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Practice Full Report

Promoting health and well-being in healthy people 2030, associated data.

Supplemental Digital Content is Available in the Text.

Healthy People 2030 describes a vision and offers benchmarks that can be used to track progress toward the goal of all people in the United States achieving their full potential for health and well-being across the life span. This vision can be realized through evidence-based interventions and policies that address the economic, physical, and social environments in which people live, learn, work, and play. Securing health and well-being for all will benefit society as a whole. Gaining such benefits requires eliminating health disparities, achieving health equity, attaining health literacy, and strengthening the physical, social, and economic environments. Implementation of Healthy People 2030 will by strengthened by engaging users from many sectors and ensuring the effective use and alignment of resources. Promoting the nation's health and well-being is a shared responsibility—at the national, state, territorial, tribal, and community levels. It requires involving the public, private, and not-for-profit sectors.

Healthy People provides science-based national objectives with 10-year targets for improving the health of the nation. Healthy People 2030—the fifth edition of the Healthy People initiative—describes a vision and offers benchmarks that can be used to track progress toward the goal of helping all people in the United States achieve their full potential for health and well-being across the life span. Healthy People 2030 expresses an expanded focus on health and well-being and an understanding that health and well-being for all people is a shared responsibility. This vision can be achieved through evidence-based interventions and policies that address the economic, physical, and social environments in which people are born, live, learn, work, play, worship, and age. High-quality data that are accurate, timely, and accessible are required to record and report on progress 1 over the course of the decade and to direct interventions to populations that are most likely to benefit from them.

Healthy People sets the federal agenda for the nation's health, guides its direction and allocation of resources, informs federal data collection and programmatic activities, and provides a model for promoting health and well-being at the state and local levels. The initiative's emphasis on promoting health and well-being signals to the nation that it is time to work across sectors to achieve health equity. This decade Healthy People 2030 is a resource for all sectors.

As part of the development of Healthy People 2030, the US Department of Health and Human Services (HHS) sought guidance from the Secretary's Advisory Committee on National Health Promotion and Disease Prevention Objectives for 2030 (Secretary's Advisory Committee), a federal advisory committee composed of nonfederal, independent subject matter experts. The Secretary's Advisory Committee presented recommendations to the HHS Secretary for developing and implementing the objectives for 2030. The Secretary's Advisory Committee convened regularly between December 2016 and September 2019, with meetings open to the public.

Health promotion has been a cornerstone of the Healthy People initiative since its inception in 1979. The Secretary's Advisory Committee recommended that the focus of Healthy People 2030 expand beyond health promotion to the broader purpose of promoting “health and well-being.” The process that has been called health promotion no longer focuses on health alone, but now leads to health and well-being for individuals in addition to society as a whole. This offers a chance to balance the needs of individuals and society. Society is defined as “a voluntary association of individuals for common ends.” 2 Health and well-being are elements among the common ends that motivate us, as individuals, to act for the good of all. In return for participating in society, individuals expect fair and just opportunities to be as healthy and well as possible. This article provides insights into defining health and well-being, promoting health and well-being, fostering user collaboration to improve health and well-being, and measuring health and well-being, in addition to implications for policy and practice.

The Secretary's Advisory Committee produced 2 detailed briefs that offered guidance for promoting health and well-being. Secretary's Advisory Committee members, joined by additional subject matter experts, developed these 2 briefs. The original documents are available on the HealthyPeople.gov Web site. 3 , 4

Defining Health and Well-being

Healthy People 2030 refers to health and well-being in every aspect of the framework, including the vision, mission, foundational principles, plan of action, and overarching goals. 5 The expanded role for health and well-being in Healthy People 2030 was supported by the Secretary's Advisory Committee's recommendations and its definition of health and well-being as how people think, feel, and function—at a personal and social level—and how they evaluate their lives as a whole. 6 How people think, feel, and function affects their beliefs about whether their lives have meaning and purpose 7 , 8 (Table ​ (Table1). 1 ). This definition recognizes the multilevel nature of health and well-being. It acknowledges that social structures, such as families, neighborhoods, communities, organizations, institutions, policies, economies, societies, cultures, and physical environments, strongly influence health and well-being. Such influence is reciprocal between individual, social, and societal health and well-being. *

The terms “health” and “well-being” describe separate but related states; health influences well-being and, conversely, well-being affects health. 9 Health incorporates both physical and mental conditions; it implies fitness under changing circumstances, such as degradation of the physical, social, or economic environments, and must be safeguarded against threats from illness, injury, or death. Safety, as a result, is an important determinant of health. Well-being is both a determinant and an outcome of health. 10 It encompasses objective and subjective elements and reflects many aspects of life and states of being. These include physical and mental, as well as emotional, social, financial, occupational, intellectual, and spiritual, elements. 11 The terms apply to individuals as well as to groups of people (eg, families, communities) and environments (eg, physical, social, economic).

The World Health Organization defines health promotion as:

The process of enabling people to increase control over, and to improve, their health. 12 Health promotion ... covers a wide range of social and environmental interventions that are designed to benefit and protect individual people's health and quality of life by addressing and preventing the root causes of ill health, not just focusing on treatment and cure. 12

The World Health Organization identifies 3 key elements for health promotion: good governance for health; health literacy; and healthy cities. Adding the concept of well-being to this definition emphasizes that promotion of health and well-being takes place across different environments and users.

Promoting Health and Well-being

The concept of promoting health and well-being at both personal and systems levels has evolved over history, starting with ancient and classical civilizations. 13 Policy strategies for promoting health have been proposed since the 1970s. 14 More than 3 decades ago, the Ottawa Charter for Health Promotion described health as a “resource for everyday life, not the objective of living.” It noted that prerequisites for health include “peace, shelter, education, food, income, a stable ecosystem, sustainable resources, social justice, and equity.” 15 This guidance remains relevant today. Promoting well-being requires engaging an expanded and diverse array of users, disciplines, and sectors that extend beyond public health, such as mental health, housing, childcare/education, business, and aging.

Interventions to promote health and well-being occur at the individual, site-specific community, and societal levels. They address economic, social, and physical environmental and political factors (“determinants of health”) that influence health and well-being. Promoting health and well-being is critical because determinants of health—the physical, social, and economic circumstances in which people are born, live, learn, work, play, worship, and age—have disparate effects on vulnerable populations. These factors interact to affect people disproportionately based on race and class. All sectors are needed to remedy such disparities and achieve health equity.

At the individual level, interventions to promote health and well-being might focus on health behaviors, employment, housing, food security, or childcare. These interventions also would apply to the community level since they target settings where people spend their time, including home, school, work, or places where they socialize such as community centers and parks. These interventions can address designs of the built environment for ease of access and to ensure safety. The Robert Wood Johnson Foundation's Culture of Health initiative is one such national model. The Foundation defines a culture of health as one in which “good health and well-being flourish across geographic, demographic, and social sectors; fostering healthy equitable communities guides public and private decision making; and everyone has the opportunity to make choices that lead to healthy lifestyles.” 16

The concept of promoting health and well-being has evolved over the decades (Figure). Health and well-being operate on more than 1 level. Broader conditions shape individual experiences of health and well-being, and organized efforts can influence those conditions. Social structures, such as families, neighborhoods and communities, and policies, economies, and cultures also play important roles. 17 – 21

How the Concept of Health Promotion Has Evolved Over Decades of Healthy People

Engaging users from many sectors and ensuring the effective use and alignment of resources will strengthen implementation of Healthy People 2030. To promote health and well-being for all people and foster equity and social justice, socioecological factors and determinants of health must be addressed at all levels. A dynamic mix of resources will be needed for long-term improvements to livability (eg, stable housing, healthy food, clean air, education, living wage jobs) and for urgent needs (eg, acute care for illness or injury, food assistance, shelter, addiction treatment, disaster relief). Such resources will need to address a more diverse range of factors than in the past.

All too often, communities and institutions function in a reactive and responsive mode, deferring or delaying long-term investments. This way of functioning generates persistent needs for urgent services, along with pressure to maintain them. Collaborative decision-making across sectors can optimize the positive impact of resources and reduce the number of crises that happen in the first place. Identifying evidence-based programs to promote health and well-being among users can serve common interests, help users expand their thinking about solutions, and set priorities for limited time, money, and other scarce resources.

Multisectoral Collaborations to Improve Health and Well-being

Achieving population-level improvements in the coming decade will require users working at all levels to function across sectors and establish or participate in multisectoral collaborations. Such efforts can improve outcomes—not only in the health sector but also in nonpublic health or health care sectors, such as education, economics, the environment, and social cohesion. Collaboration among various users groups can benefit all partners by creating win-win solutions that recognize the interrelatedness of population health status with factors that lie outside the health care and public health systems.

Achieving optimal health and well-being requires efforts that include partners from different sectors, who operate at multiple levels (eg, state, local, community) and address the circumstances of people's lives. † Such efforts could span the behavioral, psychosocial, socioeconomic, cultural, and political circumstances of the population. No single actor has sole ownership of, accountability for, or capacity to sustain the health and well-being of an entire population. 22 – 24 The 10 “causes of the causes” of poor health comprise psychological influences (eg, social gradient, stress, and social exclusion), as well as elements of community infrastructure, such as food and transportation. 25 Thus, success depends on strengthening the capacity of communities to cocreate their own futures. 26

The COVID-19 pandemic is a case study of the reciprocal, complex relationships between the health of individuals and the health of society as a whole, as well as the resulting unintended consequences. An individual's decision not to wear a mask at a grocery store or other indoor gathering place can result in the virus' spread to other people who are present. Defining some workers as essential and required to work, such as those who work in grocery stores, transportation, health care, and in other occupations that require interaction with the public, increases the risk of infection for many low-wage earners. When essential workers are compensated with low wages, lack of financial viability creates challenges to their overall health and well-being. When health insurance is tied to employment and unemployment is soaring, unemployed people often delay seeking care. When older adults stay in isolation to avoid the possibility of infection, they can experience loneliness, depression, and mental health issues. When schools are closed and children stay at home, those who lack Internet connectivity are at risk of falling behind in their schoolwork. Those who receive free school lunches may go hungry.

To help local health departments identify strategies for promoting population health and well-being and addressing determinants of health, the National Association of County and City Health Officials (NACCHO) identified 9 domains of determinants, 27 as well as data sources for each (Table ​ (Table2). 2 ). Healthy People users at the state and tribal levels may find NACCHO's domains and data sources useful for identifying and acting upon opportunities to improve and monitor measures of health and well-being. These include indicators that are important to the success of other sectors, such as high school graduation, crime reduction, and economic prosperity.

Measuring Health and Well-being

Monitoring and documenting changes to the population's health and well-being will require the use of new data sources and types of measures. The way people evaluate their own lives as a whole is one indicator of health and well-being. Yet, systems that are outside of an individual's control shape the exposures, choices, and services that people experience. An important distinction exists between individuals' subjective ratings of their own health and well-being and the objective conditions that surround and support people as they strive to improve their health and well-being.

Measures of progress that go beyond those specific to public health and health care settings will require tapping into existing data sources across other domains and sectors. For example, data used by agricultural extension offices, planning departments at all levels, schools, businesses, parks and recreation agencies, transportation systems, the Bureau of the Census, aging services, and the financial sector, among others, can inform health and well-being. Data partnerships between public health, health care settings, and other sectors can often benefit collaborators by providing a much richer source of information for each partner as well as for the entire partnership. 28

Healthy People 2020 used functional measures, including Healthy Life Expectancy, ‡ Summary Mortality and Population Health, § and Disparities, as global health measures for assessing progress. Earlier iterations of Healthy People used life expectancy and other measures. ∥ Holistic evaluations of health and well-being status of individuals, communities, and systems require broad measures, such as life satisfaction or social cohesion. 29 – 33 Assessing progress toward improved health and well-being must consider health disparities, health literacy, multisectoral policies, and determinants of health and well-being.

Realizing the potential of Healthy People 2030 will require accurate data from credible sources at all levels, with a renewed emphasis on local action. There are barriers to generating high-quality data (eg, funding, staffing, technology). Healthy People supports local action by providing guidance for consistent data collection methods and measures, as well as examples of best practices and innovations. A data partnership infrastructure and network focused on Healthy People objectives could address and respond to new developments in data sources and data analytics. For example, a data partnership could expand the availability of locally relevant data, stimulate access to new data sources to measure determinants of health and health equity, and enable linkage of geographic and demographic data in presentation formats for Healthy People users.

Partners would be able to share data, methods, and analyses and access guidance on data developments relevant to all 3 Healthy People objective types—core, developmental, and research. A data partnership infrastructure and network that links national, tribal, state, territorial, and local data through partnerships and collaborations could enhance the nation's capacity to identify and record the achievement of Healthy People objectives and overarching goals.

Healthy People 2030 continues the Healthy People initiative's tradition of serving as a catalyst for action by expanding the focus of health promotion to promoting health and well-being (see Supplemental Digital Content file, available at http://links.lww.com/JPHMP/A716 ). This emphasizes the need to shift from a disease-specific orientation to more upstream policy efforts. Healthy People 2030 offers data, objectives, and tools for creating well-being and a healthier nation. Realizing the potential of Healthy People 2030 will require the active involvement of a variety of public and private institutions and organizations, including national, tribal, state, territorial, and local health departments. Health departments at all levels can contribute to this work by engaging multiple sectors in the implementation and monitoring of objectives.

Discussions within the public health community, and between public health and other sectors, around defining health and well-being offer opportunities to engage partners that historically have not been involved in Healthy People. Engaging new partners in the Healthy People initiative will require those who traditionally have led the initiative to understand what those partners need to succeed, communicate how new partners' goals complement those of Healthy People, and convey how engaging with Healthy People can benefit the new partners. For example, partnering to improve high school graduation rates benefits the education and public health sectors, as well as the financial sector and potentially the criminal justice system. Accomplishing that goal might involve engaging with the telecommunications sector to support students' access to affordable Internet service. By engaging in such partnerships, everyone would become more familiar with the goals of other sectors and discover more win-win opportunities.

In their health improvement plans, public health departments at all levels should think broadly about which partners from other sectors could help them advance health and well-being goals, while considering what public health can offer those sectors in achieving their own goals. For example, in Maryland, each county has been charged with having a local health improvement coalition that brings together key users to achieve locally identified needs for health and well-being and to eliminate health disparities. Organizations and individuals often need to see value for investing their time and resources before they agree to participate. Involving partners early allows them to be part of identifying issues and finding solutions.

Open access data portals at the state level are proliferating and can inform decision makers as well as the public. These data portals and related data dashboards provide community leaders and residents with current geographically tracked data and tools that support assessments and linkages to evidence-based interventions. These data initiatives offer yet another opportunity for partners to convene and develop collaborative programs for their respective populations.

One of Healthy People 2030's foundational principles is that “the health and well-being of all people and communities are essential to a thriving, equitable society.” Achieving health and well-being for all will benefit society as a whole. Achieving such benefits requires eliminating health disparities, achieving health equity, attaining health literacy, and strengthening the physical, social, and economic environments. Promoting the nation's health and well-being is a shared responsibility—at the national, state, territorial, tribal, and community levels. By enlisting the involvement of the public, private, and not-for-profit sectors in efforts to promote the health and well-being of our populations, we will improve the health of the nation and the achievement of Healthy People 2030's targets.

Implications for Policy & Practice

• Across the field of public health, the focus on health promotion should be expanded to include health and well-being.
• No one sector has the ability, responsibility, or needed expertise to promote health and well-being for all. Multisectoral approaches are needed to address the social, economic, and physical determinants of health and well-being.
• It will be critical to identify common data sources and indicators that can be used to measure and evaluate trends in health and well-being.

Supplementary Material

* Other definitions exist of the terms “health” and “well-being,” respectively. This is the definition proposed for Healthy People 2030, and it considers “health and well-being” as a single term.

† In the coming decade, Healthy People 2030 will highlight innovative and successful state- and local-level efforts through HealthyPeople.gov, webinars, and other channels.

‡ Healthy Life Expectancy (HLE) includes the following: HLE free from activity limitations at birth/age 65 years; HLE free from disability at birth/age 65 years; HLE in good or better health at birth/age 65 years.

§ Summary Mortality and Population Health includes the following: life expectancy at birth/age 65 years; any activity limitation at birth/age 65 years; any disability at birth/age 65 years; percentage in fair or poor health at birth/age 65 years.

∥ Healthy People 2010 used Life Expectancy, Healthy Life Expectancy, and Disparities. Healthy People 2000 used Years of Healthy Life; Disparities; and Clinical Preventive Services.

This article is based on 2 briefs that were prepared by the Secretary's Advisory Committee on National Health Promotion and Disease Prevention Objectives for 2030 and are available online at HealthyPeople.gov . The authors acknowledge and thank the following contributors to these original briefs: Tom Kottke, MD, MSPH; Bobby Milstein, PhD, MPH; Rebecca Rossom, MD, MSCR; Matt Stiefel, MPA, MS; and Elaine Auld, MPH, MCHES.

The authors declare no conflicts of interest.

Supplemental digital content is available for this article. Direct URL citation appears in the printed text and is provided in the HTML and PDF versions of this article on the journal's Web site ( http://www.JPHMP.com ).

• Open access
• Published: 19 June 2020

Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries

• Kai Ruggeri 1 , 2 ,
• Eduardo Garcia-Garzon 3 ,
• Áine Maguire 4 ,
• Sandra Matz 5 &
• Felicia A. Huppert 6 , 7  

Health and Quality of Life Outcomes volume  18 , Article number:  192 ( 2020 ) Cite this article

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Recent trends on measurement of well-being have elevated the scientific standards and rigor associated with approaches for national and international comparisons of well-being. One major theme in this has been the shift toward multidimensional approaches over reliance on traditional metrics such as single measures (e.g. happiness, life satisfaction) or economic proxies (e.g. GDP).

To produce a cohesive, multidimensional measure of well-being useful for providing meaningful insights for policy, we use data from 2006 and 2012 from the European Social Survey (ESS) to analyze well-being for 21 countries, involving approximately 40,000 individuals for each year. We refer collectively to the items used in the survey as multidimensional psychological well-being (MPWB).

The ten dimensions assessed are used to compute a single value standardized to the population, which supports broad assessment and comparison. It also increases the possibility of exploring individual dimensions of well-being useful for targeting interventions. Insights demonstrate what may be masked when limiting to single dimensions, which can create a failure to identify levers for policy interventions.

Conclusions

We conclude that both the composite score and individual dimensions from this approach constitute valuable levels of analyses for exploring appropriate policies to protect and improve well-being.

What is well-being?

Well-being has been defined as the combination of feeling good and functioning well; the experience of positive emotions such as happiness and contentment as well as the development of one’s potential, having some control over one’s life, having a sense of purpose, and experiencing positive relationships [ 23 ]. It is a sustainable condition that allows the individual or population to develop and thrive. The term subjective well-being is synonymous with positive mental health. The World Health Organization [ 45 ] defines positive mental health as “a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community”. This conceptualization of well-being goes beyond the absence of mental ill health, encompassing the perception that life is going well.

Well-being has been linked to success at professional, personal, and interpersonal levels, with those individuals high in well-being exhibiting greater productivity in the workplace, more effective learning, increased creativity, more prosocial behaviors, and positive relationships [ 10 , 27 , 37 ]. Further, longitudinal data indicates that well-being in childhood goes on to predict future well-being in adulthood [ 39 ]. Higher well-being is linked to a number of better outcomes regarding physical health and longevity [ 13 ] as well as better individual performance at work [ 30 ], and higher life satisfaction has been linked to better national economic performance [ 9 ].

Measurement of well-being

Governments and researchers have attempted to assess the well-being of populations for centuries [ 2 ]. Often in economic or political research, this has ended up being assessed using a single item about life satisfaction or happiness, or a limited set of items regarding quality of life [ 3 ]. Yet, well-being is a multidimensional construct, and cannot be adequately assessed in this manner [ 14 , 24 , 29 ]. Well-being goes beyond hedonism and the pursuit of happiness or pleasurable experience, and beyond a global evaluation (life satisfaction): it encompasses how well people are functioning, known as eudaimonic, or psychological well-being. Assessing well-being using a single subjective item approach fails to offer any insight into how people experience the aspects of their life that are fundamental to critical outcomes. An informative measure of well-being must encompass all the major components of well-being, both hedonic and eudaimonic aspects [ 2 ], and cannot be simplified to a unitary item of income, life satisfaction, or happiness.

Following acknowledgement that well-being measurement is inconsistent across studies, with myriad conceptual approaches applied [ 12 ], Huppert and So [ 27 ] attempted to take a systematic approach to comprehensively measure well-being. They proposed that positive mental health or well-being can be viewed as the complete opposite to mental ill health, and therefore attempted to define mental well-being in terms of the opposite of the symptoms of common mental disorders. Using the DSM-IV and ICD-10 symptom criteria for both anxiety and depression, ten features of psychological well-being were identified from defining the opposite of common symptoms. The features encompassed both hedonic and eudaimonic aspects of well-being: competence, emotional stability, engagement, meaning, optimism, positive emotion, positive relationships, resilience, self-esteem, and vitality. From these ten features an operational definition of flourishing, or high well-being, was developed using data from Round 3 of the European Social Survey (ESS), carried out in 2006. The items used in the Huppert and So [ 27 ] study were unique to that survey, which reflects a well-being framework based on 10 dimensions of good mental health. An extensive discussion on the development and validation of these measures for the framework is provided in this initial paper [ 27 ].

As this was part of a major, multinational social survey, each dimension was measured using a single item. As such, ‘multidimensional’ in this case refers to using available measures identified for well-being, but does not imply a fully robust measure of these individual dimensions, which would require substantially more items that may not be feasible for population-based work related to policy development. More detailed and nuanced approaches might help to better capture well-being as a multidimensional construct, and also may consider other dimensions. However, brief core measures such as the one implemented in the ESS are valuable as they provide a pragmatic way of generating pioneering empirical evidence on well-being across different populations, and help direct policies as well as the development of more nuanced instruments. While this naturally would benefit from complementary studies of robust measurement focused on a single topic, appropriate methods for using sprawling social surveys remain critical, particularly through better standardization [ 6 ]. While this paper will overview those findings, we strongly encourage more work to that end, particularly in more expansive measures to support policy considerations.

General approach and key questions

The aim of the present study was to develop a more robust measurement of well-being that allows researchers and policymakers to measure well-being both as a composite construct and at the level of its fundamental dimensions. Such a measure makes it possible to study overall well-being in a manner that goes beyond traditional single-item measures, which capture only a fraction of the dimensions of well-being, and because it allows analysts to unpack the measure into its core components to identify strengths and weaknesses. This would produce a similar approach as the most common reference for policy impacts: Gross Domestic Product (GDP), which is a composite measure of a large number of underlying dimensions.

The paper is structured as follows: in the first step, data from the ESS are used to develop a composite measure of well-being from the items suggested by Huppert & So [ 27 ] using factor analysis. In the second step, the value of the revised measure is demonstrated by generating insights into the well-being of 21 European countries, both at the level of overall well-being and at the level of individual dimensions.

The European social survey

The ESS is a biannual survey of European countries. Through comprehensive measurement and random sampling techniques, the ESS provides a representative sample of the European population for persons aged 15 and over [ 38 ]. Both Round 3 (2006–2007) and Round 6 (2012–2013) contained a supplementary well-being module. This module included over 50 items related to all aspects of well-being including psychological, social, and community well-being, as well as incorporating a brief measure of symptoms of psychological distress. As summarized by Huppert et al. [ 25 ], of the 50, only 30 items relate to personal well-being, of which only 22 are positive measures. Of those remaining, not all relate to the 10 constructs identified by Huppert and So [ 27 ], so only a single item could be used, or else the item that had the strongest face validity and distributional items were chosen.

Twenty-two countries participated in the well-being modules in both Round 3 and Round 6. As this it within a wider body of analyses, it was important to focus on those initially. Hungary did not have data for the vitality item in Round 3 and was excluded from the analysis, as appropriate models would not have been able to reliably resolve a missing item for an entire country. To be included in the analysis and remain consistent, participants therefore had to complete all 10 items used and have the age, gender, employment, and education variables completed. Employment was classified into four groups: students, employed, unemployed, retired; other groups were excluded. Education was classified into three groups: low (less than secondary school), middle (completed secondary school), and high (postsecondary study including any university and above). Using these criteria, the total sample for Round 6 was 41,825 people from 21 countries for analysis. The full sample was 52.6% female and ranged in age from 15 to 103 (M = 47.9; SD = 18.9). Other details about participation, response rates, and exclusion have been published elsewhere [ 38 ].

Huppert & So [ 27 ] defined well-being using 10 items extracted from the Round 3 items, which represent 10 dimensions of well-being. However, the items used in Round 3 to represent positive relationships and engagement exhibited ceiling effects and were removed from the questionnaire in Round 6. Four alternatives were available to replace each question. Based on their psychometric properties (i.e., absence of floor effects and wider response distributions), two new items were chosen for positive relationships and engagement (one item for each dimension). The new items and those they replaced can be seen in Table  1 (also see Supplement ).

Development of a composite measure of psychological well-being (MPWB)

A composite measure of well-being that yields an overall score for each individual was developed. From the ten indicators of well-being shown in Table 1 , a single factor score was calculated to represent MPWB. This overall MPWB score hence constitutes a summary of how an individual performs across the ten dimensions, which is akin to a summary score such as GDP, and will be of general value to policymakers. Statistical analysis was performed in R software, using lavaan [ 40 ] and lavaan.survey [ 35 ] packages. The former is a widely-used package for the R software designed for computing structural equation models and confirmatory factor analyses (CFA). The latter allows introducing complex survey design weights (combination of design and population size weights) when estimating confirmatory factor analysis models with lavaan, which ensures that MPWB scoring followed ESS guidelines regarding both country-level and survey specific weights [ 17 ]. Both packages have been previously tested and validated in various analyses using ESS data (as explained in detail in lavaan.survey documentation).

It should be noted that Round 6 was treated as the focal point of these efforts before repeating for Round 3, primarily due to the revised items that were problematic in Round 3, and considering that analyses of the 2006 data are already widely available.

Prior to analysis, all items were coded such that higher scores were more positive and lower scores more negative. Several confirmatory factor analysis models were performed in order to test several theoretical conceptualizations regarding MPWB. Finally, factor scores (expected a posteriori [ 15 ];) were calculated for the full European sample and used for descriptive purposes. The approach and final model are presented in supplemental material .

Factor scores are individual scores computed as weighted combinations of each person’s response on a given item and the factor scoring coefficients. This approach is to be preferred to using raw or sum scores: sum or raw scores fail to consider how well a given item serves as an indicator of the latent variable (i.e., all items are unrealistically assumed to be perfect and equivalent measures of MPWB). They also do not take into account that different items could present different variability, which is expected to occur if items present different scales (as in our case). Therefore, the use of such simple methods results in inaccurate individual rankings for MPWB. To resolve this, factor scores are both more informative and more accurate, as they avoid the propagation of measurement error in subsequent analyses [ 19 ].

Not without controversy (see Supplement ), factor scores are likely to be preferable to sum scores when ranking individuals on unobservable traits that are expected to be measured with noticeable measurement error (such as MPWB [ 32 ];). Similar approaches based on factor scoring have been successfully applied in large international assessment research [ 21 , 34 ]. With the aim of developing a composite well-being score, it was necessary to provide a meaningful representation of how the different well-being indicators are reflected in the single measure. A hierarchical model with one higher-order factor best approximated MPWB along with two first-order factors (see supplement Figure S 1 ). This model replicates the factor structure reported for Round 3 by Huppert & So [ 27 ]. The higher-order factor explained the relationship between two first-order factors (positive functioning and positive characteristics showed a correlation of ρ = .85). In addition, modelling standardized residuals showed that the items representing vitality and emotional stability and items representing optimism and self-esteem were highly correlated. The similarities in wording in both pairs of items (see Table 1 ) are suspected to be responsible for such high residual correlations. Thus, those correlations were included in the model. As presented in Table  2 , the hierarchical model was found to fit the data better than any other model but a bi-factor model including these correlated errors. The latter model resulted in collapsed factor structure with a weak, bi-polar positive functioning factor. However, this bi-factor model showed a problematic bi-polar group factor with weak loadings. Whether this group factor was removed (resulting in a S-1 bi-factor model, as in [ 16 ]), model fit deteriorated. Thus, neither bi-factor alternative was considered to be acceptable.

To calculate the single composite score representing MPWB, a factor scoring approach was used rather than a simplistic summing of raw scores on these items. Factor scores were computed and standardized for the sample population as a whole, which make them suitable for broad comparison [ 8 ]. This technique was selected for two reasons. First, it has the ability to take into account the different response scales used for measuring the items included in the multidimensional well-being model. The CFA model, from which MPWB scores were computed, was defined such that the metric of the MPWB was fixed, which results in a standardized scale. Alternative approaches, such as sum or raw scores, would result in ignoring the differential variability across items, and biased individual group scores. Our approach, using factor scoring, resolves this issue by means of standardization of the MPWB scores. The second reason for this technique is that it could take account of how strongly each item loaded onto the MPWB factor. It should be noted that by using only two sub-factors, the weight applied to the general factor is identical within the model for each round. This model was also checked to ensure it also was a good fit for different groups based on gender, age, education and employment.

Separate CFA analyses per each country indicate that the final model fit the data adequately in all countries (.971 < CFI < .995; .960 < TFI < .994; .020 < RMSEA < .05; 0,023 < SRMR < 0,042). All items presented substantive loadings on their respective factors, and structures consistently replicated across all tested countries. Largest variations were found when assessing the residual items’ correlations (e.g., for emotional stability and vitality correlation, values ranged from 0,076 to .394). However, for most cases, residuals correlations were of similar size and direction (for both cases, the standard deviation of estimated correlations was close of .10). Thus, strong evidence supporting our final model was systematically found across all analyzed countries. Full results are provided in the supplement (Tables S 2 -S 3 ).

Model invariance

In order to establish meaningful comparisons across groups within and between each country, a two-stage approach was followed, resulting in a structure that was successfully found to be similar across demographics. First, a descriptive comparison of the parameter estimates unveiled no major differences across groups. Second, factor scores were derived for the sample, employing univariate statistics to compare specific groups within country and round. In these analyses, neither traditional nor modern approaches to factor measurement invariance were appropriate given the large sample and number of comparisons at stake ([ 8 ]; further details in Supplement ).

From a descriptive standpoint, the hierarchical structure satisfactorily fit both Round 3 and Round 6 data. All indicators in both rounds had substantial factor loadings (i.e., λ > .35). A descriptive comparison of parameter estimates produced no major differences across the two rounds. The lack of meaningful differences in the parameter estimates confirms that this method for computing MPWB can be used in both rounds.

As MPWB scores from both rounds are obtained from different items that have different scales for responses, it is necessary to transform individual scores obtained from both rounds in order to be aligned. To do this between Round 3 and Round 6 items, a scaling approach was used. To produce common metrics, scores from Round 3 were rescaled using a mean and sigma transformation (Kolen & Brennan 2010) to align with Round 6 scales. This was used as Round 6 measures were deemed to have corrected some deficiencies found in Round 3 items. This does not change outcomes in either round but simply makes the scores match in terms of distributions relative to their scales, making them more suitable for comparison.

As extensive descriptive insights on the sample and general findings are already available (see [ 41 ]), we focus this section on the evidence derived directly from the proposed approach to MPWB scores. For the combined single score for MPWB, the overall mean (for all participants combined) is fixed to zero, and the scores represent deviation from the overall mean. In 2012 (Round 6), country scores on well-being ranged from − 0.41 in Bulgaria to 0.46 in Denmark (Fig.  1 ). There was a significant, positive relationship between national MPWB mean scores and national life satisfaction means ( r =  .56 (.55–.57), p  < .001). In addition, MPWB was negatively related with depression scores and positively associated with other well-being measurements (see Supplement ).

Distribution of national MPWB means and confidence intervals across Europe

Denmark having the highest well-being is consistent with many studies [ 4 , 18 ] and with previous work using ESS data [ 27 ]. While the pattern is typically that Nordic countries are doing the best and that eastern countries have the lowest well-being, exceptions exist. The most notable exception is Portugal, which has the third-lowest score and is not significantly higher than Ukraine, which is second lowest. Switzerland and Germany are second and third highest respectively, and show generally similar patterns to the Scandinavian countries (see Fig. 1 ). It should be noted that, for Figs.  1 , 2 , 3 , 4 , 5 , countries with the lowest well-being are at the top. This is done to highlight the greatest areas for potential impact, which are also the most of concern to policy.

Well-being by country and gender

Well-being by country and age

Well-being by country and employment

Well-being by country and education

General patterns across the key demographic variables – gender, age, education, employment – are visible across countries as seen in Figs.  1 , 2 , 3 , 4 , 5 (see also Supplement 2 ). These figures highlight patterns based on overall well-being as well as potential for inequalities. The visualizations presented here, though univariate, are for the purpose of understanding broad patterns while highlighting the need to disentangle groups and specific dimensions to generate effective policies.

For gender, women exhibited lower MPWB scores than men across Europe (β = −.09, t (36508) = − 10.37; p  < .001). However, these results must be interpreted with caution due to considerable overlap in confidence intervals for many of the countries, and greater exploration of related variables is required. This also applies for the five countries (Estonia, Finland, Ireland, Slovakia, Ukraine) where women have higher means than men. Only four countries have significant differences between genders, all of which involve men having higher scores than women: the Netherlands (β = −.12, t (1759) = − 3.24; p  < .001), Belgium (β = −.14, t (1783) = − 3.94; p  < .001), Cyprus (β = −.18, t (930) = − 2.87; p  < .001) and Portugal (β = −.19, t (1847) = − 2.50; p  < .001).

While older individuals typically exhibited lower MPWB scores compared to younger age groups across Europe (β 25–44  = −.05, t (36506) = − 3.686, p  < .001; β 45–65  = −.12, t (36506) = − 8.356, p  < .001; β 65–74  = −.16, t (36506) = − 8.807, p  < .001; β 75+  = −.28, t (36506) = − 13.568, p  < .001), the more compelling pattern shows more extreme differences within and between age groups for the six countries with the lowest well-being. This pattern is most pronounced in Bulgaria, which has the lowest overall well-being. For the three countries with the highest well-being (Denmark, Switzerland, Germany), even the mean of the oldest age group was well above the European average, while for the countries with the lowest well-being, it was only young people, particularly those under 25, who scored above the European average. With the exception of France and Denmark, countries with higher well-being typically had fewer age group differences and less variance within or between groups. Only countries with the lowest well-being showed age differences that were significant with those 75 and over showing the lowest well-being.

MPWB is consistently higher for employed individuals and students than for retired (β = −.31, t (36506) = − 21.785; p  < .00) or unemployed individuals (β = −.52, t (36556) = − 28.972; p  < .001). Unemployed groups were lowest in nearly all of the 21 countries, though the size of the distance from other groups did not consistently correlate with national MPWB mean. Unemployed individuals in the six countries with the lowest well-being were significantly below the mean, though there is little consistency across groups and countries by employment beyond that. In countries with high well-being, unemployed, and, in some cases, retired individuals, had means below the European average. In countries with the lowest well-being, it was almost exclusively students who scored above the European average. Means for retired groups appear to correlate most strongly with overall well-being. There is minimal variability for employed groups in MPWB means within and between countries.

There is a clear pattern of MPWB scores increasing with education level, though the differences were most pronounced between low and middle education groups (β = .12, t (36508) = 9.538; p  < .001). Individuals with high education were significantly higher on MPWB than those in the middle education group (β = .10, t (36508) =11.06; p  < .001). Differences between groups were noticeably larger for countries with lower overall well-being, and the difference was particularly striking in Bulgaria. In Portugal, medium and high education well-being means were above the European average (though 95% confidence intervals crossed 0), but educational attainment is significantly lower in the country, meaning the low education group represents a greater proportion of the population than the other 21 countries. In the six countries with the highest well-being, mean scores for all levels of education were above the European mean.

Utilizing ten dimensions for superior understanding of well-being

It is common to find rankings of national happiness and well-being in popular literature. Similarly, life satisfaction is routinely the only measure reported in many policy documents related to population well-being. To demonstrate why such limited descriptive approaches can be problematic, and better understood using multiple dimensions, all 21 countries were ranked individually on each of the 10 indicators of well-being and MPWB in Round 6 based on their means. Figure  6 demonstrates the variations in ranking across the 10 dimensions of well-being for each country.

Country rankings in 2012 on multidimensional psychological well-being and each of its 10 dimensions

The general pattern shows typically higher rankings for well-being dimensions in countries with higher overall well-being (and vice-versa). Yet countries can have very similar scores on the composite measure but very different underlying profiles in terms of individual dimensions. Figure  7 a presents this for two countries with similar life satisfaction and composite well-being, Belgium and the United Kingdom. Figure 7 b then demonstrates this even more vividly for two countries, Finland and Norway, which have similar composite well-being scores and identical mean life satisfaction scores (8.1), as well as have the highest two values for happiness of all 21 countries. In both pairings, the broad outcomes are similar, yet countries consistently have very different underlying profiles in individual dimensions. The results indicate that while overall scores can be useful for general assessment, specific dimensions may vary substantially, which is a relevant first step for developing interventions. Whereas the ten items are individual measures of 10 areas of well-being, had these been limited to a single domain only, the richness of the underlying patterns would have been lost, and the limitation of single item approaches amplified.

a Comparison of ranks for dimensions of well-being between two different countries with similar life satisfaction in 2012: Belgium and United Kingdom. b Comparison of ranks for dimensions of well-being between two similar countries with identical life satisfaction and composite well-being scores in 2012: Finland and Norway

The ten-item multidimensional measure provided clear patterns for well-being across 21 countries and various groups within. Whether used individually or combined into a composite score, this approach produces more insight into well-being and its components than a single item measure such as happiness or life satisfaction. Fundamentally, single items are impossible to unpack in reverse to gain insights, whereas the composite score can be used as a macro-indicator for more efficient overviews as well as deconstructed to look for strengths and weaknesses within a population, as depicted in Figs.  6 and 7 . Such deconstruction makes it possible to more appropriately target interventions. This brings measurement of well-being in policy contexts in line with approaches like GDP or national ageing indexes [ 7 ], which are composite indicators of many critical dimensions. The comparison with GDP is discussed at length in the following sections.

Patterns within and between populations

Overall, the patterns and profiles presented indicate a number of general and more nuanced insights. The most consistent among these is that the general trend in national well-being is usually matched within each of the primary indicators assessed, such as lower well-being within unemployed groups in countries with lower overall scores than in those with higher overall scores. While there are certainly exceptions, this general pattern is visible across most indicators.

The other general trend is that groups with lower MPWB scores consistently demonstrate greater variability and wider confidence intervals than groups with higher scores. This is a particularly relevant message for policymakers given that it is an indication of the complexity of inequalities: improvements for those doing well may be more similar in nature than for those doing poorly. This is particularly true for employment versus unemployment, yet reversed for educational attainment. Within each dimension, the most critical pattern is the lack of consistency for how each country ranks, as discussed further in other sections.

Examining individual dimensions of well-being makes it possible to develop a more nuanced understanding of how well-being is impacted by societal indicators, such as inequality or education. For example, it is possible that spending more money on education improves well-being on some dimensions but not others. Such an understanding is crucial for the implementation of targeted policy interventions that aim at weaker dimensions of well-being and may help avoid the development of ineffective policy programs. It is also important to note that the patterns across sociodemographic variables may differ when all groups are combined, compared to results within countries. Some effects may be larger when all are combined, whereas others may have cancelling effects.

Using these insights, one group that may be particularly important to consider is unemployed adults, who consistently have lower well-being than employed individuals. Previous research on unemployment and well-being has often focused on mental health problems among the unemployed [ 46 ] but there are also numerous studies of differences in positive aspects of well-being, mainly life satisfaction and happiness [ 22 ]. A large population-based study has demonstrated that unemployment is more strongly associated with the absence of positive well-being than with the presence of symptoms of psychological distress [ 28 ], suggesting that programs that aim to increase well-being among unemployed people may be more effective than programs that seek to reduce psychological distress.

Certainly, it is well known that higher income is related to higher subjective well-being and better health and life expectancy [ 1 , 42 ], so reduced income following unemployment is likely to lead to increased inequalities. Further work would be particularly insightful if it included links to specific dimensions of well-being, not only the comprehensive scores or overall life satisfaction for unemployed populations. As such, effective responses would involve implementation of interventions known to increase well-being in these groups in times of (or in spite of) low access to work, targeting dimensions most responsible for low overall well-being. Further work on this subject will be presented in forthcoming papers with extended use of these data.

This thinking also applies to older and retired populations in highly deprived regions where access to social services and pensions are limited. A key example of this is the absence in our data of a U-shaped curve for age, which is commonly found in studies using life satisfaction or happiness [ 5 ]. In our results, older individuals are typically lower than what would be expected in a U distribution, and in some cases, the oldest populations have the lowest MPWB scores. While previous studies have shown some decline in well-being beyond the age of 75 [ 20 ], our analysis demonstrates quite a severe fall in MPWB in most countries. What makes this insight useful – as opposed to merely unexpected – is the inclusion of the individual dimensions such as vitality and positive relationships. These dimensions are clearly much more likely to elicit lower scores than for younger age groups. For example, ageing beyond 75 is often associated with increased loneliness and isolation [ 33 , 43 ], and reduction in safe, independent mobility [ 31 ], which may therefore correspond with lower scores on positive relationships, engagement, and vitality, and ultimately lower scores on MPWB than younger populations. Unpacking the dimensions associated with the age-related decline in well-being should be the subject of future research. The moderate positive relationship of MPWB scores with life satisfaction is clear but also not absolute, indicating greater insights through multidimensional approaches without any obvious loss of information. Based on the findings presented here, it is clearly important to consider ensuring the well-being of such groups, the most vulnerable in society, during periods of major social spending limitations.

Policy implications

Critically, Fig.  6 represents the diversity of how countries reach an overall MPWB score. While countries with overall high well-being have typically higher ranks on individual items, there are clearly weak dimensions for individual countries. Conversely, even countries with overall low well-being have positive scores on some dimensions. As such, the lower items can be seen as potential policy levers in terms of targeting areas of concern through evidence-based interventions that should improve them. Similarly, stronger areas can be seen as learning opportunities to understand what may be driving results, and thus used to both sustain those levels as well as potentially to translate for individuals or groups not performing as well in that dimension. Collectively, we can view this insight as a message about specific areas to target for improvement, even in countries doing well, and that even countries doing poorly may offer strengths that can be enhanced or maintained, and could be further studied for potential applications to address deficits. We sound a note of caution however, in that these patterns are based on ranks rather than actual values, and that those ranks are based on single measures.

Figure 7 complements those insights more specifically by showing how Finland and Norway, with a number of social, demographic, and economic similarities, plus identical life satisfaction scores (8.1) arrive at similar single MPWB scores with very different profiles for individual dimensions. By understanding the levers that are specific to each country (i.e. dimensions with the lowest well-being scores), policymakers can respond with appropriate interventions, thereby maximizing the potential for impact on entire populations. Had we restricted well-being measurement to a single question about happiness, as is commonly done, we would have seen both countries had similar and extremely high means for happiness. This might have led to the conclusion that there was minimal need for interventions for improving well-being. Thus, in isolation, using happiness as the single indicator would have masked the considerable variability on several other dimensions, especially those dimensions where one or both had means among the lowest of the 21 countries. This would have resulted in similar policy recommendations, when in fact, Norway may have been best served by, for example, targeting lower dimensions such as Engagement and Self-Esteem, and Finland best served by targeting Vitality and Emotional Stability.

Targeting specific groups and relevant dimensions as opposed to comparing overall national outcomes between countries is perhaps best exemplified by Portugal, which has one of the lowest educational attainment rates in OECD countries, exceeded only by Mexico and Turkey [ 36 ]. This group thus skews the national MPWB score, which is above average for middle and high education groups, but much lower for those with low education. Though this pattern is not atypical for the 21 countries presented here, the size of the low education group proportional to Portugal’s population clearly reduces the national MPWB score. This implies that the greatest potential for improvement is likely to be through addressing the well-being of those with low education as a near-term strategy, and improving access to education as a longer-term strategy. It will be important to analyze this in the near future, given recent reports that educational attainment in Portugal has increased considerably in recent years (though remains one of the lowest in OECD countries) [ 36 ].

One topic that could not be addressed directly is whether these measures offer value as indicators of well-being beyond the 21 countries included here, or even beyond the countries included in ESS generally. In other words, are these measures relevant only to a European population or is our approach to well-being measurement translatable to other regions and purposes? Broadly speaking, the development of these measures being based on DSM and ICD criteria should make them relevant beyond just the 21 countries, as those systems are generally intended to be global. However, it can certainly be argued that these methods for designing measures are heavily influenced by North American and European medical frameworks, which may limit their appropriateness if applied in other regions. Further research on these measures should consider this by adding potential further measures deemed culturally appropriate and seeing if comparable models appear as a result.

A single well-being score

One potential weakness remains the inconsistency of scaling between ESS well-being items used for calculating MPWB. However, this also presents an opportunity to consider the relative weighting of each item within the current scales, and allow for the development of a more consistent and reliable measure. These scales could be modified to align in separate studies with new weights generated – either generically for all populations or stratified to account for various cultural or other influences. Using these insights, scales could alternatively be produced to allow for simple scoring for a more universally accessible structure (e.g. 1–100) but with appropriate values for each item that represents the dimensions, if this results in more effective communication with a general public than a standardized score with weights. Additionally, common scales would improve on attempts to use rankings for presenting national variability within and between dimensions. Researchers should be aware that factor scores are sample-dependent (as based on specific factor model parameters such as factor loadings). Nevertheless, future research focused on investigating specific item differential functioning (by means of multidimensional item response functioning or akin techniques) of these items across situations (i.e., rounds) and samples (i.e., rounds and countries) should be conducted in order to have a more nuanced understanding of this scale functioning.

What makes this discussion highly relevant is the value of a more informed measure to replace traditional indicators of well-being, predominantly life satisfaction. While life satisfaction may have an extensive history and present a useful metric for comparisons between major populations of interest, it is at best a corollary, or natural consequence, of other indicators. It is not in itself useful for informing interventions, in the same way limiting to a single item for any specific dimension of well-being should not alone inform interventions.

By contrast, a validated and standardized multidimensional measure is exceptionally useful in its suitability to identify those at risk, as well as its potential for identifying areas of strengths and weaknesses within the at-risk population. This can considerably improve the efficiency and appropriateness of interventions. It identifies well-understood dimensions (e.g. vitality, positive emotion) for direct application of evidence-based approaches that would improve areas of concern and thus overall well-being. Given these points, we strongly argue for the use of multidimensional approaches to measurement of well-being for setting local and national policy agenda.

There are other existing single-score approaches for well-being addressing its multidimensional nature. These include the Warwick-Edinburgh Mental Well-Being Scale [ 44 ] and the Flourishing Scale [ 11 ]. In these measures, although the single score is derived from items that clearly tap a number of dimensions, the dimensions have not been systematically derived and no attempt is made to measure the underlying dimensions individually. In contrast, the development approach used here – taking established dimensions from DSM and ICD – is based on years of international expertise in the field of mental illness. In other words, there have long been adequate measures for identifying and understanding illness, but there is room for improvement to better identify and understand health. With increasing support for the idea of these being a more central focus of primary outcomes within economic policies, such approaches are exceptionally useful [ 13 ].

Better measures, better insights

Naturally, it is not a compelling argument to simply state that more measures present greater information than fewer or single measures, and this is not the primary argument of this manuscript. In many instances, national measures of well-being are mandated to be restricted to a limited set of items. What is instead being argued is that well-being itself is a multidimensional construct, and if it is deemed a critical insight for establishing policy agenda or evaluating outcomes, measurements must follow suit and not treat happiness and life satisfaction values as universally indicative. The items included in ESS present a very useful step to that end, even in a context where the number of items is limited.

As has been argued by many, greater consistency in measurement of well-being is also needed [ 26 ]. This may come in the form of more consistency regarding dimensions included, the way items are scored, the number of items representing each dimension, and changes in items over time. While inconsistency may be prevalent in the literature to date for definitions and measurement, the significant number of converging findings indicates increasingly robust insights for well-being relevant to scientists and policymakers. Improvements to this end would support more systematic study of (and interventions for) population well-being, even in cases where data collection may be limited to a small number of items.

The added value of MPWB as a composite measure

While there are many published arguments (which we echo) that measures of well-being must go beyond objective features, particularly related to economic indicators such as GDP, this is not to say one replaces the other. More practically, subjective and objective approaches will covary to some degree but remain largely distinct. For example, GDP presents a useful composite of a substantial number of dimensions, such as consumption, imports, exports, specific market outcomes, and incomes. If measurement is restricted to a macro-level indicator such as GDP, we cannot be confident in selecting appropriate policies to implement. Policies are most effective when they target a specific component (of GDP, in this instance), and then are directly evaluated in terms of changes in that component. The composite can then be useful for comprehensive understanding of change over time and variation in circumstances. Specific dimensions are necessary for identifying strengths and weaknesses to guide policy, and examining direct impacts on those dimensions. In this way, a composite measure in the form of MPWB for aggregate well-being is also useful, so long as the individual dimensions are used in the development and evaluation of policies. Similar arguments for other multidimensional constructs have been made recently, such as national indexes of ageing [ 7 ].

In the specific instance of MPWB in relation to existing measures of well-being, there are several critical reasons to ensure a robust approach to measurement through systematic validation of psychometric properties. The first is that these measures are already part of the ESS, meaning they are being used to study a very large sample across a number of social challenges and not specifically a new measure for well-being. The ESS has a significant influence on policy discussions, which means the best approaches to utilizing the data are critical to present systematically, as we have attempted to do here. This approach goes beyond existing measures such as Gallup or the World Happiness Index to broadly cover psychological well-being, not individual features such as happiness or life satisfaction (though we reiterate: as we demonstrate in Fig.  7 a and b, these individual measures can and should still covary broadly with any multidimensional measure of well-being, even if not useful for predicting all dimensions). While often referred to as ‘comprehensive’ measurement, this merely describes a broad range of dimensions, though more items for each dimension – and potentially more dimensions – would certainly be preferable in an ideal scenario.

These dimensions were identified following extensive study for flourishing measures by Huppert & So [ 27 ], meaning they are not simply a mix of dimensions, but established systematically as the key features of well-being (the opposite of ill-being). Furthermore, the development of the items is in line with widely validated and practiced measures for the identification of illness. The primary adjustment has simply been the emphasis on health, but otherwise maintains the same principles of assessment. Therefore, the overall approach offers greater value than assessing only negative features and inferring absence equates to opposite (positives), or that individual measures such as happiness can sufficiently represent a multidimensional construct like well-being. Collectively, we feel the approach presented in this work is therefore a preferable method for assessing well-being, particularly on a population level, and similar approaches should replace single items used in isolation.

While the focus of this paper is on the utilization of a widely tested measure (in terms of geographic spread) that provides for assessing population well-being, it is important to provide a specific application for why this is relevant in a policy context. Additionally, because the ESS itself is a widely-recognized source of meaningful information for policymakers, providing a robust and comprehensive exploration of the data is necessary. As the well-being module was not collected in recent rounds, these insights provide clear reasoning and applications for bringing them back in the near future.

More specifically, it is critical that this approach be seen as advantageous both in using the composite measure for identifying major patterns within and between populations, and for systematically unpacking individual dimensions. Using those dimensions produces nuanced insights as well as the possibility of illuminating policy priorities for intervention.

In line with this, we argue that no composite measure can be useful for developing, implementing, or evaluating policy if individual dimensions are not disaggregated. We are not arguing that MPWB as a single composite score, nor the additional measures used in ESS, is better than other existing single composite scoring measures of well-being. Our primary argument is instead that MPWB is constructed and analyzed specifically for the purpose of having a robust measure suitable for disaggregating critical dimensions of well-being. Without such disaggregation, single composite measures are of limited use. In other words, construct a composite and target the components.

Well-being is perhaps the most critical outcome measure of policies. Each individual dimension of well-being as measured in this study represents a component linked to important areas of life, such as physical health, financial choice, and academic performance [ 26 ]. For such significant datasets as the European Social Survey, the use of the single score based on the ten dimensions included in multidimensional psychological well-being gives the ability to present national patterns and major demographic categories as well as to explore specific dimensions within specific groups. This offers a robust approach for policy purposes, on both macro and micro levels. This facilitates the implementation and evaluation of interventions aimed at directly improving outcomes in terms of population well-being.

Availability of data and materials

The datasets analysed during the current study are available in the European Social Survey repository, http://www.europeansocialsurvey.org/data/country_index.html

Abbreviations

Diagnostic and Statistical Manual of Mental Disorders

European Social Survey

Gross Domestic Product

International Classification of Disease

Multidimensional psychological well-being

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Acknowledgements

The authors would like to thank Ms. Sara Plakolm, Ms. Amel Benzerga, and Ms. Jill Hurson for assistance in proofing the final draft. We would also like to acknowledge the general involvement of the Centre for Comparative Social Surveys at City University, London, and the Centre for Wellbeing at the New Economics Foundation.

This work was supported by a grant from the UK Economic and Social Research Council (ES/LO14629/1). Additional support was also provided by the Isaac Newton Trust, Trinity College, University of Cambridge, and the UK Economic and Social Research Council (ES/P010962/1).

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Trinity College Dublin, Dublin, Ireland

Áine Maguire

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University of New South Wales, Sydney, Australia

Felicia A. Huppert

Well-being Institute, University of Cambridge, Cambridge, UK

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KR is the lead author and researcher on the study, responsible for all materials start to finish. FH was responsible for the original grant award and the general theory involved in the measurement approaches. ÁM was responsible for broad analysis and writing. EGG was responsible for psychometric models and the original factor scoring approach, plus writing the supplementary explanations. SM provided input on later drafts of the manuscript as well as the auxiliary analyses. The authors read and approved the final manuscript.

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Additional file 1: figure s1.

. Hierarchical approach to modelling comprehensive psychological well-being. Table S1 . Confirmatory Factor Structure for Round 6 and 3. Figure S2 . Well-being by country and gender. Figure S3 . Well-being by country and age. Figure S4 . Well-being by country and employment. Figure S5 . Well-being by country and education. Table S2 . Item loadings for Belgium to Great Britain. Table S3 . Item loadings for Ireland to Ukraine.

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Ruggeri, K., Garcia-Garzon, E., Maguire, Á. et al. Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries. Health Qual Life Outcomes 18 , 192 (2020). https://doi.org/10.1186/s12955-020-01423-y

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The Science of Well-Being

Melissa Hartman

Luke Kalb has spent over a decade focusing on the measurement, treatment, and epidemiology of mental health crises. It was only after earning his PhD and joining the faculty in  Mental Health and at  Kennedy Krieger Institute that he began exploring positive mental health and well-being—an experience that, he says, “stopped me in my tracks cold.”

“I was shocked to find a robust body of scientific research on well-being,” says Kalb, PhD ’17, MHS ’08, especially since he didn’t encounter the topic in any of his training. He became convinced by the evidence that there is a dimension of mental life beyond the absence of disease—and a role for public health beyond the mitigation of mental illness and crises. With funding from the Herbert Bearman Foundation, he designed the first course at the School that was solely focused on well-being:  Public Health and the Good Life .

The course was launched last year, in the heart of the pandemic. As Kalb met with students virtually and heard about their challenges in everyday life, it became clear there is a wide need for practices we can all use to protect our mental health during stressful, uncertain times.

Here, Kalb shares some important ideas that students take from the course, along with some evidence-based strategies and tools to try.

• Take advantage of the well-being toolkit. There are many evidence-based practices—including mindfulness and meditation—that can improve well-being and prevent the onset of psychological distress. The Calm app (which Johns Hopkins offers for free to all faculty and staff) is a great place to start.  
• Cultivate relationships. One of the most important influences on our well-being is our relationships with others. However, we are living in a world of deep isolation and discord. Staying in close contact with loved ones is critical, whether for a walk in the neighborhood or a phone or video call. Finding new social outlets, like joining clubs or attending socials (even if they’re virtual), can be especially helpful for students or others who are transitioning to a new location.
• Avoid the comparison trap. A number of biases are often baked into our thinking, and we need to be aware of them. For instance, we are prone to social comparisons. Historically, due to limited transportation, we could compare ourselves only to our neighbors. Now, social media allows us to compare ourselves to the most rich and famous people in the world. Those unrealistic comparisons and self-judgments can be distressing.  
• Don’t overlook the basics. Many techniques to improve our well-being are readily available to us but not often discussed, such as protecting your sleep and leveraging gratitude. These simple practices can have profound downstream effects.

Public Health and the Good Life will be offered again starting in January 2022. New for this year: a focus on mHealth technologies. Kalb will bring in mHealth expert Johannes Thrul, PhD, MS , an assistant professor in Mental Health; and Omar Jalazada, co-founder CEO of  Kin , to talk about how we can leverage digital peer supports to promote lasting behavior change.  

Melissa Hartman is the managing editor of Hopkins Bloomberg Public Health magazine and associate director of editorial at the Johns Hopkins Bloomberg School of Public Health.

• The Intersection of Mental Health and Chronic Disease
• Mental Wellness at Work
• SEE Change: Improving Health Through Self-Empowerment

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Sustainable Development Goal 3

Ensure healthy lives and promote well-being for all at all ages.

Sustainable Development Goal 3 is to “ensure healthy lives and promote well-being for all at all ages”, according to the United Nations .

The visualizations and data below present the global perspective on where the world stands today and how it has changed over time.

The UN has defined 13 targets and 28 indicators for SDG 3. Targets specify the goals and indicators represent the metrics by which the world aims to track whether these targets are achieved. Below we quote the original text of all targets and show the data on the agreed indicators.

Target 3.1 Reduce maternal mortality

Sdg indicator 3.1.1 maternal mortality ratio.

Definition of the SDG indicator: Indicator 3.1.1 is the “maternal mortality ratio” in the UN SDG framework .

The maternal mortality ratio refers to the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births.

Data for this indicator is shown in the interactive visualization.

Target: By 2030 “reduce the global maternal mortality ratio to less than 70 per 100,000 live births” per year.

More research: The Our World in Data topic page on Maternal Mortality gives a long-run perspective over the last centuries and presents research on the causes and consequences of the deaths of mothers.

Additional charts

• Number of maternal deaths by region
• Number of maternal deaths by country

SDG Indicator 3.1.2 Skilled birth attendance

Definition of the SDG indicator: Indicator 3.1.2 is the “proportion of births attended by skilled health personnel” in the UN SDG framework .

This indicator is measured as the ratio of the births attended by skilled health personnel (generally doctors, nurses, or midwives) who are trained in providing quality obstetric care, to the number of live births in the same period.

More research: Research, discussed in the Our World in Data topic page on Maternal Mortality , shows that skilled staff can reduce maternal mortality.

Target 3.2 End all preventable deaths under 5 years of age

Sdg indicator 3.2.1 under-5 mortality rate.

Definition: Indicator 3.2.1 is the “under-5 mortality rate” in the UN SDG framework .

The under-5 mortality rate measures the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.

Target: By 2030, “end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births.”

More research: Child mortality is covered more broadly, and with a longer-term perspective in the Our World in Data topic page on Child Mortality .

• Number of under-five deaths
• Number of under-five deaths by region
• Child mortality rate by sex

SDG Indicator 3.2.2 Neonatal mortality rate

Definition of the SDG indicator: Indicator 3.2.2 is the “neonatal mortality rate” in the UN SDG framework .

The neonatal mortality rate is defined as the probability per 1,000 that a child born in a given year will die during the first 28 days of life, if subject to the age-specific mortality rates of that period.

Data on this indicator is shown in the interactive visualization.

More research: The Our World in Data topic page on Child Mortality includes a section on neonatal mortality.

• Number of neonatal deaths
• Number of neonate deaths by region

Target 3.3 Fight communicable diseases

Sdg indicator 3.3.1 hiv incidence.

Definition of the SDG indicator: Indicator 3.3.1 is the “number of new HIV infections per 1,000 uninfected population, by sex, age and key populations” in the UN SDG framework .

Data for this indicator is shown in the interactive visualization, by age group in the first chart and for the 15-49 age group in the second chart. You can change the country shown in the first chart by clicking the “Change country” button in the upper left hand corner.

Target: The target for 2030 is to “end the epidemic of AIDS” across all countries. 1

The targeted level of reduction is defined by UNAIDS as a 90% reduction in new HIV infections over 2010 levels. For all age groups combined, this would imply a target of around .03 per 1,000, or 3 new infections for every 100,000 uninfected people.

More research: HIV is covered in detail by the Our World in Data topic page on HIV/AIDS .

• Share of population infected with HIV
• HIV/AIDS death rates
• Number of HIV/AIDS deaths

SDG Indicator 3.3.2 Tuberculosis incidence

Definition of the SDG indicator: Indicator 3.3.2 is “tuberculosis incidence per 100,000 population” in the UN SDG framework .

Tuberculosis incidence is the number of new and relapse cases of tuberculosis (TB) per 100,000 people, including all forms of TB.

Target: The 2030 target is to “end the epidemic of tuberculosis” in all countries. 1

The World Health Organization's End TB Strategy defines this targeted level of reduction as a decrease in incidence of 80% over 2015 levels. This would imply a target of around 28 cases per 100,000 population globally.

• Tuberculosis death rates
• Number of tuberculosis deaths

SDG Indicator 3.3.3 Malaria incidence

Definition of the SDG indicator: Indicator 3.3.3 is “malaria incidence per 1,000 population” in the UN SDG framework .

Malaria incidence is the number of new cases of malaria in one year per 1,000 people at risk.

Target: By 2030 “end the epidemic of malaria” in all countries. 1

To achieve this target, the WHO Global Technical Strategy has set a target of reducing incidence by 90% by 2030 from 2015 levels. This would imply a target of 6 or fewer cases of malaria per 1,000 people globally in 2030.

More research: More information on global and national trends in malaria prevalence, deaths and interventions can be found at the Our World in Data topic page on Malaria .

• Malaria death rates
• Number of malaria deaths

SDG Indicator 3.3.4 Hepatitis B incidence

Definition of the SDG indicator: Indicator 3.3.4 is “Hepatitis B incidence per 100,000 population” in the UN SDG framework .

Hepatitis B incidence is the number of new cases of hepatitis B in one year per 100,000 people in a given population. This is measured indirectly as the share of children under 5 years of age with an active Hepatitis B infection, as measured by an Hepatitis B surface antigen test.

Target: By 2030 “combat hepatitis” in all countries with a focus on hepatitis B. 1 The targeted level of reduction, however, is not defined.

• Hepatitis death rates

SDG Indicator 3.3.5 Neglected tropical diseases

Definition of the SDG indicator: Indicator 3.3.5 is the “number of people requiring interventions against neglected tropical diseases” in the UN SDG framework .

This is defined as the number of people who require interventions (treatment and care) for any of the 20 neglected tropical diseases (NTDs) identified by the WHO NTD Roadmap and World Health Assembly resolutions. Treatment and care is broadly defined to allow for preventive, curative, surgical or rehabilitative treatment and care.

Target: By 2030 “end the epidemic of neglected tropical diseases (NTDs)” in all countries. 1

The associated WHO target is a 90% reduction in the number of people requiring interventions against NTDs from 2010 baseline levels. This implies a target of 219 million people needing interventions against NTDs in 2030.

• Number of people requiring interventions for NTDs by region

Target 3.4 Reduce mortality from non-communicable diseases and promote mental health

Sdg indicator 3.4.1 mortality rate from non-communicable diseases.

Definition of the SDG indicator: Indicator 3.4.1 is the “mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease” in the UN SDG framework .

This is defined as the percent of 30-year-old-people who would die before their 70th birthday from cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that they would experience current mortality rates at every age and would not die from any other cause of death (e.g. injuries or HIV/AIDS).

Target: By 2030 “reduce by one third premature mortality from non-communicable diseases through prevention and treatment” in all countries. 2

More research: Further data and research on non-communicable diseases can be found at the Our World in Data topic pages on Causes of Death , Burden of Disease , and Cancer .

• Cancer death rates
• Cardiovascular disease (CVD) death rates
• Stroke death rates

SDG Indicator 3.4.2 Suicide rate

Definition of the SDG indicator: Indicator 3.4.2 is the “suicide mortality rate” in the UN SDG framework .

The suicide mortality rate is the number of deaths from suicide measured per 100,000 people in a given population.

Target: By 2030 “promote mental health and wellbeing”. 2 There is no defined target level of reduction for this indicator.

More research: Further data and research on suicide, mental health and wellbeing can be found at the Our World in Data topic pages on Suicide , Mental Health and Happiness and Life Satisfaction .

• Number of suicide deaths
• Share of population with depression

Target 3.5 Prevent and treat substance abuse

Sdg indicator 3.5.1 coverage of treatment interventions for substance use disorders.

Definition of the SDG indicator: Indicator 3.5.1 is the “coverage of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services) for substance use disorders” in the UN SDG framework .

This is the share of people with substance use disorders in a given year who receive treatment in the form of pharmacological, psychosocial, rehabilitation or aftercare services. Data coverage in household surveys of substance use disorders is limited in many countries, and efforts are currently in progress to better estimate this indicator.

Data for this indicator is shown in the interactive visualizations. The first chart shows the share of the population with an alcohol use disorder in each country, and the second chart shows coverage of treatment interventions for certain types of substance use disorder for the countries where this data is available.

Target: By 2030 “strengthen the prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcohol” across all countries. However, there is no defined target level for this indicator.

More research: The Our World in Data topic page on Substance Use provides data on substance use disorder prevalence and as well as more limited data coverage of treatment interventions.

SDG Indicator 3.5.2 Alcohol consumption per capita

Definition of the SDG indicator: Indicator 3.5.2 is the “harmful use of alcohol, defined according to the national context as alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcohol” in the UN SDG framework .

More research: Further data and research on alcohol consumption and alcohol use disorders can be found at the Our World in Data topic page on Alcohol Consumption .

• Share of population with alcohol use disorders
• Share of population with drug use disorders
• Prevalence of substance use disorders by sex

Target 3.6 Reduce road injuries and deaths

Sdg indicator 3.6.1 halve the number of road traffic deaths.

Definition of the SDG indicator: Indicator 3.6.1 is the “death rate due to road traffic injuries” in the UN SDG framework .

Road traffic deaths include vehicle drivers, passengers, motorcyclists, cyclists and pedestrians.

Data for this indicator is shown in the first chart in the series of interactive visualizations. The second chart shows the absolute number of road traffic deaths for additional context.

Target: By 2020 “halve the number of global deaths and injuries from road traffic accidents.”

While most SDG targets are set for 2030, this was set to be achieved for 2020.

Note that the SDG Indicator is the rate of road deaths while the target is set for the absolute number of road deaths. Because of this, the interactive visualization shows, in the first chart, the road traffic death rate, and in the second chart, the number of road traffic deaths.

• Road traffic deaths by user

Target 3.7 Universal access to sexual and reproductive care, family planning and education

Sdg indicator 3.7.1 family planning needs.

Definition of the SDG indicator: Indicator 3.7.1 is the “proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methods” in the UN SDG framework .

This indicator incorporates two components, the prevalence of modern methods of contraception, and the share of women of reproductive age who want to stop or delay childbearing but are not using any method of contraception.

It is measured as the percent of women of reproductive age (15-49 years) who are currently using at least one modern contraceptive method, out of the total population of women who have demand for contraceptive methods (defined as those using contraception of any form or who have unmet need for contraception).

Target: By 2030 “ensure universal access to sexual and reproductive healthcare services, including for family planning, information and education.” 3

More research: Further data and research can be found at the Our World in Data topic page on Fertility Rate .

• Unmet need for contraception
• Contraception prevalence, any methods

SDG Indicator 3.7.2 Adolescent birth rate

Definition of the SDG indicator: Indicator 3.7.2 is the “adolescent birth rate (aged 10–14 years; aged 15–19 years) per 1,000 women in that age group” in the UN SDG framework .

Data for this indicator is shown in the interactive visualizations, which show, in the first chart, adolescent birth rates per 1,000 women aged 10-14 years old, and in the second chart, women aged 15-19 years old.

Target: By 2030 “ensure universal access to sexual and reproductive healthcare services, including for family planning.” 3

Target 3.8 Achieve universal health coverage

Sdg indicator 3.8.1 coverage of essential health services.

Definition of the SDG indicator: Indicator 3.8.1 is “coverage of essential health services” in the UN SDG framework .

Coverage of essential health services is defined as the average coverage of essential services based on tracer interventions that include reproductive, maternal, newborn and child health, infectious diseases, non-communicable diseases and service capacity and access, among the general and the most disadvantaged population.

The Universal Health Coverage (UHC) Service Coverage Index is used to track progress on this indicator. The index is on a scale from 0 to 100, where 100 is the optimal value, and calculated from the geometric mean of 14 indicators measuring the coverage of essential services including reproductive, maternal, newborn and child health, infectious diseases, non-communicable diseases and service capacity and access.

Target: By 2030 “achieve universal health coverage including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all.”

More research: Further data and research can be found at the Our World in Data topic page on Financing Healthcare .

SDG Indicator 3.8.2 Household expenditures on health

Definition of the SDG indicator: Indicator 3.8.2 is the “proportion of population with large household expenditures on health as a share of total household expenditure or income” in the UN SDG framework .

Two thresholds are used for defining large household expenditures: greater than 10% or 25% of total household expenditure or income.

The interactive visualizations show data for the 25 and 10 percent thresholds.

Target: By 2030 “achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all.”

• Out-of-pocket expenditure on healthcare
• Risk of catastrophic expenditure for surgical care
• Risk of impoverishing expenditure for surgical care

Target 3.9 Reduce illnesses and deaths from hazardous chemicals and pollution

Sdg indicator 3.9.1 mortality rate from air pollution.

Definition of the SDG indicator: Indicator 3.9.1 is the “mortality rate attributed to household and ambient air pollution” in the UN SDG framework .

This is measured as the number of deaths attributed to indoor and outdoor air pollution per 100,000 people, accounting for differences in the age structure of different populations.

Data for this indicator is shown in the series of interactive visualizations, first for household and ambient air pollution combined, then for each separately, and then with a comparison of the two types of pollution in the final chart.

Target: By 2030 “substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination.” There is, however, not a defined target level for this indicator.

More research: Further data and research can be found at the Our World in Data topic pages on Air Pollution and Indoor Air Pollution .

• Mortality rate from ambient particulate air pollution
• Number of deaths from outdoor air pollution
• Mortality rate from indoor air pollution
• Number of deaths from indoor air pollution

SDG Indicator 3.9.2 Mortality rate from unsafe water, sanitation, hygiene (WASH)

Definition of the SDG indicator: Indicator 3.9.2 is the “mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene” in the UN SDG framework .

This indicator is defined as the number of deaths per 100,000 people that are attributed to unsafe water, unsafe sanitation, and lack of hygiene (defined as exposure to unsafe Water, Sanitation, and Hygiene for All (WASH) services). This definition includes deaths from diarrhoea, intestinal nematode infections, malnutrition and acute respiratory infections.

Target: By 2030 “substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination.” There is, however, not a defined quantified target level for this indicator.

More research: Further data and research can be found at the Our World in Data topic page on Water Access, Resources and Sanitation .

• Mortality rate attributable to unsafe water
• Mortality rate attributable to unsafe sanitation

SDG Indicator 3.9.3 Mortality rate from unintentional poisoning

Definition of the SDG indicator: Indicator 3.9.3 is the “mortality rate attributed to unintentional poisoning” in the UN SDG framework .

This measures the annual number of deaths per 100,000 people that are attributed to unintentional poisonings.

Target 3.a Implement the WHO framework convention on tobacco control

Sdg indicator 3.a.1 prevalence of tobacco use.

Definition of the SDG indicator: Indicator 3.a.1 is the “age-standardized prevalence of current tobacco use among persons aged 15 years and older” in the UN SDG framework .

This measures the share of people aged 15 and older who currently use any tobacco product, whether smoked or smokeless tobacco. This includes both people who use tobacco on a daily basis as well as those who use it on a non-daily basis but have used it at some point in the last 30 days before the survey. Age-standardization accounts for differences in age distributions between countries.

Target: By 2030 “strengthen the implementation of the World Health Organization Framework Convention on Tobacco Control in all countries, as appropriate.” There is no specified target level of tobacco use for this indicator.

More research: Further data and research can be found at the Our World in Data topic page on Smoking .

• Daily smoking in people aged 10 or older
• Share of men who smoke
• Share of women who smoke
• Death rate from tobacco smoking
• Deaths attributed to smoking and secondhand smoke

Target 3.b Support research, development and access to affordable vaccines and medicines

Sdg indicator 3.b.1 vaccine coverage.

Definition of the SDG indicator: Indicator 3.b.1 is the “proportion of the target population covered by all vaccines included in their national programme” in the UN SDG framework .

The UN currently includes the four following vaccines in this indicator: three-dose diphtheria, pertussis, and tetanus (DPT3); second-dose measles vaccine; recommended dose of pneumococcal conjugate vaccine (PCV3) and recommended dose of human papillomavirus vaccine.

Data on this indicator is shown across the four interactive visualizations.

Target: By 2030 “provide access to affordable essential medicines and vaccines.” 4

For this indicator, this means universal coverage of the vaccines noted above (if included in national vaccination programmes) must be achieved by 2030.

SDG Indicator 3.b.2 Development assistance to medical research & basic healthcare

Definition: Indicator 3.b.2 is the “total net official development assistance (ODA) to medical research and basic health sectors” in the UN SDG framework .

This indicator is measured as disbursements of official development assistance (ODA) and other official flows to the medical research and basic health sectors.

Official development assistance refers to flows to countries and territories on the Organization for Economic Co-operation and Development’s Development Assistance Committee (DAC) and to multilateral institutions which meet a set of criteria related to the source of the funding, the purpose of the transaction, and the concessional nature of the funding.

Data for this indicator is shown for recipient countries.

Target: By 2030 “support the research and development of vaccines and medicines for the communicable and non-communicable diseases that primarily affect developing countries, [and] provide access to affordable essential medicines and vaccines.” 4

SDG Indicator 3.b.3 Availability of essential medicines

Definition: Indicator 3.b.3 is the “proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis” in the UN SDG framework .

This indicator measures the share of surveyed healthcare facilities that had essential medicines available for purchase at prices, such that no extra daily wages would be needed for the lowest paid unskilled government sector worker to purchase a monthly dose treatment of this medicine after fulfilling their basic needs represented by the national poverty line.

The list of 32 essential medicines used in calculation is from the 2017 Model List of Essential Medicines from the WHO Expert Committee on Selection and Use of Essential Medicines, which updates its list of essential medicines every two years. Availability and affordability of specific medicines are weighted in the overall calculation based on the regional burden of disease.

Target: By 2030 “provide access to affordable essential medicines for all.” 4

Target 3.c Increase health financing and support health workforce in developing countries

Sdg indicator 3.c.1 health worker density.

Definition: Indicator 3.c.1 is “health worker density and distribution” in the UN SDG framework .

Health worker density is the size of the health workforce per 1,000 people. It is measured here based on the density of physicians, surgeons, nurses and midwives, dentistry and pharmaceutical personnel.

Target: By 2030 “substantially increase health financing and the recruitment, development, training and retention of the health workforce in developing countries.”

• Nurses and midwives (per 1,000 people)
• Surgical workforce (per 100,000 people)
• Dentistry personnel (per 1,000 people)
• Pharmaceutical personnel (per 1,000 people)

Target 3.d Improve early warning systems for global health risks

Sdg indicator 3.d.1 health emergency preparedness.

Definition: Indicator 3.d.1 is the “International Health Regulations (IHR) capacity and health emergency preparedness” in the UN SDG framework .

The IHR Core capacity index is measured in terms of 15 capacities, where each capacity is measured as the average implementation score across a set of indicators. Countries self-report progress in the following 15 capacities: (1) Policy, legal and normative instruments to implement IHR; (2) IHR Coordination and National Focal Point Functions; (3) Financing; (4) Laboratory; (5) Surveillance; (6) Human resources; (7) Health emergency management (8) Health Service Provision; (9) Infection Prevention and Control; (10) Risk communication and community engagement; (11) Points of entry and border health; (12) Zoonotic diseases; (13) Food safety; (14) Chemical events; (15) Radiation emergencies.

Target: By 2030 “strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risks.”

SDG Indicator 3.d.2 Bloodstream infections due to antimicrobial-resistant organisms

Definition of the SDG indicator: Indicator 3.d.2 is the “percentage of bloodstream infections due to selected antimicrobial-resistant organisms” in the UN SDG framework .

This is measured as the share of people who are found to have a bloodstream infection due to certain antimicrobial-resistant organisms (methicillin-resistant Staphylococcus aureus (MRSA) and Escherichia coli resistant to 3rd-generation cephalosporin), among those seeking care whose blood sample is collected and tested.

Data for this indicator is shown in the interactive visualizations.

Full text: “By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases.”

Full text: “By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being.”

Full text:” By 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmes.”

Full text: “Support the research and development of vaccines and medicines for the communicable and non-communicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade-Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for all.”

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SDG 3: Good Health & Well-Being

Sustainable Development Goal 3 (SDG 3) is one of the 17 Sustainable Development Goals established by the United Nations in 2015. The official wording of SDG 3 is: “To ensure healthy lives and promote well-being for all at all ages.” SDG 3 research focuses on key targets like: reducing maternal mortality, ending all preventable deaths for children under five, fighting communicable diseases, reducing mortality from non-communicable diseases, and promoting mental health — all with the aim of stopping needless suffering from preventable diseases and premature death.

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-  Disparities in access to sexual and reproductive health services ( BMC Women's Health & BMC Pregnancy and Childbirth )

-  Quality improvement in maternal and reproductive health services   (BMC Pregnancy and Childbirth and BMC Women's Health)

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Key topics in SDG 3

Suicide Prevention

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An exclusive collection of the latest research from across disciplines and imprints that highlight how mental health and well-being are impacted by social, cultural and environmental disparities.

Our Nurses. Our Future

We are proud to support the nursing community with a global nursing and midwifery program. Read inspiring blogs and highlighted articles and book chapters.

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The reality of climate change is inextricable from human health. Springer Nature has published a broad range of scholarship on these topics, which will help guide policy to address these challenges.

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Because OA’s enhanced and equitable visibility means that research can reach the broader audience, and findings can be translated into actionable strategy. Explore OA books that support sustainable development and learn more about publishing your book OA - including funding options.

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Your work here: information for authors.

You can add impact and power to your SDG-related research when you publish it at Springer Nature, and alongside leading research (like the examples above). Research published OA at Springer Nature gets more exposure . For example, research published in fully OA Springer Nature journals are downloaded over 7,000 times on average (up to 5x more than competitors) and cited 7.39 times on average.

Publishing with Springer Nature gives you:

• A range of journals , from Nature, Springer, and BMC, both hybrid and OA. The journals with SDG content curated above are a small selection of journals you can publish in. Clicking through on them will give you the information — including journal metrics, submission guidelines and more — that you need to plan your next submission.
• The world’s leading scholarly book program, including the option to publish your book OA. ᐅ SDG 3 OA Books
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Healthy? Maybe. But are you flourishing?

Caitlin McDermott-Murphy

Harvard Correspondent

The answer, according to a groundbreaking new global study, might be more complex than you think

What’s your blood pressure?

For most people, this is an easy question, a fundamental measurement taken at every doctor’s visit. Many supermarkets have free stations to check it. Even smart watches can gather this metric anywhere, anytime.

Now answer this: What’s your purpose in life?

That data, according to a group of researchers at Harvard University and Baylor University, might be just as important as blood pressure in gauging what the scholars view as human well-being. That is to say, the sum total of your physical and mental health, along with your happiness and life satisfaction, sense of meaning and purpose, character and virtue, and close social relationships. This view of overall health is the focus of their new $43.4 million Global Flourishing Study to be launched this month — the largest, most culturally and geographically diverse of its kind. The team will follow roughly 240,000 participants from 22 countries over five years to gather data on which individuals or nations are flourishing and why, or why not.

“Health is more than the absence of disease,” according to the Centers for Disease Control and Prevention . Well-being is harder — but not impossible — to measure. While previous studies have tried, the Global Flourishing Study, whose partners include the survey giant Gallup  and the  Center for Open Science , is the first to take a global, longitudinal approach in an attempt to find causal links between well-being and specific character traits — like extroversion or optimism — practices, communities, relationships, or religions. If successful, the survey could later be administered as a kind of diagnostic test to prescribe interventions, similar to exercise and heart-healthy diets for cardiovascular disease.

People in poorer, developing countries typically have a greater sense of meaning and purpose. They also tend to have stronger relationships. “We don’t score very highly on that in the United States,” said Tyler VanderWeele, director of the Human Flourishing Program at Harvard.

Kris Snibbe/Harvard Staff Photographer

“We study physical health very well,” said the project’s co-director, Tyler VanderWeele, the John L. Loeb and Frances Lehman Loeb Professor of Epidemiology and director of the Human Flourishing Program at Harvard. “We also study income and wealth very well.” But while these are no doubt important, people also care about being happy, having a sense of meaning and purpose, and trying to be a good person. “Why aren’t we studying these topics with the same level of empirical rigor as we study physical health and income?”

One reason is because it’s difficult. Measuring happiness, purpose, or love requires more than a medical instrument. Centuries of philosophical and theological texts offer varying and valuable takes on the meaning of life, which is why VanderWeele enlisted a senior philosopher to help develop the survey questions. The director hopes this modern effort will result in more quantitative, measurable answers to this age-old question.

“What we measure shapes what we talk about, what we focus on, what we aim for, and policies put in place to achieve it,” he said.

GDP might be as ubiquitous a metric as blood pressure, but because a country has a high GDP doesn’t always mean its citizens have a high level of well-being. People in wealthier developed countries, for example, often have higher levels of happiness and life satisfaction. But people in poorer, developing countries typically have a greater sense of meaning and purpose. They also tend to have stronger relationships. “We don’t score very highly on that in the United States,” said VanderWeele.

To study these seemingly nebulous qualities the way scientists study disease, the multidisciplinary team designed a survey in which participants respond to statements like: “I am content with my friendships and relationships,” “I feel that I’m a person of worth,” and “I have forgiven those who hurt me.” There are also more familiar questions like, “How often do you worry about safety, food, or housing?” and “About how many cigarettes do you smoke each day?”

Some critics still have doubts about whether the study can effectively measure seemingly more subjective qualities, like love. “To that, I would say, ‘Let’s see what we get,’” said Matthew Lee, director of empirical research for the Human Flourishing Program.

Stephanie Mitchell/Harvard Staff Photographer

Translating these concepts across cultures has not always been easy. Germany, for example, has two different words for “happiness,” neither of which map exactly to the English definition. Love doesn’t have one universal definition, either: There’s romantic love; love between parent and child; love of country; and spiritual love. And in some countries where humility and privacy are highly valued, said Matthew Lee, a lecturer of sociology and the director of empirical research for the Human Flourishing Program, individuals might tailor responses to avoid seeming boastful about how they’re doing or attention-seeking if they are having difficulties.

“So how do we incorporate all of that into one study?” Lee said. “The answer is we don’t. But we can become more aware of the limitations of what we’re trying to do.”

In an attempt to head off problems, the research team solicited feedback from scholars around the world and ran cognitive interviews and pilot tests to learn whether respondents in various countries interpreted the questions differently. Now, after three years, the first survey is finally set for launch, and the data will be open-access and available to anyone.

Some critics still have doubts about whether the study can effectively measure seemingly more subjective qualities, like love.

“To that, I would say, ‘Let’s see what we get,’” said Lee. “Happiness keeps going down, especially in the United States. If we’re not prioritizing deep, fulfilling, loving relationships, then [at least] our salaries can go up. And we can have bigger houses.”

But does that mean we have meaning in life? As Lee suggested, we’ll find out.

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UNICEF Data : Monitoring the situation of children and women

GOAL 3: GOOD HEALTH AND WELL-BEING

Ensure healthy lives and promote well-being for all at all ages.

Goal 3 aims to ensure healthy lives and promote well-being for all, at all ages. Health and well-being are important at every stage of one’s life, starting from the beginning. This goal addresses all major health priorities: reproductive, maternal, newborn, child and adolescent health; communicable and non-communicable diseases; universal health coverage; and access for all to safe, effective, quality and affordable medicines and vaccines.

SDG 3 aims to prevent needless suffering from preventable diseases and premature death by focusing on key targets that boost the health of a country’s overall population. Regions with the highest burden of disease and neglected population groups and regions are priority areas. Goal 3 also calls for deeper investments in research and development, health financing and health risk reduction and management.

UNICEF’s role in contributing to Goal 3 centres on healthy pregnancies ( maternal mortality and skilled birth attendant), healthy childhoods (under-five and neonatal mortality) as well as vaccine coverage. UNICEF also contributes to monitoring elements of the universal health coverage indicator.

UNICEF is custodian for global monitoring of two indicators that measure progress towards Goal 3 as it relates to children: Indicator 3.2.1 Under-five mortality rate and Indicator 3.2.2 Neonatal mortality rate. UNICEF is also co-custodian for Indicator 3.1.2 Proportion of births attended by skilled health personnel and for Indicator 3.b.1 Proportion of the target population covered by all vaccines included in their national programme.

Child-related SDG indicators

Target 3.1 by 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live births, maternal mortality ratio (number of maternal deaths per 100,000 live births).

• Indicator definition
• Computation method
• Comments & limitations

Explore the data

Maternal mortality refers to deaths due to complications from pregnancy or childbirth. Accurate measurement of maternal mortality remains challenging and many deaths still go uncounted. Many countries still lack well functioning civil registration and vital statistics (CRVS) systems, and where such systems do exist, reporting errors – whether incompleteness (unregistered deaths, also known as “missing”) or misclassification of cause of death – continue to pose a major challenge to data accuracy.

The maternal mortality ratio (MMR) is defined as the number of maternal deaths during a given time period per 100,000 live births during the same time period. It depicts the risk of maternal death relative to the number of live births and essentially captures the risk of death in a single pregnancy or a single live birth.

Maternal deaths: The annual number of female deaths from any cause related to or aggravated by pregnancy or its management (excluding accidental or incidental causes) during pregnancy and childbirth or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy, expressed per 100,000 live births, for a specified time period.

Maternal death: The death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management (from direct or indirect obstetric death), but not from accidental or incidental causes.

Pregnancy-related death: The death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the cause of death. Late maternal death: The death of a woman from direct or indirect obstetric causes, more than 42 days, but less than one year after termination of pregnancy

The maternal mortality ratio can be calculated by dividing recorded (or estimated) maternal deaths by total recorded (or estimated) live births in the same period and multiplying by 100,000. Measurement requires information on pregnancy status, timing of death (during pregnancy, childbirth, or within 42 days of termination of pregnancy), and cause of death. The maternal mortality ratio can be calculated directly from data collected through vital registration systems, household surveys or other sources. There are often data quality problems, particularly related to the underreporting and misclassification of maternal deaths. Therefore, data are often adjusted in order to take these data quality issues into account. Some countries undertake these adjustments or corrections as part of specialized/confidential enquiries or administrative efforts embedded within maternal mortality monitoring programmes.

For countries with data available on maternal mortality, the expected proportion of non-HIV- related maternal deaths was based on country and regional random effects, whereas for countries with no data available, predictions were derived using regional random effects only.

Estimation of HIV-related indirect maternal deaths For countries with generalized HIV epidemics and high HIV prevalence, HIV/AIDS is a leading cause of death during pregnancy and post-delivery. There is also some evidence from community studies that women with HIV infection have a higher risk of maternal death, although this may be offset by lower fertility. If HIV is prevalent, there will also be more incidental HIV deaths among pregnant and postpartum women. When estimating maternal mortality in these countries, it is, thus, important to differentiate between incidental HIV deaths (non-maternal deaths) and HIV-related indirect maternal deaths (maternal deaths caused by the aggravating effects of pregnancy on HIV) among HIV-positive pregnant and postpartum women who have died (i.e. among all HIV-related deaths occurring during pregnancy, childbirth and puerperium).

For observed PMs, we assumed that the total reported maternal deaths are a combination of the proportion of reported non-HIV-related maternal deaths and the proportion of reported HIV- related (indirect) maternal deaths, where the latter is given by a*v for observations with a “pregnancy-related death” definition and a*v*u for observations with a “maternal death” definition.

The extent of maternal mortality in a population is essentially the combination of two factors:

1. The risk of death in a single pregnancy or a single live birth. 2. The fertility level (i.e. the number of pregnancies or births that are experienced by women of reproductive age).

The maternal mortality ratio (MMR) is defined as the number of maternal deaths during a given time period per 100,000 live births during the same time period. It depicts the risk of maternal death relative to the number of live births and essentially captures (1) above.

By contrast, the maternal mortality rate (MMRate) is calculated as the number of maternal deaths divided by person-years lived by women of reproductive age. The MMRate captures both the risk of maternal death per pregnancy or per total birth (live birth or stillbirth), and the level of fertility in the population.

In addition to the MMR and the MMRate, it is possible to calculate the adult lifetime risk of maternal mortality for women in the population. An alternative measure of maternal mortality, the proportion of deaths among women of reproductive age that are due to maternal causes (PM), is calculated as the number of maternal deaths divided by the total deaths among women aged 15–49 years.

Related Statistical measures of maternal mortality:

Maternal mortality ratio (MMR): Number of maternal deaths during a given time period per 100,000 live births during the same time period.

Maternal mortality rate (MMRate): Number of maternal deaths divided by person-years lived by women of reproductive age.

Adult lifetime risk of maternal death: The probability that a 15-year-old woman will die eventually from a maternal cause.

The proportion of deaths among women of reproductive age that are due to maternal causes (PM): The number of maternal deaths in a given time period divided by the total deaths among women aged 15–49 years.

Click on the button below to explore the data behind this indicator.

Skilled birth attendant – Proportion of births attended by skilled health personnel

• Sources of discrepancies

Having a skilled health care provider at the time of childbirth is an important lifesaving intervention for both women and newborns. Not having access to this key assistance is detrimental to women’s and newborns’ health because it could cause adverse health outcomes such as the death of the women and/or the newborns or long lasting morbidity. Achieving universal coverage is therefore essential for reducing maternal and newborn mortality and morbidity.

Proportion of births attended by skilled health personnel (generally doctors, nurses or midwives but can refer to other health professionals providing childbirth care) is the proportion of childbirths attended by skilled health personnel. According to the current definition (1) these are competent maternal and newborn health (MNH) professionals educated, trained and regulated to national and international standards.

They are competent to:

(i) provide and promote evidence-based, human-rights based, quality, socio-culturally sensitive and dignified care to women and newborns;

(ii) facilitate physiological processes during labour and delivery to ensure a clean and positive childbirth experience; and

(iii) identify and manage or refer women and/or newborns with complications.

Discrepancies are possible if there are national figures compiled at the health facility level. These would differ from the global figures, which are typically based on survey data collected at the household level. In terms of survey data, some survey reports may present a total percentage of births attended by a skilled health professional that does not conform to the MDG definition (e.g., total includes provider that is not considered skilled, such as a community health worker). In that case, the percentage delivered by a physician, nurse, or a midwife are totalled and entered into the global database as the MDG estimate. In some countries where skilled attendant at birth is not available, birth in a health facility (institutional births) is used instead. This is frequent among Latin American countries, where the proportion of institutional births is very high. Nonetheless, it should be noted that institutional births may underestimate the percentage of births with skilled attendant.

TARGET 3.2 By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births

Under-five mortality rate.

Mortality rates among young children are a key output indicator for child health and well-being, and, more broadly, for social and economic development. This is a closely watched public health indicator because it reflects the access of children and communities to basic health interventions such as vaccination, medical treatment of infectious diseases and adequate nutrition.

Probability of dying between birth and exactly 5 years of age, expressed per 1,000 live births

The under-five mortality rate as defined here is, strictly speaking, not a rate (i.e. the number of deaths divided by the number of population at risk during a certain period of time), but a probability of death derived from a life table and expressed as a rate per 1000 live births.

The UN Inter-agency Group for Child Mortality Estimation (UN IGME) estimates are derived from national data from censuses, surveys or vital registration systems. The UN IGME does not use any covariates to derive its estimates. It only applies a curve fitting method to good-quality empirical data to derive trend estimates after data quality assessment. In most cases, the UN IGME estimates are close to the underlying data. The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates. The UN IGME applies the Bayesian B-splines bias-reduction model to empirical data to derive trend estimates of under-five mortality for all countries. See references for details.

For the underlying data mentioned above, the most frequently used methods are as follows:

Civil registration: The under-five mortality rate can be derived from a standard period abridged life table using the age-specific deaths and mid-year population counts from civil registration data to calculate death rates, which are then converted into age-specific probabilities of dying.

Census and surveys: An indirect method is used based on a summary birth history, a series of questions asked of each woman of reproductive age as to how many children she has ever given birth to and how many are still alive. The Brass method and model life tables are then used to obtain an estimate of under-five and infant mortality rates. Censuses often include questions on household deaths in the last 12 months, which can be used to calculate mortality estimates.

Surveys: A direct method is used based on a full birth history, a series of detailed questions on each child a woman has given birth to during her lifetime. Neonatal, post-neonatal, infant, child and under-five mortality estimates can be derived from the full birth history module.

The UN IGME estimates are derived based on national data. Countries often use a single source as their official estimates or apply methods different from the UN IGME methods to derive estimates. The differences between the UN IGME estimates and national official estimates are usually not large if empirical data has good quality.

Many countries lack a single source of high-quality data covering the last several decades. Data from different sources require different calculation methods and may suffer from different errors, for example random errors in sample surveys or systematic errors due to misreporting. As a result, different surveys often yield widely different estimates of under-five mortality for a given time period and available data collected by countries are often inconsistent across sources. It is important to analyse, reconcile and evaluate all data sources simultaneously for each country. Each new survey or data point must be examined in the context of all other sources, including previous data. Data suffer from sampling or non-sampling errors (such as misreporting of age and survivor selection bias; underreporting of child deaths is also common). UN IGME assesses the quality of underlying data sources and adjusts data when necessary. Furthermore, the latest data produced by countries often are not current estimates but refer to an earlier reference period. Thus, the UN IGME also projects estimates to a common reference year. In order to reconcile these differences and take better account of the systematic biases associated with the various types of data inputs, the UN IGME has developed an estimation method to fit a smoothed trend curve to a set of observations and to extrapolate that trend to a defined time point. The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates of child mortality. In the absence of error-free data, there will always be uncertainty around data and estimates. To allow for added comparability, the UN IGME generates such estimates with uncertainty bounds. Applying a consistent methodology also allows for comparisons between countries, despite the varied number and types of data sources. UN IGME applies a common methodology across countries and uses original empirical data from each country but does not report figures produced by individual countries using other methods, which would not be comparable to other country estimates.

Neonatal mortality rate

The neonatal mortality rate is the probability that a child born in a specific year or period will die during the first 28 completed days of life if subject to age-specific mortality rates of that period, expressed per 1000 live births.

Neonatal deaths (deaths among live births during the first 28 completed days of life) may be subdivided into early neonatal deaths, occurring during the first 7 days of life, and late neonatal deaths, occurring after the 7th day but before the 28th completed day of life.

The UN Inter-agency Group for Child Mortality Estimation (UN IGME) estimates are derived from nationally representative data from censuses, surveys or vital registration systems. The UN IGME does not use any covariates to derive its estimates. It only applies a curve fitting method to good-quality empirical data to derive trend estimates after data quality assessment. In most cases, the UN IGME estimates are close to the underlying data. The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates. The UN IGME produces neonatal mortality rate estimates with a Bayesian spline regression model which models the ratio of neonatal mortality rate / (under-five mortality rate – neonatal mortality rate). Estimates of NMR are obtained by recombining the estimates of the ratio with the UN IGME-estimated under-five mortality rate. See the references for details.

Civil registration: Number of children who died during the first 28 days of life and the number of births used to calculate neonatal mortality rates.

Censuses and surveys: Censuses and surveys often include questions on household deaths in the last 12 months, which can be used to calculate mortality estimates.

Many countries lack a single source of high-quality data covering the last several decades. Data from different sources require different calculation methods and may suffer from different errors, for example random errors in sample surveys or systematic errors due to misreporting. As a result, different surveys often yield widely different estimates of neonatal mortality for a given time period and available data collected by countries are often inconsistent across sources. It is important to analyse, reconcile and evaluate all data sources simultaneously for each country. Each new survey or data point must be examined in the context of all other sources, including previous data. Data suffer from sampling or non-sampling errors (such as misreporting of age and survivor selection bias; underreporting of child deaths is also common). UN IGME assesses the quality of underlying data sources and adjusts data when necessary. Furthermore, the latest data produced by countries often are not current estimates but refer to an earlier reference period. Thus, the UN IGME also projects estimates to a common reference year. In order to reconcile these differences and take better account of the systematic biases associated with the various types of data inputs, the UN IGME has developed an estimation method to fit a smoothed trend curve to a set of observations and to extrapolate that trend to a defined time point. The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates of child mortality. In the absence of error-free data, there will always be uncertainty around data and estimates. To allow for added comparability, the UN IGME generates such estimates with uncertainty bounds. Applying a consistent methodology also allows for comparisons between countries, despite the varied number and types of data sources. UN IGME applies a common methodology across countries and uses original empirical data from each country but does not report figures produced by individual countries using other methods, which would not be comparable to other country estimates.

TARGET 3.3 By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases

Estimated incidence rate (new hiv infection per 1,000 uninfected population).

This indicator is used to measure progress towards ending the AIDS epidemic. The overarching goal of the global AIDS response is to reduce the number of people newly infected to fewer than 500,000 in 2020 and fewer than 200,000 in 2030. Monitoring the rate of people newly infected over time measures the progress towards achieving this goal. Disaggregation by sex, age and key populations is important to characterize how the epidemic is evolving, to monitor equity of access to services and to support the planning of programme responses in specific age groups such as children under five, adolescents and young adults, as well as key populations.

Annual number of new HIV infections per 1,000 uninfected population

Longitudinal data on individuals are the best source of data but are rarely available for large populations. Special diagnostic tests in surveys or from health facilities can be used to obtain data on HIV incidence. HIV incidence is thus modelled using the Spectrum software.

Malaria incidence per 1,000 population

This indicator is used to measure trends in malaria morbidity and to identify locations where the risk of disease is highest. With this information, programmes can respond to unusual trends, such as epidemics, and direct resources to the populations most in need. This data also serves to inform global resource allocation for malaria such as when defining eligibility criteria for Global Fund finance.

Incidence of malaria is defined as the number of new cases of malaria per 1,000 people at risk each year.

Case of malaria is defined as the occurrence of malaria infection in a person whom the presence of malaria parasites in the blood has been confirmed by a diagnostic test. The population considered is the population at risk of the disease.

Malaria incidence (1) is expressed as the number of new cases per 100,000 population per year with the population of a country derived from projections made by the UN Population Division and the total proportion at risk estimated by a country’s National Malaria Control Programme. More specifically, the country estimates what is the proportion at high risk (H) and what is the proportion at low risk (L) and the total population at risk is estimated as UN Population x (H + L).

The total number of new cases, T, is estimated from the number of malaria cases reported by a Ministry of Health which is adjusted to take into account (i) incompleteness in reporting systems (ii) patients seeking treatment in the private sector, self-medicating or not seeking treatment at all, and (iii) potential over-diagnosis through the lack of laboratory confirmation of cases. The procedure, which is described in the World malaria report 2009 (2), combines data reported by NMCPs (reported cases, reporting completeness and likelihood that cases are parasite positive) with data obtained from nationally representative household surveys on health-service use.

𝑇=( a + (𝑐 × 𝑒) ⁄ 𝑑) × (1 + h ⁄𝑔 + ((1−𝑔−h)/2) ⁄ 𝑔)

where: a is malaria cases confirmed in public sector b is suspected cases tested c is presumed cases (not tested but treated as malaria) d is reporting completeness e is test positivity rate (malaria positive fraction) = a/b f is cases in public sector, calculated by (a + (c x e))/d g is treatment seeking fraction in public sector h is treatment seeking fraction in private sector i is the fraction not seeking treatment, calculated by (1-g-h)/2 j is cases in private sector, calculated by f x h/g k is cases not in private and not in public, calculated by f x i/g T is total cases, calculated by f + j + k.

To estimate the uncertainty around the number of cases, the test positivity rate was assumed to have a normal distribution centred on the Test positivity rate value and standard deviation defined as 0.244 × Test positivity rate 0.5547 and truncated to be in the range 0, 1.

Reporting completeness was assumed to have one of three distributions, depending on the range or value reported by the NMCP. -If the range was greater than 80% the distribution was assumed to be triangular, with limits of 0.8 and 1 and the peak at 0.8. – If the range was greater than 50% then the distribution was assumed to be rectangular, with limits of 0.5 and 0.8. -Finally, if the range was lower than 50% the distribution was assumed to be triangular, with limits of 0 and 0.5 and the peak at 0.5 (3).

If the reporting completeness was reported as a value and was greater than 80%, a beta distribution was assumed with a mean value of the reported value (maximum of 95%) and confidence intervals (CIs) of 5% round the mean value.

The proportions of children for whom care was sought in the private sector and in the public sector were assumed to have a beta distribution, with the mean value being the estimated value in the survey and the standard deviation calculated from the range of the estimated 95% confidence intervals (CI) divided by 4. The proportion of children for whom care was not sought was assumed to have a rectangular distribution, with the lower limit 0 and upper limit calculated as 1 minus the proportion that sought care in public or private sector.

Values for the proportion seeking care were linearly interpolated between the years that have a survey, and were extrapolated for the years before the first or after the last survey. Missing values for the distributions were imputed using a mixture of the distribution of the country, with equal probability for the years where values were present or, if there was no value at all for any year in the country, a mixture of the distribution of the region for that year. The data were analysed using the R statistical software.

Confidence intervals were obtained from 10000 drawns of the convoluted distributions. (Afghanistan, Bangladesh, Bolivia (Plurinational State of), Botswana, Brazil, Cambodia, Colombia, Dominican Republic, Eritrea, Ethiopia, French Guiana, Gambia, Guatemala, Guyana, Haiti, Honduras, India, Indonesia, Lao People’s Democratic Republic, Madagascar, Mauritania, Mayotte, Myanmar, Namibia, Nepal, Nicaragua, Pakistan, Panama, Papua New Guinea, Peru, Philippines, Rwanda, Senegal, Solomon Islands, Timor-Leste, Vanuatu, Venezuela (Bolivarian Republic of), Viet Nam, Yemen and Zimbabwe. For India, the values were obtained at subnational level using the same methodology, but adjusting the private sector for an additional factor due to the active case detection, estimated as the ratio of the test positivity rate in the active case detection over the test positivity rate for the passive case detection. This factor was assumed to have a normal distribution, with mean value and standard deviation calculated from the values reported in 2010. Bangladesh, Bolivia, Botswana, Brazil, Cabo Verde, Colombia, Dominican Republic, French Guiana, Guatemala, Guyana, Haiti, Honduras, Myanmar (since 2013), Rwanda, Suriname and Venezuela (Bolivarian Republic of) report cases from the private and public sector together; therefore, no adjustment for private sector seeking treatment was made.

For some high-transmission African countries the quality of case reporting is considered insufficient for the above formulae to be applied. In such cases estimates of the number of malaria cases are derived from information on parasite prevalence obtained from household surveys.

First, data on parasite prevalence from nearly 60 000 survey records were assembled within a spatiotemporal Bayesian geostatistical model, along with environmental and sociodemographic covariates, and data distribution on interventions such as ITNs, antimalarial drugs and IRS. The geospatial model enabled predictions of Plasmodium falciparum prevalence in children aged 2–10 years, at a resolution of 5 × 5 km2, throughout all malaria endemic African countries for each year from 2000 to 2016 (see http://www.map.ox.ac.uk/making-maps/ for methods on the development of maps by the Malaria Atlas Project).

Second, an ensemble model was developed to predict malaria incidence as a function of parasite prevalence.

The model was then applied to the estimated parasite prevalence in order to obtain estimates of the malaria case incidence at 5 × 5 km2 resolution for each year from 2000 to 2016.

Data for each 5 × 5 km2 area were then aggregated within country and regional boundaries to obtain both national and regional estimates of malaria cases. (Benin, Cameroon, Central African Republic, Chad, Congo, Côte d’Ivoire, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Guinea, Kenya, Malawi, Mali, Mozambique, Niger, Nigeria, Somalia, South Sudan, Sudan, Togo and Zambia). For most of the elimination countries, the number of indigenous cases registered by the NMCPs are reported without further adjustments. (Algeria, Argentina, Belize, Bhutan, Cabo Verde, China, Comoros, Costa Rica, Democratic People’s Republic of Korea, Djibuti, Ecuador, El Salvador, Iran (Islamic Republic of), Iraq, Malaysia, Mexico, Paraguay, Republic of Korea, Sao Tome and Principe, Saudi Arabia, South Africa, Suriname, Swaziland and Thailand).

The estimated incidence can differ from the incidence reported by a Ministry of Health which can be affected by: – the completeness of reporting: the number of reported cases can be lower than the estimated cases if the percentage of health facilities reporting in a month is less than 100% – the extent of malaria diagnostic testing (the number of slides examined or RDTs performed) – the use of private health facilities which are usually not included in reporting systems. – the indicator is estimated only where malaria transmission occurs.

TARGET 3.7 By 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmes

Adolescent birth rate (number of live births to adolescent women per 1,000 adolescent women).

Reducing adolescent fertility and addressing the multiple factors underlying it are essential for improving sexual and reproductive health and the social and economic well-being of adolescents. Preventing births very early in a woman’s life is an important measure to improve maternal health and reduce infant mortality. Furthermore, women having children at an early age experience a curtailment of their opportunities for socio-economic improvement, particularly because young mothers are unlikely to keep on studying and, if they need to work, may find it especially difficult to combine family and work responsibilities. The adolescent birth rate also provides indirect evidence on access to pertinent health services since young people, and in particular unmarried adolescent women, often experience difficulties in access to sexual and reproductive health services.

The adolescent birth rate represents the risk of childbearing among females in a particular age group. The adolescent birth rate among women aged 15-19 years is also referred to as the age-specific fertility rate for women aged 15-19

The adolescent birth rate represents the risk of childbearing among females in a particular age group. The adolescent birth rate (ABR) is also referred to as the age-specific fertility rate (ASFR) for ages 15-19 years, a designation commonly used in the context of calculation of total fertility estimates. A related measure is the proportion of adolescent fertility, measured as the percentage of total fertility contributed by women aged 15-19.

The adolescent birth rate is computed as a ratio.

Numerator – the number of live births to women aged 15-19 years Denominator – the estimate of the exposure to childbearing by women aged 15-19 years

The computation is the same for the age group 10-14 years. The numerator and the denominator are calculated differently for civil registration, survey and census data.

In the case of civil registration data, the numerator is the registered number of live births born to women aged 15-19 years during a given year, and the denominator is the estimated or enumerated population of women aged 15-19 years.

In the case of survey data, the numerator is the number of live births obtained from retrospective birth histories of the interviewed women who were 15-19 years of age at the time of the births during a reference period before the interview, and the denominator is person-years lived between the ages of 15 and 19 years by the interviewed women during the same reference period.

The reported observation year corresponds to the middle of the reference period. For some surveys without data on retrospective birth histories, computation of the adolescent birth rate is based on the date of last birth or the number of births in the 12 months preceding the survey.

With census data, the adolescent birth rate is computed on the basis of the date of last birth or the number of births in the 12 months preceding the enumeration. The census provides both the numerator and the denominator for the rates. In some cases, the rates based on censuses are adjusted for under-registration based on indirect methods of estimation.

For some countries with no other reliable data, the ‘own-children’ method of indirect estimation provides estimates of the adolescent birth rate for a number of years before the census.

For a thorough treatment of the different methods of computation, see Handbook on the Collection of Fertility and Mortality Data, United Nations Publication, Sales No. E.03.XVII.11 (publicly accessible at http://unstats.un.org/unsd/publication/SeriesF/SeriesF_92E.pdf ). Indirect methods of estimation are analyzed in Manual X: Indirect Techniques for Demographic Estimation, United Nations Publication, Sales No. E.83.XIII.2 (publicly accessible at http://www.un.org/esa/population/publications/Manual_X/Manual_X.htm ).

Discrepancies between the sources of data at the country level are common and the level of the adolescent birth rate depends in part on the source of the data selected.

For civil registration, rates are subject to limitations which depend on the completeness of birth registration, the treatment of infants born alive but that die before registration or within the first 24 hours of life, the quality of the reported information relating to age of the mother, and the inclusion of births from previous periods.

The population estimates may be subject to limitations connected to age misreporting and coverage. For survey and census data, both the numerator and denominator come from the same population. The main limitations concern age misreporting, birth omissions, misreporting the date of birth of the child, and sampling variability in the case of surveys.

With respect to estimates of the adolescent birth rate among females aged 10-14 years, comparative evidence suggests that a very small proportion of births in this age group occur to females below age 12. Other evidence based on retrospective birth history data from surveys indicates that women aged 15-19 years are less likely to report first births before age 15 than women from the same birth cohort when asked five years later at ages 20–24 years.

TARGET 3.8 Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all

Proportion of the target population covered by essential health services.

Coverage of essential health services (defined as the average coverage of essential services based on tracer interventions that include reproductive, maternal, newborn and child health, infectious diseases, non-communicable diseases and service capacity and access, among the general and the most disadvantaged population).

Target 3.8 is defined as “Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all”. The concern is with all people and communities receiving the quality health services they need (including medicines and other health products), without financial hardship.

Indicator 3.8.1 is for health service coverage and indicator 3.8.2 focuses on health expenditures in relation to a household’s budget to identify financial hardship caused by direct health care payments. Taken together, indicators 3.8.1 and 3.8.2 are meant to capture the service coverage and financial protection dimensions, respectively, of target 3.8. These two indicators should be always monitored jointly.

The indicator is an index reported on a unitless scale of 0 to 100, which is computed as the geometric mean of 14 tracer indicators of health service coverage.

The index of health service coverage is computed as the geometric means of 14 tracer indicators. The 14 indicators are listed below and detailed metadata for each of the components are given online ( http://www.who.int/healthinfo/universal_health_coverage/UHC_Tracer_Indicators_Metadata.pdf ) and Annex 1. The tracer indicators are as follows, organized by four broad categories of service coverage:

I. Reproductive, maternal, newborn and child health 1. Family planning: Percentage of women of reproductive age (15−49 years) who are married or in- union who have their need for family planning satisfied with modern methods 2. Pregnancy and delivery care: Percentage of women aged 15-49 years with a live birth in a given time period who received antenatal care four or more times 3. Child immunization: Percentage of infants receiving three doses of diphtheria-tetanus-pertussis containing vaccine 4. Child treatment: Percentage of children under 5 years of age with suspected pneumonia (cough and difficult breathing NOT due to a problem in the chest and a blocked nose) in the two weeks preceding the survey taken to an appropriate health facility or provider

II. Infectious diseases 5. Tuberculosis: Percentage of incident TB cases that are detected and successfully treated 6. HIV/AIDS: Percentage of people living with HIV currently receiving antiretroviral therapy 7. Malaria: Percentage of population in malaria-endemic areas who slept under an insecticide-treated net the previous night [only for countries with high malaria burden] 8. Water and sanitation: Percentage of households using at least basic sanitation facilities

III. Noncommunicable diseases 9. Hypertension: Age-standardized prevalence of non-raised blood pressure (systolic blood pressure <140 mm Hg or diastolic blood pressure <90 mm Hg) among adults aged 18 years and older 10. Diabetes: Age-standardized mean fasting plasma glucose (mmol/L) for adults aged 18 years and older 11. Tobacco: Age-standardized prevalence of adults >=15 years not smoking tobacco in last 30 days (SDG indicator 3.a.1, metadata available here)

IV. Service capacity and access 12. Hospital access: Hospital beds per capita, relative to a maximum threshold of 18 per 10,000 population 13. Health workforce: Health professionals (physicians, psychiatrists, and surgeons) per capita, relative to maximum thresholds for each cadre (partial overlap with SDG indicator 3.c.1, see metadata here) 14. Health security: International Health Regulations (IHR) core capacity index, which is the average percentage of attributes of 13 core capacities that have been attained (SDG indicator 3.d.1, see metadata here)

The index is computed with geometric means, based on the methods used for the Human Development Index. The calculation of the 3.8.1 indicator requires first preparing the 14 tracer indicators so that they can be combined into the index, and then computing the index from those values. The 14 tracer indicators are first all placed on the same scale, with 0 being the lowest value and 100 being the optimal value. For most indicators, this scale is the natural scale of measurement, e.g., the percentage of infants who have been immunized ranges from 0 to 100 percent. However, for a few indicators additional rescaling is required to obtain appropriate values from 0 to 100, as follows: – Rescaling based on a non-zero minimum to obtain finer resolution (this “stretches” the distribution across countries): prevalence of non-raised blood pressure and prevalence of non- use of tobacco are both rescaled using a minimum value of 50%. rescaled value = (X-50)/(100-50)*100 – Rescaling for a continuous measure: mean fasting plasma glucose, which is a continuous measure (units of mmol/L), is converted to a scale of 0 to 100 using the minimum theoretical biological risk (5.1 mmol/L) and observed maximum across countries (7.1 mmol/L). rescaled value = (7.1 – original value)/(7.1-5.1)*100 – Maximum thresholds for rate indicators: hospital bed density and health workforce density are both capped at maximum thresholds, and values above this threshold are held constant at 100. These thresholds are based on minimum values observed across OECD countries. rescaled hospital beds per 10,000 = minimum(100, original value / 18*100) rescaled physicians per 1,000 = minimum(100, original value / 0.9*100) rescaled psychiatrists per 100,000 = minimum(100, original value / 1*100) rescaled surgeons per 100,000 = minimum(100, original value / 14*100) Once all tracer indicator values are on a scale of 0 to 100, geometric means are computed within each of the four health service areas, and then a geometric mean is taken of those four values. If the value of a tracer indicator happens to be zero, it is set to 1 (out of 100) before computing the geometric mean. The following diagram illustrates the calculations.

Note that in countries with low malaria burden, the tracer indicator for use of insecticide-treated nets is dropped from the calculation.

These tracer indicators are meant to be indicative of service coverage, not a complete or exhaustive list of health services and interventions that are required for universal health coverage. The 14 tracer indicators were selected because they are well-established, with available data widely reported by countries (or expected to become widely available soon). Therefore, the index can be computed with existing data sources and does not require initiating new data collection efforts solely to inform the index.

TARGET 3.9 By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination

Mortality rate attributed to household and ambient air pollution.

As part of a broader project to assess major risk factors to health, the mortality resulting from exposure to ambient (outdoor) air pollution and household (indoor) air pollution from polluting fuel use for cooking was assessed. Ambient air pollution results from emissions from industrial activity, households, cars and trucks which are complex mixtures of air pollutants, many of which are harmful to health. Of all of these pollutants, fine particulate matter has the greatest effect on human health. By polluting fuels is understood as wood, coal, animal dung, charcoal, and crop wastes, as well as kerosene.

Air pollution is the biggest environmental risk to health. The majority of the burden is borne by the populations in low and middle-income countries.

The mortality attributable to the joint effects of household and ambient air pollution can be expressed as: Number of deaths, Death rate. Death rates are calculated by dividing the number of deaths by the total population (or indicated if a different population group is used, e.g. children under 5 years).

Evidence from epidemiological studies have shown that exposure to air pollution is linked, among others, to the important diseases taken into account in this estimate: – Acute respiratory infections in young children (estimated under 5 years of age) – Cerebrovascular diseases (stroke) in adults (estimated above 25 years) – Ischaemic heart diseases (IHD) in adults (estimated above 25 years) – Chronic obstructive pulmonary disease (COPD) in adults (estimated above 25 years); and – Lung cancer in adults (estimated above 25 years)

The mortality resulting from exposure to ambient (outdoor) air pollution and household (indoor) air pollution from polluting fuels use for cooking was assessed. Ambient air pollution results from emissions from industrial activity, households, cars and trucks which are complex mixtures of air pollutants, many of which are harmful to health. Of all of these pollutants, fine particulate matter has the greatest effect on human health. By polluting fuels is understood kerosene, wood, coal, animal dung, charcoal, and crop wastes.

Attributable mortality is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed, proportion of population relying primarily on polluting fuels for cooking).

This allows calculation of the ‘population attributable fraction’ (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in that case of both the annual mean concentration of particulate matter and exposure to polluting fuels for cooking).

Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths), gives the total number of deaths that results from exposure to that particular risk factor (in the example given above, to ambient and household air pollution).

To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003).

The mortality associated with household and ambient air pollution was estimated based on the calculation of the joint population attributable fractions assuming independently distributed exposures and independent hazards as described in (Ezzati et al, 2003).

The joint population attributable fraction (PAF) were calculated using the following formula: PAF=1-PRODUCT (1-PAFi) where PAFi is PAF of individual risk factors.

The PAF for ambient air pollution and the PAF for household air pollution were assessed separately, based on the Comparative Risk Assessment (Ezzati et al, 2002) and expert groups for the Global Burden of Disease (GBD) 2010 study (Lim et al, 2012; Smith et al, 2014).

For exposure to ambient air pollution, annual mean estimates of particulate matter of a diameter of less than 2.5 um (PM25) were modelled as described in (WHO 2016, forthcoming), or for Indicator 11.6.2.

For exposure to household air pollution, the proportion of population with primary reliance on polluting fuels use for cooking was modelled (see Indicator 7.1.2 [polluting fuels use=1-clean fuels use]). Details on the model are published in (Bonjour et al, 2013).

The integrated exposure-response functions (IER) developed for the GBD 2010 (Burnett et al, 2014) and further updated for the GBD 2013 study (Forouzanfar et al, 2015) were used. The percentage of the population exposed to a specific risk factor (here ambient air pollution, i.e. PM2.5) was provided by country and by increment of 1 ug/m3; relative risks were calculated for each PM2.5 increment, based on the IER. The counterfactual concentration was selected to be between 5.6 and 8.8 ug/m3, as described elsewhere (Ezzati et al, 2002; Lim et al, 2012). The country population attributable fraction for ALRI, COPD, IHD, stroke and lung cancer were calculated using the following formula :

PAF=SUM(Pi(RR-1)/(SUM(RR-1)+1)

where i is the level of PM2.5 in ug/m3, and Pi is the percentage of the population exposed to that level of air pollution, and RR is the relative risk.

The calculations for household air pollution are similar, and are explained in detailed elsewhere (WHO 2014a).

An approximation of the combined effects of risk factors is possible if independence and little correlation between risk factors with impacts on the same diseases can be assumed (Ezzati et al, 2003). In the case of air pollution, however, there are some limitations to estimate the joint effects: limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non- linear interactions (Lim et al, 2012; Smith et al, 2014). In several regions, however, household air pollution remains mainly a rural issue, while ambient air pollution is predominantly an urban problem. Also, in some continents, many countries are relatively unaffected by household air pollution, while ambient air pollution is a major concern. If assuming independence and little correlation, a rough estimate of the total impact can be calculated, which is less than the sum of the impact of the two risk factors.

TARGET 3.b Support the research and development of vaccines and medicines for the communicable and non‑communicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade-Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for all

Proportion of the target population covered by all vaccines included in their national programme.

This indicator aims to measure access to vaccines, including the newly available or underutilized vaccines, at the national level. In the past decades all countries added numerous new and underutilised vaccines in their national immunization schedule and there are several vaccines under final stage of development to be introduced by 2030. For monitoring diseases control and impact of vaccines it is important to measure coverage from each vaccine in national immunization schedule and the system is already in place for all national programmes, however direct measurement for proportion of population covered with all vaccines in the programme is only feasible if the country has a well-functioning national nominal immunization registry, usually an electronic one that will allow this coverage to be easily estimated. While countries will develop and strengthen immunization registries it is a need for an alternative measurement.

Coverage of DTP containing vaccine (3rd dose): Percentage of surviving infants who received the 3 doses of diphtheria and tetanus toxoid with pertussis containing vaccine in a given year.

Coverage of Measles containing vaccine (2nd dose): Percentage of children who received two dose of measles containing vaccine according to nationally recommended schedule through routine immunization services in a given year.

Coverage of Pneumococcal conjugate vaccine (last dose in the schedule): Percentage of surviving infants who received the nationally recommended doses of pneumococcal conjugate vaccine in a given year.

Coverage of HPV vaccine (last dose in the schedule): Percentage of 15 years old girls received the recommended doses of HPV vaccine. Currently performance of the programme in the previous calendar year based on target age group is used.

In accordance with its mandate to provide guidance to Member States on health policy matters, WHO provides global vaccine and immunization recommendations for diseases that have an international public health impact. National programmes adapt the recommendations and develop national immunization schedules, based on local disease epidemiology and national health priorities. National immunization schedules and number of recommended vaccines vary between countries, with only DTP polio and measles containing vaccines being used in all countries.

The target population for given vaccine is defined based on recommended age for administration. The primary vaccination series of most vaccines are administered in the first two years of life.

Coverage of DTP containing vaccine measure the overall system strength to deliver infant vaccination. Coverage of Measles containing vaccine ability to deliver vaccines beyond first year of life through routine immunization services. Coverage of Pneumococcal conjugate vaccine: adaptation of new vaccines for children Coverage of HPV vaccine: life cycle vaccination

WHO and UNICEF jointly developed a methodology to estimate national immunization coverage form selected vaccines in 2000. The methodology has been refined and reviewed by expert committees over time. The methodology was published and reference is available under the reference section. Estimates time series for WHO recommended vaccines produced and published annually since 2001.

The methodology uses data reported by national authorities from countries administrative systems as well as data from immunization or multi indicator household surveys.

The rational to select a set of vaccines reflects the ability of immunization programmes to deliver vaccines over the life cycle and to adapt new vaccines. Coverage for other WHO recommended vaccines are also available and can be provided.

Given that HPV vaccine is relatively new and vaccination schedule varies from countries to country coverage estimate will be made for girls vaccinated by ag 15 and at the moment data is limited to very few countries therefore reporting will start later.

To ensure healthy lives and promote the well-being of all children, UNICEF has four key asks that encourage all governments to:

• Strengthen primary healthcare systems to reach every child
• Focus on maternal, newborn and child survival
• Prioritize child and adolescent health and well-being, including mental health
• Support responses to reduce the impact on children and families of natural disasters, complex emergencies and demographic shifts

Learn more about  UNICEF’s key asks for implementing Goal 3

See more Sustainable Development Goals

ZERO HUNGER

GOOD HEALTH AND WELL-BEING

QUALITY EDUCATION

GENDER EQUALITY

CLEAN WATER AND SANITATION

AFFORDABLE AND CLEAN ENERGY

DECENT WORK AND ECONOMIC GROWTH

REDUCED INEQUALITIES

CLIMATE ACTION

PEACE, JUSTICE AND STRONG INSTITUTIONS

PARTNERSHIPS FOR THE GOALS

Here’s how you know

• U.S. Department of Health and Human Services
• National Institutes of Health

The Importance of Research on Health and Well-Being

Director’s Page Helene M. Langevin, M.D.

February 7, 2019

As I’ve dived into my role as Director at NCCIH, one of the things that’s made me so energized about the position is the smart, pragmatic thinking embedded within the Strategic Plan NCCIH adopted in 2016. It’s a twofold cogent recognition of: 1) the very real challenges faced daily by patients and their health care providers, and 2) the opportunities the research community has to offer much-needed evidence to inform decisions about patient care. Especially important is our third strategic plan objective , which focuses on how we can explore the potential of complementary health approaches to foster health promotion and disease prevention across the lifespan.

Why is this part of NCCIH’s strategic plan so important? Since the beginning of the 20th century, modern medicine and biomedical research have overwhelmingly focused on the study and treatment of disease. In contrast, health—and especially the return to health after an illness—has received comparatively little attention.

This emphasis on treating diseases is largely a byproduct of a very good thing—the tremendous gains yielded by researchers in finding treatments for diseases and the effectiveness of pharmacologic approaches in both treating and managing diseases. These strides in advancing human health can be seen in antibiotics to treat bacterial infections or medications to manage chronic illnesses, such as diabetes, hypertension, and rheumatic diseases.

Yet these critical successes in treating and managing disease may also mean that the often-painstaking task of helping the patient recuperate during the “convalescent” period after an acute illness, or following an exacerbation of a chronic relapsing condition, has not yet been adequately studied.

Though the treatment-focused model is dominant in our research and health care ecosystem, there has been a longstanding awareness that many chronic diseases can be prevented or better managed by incorporating nonpharmacologic interventions such as nutrition, exercise, and stress management. When these methods are incorporated into care and patients are able to make lasting behavioral changes, the end result can be more durable improvements in health. Many complementary and integrative health practices follow this model, and there’s preliminary evidence indicating that some complementary approaches may be useful in encouraging improved self-care, a better personal sense of well-being, and a greater commitment to a healthy lifestyle.

In fact, one of the research strategies within NCCIH’s current strategic plan is to advance understanding of the mechanisms through which mind and body approaches affect health, resilience, and well-being. This includes a focus on methodologically rigorous evaluations that will lead to a greater understanding of whether, when, how, and for whom such practices can have substantial impact. For example, we support research designed to understand the ways in which an integrative approach to treating chronic back pain can lead to lasting healing and improved function and well-being. This research may provide critically important new therapeutic approaches for those patients who have not found relief with surgery or pain medicines.

I’m very much looking forward to the National Advisory Council for Complementary and Integrative Health (NACCIH) meeting on Friday, February 8th. It will be my first Council meeting as the director of NCCIH. The meeting will feature a symposium, “NIH Research on Well-Being,” and I invite you to listen to the conversation on this important research topic, as well as hear updates on the Center’s activities, policies, and funding priorities during the meeting’s open session. NCCIH will livestream the open session of Council on NIH Videocast from 10:00 a.m. to 3:20 p.m. ET, and it will be archived.

Past Messages From the Director

NCCIH Heads to Cleveland for ICIMH April 5, 2024

Addressing Pain Requires Understanding How We Heal March 27, 2024

Let’s Leverage All of Us To Learn More About Health and Disease in Diverse Populations February 19, 2024

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The five-step wellness model that really works—and the psychology behind it

by Ben Gibson and Victoria Ruby-Granger, The Conversation

The wellness movement appears to have the answers that our burnt-out minds need. However, psychological research and practice suggests that a superficial focus on candles, juice cleanses, and a "good vibes only" approach to life is unlikely to create meaningful changes to your well-being.

It's not a surprise that wellness culture has become so popular, especially among women and young people . A US$4.4 trillion (£3.5 trillion) wellness industry promises that clean beauty, clean eating and energy-boosting supplements will provide happiness, meaning and a stress-free existence. But if wellness can be bought, why aren't we all happier?

Purchases may make us happy ( and even reduce some lingering sadness ) but genuine changes to well-being are probably limited. In fact, feminist critics , journalists and psychologists have expressed concerns that wellness culture may exacerbate destructive perfectionism , promote an unhealthy relationship with our bodies , and even draw people into conspiracy theories and multi-level marketing scams.

Wellness culture focuses on what feels good for you as an individual, providing only a surface level experience of well-being. Mihalyi Csikszentmihalyi, one of the founders of the positive psychology movement, said in his 1991 book Flow , that "it is by being fully involved with every detail of our lives, whether good or bad, that we find happiness".

Indeed, psychological research suggests that long-term well-being comes from a committed pursuit of both pleasure and meaning. Consider the psychologist Martin Seligman's model of flourishing: Perma . Seligman's model breaks well-being into distinct, workable "elements", which gives us an idea as to how to make well-being more achievable .

A 2016 study of 1,624 participants recruited online found an intervention based on the Perma model increased levels of happiness and helped decrease depression symptoms, although the intervention seemed to work best for people around the middle range of well-being.

Studies have also found Perma-based interventions promoted well-being in university students following the COVID pandemic, seem to improve the emotional states of lung cancer patients and decrease anxiety in breast cancer patients. And researchers have tested this model across different contexts , ages , and cultures .

Perma is an acronym that stands for what Seligman considers the five pillars of well-being: positive emotions, engagement, relationships, meaning and achievement. This model suggests that rather than spending money to focus on " self-care ", we should aim to meet what psychologists consider our fundamental, psychological needs for competence, autonomy, and relatedness.

Perma suggests we ask ourselves: Am I acting in ways that make me feel competent, in control, and connected with others? Here are some wellness tips that work, based on the five pillars of Perma:

1. Positive emotions

The broaden-and-build theory states that we are at our most psychologically creative, responsive and flexible when we are experiencing positive emotions . However, it's important to move beyond momentary hedonic pleasure and aim to reap the rewards of a range of positive emotions. This allows us to experience more positive emotion, as part of an upward spiral effect .

Take one (or more) of psychologist Barbara Fredrickson's top ten positive emotions , and find ways to cultivate more of it in your life. These emotions include awe, joy, inspiration, gratitude and love. For example, to cultivate gratitude try the three good things exercise : take time to list three good things that happened in your day, or three things that you felt grateful for. You can also write about the cause of those things.

Maybe combine this with nature's well-being benefits by looking for three good things in nature . If it's difficult to find green space in your area, there are creative ways to incorporate connection with nature into your daily life, such as taking the time to look at the stars at night. Notice the bumblebees or count the different types of plants you see on your walk to work.

2. Engagement

Find an activity that gets you into flow , a state of deep engagement in an intentional, inherently rewarding activity in which we lose track of time and feel at one with what we are doing. It's also sometimes known as "getting into the zone".

Flow activities stretch us just enough to keep us engaged, but not so much that we become bored or demotivated. High flow activities include music, sports and even gaming.

3. Relationships

It's quality over quantity when it comes to personal relationships . It sounds simple, but look to (or find) people who are eager to celebrate your successes and be wary of those who belittle them.

This will help you prolong the good feelings that go along with life's little wins. Personal connection is important, and features as a core component in most theories of well-being .

Find a way to connect with something larger than yourself. Volunteer , join a community group or perform a random act of kindness .

Thinking about a future best possible self can help you set goals and help you understand what gives you purpose in life.

5. Achievement

Do something challenging; something that stretches your abilities. You may want to identify and use your strengths. Some strengths, such as perseverance, are related to achievement . True positivity is not just about feeling good, but about rising to the challenges that life sets us.

Just remember: Perma pillars are independent paths to well-being, but they're also highly related. Taking up dancing, for example, might be a way to experience positive emotions and flow, allowing you to make new connections so that you stick at it long enough to develop a sense of purpose or achievement.

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• Reference work
• © 2020

Good Health and Well-Being

• Walter Leal Filho 0 ,
• Tony Wall 1 ,
• Anabela Marisa Azul 2 ,
• Luciana Brandli 3 ,
• Pinar Gökcin Özuyar 4

European School of Sustainability Science and Research, Hamburg University of Applied Sciences, Hamburg, Germany

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International Centre for Thriving, University of Chester, Chester, UK

Center for neuroscience and cell biology and institute for interdisciplinary, research of the university of coimbra, coimbra, portugal, passo fundo university faculty of engineering and architecture, passo fundo, brazil, istinye university, istanbul, turkey.

• Fosters knowledge to support the UN Sustainable Development Goal to ensure healthy lives and promote well-being for all at all ages
• Comprehensively describes research, projects and practical action
• Provides government agencies, education institutions and non-governmental agencies with a sound basis to promote sustainability efforts
• Covers many countries, very international

Part of the book series: Encyclopedia of the UN Sustainable Development Goals (ENUNSDG)

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Table of contents (234 entries)

Front matter, adaptability, algorithmic, anthropologically disrupted biogeochemical cycles and the effect on sustainable human health and well-being.

• Catherine Zeman

Applications

Applied drama, applied fantasy and well-being.

• Anna Mackenzie, Tony Wall, Simon Poole

Applied Theatre

Art for health and well-being.

• Lynette Steele

Art Psychotherapy

Art therapy, arts on prescription, arts-based therapy, assessment of exposures in vulnerable populations: exposure and response modelling for environmental contaminants through a lifetime.

• Jennifer A. Lowry

Bibliotherapy

Birth defects.

The problems related to the process of industrialisation such as biodiversity depletion, climate change and a worsening of health and living conditions, especially but not only in developing countries, intensify. Therefore, there is an increasing need to search for integrated solutions to make development more sustainable. The United Nations has acknowledged the problem and approved the “2030 Agenda for Sustainable Development”. On 1st January 2016, the 17 Sustainable Development Goals (SDGs) of the Agenda officially came into force. These goals cover the three dimensions of sustainable development: economic growth, social inclusion and environmental protection.  

• Reduce the global maternal mortality ratio to less than 70 per 100,000 live births
• End preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births
• End the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases
• Reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and wellbeing
• Strengthen the prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcohol
• Halve the number of global deaths and injuries from road traffic accidents
• Ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmes
• Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all
• Substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination
• Strengthen the implementation of the World Health Organization Framework Convention on Tobacco Control in all countries, as appropriate
• Support the research and development of vaccines and medicines for the communicable and non-communicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade-Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for all
• Substantially increase health financing and the recruitment, development, training and retention of the health workforce in developing countries, especially in least developed countries and small island developing states
• Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risks
• Sustainable intervention
• Mortality reduction
• Health promotion
• Disease prevention
• child mortality
• sanitation for health
• Life expectancy
• sustainable lifestyle
• Public health threats
• Well-being threats

Walter Leal Filho

Anabela Marisa Azul

Luciana Brandli

Pinar Gökcin Özuyar

Book Title : Good Health and Well-Being

Editors : Walter Leal Filho, Tony Wall, Anabela Marisa Azul, Luciana Brandli, Pinar Gökcin Özuyar

Series Title : Encyclopedia of the UN Sustainable Development Goals

DOI : https://doi.org/10.1007/978-3-319-95681-7

Publisher : Springer Cham

eBook Packages : Earth and Environmental Science , Reference Module Physical and Materials Science , Reference Module Earth and Environmental Sciences

Copyright Information : Springer Nature Switzerland AG 2020

Hardcover ISBN : 978-3-319-95680-0 Published: 01 October 2019

eBook ISBN : 978-3-319-95681-7 Published: 25 September 2019

Series ISSN : 2523-7403

Series E-ISSN : 2523-7411

Edition Number : 1

Number of Pages : XXIV, 822

Number of Illustrations : 1 b/w illustrations, 36 illustrations in colour

Topics : Sustainable Development , Public Health , Quality of Life Research , Pollution, general , Epidemiology , Child Well-being

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A qualitative study of leaders’ experiences of handling challenges and changes induced by the COVID-19 pandemic in rural nursing homes and homecare services

• Malin Knutsen Glette 1 , 2 ,
• Tone Kringeland 2 ,
• Lipika Samal 3 , 4 ,
• David W. Bates 3 , 4 &
• Siri Wiig 1  

BMC Health Services Research volume  24 , Article number:  442 ( 2024 ) Cite this article

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The COVID-19 pandemic had a major impact on healthcare services globally. In care settings such as small rural nursing homes and homes care services leaders were forced to confront, and adapt to, both new and ongoing challenges to protect their employees and patients and maintain their organization's operation. The aim of this study was to assess how healthcare leaders, working in rural primary healthcare services, led nursing homes and homecare services during the COVID-19 pandemic. Moreover, the study sought to explore how adaptations to changes and challenges induced by the pandemic were handled by leaders in rural nursing homes and homecare services.

The study employed a qualitative explorative design with individual interviews. Nine leaders at different levels, working in small, rural nursing homes and homecare services in western Norway were included.

Three main themes emerged from the thematic analysis: “Navigating the role of a leader during the pandemic,” “The aftermath – management of COVID-19 in rural primary healthcare services”, and “The benefits and drawbacks of being small and rural during the pandemic.”

Conclusions

Leaders in rural nursing homes and homecare services handled a multitude of immediate challenges and used a variety of adaptive strategies during the COVID-19 pandemic. While handling their own uncertainty and rapidly changing roles, they also coped with organizational challenges and adopted strategies to maintain good working conditions for their employees, as well as maintain sound healthcare management. The study results establish the intricate nature of resilient leadership, encompassing individual resilience, personality, governance, resource availability, and the capability to adjust to organizational and employee requirements, and how the rural context may affect these aspects.

Peer Review reports

In 2021, essential healthcare services in 90% of the world’s countries were disrupted by the COVID-19 pandemic [ 1 ]. Healthcare services were heavily stressed and had to address unexpected issues and sudden changes, whilst still providing high quality care over a prolonged period [ 2 , 3 ]. Despite the intense focus on hospitals during this period, other parts of the healthcare system such as nursing homes and homecare services also faced extreme challenges. These included issues such as having to introduce and constantly adapt new infection control routines, as well as being given increased responsibility in caring for infected and seriously ill patients in facilities that were not built for such circumstances [ 4 , 5 , 6 , 7 ]. Mortality rates in nursing homes were especially high [ 8 ].

Resilience in healthcare is about a system’s ability to adapt to challenges and changes at different levels (e.g., organization, leaders, health personnel) to maintain high quality care [ 9 , 10 ]. During the COVID-19 pandemic, leaders and the front line were forced to rapidly adjust to keep healthcare services afloat. It has been demonstrated in previous research that effective leadership is crucial in navigating crises and building resilience within health systems [ 11 , 12 , 13 ]. Furthermore, leaders play key roles in facilitating health personnel resilience, for example, through promoting a positive outlook on change and by developing health personnels’ competencies and strengths [ 12 , 14 , 15 ]. During the COVID-19 pandemic, this role became intensified [ 16 , 17 , 18 ], and leaders’ roles in promoting resilient healthcare services were central, for example safeguarding resources, providing emotional support and organizing systems to cope with extreme stresses [ 3 , 19 ].

Smaller, rural nursing homes and home care services are geographically dispersed and typically remote from specialized healthcare services or other nursing home and homecare services. They also tend to have reduced access to personnel due to low population density, frequently leading to the need to make independent decisions, often in complex situations [ 20 ]. Overall, rural healthcare services face different challenges than their urban counterparts [ 21 , 22 , 23 ]. The COVID-19 pandemic intensified some of these issues and created new ones which needed to be managed [ 21 , 24 , 25 ].

The research base on COVID-19 has expanded extensively the past years [ 26 ], covering areas such as clinical risks and outcomes for healthcare workers [ 27 ] and patients [ 28 ], hospital admissions [ 29 ] and healthcare utilization during the pandemic [ 30 ]. Moreover, areas like healthcare leaders' [ 16 , 17 , 31 ] and healthcare professionals’ [ 2 , 32 ] strategies to handle the pandemic challenges, and COVID related strategies’ effect on quality of care [ 33 , 34 ]. And lastly, but not exhaustively, the COVID-19 pandemic in different healthcare settings such as hospitals [ 35 ], primary healthcare services and [ 36 ] mental healthcare services [ 37 ]. However, research on rural healthcare settings, particularly leaders in rural nursing homes and homecare services, have received less attention [ 38 , 39 , 40 ]. Despite the anticipated importance of primary healthcare services in future healthcare and the prevalence of rural healthcare options [ 41 , 42 ]. Overall, there are still lessons to be learned from the COVID-19 pandemic, specifically identifying resilience promoting and inhibiting factors in different health care settings during crisis, how leaders deal with crisis management, and furthermore, to understand and draw lessons from challenges that were overcome during the pandemic[ 43 , 44 ].

Aim and research question

The aim of this study was to assess how healthcare leaders in rural primary healthcare services managed nursing homes and homecare services during the COVID-19 pandemic. Moreover, the study aimed to explore how adaptations to changes and challenges induced by the pandemic were handled by these leaders.

The research question guiding the study was: How did primary healthcare leaders in rural areas experience their leadership during the COVID-19 pandemic, and how did they adapt to the rapid onset changes demanded by the COVID-19 outbreak?

The study employed a qualitative explorative design to study in-depth, how nursing home and homecare leaders in Norwegian rural primary healthcare services experienced and addressed the extreme challenges and needs for change induced by the COVID-19 pandemic [ 45 , 46 ]. Four rural municipalities of different sizes were included in the study. Nursing home and homecare leaders at different organizational levels participated in individual interviews (See Table  1 ).

Norway is divided into 356 municipalities. These municipalities have the autonomy to administer and manage their primary healthcare services, subject to certain laws and regulations (e.g., Act on municipal health and care services [ 47 ], Act on patient and user rights [ 48 ] and Regulation on quality in nursing and care services for service provision [ 49 ]). All municipalities are obligated to offer specified healthcare services independent of their size and inhabitant number (Se Fig.  1 for a brief overview of healthcare services provided by the Norwegian municipalities, comprising nursing homes and home care services, and included municipalities).

Brief overview of healthcare services provided by the Norwegian municipalities, comprising nursing homes and home care services, and the included municipalities

Recruitment and participants

Recruitment was anchored in the municipal management. The municipal manager of health and care in 11 municipalities across the Norwegian west coast were first contacted via email, then by telephone (se Fig.  1 ). Most managers who responded to our contact were positive, but many had to decline due to time constraints related to pandemic management. Four managers agreed to data collection in their municipality with the stipulation that the nursing home- and homecare leaders wanted to participate. All levels of leaders were eligible for inclusion due to the small size of the healthcare services. We contacted the leaders of nursing homes and home care services in the four municipalities, first by email, then by telephone. Nine leaders agreed to participate. One leader declined. All included leaders were female, registered nurses (RNs), and had long and broad experiences with working as RNs either in the healthcare service they now were leaders in, or in other healthcare settings. Some leaders stated that they had continued education or Master’s degrees, but more leader specific qualifications such as leader education, training or courses were not disclosed (Table  1 . Overview of participants and setting).

Data collection

Individual interviews were conducted from November 2021 to November 2022 by the first author (MKG). Leaders in one of the municipalities (municipality B) wished to do the interview in a group interview (three leaders), which we arranged. All but one interview was conducted at the leaders’ work premises (in their offices or in meeting rooms). One leader was interviewed via Zoom due to a temporary need for increased infection precautions. All interviews were guided by a predeveloped interview guide which was based on resilience in healthcare theory [ 50 , 51 ] and contained subject such as: Success factors and challenges with handling the COVID-19 pandemic; New solutions and how new knowledge and information was handled; and Lessons learned from the pandemic.

Data analysis

The interviews were audio recorded and transcribed. The analysis followed the steps in Braun and Clarkes thematic approach [ 52 ]. This involved reading through the transcripts multiple times to find meanings related to the overall research question. Text with meaning was inserted into a Word table which provided initial codes. After the coding process, which involved creating and continuously revising codes, there were 47 codes. The codes were then organized into categories and categories were sorted into initial main themes. Themes and categories were assessed to determine whether any of them should be merged, refined, split or eliminated [ 52 ] (see Table  2 for example of the analysis process). The author team reviewed and approved categories and themes to ensure that each theme illuminated its essence [ 52 ].

We analyzed the interviews and identified three main themes and eight categories (Table  3 ). The results are presented according to identified main themes.

Navigating the role of a leader during the pandemic

Overall, the leaders seemed to have two primary focuses when they talked about how they had experienced the COVID-19 pandemic. These were their personal coping, and how they managed the organizational challenges arising throughout the pandemic period. Particularly in the beginning, they reported feelings of fear and insecurity. Leaders dreaded the consequences which could result from mistakes, such as providing wrong, or missing essential information.

“Having such a responsibility is a burden, and even though you’re not alone, you still feel like you’re the one responsible for the safety of the employees and the patients. Ensuring the safety of everyone was the priority, which is why it was critical to make sure that the protocols we were distributing were the correct ones…” (L1 nursing home municipality C)

Additionally, several leaders stated that they were concerned about personnel who had contracted COVID-19 (some of whom had serious symptoms), and even felt responsible for their situation. Leaders of two of the municipalities reported feelings of frustration, and despair, and all leaders reported long working hours. Leaders expressed that they felt that they had been “on call” for the last two years, and described long working days, with limited consideration for evenings, nights, weekends, or vacations.

A range of organizational challenges was described (e.g., dealing with a stressed economy, experiencing task overload, working within an unprepared organization and the struggle to get a hold on enough personal protective equipment. One of the most prominent challenges in the data set, was the acquisition, interpretation, and distribution of information issued by the authorities. The leaders described that new information was issued frequently along with constantly changing routines. New routines where developed, distributed, and discarded nonstop in the attempt to “get the organization in line with the state authorities”.

“There was new information issued [from the Norwegian directorate of health] almost hourly… we had more than enough to, in a way, keep up with all these procedures that came, or all the new messages that came, and these [information and routines] had to be issued out to the employees and to the next of kin…” (L1 nursing home municipality A)

Despite the difficulties related to information flow, or lack thereof, the leaders devised a range of solutions to make information more accessible to their staff (e.g., informational e-mails, developing short information sheets, making information binders, and meeting up physically to go through new routines with their employees). The data indicated that it was hard to gauge how much information to make available to their staff, who were eager for knowledge, yet still found it hard to process everything. On occasion, the leaders desired assistance or someone to assume authority, or as one leader articulated: “someone to push the red button” (L1 homecare municipality C), due to their struggles to keep up with information, regulations, and routines in the face of rapid changes.

Not surprisingly, leaders felt a heightened need to take the lead during the COVID-19 pandemic. This was a long-running crisis, and they had to be present, approachable and a source of support for their staff, while also striving to gain the employees’ understanding. For example, in one healthcare service the employees wanted more strict rules than necessary and had strong opinions on how things should be done in “in their healthcare service”, while the leader was stringent with sticking to national regulations which were less strict. Another aspect was handling disagreement with measures among employees. Often measures were not in line with the employees’ wishes, which created friction.

The pandemic highlighted the importance of leaders taking on the task of creating a secure working environment for their employees. The leaders noted considerable anxiety among the staff, particularly in facilities that had not experienced any COVID-19 cases. Leaders came to understand the importance of tending to all wards, regardless of whether they had been affected by the infection, even though it was perceived as taxing. Overall, the leaders worked actively to make the situation in wards with infection outbreaks as best as possible. A leader from a healthcare service which had a major COVID-19 outbreak stated:

“We constantly tried to create new procedures to make it as easy as possible for them [So] that they didn’t have to think about anything. That they [didn’t have to think about] bringing food to work, that they had to [remember] this or that. That they were provided with everything they needed…” (L2 nursing home municipality C)

Another recurring topic in the dataset, was the constant challenges and changes the leaders had to overcome and adapt to during the COVID-19 pandemic. For example, there was a need to plan for all possible scenarios, particularly if they were to have a major infection outbreak among the staff (e.g., how to limit the infection outbreak, how to deal with staffing, how to arrange the wards in case of an outbreak). One healthcare service experienced such a scenario, which demanded a rapid response, when they had a major COVID-19 outbreak with over twenty infected employees almost overnight. The leaders were left with the impossible task of covering a range of shifts, and they were forced to adopt a strategy of reaching out to other healthcare services within their municipality (other wards, nursing homes, the home care services and psychiatric services) asking if they had any nurses “to spare.” Eventually, they managed to cover their staffing needs without using a temp agency.

The leaders of this nursing home also had to deal with numerous small, but important challenges such as how to deal with dirty laundry, what to do with food scraps, where to put decorations and knick-knacks, how to provide wardrobes and lunchrooms, and generally, how to handle an infection outbreak in facilities not designed for this purpose.

Leaders in all primary healthcare services implemented strategies to prevent infection or spread of infection. They introduced longer shifts, split up the personnel in teams, made cleaning routines for lunchrooms and on-call rooms, set up a temporary visiting room for next of kin, developed routines for patient visits, regularly debriefed personnel of infection routines, made temporary wardrobes, and removed unnecessary tasks from the work schedule. New digital tools were introduced, particularly for distributing instructional videos and information among employees, and to keep contact with other leaders.

Although many leaders described the situation as challenging, particularly in the beginning, many found themselves gaining increased control over the situation as time went by.

“Little by little, in some way, the routine of everyday life has become more settled… you can’t completely relax yet, but you can certainly feel a bit more organized, and more confident in your decisions, since we have been doing it for a while [ca 1 year]. (L1 nursing home municipality C)

The aftermath—management of covid-19 in rural primary healthcare services

Despite organizational as well as personal challenges, leaders’ overall impression of the COVID-19 management was positive. The leaders firmly believed that the quality of healthcare services had been preserved, and all the physical healthcare needs of the patients had been properly cared for. According to leaders, there was not a rise in adverse events (e.g., falls, wounds) and patients and next of kin were positive in their feedback. The one main concern regarding quality of care was, however, the aspect of the patients’ sociopsychological state. Patients became isolated and lonely when they could not receive visitors or had to be isolated in their rooms or their homes during COVID. Nevertheless, the leaders expressed admiration for the healthcare personnel's work in addressing psychosocial needs to the best of their capacity. Overall, the leaders were proud of how the front-line healthcare personnel had handled the pandemic, and the extraordinary effort they put in to keeping the healthcare services running.

Several leaders stated that they now felt better prepared for “a next pandemic”, but they also had multiple suggestions for organizational improvements. These suggestions included: set up a visit coordinator, develop a better pandemic plan, be better prepared nationally, develop local PPE storage sites, introduce digital supervision for isolation rooms (for example RoomMate [ 53 ]), provide more psychological help for employees who struggled in the aftermath of an infection outbreak, have designated staff on standby for emergency situations, establish clear communication channels for obtaining information and, when constructing new nursing homes and healthcare facilities, consider infection control measures.

The leaders also discussed the knowledge they had acquired during this period. Many talked about learning how to use digital tools, but mostly they talked about the experience they had gained in handling crisis:

“I believe we are equipped in a whole different way now. There’s no doubt about that. Both employees and leaders and the healthcare service in general, I think… I have no doubt about that… so… there have been lessons learned, no doubt about it….” (L1 nursing home municipality C)

Leaders also talked about what they experienced as success factors in handling the pandemic: Long shifts (11,5 h), with the same shift going 4 days in a row to avoid contacts between different shift, the use of Microsoft Teams and other communication tools to increased and ease intermunicipal cooperation, and the possibility to share experiences, making quick decisions and take action quickly, developing close cooperation with the municipality chief medical officer and the nursing home physician, the involvement of the occupational healthcare service (take the employees’ work situation seriously) and the conduct of “Risk, Vulnerability and Preparedness” analysis (a tool to identify possible threats in order to implement preventive measures and necessary emergency response). The leaders also talked about the advantages of getting input from employees (e.g., through close cooperation with the employee representatives).

The benefits and drawbacks of being small and rural during a pandemic

Aspects of being a small healthcare service within a small municipality were highlighted by several of the leaders. For example, the leader of one the smaller healthcare service included in the study, addressed the challenge of acquiring enough competent staff. To be able to fulfill their requirements for competent staff, the municipality needed to buy healthcare services from neighboring municipalities. Another drawback was that employees who had competence or healthcare education often lacked experience in infection control and infection control routines, because they had rarely or never had infectious outbreaks of any kind. This made it particularly challenging to implement infection control measures. In one of the larger municipalities in this study, they had worked targeted for years to increase the competence in their municipality by focusing on full time positions to all and educating assistants to become Licensed practical nurses (LPN). They benefited from these measures during the pandemic.

Another aspect which was emphasized as essential to survive a pandemic in a small municipality, was intermunicipal cooperation. Leaders of all four healthcare services stated that they built increased cooperation with nearby municipalities during the pandemic. Leaders from the different municipalities met often, sometimes several times a week, and helped each other, shared routines, and methods, asked each other questions, coordinated covid-19 testing and developed intermunicipal corona wards, kept each other updated on infection status locally, and relied on each other’s strengths.

“We established a very good intermunicipal cooperation within the health and care services. We helped each other. Shared both routines and procedures, and actually had Teams meetings twice a week, where I could ask questions…and… we all had different strengths in the roles we held, not all of them [group members] were healthcare personnel either, and they had a lot of questions regarding the practical [handling of the pandemic]. At the same time, they [people who were not healthcare personnel] were good at developing routines and procedures, which they shared with the rest. In other words, the cooperation between the municipalities was very good, and for a small municipality, it was worth its weight in gold”. (L1, nursing home/homecare Municipality D)

The same leader stated that they could not have managed the pandemic without support from other larger municipalities and advised closer cooperation following the pandemic as well. An advantage of being small was the ability to easily track and monitor the virus spread within the municipality. Moreover, it was easy to have close cooperation with the infectious disease physician, the municipal chief medical officer, and the nursing home physician, as one person often held several of these roles. Some leaders also had several roles themselves such as a combination of nursing home leader and homecare leader or a combination of nursing home leader and health and care manager (overseeing all health and care services in the municipality). This was perceived as both an advantage and a disadvantage. This was an advantage because they gained a full overview of the situation due to their multiple areas of responsibility, but a disadvantage because it was demanding for one person to handle everything alone, making the system vulnerable. Another challenging aspect was a lack of people to fill all the necessary roles. For example, in one municipality they did not have a public health officer (a physician in charge of the healthcare services in a municipality, and the municipal management’s medical adviser), and had to hire a private practicing physician, who was not resident in the municipality to take on this role.

The economy was also a continuous source of worry. Running a small healthcare service within a small municipality was stated as expensive because the municipalities were obligated to provide the same healthcare services as the larger municipalities, but with less income (e.g., tax payment per inhabitant). The pandemic led to new expenses such as overtime payment, and wage supplement for changed work hours. Leader had to continuously balance a sound use of resources, and responsible operation.

Table 4 provides and overview of the challenges leaders encountered, how they were handled, and leaders’ suggestions for further improvement.

We assessed how leaders in rural primary healthcare services coped with unprecedented challenges during the COVID-19 pandemic. On one hand, they had to manage personal struggles such as insecurity, guilt, and excessive workload. At the same time, they had to confront major organizational issues such as financial instability, lack of resources, and information overload. Moreover, their roles changed, and the need to lead, make more decisions and be more supportive was heightened. While adapting to these changing roles, the leaders continuously introduced new measures to handle pandemic induced challenges including development of new routines, distilled and distributed information, reorganized staffing plans and rearranged wards. Although patients’ safety and quality of care was perceived as safeguarded throughout the COVID-19 pandemic period, leaders had several suggestions for improvements in case of future crises.

Previous research on primary healthcare services during COVID-19 support several of the findings identified here. Similar challenges requiring leaders to adapt their ways of working such as insufficient contingency plans and infection control, lack of staffing, changing guidelines and routines and challenges related to information flow were found [ 17 , 31 , 54 , 55 , 56 ]. Leader strategies to handle these challenges included reallocation of staff, providing support, provide training and distill and distribute information [ 16 , 31 , 55 , 57 ]. Some findings in this study, particularly related to the rural context, has not been found elsewhere. We found that 1) the leaders’ and healthcare services’ increased their dependency on neighboring municipalities during the pandemic and 2) we identified both the advantages and drawbacks of leaders having to function in multiple roles during the pandemic. The heightened importance of cooperation within municipalities and healthcare services in rural areas as opposed to urban areas, has however, been highlighted both before and during the pandemic [ 17 , 23 ].

The pandemic prompted organizations like the World Health Organization (WHO), International Council of Nurses (ICN), and Organization for Economic Co-operation and Development (OECD) to advocate for the advancement of more resilient healthcare services to be able to overcome current and future health system challenges [ 3 , 58 , 59 ]. To achieve the goal of resilient healthcare services, a multi-focal perspective incorporating both individual, teams and systems, is needed. This is because health system organization and leadership on all levels will impact how resilience can be built on team and individual level and thereby reinforce resilience in organizations [ 12 , 51 , 60 , 61 , 62 ].

The multiple aspects of resilient leadership

Leadership style, leaders’ facilitation for flexibility and leaders’ management of resources, competence, and equipment, will affect the resilience of health personnel and thereby the organizational resilience [ 12 , 15 , 63 ]. However, resilient leadership is affected by multiple aspects. For one, leaders inherent individual resilience will influence how and if, they lead resiliently [ 64 ]. Individual resilience is a multifaceted concept consisting of the person’s determination, persistence, adaptability and recuperative capacity, and is impacted by their personal qualities, conduct and cultural outlook [ 12 ]. Similar to previous literature [ 65 , 66 ], the current study found that leaders had to cope with personal challenges such as fear, guilt, adapting to changed roles and increased workload, while performing their everyday tasks. Literature have shown that leaders' responses to challenges can be influenced by their unique personality traits, ultimately shaping their resilience and leadership style [ 67 , 68 ]. Personal qualities needed to “lead well” have also shown to vary between rural and urban healthcare services. For example, Doshi [ 69 ] found that being social, passionate and extrovert was more important in urban areas than in rural areas. This indicate that leaders’ personality traits affect resilience in healthcare, and that resilience promoting personality traits may vary across urban and rural areas. More research is needed to study these relationships.

Although measures to increase personal resilience can be effective (e.g., mindfulness, workshops/training, therapy) [ 70 , 71 , 72 , 73 ] it is not sufficient to base resilience building on these aspects alone [ 74 ]. There is a need to consider how leaders are influenced and supported by the system they are working within to become, and act more resiliently. This includes the support leaders have in their community (e.g., peer support, leader support and proper guidance), their access to resources and their freedom to make decisions [ 60 , 75 , 76 ]. In the current study, it appeared to be a connection between leaders’ coping and the amount of support they had from colleagues. In our interpretation, leaders who talked about their cooperation with others, also talked more positively of their COVID-19 experiences (e.g., how much they had learned or what they had accomplished, rather than how pressured and anxious they were). Similar results have previously been found. For example, leaders in Marshall and colleagues’ study [ 65 ] felt isolated and struggled to make sense of the situation (COVID-19 induced challenges), while leaders in Seljemo and colleagues’ study stated that support from other managers made it easier to cope with high workloads [ 31 ]. In smaller rural healthcare settings, obtaining support can be challenging due to the limited presence of leader colleagues in close proximity [ 77 ]. Additionally, Gray & Jones [ 78 ] suggests that resilient leaders are leaders who ask for help when needed. This indicates that leaders in more isolated areas may require more effort to form connections beyond their organization, and rural healthcare systems must afford greater attention to enabling peer networking (e.g., by providing time and resources).

Through recurrent intermunicipal, online meetings, leaders in the current study attained to initiate, and preserve contact with other leaders in other healthcare settings, much more than before the COVID-19 pandemic. This was particularly important for the smallest, most rural municipalities, where one leader held many roles, and was by one leader, stated as the main reason they were able to manage the COVID-19 pandemic in their primary healthcare service. The tendency to increase intermunicipal cooperation during this period, and the overall need for smaller, rural healthcare services to cooperate with others is found in other literature [ 23 , 79 ]. However, mostly as collaboration within primary healthcare services, and not across organizations. Although recommended by leaders, it is not clear if this close contact has been maintained after the pandemic.

The governance leaders are working under will affect leaders’ possibility to lead resiliently. The governance allows for effective coordination of financing, resource generation, and service delivery activities, ensuring optimal system performance [ 80 ]. Yet, governing for resilience has proven to be a major challenge, because it requires systems to be both flexible and stable at the same time [ 76 ]. Flexibility presupposes systems’, health personnel’, and leaders’ ability to adapt to current conditions, and is essential for systems to cope with unpredictable, non-linear, and ever-changing social and environmental conditions. Conversely, stability must also be implemented to ensure that new policies are sustained and effective, and to stabilize expectations and promote coordination over time [ 76 ]. This means that leaders need flexibility to make their own decisions, as well as the stability that proper guidelines and direction provides [ 81 ]. In this study, some leaders reported experiencing chaos and loss of control when routines and guidelines lacked in the beginning of the pandemic. Similar results have been found among other healthcare leaders, as well as healthcare personnel [ 32 , 66 ]. In contrast, the leaders’ need for flexibility to be able to adapt to the everchanging work environment brought on by the pandemic (examples in Table  3 ) was demonstrated in this, and other studies [ 16 , 17 ]. It can, however, be argued that the balance between flexibility and stability is often skewed more towards flexibility in rural regions. Rural leaders must make unsupported decisions more often than urban leaders as they face higher demands and fewer available resources (such as competence, staff, and funding) [ 77 ]. This requires rural leaders to be more innovative and adaptable to current circumstances [ 23 , 69 , 77 , 79 ]. That said, the availability of resources have shown to impact a system's flexibility, often by influencing the quality of its adaptations [ 2 ].

In low-resource healthcare settings across the globe, certain adaptations made to combat pandemic challenges ended up causing damage (e.g., reuse or misuse of PPE, overexploitation of healthcare personnel and the use of unconventional treatment methods) [ 2 , 82 ]. In high resource healthcare services, as included in this study, adaptations were often described as beneficial, and potential long-lasting solutions (Table  3 ) [ 16 , 17 , 31 ]. Although not comparable to low resource healthcare services, variation in resource availability and economy between the included healthcare services was also expressed in this study. Norwegian municipalities’ income is closely tied to their tax revenue and population size [ 83 ], and regardless of income, the municipalities are required to provide specific healthcare services to their inhabitants. Thus, the financial foundation of smaller more rural municipalities is not as strong as that of larger municipalities. These inequalities were expressed as notable by both leaders and by healthcare personnel in a preceding study exploring the same primary rural healthcare services as included here [ 32 ]. Since resilience in healthcare is also highly dependent on the competence and experience of employees and leaders, the combination of resource and financial deficiencies, more often experienced in rural healthcare services than in urban healthcare services, may pose particular challenges in resilience building in rural areas [ 23 , 84 ]. This is worth exploring further, along with the rural healthcare services’ particular need to be flexible versus the potential difficulty they may have in making beneficial adaptations because of a weaker financial foundation.

Resilience and leadership style

Providing support to employees was an important leader task during the pandemic [ 55 , 66 ] and have further, been found to be particularly vital in rural areas, where employees have a smaller network of colleagues to turn to [ 84 ]. Other vital leadership tasks, recognizable from crisis leadership literature and also found in this study, were the importance of organizing, directing and implementing actions, forging cooperation, enabling work- arounds or adaptation, direct and guide and the importance of communication and dissemination of information [ 85 , 86 ]. Although charismatic leadership Footnote 1 has been found to be most valuable during crisis [ 87 ], there is an ongoing discussion of what leadership style is best suited to promote resilience in healthcare [ 11 , 14 , 66 , 88 ]. For example, both transformational and transactional leadership 1 [ 89 ] have been stated as resilience promoting leadership styles [ 15 ]. However, as found in other literature [ 66 , 88 ], the results of this study indicated that leaders oscillated between different styles during the COVID-19 pandemic period. For example, in the beginning of the pandemic when uncertainty characterized the healthcare system, leaders became stricter with rules and regulations, demonstrating an authoritative leadership style 1 . Further, stepping in, lecturing about infection control procedures and use of PPE, indicated a coaching leadership 1 style and lastly, when the leaders went against employees wishes to ensure safe maintenance of operation, it showed similarities to a transformational leadership style 1 [ 90 ]. Interestingly, leaders did not speak directly about how their leadership styles changed, and seemed unaware of their leadership style adaptation. Similarly, in Sihvola et al. [ 66 ] leaders found it surprising how novel conditions could influence their leadership style.

On one side, these results, suggest that an adaptive leadership style can be necessary during crisis. On the other side, this and other studies [ 31 , 54 ] indicate that leaders need more knowledge on crisis leadership, for example, to be made aware of the potential need to oscillate between different leadership styles during a crisis, and the possible subsequent challenges. For example, a study conducted by Boyle og Mervin [ 91 ] found that being a “nurse leader” (all leaders in this study were nurses), showed challenging because the leaders were judged as a peer rather than a leader. This can cause challenges, particularly when stepping into an authoritative leadership style. Such conflicts were not reported in this study, however, these are all aspects which should be given more attention when investigating resilience in healthcare and leadership styles [ 88 ]. Furthermore, it is crucial to acquire further understanding on the distinctions between leading in rural and urban areas, and how various leadership approaches may be impacted by managing tight-knit employee teams, which is often the case in small rural nursing home and homecare services. And finally, there is a need to provide a deeper understanding of the factors that promote or impede resilience in rural primary healthcare services, and the influence of the contextual aspects on resilience in healthcare.

Limitations

This study has limitations which need to be addressed. A larger number of included primary healthcare leaders over a wider geographical area and across boarders would have provided a broader view of leader experiences during the COVID-19 pandemic. However, it was very difficult to get leaders to take time to reflect during this crisis. This study does provide insight into a variety of different municipalities of different sizes, organization and locations in the Norwegian context, providing a variety of rural primary healthcare leaders experiences during the pandemic. Interviews were conducted in different ways (focus group, digital and individually) this could have influenced leaders description of their experiences. Furthermore, interviews were held at different points throughout the pandemic phases, leading to a mix of leaders with both current and reflective experiences of navigating the pandemic. This should be taken into consideration when reading the results.

By exploring nursing home and home care leaders’ experiences with the COVID-19 pandemic in rural areas, we found that the leaders met a range of rapid onset challenges of different nature, many of which demanded fast decisions and solutions. Leaders handled these challenges and changes in a variety of ways in their different contexts. In addition to health system challenges, leaders also had to cope with rapidly changing roles, while managing their own and employees’ insecurities. This study’s results demonstrate the intricate nature of resilient leadership, encompassing individual resilience, personality, governance, resource availability, and the capability to adjust to organizational and employee requirements. In addition, there may be differences between how resilience in healthcare is built and progresses in rural healthcare services versus urban contexts. Further research to understand the interplay between these aspects is needed, and it is critical to consider context.

Availability of data and materials

Data are available from the corresponding author upon reasonable request.

Charismatic leadership : influence and persuasion of others to help the fulfill their mandate, also in face of adversity; Transformational leadership: pushing to work and think in new ways; Authoritative leadership : the leader in control, low autonomy; Coaching leadership : the leader support employee’s skill advancement; Transactional leadership : exchange of rewards for fulfilling expectations.

Abbreviations

International Council of Nurses

Licensed practical nurse

Organization for Economic Co-operation and Development

Personal protective equipment

Registered Nurse

The World Health Organization

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Acknowledgements

The authors would like to thank participating leaders for their contribution to the study. We would also like to acknowledge Ole-Jørn Borum for graphical design on fig. 1 .

Open access funding provided by University of Stavanger & Stavanger University Hospital The publication processing charge was covered by the University of Stavanger.

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Malin Knutsen Glette & Siri Wiig

Department of Health and Caring Sciences, Western Norway University of Applied Sciences, Haugesund, Norway

Malin Knutsen Glette & Tone Kringeland

Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA

Lipika Samal & David W. Bates

Department of Health Policy and Management, Harvard T. H. Chan School of Public Health, Boston, MA, USA

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Contributions

MKG, SW, TK, and DWB was involved in discussions regarding the project’s development. MKG conducted interviews and led the analysis of the transcribed data. The manuscript was a collaborative effort between MKG, SW, TK, DWB and LS, where all authors provided feedback. The author team approved the manuscript before submission.

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Correspondence to Malin Knutsen Glette .

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Ethics approval and consent to participate.

The study was approved by the Norwegian Agency for Shared Services in Education and Research (SIKT) in 2022 and provides the ethical approval, information security and privacy services as a part of the HK-dir (Norwegian Directorate for Higher Education and Skills). An informed consent form was signed by all leaders prior to the interviews, and information about the aim of the study and their right to redraw was repeated immediately before the interviews started.

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Dr. Bates reports grants and personal fees from EarlySense, personal fees from CDI Negev, equity from ValeraHealth, equity from Clew, equity from MDClone, personal fees and equity from AESOP, personal fees and equity from Feelbet-ter, equity from Guided Clinical Solutions, and grants from IBM Watson Health, outside the submitted work. Dr. Bates has a patent pending (PHC-028564 US PCT), on intraoperative clinical decision support. The other authors report no competing interests.

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Glette, M.K., Kringeland, T., Samal, L. et al. A qualitative study of leaders’ experiences of handling challenges and changes induced by the COVID-19 pandemic in rural nursing homes and homecare services. BMC Health Serv Res 24 , 442 (2024). https://doi.org/10.1186/s12913-024-10935-y

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Workplace health benefits don’t move the needle on improving employee happiness and well-being. With one exception

F rom virtual therapy to time management classes and mindfulness programs, workplaces are hoping their commitment to well-being pays off in the long run with a happier, healthier cohort of workers. 

However, in an assessment of 90-workplace interventions to improve well-being, William Fleming, PhD, a researcher at the Wellbeing Research Centre at the University of Oxford, found the success rates are murky. 

 “Across multiple subjective well-being indicators, participants appear no better off,” Fleming concludes in his paper published in the Industrial Relations Journal this month. Fleming also notes in his paper that at least half of employers in the UK have official well-being strategies. Workplace well-being, broadly defined, refers to how positive an employee feels in their job, which inevitably influences their sense of purpose, belonging, and productivity. 

The study, which analyzed data from 46,336 workers across over 230 companies, found nearly all interventions, including resilience training, access to sleep apps, and online coaching, did not benefit employee well-being. There was one notable exception, however: Volunteering did positively correlate with improved workplace well-being. 

Previous research has found conservative benefits in individualistic workplace mental health interventions, and Fleming aimed to understand how workers who partake in these benefits fair compared to those who opt-out. As more people seek inclusive workplaces that support work-life balance, interventions are timely. However, standard well-being programming may be too simple of a solution, and experts say employers are missing the mark on how they can thoughtfully improve well-being. 

The ‘mental health buffet’ is not working 

Ariela Safira , founder and CEO of Real, a mental wellness platform that has expanded to address workforce needs, agrees with Fleming’s conclusions. “It’s the very reason why we set out to build an entirely new care model in the first place,” she says. Despite attention on diminishing mental health within the workplace and beyond, more benefits without intention don’t solve the problem, Safira says. 

“When it comes to mental health, mental illness has been skyrocketing and yet, as an industry, we have barely skimmed the surface in terms of creating new forms of care to meet the need,” Safira says. “As a result, people have been continuing to struggle, regardless of how many mental health benefits their employer offers them. This is hurting individuals and it's hurting workplaces.”

Dr. Richard Safeer , the chief medical director of employee health and well-being at Johns Hopkins Medicine and author of A Cure for the Common Company , says well-being benefits can make a difference. However, it’s not one-size-fits-all across companies. 

“It’s easy to put resources on the workplace mental health buffet, but like so many ‘all you can eat’ menus, the quality of what’s served is not usually great,” he says. “The workplace culture is complex and the expectation that simple solutions will prevail is naïve.”

While the pandemic underscored our need for human connection and community, Safeer says more intentional innovation must go into how well-being benefits and programs are developed and integrated into the culture. 

“Well-being is more than a program, prize or portal. Well-being requires the compilation of an intentionally crafted well-being culture, whereby every member of the organization plays a role not only in their own well-being, but also a role in supporting those with whom they work,” says Safeer, who points to what makes a workplace psychologically safe . For example, beyond benefits, it’s about fostering “ human-centered leaders .” 

The well-being policies that workers want 

What’s more, if systemic changes propelling burnout and stress stay intact, benefits have little effect. “Unless the stressors and barriers that eat away at employee well-being are addressed, it will be difficult for any program to overcome those negative forces,” Safeer says. 

A 2023 survey from Gallup and Bentley University found the three workplace policies workers say will help their well-being relate more to the structure of working than individualistic improvements. Limiting work outside of typical hours, implementing a 4-day workweek, and incorporating mental health days topped the list for preferred well-being policies. Jennifer Moss , a workplace culture strategist, speaker author of The Burnout Epidemic , echoes this sentiment and says the root causes of poor well-being and chronic stress cannot be solved with merely workplace mindfulness guides if staff are overworked and don’t feel supported.  

“When we aren’t giving people space to focus on self-care then it just ends up being another task for employees to add into their already busy days,” she says. “Instead of trying to put bandaids on broken systems, let’s transform the system instead. Instead of, ‘How do we make people more resilient to the stress we’ve caused?’ How about ‘Let’s figure out where our people are experiencing stress and eliminate that!’ instead.” 

One suggestion? Moss says to find time in the workday to streamline when employees will have time to focus on their health. “We can start by reducing meeting fatigue and giving people the right to disconnect,” she says. 

Safira, whose platform provides mental wellness support around the clock and aims to create mentally healthy workplaces by providing events, stories, and engagement on wellness topics, says to think outside of the box on what can serve people at work. 

“We need to innovate new care models that speak to the individual needs of today’s people and also to the unique needs of today's workplace,” she says. “We need care that prevents mental illness, that is available immediately (without a 2-month waitlist), that is accessible and clinically effective for people of all races, that supports people at all hours of the day, and that on a community-level, builds more mentally well workplaces.” 

It must be noted that the study’s data was collected between 2017 and 2018 from the BHQ survey before the pandemic, which has laid bare many workplace well-being problems in the age of a distributed workforce. The study also did not examine the specificity of the programs over the course of their duration, or how different programs can influence the outcomes for workers overtime. 

Safeer also says it’s important not to lump all employers into the same bin, which will simplify needed solutions. “Some organizations are taking meaningful steps to lay the foundation whereby the additional application of individual resources, such as trainings and well-being apps might prove helpful,” he says.

This story was originally featured on Fortune.com

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If you've heard of or read about mindfulness meditation — also known as mindfulness — you might be curious about how to practice it. Find out how to do mindfulness exercises and how they might benefit you.

What is mindfulness?

Mindfulness is a type of meditation in which you focus on being intensely aware of what you're sensing and feeling in the moment, without interpretation or judgment. Practicing mindfulness involves breathing methods, guided imagery, and other practices to relax the body and mind and help reduce stress.

Spending too much time planning, problem-solving, daydreaming, or thinking negative or random thoughts can be draining. It can also make you more likely to experience stress, anxiety and symptoms of depression. Practicing mindfulness exercises can help you direct your attention away from this kind of thinking and engage with the world around you.

What are the benefits of meditation?

Meditation has been studied in many clinical trials. The overall evidence supports the effectiveness of meditation for various conditions, including:

• High blood pressure (hypertension)

Preliminary research indicates that meditation can also help people with asthma and fibromyalgia.

Meditation can help you experience thoughts and emotions with greater balance and acceptance. Meditation also has been shown to:

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What are some examples of mindfulness exercises?

There are many simple ways to practice mindfulness. Some examples include:

• Pay attention. It's hard to slow down and notice things in a busy world. Try to take the time to experience your environment with all of your senses — touch, sound, sight, smell and taste. For example, when you eat a favorite food, take the time to smell, taste and truly enjoy it.
• Live in the moment. Try to intentionally bring an open, accepting and discerning attention to everything you do. Find joy in simple pleasures.
• Accept yourself. Treat yourself the way you would treat a good friend.
• Focus on your breathing. When you have negative thoughts, try to sit down, take a deep breath and close your eyes. Focus on your breath as it moves in and out of your body. Sitting and breathing for even just a minute can help.

You can also try more structured mindfulness exercises, such as:

• Body scan meditation. Lie on your back with your legs extended and arms at your sides, palms facing up. Focus your attention slowly and deliberately on each part of your body, in order, from toe to head or head to toe. Be aware of any sensations, emotions or thoughts associated with each part of your body.
• Sitting meditation. Sit comfortably with your back straight, feet flat on the floor and hands in your lap. Breathing through your nose, focus on your breath moving in and out of your body. If physical sensations or thoughts interrupt your meditation, note the experience and then return your focus to your breath.
• Walking meditation. Find a quiet place 10 to 20 feet in length, and begin to walk slowly. Focus on the experience of walking, being aware of the sensations of standing and the subtle movements that keep your balance. When you reach the end of your path, turn and continue walking, maintaining awareness of your sensations.

When and how often should I practice mindfulness exercises?

It depends on what kind of mindfulness exercise you plan to do.

Simple mindfulness exercises can be practiced anywhere and anytime. Research indicates that engaging your senses outdoors is especially beneficial.

For more structured mindfulness exercises, such as body scan meditation or sitting meditation, you'll need to set aside time when you can be in a quiet place without distractions or interruptions. You might choose to practice this type of exercise early in the morning before you begin your daily routine.

Aim to practice mindfulness every day for about six months. Over time, you might find that mindfulness becomes effortless. Think of it as a commitment to reconnecting with and nurturing yourself.

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• Bystritsky A. Complementary and alternative treatments for anxiety symptoms and disorders: Physical, cognitive, and spiritual interventions. https://uptodate.com/contents/search. Accessed June 14, 2018.
• Seaward BL. Meditation and mindfulness. In: Managing Stress: Principles and Strategies for Health and Well-being. 9th ed. Burlington, Mass.: Jones & Bartlett Learning; 2018.
• Shapiro SL, et al. The Art and Science of Mindfulness: Integrating Mindfulness into Psychology and the Helping Professions. 2nd ed. Washington, D.C.: American Psychological Association; 2017.
• Lymeus F, et al. Building mindfulness bottom-up: Meditation in natural settings supports open monitoring and attention restoration. Consciousness and Cognition. 2018;59:40.
• Blanck P, et al. Effects of mindfulness exercises as stand-alone interventions on symptoms of anxiety and depression: Systematic review and meta-analysis. Behaviour Research and Therapy. 2018;102:25.
• AskMayoExpert. Meditation. Rochester, Minn.: Mayo Foundation for Medical Education and Research; 2018.
• Khoury B, et al. Mindfulness-based stress reduction for healthy individuals: A meta-analysis. Journal of Psychosomatic Research. 2015;78:519.
• Practice mindfulness and relaxation. Springboard Beyond Cancer. https://survivorship.cancer.gov/springboard/stress-mood/practice-mindfulness. Accessed June 14, 2018.

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How can I plan what to eat or drink when I have diabetes?

How can physical activity help manage my diabetes, what can i do to reach or maintain a healthy weight, should i quit smoking, how can i take care of my mental health, clinical trials for healthy living with diabetes.

Healthy living is a way to manage diabetes . To have a healthy lifestyle, take steps now to plan healthy meals and snacks, do physical activities, get enough sleep, and quit smoking or using tobacco products.

Healthy living may help keep your body’s blood pressure , cholesterol , and blood glucose level, also called blood sugar level, in the range your primary health care professional recommends. Your primary health care professional may be a doctor, a physician assistant, or a nurse practitioner. Healthy living may also help prevent or delay health problems  from diabetes that can affect your heart, kidneys, eyes, brain, and other parts of your body.

Making lifestyle changes can be hard, but starting with small changes and building from there may benefit your health. You may want to get help from family, loved ones, friends, and other trusted people in your community. You can also get information from your health care professionals.

What you choose to eat, how much you eat, and when you eat are parts of a meal plan. Having healthy foods and drinks can help keep your blood glucose, blood pressure, and cholesterol levels in the ranges your health care professional recommends. If you have overweight or obesity, a healthy meal plan—along with regular physical activity, getting enough sleep, and other healthy behaviors—may help you reach and maintain a healthy weight. In some cases, health care professionals may also recommend diabetes medicines that may help you lose weight, or weight-loss surgery, also called metabolic and bariatric surgery.

Choose healthy foods and drinks

There is no right or wrong way to choose healthy foods and drinks that may help manage your diabetes. Healthy meal plans for people who have diabetes may include

• dairy or plant-based dairy products
• nonstarchy vegetables
• protein foods
• whole grains

Try to choose foods that include nutrients such as vitamins, calcium , fiber , and healthy fats . Also try to choose drinks with little or no added sugar , such as tap or bottled water, low-fat or non-fat milk, and unsweetened tea, coffee, or sparkling water.

Try to plan meals and snacks that have fewer

• foods high in saturated fat
• foods high in sodium, a mineral found in salt
• sugary foods , such as cookies and cakes, and sweet drinks, such as soda, juice, flavored coffee, and sports drinks

Your body turns carbohydrates , or carbs, from food into glucose, which can raise your blood glucose level. Some fruits, beans, and starchy vegetables—such as potatoes and corn—have more carbs than other foods. Keep carbs in mind when planning your meals.

You should also limit how much alcohol you drink. If you take insulin  or certain diabetes medicines , drinking alcohol can make your blood glucose level drop too low, which is called hypoglycemia . If you do drink alcohol, be sure to eat food when you drink and remember to check your blood glucose level after drinking. Talk with your health care team about your alcohol-drinking habits.

Find the best times to eat or drink

Talk with your health care professional or health care team about when you should eat or drink. The best time to have meals and snacks may depend on

• what medicines you take for diabetes
• what your level of physical activity or your work schedule is
• whether you have other health conditions or diseases

Ask your health care team if you should eat before, during, or after physical activity. Some diabetes medicines, such as sulfonylureas  or insulin, may make your blood glucose level drop too low during exercise or if you skip or delay a meal.

Plan how much to eat or drink

You may worry that having diabetes means giving up foods and drinks you enjoy. The good news is you can still have your favorite foods and drinks, but you might need to have them in smaller portions  or enjoy them less often.

For people who have diabetes, carb counting and the plate method are two common ways to plan how much to eat or drink. Talk with your health care professional or health care team to find a method that works for you.

Carb counting

Carbohydrate counting , or carb counting, means planning and keeping track of the amount of carbs you eat and drink in each meal or snack. Not all people with diabetes need to count carbs. However, if you take insulin, counting carbs can help you know how much insulin to take.

Plate method

The plate method helps you control portion sizes  without counting and measuring. This method divides a 9-inch plate into the following three sections to help you choose the types and amounts of foods to eat for each meal.

• Nonstarchy vegetables—such as leafy greens, peppers, carrots, or green beans—should make up half of your plate.
• Carb foods that are high in fiber—such as brown rice, whole grains, beans, or fruits—should make up one-quarter of your plate.
• Protein foods—such as lean meats, fish, dairy, or tofu or other soy products—should make up one quarter of your plate.

If you are not taking insulin, you may not need to count carbs when using the plate method.

Work with your health care team to create a meal plan that works for you. You may want to have a diabetes educator  or a registered dietitian  on your team. A registered dietitian can provide medical nutrition therapy , which includes counseling to help you create and follow a meal plan. Your health care team may be able to recommend other resources, such as a healthy lifestyle coach, to help you with making changes. Ask your health care team or your insurance company if your benefits include medical nutrition therapy or other diabetes care resources.

Talk with your health care professional before taking dietary supplements

There is no clear proof that specific foods, herbs, spices, or dietary supplements —such as vitamins or minerals—can help manage diabetes. Your health care professional may ask you to take vitamins or minerals if you can’t get enough from foods. Talk with your health care professional before you take any supplements, because some may cause side effects or affect how well your diabetes medicines work.

Research shows that regular physical activity helps people manage their diabetes and stay healthy. Benefits of physical activity may include

• lower blood glucose, blood pressure, and cholesterol levels
• better heart health
• healthier weight
• better mood and sleep
• better balance and memory

Talk with your health care professional before starting a new physical activity or changing how much physical activity you do. They may suggest types of activities based on your ability, schedule, meal plan, interests, and diabetes medicines. Your health care professional may also tell you the best times of day to be active or what to do if your blood glucose level goes out of the range recommended for you.

Do different types of physical activity

People with diabetes can be active, even if they take insulin or use technology such as insulin pumps .

Try to do different kinds of activities . While being more active may have more health benefits, any physical activity is better than none. Start slowly with activities you enjoy. You may be able to change your level of effort and try other activities over time. Having a friend or family member join you may help you stick to your routine.

The physical activities you do may need to be different if you are age 65 or older , are pregnant , or have a disability or health condition . Physical activities may also need to be different for children and teens . Ask your health care professional or health care team about activities that are safe for you.

Aerobic activities

Aerobic activities make you breathe harder and make your heart beat faster. You can try walking, dancing, wheelchair rolling, or swimming. Most adults should try to get at least 150 minutes of moderate-intensity physical activity each week. Aim to do 30 minutes a day on most days of the week. You don’t have to do all 30 minutes at one time. You can break up physical activity into small amounts during your day and still get the benefit. 1

Strength training or resistance training

Strength training or resistance training may make your muscles and bones stronger. You can try lifting weights or doing other exercises such as wall pushups or arm raises. Try to do this kind of training two times a week. 1

Balance and stretching activities

Balance and stretching activities may help you move better and have stronger muscles and bones. You may want to try standing on one leg or stretching your legs when sitting on the floor. Try to do these kinds of activities two or three times a week. 1

Some activities that need balance may be unsafe for people with nerve damage or vision problems caused by diabetes. Ask your health care professional or health care team about activities that are safe for you.

Stay safe during physical activity

Staying safe during physical activity is important. Here are some tips to keep in mind.

Drink liquids

Drinking liquids helps prevent dehydration , or the loss of too much water in your body. Drinking water is a way to stay hydrated. Sports drinks often have a lot of sugar and calories , and you don’t need them for most moderate physical activities.

Avoid low blood glucose

Check your blood glucose level before, during, and right after physical activity. Physical activity often lowers the level of glucose in your blood. Low blood glucose levels may last for hours or days after physical activity. You are most likely to have low blood glucose if you take insulin or some other diabetes medicines, such as sulfonylureas.

Ask your health care professional if you should take less insulin or eat carbs before, during, or after physical activity. Low blood glucose can be a serious medical emergency that must be treated right away. Take steps to protect yourself. You can learn how to treat low blood glucose , let other people know what to do if you need help, and use a medical alert bracelet.

Avoid high blood glucose and ketoacidosis

Taking less insulin before physical activity may help prevent low blood glucose, but it may also make you more likely to have high blood glucose. If your body does not have enough insulin, it can’t use glucose as a source of energy and will use fat instead. When your body uses fat for energy, your body makes chemicals called ketones .

High levels of ketones in your blood can lead to a condition called diabetic ketoacidosis (DKA) . DKA is a medical emergency that should be treated right away. DKA is most common in people with type 1 diabetes . Occasionally, DKA may affect people with type 2 diabetes  who have lost their ability to produce insulin. Ask your health care professional how much insulin you should take before physical activity, whether you need to test your urine for ketones, and what level of ketones is dangerous for you.

Take care of your feet

People with diabetes may have problems with their feet because high blood glucose levels can damage blood vessels and nerves. To help prevent foot problems, wear comfortable and supportive shoes and take care of your feet  before, during, and after physical activity.

If you have diabetes, managing your weight  may bring you several health benefits. Ask your health care professional or health care team if you are at a healthy weight  or if you should try to lose weight.

If you are an adult with overweight or obesity, work with your health care team to create a weight-loss plan. Losing 5% to 7% of your current weight may help you prevent or improve some health problems  and manage your blood glucose, cholesterol, and blood pressure levels. 2 If you are worried about your child’s weight  and they have diabetes, talk with their health care professional before your child starts a new weight-loss plan.

You may be able to reach and maintain a healthy weight by

• following a healthy meal plan
• consuming fewer calories
• being physically active
• getting 7 to 8 hours of sleep each night 3

If you have type 2 diabetes, your health care professional may recommend diabetes medicines that may help you lose weight.

Online tools such as the Body Weight Planner  may help you create eating and physical activity plans. You may want to talk with your health care professional about other options for managing your weight, including joining a weight-loss program  that can provide helpful information, support, and behavioral or lifestyle counseling. These options may have a cost, so make sure to check the details of the programs.

Your health care professional may recommend weight-loss surgery  if you aren’t able to reach a healthy weight with meal planning, physical activity, and taking diabetes medicines that help with weight loss.

If you are pregnant , trying to lose weight may not be healthy. However, you should ask your health care professional whether it makes sense to monitor or limit your weight gain during pregnancy.

Both diabetes and smoking —including using tobacco products and e-cigarettes—cause your blood vessels to narrow. Both diabetes and smoking increase your risk of having a heart attack or stroke , nerve damage , kidney disease , eye disease , or amputation . Secondhand smoke can also affect the health of your family or others who live with you.

If you smoke or use other tobacco products, stop. Ask for help . You don’t have to do it alone.

Feeling stressed, sad, or angry can be common for people with diabetes. Managing diabetes or learning to cope with new information about your health can be hard. People with chronic illnesses such as diabetes may develop anxiety or other mental health conditions .

Learn healthy ways to lower your stress , and ask for help from your health care team or a mental health professional. While it may be uncomfortable to talk about your feelings, finding a health care professional whom you trust and want to talk with may help you

• lower your feelings of stress, depression, or anxiety
• manage problems sleeping or remembering things
• see how diabetes affects your family, school, work, or financial situation

Ask your health care team for mental health resources for people with diabetes.

Sleeping too much or too little may raise your blood glucose levels. Your sleep habits may also affect your mental health and vice versa. People with diabetes and overweight or obesity can also have other health conditions that affect sleep, such as sleep apnea , which can raise your blood pressure and risk of heart disease.

NIDDK conducts and supports clinical trials in many diseases and conditions, including diabetes. The trials look to find new ways to prevent, detect, or treat disease and improve quality of life.

What are clinical trials for healthy living with diabetes?

Clinical trials—and other types of clinical studies —are part of medical research and involve people like you. When you volunteer to take part in a clinical study, you help health care professionals and researchers learn more about disease and improve health care for people in the future.

Researchers are studying many aspects of healthy living for people with diabetes, such as

• how changing when you eat may affect body weight and metabolism
• how less access to healthy foods may affect diabetes management, other health problems, and risk of dying
• whether low-carbohydrate meal plans can help lower blood glucose levels
• which diabetes medicines are more likely to help people lose weight

Find out if clinical trials are right for you .

Watch a video of NIDDK Director Dr. Griffin P. Rodgers explaining the importance of participating in clinical trials.

What clinical trials for healthy living with diabetes are looking for participants?

You can view a filtered list of clinical studies on healthy living with diabetes that are federally funded, open, and recruiting at www.ClinicalTrials.gov . You can expand or narrow the list to include clinical studies from industry, universities, and individuals; however, the National Institutes of Health does not review these studies and cannot ensure they are safe for you. Always talk with your primary health care professional before you participate in a clinical study.

This content is provided as a service of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), part of the National Institutes of Health. NIDDK translates and disseminates research findings to increase knowledge and understanding about health and disease among patients, health professionals, and the public. Content produced by NIDDK is carefully reviewed by NIDDK scientists and other experts.

NIDDK would like to thank: Elizabeth M. Venditti, Ph.D., University of Pittsburgh School of Medicine.