health research paper pdf

Public health and research: an overview

Journal of Health Research

ISSN : 2586-940X

Article publication date: 15 October 2020

Issue publication date: 15 June 2021

This paper was to describe the overview of public health research.

Design/methodology/approach

It is a commentary piece of work from own long experience in working with the World Health Organization.

This study has innovative ideas in approaching priority areas in public health research.

Originality/value

This study opens up new thought in public health research.

• Public health

Public health research

Plianbangchang, S. (2021), "Public health and research: an overview", Journal of Health Research , Vol. 35 No. 4, pp. 374-378. https://doi.org/10.1108/JHR-03-2020-0074

Emerald Publishing Limited

Copyright © 2020, Samlee Plianbangchang

Published in Journal of Health Research . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

An overview of public health

Public health is the science and art of preventing disease, prolonging life and promoting human health through organized community efforts as well as the informed choice of society, public, private and voluntary organizations and communities at large. Analyzing the health of a population and the threats to that health forms the basis for public health.

Additionally, “health”, as defined in the WHO Constitution in 1948 [ 1 ], considers physical, mental and social well-being and is not merely the absence of disease or infirmity. Furthermore, public health is interdisciplinary, thus, a “public health team” may include several related disciplines in health and other social fields. Access to public health initiatives for comprehensive and integrated health care and services is always a difficult challenge in any population setting.

The objective of this commentary is to provide an overview of public health research and highlight innovative thinking in the field.

Public health systems

Public health systems are commonly defined as “all public, private, and voluntary entities that contribute to the delivery of essential public health services to people within a jurisdiction” [ 2 ]. This concept is to ensure, among other things, that all contributions to the improved health and well-being of the community or state are appropriately recognized and counted in the assessment of the provision of public health services to the community. The public health system includes public health agencies at state and local levels; all healthcare providers; public safety agencies; human services and charity organizations; education and youth development agencies; recreation and art-related organizations; economic and philanthropic agencies and environmental organizations.

The 10 essential public health services/functions

Monitoring community health situations to identify and solve health problems and prevent any health risks in the community;

Investigating and diagnosing specific health threats and health hazards in the community with the view to their early prevention;

Through modern technology in communication, informing, educating and empowering people in the community about health issues and challenges and their interventions;

Identifying/investigating and solving any problems of public health importance;

Through the full participation of people, developing policies and plans that support individual and combined health efforts in the community;

Ensuring effective enforcement of laws and regulations that protect environmental health and assure the safety of the population;

Linking people to needed personal health services and ensuring the provision of quality health care when otherwise unavailable;

Assuring the availability of effective public health workforce and competent healthcare personnel in both public and private facilities;

Objectively evaluating efficiency, effectiveness, accessibility and quality of personal as well as population-based health care facilities and services and

Undertaking study/research for new insights and innovative solutions to effectively counter prevailing and emerging health problems.

Public health rsearch aims to elucidate the influence of factors that determine the health of a population, i.e. genetic, environmental, social-cultural, economic, political, etc. The objective of public health research is to use the knowledge gained to propose policies and interventions, based on scientific evidence, and to help improve the health and well-being of the population and ultimately reduce/eradicate health inequalities.

Public health research is characterized by its multidisciplinary approach. It draws on several disciplines in its development and management, especially epidemiology/human ecology; biostatistics; physical and social sciences as applied to health; biology; genetics and toxicology. It usually entails the analysis of data on population samples on varying scales, depending on the scope and purpose of the research [ 4 ].

In practical terms, public health researchers study the statuses of population health and well-being, disability and loss of independence. They analyze the determining risk factors of these statuses or conditions, whether biological, behavioral, psycho-social or environmental. In addition, the researchers develop and assess the interventions aiming to effectively promote population health, prevent disease and compensate for disabilities and loss of independence. They also develop and assess innovation to improve efficiency and effectiveness of the organization of healthcare facilities and personnel, in public health, medical and other social service areas.

Research in health may be in the fields of health research, medical research, public health research, health services research, health systems research, environmental health research and others. These are interlinked and need to be pursued in a parallel manner in public health development.

Funding agencies

Mission and role of funding agencies

(2)Quality of research proposal

Relevance to current health-related issues and problems; responding to priority public health needs and the challenges of community and country are crucial. Applications should also be in the interest of the international/global health community.

Formulation with rational and logical thinking; Research proposals should be well-conceived and developed according to sound research methodology/protocol on epidemiological principles.

Expected result of the proposal; The result must be of high quality and should imply strong feasibility in its subsequent application/implementation in both technical, managerial and financial terms.

Public health system development research

DEIDS (development and evaluation of integrated health delivery system) (Thailand Lampang Health Development Project), 1973-1978 [ 6 ].

(2)District Health Services Development Project based on the principle of the primary health care approach, Mongar Health Development Project in Bhutan, 1984–1990 [ 7 ].

(3)Comprehensive and Integrated Health Research Development Project on hill- tribe population, 2014–present (still ongoing)

This is a long-term large research and development project with many sub-projects focusing on various specific issues of the target population. It is a multi-agency and interdisciplinary endeavor developed and implemented through the coordination of the Maefah Luang University Center of Excellence on health of the hill-tribe population. It includes the development of integrated and comprehensive social and health care services with an emphasis on equity, social justice and self-reliance in the community. There are multiple sources of funding, however mainly from the National Research Council of Thailand.

Some suggested areas for contemporary public health research

Universal health coverage for all

Financial sustainability in the long term

Equal accessibility by all people, regardless of their social and economic status

The right approach to development, proactive or passive strategy

Affordability in financial and managerial terms of the country concerned.

(2)Social impact of drug abuse /addiction

The social and economic impact of (chaotic situations created by) drug abuse and addiction

The general health of a population that is gradually undermined by drug abuse and drug addiction, leading, among other things to worsened national social and economic growth.

(3)Health and social care of elderly population

Intervention to reduce the degree of morbidity/disability and dependence

Efforts to increase social and economic productivity/independence

Program for self-help and self-reliance in an aged population

Preference between aging and aged programs, the difference between the two.

(4)Emerging infectious disease (EID)

The epidemiological, environmental and ecological approach in an integrated manner

The importance of social and behavioral change

Emerging mutation, assortment and genetic change in infectious agents

Impact of global climate change on EID, etc.

Even though it is mainly conceptual, the article may be able to help open new visions in public health research for better and sustainable health in the entire population in various localities.

1 World Health Organization [WHO] . WHO basic document . 47th ed. Geneva : WHO ; 2009 [cited 2019 Nov 28]. Available from: https://apps.who.int/iris/handle/10665/44192 .

2 World Health Organization [WHO] . What is health policy and systems research (HPSR)? [cited 2019 Nov 28]. Available from: https://www.who.int/alliance-hpsr/about/hpsr/en/ .

3 Centers for Disease Control and Prevention [CDC] , Office for state, tribal, local and territorial support . The 10 Essential Public Health Services: An Overview [cited 2019 Nov 28]. Available from: https://www.cdc.gov/publichealthgateway/publichealthservices/pdf/essential-phs.pdf .

4 Centers for Disease Control and Prevention [CDC] . Public health 101 series [cited 2019 Nov 28]. Available from: https://www.cdc.gov/publichealth101/ .

5 Centers for Disease Control and Prevention [CDC] . 24/7 Saving Lives, Protecting People [cited 2019 Nov 28]. Available from: https://www.cdcfoundation.org/cdc-247-saving-lives-protecting-people# .

6 Memoir on Occasion of the Death Anniversary of Dr. Somboon Vacharothai . Bangkok ; 2014 : 39 - 43 . (On Thailand DEIDS Project) .

7 World Health Organization [WHO] , Regional office for south-east asia [SEARO]. Sasakawa health prize: stories from south-east asia . New Delhi : WHO/SEARO ; 2012 : 125 - 146 [cited 2019 Nov 28]. Available from: https://apps.who.int/iris/bitstream/handle/10665/205878/B4905.pdf?sequence=1&isAllowed=y .

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• v.21(6); 2018 Dec

Public and patient involvement in quantitative health research: A statistical perspective

Ailish hannigan.

1 Public and Patient Involvement Research Unit, Graduate Entry Medical School, University of Limerick, Limerick, Ireland

2 Health Research Institute, University of Limerick, Limerick, Ireland

The majority of studies included in recent reviews of impact for public and patient involvement (PPI) in health research had a qualitative design. PPI in solely quantitative designs is underexplored, particularly its impact on statistical analysis. Statisticians in practice have a long history of working in both consultative (indirect) and collaborative (direct) roles in health research, yet their perspective on PPI in quantitative health research has never been explicitly examined.

To explore the potential and challenges of PPI from a statistical perspective at distinct stages of quantitative research, that is sampling, measurement and statistical analysis, distinguishing between indirect and direct PPI.

Conclusions

Statistical analysis is underpinned by having a representative sample, and a collaborative or direct approach to PPI may help achieve that by supporting access to and increasing participation of under‐represented groups in the population. Acknowledging and valuing the role of lay knowledge of the context in statistical analysis and in deciding what variables to measure may support collective learning and advance scientific understanding, as evidenced by the use of participatory modelling in other disciplines. A recurring issue for quantitative researchers, which reflects quantitative sampling methods, is the selection and required number of PPI contributors, and this requires further methodological development. Direct approaches to PPI in quantitative health research may potentially increase its impact, but the facilitation and partnership skills required may require further training for all stakeholders, including statisticians.

1. BACKGROUND

Public and patient involvement (PPI) in health research has been defined as research being carried out “with” or “by” members of the public rather than “to,” “about” or “for” them. 1 PPI covers a diverse range of approaches from “one off” information gathering to sustained partnerships. Tritter's conceptual framework for PPI distinguished between indirect involvement where information is gathered from patients and the public, but they do not have the power to make final decisions and direct involvement where patients and the public take part in the decision‐making. 2

A bibliometric review of the literature reported strong growth in the number of published empirical health research studies with public involvement. 3 In a systematic review of the impact of PPI on health and social care research, Brett et al 4 reported positive impacts at all stages of research from planning and undertaking the study to analysis, dissemination and implementation. The design of the majority of empirical research studies included in both reviews was qualitative (70% of studies in Brett. et al 4 and 73% in Boote et al 3 ). More significant tensions have been reported in community‐academic partnerships that use quantitative methods rather than solely qualitative methods, for example tensions with the community about having and recruiting to a “no intervention” comparison group. 5 Particular challenges for PPI have been reported in the most structured and regulated of quantitative designs, that is a randomized controlled trial (RCT), where there is little opportunity for flexibility once the trial has started 6 and Boote et al 3 concluded that researchers may find it easier to involve the public in qualitative rather than quantitative research.

If the full potential of PPI for health research is to be realized, its potential and challenges in quantitative research require more exploration, particularly the features of quantitative research which are different from qualitative research, for example, sampling, measurement and statistical analysis. Statisticians in practice have a long history of working with a variety of stakeholders in health research and have examined the difference between an indirect or consulting role for the statistician and a more direct, collaborative role, 7 yet their perspective has never been explicitly explored in health research with PPI. The objective of this study therefore was to critically reflect on the potential and challenges for PPI at distinct stages of quantitative research from a statistical perspective, distinguishing between direct and indirect approaches to PPI. 2

2. SAMPLE SIZE AND SELECTION

Quantitative research usually aims to provide precise, unbiased estimates of parameters of interest for the entire population which requires a large, randomly selected sample. Brett et al 4 reported a positive impact of PPI on recruitment in studies, but the representativeness of the sample is as important in quantitative research as sample size. Studies have shown that even when accrual targets have been met, the sample may not be fully representative of the population of interest. In cancer clinical trials, for example, those with health insurance and from higher socio‐economic backgrounds can be over‐represented, while older patients, ethnic minorities and so‐called hard‐to‐reach groups (often with higher cancer mortality rates) are under‐represented. 8 This limits the ability to generalize the results of the trials to all those with cancer. There is evidence that a direct approach to PPI with sustained partnerships between community leaders, primary care providers and clinical trial researchers can be effective in increasing awareness and participation of under‐represented groups in cancer clinical trials 9 , 10 and therefore help to achieve the goal of a population‐representative sample.

Collecting representative health data for some groups in the population may only be possible with their involvement. Marin et al 11 reports on the challenges of identifying an appropriate sampling frame for a health survey of Aboriginal adults in Southern Australia. Access to information identifying Aboriginal dwellings was not publically available, making it difficult to randomly select participants for large population household surveys. Trying to overcome this challenge involved reaching agreement on the process of research for Aboriginal adults with their local communities. An 8‐month consultation process was undertaken with representatives from multiple locations including Aboriginal owned lands in one region; however, it was ultimately agreed that it was culturally inappropriate for the research team to survey this region. The study demonstrated the opportunities for PPI in quantitative research with a representative sample of randomly chosen Aboriginal adults (excluding those resident in one region) ultimately achieved but also the challenges for PPI. The direct approach to involvement in this study, after a lengthy consultation process, resulted in a decision not to carry out the planned sampling and data collection in one region with implications for generalization of results and overall sample size.

Of course, given the importance of representativeness in quantitative research, there may be particular challenges for statisticians and quantitative researchers in accepting the term patient or public representative with some suggesting PPI contributor as a more appropriate term. 6 PPI representative may suggest to a quantitative researcher that an individual patient or member of the public is typical of an often diverse population, yet there is evidence that the opportunities and capacity to be involved as PPI contributors vary by level of education, income, cognitive skills and cultural background. 12 Dudley et al carried out a qualitative study of the impact of PPI in RCTs with patients and researchers from a cohort of RCTs. 6 The types of roles of PPI contributors described by researchers involved in the RCTs were grouped into oversight, managerial and responsive roles. Responsive PPI was described as informal and impromptu with researchers approaching multiple “responsive” PPI contributors as difficulties arose, for example advising on patient information sheets and follow‐up of patients. It was reported that contributions from responsive roles may carry more weight with the researchers in RCTs because it allowed access to a more diverse range of contributors who researchers saw as more “representative” of the target population.

3. MEASUREMENT

Measurement of quantitative data involves decisions about what to measure, how to measure it and how often to measure it with these decisions typically made by the research team. Without the involvement of patients and the public, however, important outcomes for people living with a condition have been missed or overlooked, for example fatigue for people with rheumatoid arthritis 13 or the long‐term effects of therapy for children with asthma. 14

Core outcome sets (COS) are a minimum set of agreed important outcomes to be measured in research on particular illnesses, conditions or treatments to ensure important outcomes are consistently reported and allow the results from multiple studies to be easily combined and compared. Young reported on workshops to explore what principles, methods and strategies that COS developers may need to consider when seeking patient input into the development of a COS. 15 The importance of distinguishing between an indirect role for patients in COS development where patients respond to a consensus survey or a direct role where patients are partners in planning, running and disseminating a COS study was highlighted by delegates in the workshops. While all delegates agreed that participation by patients should be meaningful and on an equal footing with other stakeholders, there was considerable uncertainty on how to achieve this, for example how many patients are needed in the COS development process or what proportion of patients relative to other stakeholders should be included. This raises the issue again of the number and selection of PPI contributors for quantitative researchers, and it was concluded that methodological work was needed to understand the COS development process from the perspective of patients and how the process may be improved for them.

Important considerations in longitudinal research are the number and timing of repeated measurements. From a statistical perspective, measurements on the same subject at different times are almost always correlated, with measurements taken close together in time being more highly correlated than measurements taken far apart in time. Unequal spacing of observation times may be more computationally challenging in statistical analysis of repeated measurements and missing data within subjects over time can be particularly challenging depending on the amount, cause and pattern of missing data. 16 There are therefore important statistical considerations to be taken into account in the design of longitudinal studies but these have to be balanced with input from PPI contributors on appropriate timing and frequency of data collection for potential participants.

Lucas et al reported on how European birth cohorts are engaging and consulting with young birth cohort members. 17 Of the 84 individual cohorts identified, only eight had a mechanism for consulting with parents and three a mechanism for consulting with young people themselves (usually “one off” consultations). Very varied follow‐up rates were reported from 13% to 84% more than 10 years after enrolment for individual data rounds of the birth cohorts. 17 Being motivated to continue to participate may be influenced by whether a participant believes the study is interesting, important, or relevant to them. 18 One of the key strategies for retention in the Australian Aboriginal Birth Cohort study was partnerships with community members with local knowledge who were involved in all phases of the follow‐up. 19 Retention rates of 86% at 11‐year follow‐up and 72% at 18‐year follow‐up were reported which demonstrates the potential of a direct approach to PPI. Ethical approval for the study involved an Aboriginal Ethical Sub‐committee which had the power of veto and a staged consent was used where participants had the right to refuse individual procedures at each wave. As with all missing data, this has implications for the statistical analysis yet only 10% of participants in this study chose to opt out of different assessments at follow‐up.

3.1. Statistical analysis

A report on the impact of PPI found that it had a positive impact at all stages of qualitative research including data analysis but that there was little evidence of its impact on quantitative data analysis. 20 It was concluded this lack of evidence may reflect a lack of involvement rather than an evidence gap. Booth et al 3 also suggested that the public may be more comfortable with interpreting interview and focus group data compared with numeric data. Low levels of numerical and statistical literacy in the general population may contribute to this.

Statistical analysis involves describing the data using appropriate graphical and numerical summaries (descriptive statistics) and using more advanced statistical methods to draw inferences about the population using the data from a sample (statistical inference). Choosing appropriate methods for statistical inference, testing the underlying assumptions and checking the adequacy of the models produced requires advanced statistical training and implementing them typically involves the use of statistical software or programming. Statisticians bring this expertise to quantitative health research and while it is important that the chosen methods are adequately communicated to all stakeholders, replicating this type of expertise in PPI contributors seems like an inefficient use of resources for PPI.

Quantitative data are, however, “not just numbers, they are numbers with a context” 21 and most practising statisticians agree that knowledge of the context is needed to carry out even a purely technical role effectively. 22 While many associate statistical analysis with objectivity, in practice, statisticians routinely use “subjective” external information to guide, for example the decision on what is a meaningful effect size; whether an outlier is an error in data entry or represents an unusual but meaningful observation; and potential issues with measurement of variables and confounding. 23 Gelman and Hennin argue that we should move beyond the discussion of objectivity and subjectivity in statistics and “replace each of them with broader collections of attributes, with objectivity replaced by transparency, consensus, impartiality and correspondence to observable reality, and subjectivity replaced by awareness of multiple perspectives and context dependence.” 23 This debate within statistics is relevant for PPI where the perceived objectivity and standardization of statistical analysis can be used as a reason for lack of involvement.

External information and context are particularly important in statistical modelling where statisticians are often faced with many potential predictors of an outcome. The “best” way of selecting a multivariable model is still unresolved from a statistical perspective, and it is generally agreed that subject matter knowledge, when available, should guide model building. 24 Even when the potential predictors are known, understanding the causal pathways of exposure on an outcome is challenging where the effect of a variable on the outcome can be direct or indirect. Christiaens et al 25 used a causal diagram to visualize the relationship between pain acceptance and personal control of women in labour and the use of pain medication during labour. Their analysis accounted for the maternal care context of the country where the women were giving birth and other characteristics such as age of the woman and duration of labour. The choice of these characteristics was underpinned by a literature review but women who have given birth also have expert knowledge on why they use pain relief and how other variables such as their personal beliefs and social context might influence that decision. 26

Collaborative or participatory modelling is an approach to scientific modelling in areas such as natural resource management which involves all stakeholders in the model building process. Participants can suggest characteristics for inclusion in the model and how they may impact on the outcome. Causal diagrams are then used to create a shared view across stakeholders. 27 Rockman et al 28 concluded, in the context of marine policy, that “participatory modelling has the potential to facilitate and structure discussions between scientists and stakeholders about uncertainties and the quality of the knowledge base. It can also contribute to collective learning, increase legitimacy and advance scientific understanding.”

There is emerging evidence that the importance of PPI in the development and application of modelling in health research is being recognized. Van Voorn 29 discussed the benefits and risks of PPI in health economic modelling of cost‐effectiveness of new drugs and treatment strategies, with public and patients described as the missing stakeholder group in the modelling process. The potential benefits included the expertise that patients could bring to the process, a greater understanding and possible acceptance by patients of the results of the models and improved model validation. The risks included potential patient bias and the increased resources required for training. The number and selection of patients to contribute to the process was also discussed with a suggestion to include patients “who were able to take a neutral view” and “at least five patients that differ significantly in their background,” again highlighting the focus of quantitative researchers on bias and sample size. The role for this type of participatory modelling in informing debate on public health problems is increasingly being recognized, drawing on the experience of its use in other areas where optimal use of limited resources is required to address complex problems in society. 30

4. CONCLUSIONS

Statistical analysis of quantitative data is underpinned by having a representative sample, and there is evidence that a direct approach to PPI can help achieve that by supporting access to and increasing participation of under‐represented groups in the population. The direct approach has also demonstrated its potential in the retention of those recruited over time, thus reducing bias caused by missing data in longitudinal studies. At all stages of statistical analysis, a statistician continuously refers back to the context of the data collected. 22 Lay knowledge of PPI contributors has an important role in providing this context, and there is evidence from other disciplines of the benefits of including this knowledge in analysis to support collective learning and advance scientific understanding.

The direct approach to PPI where patients and the public have the power to make decisions also brings challenges and the statistician needs to be able to clearly communicate the impact of each decision on the scientific rigour and validity of sampling, measurement and analysis to all stakeholders. Decisions made on participation impact on generalizability. Participatory modelling requires facilitation and partnership skills which may require further training for all stakeholders, including statisticians.

The direct and indirect role for PPI contributors mirrors what happens for statisticians in practice. Statisticians can have a consultative role, that is answering a specific statistical question or a collaborative role where a statistician works with others as equal partners to create new knowledge, with professional organizations for statisticians providing guidance and mentorship on moving from consulting to collaboration to leadership roles. 7 , 31 Statisticians therefore bring very relevant experience and understanding for PPI contributors on the ladder of participation in health research. Further exploration is required on the impact of direct compared to indirect involvement in quantitative research, drawing on the evidence base for community‐based participatory research in quantitative designs 9 and the framework for participatory health research and epidemiology. 32 , 33

CONFLICT OF INTERESTS

No conflict of interests.

ACKNOWLEDGEMENTS

Prof. Anne MacFarlane, Public and Patient Involvement Research Unit, University of Limerick, for discussion of ideas and comments on drafts.

Hannigan A. Public and patient involvement in quantitative health research: A statistical perspective . Health Expect . 2018; 21 :939–943. 10.1111/hex.12800 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

• Open access
• Published: 12 December 2023

Examining the role of community resilience and social capital on mental health in public health emergency and disaster response: a scoping review

• C. E. Hall 1 , 2 ,
• H. Wehling 1 ,
• J. Stansfield 3 ,
• J. South 3 ,
• S. K. Brooks 2 ,
• N. Greenberg 2 , 4 ,
• R. Amlôt 1 &
• D. Weston 1  

BMC Public Health volume  23 , Article number:  2482 ( 2023 ) Cite this article

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The ability of the public to remain psychologically resilient in the face of public health emergencies and disasters (such as the COVID-19 pandemic) is a key factor in the effectiveness of a national response to such events. Community resilience and social capital are often perceived as beneficial and ensuring that a community is socially and psychologically resilient may aid emergency response and recovery. This review presents a synthesis of literature which answers the following research questions: How are community resilience and social capital quantified in research?; What is the impact of community resilience on mental wellbeing?; What is the impact of infectious disease outbreaks, disasters and emergencies on community resilience and social capital?; and, What types of interventions enhance community resilience and social capital?

A scoping review procedure was followed. Searches were run across Medline, PsycInfo, and EMBASE, with search terms covering both community resilience and social capital, public health emergencies, and mental health. 26 papers met the inclusion criteria.

The majority of retained papers originated in the USA, used a survey methodology to collect data, and involved a natural disaster. There was no common method for measuring community resilience or social capital. The association between community resilience and social capital with mental health was regarded as positive in most cases. However, we found that community resilience, and social capital, were initially negatively impacted by public health emergencies and enhanced by social group activities.

Several key recommendations are proposed based on the outcomes from the review, which include: the need for a standardised and validated approach to measuring both community resilience and social capital; that there should be enhanced effort to improve preparedness to public health emergencies in communities by gauging current levels of community resilience and social capital; that community resilience and social capital should be bolstered if areas are at risk of disasters or public health emergencies; the need to ensure that suitable short-term support is provided to communities with high resilience in the immediate aftermath of a public health emergency or disaster; the importance of conducting robust evaluation of community resilience initiatives deployed during the COVID-19 pandemic.

Peer Review reports

For the general population, public health emergencies and disasters (e.g., natural disasters; infectious disease outbreaks; Chemical, Biological, Radiological or Nuclear incidents) can give rise to a plethora of negative outcomes relating to both health (e.g. increased mental health problems [ 1 , 2 , 3 , 4 ]) and the economy (e.g., increased unemployment and decreased levels of tourism [ 4 , 5 , 6 ]). COVID-19 is a current, and ongoing, example of a public health emergency which has affected over 421 million individuals worldwide [ 7 ]. The long term implications of COVID-19 are not yet known, but there are likely to be repercussions for physical health, mental health, and other non-health related outcomes for a substantial time to come [ 8 , 9 ]. As a result, it is critical to establish methods which may inform approaches to alleviate the longer-term negative consequences that are likely to emerge in the aftermath of both COVID-19 and any future public health emergency.

The definition of resilience often differs within the literature, but ultimately resilience is considered a dynamic process of adaptation. It is related to processes and capabilities at the individual, community and system level that result in good health and social outcomes, in spite of negative events, serious threats and hazards [ 10 ]. Furthermore, Ziglio [ 10 ] refers to four key types of resilience capacity: adaptive, the ability to withstand and adjust to unfavourable conditions and shocks; absorptive, the ability to withstand but also to recover and manage using available assets and skills; anticipatory, the ability to predict and minimize vulnerability; and transformative, transformative change so that systems better cope with new conditions.

There is no one settled definition of community resilience (CR). However, it generally relates to the ability of a community to withstand, adapt and permit growth in adverse circumstances due to social structures, networks and interdependencies within the community [ 11 ]. Social capital (SC) is considered a major determinant of CR [ 12 , 13 ], and reflects strength of a social network, community reciprocity, and trust in people and institutions [ 14 ]. These aspects of community are usually conceptualised primarily as protective factors that enable communities to cope and adapt collectively to threats. SC is often broken down into further categories [ 15 ], for example: cognitive SC (i.e. perceptions of community relations, such as trust, mutual help and attachment) and structural SC (i.e. what actually happens within the community, such as participation, socialising) [ 16 ]; or, bonding SC (i.e. connections among individuals who are emotionally close, and result in bonds to a particular group [ 17 ]) and bridging SC (i.e. acquaintances or individuals loosely connected that span different social groups [ 18 ]). Generally, CR is perceived to be primarily beneficial for multiple reasons (e.g. increased social support [ 18 , 19 ], protection of mental health [ 20 , 21 ]), and strengthening community resilience is a stated health goal of the World Health Organisation [ 22 ] when aiming to alleviate health inequalities and protect wellbeing. This is also reflected by organisations such as Public Health England (now split into the UK Health Security Agency and the Office for Health Improvement and Disparities) [ 23 ] and more recently, CR has been targeted through the endorsement of Community Champions (who are volunteers trained to support and to help improve health and wellbeing. Community Champions also reflect their local communities in terms of population demographics for example age, ethnicity and gender) as part of the COVID-19 response in the UK (e.g. [ 24 , 25 ]).

Despite the vested interest in bolstering communities, the research base establishing: how to understand and measure CR and SC; the effect of CR and SC, both during and following a public health emergency (such as the COVID-19 pandemic); and which types of CR or SC are the most effective to engage, is relatively small. Given the importance of ensuring resilience against, and swift recovery from, public health emergencies, it is critically important to establish and understand the evidence base for these approaches. As a result, the current review sought to answer the following research questions: (1) How are CR and SC quantified in research?; (2) What is the impact of community resilience on mental wellbeing?; (3) What is the impact of infectious disease outbreaks, disasters and emergencies on community resilience and social capital?; and, (4) What types of interventions enhance community resilience and social capital?

By collating research in order to answer these research questions, the authors have been able to propose several key recommendations that could be used to both enhance and evaluate CR and SC effectively to facilitate the long-term recovery from COVID-19, and also to inform the use of CR and SC in any future public health disasters and emergencies.

A scoping review methodology was followed due to the ease of summarising literature on a given topic for policy makers and practitioners [ 26 ], and is detailed in the following sections.

Identification of relevant studies

An initial search strategy was developed by authors CH and DW and included terms which related to: CR and SC, given the absence of a consistent definition of CR, and the link between CR and SC, the review focuses on both CR and SC to identify as much relevant literature as possible (adapted for purpose from Annex 1: [ 27 ], as well as through consultation with review commissioners); public health emergencies and disasters [ 28 , 29 , 30 , 31 ], and psychological wellbeing and recovery (derived a priori from literature). To ensure a focus on both public health and psychological research, the final search was carried across Medline, PsycInfo, and EMBASE using OVID. The final search took place on the 18th of May 2020, the search strategy used for all three databases can be found in Supplementary file 1 .

Selection criteria

The inclusion and exclusion criteria were developed alongside the search strategy. Initially the criteria were relatively inclusive and were subject to iterative development to reflect the authors’ familiarisation with the literature. For example, the decision was taken to exclude research which focused exclusively on social support and did not mention communities as an initial title/abstract search suggested that the majority of this literature did not meet the requirements of our research question.

The full and final inclusion and exclusion criteria used can be found in Supplementary file 2 . In summary, authors decided to focus on the general population (i.e., non-specialist, e.g. non-healthcare worker or government official) to allow the review to remain community focused. The research must also have assessed the impact of CR and/or SC on mental health and wellbeing, resilience, and recovery during and following public health emergencies and infectious disease outbreaks which affect communities (to ensure the research is relevant to the review aims), have conducted primary research, and have a full text available or provided by the first author when contacted.

Charting the data

All papers were first title and abstract screened by CH or DW. Papers then were full text reviewed by CH to ensure each paper met the required eligibility criteria, if unsure about a paper it was also full text reviewed by DW. All papers that were retained post full-text review were subjected to a standardised data extraction procedure. A table was made for the purpose of extracting the following data: title, authors, origin, year of publication, study design, aim, disaster type, sample size and characteristics, variables examined, results, restrictions/limitations, and recommendations. Supplementary file 3 details the charting the data process.

Analytical method

Data was synthesised using a Framework approach [ 32 ], a common method for analysing qualitative research. This method was chosen as it was originally used for large-scale social policy research [ 33 ] as it seeks to identify: what works, for whom, in what conditions, and why [ 34 ]. This approach is also useful for identifying commonalities and differences in qualitative data and potential relationships between different parts of the data [ 33 ]. An a priori framework was established by CH and DW. Extracted data was synthesised in relation to each research question, and the process was iterative to ensure maximum saturation using the available data.

Study selection

The final search strategy yielded 3584 records. Following the removal of duplicates, 2191 records remained and were included in title and abstract screening. A PRISMA flow diagram is presented in Fig.  1 .

PRISMA flow diagram

At the title and abstract screening stage, the process became more iterative as the inclusion criteria were developed and refined. For the first iteration of screening, CH or DW sorted all records into ‘include,’ ‘exclude,’ and ‘unsure’. All ‘unsure’ papers were re-assessed by CH, and a random selection of ~ 20% of these were also assessed by DW. Where there was disagreement between authors the records were retained, and full text screened. The remaining papers were reviewed by CH, and all records were categorised into ‘include’ and ‘exclude’. Following full-text screening, 26 papers were retained for use in the review.

Study characteristics

This section of the review addresses study characteristics of those which met the inclusion criteria, which comprises: date of publication, country of origin, study design, study location, disaster, and variables examined.

Date of publication

Publication dates across the 26 papers spanned from 2008 to 2020 (see Fig.  2 ). The number of papers published was relatively low and consistent across this timescale (i.e. 1–2 per year, except 2010 and 2013 when none were published) up until 2017 where the number of papers peaked at 5. From 2017 to 2020 there were 15 papers published in total. The amount of papers published in recent years suggests a shift in research and interest towards CR and SC in a disaster/ public health emergency context.

Graph to show retained papers date of publication

Country of origin

The locations of the first authors’ institutes at the time of publication were extracted to provide a geographical spread of the retained papers. The majority originated from the USA [ 35 , 36 , 37 , 38 , 39 , 40 , 41 ], followed by China [ 42 , 43 , 44 , 45 , 46 ], Japan [ 47 , 48 , 49 , 50 ], Australia [ 51 , 52 , 53 ], The Netherlands [ 54 , 55 ], New Zealand [ 56 ], Peru [ 57 ], Iran [ 58 ], Austria [ 59 ], and Croatia [ 60 ].

There were multiple methodological approaches carried out across retained papers. The most common formats included surveys or questionnaires [ 36 , 37 , 38 , 42 , 46 , 47 , 48 , 49 , 50 , 53 , 54 , 55 , 57 , 59 ], followed by interviews [ 39 , 40 , 43 , 51 , 52 , 60 ]. Four papers used both surveys and interviews [ 35 , 41 , 45 , 58 ], and two papers conducted data analysis (one using open access data from a Social Survey [ 44 ] and one using a Primary Health Organisations Register [ 56 ]).

Study location

The majority of the studies were carried out in Japan [ 36 , 42 , 44 , 47 , 48 , 49 , 50 ], followed by the USA [ 35 , 37 , 38 , 39 , 40 , 41 ], China [ 43 , 45 , 46 , 53 ], Australia [ 51 , 52 ], and the UK [ 54 , 55 ]. The remaining studies were carried out in Croatia [ 60 ], Peru [ 57 ], Austria [ 59 ], New Zealand [ 56 ] and Iran [ 58 ].

Multiple different types of disaster were researched across the retained papers. Earthquakes were the most common type of disaster examined [ 45 , 47 , 49 , 50 , 53 , 56 , 57 , 58 ], followed by research which assessed the impact of two disastrous events which had happened in the same area (e.g. Hurricane Katrina and the Deepwater Horizon oil spill in Mississippi, and the Great East Japan earthquake and Tsunami; [ 36 , 37 , 38 , 42 , 44 , 48 ]). Other disaster types included: flooding [ 51 , 54 , 55 , 59 , 60 ], hurricanes [ 35 , 39 , 41 ], infectious disease outbreaks [ 43 , 46 ], oil spillage [ 40 ], and drought [ 52 ].

Variables of interest examined

Across the 26 retained papers: eight referred to examining the impact of SC [ 35 , 37 , 39 , 41 , 46 , 49 , 55 , 60 ]; eight examined the impact of cognitive and structural SC as separate entities [ 40 , 42 , 45 , 48 , 50 , 54 , 57 , 59 ]; one examined bridging and bonding SC as separate entities [ 58 ]; two examined the impact of CR [ 38 , 56 ]; and two employed a qualitative methodology but drew findings in relation to bonding and bridging SC, and SC generally [ 51 , 52 ]. Additionally, five papers examined the impact of the following variables: ‘community social cohesion’ [ 36 ], ‘neighbourhood connectedness’ [ 44 ], ‘social support at the community level’ [ 47 ], ‘community connectedness’ [ 43 ] and ‘sense of community’ [ 53 ]. Table  1 provides additional details on this.

How is CR and SC measured or quantified in research?

The measures used to examine CR and SC are presented Table  1 . It is apparent that there is no uniformity in how SC or CR is measured across the research. Multiple measures are used throughout the retained studies, and nearly all are unique. Additionally, SC was examined at multiple different levels (e.g. cognitive and structural, bonding and bridging), and in multiple different forms (e.g. community connectedness, community cohesion).

What is the association between CR and SC on mental wellbeing?

To best compare research, the following section reports on CR, and facets of SC separately. Please see Supplementary file 4  for additional information on retained papers methods of measuring mental wellbeing.

• Community resilience

CR relates to the ability of a community to withstand, adapt and permit growth in adverse circumstances due to social structures, networks and interdependencies within the community [ 11 ].

The impact of CR on mental wellbeing was consistently positive. For example, research indicated that there was a positive association between CR and number of common mental health (i.e. anxiety and mood) treatments post-disaster [ 56 ]. Similarly, other research suggests that CR is positively related to psychological resilience, which is inversely related to depressive symptoms) [ 37 ]. The same research also concluded that CR is protective of psychological resilience and is therefore protective of depressive symptoms [ 37 ].

• Social capital

SC reflects the strength of a social network, community reciprocity, and trust in people and institutions [ 14 ]. These aspects of community are usually conceptualised primarily as protective factors that enable communities to cope and adapt collectively to threats.

There were inconsistencies across research which examined the impact of abstract SC (i.e. not refined into bonding/bridging or structural/cognitive) on mental wellbeing. However, for the majority of cases, research deems SC to be beneficial. For example, research has concluded that, SC is protective against post-traumatic stress disorder [ 55 ], anxiety [ 46 ], psychological distress [ 50 ], and stress [ 46 ]. Additionally, SC has been found to facilitate post-traumatic growth [ 38 ], and also to be useful to be drawn upon in times of stress [ 52 ], both of which could be protective of mental health. Similarly, research has also found that emotional recovery following a disaster is more difficult for those who report to have low levels of SC [ 51 ].

Conversely, however, research has also concluded that when other situational factors (e.g. personal resources) were controlled for, a positive relationship between community resources and life satisfaction was no longer significant [ 60 ]. Furthermore, some research has concluded that a high level of SC can result in a community facing greater stress immediately post disaster. Indeed, one retained paper found that high levels of SC correlate with higher levels of post-traumatic stress immediately following a disaster [ 39 ]. However, in the later stages following a disaster, this relationship can reverse, with SC subsequently providing an aid to recovery [ 41 ]. By way of explanation, some researchers have suggested that communities with stronger SC carry the greatest load in terms of helping others (i.e. family, friends and neighbours) as well as themselves immediately following the disaster, but then as time passes the communities recover at a faster rate as they are able to rely on their social networks for support [ 41 ].

Cognitive and structural social capital

Cognitive SC refers to perceptions of community relations, such as trust, mutual help and attachment, and structural SC refers to what actually happens within the community, such as participation, socialising [ 16 ].

Cognitive SC has been found to be protective [ 49 ] against PTSD [ 54 , 57 ], depression [ 40 , 54 ]) mild mood disorder; [ 48 ]), anxiety [ 48 , 54 ] and increase self-efficacy [ 59 ].

For structural SC, research is again inconsistent. On the one hand, structural SC has been found to: increase perceived self-efficacy, be protective of depression [ 40 ], buffer the impact of housing damage on cognitive decline [ 42 ] and provide support during disasters and over the recovery period [ 59 ]. However, on the other hand, it has been found to have no association with PTSD [ 54 , 57 ] or depression, and is also associated with a higher prevalence of anxiety [ 54 ]. Similarly, it is also suggested by additional research that structural SC can harm women’s mental health, either due to the pressure of expectations to help and support others or feelings of isolation [ 49 ].

Bonding and bridging social capital

Bonding SC refers to connections among individuals who are emotionally close, and result in bonds to a particular group [ 17 ], and bridging SC refers to acquaintances or individuals loosely connected that span different social groups [ 18 ].

One research study concluded that both bonding and bridging SC were protective against post-traumatic stress disorder symptoms [ 58 ]. Bridging capital was deemed to be around twice as effective in buffering against post-traumatic stress disorder than bonding SC [ 58 ].

Other community variables

Community social cohesion was significantly associated with a lower risk of post-traumatic stress disorder symptom development [ 35 ], and this was apparent even whilst controlling for depressive symptoms at baseline and disaster impact variables (e.g. loss of family member or housing damage) [ 36 ]. Similarly, sense of community, community connectedness, social support at the community level and neighbourhood connectedness all provided protective benefits for a range of mental health, wellbeing and recovery variables, including: depression [ 53 ], subjective wellbeing (in older adults only) [ 43 ], psychological distress [ 47 ], happiness [ 44 ] and life satisfaction [ 53 ].

Research has also concluded that community level social support is protective against mild mood and anxiety disorder, but only for individuals who have had no previous disaster experience [ 48 ]. Additionally, a study which separated SC into social cohesion and social participation concluded that at a community level, social cohesion is protective against depression [ 49 ] whereas social participation at community level is associated with an increased risk of depression amongst women [ 49 ].

What is the impact of Infectious disease outbreaks / disasters and emergencies on community resilience?

From a cross-sectional perspective, research has indicated that disasters and emergencies can have a negative effect on certain types of SC. Specifically, cognitive SC has been found to be impacted by disaster impact, whereas structural SC has gone unaffected [ 45 ]. Disaster impact has also been shown to have a negative effect on community relationships more generally [ 52 ].

Additionally, of the eight studies which collected data at multiple time points [ 35 , 36 , 41 , 42 , 47 , 49 , 56 , 60 ], three reported the effect of a disaster on the level of SC within a community [ 40 , 42 , 49 ]. All three of these studies concluded that disasters may have a negative impact on the levels of SC within a community. The first study found that the Deepwater Horizon oil spill had a negative effect on SC and social support, and this in turn explained an overall increase in the levels of depression within the community [ 40 ]. A possible explanation for the negative effect lays in ‘corrosive communities’, known for increased social conflict and reduced social support, that are sometimes created following oil spills [ 40 ]. It is proposed that corrosive communities often emerge due to a loss of natural resources that bring social groups together (e.g., for recreational activities), as well as social disparity (e.g., due to unequal distribution of economic impact) becoming apparent in the community following disaster [ 40 ]. The second study found that SC (in the form of social cohesion, informal socialising and social participation) decreased after the 2011 earthquake and tsunami in Japan; it was suggested that this change correlated with incidence of cognitive decline [ 42 ]. However, the third study reported more mixed effects based on physical circumstances of the communities’ natural environment: Following an earthquake, those who lived in mountainous areas with an initial high level of pre-community SC saw a decrease in SC post disaster [ 49 ]. However, communities in flat areas (which were home to younger residents and had a higher population density) saw an increase in SC [ 49 ]. It was proposed that this difference could be due to the need for those who lived in mountainous areas to seek prolonged refuge due to subsequent landslides [ 49 ].

What types of intervention enhance CR and SC and protect survivors?

There were mixed effects across the 26 retained papers when examining the effect of CR and SC on mental wellbeing. However, there is evidence that an increase in SC [ 56 , 57 ], with a focus on cognitive SC [ 57 ], namely by: building social networks [ 45 , 51 , 53 ], enhancing feelings of social cohesion [ 35 , 36 ] and promoting a sense of community [ 53 ], can result in an increase in CR and potentially protect survivors’ wellbeing and mental health following a disaster. An increase in SC may also aid in decreasing the need for individual psychological interventions in the aftermath of a disaster [ 55 ]. As a result, recommendations and suggested methods to bolster CR and SC from the retained papers have been extracted and separated into general methods, preparedness and policy level implementation.

General methods

Suggested methods to build SC included organising recreational activity-based groups [ 44 ] to broaden [ 51 , 53 ] and preserve current social networks [ 42 ], introducing initiatives to increase social cohesion and trust [ 51 ], and volunteering to increase the number of social ties between residents [ 59 ]. Research also notes that it is important to take a ‘no one left behind approach’ when organising recreational and social community events, as failure to do so could induce feelings of isolation for some members of the community [ 49 ]. Furthermore, gender differences should also be considered as research indicates that males and females may react differently to community level SC (as evidence suggests males are instead more impacted by individual level SC; in comparison to women who have larger and more diverse social networks [ 49 ]). Therefore, interventions which aim to raise community level social participation, with the aim of expanding social connections and gaining support, may be beneficial [ 42 , 47 ].

Preparedness

In order to prepare for disasters, it may be beneficial to introduce community-targeted methods or interventions to increase levels of SC and CR as these may aid in ameliorating the consequences of a public health emergency or disaster [ 57 ]. To indicate which communities have low levels of SC, one study suggests implementing a 3-item scale of social cohesion to map areas and target interventions [ 42 ].

It is important to consider that communities with a high level of SC may have a lower level of risk perception, due to the established connections and supportive network they have with those around them [ 61 ]. However, for the purpose of preparedness, this is not ideal as perception of risk is a key factor when seeking to encourage behavioural adherence. This could be overcome by introducing communication strategies which emphasise the necessity of social support, but also highlights the need for additional measures to reduce residual risk [ 59 ]. Furthermore, support in the form of financial assistance to foster current community initiatives may prove beneficial to rural areas, for example through the use of an asset-based community development framework [ 52 ].

Policy level

At a policy level, the included papers suggest a range of ways that CR and SC could be bolstered and used. These include: providing financial support for community initiatives and collective coping strategies, (e.g. using asset-based community development [ 52 ]); ensuring policies for long-term recovery focus on community sustainable development (e.g. community festival and community centre activities) [ 44 ]; and development of a network amongst cooperative corporations formed for reconstruction and to organise self-help recovery sessions among residents of adjacent areas [ 58 ].

This scoping review sought to synthesise literature concerning the role of SC and CR during public health emergencies and disasters. Specifically, in this review we have examined: the methods used to measure CR and SC; the impact of CR and SC on mental wellbeing during disasters and emergencies; the impact of disasters and emergencies on CR and SC; and the types of interventions which can be used to enhance CR. To do this, data was extracted from 26 peer-reviewed journal articles. From this synthesis, several key themes have been identified, which can be used to develop guidelines and recommendations for deploying CR and SC in a public health emergency or disaster context. These key themes and resulting recommendations are summarised below.

Firstly, this review established that there is no consistent or standardised approach to measuring CR or SC within the general population. This finding is consistent with a review conducted by the World Health Organization which concludes that despite there being a number of frameworks that contain indicators across different determinants of health, there is a lack of consensus on priority areas for measurement and no widely accepted indicator [ 27 ]. As a result, there are many measures of CR and SC apparent within the literature (e.g., [ 62 , 63 ]), an example of a developed and validated measure is provided by Sherrieb, Norris and Galea [ 64 ]. Similarly, the definitions of CR and SC differ widely between researchers, which created a barrier to comparing and summarising information. Therefore, future research could seek to compare various interpretations of CR and to identify any overlapping concepts. However, a previous systemic review conducted by Patel et al. (2017) concludes that there are nine core elements of CR (local knowledge, community networks and relationships, communication, health, governance and leadership, resources, economic investment, preparedness, and mental outlook), with 19 further sub-elements therein [ 30 ]. Therefore, as CR is a multi-dimensional construct, the implications from the findings are that multiple aspects of social infrastructure may need to be considered.

Secondly, our synthesis of research concerning the role of CR and SC for ensuring mental health and wellbeing during, or following, a public health emergency or disaster revealed mixed effects. Much of the research indicates either a generally protective effect on mental health and wellbeing, or no effect; however, the literature demonstrates some potential for a high level of CR/SC to backfire and result in a negative effect for populations during, or following, a public health emergency or disaster. Considered together, our synthesis indicates that cognitive SC is the only facet of SC which was perceived as universally protective across all retained papers. This is consistent with a systematic review which also concludes that: (a) community level cognitive SC is associated with a lower risk of common mental disorders, while; (b) community level structural SC had inconsistent effects [ 65 ].

Further examination of additional data extracted from studies which found that CR/SC had a negative effect on mental health and wellbeing revealed no commonalities that might explain these effects (Please see Supplementary file 5 for additional information)

One potential explanation may come from a retained paper which found that high levels of SC result in an increase in stress level immediately post disaster [ 41 ]. This was suggested to be due to individuals having greater burdens due to wishing to help and support their wide networks as well as themselves. However, as time passes the levels of SC allow the community to come together and recover at a faster rate [ 41 ]. As this was the only retained paper which produced this finding, it would be beneficial for future research to examine boundary conditions for the positive effects of CR/SC; that is, to explore circumstances under which CR/SC may be more likely to put communities at greater risk. This further research should also include additional longitudinal research to validate the conclusions drawn by [ 41 ] as resilience is a dynamic process of adaption.

Thirdly, disasters and emergencies were generally found to have a negative effect on levels of SC. One retained paper found a mixed effect of SC in relation to an earthquake, however this paper separated participants by area in which they lived (i.e., mountainous vs. flat), which explains this inconsistent effect [ 49 ]. Dangerous areas (i.e. mountainous) saw a decrease in community SC in comparison to safer areas following the earthquake (an effect the authors attributed to the need to seek prolonged refuge), whereas participants from the safer areas (which are home to younger residents with a higher population density) saw an increase in SC [ 49 ]. This is consistent with the idea that being able to participate socially is a key element of SC [ 12 ]. Overall, however, this was the only retained paper which produced a variable finding in relation to the effect of disaster on levels of CR/SC.

Finally, research identified through our synthesis promotes the idea of bolstering SC (particularly cognitive SC) and cohesion in communities likely to be affected by disaster to improve levels of CR. This finding provides further understanding of the relationship between CR and SC; an association that has been reported in various articles seeking to provide conceptual frameworks (e.g., [ 66 , 67 ]) as well as indicator/measurement frameworks [ 27 ]. Therefore, this could be done by creating and promoting initiatives which foster SC and create bonds within the community. Papers included in the current review suggest that recreational-based activity groups and volunteering are potential methods for fostering SC and creating community bonds [ 44 , 51 , 59 ]. Similarly, further research demonstrates that feelings of social cohesion are enhanced by general social activities (e.g. fairs and parades [ 18 ]). Also, actively encouraging activities, programs and interventions which enhance connectedness and SC have been reported to be desirable to increase CR [ 68 ]. This suggestion is supported by a recent scoping review of literature [ 67 ] examined community champion approaches for the COVID-19 pandemic response and recovery and established that creating and promoting SC focused initiatives within the community during pandemic response is highly beneficial [ 67 ]. In terms of preparedness, research states that it may be beneficial for levels of SC and CR in communities at risk to be assessed, to allow targeted interventions where the population may be at most risk following an incident [ 42 , 44 ]. Additionally, from a more critical perspective, we acknowledge that ‘resilience’ can often be perceived as a focus on individual capacity to adapt to adversity rather than changing or mitigating the causes of adverse conditions [ 69 , 70 ]. Therefore, CR requires an integrated system approach across individual, community and structural levels [ 17 ]. Also, it is important that community members are engaged in defining and agreeing how community resilience is measured [ 27 ] rather than it being imposed by system leads or decision-makers.

In the aftermath of the pandemic, is it expected that there will be long-term repercussions both from an economic [ 8 ] and a mental health perspective [ 71 ]. Furthermore, the findings from this review suggest that although those in areas with high levels of SC may be negatively affected in the acute stage, as time passes, they have potential to rebound at a faster rate than those with lower levels of SC. Ongoing evaluation of the effectiveness of current initiatives as the COVID-19 pandemic progresses into a recovery phase will be invaluable for supplementing the evidence base identified through this review.

• Recommendations

As a result of this review, a number of recommendations are suggested for policy and practice during public health emergencies and recovery.

Future research should seek to establish a standardised and validated approach to measuring and defining CR and SC within communities. There are ongoing efforts in this area, for example [ 72 ]. Additionally, community members should be involved in the process of defining how CR is measured.

There should be an enhanced effort to improve preparedness for public health emergencies and disasters in local communities by gauging current levels of SC and CR within communities using a standardised measure. This approach could support specific targeting of populations with low levels of CR/SC in case of a disaster or public health emergency, whilst also allowing for consideration of support for those with high levels of CR (as these populations can be heavily impacted initially following a disaster). By distinguishing levels of SC and CR, tailored community-centred approaches could be implemented, such as those listed in a guide released by PHE in 2015 [ 73 ].

CR and SC (specifically cognitive SC) should be bolstered if communities are at risk of experiencing a disaster or public health emergency. This can be achieved by using interventions which aim to increase a sense of community and create new social ties (e.g., recreational group activities, volunteering). Additionally, when aiming to achieve this, it is important to be mindful of the risk of increased levels of CR/SC to backfire, as well as seeking to advocate an integrated system approach across individual, community and structural levels.

It is necessary to be aware that although communities with high existing levels of resilience / SC may experience short-term negative consequences following a disaster, over time these communities might be able to recover at a faster rate. It is therefore important to ensure that suitable short-term support is provided to these communities in the immediate aftermath of a public health emergency or disaster.

Robust evaluation of the community resilience initiatives deployed during the COVID-19 pandemic response is essential to inform the evidence base concerning the effectiveness of CR/ SC. These evaluations should continue through the response phase and into the recovery phase to help develop our understanding of the long-term consequences of such interventions.

Limitations

Despite this review being the first in this specific topic area, there are limitations that must be considered. Firstly, it is necessary to note that communities are generally highly diverse and the term ‘community’ in academic literature is a subject of much debate (see: [ 74 ]), therefore this must be considered when comparing and collating research involving communities. Additionally, the measures of CR and SC differ substantially across research, including across the 26 retained papers used in the current review. This makes the act of comparing and collating research findings very difficult. This issue is highlighted as a key outcome from this review, and suggestions for how to overcome this in future research are provided. Additionally, we acknowledge that there will be a relationship between CR & SC even where studies measure only at individual or community level. A review [ 75 ] on articulating a hypothesis of the link to health inequalities suggests that wider structural determinants of health need to be accounted for. Secondly, despite the final search strategy encompassing terms for both CR and SC, only one retained paper directly measured CR; thus, making the research findings more relevant to SC. Future research could seek to focus on CR to allow for a comparison of findings. Thirdly, the review was conducted early in the COVID-19 pandemic and so does not include more recent publications focusing on resilience specifically in the context of COVID-19. Regardless of this fact, the synthesis of, and recommendations drawn from, the reviewed studies are agnostic to time and specific incident and contain critical elements necessary to address as the pandemic moves from response to recovery. Further research should review the effectiveness of specific interventions during the COVID-19 pandemic for collation in a subsequent update to this current paper. Fourthly, the current review synthesises findings from countries with individualistic and collectivistic cultures, which may account for some variation in the findings. Lastly, despite choosing a scoping review method for ease of synthesising a wide literature base for use by public health emergency researchers in a relatively tight timeframe, there are disadvantages of a scoping review approach to consider: (1) quality appraisal of retained studies was not carried out; (2) due to the broad nature of a scoping review, more refined and targeted reviews of literature (e.g., systematic reviews) may be able to provide more detailed research outcomes. Therefore, future research should seek to use alternative methods (e.g., empirical research, systematic reviews of literature) to add to the evidence base on CR and SC impact and use in public health practice.

This review sought to establish: (1) How CR and SC are quantified in research?; (2) The impact of community resilience on mental wellbeing?; (3) The impact of infectious disease outbreaks, disasters and emergencies on community resilience and social capital?; and, (4) What types of interventions enhance community resilience and social capital?. The chosen search strategy yielded 26 relevant papers from which we were able extract information relating to the aims of this review.

Results from the review revealed that CR and SC are not measured consistently across research. The impact of CR / SC on mental health and wellbeing during emergencies and disasters is mixed (with some potential for backlash), however the literature does identify cognitive SC as particularly protective. Although only a small number of papers compared CR or SC before and after a disaster, the findings were relatively consistent: SC or CR is negatively impacted by a disaster. Methods suggested to bolster SC in communities were centred around social activities, such as recreational group activities and volunteering. Recommendations for both research and practice (with a particular focus on the ongoing COVID-19 pandemic) are also presented.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Social Capital

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This study was supported by the National Institute for Health Research Research Unit (NIHR HPRU) in Emergency Preparedness and Response, a partnership between Public Health England, King’s College London and the University of East Anglia. The views expressed are those of the author(s) and not necessarily those of the NIHR, Public Health England, the UK Health Security Agency or the Department of Health and Social Care [Grant number: NIHR20008900]. Part of this work has been funded by the Office for Health Improvement and Disparities, Department of Health and Social Care, as part of a Collaborative Agreement with Leeds Beckett University.

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Hall, C.E., Wehling, H., Stansfield, J. et al. Examining the role of community resilience and social capital on mental health in public health emergency and disaster response: a scoping review. BMC Public Health 23 , 2482 (2023). https://doi.org/10.1186/s12889-023-17242-x

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Fatal Traffic Risks With a Total Solar Eclipse in the US

• 1 Department of Medicine, University of Toronto, Toronto, Ontario, Canada
• 2 Evaluative Clinical Science Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
• 3 Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
• 4 Division of General Internal Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
• 5 Center for Leading Injury Prevention Practice Education & Research, Toronto, Ontario, Canada
• 6 Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
• 7 Centre for Clinical Epidemiology & Evaluation, University of British Columbia, Vancouver, British Columbia, Canada

A total solar eclipse occurs when the moon temporarily obscures the sun and casts a dark shadow across the earth. This astronomical spectacle has been described for more than 3 millennia and can be predicted with high precision. Eclipse-related solar retinopathy (vision loss from staring at the sun) is an established medical complication; however, other medical outcomes have received little attention. 1

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Redelmeier DA , Staples JA. Fatal Traffic Risks With a Total Solar Eclipse in the US. JAMA Intern Med. Published online March 25, 2024. doi:10.1001/jamainternmed.2023.5234

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