future perspectives of the research

Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies (2019)

Chapter: chapter 6 final perspectives and future research opportunities.

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111 Final Perspectives and Future Research Opportunities Research for this project has examined a wide range of existing studies of air passenger demand and air passenger activity by many types of researchers, as well as the types of data typically used to design and conduct these studies, along with the sources of these data. This effort has focused both on understanding how and why these studies were conducted and the extent to which the studies made use of disaggregated socioeconomic data. The research team used what was learned about the state of practice in modeling air passenger demand as a starting point for new research into how disaggregated socio­ economic data could be used to improve the modeling of passenger demand and observed enplanements. This new research proceeded along several paths. One of these research paths investigated what could be learned from air passenger, household travel, and consumer survey data about how air passenger travel demand varies with the disaggregated demo­ graphic and socioeconomic information about air travelers that is typically collected in these surveys. A second research path investigated the effects of including a disaggregated socioeconomic variable in traditional models of air passenger demand at individual airports (which already make use of aggregated regional socioeconomic variables). This research took a case study approach, evaluating the results from including a disaggregated socioeconomic variable in traditionally structured regression models of annual O&D enplanements for eight U.S. airports or airport systems. A third path examined alternative ways new specifications and equation forms could incor­ porate disaggregated socioeconomic variables or relationships in models of air passenger activity at an airport or in an airport system. This effort used insights from the analysis of past air pas­ senger surveys and the differences in the propensities to travel by air of different socioeconomic cohorts revealed by those analyses. The fourth research path evaluated the usefulness of new forms of disaggregated socio­ economic data about air passengers. These data were developed from de­identified financial transaction records of individuals who made use of personal financial management software. While only a small fraction of such consumer spending is directly related to air travel choices and behavior, these digital forms of data collection represent a rapidly evolving frontier for the development of information about consumer purchase choices in a wide range of markets, including passenger aviation. These four research avenues pursued in this project together comprise a multifaceted approach to developing an improved understanding of the potential value of disaggregated socioeconomic data in the analysis of air passenger demand for airports. Aspects of this overall C H A P T E R 6

112 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies approach directly address the four forward­looking questions identified in the introduction to this report: • How does air passenger behavior differ across socioeconomic cohorts or subgroups, especially with respect to individual propensities to choose to travel by air? • Can disaggregated socioeconomic factors be introduced into traditional approaches to modeling air passenger demand at individual airports or systems of airports, and do those variables provide new information compared to a baseline of traditional air passenger demand modeling using aggregate socioeconomic variables? • Can new approaches to structuring econometric models or other approaches be developed and used to realize the value of incorporating disaggregated socioeconomic data in under­ standing or modeling air passenger demand? • Are there new forms of disaggregated socioeconomic data, or new ways of collecting such data, that can be used to model or study air passenger demand at airports and in regions? In this report we have summarized how we conducted project research to address these questions, and also reported the results from these research efforts. In the remainder of this chapter, we present the findings from our research and the research opportunities that we believe these findings open for future researchers. Summary of Research Approach This research project has investigated the effectiveness of incorporating the use of disaggre­ gated socioeconomic variables in studies of air passenger demand and air passenger activity at airports. The research was organized to first identify how such studies have been conducted and reported in the past. The review of these past examples extended across research conducted by a wide range of individuals and organizations, including academics, governmental and industry organizations, and airport practitioners and consultants. The research next covered the types of data that have been used in studies of air passenger demand as well as the types and sources of disaggregated socioeconomic that could also be used in such studies. This research identi­ fied trends in the evolution of age and household income cohorts in the United States. The research then extended to an analysis of air passenger and consumer surveys to identify ways the propensity of individuals to travel by air vary by their demographic and socioeconomic char­ acteristics, such as age, gender, or household income. The research then used a case study approach to examine the effects of adding a disaggre­ gated household income variable as an independent regressor to more traditional models using aggregate socioeconomic variables. This model performance comparison was done for eight case study airports using observations of annual O&D enplanements between 1990 and 2010 for seven U.S. airports and one U.S. airport system. This approach permitted the assessment along several dimensions of the impact of incorporating this type of data in air passenger demand modeling. A second case study analysis was conducted using data from the Baltimore–Washington airport system to examine the effectiveness of more sophisticated model formulations, again comparing model performance without and with the inclusion of a form of disaggregated socio­ economic variable among the independent variables of the regressions. The research then examined the potential for using new forms of disaggregated (or more individualized) socioeconomic data for air passenger demand modeling, based on individual financial transactions data from a group of individuals.

Final Perspectives and Future Research Opportunities 113 Summary of Findings from Research The primary goal of the research project was to investigate the extent emerging or ongoing socioeconomic changes in the population (such as the age structure of society, increased immi­ gration, wealth concentration, geographic redistribution of the population, and changing views on the use of disposable income) are likely to influence the future demand for air travel and may not be well captured in current approaches to air passenger demand modeling. More specifi­ cally the research explored whether the inclusion of disaggregated socioeconomic data, such as regional distributions of age, gender, or household incomes, in air passenger demand models can improve the ability of those models to anticipate future changes in the overall demand for air travel and composition of the air traveling public. The research found that the use of air travel by different subsets of the population (in terms of household income, age, race and ethnicity) varies widely. It is clear that changes in the dis­ tribution of these characteristics across the population are likely to have a significant impact on future air travel demand. In particular, an increasing concentration of wealth and income in the wealthiest segment of society appears likely to reduce the amount of air travel compared to a less concentrated distribution for any given level of total income, excluding the effect of other factors, for reasons discussed earlier in this report. At the same time, increasing concentration of wealth and income may continue to change air travel in more qualitative ways, with airlines charging separately for service amenities that some segments of the traveling public are willing to forego in order to obtain a lower airfare. Similarly, an aging population will move an increasing proportion of the population into age ranges that make fewer air trips per year than those in the age range from 45 to 65 that make the most air trips per year on average. The research explored a number of different ways of reflecting these trends in air passenger demand studies and models, although an attempt to develop models of enplaned air passenger traffic for eight case studies of individual airports and one regional airport system that incor­ porated disaggregated variables that reflected the distribution of household incomes met with mixed success. Adding a disaggregated household income variable to relatively simple models that used only two aggregate socioeconomic variables did not noticeably improve the ability of the models to either explain past air passenger enplanements or predict future passenger traf­ fic levels in a simulated forecasting exercise. However, a more complex model using a different disaggregated household income variable did show improvement in either its ability to explain past levels of air passenger enplanements or its ability to predict future air passenger traffic. While this improvement was not large, the implications for future levels of air passenger demand of the change in the implied demand elasticity (model coefficients) between the model without the disaggregated income variable and the model with the variable were significant. This finding has broader implications beyond the specific models estimated in the research. Elasticity values obtained from air passenger demand models are sometimes used or quoted in other studies. If these values are biased due to the omission of disaggregated socioeconomic variables in the models from which they were obtained, this could distort the results of these other studies. Future Research Opportunities The analysis of disaggregated response data from air passenger, household travel, and con­ sumer expenditure surveys raised a number of issues that appear deserving of further research. These are discussed in more detail in the description of the analysis findings in Chapter 3, but can be summarized as follows: • The analysis of the three broad categories of survey (airport intercept, household travel, and consumer expenditure) gave different estimates of air travel propensity for given population

114 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies subgroups. In particular, the California Household Travel Survey appeared to undercount air trips by California residents. Further work to resolve these differences in findings from different surveys is needed. • The analysis of the survey results looked at different socioeconomic factors (income, age, etc.) separately, but in reality they are most likely correlated. Further research to explore the impli­ cations of this correlation and develop techniques to account for different socioeconomic factors simultaneously would be valuable. • The demand for air travel depends not only on socioeconomic factors of the population but also on the level of airfares experienced by travelers from a given region. It would be valuable to develop techniques to account for differences in airfare levels between airports or regions in order to be better able to compare findings on air travel propensity by household or air traveler socioeconomic characteristics across different airports or regions. The case study regression analysis relied on a relatively simple model specification to investi­ gate the effects of incorporating a specific example of a disaggregated socioeconomic variable on the performance of baseline models relying on aggregate socioeconomic variables (and another independent variable unrelated to socioeconomic factors). While the specific dis aggregated socioeconomic variable chosen for these comparisons—one reflecting regional household income distributions—is a natural candidate for consideration in a model of air passenger demand, there may be other disaggregated socioeconomic variables that could be analyzed. In addition, it may be valuable to analyze other ways of introducing the information from the dis­ aggregated variable into the regression, since the comparisons of baseline and alternative models were affected by the strong correlation between the aggregate and disaggregated variables used in the case study regressions. The more detailed analysis of the passenger enplanements in the Baltimore–Washington regional airport system performed as part of the overall case study analysis developed a model that provided an excellent fit to the historical data using a range of independent variables that make intuitive sense and had estimated coefficients that had values that appeared reasonable and were estimated with a high level of statistical significance. As noted above, including a disaggre­ gated variable for household income distribution not only improved the fit of the model to the data and its predictive ability but also changed the estimated values and increased the statistical significance of the other variables, suggesting that omitting such variables from air passenger demand models could lead to biased estimates of the model coefficients. However, there were aspects to this model that are deserving of further research, in particular: • In addition to the continuous variables the model made use of year­specific dummy variables that accounted for factors not reflected in the continuous variables. These played a major role in the model fit. It would be valuable to explore other continuous variables that could account for these effects without relying on year­specific dummy variables (which are problematic in using models for forecasting). • The model used average airline yield at a national level as the airfare price variable. However, this may not be a good reflection of airfares for any particular airport. It would be valuable to explore the use of a variable that better reflects the average airfares at the airport(s) in questions. • The model used a disaggregated variable for household income distribution but did not con­ sider changes in the distribution of other socioeconomic factors, such as the age of air travelers. It would be valuable to explore how to incorporate changes in the distribution of other socio­ economic factors in similar models. • The model variables reflected socioeconomic factors for residents of the Baltimore­ Washington region, but of course a large proportion of the passengers at the region’s airports (over half in the case of the Baltimore­Washington region) are visitors. It would be valuable to explore how to account for socioeconomic factors of visitors as well as residents.

Final Perspectives and Future Research Opportunities 115 More broadly, the approach followed in developing the more detailed model of the Baltimore­Washington region needs to be applied to other airports and regions to determine whether the resulting models for those airports or regions also show a similar improvement or whether the results found for the Baltimore­Washington region are somehow unique to that region or maybe even an artifact of the data used in the analysis. In addition, the Baltimore­ Washington region is one of the largest multi­airport regions in the country and the airports serving the region are much larger than most other individual airports to which the approach could be applied. It would be valuable to explore whether such differences in scale or the extent of the airport system being modeled also affect the applicability or performance of this modeling approach. There are several research opportunities related to using the new data sources for airport demand modeling studies discussed in Chapter 5. We found that many of the most success­ ful applications of using cell phone or location­based data for airports were for revenue­ generating and cost­reduction purposes. In the context of airport demand studies, we found successful applications using cell phone and ticketing data to analyze airport catchment areas. In terms of future research, cell phone usage could be a source of new data on passenger choices and passenger movements through terminals. Future research could explore linking cell phone tracking data to small­area demographic and socioeconomic data to explore air travel propensity by household characteristics without the need for air passenger surveys (or to track changes between surveys). Cell phones could also be used as a tool to gather data simi­ lar to the types of data collected in traditional airport survey efforts. For example, cell phone beacons and airport­specific phone apps could invite air travelers to participate in an online air passenger survey. Travelers could download a survey form that they could complete at leisure (for example, on their flights) and then submit online when they next have an internet connection. Both of these future research opportunities help address one of the key challenges to using new sources of disaggregated data based on cell phone, GPS, or financial transactions, namely that due to the need to protect individuals’ privacy there is limited socioeconomic information available from these sources directly. Chapter 5 also discussed alternative potential approaches to incorporating disaggregated socioeconomic factors in air passenger demand studies and models. However, the resources of the project only allowed one of these approaches to be explored and then only with a very limited number of variables and for only one socioeconomic factor (household income). It would be valuable to develop techniques for applying the other approaches or including a broader range of factors and to explore their relative feasibility and effectiveness. Because the sources of data on air passenger enplanements do not typically distinguish between travel by residents of a region and visitors to a region or between travel for personal and business purposes, models of air travel demand have generally attempted to model total air travel. Since the survey analysis undertaken in the course of the project shows, not surprisingly, that the average number of annual air trips made for business and personal purposes differs considerably by household or individual characteristics, while it can also be expected that the factors influencing air travel by residents of a region and visitors to the region are different, it would be valuable to explore the feasibility and effectiveness of developing air passenger demand models that can distinguish between travel by residents of a region and visitors to the region or predict travel by trip purpose. The foregoing potential research opportunities represent a wide range of possible research projects that would build on the work performed in the current project and that could be pur­ sued through future research. The description of the research performed for the current project and the discussion of potential research opportunities in this report provide a fairly clear indication of how potential future research projects could be undertaken. Nonetheless, more detailed research problem statements could be developed following publication of this report.

116 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies This would allow other interested researchers to contribute suggestions and comments on the scope of future research. Although each of the potential research opportunities would contribute to a better under­ standing of how air passenger demand is influenced by disaggregated socioeconomic factors and how to best reflect this in air passenger demand models, and hence there is no obvious priority between the various research needs, it would seem reasonable to focus initially on two issues: explore applying the modeling approach used for the more detailed analysis of the Baltimore–Washington region to other airports and regions in order to determine how trans­ ferable these findings are and investigate the reasons for the differences in air travel propensity between air passenger intercept surveys and household travel and expenditure surveys. A clearer understanding of the reasons for these differences would allow future research on air passenger demand to draw on a broader range of disaggregated socioeconomic data with greater confi­ dence, as well as potentially provide useful guidance for the design of such surveys in the future. Conclusions Passenger aviation is often described as an important catalyst for broad economic growth and is often depicted, qualitatively and in formal models of air passenger demand, as being driven by aggregate measures of regional economic activity and growth. Yet it is also under­ stood that air transportation is not used to the same extent by all segments of society. This project has examined whether this growing understanding of the differences in propensities to fly that depend on individual and household characteristics such as household income, age, or educational status can be used to improve models and forecasts of air passenger activity. These differences were consistently seen in analyses of a number of surveys of air passengers and consumers. While data about distinct subgroups of individuals and households—disaggregated socio­ economic data—and about how their distributions of characteristics are changing are avail­ able, these data are sometimes correlated with the aggregate regional socioeconomic data used in the past for the airport passenger demand models used in a variety of airport studies and planning efforts. This correlation can raise statistical challenges for simple approaches to incorporating some forms of disaggregated socioeconomic data into existing models of air passenger demand. The project’s analysis of the effect of incorporating a particular disaggre­ gated socioeconomic variable into fairly simple regression models of annual enplanements in case studies of seven individual U.S. airports and one regional airport system ran into such challenges, while resulting in only modest improvements in the performance of the models that included the disaggregated socioeconomic variable. Therefore, care must be taken in including disaggregated socioeconomic variables in air passenger demand models to avoid or control for potential correlation between aggregate and disaggregated socioeconomic vari­ ables to the extent possible. It should also be recognized that there may be tradeoffs in model performance between omitted­variable bias in models that only include aggregate socio­ economic variables and a loss of statistical significance and potential bias in coefficient esti­ mates if disaggregated socioeconomic variables are included that are partially correlated with the aggregate socio economic variables. As more experience is gained in using disaggregated socioeconomic variables in air passenger demand models, it should become clearer how best to resolve such trade­offs. Although including a disaggregated household income variable in the simple case study regression models did not show a significant improvement in the predictive ability of the models, including a different disaggregated household income variable in a more complex model speci­ fication for the case study of the Baltimore–Washington regional airport system did provide an

Final Perspectives and Future Research Opportunities 117 improvement in both model fit to the historical data and predictive ability. Furthermore, includ­ ing the disaggregated income variable resulted in a significant change to the demand elasticities implied by the estimated model coefficients. Three important conclusions can be drawn from this experience: • The way in which disaggregated socioeconomic variables are defined is important. If they are defined in a way that partially reflects the factors measured by the aggregate socioeconomic variables in a model, their addition to the model may not result in any improvement in model fit and may reduce the statistical significance of the estimated coefficients of the aggregate variables in the model. • Simple models with relatively few independent variables that do not fit the historical data very closely are not likely to show much improvement by including variables that reflect the distribution of aggregate socioeconomic factors included in the model. This is because there are clearly factors influencing the dependent variable that are not well represented by the independent variables. It is not likely that including a variable that reflects the dis­ tribution of one of the factors measured by an aggregate variable will rectify this problem and the model estimation process may use the additional variable to account for factors that happen to be correlated with it, distorting not only the estimated coefficient of that variable but those of other variables as well. • For use in air travel demand forecasting what matters most in a model is that the demand elasticities implied by the model coefficients accurately reflect the likely effect of the inde­ pendent variables in question. Good fit to the historical data is desirable and provides some assurance that the model is reasonable, but if this is achieved through biased coefficients of the independent variables the ability of the model to correctly anticipate the effect of any given scenario for future values of the independent variables will be compromised. An alternative approach to the modeling undertaken in the case study analysis, given initial investigation in this project, would be to develop new models or new forms of dis­ aggregated data that may avoid some of the difficulties encountered in the case study analy­ sis. The modest but real improvements in model performance observed in the project case studies and the consistent results from survey analysis linking respondent socioeconomic characteristics and differences in household propensity to travel by air suggest that contin­ ued research would be worthwhile in this area. Further investigation and exposition of this alternative approach to air passenger demand modeling could be the basis of additional research. In addition, a second project could focus on further development of the model specifications developed for the Baltimore­Washington region, applying them to other air­ ports and airport systems, and explaining the modeling results and their implications. Such research may bring new insights to airport managers and decision makers facing a changing air travel marketplace. It was noted that airports use air passenger demand studies for many purposes, from analy­ sis of behaviors and choices of the passengers they currently serve or hope to serve to the sup­ port of an airport’s planning and preparation for future passengers and services. The research conducted in this project can provide value for those who will prepare these studies for airport managers as well as for the airport managers, planners, and decision makers who will use the studies. • Airport staff and airport consultants who prepare models and reports on air passenger demand for airport managers may find new insights in the report’s analysis of air passenger surveys and the variability in the propensity to travel by air that is revealed across different demographic and socioeconomic subgroups. These new insights may influence the formula­ tion of new models for air passenger demand or may contribute to the interpretation of results from more familiar modeling specification.

118 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies • Those engaged in air passenger modeling may also find the case study analysis and especially the investigation of new modeling specifications for air passenger demand at the Baltimore­ Washington airport system suggestive of new approaches that could be taken in their own assignments. • Airport planners and managers (and other users of air passenger demand studies) may find the report’s presentations of demographic and socioeconomic trends, air passenger choices and behaviors, and the shortcomings and opportunities that are identified for formal passenger activity modeling valuable to their own needs to interpret air passenger demand studies and act on them. • Finally, air passenger demand models are also developed and used by other aviation indus­ try participants, such as government agencies, aircraft manufacturers, airlines, and academic researchers. Although these parties may look to models at different levels of generality or complexity, they may also find that the cautions and innovations for air passenger modeling techniques developed for this report can also contribute to their own modeling efforts. The current research project represents an initial effort to both understand how the distri­ bution of socioeconomic factors across the population affects the demand for air travel and to explore how to incorporate these effects in air passenger demand studies and models. It has generated a large amount of relevant information from a detailed analysis of air passenger, household travel, and other surveys, the full analysis of which will require much further work. It has also demonstrated that incorporating these effects in air travel demand models not only can improve the predictive ability of those models, but indeed is essential for them to have good predictive ability, given the underlying changes in the socioeconomic composition of society. However, much work remains to be done to develop robust models of air passenger demand that can be applied in a range of settings. This will require a sustained research effort over many years. As the literature review undertaken in the current project has demonstrated, the state of practice of air travel demand analysis has hardly evolved at all over the past 40 years. However, the current project has laid the foundations upon which a significantly improved understand­ ing or air travel demand and an evolving state of practice can be built through continuing future research efforts.

TRB’s Airport Cooperative Research Program (ACRP) Research Report: 194: Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies explores the potential benefits of using disaggregated socioeconomic data, such as regional household income distributions and air passenger and travel survey data, for air passenger demand studies.

Aviation demand is strongly correlated to socioeconomic activity, and analysts typically use aggregate socioeconomic data, such as gross regional product or average regional household income, to better understand current and potential future aviation demand.

Since the United States is experiencing significant and ongoing demographic trends there is a question as to whether traditional methods and data sources will adequately capture these trends or would more detailed, disaggregated socioeconomic data, or even nontraditional data provide more accurate results.

This report summarizes long-term socioeconomic trends, attempts to understand their potential impact, and provides guidance for incorporating disaggregated socioeconomic data into air passenger demand studies.

The following appendices to ACRP Research Report 194 are available online:

Appendix A : Detailed Survey of Past Analyses of Air Passenger Demand

Appendix B : Airport Industry Use of Socioeconomic Data for Air Passenger Demand Studies

Appendix C : Additional Material on Sources of Disaggregated Socioeconomic Data

Appendix D : Detailed Case Study Analysis Results

Appendix E : Background on Other Analytic Approaches

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Home » Future Research – Thesis Guide

Future Research – Thesis Guide

Table of Contents

Future Research

Definition:

Future research refers to investigations and studies that are yet to be conducted, and are aimed at expanding our understanding of a particular subject or area of interest. Future research is typically based on the current state of knowledge and seeks to address unanswered questions, gaps in knowledge, and new areas of inquiry.

How to Write Future Research in Thesis

Here are some steps to help you write effectively about future research in your thesis :

• Identify a research gap: Before you start writing about future research, identify the areas that need further investigation. Look for research gaps and inconsistencies in the literature , and note them down.
• Specify research questions : Once you have identified a research gap, create a list of research questions that you would like to explore in future research. These research questions should be specific, measurable, and relevant to your thesis.
• Discuss limitations: Be sure to discuss any limitations of your research that may require further exploration. This will help to highlight the need for future research and provide a basis for further investigation.
• Suggest methodologies: Provide suggestions for methodologies that could be used to explore the research questions you have identified. Discuss the pros and cons of each methodology and how they would be suitable for your research.
• Explain significance: Explain the significance of the research you have proposed, and how it will contribute to the field. This will help to justify the need for future research and provide a basis for further investigation.
• Provide a timeline : Provide a timeline for the proposed research , indicating when each stage of the research would be conducted. This will help to give a sense of the practicalities involved in conducting the research.
• Conclusion : Summarize the key points you have made about future research and emphasize the importance of exploring the research questions you have identified.

Examples of Future Research in Thesis

SomeExamples of Future Research in Thesis are as follows:

Future Research:

Although this study provides valuable insights into the effects of social media on self-esteem, there are several avenues for future research that could build upon our findings. Firstly, our sample consisted solely of college students, so it would be beneficial to extend this research to other age groups and demographics. Additionally, our study focused only on the impact of social media use on self-esteem, but there are likely other factors that influence how social media affects individuals, such as personality traits and social support. Future research could examine these factors in greater depth. Lastly, while our study looked at the short-term effects of social media use on self-esteem, it would be interesting to explore the long-term effects over time. This could involve conducting longitudinal studies that follow individuals over a period of several years to assess changes in self-esteem and social media use.

While this study provides important insights into the relationship between sleep patterns and academic performance among college students, there are several avenues for future research that could further advance our understanding of this topic.

• This study relied on self-reported sleep patterns, which may be subject to reporting biases. Future research could benefit from using objective measures of sleep, such as actigraphy or polysomnography, to more accurately assess sleep duration and quality.
• This study focused on academic performance as the outcome variable, but there may be other important outcomes to consider, such as mental health or well-being. Future research could explore the relationship between sleep patterns and these other outcomes.
• This study only included college students, and it is unclear if these findings generalize to other populations, such as high school students or working adults. Future research could investigate whether the relationship between sleep patterns and academic performance varies across different populations.
• Fourth, this study did not explore the potential mechanisms underlying the relationship between sleep patterns and academic performance. Future research could investigate the role of factors such as cognitive functioning, motivation, and stress in this relationship.

Overall, there is a need for continued research on the relationship between sleep patterns and academic performance, as this has important implications for the health and well-being of students.

Further research could investigate the long-term effects of mindfulness-based interventions on mental health outcomes among individuals with chronic pain. A longitudinal study could be conducted to examine the sustainability of mindfulness practices in reducing pain-related distress and improving psychological well-being over time. The study could also explore the potential mediating and moderating factors that influence the relationship between mindfulness and mental health outcomes, such as emotional regulation, pain catastrophizing, and social support.

Purpose of Future Research in Thesis

Here are some general purposes of future research that you might consider including in your thesis:

• To address limitations: Your research may have limitations or unanswered questions that could be addressed by future studies. Identify these limitations and suggest potential areas for further research.
• To extend the research : You may have found interesting results in your research, but future studies could help to extend or replicate your findings. Identify these areas where future research could help to build on your work.
• To explore related topics : Your research may have uncovered related topics that were outside the scope of your study. Suggest areas where future research could explore these related topics in more depth.
• To compare different approaches : Your research may have used a particular methodology or approach, but there may be other approaches that could be compared to your approach. Identify these other approaches and suggest areas where future research could compare and contrast them.
• To test hypotheses : Your research may have generated hypotheses that could be tested in future studies. Identify these hypotheses and suggest areas where future research could test them.
• To address practical implications : Your research may have practical implications that could be explored in future studies. Identify these practical implications and suggest areas where future research could investigate how to apply them in practice.

Applications of Future Research

Some examples of applications of future research that you could include in your thesis are:

• Development of new technologies or methods: If your research involves the development of new technologies or methods, you could discuss potential applications of these innovations in future research or practical settings. For example, if you have developed a new drug delivery system, you could speculate about how it might be used in the treatment of other diseases or conditions.
• Extension of your research: If your research only scratches the surface of a particular topic, you could suggest potential avenues for future research that could build upon your findings. For example, if you have studied the effects of a particular drug on a specific population, you could suggest future research that explores the drug’s effects on different populations or in combination with other treatments.
• Investigation of related topics: If your research is part of a larger field or area of inquiry, you could suggest potential research topics that are related to your work. For example, if you have studied the effects of climate change on a particular species, you could suggest future research that explores the impacts of climate change on other species or ecosystems.
• Testing of hypotheses: If your research has generated hypotheses or theories, you could suggest potential experiments or studies that could test these hypotheses in future research. For example, if you have proposed a new theory about the mechanisms of a particular disease, you could suggest experiments that could test this theory in other populations or in different disease contexts.

Advantage of Future Research

Including future research in a thesis has several advantages:

• Demonstrates critical thinking: Including future research shows that the author has thought deeply about the topic and recognizes its limitations. It also demonstrates that the author is interested in advancing the field and is not satisfied with only providing a narrow analysis of the issue at hand.
• Provides a roadmap for future research : Including future research can help guide researchers in the field by suggesting areas that require further investigation. This can help to prevent researchers from repeating the same work and can lead to more efficient use of resources.
• Shows engagement with the field : By including future research, the author demonstrates their engagement with the field and their understanding of ongoing debates and discussions. This can be especially important for students who are just entering the field and want to show their commitment to ongoing research.
• I ncreases the impact of the thesis : Including future research can help to increase the impact of the thesis by highlighting its potential implications for future research and practical applications. This can help to generate interest in the work and attract attention from researchers and practitioners in the field.

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Research + Insights: Navigating AI Now and in the Future: Perspectives from HR Leaders and Employees

To gain insights into AI in the workplace, SHRM Research surveyed 1,993 U.S. workers and 1,220 HR leaders in January and February 2024. The results reveal a rapidly changing business landscape as organizations embrace the power of AI, either now or with plans to adopt the technology within the next year or two. Notably, HR is emerging as a key area that is ripe for AI investment and opportunity. While AI holds great potential, human intelligence and collaboration remain critical to its success. To successfully navigate this future, individuals will need to cultivate a diverse skill set, with HR taking center stage in upskilling and reskilling efforts.

Note: HR leaders were asked to select all the changes that apply with the exception of “AI has not changed the business environment.”

                                                                                                                                                                                      .

MESSAGE FROM THE RESEARCHERS

Upskilling and reskilling for the ai-driven future.

By Ragan Decker, Ph.D.

The rise of artificial intelligence in the workplace demands a reimagining of the skill sets essential for employees. Recent SHRM research underscores this point, revealing that 83 percent of HR leaders believe that upskilling will be essential for workers to remain in a job market shaped by AI.

This sentiment aligns with the prevailing understanding that AI is more likely to reshape existing jobs than entirely replace them, as affirmed by 58 percent of HR leaders (with only 16 percent of HR leaders in disagreement).

The ability to collaborate with AI will likely be critical in the future, as 88 percent of HR leaders and 86 percent of U.S. workers believe that AI needs humans for optimal functionality. What skills will be needed for effective human-machine collaboration in the future? U.S. workers say the most important competencies will be technical skills for navigating interfaces and tools (72 percent), digital literacy (68 percent), and critical thinking skills (68 percent).

However, teaching these skills presents some challenges. When HR leaders were asked why their organization hadn’t implemented various types of AI, lack of knowledge and skills among their workforce was a common theme. This highlights a critical skills gap that needs to be addressed for widespread AI adoption to happen.

Despite this skills gap, the implementation of AI training and upskilling programs remains limited. Among organizations utilizing AI, only 29 percent have taken proactive measures to train and upskill employees working alongside AI technologies ( Talent Trends , SHRM Research, 2024).

Furthermore, the research highlights a lack of strategic foresight among many organizations. More than three-quarters of HR leaders have not yet been tasked with evaluating the influence of AI on the composition of the workforce (77 percent, see below), and only 17 percent have been asked to examine the impact of AI on skill requirements. This lack of proactive planning hinders an organization’s ability to adapt and thrive in the AI-driven future.

3 Smart Steps for HR

To bridge this gap and prepare for the future, organizations must adopt a proactive approach to workforce planning and development. SHRM Research recommends that HR leaders take the following steps:

• Play an active role, often taking the lead, in planning for the AI-driven future , including evaluating the impact of AI on skill requirements, educational requirements, the organizational structure and workforce composition overall.
• Foster a culture of continuous learning  by encouraging employees to embrace lifelong learning and skill development.
• Invest in comprehensive upskilling and reskilling programs  tailored to the specific needs of the workforce.

By taking these steps, organizations can ensure their workforce is well equipped to thrive alongside AI, unlocking its potential for greater efficiency, productivity and innovation in the years to come.

Ragan Decker, Ph.D., Manager EN/ES Research, SHRM  

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Publications, ai in education: the future is now, artificial intelligence, academics and how gonzaga is handling the new technology..

[Notice: A human wrote this article.]

Tech company OpenAI released its ChatGPT tool at the end of November 2022, and it took only a couple of weeks for Gonzaga professors to notice students had quickly adopted the program that miraculously seemed capable of “writing” essays.

Justin Marquis remembers one professor mentioning that some papers she’d received for an assignment seemed odd. The papers were a little too perfect. Perfect grammar. Perfect spelling. Perfect Spanish usage. Perfect to a degree that just doesn’t happen at the undergraduate level, or even among graduate students. And each of the essays had the same structure: five paragraphs, the last one beginning with the words “in conclusion.”

Marquis saw the signs of assignments “written” by ChatGPT or similar tools like Google’s Bard or Microsoft’s Copilot. As director of GU’s Instructional Design and Delivery, Marquis and his team help faculty and staff harness the latest technology, including AI, to deliver innovative classroom experiences. And he’s used ChatGPT since its debut to assist in brainstorming blog posts, designing class materials and helping fellow faculty approach courses in creative new ways.

“AI is basically math. It’s taken the information on the internet and turned it into equations. It seems like magic because it actually does work, because it’s parsing so much information and doing computations to make things make sense. But it doesn’t understand context in any way. So, when you prompt it to write an essay about “Beowulf,” it might process all early English literature and pull random characters from other stories, completely unrelated.”

ChatGPT’s foibles didn’t stop people from embracing it. Two months after its launch, it had reached more than 100 million monthly users, according to Reuters. By comparison, it took TikTok nine months to reach that many users, and Instagram more than two years.

At Gonzaga, Marquis has worked with offices like Human Resources to improve training programs via ChatGPT brainstorm sessions, and collaborated with the Center for Teaching and Advising to provide workshops for teachers interested in adopting some AI in their work – or at least getting a better understanding.

'Cheating' is Changing

Is a student cheating if using ChatGPT to write a paper?

That depends on each faculty member’s expectations for each class. Some actively encourage students to use ChatGPT to hone arguments, spark new directions for writing, or simply to learn new technology that will be part of life from this point forward. Others strictly prohibit AI for class work.

The key for faculty is making expectations crystal clear at the beginning of any class, something Marquis encourages his peers to do both as Gonzaga’s resident AI expert and interim chair of the Academic Integrity Board, the entity that handles any accusations of cheating on campus.

“The thing I’d like students to understand, and for faculty to impress, is that you are responsible for the thing AI creates. Whether you write something or make art, you put it out under your name,” Marquis says. “This is a representation of you and you are the one who will bear any consequences from it.”

While the University doesn’t have a specific policy aimed at AI, any student who might hand in a ChatGPT-written assignment without permission to use the tool and/or without citing they’d used AI would be in violation of GU’s Academic Integrity Policy, which prohibits submitting a paper without proper attribution.

The challenge for instructors trying to prohibit use of AI is that proving an assignment was generated by AI is exceedingly difficult. Electronic tools designed to determine if an assignment is AI-generated have proved unreliable, Marquis says, and could even falsely accuse a student of academic malfeasance, with possible significant consequences.

How should teachers draw the line on what they accept? No one argues against a student using Spellcheck or Grammarly as they write. How about a student whose first language isn’t English using an AI translation app to help understand a professor’s lecture or some assigned reading?

As experts in their respective fields, faculty are well-positioned to spot AI-generated assignments. Besides those predictable, unrealistically clean essays, they’ll quickly notice when Macbeth inexplicably appears in an essay about “Twelfth Night.” But the very concept of “cheating” might get tougher to define as AI is used by more students and teachers. It will remain, as now, the faculty’s obligation to make sure expectations are clearly defined as students born into an AI-dominated world matriculate.

Challenges and Opportunities

Another important lesson for unsuspecting students and AI advocates goes back to the idea that the “magic” is based in math.

AI’s ingestion of human history’s knowledge means it also absorbs all the biases that humans have inserted into that history for hundreds of years. That means the “answers” it generates to innocent queries could be racist or misogynistic.

“Everything we’ve ever written or created and put on the internet has bias built into it. AI will probably not only replicate that bias, but likely amplify it,” Marquis says. “We’re seeing examples where AI is saying really offensive and strange things, and people ask, ‘Why did it do that?’”

“Different perspectives or understandings of the world are not the most popular viewpoints,” Marquis says, noting that this is where Gonzaga’s liberal arts approach to a Catholic, Jesuit, humanistic education is vital when it comes to AI.

Users also need to know that AI is created to deliver the most common answers to a question, the most popular viewpoints, not necessarily the most interesting or most correct.

That human quality, Marquis adds, is how a faculty member can “hedge their bets” against AI, writing assignment prompts that require students to think outside the box of what AI is able to do, prompts for both personal reflection on material studied and demonstration of perspectives beyond the obvious.

AI isn’t bad, it’s just a tool to be used ethically and responsibly. In his role to help faculty to best deliver on Gonzaga’s educational mission, Marquis is excited that AI can help reduce faculty video editing processes from three hours to 15 minutes. For multiple functions, using AI tools to streamline basic tasks is not a possibility – it’s a reality.

And Marquis believes there’s no reason to be afraid of that.

“AI is probably not going to cause the world to end. It will evolve into a tool that we learn how to use, and it will change the way we function. We have to adapt all our practices to that understanding.”

At the Intersection of History and AI

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AI Around Gonzaga's Campus

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A review on existing and emerging approaches for textile wastewater treatments: challenges and future perspectives

• Review Article
• Published: 06 December 2023
• Volume 31 , pages 1748–1789, ( 2024 )

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• Gauri A. Kallawar 1 , 2 &
• Bharat A. Bhanvase 2  

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This comprehensive review explores the complex environment of textile wastewater treatment technologies, highlighting both well-established and emerging techniques. Textile wastewater poses a significant environmental challenge, containing diverse contaminants and chemicals. The review presents a detailed examination of conventional treatments such as coagulation, flocculation, and biological processes, highlighting their effectiveness and limitations. In textile industry, various textile operations such as sizing, de-sizing, dyeing, bleaching, and mercerization consume large quantities of water generating effluent high in color, chemical oxygen demand, and solids. The dyes, mordants, and variety of other chemicals used in textile processing lead to effluent variable in characteristics. Furthermore, it explores innovative and emerging techniques, including advanced oxidation processes, membrane filtration, and nanotechnology-based solutions. Future perspectives in textile wastewater treatment are discussed in-depth, emphasizing the importance of interdisciplinary research, technological advancements, and the integration of circular economy principles. Numerous dyes used in the textile industry have been shown to have mutagenic, cytotoxic, and ecotoxic potential in studies. Therefore, it is necessary to assess the methods used to remediate textile waste water. Major topics including the chemical composition of textile waste water, the chemistry of the dye molecules, the selection of a treatment technique, the benefits and drawbacks of the various treatment options, and the cost of operation are also addressed. Overall, this review offers a valuable resource for researchers and industry professionals working in the textile industry, pointing towards a more sustainable and environmentally responsible future.

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This is a review article. All required data are added in this manuscript.

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Kallawar, G.A., Bhanvase, B.A. A review on existing and emerging approaches for textile wastewater treatments: challenges and future perspectives. Environ Sci Pollut Res 31 , 1748–1789 (2024). https://doi.org/10.1007/s11356-023-31175-3

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Current Challenges and Future Perspectives of Diagnosis of Hepatitis B Virus

Manoj kumar.

1 National Institute of Biologicals, Noida 201309, India

Sangeeta Pahuja

2 Department of Immunohaematology and Blood Transfusion, Lady Hardinge Medical College and Associated Hospitals, New Delhi 110001, India

Prashant Khare

3 Center for Advanced Biotechnology Research, Xenesis Institute, 5th Floor, Plot 68, Sector 44, Gurugram 122003, India

Anoop Kumar

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It is estimated that approximately 260 million people worldwide are infected with the hepatitis B virus (HBV), which is one of the leading causes of liver disease and liver cancer throughout the world. Compared with developed countries, low-income and middle-income countries have limited access to resources and advanced technologies that require highly specialized staff for HBV diagnosis. In spite of the heavy burden caused by hepatitis B virus, 90% of people are still undiagnosed. The World Health Organization (WHO) goal of eliminating hepatitis B by 2030 seems very difficult to achieve due to the existing diagnostic infrastructure in low-resource regions. The majority of diagnostic laboratories still use hepatitis B surface antigen (HBsAg)-based tests. WHO’s elimination plan is at risk of derailment due to phases like the window period, immune control, and occult HBV infection (OBI) not being detected by standard tests. Here, in this article, we are focusing on various diagnostic platforms for the better diagnosis of HBV. The aim of the elimination of HBV can only be achieved by detecting all phases of HBV infection, which can be executed by a combined approach of using new marker assays along with advanced pretesting and testing methods.

1. Introduction

Hepatitis B virus (HBV) infection remains a major health problem despite an extensive vaccination program worldwide. Globally, 260 million people are chronically infected with HBV and 890,000 are dying yearly from complications due to the advancement of HBV infection [ 1 , 2 ]. HBV may play a role in the pathogenesis of chronic liver disease, cirrhosis, and hepatocellular carcinoma (HCC). About one third of the global population is infected with HBV at some point in their lives, and the self-limiting acute hepatitis B infection ends once the virus is cleared [ 3 , 4 ]. When HBV is acquired in adulthood, about 5% persists and progresses into chronic hepatitis B infection (CHB) while in 95% and 20–30% cases of chronic liver disease, cirrhosis, and hepatocellular carcinomas (HCC), it is acquired in infancy and childhood, respectively [ 5 , 6 ]. It is one of the major blood-borne viruses, mostly transmitted through blood and body fluids, and its incubation period is around 2–5 weeks [ 7 ]. It infects and replicates in hepatocytes (liver cells), which results in severe diseases, such as cirrhosis and hepatocellular carcinoma [ 8 ]. The majority of the hepatitis B endemicity in low–medium resource countries (LMIC) is caused by the transmission of the disease from mother to child [ 9 ]. Other additional factors for HBV transmission are intravenous (IV) drug abuse, occupational exposure to infected blood products, having multiple sexual partners, and lack of awareness [ 10 , 11 ].

HBV is a partially double-stranded hepatotropic enveloped DNA virus and belongs to the family Hepadnaviridae [ 12 ]. It is the etiological factor for both acute and chronic hepatitis B infection in humans. Even though the occurrence of HBV infections is decreasing due to vaccination and use of antiviral therapy (by reducing the viral load of chronically infected patients), around still 3.5% of the global population is chronically infected with HBV [ 3 ]. In 2016, the WHO Global Hepatitis Health Sector Strategy articulated the elimination of viral hepatitis by 2030 [ 13 ]. As per this strategy, the target of the elimination of viral hepatitis can be achieved by increasing diagnosis for timely testing, care, and treatment [ 14 , 15 ]. In this article, we discuss the current challenges in diagnosing HBV and recent updates in the field.

2. HBV Genome

In 1965, Blumberg et al. discovered an antigen known as an “Australian antigen” in an Australian aborigine, which was later termed as the hepatitis B surface antigen [ 16 ]. The HBV particles were first visualized under an electron microscope in 1970 by Dane and colleagues. In a serum of HBV-infected patients, three types of HBV particles were observed, out of which two spherical structures had 42 and 22 nm diameter and the third filament-like structure had 22 nm diameter with variable lengths [ 17 , 18 ]. The larger spherical structure of 42 nm diameter, termed as the Dane particle, is the infectious virion. It consists of a lipid membrane with three viral surface antigens (HBs) which surround a nucleocapsid composed of hepatitis B core protein (HBc), viral polymerase (Pol), and viral genome DNA. The 3.2 kb genome of HBV is comprised of circular partially double-stranded DNA. This relaxed-circular DNA (rcDNA) consists of a closed (−) strand and an open (+) strand. The HBV genome encodes 4 overlapping open reading frames (ORFs), namely C, P, S, and X. The functional proteins of HBV are encoded by these overlapping ORFs as follows:

• ORF C: 22-kDa precore protein (p22cr), HBc and HBeAg;
• ORF P: viral polymerase (Pol);
• ORF S: viral surface antigen, L(Large)-HBs, M(Middle)-HBs, and S(small)-HBs;
• ORF X: HBV X protein (HBx).

Hepatitis B virus e antigen (HBeAg) is translated from an HBc reading frame and is an indicator of cccDNA replication. The rcDNA of HBV, on entering the cells, is converted into covalently closed circular DNA (cccDNA), which generates viral RNAs of different lengths due to the initiation of transcription from different promoters. The lengths of these RNAs are around 3.5 Kb, 2.4 Kb, 2.1 Kb, and 0.7 Kb. In 1983, Rall et al. showed that transcription from the HBV genome is facilitated by RNA polymerase II of an infected host and controlled by 4 different promoters and 2 enhancers (Enhancer I and Enhancer II), for preS1, preS2, core, and X sequences [ 19 ].

3. Genotypes of HBV and Their Clinical Importance

The HBV genotypes have been classified based on 8% or more divergence in the HBV genome sequences. To date, around 10 genotypes (A to J) along with some subtypes have been identified and their prevalence varies in different geographical regions. The circulation of particular HBV genotypes in a particular population may have some epidemiological importance, can reveal the country of origin, and can help in tracking the pattern of transmission. The HBV genotypes of immigrants are mostly traced to their native countries [ 20 ].

Globally, four HBV genotypes (A, B, C, and D) are frequently found, but the presence of the genotype B and C is common in Eastern and Western Asia, genotypes A and D in North America, Africa, and Europe, and genotype E in West Africa [ 21 , 22 ]. The genotype of HBV may be linked with disease progression, outcome in the chronicity of the infection, and response to the therapy. The patients infected with different HBV genotypes may show distinctive disease progression, distinctive outcomes, and antiviral therapy response [ 23 , 24 , 25 ]; however, the approved HBV vaccines are effective against all genotypes of HBV [ 21 , 25 ].

4. The Natural History of HBV Infection

The pathogenicity of HBV infection is governed by the immune response of the host interaction, virus replication, evolution, and environmental factors. For chronic HBV infection, the age at which a person acquires the infection plays an important role. The risk of progression of acute to chronic HBV infection is around 95% during the perinatal period, 20–30% in 1–5-year-old children, and less than 5% in adults [ 6 , 26 ]. Chronic HBV infection can be categorized into four phases ( Table 1 ).

Phases of Chronic HBV Infections.

4.1. HBeAg-Positive Chronic Infection or Immune-Tolerant Phase

This is a high-replicative, low-inflammatory phase, characterized by high viral loads. Typically, there are high levels of HBV DNA (generally > 10 7 IU/mL), positivity for HBeAg and HBsAg, but normal alanine aminotransferase (ALT) levels and normal liver histology [ 6 , 27 ].

4.2. HBeAg-Positive Chronic Hepatitis or Immune-Active Phase

This phase occurs as a result of the host immune response against HBV, resulting in liver cell injury. This manifests as elevated ALT levels together with the sign of moderate to severe liver injury. HBV replication is reduced and HBV DNA, HBeAg, and HBsAg levels decline. This phase ends with the reduction of HBV DNA and HBeAg seroconversion to anti HBe positivity [ 3 ].

4.3. HBeAg-Negative Chronic Infection or Immune Control Phase

This phase is characterized by HBeAg seroconversion to antibody to HBeAg (anti-HBe), low to undetectable HBV DNA, and normal ALT level. However, about 10–30% of HBeAg seroconversion patients still have elevated levels of ALT and HBV DNA; consequently, they are classified as HBeAg-negative CHB patients [ 6 ] and mostly have a mutation in the core promoter or pre-core region (antibody HBe, only core).

4.4. HBeAg-Negative Chronic Hepatitis or HBeAg-Negative Immune Reactivation Phase

Recurrence of replication of HBV DNA is seen in around 10–20% of inactive carriers after years of quiescence. Most of them have a mutation in the core promoter or pre-core region and show necroinflammation and fibrosis in liver histology [ 6 ].

4.5. Acute HBV Infection

Acute HBV infection is a condition where an increased level of HBsAg and alanine aminotransferase (ALT) is observed due to HBV infection and clears in less than 6 months. The elevated level of HBsAg and ALT were eliminated within 6 months. Acute HBV infections are mainly asymptomatic and only 30% of infected persons showed clinical signs of jaundice and hepatitis [ 28 ]. Acute HBV infection has an “eclipse” period of around 8 days, in which no evident phase of infection is observed [ 29 ]. Acute HBV infections are self-limiting but can persist as a residual infection and can be active in immune-compromised individuals. Initially, it was assumed that the DNA of HBV is eradicated but sensitive PCR assays may be able to detect DNA traces from serum and liver [ 30 ]. HBV infections that cannot clear within 6 months can lead to the chronic HBV infections. Chronic HBV infection is characterized by a high levels of HBsAg and ALT due to weak cytotoxic T-cell response.

4.6. Occult HBV Infection (OBI)

OBI is defined as presence of replication competent HBV DNA (i.e., episomal covalently closed circular DNA) in the liver and/or HBV DNA in the blood of persons having undetectable HBsAg by presently available tests [ 31 , 32 ].

In OBI, there is suppression of viral replication activity and protein expression, due to host’s immunologic and epigenetic mechanisms, resulting in absence of HBsAg. OBI can be categorized as:

• Sero-positive-OBI: positive for anti-HBc and/or anti-HBs antibodies;
• Sero-negative-OBI (1–20% of all OBIs): Negative anti-HBc and/or anti-HBs antibodies.

Missing out on HBsAg positivity due to the inadequate sensitivity of assay or inability to detect HBV S variants may lead to false negative HBsAg and misdiagnosis/false positive OBI. Though HBsAg is negative, episomal cccDNA in OBI cases is fully replication competent (unlike integrated HBV DNA) and can lead to a reactivation of infection in patient or transfusion-transmitted hepatitis B virus infection in a recipient of blood from an OBI positive donor. Diagnosis of OBI depends on detection of HBV DNA in the liver (gold standard) or blood (more commonly used), with absence of HBsAg. Presence of anti HBcAb is often used as a surrogate marker. Detection of HBV DNA in blood is challenging as it is present in low concentrations and may only be intermittently detected. Hence, the use of sensitive methods for detection of low levels of HBV DNA is recommended. The presence of anti HBc with absence of HBs Ag and presence of anti HBs is also seen in recovered patients, however, HBV DNA is negative in such patients [ 29 , 31 , 32 ]. A resolved HBV infection is defined as a positive HBc antibody without detectable serum HBV DNA or negative HBsAg.

5. Diagnosis of HBV

WHO’s plan to eliminate viral hepatitis by 2030 can be accomplished by increasing diagnosis, care, and treatment [ 13 ]. The elimination of hepatitis refers to the reduction of 90% incidence and 65% of deaths from the 2015 baseline [ 14 , 15 , 33 ]. Screening and diagnosis should be made available to the people unacquainted with their status of HBV infection or who have not so far entered into care and treatment [ 34 , 35 ]. These unaware infected people recurrently carry on a spread of the virus [ 36 ].

The diagnosis and follow-up of chronic infection rely on laboratory viral biomarkers. There are two key categories of HBV biomarker assays: one is serology, a term encompassing the detection and quantification of viral-specific antibodies and/or antigens, and the second is nucleic acid testing (NAT) for the detection and quantification of the HBV genome and its RNA transcripts [ 28 ].

Serology tests that identify or measure HB surface antigen (HBsAg) serum levels, HB surface antibodies (anti-HBs), and HB core antibodies (anti-HBcs) are used to detect patients who have been exposed to HBV, whereas NAT tests provide information on the level of virus replication, the manifestation of particular variants, and occurrence of virus reservoirs. Tests are being advanced to measure levels of intrahepatic HBV replication. These biomarkers are to be used to identify patients with HBV infection, follow disease progression, and determine response to therapy and efficacy of new agents in clinical trials.

HBV can be diagnosed by various HBV markers such as hepatitis B surface antigens (HBsAgs), hepatitis B surface antibodies (anti-HBs), hepatitis B e-antigen (HBeAgs), hepatitis B e-antibodies (anti-HBes), and hepatitis core antigens (anti-HBcs) ( Table 2 )

HBV markers and their significance in diagnosis.

5.1. Routinely Used HBV Markers

Hepatitis B surface antigen (HBsAg): the key serological marker for acute and chronic hepatitis B infection. HBsAg is used as a marker to establish the prevalence of chronic HBV infection in epidemiological studies [ 37 ]. Usually, detection of the HBsAg in repeat testing (after 6 months) is used as marker of chronic HBV infection, whereas the absence of HBsAg in serum indicates recovery from acute HBV infection [ 38 ].

It is encoded by ORF S and synthesized as a small protein (SHBs), a medium protein (MHBs), and a large protein (LHBs) viral surface antigen [ 39 ]. Viral transmission occurs when SHBs and/or LHBs which contain the pre-S1 region binds to the sodium taurocholate co-transporting polypeptide and heparan sulphate proteoglycan (Urban S, et al., 2014). HBsAg is part of the viral envelope and also occurs as a non-infectious subviral particle (which may inhibit the host immune system) and its measurement in body fluid has been used as an important diagnosis parameter in clinical practice and trials [ 40 , 41 ].

HBsAg is the key marker of chronic HBV infection when persistence is more than 6 months, whereas after recovery from acute HBV infections, levels of HBsAg become undetectable.

The levels of HBsAg in the body fluid are important to reveal the infection stage because it reflects the transcriptional activity of cccDNA, which is higher in HBeAg-positive infection than in HBeAg-negative patients [ 42 , 43 ].

HBsAg quantification has prognostic significance and has been incorporated into risk scores to predict the risk of HCC and possibly specify rebound viral risk after stopping NUCs [ 40 ]. Currently, commercially available standardized assays for the quantification of HBsAg are the Architect HBsAg assay developed by Abbott Diagnostics, USA, the ElecsysHBsAg II quant assay developed by Roche Diagnostics, USA, and the DiaSorin Liaison XL, developed by DiaSorin, Italy. These assays can detect and quantify the HBsAg but cannot differentiate three HBs proteins. These three HBs proteins can be detected and distinguished by in-house ELISA or Western blot analysis [ 44 ].

Anti-HBs (Antibody to HBsAg): the main marker to show the presence of antibodies that neutralize HBV and generally symbolize recovery from an acute infection. After recovery, anti-HBs and anti-HBc may be detectable. A positive anti-HBs with negative HBsAg can be seen in response to HBV vaccination, recovery after acute hepatitis, or HBsAg seroconversion in chronic HBV infection. The anti-HBs titer of less than 10 IU/l is considered negative, whereas 10–100 IU/l is considered moderate. The titer higher than 100 IU/l is considered protective even if the person is exposed to a high HBV viral load [ 28 , 29 ].

Hepatitis B core antigen (HBcAg) and anti-HBc: the core protein is covered with HBsAg and thus not freely detected in serum. Few assays are available for anti-HBc but not for detection of HBc antigen [ 29 ]. The detection of Anti-HBc is considered to be suggestive of HBV contact and predictive of three outcomes:

• Recovery indicated by the development of anti-HBs;
• Chronicity indicated by long-term HBsAg;
• Occult HBV infection (OBI) is indicated by low levels of HBV DNA and absence of HBsAg.

In acute HBV infection, the first detectable antibody is IgM anti-HBc, which can be detected within 1 month after HBsAg appearance. The recent infection shows elevated levels of IgM anti-HBc and it lasts for around 4–6 months. During recovery from the acute infection, the levels of IgG anti-HBc increase whereas IgM anti-HBc levels decrease. Low levels of the IgM anti-HBc can persevere in HBV chronic infection and can increase with the severity of chronic hepatitis B. Detection of anti-HBc in the blood may be used as a surrogate marker for identifying OBI in blood or organ donors, in persons who are about to receive immunosuppressive therapy, and for epidemiological studies [ 29 ].

Hepatitis B e antigen (HBeAg) and anti-HBe: HBeAg is coded by the precore (Pre-C) region of the core gene (C). HBeAg helps in distinguishing HBeAg-positive and HBeAg-negative CHB infection. HBeAg seroconversion marks the transition from the immune clearance phase to the immune control phase of CBH. HBeAg has been used as a marker of chronic active HBV infection in its immune tolerant phase and reactivation of low-replicative chronic infection [ 45 , 46 ]. The existence of HBeAg in the serum of an HBsAg-positive carrier indicates frequent viral replication and greater infectivity. Assays for detecting HBeAg and anti-HBe are generally combined in the same enzyme immunoassay kits as these markers are essentially mutually exclusive. HBeAg can be used as a cost-effective substitute for HBV DNA, which help in therapeutic management and efficacy monitoring against HBV in low resource countries [ 47 , 48 ].

Hepatitis B virus DNA (HBV DNA): testing of the DNA in serum is used to assess HBV viral replication and should be performed regularly, around 6 months in chronic HBV patients. The viral load of HBV DNA provides insight for current therapy guidelines and treatment efficacy [ 6 , 45 ]. The viral cccDNA was not usually used to detect HBV, though used to quantify the viral load [ 49 , 50 ]. The occurrence of the cccDNA in liver cells is a critical cause of difficult HBV eradication as cccDNA acts as a template for the new virions replication [ 51 ]. The absence of HBsAg and cccDNA indicates the true HBV cure [ 26 ]; however, an increase in HBV DNA concentration indicates resistance to given therapy [ 3 ].

5.2. Emerging Marker for HBV

Newer markers are continuously being sought for better diagnosis, prognostication, and treatment management. Recent markers include HBcrAg and HBV RNA.

Hepatitis B core-related antigen (HBcrAg): chronic HBV infection cannot be eliminated due to the presence of cccDNA in the nucleus of infected liver cells. The biopsy of liver cells, required to quantitate the cccDNA, is a difficult and invasive procedure. The serological marker, HBcrAg, may be an alternative non-invasive marker for intrahepatic viral replicative activity. Some studies showed a good association between the amount of HBcrAg and cccDNA in both HBeAg negative and positive patients [ 52 , 53 , 54 ] but a feeble association with HBsAg [ 55 ]. HBcrAg comprises of three proteins HBeAg, p22cr, and HBcAg, which are coded by the precore/core region and can be used in serologic testing [ 55 , 56 , 57 ]. These three proteins share an identical 149 amino acid sequence and are detectable when HBV DNA and HBsAg are undetectable [ 58 , 59 ]. HBeAg, encoded by a core gene, is a circulating peptide transformed and secreted in hepatocytes. HBcAg nucleocapsid protein surrounds viral DNA whereas p22Cr exists in HBV DNA and HBcAg negative Dane-like particles [ 58 ]. HBcrAg is a prospective alternate indicator of cccDNA and may soon turn into advantageous marker for outcome and management of the HBV related infections or diseases [ 60 ].

Hepatitis B virus RNA (HBV RNA): transcripts (HBV mRNA) act as a template for synthesis of viral proteins, therefore, mRNAs and pregenomic RNAs can act as a viral replication markers which are present in the serum of infected patients [ 61 ]. Interestingly, many commercial assays are available for the detection of HBV DNA, but no commercial assay is available to detect HBV RNA. In absence of commercial assays, in-house assays may be used to detect HBV RNA that correlates with HBV DNA in untreated patients. It is important to note that detection of HBV RNA may be influenced by variables such as the genotype and presence of mutations. Similar assumptions can be made about other diagnostic markers. The knowledge of these variables may become advantageous for future studies to make HBV RNA detection assays more reliable [ 62 , 63 ].

The HBV biomarkers, such as the hepatitis B surface antigen (HBsAg), hepatitis core antigen (anti-HBc), hepatitis B e-antigen (HBeAg), hepatitis B surface antibody (anti-HB), and hepatitis B e antibody (anti-HBe), have been used for the detection the natural infection and infection phases of HBV. However, the levels of HBV DNA play a vital role in management of HBV infection [ 64 ].

6. Common Methods for Detection of Hepatitis B Virus

The detection and quantification of the hepatitis B viral markers in body fluids is mostly carried out by enzyme-linked immunosorbent assays (ELISA), radioimmunoassay (RIA), enzyme immunoassay (EIA), polymerase chain reaction (PCR), and recently developed techniques such as microparticle enzyme immunoassay (MEIA), electrochemiluminescence immunoassay (ECLIA) and chemiluminescence microparticle immunoassay (CMIA). The advanced techniques for HBV testing are often expensive and need bulk instrumentation and trained manpower [ 15 , 65 , 66 ]. The advantages and disadvantages of the detection methods of HBV are given in Table 3 .

Advantages and disadvantages of the HBV diagnostic methods.

6.1. Point-of-Care Tests (POCT)

The fast and more accessible diagnosis of the HBV is aided by POCTs, also known as rapid diagnostic tests (RDT). These tests require 1–2 drops of sample and are easy to use, and do not require specialized training, which makes them ideal for variety of community and outreach locations. The sensitivity of these tests kits are less in comparison to other tests [ 65 ].

6.2. Dried Blood Spot (DBS)

A sampling method which aids for practical solution for the large population screening or testing in low-resource settings having limited accesses to the testing facility. A drop of patient blood by finger-prick is collected on a chemically treated paper card. The chemical on the paper card preserves the HBV marker during transport at ambient temperature from field to laboratory, where these samples are tested using advanced molecular or immunoassays [ 67 , 68 ].

7. Challenges in HBV Diagnosis

In low-resource settings or countries, collecting blood for HBV testing at a remote location usually requires additional logistic support for transport to the testing facility. HBV diagnosis usually relies on plasma or serum for most commonly available tests. Low-resource setting sub-centers usually lack centrifugation machines needed to separate plasma and serum from blood samples. Some sub-centers may have centrifugation machines, but it is difficult to arrange supplies such as consumables and power. Samples transported at room temperature do not usually affect serological test markers, but can degrade molecular markers. This issue can be overcome by using the DBS method of sample collection [ 69 , 70 , 71 , 72 ] and using point-of-care tests. These measures may help in overcoming the lack of testing and loss of follow-up in low resource settings [ 73 ]. Nucleic acid testing for screening HBV is being used extensively in the developed countries but it has limited use in low-income countries due to a higher cost and requirement of trained manpower.

8. Future Perspectives

The proper understanding and interpretation of diagnostic methods is necessary for successful therapy against hepatitis B, because different type of therapies target various markers of hepatitis B infection differently. Therefore, diagnosis based on different combinations of markers of hepatitis B infection needs to be carried out to monitor the effectiveness of a therapy. The seroclearance of HBsAg and appearance of anti-HBs [ 26 , 45 ], which further indicates protective immunity, can be diagnosed with a combination of HBs and anti-HBs markers. HBsAg serum levels and their source are different in the inactive and immune–tolerant phase and, therefore, need to be diagnosed by different strategies in these phases. In HBeAg-negative patients, less than 100 IU/mL of HBsAg may indicate gradual HBsAg clearance that can only be diagnosed together with HBeAg and HBsAg markers [ 74 ]. Both markers, along with knowledge of HBV genotypes, may also be helpful in monitoring the response of antiviral therapy. The genotypes of HBV in the infected patient is known to influence the effect of antiviral therapy [ 75 ]. Therefore, the foolproof diagnosis for HBV would be to obtain information on HBsAg, HBeAg, hepatitis B viral load, and genotypes of HBV. These four markers may help in accomplishing the WHO’s goal because these will help in determining the stage of infection and further decision-making in the type of therapy to be given.

9. Conclusions

In spite of the fact that there are several unresolved and controversial issues in the diagnosis of hepatitis B, there is a necessity for mass testing, especially in areas where hepatitis B is more prevalent and where resources are limited. There is, however, an urgent need for the development of sensitive, standardized, and validated test procedures for the detection of HBsAg, HBV DNA (in blood and in liver), and other viral markers. These tests should identify HBV S variants as well as HBsAg present within immune complexes with anti-HBs. A standard report on occult blood infections should be adopted, so that this type of infection can be reported in the future. As part of the WHO’s goal to eliminate viral hepatitis from the public health agenda by 2030, the improvement of awareness of the disease, case identification, surveillance strategies, and treatment optimization are crucial steps towards achieving this goal.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, writing—original draft preparation, and final approval M.K. and A.K.; review and editing, P.K.; Writing, review, supervision, and final approval S.P. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

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