Depression and assets during the COVID-19 pandemic: A longitudinal study of mental health across income and savings groups.

The prevalence of depression in U.S. adults during the COVID-19 pandemic has been high overall and particularly high among persons with fewer assets. Building on previous work on assets and mental health, we document the burden of depression in groups based on income and savings during the first two years of the COVID-19 pandemic. Using a nationally representative, longitudinal panel study of U.S. adults (N = 1,271) collected in April-May 2020 (T1), April-May 2021 (T2), and April-May 2022 (T3), we estimated the adjusted odds of reporting probable depression at any time during the COVID-19 pandemic with generalized estimating equations (GEE). We explored probable depression-defined as a score of ≥10 on the Patient Health Questionnaire-9 (PHQ-9)-by four asset groups, defined by median income (≥$65,000) and savings (≥$20,000) categories. The prevalence of probable depression was consistently high in Spring 2020, Spring 2021, and Spring 2022 with 27.9% of U.S. adults reporting probable depression in Spring 2022. We found that there were four distinct asset groups that experienced different depression trajectories over the COVID-19 pandemic. Low income-low savings asset groups had the highest level of probable depression across time, reporting 3.7 times the odds (95% CI: 2.6, 5.3) of probable depression at any time relative to high income-high savings asset groups. While probable depression stayed relatively stable across time for most groups, the low income-low savings group reported significantly higher levels of probable depression at T2, compared to T1, and the high income-low savings group reported significantly higher levels of probable depression at T3 than T1. The weighted average of probable depression across time was 42.9% for low income-low savings groups, 24.3% for high income-low savings groups, 19.4% for low income-high savings groups, and 14.0% for high income-high savings groups. Efforts to ameliorate both savings and income may be necessary to mitigate the mental health consequences of pandemics.

Pandemic stressors and mental health indicators in eight countries.

PURPOSE: The Covid-19 pandemic has exacted a significant physical, financial, social, and emotional toll on populations throughout the world. This study aimed to document the association between pandemic stressors and mental health during the pandemic across countries that differ in cultural, geographic, economic, and demographic factors. METHODS: We administered an online survey randomly in Brazil, China, Germany, Egypt, India, Indonesia, Nigeria, and the United States from September 2020 to November 2020. This survey included questions on Covid-19-related stressors as well as the Patient Health Questionnaire-2 and the Primary Care PTSD Checklist to screen for depression and post-traumatic stress disorder (PTSD) symptoms, respectively. We performed bivariable and multivariable regression analyses to assess the prevalence and odds ratios of overall depression symptoms and probable PTSD and in relation to stressors across countries. RESULTS: Among 8754 respondents, 28.9% (95% CI 27.5-30.0%) experienced depression symptoms, and 5.1% (95% CI 4.5-6.0%) experienced probable PTSD. The highest prevalence of depression symptoms was in Egypt (41.3%, 95% CI 37.6-45.0%) and lowest in the United States (24.9%, 95% CI 22.3-27.7%). The highest prevalence of probable PTSD was in Brazil (7.3%, 95% CI 5.6-9.4%) and the lowest in China (1.2%, 95% CI 0.7-2.0%). Overall, experiencing six or more Covid-19-related stressors was associated with both depression symptoms (OR 1.90, 95% CI 1.46-2.48) and probable PTSD (OR 13.8, 95% CI 9.66-19.6). CONCLUSION: The association between pandemic related stressors and the burden of adverse mental health indicators early in the Covid-19 pandemic transcended geographic, economic, cultural, and demographic differences between countries. The short-term and long-term impacts of the pandemic on mental health should be incorporated in efforts to tackle the consequences of Covid-19.

Symptoms of Posttraumatic Stress During the COVID-19 Pandemic in the Governmental Public Health Workforce and General Population.

We aimed to estimate the prevalence of COVID-19-related posttraumatic stress symptoms (PTSS) in the governmental public health workforce and in US adults, assess differences in reporting PTSS within subgroups, and evaluate whether frontline workers reported higher levels of PTSS than persons in other jobs. We used data from 2 nationally representative studies: the 2021 Public Health Workforce Interests and Needs Survey (PH WINS) and the COVID-19 and Life Stressors Impact on Mental Health and Well-being (CLIMB) study. Our study found that the state and local governmental public health workforce was more likely to report PTSS than the general adult population. Almost a quarter of public health agency employees (24.7%) and 21.1% of adults reported at least 3 symptoms of posttraumatic stress. Differences in levels of PTSS appeared within demographic groups for both samples. Personal care and service frontline workers had 4.3 times the odds of reporting symptoms of posttraumatic stress than non-frontline workers.

Data to Improve Global Health Equity-Key Challenges.

This JAMA Forum discusses how to use data to help achieve global health equity, the challenges in using data to narrow health gaps, and ways to work toward a world with fewer health gaps.

Global Health Equity Requires Global Equity.

Many global health challenges are characterized by the inequitable patterning of their health and economic consequences, which are etched along the lines of pre-existing inequalities in resources, power, and opportunity. These links require us to reconsider how we define global health equity, and what we consider as most consequential in its pursuit. In this article, we discuss the extent to which improving underlying global equity is an essential prerequisite to global health equity. We conclude that if we are to improve global health equity, there is a need to focus more on foundational-rather than proximal-causes of ill health and propose ways in which this can be achieved.

Economic precarity, loneliness, and suicidal ideation during the COVID-19 pandemic.

The US population faced stressors associated with suicide brought on by the COVID-19 pandemic. Understanding the relationship between stressors and suicidal ideation in the context of the pandemic may inform policies and programs to prevent suicidality and suicide. We compared suicidal ideation between two cross-sectional, nationally representative surveys of adults in the United States: the 2017-2018 National Health and Nutrition Examination Survey (NHANES) and the 2020 COVID-19 and Life Stressors Impact on Mental Health and Well-being (CLIMB) study (conducted March 31 to April 13). We estimated the association between stressors and suicidal ideation in bivariable and multivariable Poisson regression models with robust variance to generate unadjusted and adjusted prevalence ratios (PR and aPR). Suicidal ideation increased from 3.4% in the 2017-2018 NHANES to 16.3% in the 2020 CLIMB survey, and from 5.8% to 26.4% among participants in low-income households. In the multivariable model, difficulty paying rent (aPR: 1.5, 95% CI: 1.2-2.1) and feeling alone (aPR: 1.9, 95% CI: 1.5-2.4) were associated with suicidal ideation but job loss was not (aPR: 0.9, 95% CI: 0.6 to 1.2). Suicidal ideation increased by 12.9 percentage points and was almost 4.8 times higher during the COVID-19 pandemic. Suicidal ideation was more prevalent among people facing difficulty paying rent (31.5%), job loss (24.1%), and loneliness (25.1%), with each stressor associated with suicidal ideation in bivariable models. Difficulty paying rent and loneliness were most associated with suicidal ideation. Policies and programs to support people experiencing economic precarity and loneliness may contribute to suicide prevention.

Pandemic preparedness and response: exploring the role of universal health coverage within the global health security architecture.

In response to the COVID-19 pandemic, several international initiatives have been developed to strengthen and reform the global architecture for pandemic preparedness and response, including proposals for a pandemic treaty, a Pandemic Fund, and mechanisms for equitable access to medical countermeasures. These initiatives seek to make use of crucial lessons gleaned from the ongoing pandemic by addressing gaps in health security and traditional public health functions. However, there has been insufficient consideration of the vital role of universal health coverage in sustainably mitigating outbreaks, and the importance of robust primary health care in equitably and efficiently safeguarding communities from future health threats. The international community should not repeat the mistakes of past health security efforts that ultimately contributed to the rapid spread of the COVID-19 pandemic and disproportionately affected vulnerable and marginalised populations, especially by overlooking the importance of coherent, multisectoral health systems. This Health Policy paper outlines major (although often neglected) gaps in pandemic preparedness and response, which are applicable to broader health emergency preparedness and response efforts, and identifies opportunities to reconceptualise health security by scaling up universal health coverage. We then offer a comprehensive set of recommendations to help inform the development of key pandemic preparedness and response proposals across three themes-governance, financing, and supporting initiatives. By identifying approaches that simultaneously strengthen health systems through global health security and universal health coverage, we aim to provide tangible solutions that equitably meet the needs of all communities while ensuring resilience to future pandemic threats.

What matters for health? Public views from eight countries.

INTRODUCTION: Despite growing scholarship on the social determinants of health (SDoH), wider action remains in its early stages. Broad public understanding of SDoH can help catalyse such action. This paper aimed to document public perception of what matters for health from countries with broad geographic, cultural, linguistic, population composition, language and income level variation. METHODS: We conducted an online survey in Brazil, China, Germany, Egypt, India, Indonesia, Nigeria and the USA to assess rankings of what respondents thought matters for health and what they perceived decision makers think matters for health. We analysed the percentages of each determinant rated as the most important for good health using two metrics: the top selection and a composite of the top three selections. We used two-tailed χ2 test for significance testing between groups. RESULTS: Of 8753 respondents, 56.2% (95% CI 55.1% to 57.2%) ranked healthcare as the most important determinant of good health using the composite metric. This ranking was consistent across countries except in China where it appeared second. While genetics was cited as the most important determinant by 22.3% (95% CI 21.5% to 23.2%) of the overall sample with comparable rates in most countries, the percentage increased to 33.3% (95% CI 30.5% to 36.3%) in Germany and 35.9% (95% CI 33.0% to 38.8%) in the USA. Politics was the determinant with the greatest absolute difference (18.5%, 95% CI 17.3% to 19.6%) between what respondents considered matters for health versus what they perceived decision makers think matters for health. CONCLUSION: The majority of people consider healthcare the most important determinant of health, well above other social determinants. This highlights the need for more investment in communication efforts around the importance of SDoH.

Data Sources for Understanding the Social Determinants of Health: Examples from Two Middle-Income Countries: the 3-D Commission.

The expansion in the scope, scale, and sources of data on the wider social determinants of health (SDH) in the last decades could bridge gaps in information available for decision-making. However, challenges remain in making data widely available, accessible, and useful towards improving population health. While traditional, government-supported data sources and comparable data are most often used to characterize social determinants, there are still capacity and management constraints on data availability and use. Conversely, privately held data may not be shared. This study reviews and discusses the nature, sources, and uses of data on SDH, with illustrations from two middle-income countries: Kenya and the Philippines. The review highlights opportunities presented by new data sources, including the use of big data technologies, to capture data on social determinants that can be useful to inform population health. We conducted a search between October 2010 and September 2020 for grey and scientific publications on social determinants using a search strategy in PubMed and a manual snowball search. We assessed data sources and the data environment in both Kenya and the Philippines. We found limited evidence of the use of new sources of data to study the wider SDH, as most of the studies available used traditional sources. There was also no evidence of qualitative big data being used. Kenya has more publications using new data sources, except on the labor determinant, than the Philippines. The Philippines has a more consistent distribution of the use of new data sources across the HEALTHY determinants than Kenya, where there is greater variation of the number of publications across determinants. The results suggest that both countries use limited SDH data from new data sources. This limited use could be due to a number of factors including the absence of standardized indicators of SDH, inadequate trust and acceptability of data collection methods, and limited infrastructure to pool, analyze, and translate data.

Housing as a Social Determinant of Health: Evidence from Singapore, the UK, and Kenya: the 3-D Commission.

Housing is a paradigmatic example of a social determinant of health, as it influences and is influenced by structural determinants, such as social, macroeconomic, and public policies, politics, education, income, and ethnicity/race, all intersecting to shaping the health and well-being of populations. It can therefore be argued that housing policy is critically linked to health policy. However, the extent to which this linkage is understood and addressed in public policies is limited and highly diverse across and within countries. This analysis seeks to describe the linkages between housing policies and health and well-being using examples from three countries at different levels of the wealth spectrum: Singapore, the UK, and Kenya.We conducted a comparative policy analysis across three country contexts (Singapore, the UK, and Kenya) to document the extent to which housing policies address health and well-being, highlighting commonalities and differences among them. To guide our analysis, we used the United Nations (UN) definition of adequate housing as it offers a broad framework to analyze the impact of housing on health and well-being.The anatomy of housing policies has a strong correlation to the provision of adequate housing across Singapore, the UK, and Kenya, especially for vulnerable groups. The paper demonstrates that contextual factors including population composition (i.e., aging versus youthful), political ideologies, legal frameworks (i.e., welfare versus market-based provision of housing), and presence (or absence) of adequate, quality, timely, reliable, robust data systems for decision-making, which are taken up by stakeholders/state, have strong implications of the type of housing policies developed and implemented, in turn directly and indirectly impacting the overall health and well-being of populations.This analysis demonstrates the value of viewing housing policies as public health policies that could significantly impact the health and well-being of populations, especially vulnerable groups. Moreover, the findings highlight the importance of the Health in All Policies approach to facilitate integrated policy responses to address social determinants of health such as housing. This is more critical than ever, given the context of the global pandemic that has led to worsening overall health and well-being.

Use of Data to Understand the Social Determinants of Depression in Two Middle-Income Countries: the 3-D Commission.

Depression accounts for a large share of the global disease burden, with an estimated 264 million people globally suffering from depression. Despite being one of the most common kinds of mental health (MH) disorders, much about depression remains unknown. There are limited data about depression, in terms of its occurrence, distribution, and wider social determinants. This work examined the use of novel data sources for assessing the scope and social determinants of depression, with a view to informing the reduction of the global burden of depression.This study focused on new and traditional sources of data on depression and its social determinants in two middle-income countries (LMICs), namely, Brazil and India. We identified data sources using a combination of a targeted PubMed search, Google search, expert consultations, and snowball sampling of the relevant literature published between October 2010 and September 2020. Our search focused on data sources on the following HEALTHY subset of determinants: healthcare (H), education (E), access to healthy choices (A), labor/employment (L), transportation (T), housing (H), and income (Y).Despite the emergence of a variety of data sources, their use in the study of depression and its HEALTHY determinants in India and Brazil are still limited. Survey-based data are still the most widely used source. In instances where new data sources are used, the most commonly used data sources include social media (twitter data in particular), geographic information systems/global positioning systems (GIS/GPS), mobile phone, and satellite imagery. Often, the new data sources are used in conjunction with traditional sources of data. In Brazil, the limited use of new data sources to study depression and its HEALTHY determinants may be linked to (a) the government's outsized role in coordinating healthcare delivery and controlling the data system, thus limiting innovation that may be expected from the private sector; (b) the government routinely collecting data on depression and other MH disorders (and therefore, does not see the need for other data sources); and (c) insufficient prioritization of MH as a whole. In India, the limited use of new data sources to study depression and its HEALTHY determinants could be a function of (a) the lack of appropriate regulation and incentives to encourage data sharing by and within the private sector, (b) absence of purposeful data collection at subnational levels, and (c) inadequate prioritization of MH. There is a continuing gap in the collection and analysis of data on depression, possibly reflecting the limited priority accorded to mental health as a whole. The relatively limited use of data to inform our understanding of the HEALTHY determinants of depression suggests a substantial need for support of independent research using new data sources. Finally, there is a need to revisit the universal health coverage (UHC) frameworks, as these frameworks currently do not include depression and other mental health-related indicators so as to enable tracking of progress (or lack thereof) on such indicators.

Food, Big Data, and Decision-making: a Scoping Review-the 3-D Commission.

Food is an important determinant of health, featuring prominently in the Sustainable Development Goals. The term "big data" is seldom used in relation to food, partly because food data are scattered across different sectors. The increasing availability of food-related data presents an opportunity to glean new insights on food and food systems. These insights may enhance the quality of products and services and improve decision-making on optimizing food availability, all to the end of producing better health. Yet, knowledge gaps remain about the unique opportunities and challenges linked to big data on food and their use in decision-making. This scoping review explored the available literature linking food with big data and decision-making, using the following research question: What is the current literature on data about food, and how are these data used in decision-making? We searched PubMed until 29 February 2020 and Embase, Web of Sciences, and the Cochrane Database of Systematic Reviews until 8 March 2020. We included studies written in English and conducted narrative analyses to identify relevant themes from included studies. Sixteen studies fulfilled our eligibility criteria, including big data analyses, modelling studies, and reviews. These studies described the added value of using big data and how evidence from big data had or can be used for decision-making, as well as challenges and opportunities for such use. The majority of the included studies examined the link between food and big data, while hypothesizing of how these insights could inform decision-making, including policies, interventions, programs, and financing. There were only two examples wherein big data on food informed decision-making directly. The review highlights several false dichotomies in how the subject is approached in the literature and the importance of context, both between and within countries, in shaping the availability and types of data that can be used as meaningful evidence to inform decision-making. This review shows the paucity of research around the intersection of food, big data, and decision-making, as well as the potential in using big data on food systems to the end of informing decisions to improve the health of populations. Future research and decision-making around health systems can benefit from examining the full spectrum of perspectives on the subject. Future research and decision-making around health systems can also employ the steadfast embrace of technology, which will potentially reduce disparities in big data availability, to the end of improving the health of populations.

Data, Social Determinants, and Better Decision-making for Health: the 3-D Commission.

More than a decade after the World Health Organization Commission on the Social Determinants of Health (SDoH), it is becoming widely accepted that social and economic factors, including but not limited to education, energy, income, race, ethnicity, and housing, are important drivers of health in populations. Despite this understanding, in most contexts, social determinants are not central to local, national, or global decision-making. Greater clarity in conceptualizing social determinants, and more specificity in measuring them, can move us forward towards better incorporating social determinants in decision-making for health. In this paper, first, we summarize the evolution of the social framing of health. Second, we describe how the social determinants are conceptualized and contextualized differently at the global, national, and local levels. With this, we seek to demonstrate the importance of analyzing and understanding SDoH relative to the contexts in which they are experienced. Third, we problematize the gap in data across contexts on different dimensions of social determinants and describe data that could be curated to better understand the influence of social determinants at the local and national levels. Fourth, we describe the necessity of using data to understand social determinants and inform decision-making to improve health. Our overall goal is to provide a path for our collective understanding of the foundational causes of health, facilitated by advances in data access and quality, and realized through improved decision-making.

Energy, Data, and Decision-Making: a Scoping Review-the 3D Commission.

Access to energy is an important social determinant of health, and expanding the availability of affordable, clean energy is one of the Sustainable Development Goals. It has been argued that climate mitigation policies can, if well-designed in response to contextual factors, also achieve environmental, economic, and social progress, but otherwise pose risks to economic inequity generally and health inequity specifically. Decisions around such policies are hampered by data gaps, particularly in low- and middle-income countries (LMICs) and among vulnerable populations in high-income countries (HICs). The rise of "big data" offers the potential to address some of these gaps. This scoping review sought to explore the literature linking energy, big data, health, and decision-making.Literature searches in PubMed, Embase, and Web of Science were conducted. English language articles up to April 1, 2020, were included. Pre-agreed study characteristics including geographic location, data collected, and study design were extracted and presented descriptively, and a qualitative thematic analysis was performed on the articles using NVivo.Thirty-nine articles fulfilled eligibility criteria. These included a combination of review articles and research articles using primary or secondary data sources. The articles described health and economic effects of a wide range of energy types and uses, and attempted to model effects of a range of technological and policy innovations, in a variety of geographic contexts. Key themes identified in our analysis included the link between energy consumption and economic development, the role of inequality in understanding and predicting harms and benefits associated with energy production and use, the lack of available data on LMICs in general, and on the local contexts within them in particular. Examples of using "big data," and areas in which the articles themselves described challenges with data limitations, were identified.The findings of this scoping review demonstrate the challenges decision-makers face in achieving energy efficiency gains and reducing emissions, while avoiding the exacerbation of existing inequities. Understanding how to maximize gains in energy efficiency and uptake of new technologies requires a deeper understanding of how work and life is shaped by socioeconomic inequalities between and within countries. This is particularly the case for LMICs and in local contexts where few data are currently available, and for whom existing evidence may not be directly applicable. Big data approaches may offer some value in tracking the uptake of new approaches, provide greater data granularity, and help compensate for evidence gaps in low resource settings.

Improving Decision-Making for Population Health in Nonhealth Sectors in Urban Environments: the Example of the Transportation Sector in Three Megacities-the 3-D Commission.

Noncommunicable diseases (NCDs) represent a significant global public health burden. As more countries experience both epidemiologic transition and increasing urbanization, it is clear that we need approaches to mitigate the growing burden of NCDs. Large and growing urban environments play an important role in shaping risk factors that influence NCDs, pointing to the ineluctable need to engage sectors beyond the health sector in these settings if we are to improve health. By way of one example, the transportation sector plays a critical role in building and sustaining health outcomes in urban environments in general and in megacities in particular. We conducted a qualitative comparative case study design. We compared Bus Rapid Transit (BRT) policies in 3 megacities-Lagos (Africa), Bogotá (South America), and Beijing (Asia). We examined the extent to which data on the social determinants of health, equity considerations, and multisectoral approaches were incorporated into local politics and the decision-making processes surrounding BRT. We found that all three megacities paid inadequate attention to health in their agenda-setting, despite having considerable healthy transportation policies in principle. BRT system policies have the opportunity to improve lifestyle choices for NCDs through a focus on safe, affordable, and effective forms of transportation. There are opportunities to improve decision-making for health by involving more available data for health, building on existing infrastructures, building stronger political leadership and commitments, and establishing formal frameworks to improve multisectoral collaborations within megacities. Future research will benefit from addressing the political and bureaucratic processes of using health data when designing public transportation services, the political and social obstacles involved, and the cross-national lessons that can be learned from other megacities.