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.

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.

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.