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.