Use of data systems to address social determinants of health: a need to do more.

Abstract

This supplement to Public Health Reports (PHR) focuses on data systems and their use in addressing social determinants of health (SDH). This particular topic requires attention now given the evidence of increasing burden and worsening inequities in some health outcomes, in spite of decades of work to change individual behaviors, as well as the need to be efficient in our use of existing data. A holistic approach to disease prevention is urgently needed to reduce the inequities that have been perpetuated in our society for so long. Despite concerted, targeted, and coordinated efforts to reduce inequities in health outcomes, gross inequities still exist,1–4 and some evidence indicates that the gap between the best health outcomes and the worst health outcomes is growing.1,3–5 Well-meaning efforts have substantially focused on individual-related behavior changes, with less focus on wider social and structural determinants of health, which can be defined as follows:6,7 Structural factors include those physical, social, cultural, organizational, community, economic, legal, or policy aspects of the environment that impede or facilitate efforts to avoid disease transmission. Social factors include the economic and social conditions that influence the health of people and communities as a whole, and include the conditions for early childhood development, education, employment, income and job security, food security, health services, and access to services, housing, social exclusion, and stigma.8 In addition to addressing individual factors, there is an urgent need to address social and structural factors and to better understand their relationship to each other as we develop effective programs and policies to reduce inequities. A holistic approach to disease prevention involves not only addressing individual, social, structural, and environmental determinants, but also working with a wide array of sectors, such as health, education, justice, environment, and labor. Additionally, it means working with diverse kinds of data, including disease surveillance, legal, land use, marketing, workforce, education, and financial. Making the best use of a wide variety of data at the individual, neighborhood, community, and county levels, for example, can provide a more complete description of the underlying factors that may influence health outcomes than using disease surveillance data alone. As a matter of fact, using disease surveillance data alone, which often are limited to variables such as disease of interest, age, sex or gender, and race/ethnicity, can be stigmatizing and only tells part of the story. Public health professionals have an obligation to fairly and accurately describe disease occurrence in populations. As a result, we should be compelled to use data from available sources to provide a complete picture of the environment in which the disease occurs and any underlying factors contributing to its occurrence. Addressing underlying factors of health has been advocated by many health practitioners for decades.1,9–12 The Institute of Medicine Committee on Public Health Strategies to Improve Health released a report in 2010 that recommended gathering, analyzing, and communicating health information that includes not only disease-outcome data, but also data on underlying factors contributing to poor health.13 In many cases, national disease surveillance systems do not include information on underlying determinants of disease, necessitating linking to existing sources of social, structural, legal, environmental, and financial data to provide a more comprehensive description of the affected population.14 This special issue of PHRaims to reflect on the types of data we routinely gather, analyze, report, and communicate, and it calls us to take a holistic approach to data use both in the sources (e.g., United Nations, Centers for Disease Control and Prevention [CDC], Census Bureau, Department of Transportation, and Department of Justice) and kinds (e.g., disease outcome, policy, financial, land use, service usage, achievement, and segregation) of data used in public health. It calls us to be good public health stewards by challenging us to move beyond our routine analyses based mostly on individual-level data and include data from other sectors and levels in the work we do. This supplement provides examples of innovative uses and analyses of data for local, state, and national governments and organizations to consider. Promoting health equity through a holistic approach is a major strategic priority of CDC's National Center for HIV/AIDS, Viral Hepatitis, STD, and TB -Prevention (NCHHSTP).15 NCHHSTP's recent white paper entitled “Establishing a Holistic Framework to Reduce Inequities in HIV, Viral Hepatitis, STDs, and Tuberculosis in the United States” calls for a systematic approach to monitoring disease by simultaneously reporting on disease outcomes and underlying factors of poor health.16 NCHHSTP is also placing more emphasis on addressing structural determinants of health, including health policy, economic and social interventions, and cross-sectoral collaborations. The articles in this supplement clearly expand the knowledge base on social determinants and data use and are examples of the holistic approach to public health suggested in the CDC white paper.