Real world data: a health equity lens for research

Health inequalities around the world create staggering differences in size. For example, life expectancy at birth is between 55 and 85 years, depending on the country of birth. Linked to degrees of social disadvantage, these profound differences can be seen across the borders of a single country, including the United States. Emerging from the circumstances in which people grow, live, work, and age and are established to deal with disease, these health inequalities are profound, but they can be resolved with an effort to understand and intervene in their origins.

It is a way of judging the development of societies by the correct distribution of health across the social spectrum and how societies protect individuals from the disadvantages of disease. In order to achieve health equity, we must first take steps to understand the social determinants of health (SDoH) that affect the unfair distribution of disease. In assessing SDoH, one of the main research objectives is to find out how health is different among different groups of patients defined by different social characteristics. The second step is to take advantage of these findings to investigate how to intervene in these significant factors or their consequences in order to equalize the observed differences.

Historically, randomized controlled trials (RCTs) have been considered the gold standard for generating evidence of comparative efficacy. However, most trials are poorly positioned to address health equity issues, including addressing differences in access to and effectiveness of treatment across different populations, including those defined by age, gender, race, and ethnicity. Analyzes of the composition of randomized trial populations indicate that groups of patients defined by sex, age, race / ethnicity are often highlighted. As an example, a systematic review of ECTs for patients with cardiovascular disease, referred to in the American Health Association’s practice guidelines, revealed that 80-85% of these patients were white and approximately 70% were male.

In April, the U.S. Food and Drug Administration (FDA) released a guidance document to improve test enrollment for underprivileged races and ethnic groups. Although the document addresses racial and ethnic diversity, the FDA defines a much broader set of underrepresentation groups — including gender, gender identity, age, socioeconomic status, disability, pregnancy, breastfeeding status, and other clinical features — and promotes sponsorships. to enrich test populations through these axes.

Advantages of Real World Data for Health Equity Research

Although trial design will evolve to be more relevant to representative patient populations, real-world data (RWD) provides a powerful immediate tool for analyzing health equity. These are any data that is commonly collected in relation to the patient’s health status and / or the provision of health care. RWD can be extracted from electronic health records (EHRs), claims, reported patient outcomes (PROs), registry data, and many other sources. Due to its potential for breadth, richness, and high multidimensional breadth, these data offer strong advantages and efficiencies for health equity research, including:

  • Patient representation and heterogeneity: Using RWD, researchers can study the effects or access of treatment in populations that are representative of real-world practice and in a variety of populations that are not normally included in trials. Wider generalization is expected in age, race / ethnicity, socioeconomic status, disability, and other clinical features, among others. The data provide significant opportunities to reveal differences in results and access. Heterogeneity may also exhibit safety and efficacy effects that are not apparent in randomized test environments.
  • Efficiency: RWD allows larger sample sizes to be analyzed in subgroups; the ability to create perspectives quickly; and greater statistical capacity to accurately characterize represented populations. Analyzes can examine complex networks of variables, including the simultaneous analysis of multiple SDoHs, their relative contributions, and their potential for interaction.
  • Various data sources enriched with SDoH: Ability to link patients deterministically across data sources to characterize social, behavioral, and clinical determinants of health. RWD can also be linked to other non-clinical data sets, such as employment records.

A case in point: Take advantage of RWD to achieve Breast Health Equity

Breast cancer has one of the largest racial differences observed in mortality and is a priority for understanding the role of SDoH in health inequalities in care and outcomes. OM1 is working with a leading mammography device company on a health-focused RWD platform in minimal and high-risk subgroups that study the comparative effectiveness of 3D and 2D mammography, including black and Asian women.

This study draws from a mix of more than a million women and millions of screen-targeted academic enrollment sites. Due to the design of the study, the cohort has a higher proportion of black women than the U.S. population and is significantly more represented than clinical trials of breast cancer screening modalities. One of the latest findings from this platform is that black women have reduced access to cutting-edge 3D mammography technology compared to conventional women for regular screening. In addition, the comparative efficacy of 3D and 2D mammography was analyzed in subgroups defined by race and ethnicity, and demonstrated that although black women have reduced access, they were less likely to benefit from improved screening results on 3D mammography than white women.

Beyond the case study, SDoH data can be analyzed in almost all disease areas (e.g., mental health, cardiology, and dermatology). SDoH analyzes can measure and track differences in patients ’geographic location or health system location. SDoH analyzes can also be used to facilitate targeted interventions to target areas where patients may have a particular need or barrier to access. SDoH analyzes can also provide insight into the factors that cause the observed differences. By a priori naming hypotheses about potential pathways or mechanisms underlying observed health inequalities, important data elements can be gathered to design analyzes that investigate the potential contribution of multiple variables, such as income and education, and so on.

Be aware when working with SDoH data

It is important to be clear when researching the relationships between SDoH data, such as race, and different health outcomes, that there is a broad consensus that these are social constructs and not biological determinants. Humans are very similar to each other genetically and the part of genetic variation that occurs within groups is as large as the part that occurs between groups defined by race or ethnicity.

Because of the long and embarrassing history of the misuse of racial classifications in research and medicine, there are disagreements over whether to continue to collect and use race in the U.S. for research purposes, and if so, how to use it. It is noteworthy that there are significant differences in how SDoH is collected and analyzed for health around the world. France does not collect census or other data on the race (or ethnicity) of its citizens; this limits the ability to assess differences by race. In the UK, rather than race / ethnicity, health inequalities are assessed by social class. However, significant health-related differences in many health conditions in the United States were observed in health outcomes. A health equity lens promotes understanding of these aspects by carefully collecting data on SDoH and designing equity research in the service of identifying interventions to address health-related inequalities.

Additional considerations include recognizing diversity within racial groups; and study carefully how to define and group group categories by race / ethnicity. It is also important to analyze and determine why race information should be collected during the design phase of the research (e.g., due to a previously documented difference); and describe how the race was measured (coded vs. self-reported by observers, number and categories of categories, whether multiple responses were allowed). Although SDoH research often focuses on “one variable at a time,” in the research design phase, researchers should consider the full set of SDoH required to fully explore associations and investigate possible hypotheses, including possible explanatory mechanisms. To this end, it is often important to consider whether data collection could include measures of racism, social class, culture, ancestry, migration history, language, and genetic variation (if any) to determine the basis for observed differences.

The future of health equity

With careful attention to access to high-quality data sources and robust scientific methodological methods, RWD and SDoH data are designed to provide a tremendous opportunity to address gaps in attention, access, and treatment sensitivity. Using a set of characteristics that are significant determinants of health outcomes, we can begin to understand the underlying causes of health inequalities and begin to develop policies and therapies in a more personalized and equitable manner.


Bridging the gap in a generation: health equity through action on social determinants of health – Final report of the Committee on Social Determinants of Health. WHO / IER / CSDH / 08.1. August 27, 2008

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