A Typology For Health Equity Measures

After George Floyd’s murder in May 2020, state governments and our health care system have increased attention to racism and its impact on health equity. There has also been heightened awareness that health equity is a problem for communities other than racial and ethnic minorities, including persons with disabilities, people residing in rural communities, non-binary and/or non-cisgender people, and others.

With this elevation of health equity as a priority concern came a need to better understand it through measurement and to be able to assess the effectiveness of efforts to improve equity. 

Almost immediately, two problems became apparent. First, there is no national standard method for capturing data on key variables needed to assess health equity. Some organizations, such as the Office of Management and Budget, the Health Resources and Services Administration, and the National Committee for Quality Assurance, have developed guidelines for collection of race and ethnicity data, but they are not always consistent. Furthermore, there is even less consensus on what categories to use to capture data on other variables, such as language, sexual orientation, gender, and disability status. Second, there are no standard measures for assessing performance in improving health equity. The latter are essential to creating accountability and transparency for health equity.

This article begins to address the second problem by suggesting a typology of health equity measures. We offer this typology with the hope that others can build from this work, and we can move toward a better understanding of how to measure health equity and, from there, improve health equity by reducing disparities in performance across groups.

We suggest that there are four types of health equity performance measures:

  1. Data infrastructure
  2. Process and outcome measures stratified by subpopulation
  3. Process and outcomes measures targeted at specific subpopulations
  4. Process and outcome measures targeted at strategies intended to reduce inequities

Data Infrastructure

As noted earlier, the lack of standardized variables to assess equity across populations is an enormous impediment to advancing health equity. Data infrastructure measures assess how providers, payers, and states capture information such as race, ethnicity, and language (REL) data; disability status data; and sexual orientation and gender identity (SOGI) data. These data infrastructure measures could specify that data are collected using a standard categorization model and are member/patient reported. It is important that staff who collect REL, disability status, and SOGI data are asking for this information in a culturally sensitive manner so as to improve collection rates and avoid perpetuating harm. 

Examples: percentage of insurer members for which the insurer has complete member-reported REL data, percentage of patients attributed to a provider with complete patient-reported SOGI data.

Process And Outcome Measures Stratified By Subpopulation

This category represents the most commonly identified method for assessing health equity. It involves taking existing measures and stratifying them by REL, SOGI, disability status, rurality, and other variables. Many states, insurers, and provider organizations have begun to perform this type of measurement. Unfortunately, their efforts are constrained by the incompleteness and lack of integrity of the underlying data by which they are stratifying. Entities will also be limited by the challenges surrounding how data are shared and verified across entities. For example, it is not clear which source is correct when a provider organization and an insurer have different race/ethnicity information for an individual. Given challenges associated with clinical data collection and reporting, it may be easier to focus on stratifying process measures before moving to outcome measures that aim to reduce disparities across populations. 

Examples: colorectal cancer screening rates by disability status, c-section rates by race, and ethnicity.

Process And Outcomes Measures Targeted At Specific Subpopulations

This category includes measures with a denominator defined as the subpopulation of interest. This type of measure doesn’t focus on identifying disparities but rather on assessing care for populations with known disparities. Its purpose is subtly different from the prior category in that it is intended to support quality improvement rather than inequity reduction. 

Examples: emergency department utilization rate for persons with serious mental illness, appropriate antibiotic prophylaxis for children with sickle cell anemia.

Process And Outcome Measures Targeted At Strategies Intended To Reduce Inequities

Finally, there are measures that focus on assessing performance for interventions intended to reduce inequities. Over time, these measures evolve from assessing whether patients receive the intervention to measuring the impact of those actions. 

Examples: percentage of patients screened for social risk factors, percentage of members with timely and appropriate access to translator services, improved health outcomes following receipt of services for an identified need.

Envisioning A System With Equitable Outcomes For All Populations

We put forth the aforementioned typology to help states, insurers, and providers to think about the different ways to approach health equity measurement and to recognize that different types of measures can support different uses and ends. A comprehensive approach to health equity improvement will necessitate multiple types of measures, with the focus over time (hopefully) moving away from data infrastructure and process measures to more outcome-focused measures. We hope that using health equity measures first to assess and then to reduce inequities in health care will lead to a system with equitable outcomes for all populations.

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