What We're Reading

Krieger N. Measures of racism, sexism, heterosexism, and gender binarism for health equity research: from structural injustice to embodied harm - an ecosocial analysis. Annu Rev Public Health. 2020;41:4.1-4.26. 

 

The reason to study injustice is not to prove that injustice is wrong; it is by definition. Instead, the purpose is to understand how injustice shapes population health, contest narratives that naturalize inequities, and generate evidence for change and accountability.

 

Health equity can be measured in research using structural measures and individual measures. Structural measures include explicit laws and rules (e.g. bans on same sex relationships), nonexplicit laws and rules which lead to inequities (e.g. the war on drugs), and area-based or institutional non-rule measures representing the inferred result of a rule based structural injustice (population based data examining differences between groups on employment, access to healthcare, etc.). Individual measures include exposure (e.g., self-report of exposure) or internalized views of the privileged group.

A roadmap for promoting health equity and eliminating disparities: the four I's for health equity. September 2017. National Quality Forum. 

The National Quality Forum (NQF) defines domains of health equity performance measurement into 5 domains:

• High quality care
• Access to care
• Structure for equity
• Culture of equity
• Partnerships and collaboration
  

The high quality care and access to care domains are particularly applicable for accountability purposes; measures in the other domains may be primarily used for internal quality improvement.

The NQF did an environmental scan of disparities-sensitive and health equity measures and found 886 performance measures. Of these, the majority were in the high-quality care (755 measures) or access to care categories, and the fewest measures were available for collaboration and partnerships. While most measures were in the high quality care domain, few are currently being utilized to assess disparities.

Comments from discussion participants:

• It is hard to measure equity. It is still worth it.
• We don’t have the tools yet to do this work. It will take time to figure out how to measure it, and then longer for how best to act on it. There is a need for a rapid cycle process to move things forward.
• It is important to have a process to decommission measures – i.e. not continuing to measure things that are of little importance.
• Definitions for equity have traditionally not been broad enough to include rural and the intersectionality of race and immigration status with rurality.
• We more often look at improving the outlook for a group with disparities in outcomes rather than decreasing the disparity itself.
• A push for “equity” presumes the people on top are getting a good outcome that should be available to everyone. That is not true in much of our health system. Outcome and cost improvements are needed across the board.
• Consider developing alternative payment models that reward short term measures and show progress over time in long term measures.

A roadmap for promoting health equity and eliminating disparities: the four I's for health equity. September 2017. National Quality Forum. 

Recommendations and considerations from the National Quality Forum (NQF) on social risk factor data collection and prioritization of measures include:

• Use electronic health record data rather than claims when possible (resource: National Academies of Medicine report on Capturing Social and Behavioral Domains and Measures in Electronic Health Records)
• Data collection could be incentivized by payers 
• Social risk factor data collection should occur at the community level in addition to the individual and household/family level
• Prioritization of measures can include consideration of prevalence of risk factors, impact of a condition, size of disparity, strength of evidence, and ease and feasibility of making an improvement 

Recommendations and considerations on redesigned approaches to payment include:

• Include upfront funds for infrastructure to address the social determinants of health
• Consider pay for performance for decreasing disparities in quality and access
• Ensure organizations disproportionately serving individuals with social risk factors can compete in value based purchasing by using peer group comparisons and tying incentives to the size of the disparity group
• Provide additional payment for inpatient and outpatient services for patients with social risk factors (e.g., adjusting diagnosis-related group payments for homelessness to account for additional discharge planning needs)
• Fund care delivery and payment reform demonstration projects to specifically reduce disparities (resource: Advancing Health Equity: Leading care, payment, and systems transformation)

Saha S, Cohen BB, Nagy J, McPherson ME, Phillips R. Well-being in the nation: a living library of measures to drive multi-sector population health improvement and address social determinants. Milbank Quarterly. 2020;98(3):641-663. 

The WIN measures include 9 core measures to be used across initiatives:

• Wellbeing of people: person-reported wellbeing (e.g., Cantril’s ladder), life expectancy
• Wellbeing of places: child poverty, healthy community indices (e.g. county health rankings)
• Equity: differences in wellbeing, years of life lost, income inequality (e.g., Gini index), high school graduation rates, demographic variables used in a standard way for equity analysis (race, zip code, gender, language, rural status)
  

Additional measures to be used depending on the initiative are organized into leading indicators or flexible expanded sets (promising measures that require additional research or data) in several categories: community vitality, economy, education, environment and infrastructure, equity, food and agriculture, health, housing, public safety, transportation, wellbeing, and demographics.

The WIN measures are intended to be a living library, updated as additional research and data becomes available.

Stiglitz JE. GDP is the wrong tool for measuring what matters. Scientific American. August 1, 2020. 

GDP is a measure of market activity. It does not reflect health, education, equality of opportunity, state of the environment, or even the state or sustainability of the economy. Indeed, a singular focus on GDP has led to using resources more efficiently in the short term while compromising long term impacts on the economy and society.  The GDP includes goods and services of limited value and excludes others that are free but valuable to society such as unpaid caregiving by family members. It gives equal value to energy production from renewable sources such as solar or wind and from sources that cause environmental degradation such as coal.

Expert economists recommend “dethroning” GDP and instead using a dashboard of a set of metrics for health, sustainability, and other values the people of the nation aspire to – and harms they wish to diminish (e.g., inequality). Trying to use one single measure loses too much information in aggregation. The Organization for Economic Cooperation and Development’s Better Life initiative includes 11 categories of indicators: housing, income, jobs, community, education, environment, civic engagement, health, life satisfaction, safety, and work life balance. New Zealand embedded well being indicators into their budgetary processes starting in 2019.