All Health is Local: Measuring the Burden of Disease by U.S. County, Race/Ethnicity, and Socioeconomic Status

Ali H. Mokdad, Ph.D.
Chief Strategy Officer, Population Health

Professor, Health Metrics Sciences
Institute for Health Metrics and Evaluation
University of Washington, Seattle

Despite greater public awareness about the social determinants of health, health inequities in the United States remain severe. Reducing disparities in health outcomes are a persistent challenge for policymakers, public health officials, and medical professionals. Due in part to these gaps, the U.S. underperforms against other industrialized countries in key health metrics, such as overall and healthy life expectancy. The reasons that the U.S. lags behind its peers are manifold. Most importantly, however, are the health discrepancies by geographic location, race/ethnicity, and socioeconomic status (SES). Understanding and reducing disparities among those most affected must be of central interest to policymakers to ensure that every person in the U.S. can lead a healthy life. A dearth of sufficient evidence on local health patterns produced from high-quality scientific research weakens our ability to understand the problem and design interventions. A particularly pressing need is for comprehensive and comparable examination of health outcomes for individuals in the U.S. by race/ethnicity and SES at the local level.

Measuring the burden of disease at the local level in the U.S. is a tremendous undertaking with significant analytical and structural problems. A principal challenge is developing the linkages of health outcomes with location, SES, and race/ethnicity identifiers. Additionally, geographic boundaries are not stable; they shift over time as municipalities are incorporated or new counties are formed. Race/ethnicity categories used for data collection are likewise not consistent across time and location and can vary by data source. It is also common for discrepancies to exist between self-reported race/ethnicity and race/ethnicity as recorded on death certificates. There is also a need for the resources and technical capacity to conduct such work. The computational and analytical infrastructure needed to effectively incorporate all data into intensive computational approaches is massive.

The Institute for Health Metrics and Evaluation (IHME) at the University of Washington is the lead organization for the Global Burden of Disease (GBD) Study. GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to diseases and injuries for 204 countries and territories. The GBD results are presented in the context of the Socio-Demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in women younger than 25 years.

IHME has undertaken a variety of analyses within the U.S. at the subnational level aimed at improving the quantification of health disparities. These studies shed light on U.S. health disparities and their relative role in U.S. health system performance. IHME documented that many U.S. counties stagnated in their life expectancy trends,1 and in some cases, even declined. Health disparities at the county level are also significant. In Seattle and King County, for example, men face a staggering life expectancy discrepancy of 18 years.2

The project to produce disease estimates at the U.S. county level by race/ethnicity and SES continues to evolve. IHME is now collaborating with NIMHD and the U.S. Burden of Health Disparities Working Group to extend use of the full GBD analytic framework from the national and state level to produce estimates for all 3,142 U.S. counties. Disaggregating results by race/ethnicity and SES is a cornerstone of the project. This joint effort aims to improve access to health data resources, bolster analytic approaches, and to deliver user-friendly estimates to the wider health policy community.

For maximum value and impact, the quantification of health and health loss at the county level must be translated into policy. At IHME, a fledgling area of research uses GBD metrics to inform and evaluate health policy interventions. IHME is building a dynamic model to predict the impact of an intervention on a specific population within a defined geographic region. Integrating the model with GBD estimates increases the power, accuracy, and efficacy of a tool that can predict the best ways to reduce mortality, disability, prevalence, and cost in any given location for a risk factor or disease. This is a crucial step to helping policy makers, donors, ministries of health, public health workers, and others effectively select, apply, and scale up health interventions by empowering them to review and compare the relative costs, efficacy, and impact of potential interventions on combatting challenges.

The global COVID-19 pandemic has revealed stark divides in U.S. health outcomes in tragic and profound ways. One way to close the gap and address existing health inequities is through better evidence. Over the next few years, IHME, NIMHD, and the U.S. Burden of Health Disparities Working Group will be producing estimates of burden by race/ethnicity and SES for all U.S. counties. The health policy, research, and practice communities may soon be furnished with evidence to better serve different populations at the local level. This data can help us target interventions more precisely to local needs, thus putting them at our fingertips.

Dr. Mokdad was the guest presenter for the NIMHD Director’s Seminar Series (DSS) on March 11, 2021. Learn more about his presentation at the DSS website.


  1. Dwyer-Lindgren L., et al. Inequalities in Life Expectancy Among US Counties, 1980 to 2014: Temporal Trends and Key Drivers. JAMA Intern Med. 2017.
  2. Dwyer-Lindgren L., et al. Variation in life expectancy and mortality by cause among neighbourhoods in King County, WA, USA, 1990-2014: a census tract-level analysis for the Global Burden of Disease Study 2015. Lancet Public Healt 2017.

Categories: Scientific Research
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