Home Research Research Library Impact of Community Health Center Losses on County-Level Mortality: A Natural Experiment in the United States, 2011–2019 Impact of Community Health Center Losses on County-Level Mortality: A Natural Experiment in the United States, 2011–2019 2025 Author(s) Basu, Sanjay, Phillips, Robert L, and Hoang, Hank Topic(s) Achieving Health System Goals, and What Family Physicians Do Volume Health Services Research Source Health Services Research Objective To estimate the effect of Community Health Center (CHC) site losses on county-level mortality rates. Study Setting and Design We conducted a natural experiment study using difference-in-differences analysis of propensity score–matched US counties from 2011 through 2019. Data Sources and Analytic Sample The study included 3142 US counties, with 177 counties experiencing CHC site losses in 2014, per data from the health resources and services administration. Principal Findings Loss of CHC sites was associated with an increase in age-adjusted all-cause mortality of 3.54 deaths per 100 000 population (95% CI: 1.19, 5.90; p = 0.003) in the year following the loss. The largest increase was observed for cancer mortality (2.61 per 100 000; 95% CI: 0.59, 4.62; p = 0.011). Primary care physician density and patient volume loss both mediated the relationship. Conclusions CHC site losses were associated with increases in mortality. Preserving CHC access may be important for maintaining population health, particularly in underserved areas. ABFM Research Read all 2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone 1990 American Board of Family Practice statistics Go to American Board of Family Practice statistics 2020 Primary Care Spending in the United States, 2002-2016 Go to Primary Care Spending in the United States, 2002-2016 2019 Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models Go to Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models
Author(s) Basu, Sanjay, Phillips, Robert L, and Hoang, Hank Topic(s) Achieving Health System Goals, and What Family Physicians Do Volume Health Services Research Source Health Services Research
ABFM Research Read all 2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone 1990 American Board of Family Practice statistics Go to American Board of Family Practice statistics 2020 Primary Care Spending in the United States, 2002-2016 Go to Primary Care Spending in the United States, 2002-2016 2019 Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models Go to Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models
2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone
2020 Primary Care Spending in the United States, 2002-2016 Go to Primary Care Spending in the United States, 2002-2016
2019 Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models Go to Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models