Reducing primary care physician shortage in low-density areas increases life expectancy

1. Reducing Primary Care Physicians (PCP) shortages in low-density areas increased the mean life expectancy of patients.

2. The addition of PCPs in low-density areas was shown to eliminate the life expectancy gap between low- and high-density counties.

Evidence Rating Level: 2 (Good)

Study Rundown: Primary care physician density is associated with the degree of health outcomes in the community. This study estimated the effect of alleviating PCP shortages, in low-density counties, on life expectancy. The study determined patients living in low-density PCP areas had a shorter average life expectancy compared to patients living in higher-density PCP areas. Furthermore, by reducing the PCP-density gap in the low-density communities, the average all-cause mortality rate in low-density areas was lowered. This study provided a quantified projection of the benefit low-density counties would receive from an increase in the PCP-to-patient ratio. The statistical modeling was robust as demonstrated by a comparison done of the primary modeling to alternative modeling methods and sensitivity analyses. The study is limited by the observational nature and inability to randomize PCP densities to counties. Nonetheless, the study’s results are significant by demonstrating the positive impact of increasing PCP densities.

Click to read the study in Annals of Internal Medicine

Relevant Reading: Association of Primary Care Physician Supply With Population Mortality in the United States, 2005-2015

In-Depth [retrospective cohort]: This study was a statistical modeling study using retrospective data from 2017. From the 3,143 counties in the United States, the study included 3,104 counties for which there were complete data. Primary Care Physicians were defined as non-resident physicians less than 75 years of age with a general practice. The investigators fit the data into three models: a generalized additive model (GAM), a mixed-effects model, and a generalized estimating equations (GEE) model. The GAM model was ultimately used as it was the most appropriate for capturing the nonlinear relationship between life expectancy and PCP density. From the 3,104 counties, 1,218 counties were categorized as low-density areas, while 1,886 counties were deemed high-density areas. A low-density area was defined as a county with less than one PCP per 3,500 persons in 2017.  The primary outcome was average life expectancy. The average life expectancy in low-density areas was 77.3 years (median, 77.4 years; interquartile range [IQR], 75.6 to 79.0 years) compared to 78.1 years in the high-density areas (median,78.4 years; IQR, 76.5 to 79.9 years). The model projected improving low-density counties to the 1:3,500 benchmark would increase life expectancy by 22.4 days (median = 19.4 days; 95% confidence interval [95% CI], 0.9 to 45.6 days). The secondary outcome of all-cause mortality was projected to decrease by 0.77 deaths per 100,000 persons per year (median = 0.68; 95% CI, 0.04 to1.79). Overall, increasing the PCP-to-patient ratio to the 1:3,500 benchmark would increase average life expectancy and reduce all-cause mortality.

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