Drivers of Variation in Health Care Spending Across US Counties

Abstract

Importance: Understanding the drivers of health care spending across US counties is important for developing policies and assessing the allocation of health care services.

Objective: To estimate the amount of cross-county health care spending variation explained by (1) population age, (2) health condition prevalence, (3) service utilization, and (4) service price and intensity.

Design, setting, and participants: In this cross-sectional study, data for 4 key drivers of per capita spending were extracted for 3110 US counties, 148 health conditions, 38 age-sex groups, 4 payers, and 7 types of care for 2019. Service utilization was measured as service volume per prevalent case, while price and intensity was measured as spending per visit, admission, or prescription. Das Gupta and Shapley decomposition methods and linear regression were used to estimate the contribution of each factor. The data analysis was conducted between March 2024 and July 2024.

Exposures: Age, disease prevalence, service utilization, or service price and intensity.

Main outcomes and measures: Variation in health care spending across US counties.

Results: In 2019, 76.6% of personal health care spending was included in this study. Overall, 64.8% of cross-county health care spending variation among 3110 US counties was explained by service utilization, while population age, disease prevalence, and price and intensity of services explained 4.1%, 7.0%, and 24.1%, respectively. The rate at which these factors contributed to variation in spending differed by payer, type of care, and health condition. Service utilization was associated with insurance coverage, median income, and education. An increase in each of these from the median to the 75th percentile was associated with a 7.8%, 4.4%, and 3.8% increase in ambulatory care utilization, respectively. The fraction of Medicare beneficiaries with Medicare Advantage was associated with less utilization. An increase in Medicare Advantage coverage from the median to the 75th percentile was associated with a 1.9% decrease in ambulatory care utilization. Differences in cross-state spending levels were also attributed to different factors. For Utah, the state with the least health care spending per capita, spending rates were lower for all types of care due principally to the young age profile. For New York, the state with the highest spending, spending rates were relatively high for hospital inpatient and prescribed pharmaceutical spending. For both types of care, high service price and intensity contributed to the above-average spending.

Conclusions and relevance: In this cross-sectional study, variation in health care spending among US counties was largely related to variation in service utilization. Understanding the drivers of spending variation in the US may help policymakers assess the allocation of health care resources.

Figure 1.. Health Care Spending Per Capita Across US Counties

Spending is inclusive of all personal health care spending, excluding spending on durable medical equipment and over-the-counter drugs, and from Veteran Affairs, Department of Defense, and Indian Health Services.

Figure 2.. Factors That Explain Cross-County Variation in Spending Per Capita

Factors explaining variation in total spending and by payer (A), type of care (B), and 9 highest-spending health conditions (C). Each bar shows the total variation explained by each factor. Because all variation was explained these percentages add up to 100%.

Figure 3.. Factors Associated With Cross-County Service Utilization and Service Price and Intensity

The estimated coefficient reports the relative change in service utilization or service price and intensity relative to an associated increase from the median to the 75th percentile of each independent variable. For example, an increase in health insurance rate from the median to the 75th percentile was associated with a 7.7% increase in ambulatory care utilization. Each bivariate regression was type-of-care-specific, assessing the relationship between type-of-care-specific service utilization and service price and intensity with county characteristics. Standard errors are clustered by payer, cause, and age and sex group and were adjusted for multiple hypothesis testing using the Bonferroni adjustment. This analysis considered 78 health conditions composing 55% of all health care spending.

Figure 4.. Factors Explaining State-Specific Spending Per Capita Relative to National Spending Per Capita by Type of Care for Highest- and Lowest-Spending States

This analysis was completed including 78 health conditions that have disease prevalence estimated. The black dot is the difference between the state health care spending per capita estimate and the national health care spending per capita estimate for each type of care. The bars quantify the factors contributing to spending differences. For example, Utah spent 33.6% less on ambulatory care per capita than the national ambulatory care spending per capita level. All 4 factors contributed to this lower spending. AM indicates ambulatory care; ED, emergency department care; HH, home health; IP, inpatient care; NF, nursing facility care; and RX, retail prescribed pharmaceutical.