Exploring the Links Between 340B Growth and Overall Health Care Spending

ABSTRACT

Objectives: To examine associations between the 340B Drug Pricing Program’s expansion and total health care spending and patient out-of-pocket (OOP) costs in the United States.

Study Design: Retrospective panel analysis using county-level data from 2014 to 2019 with national extrapolations through 2024.

Methods: The author estimated multivariable, log-linear panel regression models of per capita total and OOP health care spending as a function of 340B program growth, controlling for income, insurance coverage, unemployment, demographics, and hospital market concentration. The key independent variables included the number of Disproportionate Share Hospital (DSH) and Critical Access Hospital (CAH) sites and their affiliated contract pharmacies (CPs). State fixed effects and year dummies were included to control for unobserved heterogeneity.

Results: As of 2024, the 340B program expansion was associated with approximately $75 billion in additional annual health care spending, including $15 billion in extra annual OOP spending. DSH hospitals and their contract pharmacy arrangements accounted for nearly all of the observed increases, with growth in CP arrangements associated with approximately 70% of 340B-attributable spending. Counties with higher 340B activity exhibited 1.5% higher total spending and 2.8% higher OOP spending, on average. From 2013 to 2024, the author estimated that 340B-related growth was associated with 4.4% of overall inflation-adjusted health spending growth and 15% of the increase in OOP spending, or $60 billion and $15 billion, respectively.

Conclusions: The 340B program was associated with material increases in US health care and patient spending. Policy makers should reassess the 340B program’s structure and oversight to mitigate its unintended inflationary effects on health care costs.

Am J Manag Care. 2026;32(11):614-618

doi:10.37765/ajmc.2026.90040

Takeaway Points

This research tests whether increases in local 340B activity are associated with local health care spending and finds the following:

• Counties with faster 340B growth also experience faster growth in health care spending after controlling for other factors typically associated with spending growth.
• Counties with higher absolute levels of 340B activity also have higher health care spending after controlling for other spending factors.
• Patient out-of-pocket spending shows a stronger association with 340B activity: Greater participation in the program is linked to higher, not lower, patient expenditures.

The 340B Drug Pricing Program requires drug manufacturers to sell deeply discounted outpatient drugs to covered entities (CEs), including hospitals, federal grantees, and clinics.¹ Initially, the program included fewer than 100 safety-net hospitals in 1992, but by 2025, more than 2500 hospitals participated in the program.^1,2 340B hospitals use a sophisticated network of affiliated outpatient facilities (child sites) and for-profit partner contract pharmacies (CPs) with affiliated third-party administrators to maximize the number of prescriptions they can claim as eligible for 340B pricing and fulfillment.

CEs are not obliged to pass discounts along to patients, and recent research has found that doing so is rare.³ Instead, CEs earn substantial profits for administering 340B-acquired medicines and charging standard rates to patients, their insurers, or government payers. Prior research has shown that 340B profits retained by CEs and their partners run into tens of billions of dollars annually.⁴ CEs purchased over $66 billion of inventory at the 340B price in 2023,⁵ resulting in an estimated $64 billion in gross profit for CEs and their partners.⁶ Research has demonstrated that at least 75% of 340B gross profit is kept by CEs or their CPs, and that 340B hospitals have lower levels of charity spending.⁷

There are now more than 64,000 CEs and child sites with more than 230,000 CP arrangements,⁸ and recent literature has shown that 340B expansion introduces potential market distortions that may affect many facets of the health care delivery ecosystem.⁹ In theory, 340B discounts could reduce health spending for vulnerable patients and communities, and in general, proponents often describe the 340B program as “costless” to taxpayers.¹⁰ In practice, a recent Congressional Budget Office report found that the program introduces perverse incentives that may contribute to rising overall health care spending.¹¹ The 340B program has been shown to influence decisions made about the site of care for oncology patients, increasing Medicare spending by over $1100 per patient.¹² Researchers have also found that the 340B program impacts the employer-based market, raising costs for employers and employees,¹³ and 340B participation has fueled provider consolidation,¹⁴ which has been shown to raise prices for many health care services.¹⁵ CEs’ child sites tend to be in higher-income, better-insured communities, amplifying the risks that the 340B program may impact commercial spending.¹⁶ Recent estimates have examined the net impact of these costs and found that per-enrollee Medicaid costs, Affordable Care Act health plan costs, and employer-paid insurance premiums all increased faster and are higher in states with elevated 340B activity when other factors are held constant.^17-19

The cumulative evidence raises important questions about whether the 340B program’s structure is inadvertently driving up health care spending. Several mechanisms may link 340B participation to elevated spending beyond drug costs. First, because CEs profit from the spread between 340B acquisition prices and reimbursement rates, providers face financial incentives to shift care to higher-cost hospital outpatient settings where 340B program pricing applies, rather than physician offices or lower-cost sites. Second, 340B participation is associated with hospital consolidation—which independently raises prices for a broad range of services—creating a multiplier effect on total spending. Third, because CEs are not required to share discounts with commercially insured patients, insurers face the full reimbursement rate; over time, plan sponsors and patients absorb these costs through higher premiums and cost sharing. These intersecting pathways make the 340B program’s net effect on total spending an empirical question that this study addresses. This study examined the combined association between the 340B program’s effects and local health care spending. The net association of 340B participation with overall and out-of-pocket (OOP) spending was estimated using comprehensive, county-level data on both spending and 340B activity from 2014 through 2019, with national estimates extrapolated through 2024.

Methods

Data on 340B participation were collected for all nonprofit hospitals and their active child site and CP relationships between 2014 and 2019 from the Health Resources and Services Administration’s Office of Pharmacy Affairs Information System database. Hospitals qualify for the 340B program under various eligibility categories; the most common type of 340B hospital is a Disproportionate Share Hospital (DSH), which qualifies based on achieving a DSH adjustment percentage of greater than 11.75%. DSHs purchased approximately 80% of all 340B medicines in 2023.⁶ Small, rural Critical Access Hospitals (CAHs), which automatically qualify for the program, are nearly as numerous but account for much smaller purchase volumes. Pediatric hospitals, cancer hospitals, rural referral centers, and other types of hospitals account for a significantly smaller share of total 340B purchases and overall participation and are excluded from the analysis. The number of active DSH and CAH 340B hospitals and affiliated 340B sites within a given county was tabulated for each year, considering an entity “active” if it is active for any part of the year. Data on active CP arrangements were also collected and were only included if they were with community pharmacies located within the same county as the CE, to exclude mail-order and specialty pharmacy participants. Growth in the mean number of sites and CP arrangements by county is shown in Figure 1.

A model of local health spending must account for local market and demographic characteristics. Annual data were collected from the Bureau of Labor Statistics on county-level mean per capita household income, under-65 insurance coverage, median age, and the unemployment rate. Hospital financial data were gathered from annual Medicare Cost Reports²⁰ and used to calculate the county-level Herfindahl-Hirschman Index (HHI; a standard measure of hospital concentration) for hospital revenues to capture the potential pricing power of hospitals and related spending increases.¹⁵ Capturing HHI is especially relevant because recent research has shown that the 340B program is an important driver for—and beneficiary of—hospital consolidation.²¹

The author gathered county-level total and OOP health care spending for the same years from the Institute for Health Metrics and Evaluation (IHME) Disease Expenditure Project (DEX) county-level spending database.²² The DEX provides estimates of per capita health care spending for 3110 US counties by payer, age group, and type of care for 2010-2019, compiled from over 40 billion insurance claims and nearly 1 billion facility records. It covers Medicare, Medicaid, private insurance, and OOP payments, making it the most comprehensive publicly available source of county-level health spending in the United States.

The author estimated 2 log-linear multivariable panel regressions for county-level health care spending for 2014-2019. Total spending and OOP spending are available separately, which directly correspond to 2 dependent variables. The dependent variables were the natural logarithm of per capita total or OOP spending, and the independent variables included the number of 340B sites and CPs; all independent variables were measured in levels. The coefficients in such a regression are interpreted as the percentage change in the dependent variable for a 1-unit change in the independent variable. The regressions included both time effects and state-level fixed effects to account for unobserved, state-specific factors.

State fixed effects captured the dominant policy-level drivers of 340B eligibility, mainly state hospital ownership rules and Medicaid program designs that directly determine DSH adjustment percentages and therefore hospital qualification for the program. Although county-level fixed effects would be ideal, adding them would consume nearly all available degrees of freedom and render estimates unreliable. Most of the identifying variation in the 340B variables is cross-sectional, with counties differing substantially in their levels of 340B participation; within-county year-to-year changes are more modest. For example, the DSH percentage depends on the share of inpatients covered by Medicaid; thus, state differences in Medicaid coverage indirectly affect the level of 340B activity in each state. Some states, such as New York, prohibit for-profit hospital ownership, thereby increasing the number of 340B hospitals. Every state has experienced significant growth in 340B activity in recent years, but growth has differed significantly across states. The author also included interaction terms between the number of sites and the number of CPs to help distinguish between the effects of each factor, because the number of CP arrangements and the number of sites are correlated. The correlation coefficient between sites and CPs is 0.62. Results do not meaningfully differ if the interaction terms are excluded.

Results

The regression results are shown in the right-hand columns of the Table. For context, the left column shows the mean values for counties in 2024 for 340B variables and 2019 (the last year of the analytic set) for the other variables. The results indicate that the number of DSH sites and DSH CPs has large and statistically significant associations with both total and OOP health care spending, with a larger association for OOP spending. The average county in 2024 had 9.1 DSH sites and 14 DSH CP arrangements. Applying those levels to the regression coefficients implies that 340B participation was associated with 1.5% higher total spending per average county and 2.4% higher patient OOP spending. CAH sites and CAH CP arrangements have a very small but still marginally statistically significant association as well—for overall spending, the association is just 0.01% (vs 1.2% for DSH hospitals), and for OOP spending, the association is 0.3% (vs 2.4% for DSH hospitals). In 2024, the 340B program added an estimated 1.5% to total spending and 2.8% to OOP spending, with nearly all of the higher spending attributable to DSH hospitals and affiliated CP arrangements. Based on National Health Expenditure data from CMS,²³ at the national level, this translates to approximately $75 billion in additional annual spending, including $15 billion in patient OOP costs.

The regression coefficients allowed for comparison of the relative importance of sites and CPs. Overall, CP arrangements appear to account for approximately 70% of 340B-attributable spending, whereas hospital sites account for the remaining 30%. CP arrangements have grown faster than the number of sites, as shown in Figure 1, consistent with this finding. The Table shows that hospital concentration is strongly correlated with higher spending, consistent with standard economic research.

Higher income is associated with higher total spending—every $1000 increase in mean income corresponds to 3.7% higher total spending. The income effect on OOP spending is even higher, with every $1000 in income leading to a 5.6% increase in OOP spending. Better insurance coverage, lower unemployment, and higher age are all associated with higher average spending, consistent with expectations.

Discussion

To interpret these results and place them into perspective, the author calculated the growth in key 340B parameters from 2013 through 2024 and compared the implied spending growth with overall national spending growth over the same period. Applying the estimates this way is inherently limited because 340B has grown exponentially since the sample set ended in 2019, though it still provides some context. Figure 2 shows the increase in DSH sites, DSH CP arrangements, CAH sites, and CAH CP arrangements from 2013 to 2024 and calculates the implied growth rates in total and OOP spending over the same period by applying the relevant regression coefficients to these increases.

Together, 340B site growth was associated with a 0.36% increase in overall spending and a 0.51% increase in patient OOP spending, and 340B CP arrangements growth was associated with a 1.0% increase in total spending and a 1.8% increase in OOP spending. Combining these effects, the 340B-associated growth effect (after subtracting interaction terms) was 1.2% for total spending and 2.2% for patient OOP spending. Over the same period, total inflation-adjusted US health care spending grew by 28.1%, whereas inflation-adjusted patient OOP spending grew by 15%.²⁴ Dividing the 340B-associated spending growth by total spending growth, 340B growth was associated with an estimated 4.4% of total spending growth and approximately 15% of OOP spending growth over the period. Applying these to the inflation-adjusted increases of $1.36 trillion in total spending and $98 billion in OOP spending from 2013 to 2024,²⁴ 340B growth was associated with $59.8 billion in higher total spending and $14.7 billion in OOP spending over the period.

The analysis is limited to hospital 340B sites and does not contemplate any potential benefits that may arise from the increased spending associated with the program. Specific spending cannot be attributed to 340B activity, mainly because the program does not require enumeration of how CEs spend 340B revenues. However, the results are consistent with prior research indicating that 340B appears to encourage greater overall spending across various channels, including higher employer premiums, Medicaid spending, and Medicare costs.^11,12,16-18 Several mechanisms likely explain the observed associations between 340B participation and higher overall health care spending. The program creates financial incentives for hospitals to expand outpatient facilities and contract pharmacy arrangements, shifting care to higher-cost settings where 340B pricing applies. This site-of-care shift has been documented for oncology patients¹² and plausibly extends to other therapeutic areas. Separately, 340B profits can cross-subsidize hospital expansion and consolidation, which raises prices for a broad range of nondrug services. Finally, the absence of an obligation to pass discounts to commercially insured patients means that plan sponsors and patients face the full reimbursement rate while the hospital retains the spread. The analysis captures the net effect of these pathways on county-level spending but cannot attribute specific shares to each mechanism, given the lack of 340B revenue disclosure requirements.

Several limitations should be noted. First, the observational design does not support causal inference; unobserved confounders may account for some of the observed associations. Counties that attracted more 340B activity may have experienced faster growth in health care spending for reasons unrelated to the 340B program, a possibility that cannot be fully ruled out. Second, the use of state rather than county fixed effects, although appropriate given the data structure, means that time-invariant county-level differences are not directly absorbed, which the author attempted to address through explicit controls. Third, the sample covers 2014 to 2019; earlier data on the relevant independent variables were not available in the same data. Extrapolating coefficients to 2024 assumes stable relationships, as 340B continued to grow rapidly, which may not hold. Fourth, the IHME DEX spending data, although comprehensive, are model-based estimates rather than direct administrative claims, and results may differ from estimates derived from other data sources. Finally, reverse causality—in which high-spending counties attracted more 340B participation—cannot be ruled out.

Conclusions

The 340B program’s exponential growth was associated with a significant increase in total and OOP health care spending. The most prominent association with higher spending was greater DSH hospital participation and the expanded network of DSH CP arrangements. Policy makers who view the 340B program as a costless transfer of funds from pharmaceutical manufacturers to hospitals are missing an important structural driver of overall US health care costs, and especially of patient OOP spending. Over time, the fiscally challenged trajectory of the US taxpayer-funded health care system will continue to be stressed by unfettered growth in the 340B program.

Author Affiliations: Columbia University, New York, NY; Health Capital Group LLC, Princeton, NJ.

Source of Funding: This research was funded by the Pharmaceutical Research and Manufacturers of America (PhRMA).

Author Disclosures: Neal Masia, PhD, provides consulting services to multiple pharmaceutical companies.

Authorship Information: Concept and design; acquisition of data; analysis and interpretation of data; drafting of the manuscript; critical revision of the manuscript for important intellectual content; statistical analysis; obtaining funding; and administrative, technical, or logistical support.

Address Correspondence to: Neal Masia, PhD, Health Capital Group LLC, 18117 Biscayne Blvd, Suite 4901, Miami, FL 33160. Email: nm2724@columbia.edu.

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