Study Design

To isolate the effects of the program, we used a regression-discontinuity design12,13 that took advantage of the eligibility rules of the 340B Program for general acute care hospitals, which establish eligibility above a threshold of 11.75% in the Disproportionate Share Hospital adjustment percentage (DSH percentage) of each hospital. The DSH percentage, a federally defined measure that determines additional payments for uncompensated care, is largely based on the percentage of admissions at a hospital that are for Medicaid patients and low-income Medicare patients (see the Supplementary Appendix, available with the full text of this article at NEJM.org).14

In the context of our study, a regression-discontinuity approach assumes that all determinants of hospital behavior for hospitals just above or just below the eligibility threshold were similar with the exception of exposure to the program. Equivalently, hospitals with minimally different DSH percentages within a sufficiently narrow range around the threshold are considered to be quasi-randomly assigned to program eligibility. In accordance with standard practice when there may be too few observations within such a range, we included hospitals from a broader range of DSH percentages and used regression to estimate threshold-related discontinuities (level shifts) in the cross-sectional relationship between hospital DSH percentages and each study outcome. This approach assumes that the relationship would have continued uninterrupted across the threshold in the absence of the program. Unlike comparisons of longitudinal changes (e.g., a difference-in-differences approach), our cross-sectional regression-discontinuity approach did not require hard-to-justify assumptions about how hospitals would have evolved in the absence of the program during a period of rapid hospital–physician consolidation.

Study Data and Population

Our study included general acute care hospitals with 50 or more beds. We excluded for-profit hospitals because they are not eligible for the 340B Program and other categories of hospitals because they are subject to different eligibility criteria or payment systems.15 We further limited our analysis to hospitals with a DSH percentage within 10 percentage points of the 11.75% eligibility thresholds (i.e., 1.75% to 21.75%) and assessed the robustness of our results to narrower ranges in sensitivity analyses (see the Supplementary Appendix).

For hospital-level analyses, we constructed hospital-level variables for each year from 2008 through 2012 using Medicare claims and enrollment data for a random 20% sample of fee-for-service beneficiaries and data from the Centers for Medicare and Medicaid Services Hospital Cost Report Information System (HCRIS). For patient-level analyses of mortality in communities served by the hospitals in our study, we restricted the sample of fee-for-service Medicare beneficiaries to those living in ZIP Codes occupied by a single hospital (75% of the study hospitals fit this description).

Study Variables

340B Program Participation and Eligibility

Using data from the Health Resources and Services Administration, we categorized a hospital as a 340B Program participant in a given year if it was a registered participant at any point during the year.16 To assess the program eligibility of each hospital, we used the DSH percentage of the hospital from the previous year, as reported in the HCRIS, because eligibility is determined prospectively (see the Supplementary Appendix).

Dependent Variables

Our primary hospital-level analyses included several prespecified and closely related outcome measures, which are described in more detail in the Supplementary Appendix. For each hospital in each year, we adapted previously described methods, using Medicare outpatient and carrier claims to determine the number of physicians in hematology–oncology, ophthalmology, or rheumatology who were practicing in a facility owned by the hospital.17 We prespecified these three specialties because they account for the most Part B drug spending in Medicare and have the highest proportions of revenue attributable to parenteral drugs among all specialties.15,18 We focused on these specialties because of the emphasis of our study on parenteral drugs. However, the 340B Program may have accelerated hospital–physician integration in other specialties, too, because the discounts also apply to prescription drugs and may have encouraged hospital acquisitions of multispecialty groups.19

For each of the three specialties in each year and each hospital (including all outpatient practices and facilities owned by the hospital), we used Medicare claims and enrollment data to determine the number of Medicare patients served in outpatient facilities of the hospital by a physician in the specialty (see the Supplementary Appendix), the number of these patients receiving Part B drugs from the hospital, the number of reimbursed Part B drug claims billed by the hospital for these patients (and associated Medicare revenue), and the proportion of these patients who were dually enrolled in Medicaid and Medicare or received state assistance for Medicare cost-sharing. These dually eligible beneficiaries have less generous coverage or coverage that reimburses hospitals at lower rates for Part B drugs and other services than do persons with private supplemental insurance.

For secondary hospital-level analyses assessing hospital investments in the safety net, we used HCRIS data to assess the following variables yearly for federally qualified health centers (FQHCs) integrated with each hospital: the number of health care professionals employed, the number of patient encounters, and Medicare spending for FQHC care. We also assessed from claims the number of inpatient admissions for low-income groups. For secondary patient-level analyses of Medicare beneficiaries in the ZIP Codes of the hospitals, we assessed annual mortality from Master Beneficiary Summary files.

Covariates

As covariates for hospital-level and patient-level analyses, we assessed hospital teaching status, urban or rural classification, and Census region. As covariates for patient-level analyses of mortality, we additionally assessed the following patient characteristics: age, sex, race and ethnic group, whether disability was the original reason for Medicare enrollment, presence of end-stage renal disease, chronic conditions from the Chronic Conditions Data Warehouse, and the Hierarchical Condition Category score.

Statistical Analysis

For each hospital-level dependent variable, we fit the following model to estimate the eligibility threshold–related discontinuity in the relationship between the variable and the hospital DSH percentage:

E(Y it ) = β 0 +β 1 340BEligible it +β 2 DSH it +β 3 (340BEligible it ×DSH it )+γX it +α t ,

where E(Y it ) denotes the expected value of the outcome for hospital i in year t, 340BEligible it indicates whether the DSH percentage of the hospital exceeded the eligibility threshold, DSH it is the DSH percentage of the hospital, X it is a set of hospital-year level characteristics, and α t denotes fixed effects for year. The terms DSH it and (340BEligible it ×DSH it ) allow the slopes of the linear relationship between the hospital DSH percentage and outcome to differ on either side of the eligibility threshold.

The coefficient of interest, β 1 , is the adjusted discontinuity, or the difference in the outcome between hospitals above versus below the program eligibility threshold after adjustment for covariates and the relationship between hospital DSH percentage and the outcome. This quantity represents the estimated effect of 340B eligibility on the outcome variable. Because some eligible hospitals do not enroll in the 340B Program, we used instrumental-variables methods to estimate discontinuities associated with program participation. To aid interpretation of the multiple tests in our primary analyses, we conducted post hoc significance tests using a modified Hochberg procedure20 that accounted for the multiplicity of outcomes and the high degree of correlation among them. Additional details about these analyses are provided in the Supplementary Appendix.

For analyses of mortality in the local communities of hospitals, we estimated similar models at the patient level after restricting the sample to Medicare beneficiaries living in ZIP Codes occupied by a single hospital and assigning the DSH percentage of that hospital to all beneficiaries residing in its ZIP Code. In a supplemental analysis, we used a similar strategy to examine overall Part B drug use and spending among beneficiaries in the local communities of hospitals (see the Supplementary Appendix).

In all analyses, we excluded hospitals with DSH percentages that were within 1 percentage point of the eligibility threshold in order to reduce measurement error introduced by misclassification of hospital eligibility among hospitals that were close to the threshold.21 This misclassification resulted from misalignment for some hospitals between annual periods for DSH reporting in the HCRIS and calendar-year periods used for determining eligibility. In hospital-level analyses, hospitals were weighted by their number of beds. All analyses used robust variance estimators to account for clustering at the hospital level.22

In sensitivity analyses, we tested the robustness of our estimates to adjustment for different specifications of the relationship between DSH percentage and outcomes. We also tested for eligibility-related discontinuities in hospital characteristics that should not be affected by the program and, for mortality analyses, in patient characteristics to test the assumption that potential confounders trended continuously across the eligibility threshold. We conducted additional analyses to assess the extent to which hospitals might have manipulated their DSH percentage to become eligible for the program, including analyses using the DSH percentages and hypothetical eligibility of hospitals in 2002 (before program expansion). In falsification tests, we repeated our hospital-level analyses among for-profit hospitals (which are not 340B-eligible) and among study hospitals in 2002 (when few were eligible), and we reestimated models using a range of alternate hypothetical eligibility thresholds.