US Hospital Service Availability and New 340B Program Participation.

Importance: The US 340B Drug Pricing Program enables eligible hospitals to receive substantial discounts on outpatient drugs to improve hospitals' financial sustainability and maintain access to care for patients who have low income and/or are uninsured. However, it is unclear whether hospitals use program savings to subsidize access as intended. Objective: To evaluate whether the 340B program is associated with improvements in access to hospital-based services and to test whether the association varies by hospital ownership. Design, Setting, and Participants: Difference-in-differences and cohort analysis from 2010 to 2019. Never and newly participating 340B general, acute, nonfederal hospitals in the US using data from the American Hospital Association's Annual Survey of Hospitals merged with hospital and market characteristics. Data were analyzed from January 1, 2023, to January 31, 2024. Exposures: New enrollment in 340B between 2012 and 2018. Main Outcomes and Measures: Total number of unprofitable service lines, ie, substance use, psychiatric (inpatient and outpatient), burn clinic, and obstetrics services; and profitable services, ie, cardiac surgery and orthopedic, oncologic, neurologic, and neonatal intensive services. Results: The study sample comprised a total of 2152 hospitals, 1074 newly participating and 1078 not participating in the 340B program. Participating hospitals were more likely than nonparticipating hospitals to be critical access and teaching hospitals, have higher Medicaid shares, and be located in rural areas and in Medicaid expansion states. At public hospitals, participation in the 340B program was associated with a significant increase in total unprofitable services (0.21; 95% CI, 0.04 to 0.38; P = .02) and marginal increases in substance use (5.4 percentage points [pp]; 95% CI, -0.8 pp to 11.6 pp; P = .09) and inpatient psychiatric (6.5 pp; 95% CI, -0.7 pp to 13.7 pp; P = .09) services. Among nonprofit hospitals, there was no significant association between 340B and service offerings (profitable and unprofitable) except for an increase in oncologic services (2.5 pp; 95% CI, 0.0 pp to 5.0 pp; P = .05). Conclusions and Relevance: The finding of the cohort study indicate that participation in the 340B program was associated with an increase in unprofitable services among newly participating public hospitals. Nonprofit hospitals were largely unaffected. These findings suggest that public hospitals responded to 340B savings by improving patient access, whereas nonprofits did not. This heterogeneous response should be considered when evaluating the eligibility criteria for the 340B program and how it affects social welfare.

Community social vulnerability and the 340B Drug Pricing Program: Evaluating predictors of 340B participation among critical access hospital.

PURPOSE: The federal 340B Drug Pricing Program allows eligible hospitals, including critical access hospitals (CAHs), to obtain outpatient drugs at a discounted rate. CAHs likely benefit from 340B participation because they are often under-resourced and serve at-risk patient populations. The objective of this study was to understand predictors of 340B program participation among CAHs, and how participation varies with community-level social vulnerability. METHODS: We used a cross-sectional study design to assess the relationship between 340B participation in 2019 and community vulnerability status using 2018 data from the CDC's social vulnerability index (SVI) among acute care CAHs. Analyses used linear probability models adjusted for hospital-level characteristics. FINDINGS: In bivariate analyses, CAHs participating in the 340B program had lower overall social vulnerability scores, relative to nonparticipating, eligible, and ineligible CAHs, respectively (43.8 vs. 48.7 vs. 64.7, p < 0.10). In adjusted regression models, greater community vulnerability rankings due to socioeconomic status (-0.129, p < 0.05) and minority status and language (-0.092, p < 0.05) were associated with decreased 340B participation. Higher hospital operating margin was associated with increased 340B participation (0.163, p < 0.05). Although the number of for-profit CAHs ineligible for 340B was small, they had the highest community-level social vulnerability score and lowest hospital operating margin on average. CONCLUSIONS: CAHs located in areas of high community vulnerability are less likely to participate in the 340B program. Some vulnerable patient populations served by CAHs may be excluded from 340B program benefits.

Much work to do about measuring work.

Work ability is a critical economic and well-being indicator in cancer care. Yet, work ability is understudied in clinical trials and observational research and is often undocumented in medical records. Despite agreement on the importance of work from well-being, health insurance, and financial perspectives, standardized approaches for collecting, measuring, and analyzing work outcomes are lacking in the health-care setting. The necessary components for closing the gap in patient and caregiver employment research in health-care settings involve a common set of measures, including those that replace or translate generic measures of mental and physical functioning into work outcomes in observational and clinical trial research, standardized approaches to data collection and documentation, and the use of longitudinal data to understand the consequences of reduced work ability over time. We present a conceptual framework for the inclusion of work ability in outcomes research. We cover constructs for employment and work ability measurement that can be adopted in research, recorded as patient-level data, and used to guide treatment decisions. The inclusion of return to work and hours worked, productivity, and ability to perform in a similar job can support conversations that guide treatment decisions and minimize economic consequences. Our hope is that by considering impact on work ability, improved treatments will be developed, health inequities reduced, and resources directed toward aiding patients and their caregivers in balancing work and health demands.

Access To Oncology Services In Rural Areas: Influence Of The 340B Drug Pricing Program.

Rural-urban cancer disparities, including greater mortality rates, are partially attributable to the limited availability of oncology services in rural communities. Without these services, rural residents may experience delays in timely treatment and may be less likely to complete recommended care. The 340B Drug Pricing Program allows eligible not-for-profit and public hospitals to purchase covered outpatient drugs, including high-cost oncology drugs, at discounted prices. Using 2011-20 data, we evaluated the relationship between new enrollment in the 340B program and oncology services initiation in rural general acute care hospitals that lacked oncology services in 2011. Compared with hospitals that remained unenrolled in the 340B program through 2020, hospitals that enrolled during 2012-18 were 8.3 percentage points more likely to have added oncology services as of 2020. The newly participating hospitals that added oncology services were disproportionately located in Medicaid expansion states and in counties with lower uninsurance rates. These findings suggest that the 340B program facilitates expanded access to oncology services in some rural communities, but opportunities remain to address disparities in the most disadvantaged service areas.

Working, Low Income, and Cancer Caregiving: Financial and Mental Health Impacts.

PURPOSE: Approximately 6 million people provide caregiving to people diagnosed with cancer. Many must remain employed to support their household and to have access to health insurance. It is unknown if caregiving for a spouse diagnosed with cancer is associated with greater financial and mental stress relative to providing care for a spouse with different conditions. METHODS: Health and Retirement Study (2002-2020) data were used to compare employed caregivers, younger than age 65 years, caring for a spouse diagnosed with cancer (n = 103) and a matched control group caring for a spouse with other conditions (n = 515). We used logistic regression to examine a decrease in household income, increase in household debt, stopping work, and a new report of a mental health condition over a 4-year period, adjusting for socioeconomic and demographic characteristics, and health insurance status. Subanalyses stratified estimations by median household income. RESULTS: Around a third of cancer caregivers reported they stopped working (35%) and had an increase in household debt (30%). Cancer caregivers in households below the median household income were more likely to report decreased income (13.4 percentage points [pp]; P < .10), increased household debt (14.5 pp; P < .10), and stopping work (18.8 pp; P < .05) than similar noncancer caregivers. Mixed results were found for a change in mental health domains. The results were robust to multiple sensitivity analyses. CONCLUSION: Cancer caregivers from low-income households were more likely to increase debt and incur work loss compared with noncancer caregivers in similar households. Policies such as paid sick leave and family leave are needed for this strained and important population who have financial and employment responsibilities in addition to caregiving.

Understanding the relationship between nonprofit hospital community benefit spending and system membership: An analysis of independent hospital acquisitions.

The Internal Revenue Service (IRS) requires nonprofit hospitals to report community benefit spending to justify their nonprofit tax exemption. We examined whether nonprofit hospital acquisitions influence the amount and type community benefit spending. We analyzed 2011-2018 data on urban, nonprofit hospitals. The analysis dataset included 57 hospitals that were acquired and a matched control group. We estimated difference-in-differences specifications to measure the effect of acquisitions on total community benefit spending, and three subcategories - clinical, population health, and other spending types. We found that acquisitions led to decreased population health spending (-$0.32 million, p < 0.01) and other spending categories (-$1.5 million, p < 0.05), but no significant change in total or clinical spending. If the acquirer was located out-of-state, total community benefit spending declined by $2.4 million (p < 0.10). Our findings support the need for community benefit spending to be considered, along with quality, efficiency, and prices, when evaluating the welfare impact of acquisitions.

Retirement behavior of cancer survivors: role of health insurance.

PURPOSE: Workers who rely on employment for health insurance may be unable to reduce work during and following treatment for a serious health condition, potentially harming their health in retirement. In this study, we examine the influence of retiree and employment-contingent insurance on the retirement and health of workers diagnosed with cancer. METHODS: This longitudinal cohort study used 2000-2018 Health and Retirement Study data to examine changes in employment, weekly hours worked, and health status measures following a cancer diagnosis. We selected respondents who reported a new cancer diagnosis (n = 354) and a matched, non-cancer sample (n = 1770), restricting both samples to those employed and younger than age 63. RESULTS: Following a cancer diagnosis, women with retiree health insurance were 18.6 percentage points less likely to work (95% CI: - 36.3 to - 1.0; p < 0.05) relative to women with employer health insurance, but no retiree insurance. Employed women with cancer but without employment-contingent health insurance increased weekly hours worked by 34% relative to similar non-cancer controls. Men and women with a cancer diagnosis and without employment-contingent health or retiree insurance were also less likely to work (p < 0.05). Among those who stopped working, respondents with cancer and employment-contingent health insurance reported better health status than respondents without employment-contingent health insurance. CONCLUSIONS: Cancer survivors with employer and retiree health insurance leave the workforce earlier and report better health status when they stop working than those without equivalent insurance. IMPLICATIONS FOR CANCER SURVIVORS: Policies to support health insurance outside of employment may allow cancer survivors to retire earlier and may have positive health benefits.

Treatment preferences among adults with normal cognition and cognitive impairment.

BACKGROUND: Although patient participation in treatment decisions is important for preference-concordant care delivery, it is largely unknown how cognitive impairment influences treatment preferences. We investigated whether treatment preferences for the care of serious illness differ between adults with and without cognitive impairment in hypothetical clinical scenarios. METHODS: Data from the 2018 Health and Retirement Study were used. The sample included 1291 self-respondents (201 respondents with cognitive impairment, and 1090 with normal cognition). We examined treatment preferences for life-extending, limited, and comfort care options in two hypothetical clinical scenarios where the respondent imagines a patient with (1) good physical health with severe cognitive impairment consistent with dementia; and (2) with physical impairment due to a heart attack, but normal cognition. Respondents specified whether they were unsure, or if they would want or not want each treatment option. Linear probability models were used to compare treatment preferences by cognitive status. RESULTS: Respondents with cognitive impairment were more likely to report that they were unsure about treatment options across both clinical scenarios compared to those with normal cognition. For the limited treatment option, cognitive impairment was associated with a lower rate of expressing a treatment preference by 7.3 (p = 0.070) and 8.5 (p = 0.035) percentage points for dementia and heart attack scenarios, respectively. Among those who articulated preferences, cognitive impairment was associated with a higher rate of preference for life-extending treatment in both dementia (30.1% vs. 20.0%, p = 0.044) and heart attack scenarios (30.0% vs. 20.2%, p = 0.033). CONCLUSIONS: Compared to those with normal cognition, cognitive impairment was associated with greater uncertainty about treatment preferences and higher rates of aggressive care preferences among those who specified preferences. Further research should assess whether preferences for aggressive care become more common as cognition declines in order to improve preference-concordant care delivery for patients with cognitive impairment.

Best of ARM: Evaluating engagement in multisector community health networks: The case of tribal organizations.

OBJECTIVE: To quantify variation in public health system engagement with tribal organizations across a national sample of communities and to identify predictors of engagement. DATA SOURCES: We used 2018 National Longitudinal Survey of Public Health Systems data, a nationally representative cohort of the US public health systems. STUDY DESIGN: Social network analysis measures were computed to indicate the extent of tribal organization participation in public health networks and to understand the sectors and social services that engage with tribal organizations in public health activities. Two-part regression models estimated predictors of tribal engagement. DATA COLLECTION: A stratified random sample of local public health agencies was surveyed, yielding 574 respondents. An additional cohort of oversampled respondents was also surveyed to include jurisdictions from the entire state upon the request of their respective state health departments (n = 154). Analyses were restricted to jurisdictions with a nearby American Indian and Alaska Native (AI/AN) serving health facility, yielding a final sample size of 258 local public health systems. PRINCIPAL FINDINGS: When an AI/AN serving health facility was present in the region, tribal organizations participated in 28% of public health networks and 9% of implemented public health activities. Networks with tribal engagement were more comprehensive in terms of the breadth of sectors and social services participating in the network and the scope of public health activities implemented relative to networks without tribal engagement. The likelihood of tribal engagement increased significantly with the size of the AI/AN population, the presence of a tribal facility with Indian Health Service funding in the region, and geographic proximity to reservation land (p < 0.10). CONCLUSIONS: The vast majority of public health networks do not report engagement with tribal organizations. Even when AI/AN serving health facilities are present, reported engagement of tribal organizations remains low.

Cancer's Lasting Financial Burden: Evidence From a Longitudinal Assessment.

BACKGROUND: The purpose of this study was to conduct a longitudinal analysis of out-of-pocket expenditure (OOPE) trajectories for the assessment of cancer's lasting financial impact. METHODS: We identified newly diagnosed cancer patients and constructed matched control group of noncancer participants from the 2002-2018 Health and Retirement Study. Outcomes included monthly OOPE for prescription drugs (RX-OOPE_MONTHLY) and OOPE for medical services other than drugs in the past 2 years (non-RX-OOPE_2YR), consumer debt, and new individual retirement account (IRA) withdrawals. Generalized linear models were used to compare OOPEs between cancer and matched control groups. Logistic regressions were used to compare household-level consumer debt or early IRA withdrawal. Subgroup analysis stratified patients by age, health status, and household income, with the low-income group stratified by Medicaid coverage. All statistical tests were 2-sided. RESULTS: The study cohort included 2022 cancer patients and 10 110 participants in the matched noncancer control group. Mean non-RX-OOPE_2YR of cancer patients was similar to that of participants in the matched control group before diagnosis but statistically significantly higher at diagnosis ($1157, P < .001), 2 ($511, P < .001) years, 4 ($360, P = .006) years, and 6 ($430, P = .01) years after diagnosis. A similar pattern was observed in RX-OOPE_MONTHLY. A statistically significantly higher proportion of cancer patients incurred consumer debt at diagnosis (34.5% vs 29.9%; P < .001) and 2 years after (32.5% vs 28.2%; P = .002). There was no statistically significant difference in new IRA withdrawals. Patients experienced lasting financial consequences following cancer diagnosis that were most pronounced among patients aged 65 years and older, in good-to-excellent health at baseline, and with low income, but without Medicaid coverage. CONCLUSIONS: Policies to reduce costs and expand insurance coverage options while reducing cost-sharing are needed.

The Growing Divide in the Composition of Public Health Delivery Systems in US Rural and Urban Communities, 2014-2018.

Objectives. To examine changes in the scope of activity and organizational composition of public health delivery systems serving rural and urban US communities between 2014 and 2018.Methods. We used data from the National Longitudinal Survey of Public Health Systems to measure the implementation of recommended public health activities and the network of organizations contributing to these activities in a nationally representative cohort of US communities. We used multivariable regression models to test for rural-urban differences between 2014 and 2018.Results. The scope of recommended activities implemented in rural areas declined by 3.4 percentage points between 2014 and 2018, whereas it increased by 1.4 percentage points in urban areas. The rural-urban disparity in scope of activities grew by a total of 4.8 percentage points (P < .05) over this time. The disparity in network density grew by 2.3 percentage points (P < .05).Conclusions. Urban public health systems have enhanced their scope of activities and organizational networks since 2014, whereas rural systems have lost capacity. These trends suggest that system improvement initiatives have had uneven success, and they may contribute to growing rural-urban disparities in population health status.