Health affairs scholar最新文献

Introduction: The US faces a growing mismatch between demand for inpatient psychiatric care and available capacity. Little is known about the characteristics of regions affected by inpatient psychiatric bed shortages, which hospitals have faced decreases in bed supply, and whether other psychiatric services have emerged to fill the gap.

Methods: Using data from the American Hospital Association Annual Survey, we conducted a descriptive analysis of inpatient psychiatric bed supply across hospital referral regions (HRRs) from 2012 to 2022. We examined the demographic patterns of regions affected by shortages, assessed hospital characteristics associated with reductions in psychiatric capacity, and evaluated the presence of alternative psychiatric services that may substitute for inpatient care.

Results: More than 60% of the US population consistently lived in HRRs with psychiatric bed shortages during this period, defined as fewer than 30 beds per 100 000 people. By 2022, HRRs with severe shortages, relative to those without them, were more likely to be in the West and had higher proportions of Hispanic residents, raising concerns about inequities in behavioral health care access. Hospitals most likely to reduce psychiatric capacity were general, non-profit, and system-affiliated institutions with lower total margins. Importantly, hospitals in severe shortage areas were less likely to have outpatient psychiatric services, indicating that alternative hospital-based resources may not fully offset inpatient shortfalls.

Conclusion: Addressing the nation's psychiatric bed shortage will require targeted financial support for general hospitals at risk of closing psychiatric units and investment in broader psychiatric infrastructure to ensure equitable access across regions.

The Congressional Budget Office (CBO) created a model of new drug development with the aim of informing the US Congress about the potential impact of policy changes affecting expected biopharmaceutical revenue on future innovation. While models are by their nature simplifications of reality, biopharmaceutical investment decision-making is particularly complex and poorly understood outside the investment ecosystem, raising questions about the adequacy of such models to inform policymaking. To better understand how CBO's model compares to real-world investment decision processes, we conducted semi-structured interviews with investors representing venture capital, private equity, corporate venture capital, and biopharmaceutical companies. The interviews with investors suggest that the CBO's model does not adequately reflect investment decisions for drug development. These findings highlight the risks of using models to guide policymaking and the need to improve the existing model with the help of stakeholder input before such models are adopted.

Introduction: Medical debt is widely regarded as a social problem that reflects growing out-of-pocket costs. Yet whether medical debt acts a social determinant of health by discouraging people to seek additional care may depend on one's ability to repay this debt.

Methods: We use data from the first wave of a survey of a nationally representative sample of 2090 low-wage workers in the U.S. We ran linear probability models to predict putting off filling prescriptions, receiving primary medical care, and receiving specialty medical care based on medical debt disposition.

Results: We find that workers with medical debt they cannot afford to repay are more likely to defer three types of health care and are confronted with several other financial risk factors compared with workers with medical debt they are repaying. Also, concerning putting off needed health care, there is no difference between workers who are repaying their medical debt and those with no medical debt.

Discussion: These findings suggest the need to strengthen financial assistance policies and programs and ensure access to low-cost health coverage for low-wage workers.

Changes in biopharmaceutical policy, specifically the Inflation Reduction Act (IRA), introduced administrative drug price setting in the U.S., prompting questions about the impact on drug development of this and future policies under consideration. Existing models used to inform policymakers, such as those from the Congressional Budget Office (CBO), attempted to quantify the relationship between investment and financial return but overlooked the effect on post-approval research. This research, essential for expanding drug indications and demonstrating efficacy in new populations, is often pursued years after initial approval, at the time when IRA price controls take effect. As a result, the expected financial return from secondary indications is diminished, potentially discouraging investment in post-market studies. This commentary emphasizes the importance of models that incorporate the impact of policy on both new and post-approval drug development. Without such analysis, policymakers risk underestimating the broader consequences. Given the significant role post-approval research plays in improving health outcomes, particularly for chronic disease, its exclusion from policy impact models is a notable gap. We urge the research community to generate evidence that informs more comprehensive modeling, ensuring that future policy decisions support investment in the entire lifecycle of drug development.

Introduction: Foster care admissions are highly traumatic for children and their families, often causing serious adverse outcomes. We seek to assess the viability of machine learning methods to identify children at risk of future foster care admission to facilitate diversion.

Methods: We use claims data for children enrolled in a Medicaid health plan in Ohio as well as for linked adults, along with data on individual and geographic social determinants of health (SDOH) factors. We test the performance of a gradient-boosted tree machine learning algorithm as compared to logistic regression. Of the children, 85% have SDOH data available.

Results: Using a gradient-boosted tree machine learning algorithm, we built a model that identifies 2408 children (1.32%) as at risk of foster care admission in a sample of 181 841, of whom 1599 entered foster care within 1 year, resulting in a positive predictive value (PPV) of 66.4% (F ₁ = 55.5%, specificity = 99.5%, sensitivity = 47.67%), outperforming logistic regression. Accuracy was substantially better when using SDOH data (PPV of 84.72% with SDOH data compared to 27.44% without).

Conclusions: These results highlight the importance of SDOH factors in predicting foster care admission. They also point to the potential of machine learning for facilitating early intervention to prevent foster care admissions.

Introduction: As people age, many require help with personal care-often provided by family members or friends or direct care workers. Over the last three decades, driven by the disability rights movement, direct care worker shortages, and caregiver burden, self-direction has emerged as a Medicaid option that allows individuals to hire and pay their own caregivers, including family members.

Methods: We use 2021 TMSIS Medicaid claims data linked to Medicare Master Beneficiary Summary File to identify dually eligible beneficiaries 65+ receiving personal care. We describe the demographic characteristics of self-direction users compared to non-self-direction users and present the percentage of self-direction users across states.

Results: We find that over half of dually eligible beneficiaries 65+ receiving personal care use self-direction. Compared to individuals who use agency-based personal care, self-direction users have higher prevalence of chronic disease, higher home health use, and higher Medicare costs.

Conclusion: Self-direction has become a common model of personal care among older adults enrolled in Medicaid. Examining how funds allocated for self-direction are spent; the effects of self-direction on consumers and their caregivers; and how self-direction may impact Medicare and Medicaid costs is critical to inform the expansion and funding of Medicaid self-direction programs.