Health affairs scholar最新文献

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.

Despite acknowledgment that social and economic policies fundamentally shape health, persistent geographic and sociodemographic inequities in the United States reflect the deliberate choices embedded in policy decisions. There is a critical need for policy research that illuminates not just associations of social policies with health, but also mechanisms and pathways to equitable impact. The special collection of articles in Health Affairs Scholar on "Intersections of Social Policies and Health" advances the field by examining how the effects of social policies are shaped by legal, political, and cultural contexts; the uneven implementation and enforcement that contribute to health disparities; and the crucial role of narratives and administrative processes in mediating policy impact. Through nuanced analyses-such as exploring policy ecosystems, administrative burdens, and media framing-these studies move beyond single-policy assessments to explore the complex realities of translating policy intent into population health improvements. Collectively, the collection points toward new research priorities: advancing nuanced measurement of policy contexts, integrating equity and subgroup analyses, prioritizing implementation science, incorporating political and narrative determinants, and embracing intersectoral approaches. By deepening our understanding of how and why policy effects unfold unevenly, this scholarship charts a course for more effective and equitable policy research and action.

Introduction: Cannabis use in the United States is increasing. However, the prevalence and trends in cannabis use disorder (CUD) and cannabis poisoning among Medicaid enrollees, a vulnerable population, are not evident.

Methods: Using the Merative MarketScan Multistate Medicaid Claims and Encounters Database from 2011-2022, we examined adjusted prevalence and trends in CUD and cannabis poisoning among Medicaid enrollees and by age, sex, and insurance type (managed care [MC]/fee-for-service [FFS]).

Results: During the 2011 quarter (Q) 1-2022 Q4, the CUD rate increased from 336.54 to 548.96 per 100 000 enrollees per quarter-a 1.63-fold increase; the cannabis poisoning rate increased from 1.45 to 7.04 per 100 000 enrollees per quarter-a 4.86-fold increase. CUD rates were highest among those aged 18-34 years, while cannabis poisoning rates among those aged 0-17 years surpassed the rate of those aged 18-34 years by 2020 Q3. CUD and cannabis poisoning rates increased among both males and females and among those with FFS and MC, although females and MC enrollees experienced higher increases than males and FFS enrollees, respectively.

Conclusion: CUD and cannabis poisoning rates among Medicaid enrollees increased significantly during 2011-2022, especially among older adults, females, and MC enrollees. Targeted education campaigns on safe use and storage of cannabis may help reduce increasing trends in CUD and cannabis poisonings.

The National Institutes of Health (NIH) recently released a document titled "Leading in Gold Standard Science-An NIH Implementation Plan." We offer reflections on 4 of the 9 "tenets" of gold standard research and recommendations for improving scholarly publication.

The FDA Commissioner's new National Priority Voucher program seeks to accelerate drug approvals for products meeting certain criteria. Interestingly, the program intends to increase affordability of new drugs. With few specifics available as to how the program will achieve that goal, this paper proposes a framework for leveraging independent value assessments to achieve affordable access while incentivizing evidence development and innovation.