Optimizing Care
The Early Opioid Epidemic and Medicaid: Is Prescription Access to Blame?
John Anders
American Journal of Health Economics, January 2025, Pages 91-123
Abstract:
Is the opioid epidemic attributable to prescription painkillers becoming more accessible? I find that, for an average county, Medicaid expansions under the Affordable Care Act caused approximately 175,000 more opioid units to be prescribed per year, and 4 additional opioid-related deaths per year. Medicaid expansions explain nearly one-sixth of the overall death toll from 2012 to 2016. These results are driven largely by deaths of White men aged 18–65, and vary by local access to marijuana (an opioid substitute). Results are robust to treatment heterogeneity concerns. After estimating the interactive impact of Medicaid expansions and marijuana legalization on opioid-related deaths, I conclude that opioid mortality can be reduced without restricting opioid access.
Scope of practice and opioid prescribing behavior of nurse practitioners serving Medicare beneficiaries
Shishir Shakya & Alicia Plemmons
Health Economics, February 2025, Pages 225-245
Abstract:
Policymakers aiming to increase access to health care while simultaneously keeping costs low and quality high are considering expanding the practice authority and prescriptive authority of nurse practitioners in order to address primary care shortages. While we know this increases access, some researchers argue that the expansion of job autonomy of nurse practitioners can compromise the quality and safety of rendered medical services. This paper investigates quality and safety outcomes in prescribing behaviors of nurse practitioners who have prescribed opioids for Medicare Part D beneficiaries using a unique source of policy variation, nurse practitioners with the ability to prescribe medication who move to either states with or without physician supervision. We find that scope of practice expansions do not compromise quality and safety in terms of potential abuse or misuse of prescriptive authority.
Prediction with Differential Covariate Classification: Illustrated by Racial/Ethnic Classification in Medical Risk Assessment
Charles Manski, John Mullahy & Atheendar Venkataramani
NBER Working Paper, January 2025
Abstract:
A common practice in evidence-based decision-making uses estimates of conditional probabilities P(y|x) obtained from research studies to predict outcomes y on the basis of observed covariates x. Given this information, decisions are then based on the predicted outcomes. Researchers commonly assume that the predictors used in the generation of the evidence are the same as those used in applying the evidence: i.e., the meaning of x in the two circumstances is the same. This may not be the case in real-world settings. Across a wide-range of settings, ranging from clinical practice to education policy, demographic attributes (e.g., age, race, ethnicity) are often classified differently in research studies than in decision settings. This paper studies identification in such settings. We propose a formal framework for prediction with what we term differential covariate classification (DCC). Using this framework, we analyze partial identification of probabilistic predictions and assess how various assumptions influence the identification regions. We apply the findings to a range of settings, focusing mainly on differential classification of individuals' race and ethnicity in clinical medicine. We find that bounds on P(y|x) can be wide, and the information needed to narrow them available only in special cases. These findings highlight an important problem in using evidence in decision making, a problem that has not yet been fully appreciated in debates on classification in public policy and medicine.
Selection of Movers on Observable Characteristics and the Effect of Place on Health and Healthcare Spending
Ryan Gallagher, Robert Kaestner & Cuiping Schiman
American Journal of Health Economics, forthcoming
Abstract:
Understanding the effect of place (geography) on health and healthcare spending is a longstanding research question with important implications for improving health and the efficiency of healthcare spending. To answer this question, recent studies have exploited variation in place resulting from elderly persons moving. The key assumption of these studies is that moving is exogenous conditional on observed characteristics (e.g., age, sex, and race) and place of origin fixed effects (or person fixed effects). In this article, we document the extent of selection among elderly movers on a set of observable characteristics and estimate differences in Medicare spending and hospitalizations associated with such selection. Specifically, we measured the amount of selection between movers and non-movers, and among movers by the type of move made, as characterized by differences in Medicare spending and hospitalization between the mover's origin and destination locations. Our analysis shows that there is a substantial amount of selection among movers on observable characteristics not used in previous studies and that such selection is associated with large and economically important differences in Medicare spending and hospitalizations. We also show that the inclusion of person fixed effects does not eliminate the problem from unmeasured confounding due to time-varying effects of the observed characteristics on individual outcomes. In sum, our analysis suggests there is likely significant confounding unaccounted for in studies that estimate the effect of "place" on health and healthcare spending using movers.
Quality Improvement Spillovers: Evidence from the Hospital Readmissions Reduction Program
Mohamad Soltani, Robert Batt & Hessam Bavafa
Management Science, forthcoming
Abstract:
Quality knowledge spillovers can enhance the overall effectiveness of quality improvement initiatives. We study the presence and moderators of such spillovers in a multitask service setting, specifically in hospital inpatient care. Leveraging a national quality improvement regulation, the Hospital Readmissions Reduction Program (HRRP), which offers partial incentives for hospitals to reduce readmissions, we employ difference-in-differences econometric models on a nationwide database and find positive quality spillovers in the healthcare sector. Our findings indicate that the implementation of HRRP led to a significant decrease in 30-day readmissions among patients with clinical conditions or insurance types that were not targeted by the policy. Additionally, we find that task similarity played a positive role in promoting quality spillovers, while a hospital’s operational focus on target patients (i.e., the proportion of hospital volume targeted by the policy) did not moderate these spillovers. Notably, we observe that hospitals achieved these quality improvements without increasing the intensity of care provided, and that meaningful improvements in quality were associated with up to a 3% reduction in hospitalization costs. This paper contributes novel insights into how regulators and policymakers can design narrow public policies and regulations that achieve broader results by exploiting the beneficial quality improvement spillovers of partial incentives.
Changes in healthcare costs and utilization for Medicaid recipients who received supportive housing through a payer-community-based housing partnership
John Lovelace et al.
Health Services Research, forthcoming
Study Setting and Design: Healthcare claims were reviewed retrospectively for 80 program participants in one urban Pennsylvania county between 1/1/2018 and 9/28/2023 who had ≥6 months of claims data in both pre- and post-housing periods. Eligibility included age >18 years, Medicaid/Special Needs Plan enrollment, and housing need. Due to limited housing units, potential participants were prioritized by medical need and history of unplanned care.
Principal Findings: Compared to the pre-period, significantly lower medical (−40.4%, p = 0.004), emergency department (−62.7%, p = 0.02), and total (−33.3%, p = 0.02) costs of care were observed in the post-period. Significantly lower primary care (−50.0%, p = 0.0003), specialist (−31.3%, p = 0.02), and emergency department (−50.0%, p = 0.03) utilization were also observed.
Nurse practitioner oversight ratios and labor market outcomes
Andrew Smith & Sara Markowitz
Contemporary Economic Policy, January 2025, Pages 52-78
Abstract:
Nurse practitioners are important to the US healthcare system; however, many states impose physician oversight ratios, which limit the number of NPs a physician may supervise. In this study, we evaluate the effect of eliminating physician oversight ratios on the supply of NPs by conducting case studies of three states -- New York, Nevada, and Pennsylvania -- using synthetic control and related approaches. Surprisingly, we find that eliminating oversight ratios had little effect on the labor supply of NPs. For New York, we also analyze the effects of eliminating oversight ratios on wages and hours worked and again find null results.
Information Disclosure and Competitive Dynamics: Evidence from the Pharmaceutical Industry
Jennifer Kao
Management Science, forthcoming
Abstract:
This paper studies how competitive dynamics shape innovative firms’ voluntary disclosure of product quality information. Our empirical context is the pharmaceutical industry, where firms must decide whether to disclose private drug quality information acquired in clinical trials. Using a difference-in-difference strategy, we show that the approval of a competitor’s drug lowers the likelihood of a firm reporting its clinical trial results by 13%. We explore how these effects vary based on the project quality, competitor type, and firm experience. These findings suggest that strategic considerations play a role in firms’ disclosure decisions; in response to a competitor’s drug approval, firms may selectively withhold information to maintain and improve their competitive position.