Health economics最新文献

In March of 2017 Utah announced its intent to lower the legal blood alcohol content (BAC) for driving from 0.08 to 0.05 g/dL. However, this change did not take effect until 2019. We employ a difference-in- differences strategy on Utah counties using neighboring states as controls to test whether this policy change significantly affected the number of traffic accidents or the severity of those accidents. Results show the policy appears to temporarily decrease the total number of accidents, limited primarily to property damage- only accidents. We believe these results may be partially explained by drivers who, after the policy is enacted, avoid reporting property damage-only accidents if possible. Using insurance claims data, we show there is no corresponding fall in insurance claims or payouts suggesting that the fall in total accidents likely comes from under-reporting.

In this paper, I examine how patient death affects referrals from referring physicians to cardiac surgeons. I use Medicare data to identify pairs of referring physicians and cardiac surgeons who experience a patient death after a major surgical procedure to examine how these events affect referrals. I construct counterfactuals for affected pairs using pairs that experience a patient death but five quarters in the future. I find that there is a significant decline in the number of referrals and probability of a referral from the referring physician to the cardiac surgeon after the patient's death.

Physicians often face tight resource constraints, meaning they have to make trade-offs between which patients they care for and the amount of care received. Studies show that patients requiring many resources disproportionately suffer a loss of care when resources are constrained. This study uncovers whether physicians' attitudes toward prioritization of healthcare predicts poor-health patients' access to care. We combine unique survey data on Danish GPs' preferred prioritization principle with register data on their patients' contacts in general practice. We consider different types of contacts as the required effort could impact the need for prioritization. Our results show variation in GPs' prioritization principles, where a majority prefers a principle that may lead to an unequal distribution of services. We further find that GPs' attitudes toward prioritization predict some poor-health patients' access to general practice. GPs who state they prefer the principle of prioritizing patients in the poorest health state when resources tightened provide more contacts to poor-health patients. The additional contacts are typically high-effort contacts such as annual status meetings and home visits, but also low-effort contacts such as emails. Our findings indicate inequity in poor-health patients' access to care across general practices.

This paper examines the effect of new medical technology (robotic surgery) on efficiency gains and productivity changes for surgical treatment in patients with prostate cancer from the perspective of a public health sector organization. In particular, we consider three interrelated surgical technologies within the English National Health System: robotic, laparoscopic and open radical prostatectomy. Robotic and laparoscopic techniques are minimally invasive procedures with similar clinical benefits. While the clinical benefits in adopting robotic surgery over laparoscopic intervention are unproven, it requires a high initial investment cost and carries high on-going maintenance costs. Using data from Hospital Episode Statistics for the period 2000–2018, we observe growing volumes of prostatectomies over time, mostly driven by an increase in robotic-assisted surgeries, and further analyze whether hospital providers that adopted a robot see improved measures of throughput. We then quantify changes in total factor and labor productivity arising from the use of this technology. We examine the impact of robotic adoption on efficiency gains employing a staggered difference-in-difference estimator and find evidence of a 50% reduction in length of stay (LoS), 49% decrease in post-LoS and 44% and 46% decrease in postoperative visits after 1 year and 2 years, respectively. Productivity analysis shows the growth in radical prostatectomy volume is sustained with a relatively stable number of urology surgeons. The robotic technique increases total production at the hospital level between 21% and 26%, coupled with a 29% improvement in labor productivity. These benefits lend some, but not overwhelming support for the large-scale hospital investments in such costly technology.

We utilize the phased rollout of COVID-19 vaccines by exact birth date in South Korea as a natural experiment for testing risk compensation. People may resume face-to-face social activities following vaccination because they perceive lower risk of infection. Applying a regression discontinuity design based on birth date cutoffs for vaccine eligibility, we find no evidence of risk-compensating behaviors, as measured by large, high-frequency data from credit card and airline companies as well as survey data. We find some evidence of self-selection into vaccine take-up based on perception toward vaccine effectiveness and side effects, but the treatment effects do not differ between compliers and never-takers.

I investigate heterogeneity across occupational characteristics in the effect of retirement eligibility on mental health in the United Kingdom. I use K-means clustering to define three occupational clusters, differing across multiple dimensions. I estimate the effect of retirement eligibility using a Regression Discontinuity Design, allowing the effect to differ by cluster. The effects of retirement eligibility are beneficial, and greater in two clusters: one comprised of white-collar jobs in an office setting and another of blue-collar jobs with high physical demands and hazards. The cluster with smaller benefits mixes blue- and white-collar uncompetitive jobs with high levels of customer interaction. The results have implications for the distributional effect of raising the retirement age.

Changes in the dynamics of prescription drug markets have raised issues regarding whether the United States Bureau of Labor Statistics' (BLS’) Prescription Drug Consumer Price Index (CPI-Rx) has adequately kept up with the evolving marketplace. The CPI-Rx limits its sampling frame to retail outpatient outlets and excludes prescription pharmaceuticals dispensed in non-retail settings such as hospitals, physician/clinic outpatient facilities, and nursing homes. Thus, the CPI-Rx overlooks the increasingly important specialty pharmaceuticals dispensed in non-retail settings, whose transactions are instead captured in the overall hospital and professional services component of the medical care CPI. Specialty drugs now account for about 55% of all U.S. drug spending, double the share of a decade earlier. To the extent specialty drug price growth differs from that of traditional pharmaceuticals, the CPI-Rx could provide an inaccurate measure of overall drug price inflation. We calculate a chained Laspeyres CPI using data from the Merative™ MarketScan® Databases for the years 2010–2019 and IQVIA-designated specialty drugs and offer evidence showing that by not sampling specialty drugs in non-retail settings, the CPI-Rx has understated overall U.S. prescription drug inflation by just under 75 basis points annually. We discuss implications for health care policy and suggest the BLS examine the feasibility of publishing an overall pharmaceutical price index incorporating both traditional and specialty pharmaceuticals dispensed in retail and non-retail settings.

There is increasing interest in moving away from “one size fits all (OSFA)” approaches toward stratifying treatment decisions. Understanding how expected effectiveness and cost-effectiveness varies with patient covariates is a key aspect of stratified decision making. Recently proposed machine learning (ML) methods can learn heterogeneity in outcomes without pre-specifying subgroups or functional forms, enabling the construction of decision rules (‘policies’) that map individual covariates into a treatment decision. However, these methods do not yet integrate ML estimates into a decision modeling framework in order to reflect long-term policy-relevant outcomes and synthesize information from multiple sources. In this paper, we propose a method to integrate ML and decision modeling, when individual patient data is available to estimate treatment-specific survival time. We also propose a novel implementation of policy tree algorithms to define subgroups using decision model output. We demonstrate these methods using the SPRINT (Systolic Blood Pressure Intervention Trial), comparing outcomes for “standard” and “intensive” blood pressure targets. We find that including ML into a decision model can impact the estimate of incremental net health benefit (INHB) for OSFA policies. We also find evidence that stratifying treatment using subgroups defined by a tree-based algorithm can increase the estimates of the INHB.

This paper identifies the health penalty experienced by girls due to having a brother from endogenous sibling gender composition. We propose a girls-to-girls comparison strategy and rule out the confounding effect from the sibship size, birth interval, and birth order. Employing an instrumental variable approach and data from the Chinese Family Panel Studies, we find that girls with a brother are demonstrably shorter and report poorer health. This “brother's penalty” manifests even prenatally. Alternative explanations, such as birth order disadvantages, are carefully addressed and ruled out. The results hold even after excluding gender-neutral ethnic minorities. This observed penalty is likely attributed to unequal resource allocation within families and potential parental neglect. This penalty is amplified in families with lower income and maternal education, implying resource constraints contribute to gender discrimination. Our findings highlight the importance of addressing intrafamily gender bias for ensuring equal opportunities and health outcomes.

Clinical trial registration: Not applicable.

We investigate the effects of regulations governing the practice autonomy of dental hygienists on dental care use with the 2001–2014 Medical Expenditure Panel Survey. We measure the strength of autonomy regulations by extending the Dental Hygiene Professional Practice Index to the years 2001–2014, allowing us to capture changes in regulations within states over time. Using a difference-in-differences framework applied to selected states, we find that relaxing supervision requirements to provide dental hygienists moderate autonomy results in an increase in total dental visits due to greater use of preventive dental care. However, the use of dental treatment decreases when states adopt the highest level of autonomy. Both sets of estimates increase in magnitude when we subset the sample to dental care provider shortage areas. In support of these findings, we show that dental visits shift to dental hygienists in shortage areas when states expand the scope of practice of hygienists, and that there is an increase in tasks performed by hygienists, such as cleanings and dental exams.