Health economics最新文献

The Medicaid coverage “cliff” occurs when Medicare beneficiaries with household income exceeding 100% of the federal poverty level lose eligibility for supplemental Medicaid coverage. Using a regression discontinuity design with data from Medical Expenditure Panel Survey and National Health and Nutrition Examination Survey for 2007–2019, we demonstrate that the cliff increases out-of-pocket spending by 25% and the probability of experiencing problems paying medical bills by 44.4% without decreases in overall health care spending. However, there is evidence that near-poor Medicare beneficiaries changed behavior in response to the cliff, increasing the use of high-value diagnostic and preventive testing by 8.8% and enrollment in a more affordable plan by 12.2%. The cliff does not encourage healthy behavior.

Methods have been developed for transporting evidence from randomised controlled trials (RCTs) to target populations. However, these approaches allow only for differences in characteristics observed in the RCT and real-world data (overt heterogeneity). These approaches do not recognise heterogeneity of treatment effects (HTE) according to unmeasured characteristics (essential heterogeneity). We use a target trial design and apply a local instrumental variable (LIV) approach to electronic health records from the Clinical Practice Research Datalink, and examine both forms of heterogeneity in assessing the comparative effectiveness of two second-line treatments for type 2 diabetes mellitus. We first estimate individualised estimates of HTE across the entire target population defined by applying eligibility criteria from national guidelines (n = 13,240) within an overall target trial framework. We define a subpopulation who meet a published RCT's eligibility criteria (‘RCT-eligible’, n = 6497), and a subpopulation who do not (‘RCT-ineligible’, n = 6743). We compare average treatment effects for pre-specified subgroups within the RCT-eligible subpopulation, the RCT-ineligible subpopulation, and within the overall target population. We find differences across these subpopulations in the magnitude of subgroup-level treatment effects, but that the direction of estimated effects is stable. Our results highlight that LIV methods can provide useful evidence about treatment effect heterogeneity including for those subpopulations excluded from RCTs.

This paper theoretically investigates externalities and policy interventions in travel during a pandemic. We develop a tractable static model of two regions from a short-run perspective. The model shows that the externalities can be both negative and positive, depending on regional asymmetry. Thus, even when infectious diseases are widespread, travel restrictions do not necessarily reduce infections and do not necessarily improve social welfare. A formula for the optimal policy intervention is derived and shown to be the weighted average of four types of externalities defined by the direction of travel and the epidemiological status of a traveler.

Despite a growing interest in the impacts of prenatal factors on adulthood quality of life, economists have not estimated the income effects of sunlight exposures by mothers during pregnancy. This paper estimates such effects using data from China General Social Survey and China National Meteorological Data Service Center. The results show that the income effects of prenatal sunlight exposure in the second trimester are significantly positive. The effects differ for individuals born in different months and the effects are larger for female employees, older employees, those born in rural areas, in the pre-reform period, or whose mothers are less-educated. Finally, we investigate the possible mechanisms via the human capital pathway, discovering that fetuses with longer sunlight exposure in the second trimester are healthier and do more exercises in adulthood. It is suggested that families, communities, policymakers should pay attention to prenatal sunlight exposure, especially for pregnant women in the developing world who are less educated or live in rural areas.

Lower levels of education are associated with higher mortality. Lower levels of education are also associated with lower income, which is also associated with higher mortality. We investigate the impact of education on mortality and the extent to which this is mediated through income over the life course. We account for both selective educational attainment and selective income over the life course, through inverse propensity weighting (IPW) of the mortality hazard. We decompose the educational gain, that is, the decrease in mortality from more education, in the hazard into an indirect effect of education, running through changes in income and a direct effect of education, running through other factors. We use Swedish conscription data (men only), linked to parental information and individual annual income for the period 1968 till 2012. Our empirical results indicate large educational gains in mortality. We also find that this educational gradient runs through changes in income, especially for the more educated, and does not run through other factors related to education. We conduct several robustness and sensitivity checks that indicate that the results are robust.

The literature on health shocks finds that minor injuries have only short-term labor market impacts. However, mild traumatic brain injuries (mTBIs, commonly referred to as concussions) may be different as the medical literature highlights that they can have longer-term health and cognitive effects. Moreover, TBIs are one of the most common causes of disability globally, with the vast majority being mild. Thus, it is important to understand the impact of mTBIs on labor market outcomes. We use administrative data on all medically-diagnosed mild traumatic brain injuries (mTBIs) in New Zealand linked to monthly tax records to examine the labor market effects of a mTBI. We use a comparison group of those who suffer a mTBI at a later date to overcome potential endogeneity issues, and employ a doubly-robust difference-in-differences method. We find that suffering a mTBI has negative labor market effects. Rather than dissipating over time, these negative effects grow, representing a decrease in employment of 20 percentage points and earning losses of about a third after 48 months. Our results highlight the need for timely diagnosis and treatment to mitigate the effect of mTBIs to reduce economic and social costs.

Age is one of the most relevant observable risk attributes in determining the value of health insurance premiums. Empirical evidence indicates that the cost of health insurance is the leading cause of contract switching, which can compromise access to healthcare services and potentially result in treatment discontinuities. Using data from a Health Maintenance Organization in the Southern region of Brazil, we examined the effect of health plan price readjustment resulting from changes in the beneficiary's age group on disenrollment or switches to a more limited coverage plan. The estimates were obtained using the method of regression discontinuity. The main findings indicate that for age group transitions at 59 years old, the price readjustment effect led to an increase in contract cancellations and switching to cheaper plans. These findings highlight that an important consequence of the difference in premium sensitivity among age groups is that the exit of individuals from the health insurance sector is selective in age. The results of this paper can support policymaking to improve access to health insurance.

This study investigates the impacts of health shocks among older individuals on the dynamics of their living arrangements in Europe and the United States. Exploiting unpredicted health shocks, we use an event-study difference-in-differences approach to demonstrate that health shocks increase difficulties with activities of daily living and instrumental activities of daily living, thereby increasing the need for care. Our findings indicate that health shocks raise the probability of nursing home residency and co-residence with adult children by 0.7 and 1.4 percentage points in Europe, and by 2.1 and 1.8 percentage points in the U.S., respectively. Further analyses reveal that more generous long-term care public policies correlate with a higher probability of nursing home residency and a lower probability of co-residing with adult children, highlighting the significant role of public policies in household responses to health shocks. Additionally, we find that health shocks negatively impact adult children's labor supply, particularly in the U.S.

This paper empirically investigates the impact of public long-term care insurance (LTCI) on the utilization of inpatient services and associated expenditures among disabled Chinese individuals, using data from the China Health and Retirement Longitudinal Study from 2011 to 2018. Employing a staggered difference-in-difference approach within a propensity score matching framework (PSM-DID), the study finds that the introduction of LTCI significantly reduces the likelihood of inpatient service usage by 4.2%, the annual number of inpatient admissions by 10.2%, the annual inpatient cost by 16.2%, the out-of-pocket expenses by 20.7%, and the reimbursement expenditure by the public medical insurer by 9.9%. The study further explores the mechanisms underlying these effects and identifies that the Substitution Effect, where care services in community healthcare centers and nursing homes replace hospitalizations, outweighs the Income Effect generated by LTCI benefits. By leveraging the quasi-natural experimental setting of diverse LTCI policies across cities, the study also examines the heterogeneous impacts of LTCI based on household income, eligibility criteria, and reimbursement methods. The findings underscore the positive role of LTCI in controlling medical expenses and alleviating congestion in urban hospitals, offering valuable insights for promoting “Healthy Aging”.

Estimating the causal effects of health policy interventions is crucial for policymaking but is challenging when using real-world administrative health care data due to a lack of methodological guidance. To help fill this gap, we conducted a plasmode simulation using such data from a recent policy initiative launched in a deprived urban area in Germany. Our aim was to evaluate and compare the following methods for estimating causal effects: propensity score matching, inverse probability of treatment weighting, and entropy balancing, all combined with difference-in-differences analysis, augmented inverse probability weighting, and targeted maximum likelihood estimation. Additionally, we estimated nuisance parameters using regression models and an ensemble learner called superlearner. We focused on treatment effects related to the number of physician visits, total health care cost, and hospitalization. While each approach has its strengths and weaknesses, our results demonstrate that the superlearner generally worked well for handling nuisance terms in large covariate sets when combined with doubly robust estimation methods to estimate the causal contrast of interest. In contrast, regression-based nuisance parameter estimation worked best in small covariate sets when combined with singly robust methods.