Health Economics

doi: 10.1002/hec.3597pmid: N/A

No abstract is available for this article.

Frew, Emma; Platt, Maia; Brown, Heather; Goodman, Allen; Seidler, Elizabeth; Spetz, Joanne; McIntyre, Di

doi: 10.1002/hec.3655pmid: 29722182

Ahammer, Alexander

doi: 10.1002/hec.3646pmid: 29573056

I analyze how general practitioners (GPs) indirectly affect their patients' employment outcomes by deciding the length of sick leaves. I use an instrumental variables framework where spell durations are identified through supply‐side certification measures. I find that a day of sick leave certified only because the worker's GP has a high propensity to certify sick leaves decreases the employment probability persistently by 0.45–0.69 percentage points, but increases the risk of becoming unemployed by 0.28–0.44 percentage points. These effects are mostly driven by workers with low job tenure. Several robustness checks show that endogenous matching between patients and GPs does not impair identification. My results bear important implications for doctors: Whenever medically justifiable, certifying shorter sick leaves to protect the employment status of the patient may be beneficial.

Basu, Anirban; Coe, Norma B.; Chapman, Cole G.

doi: 10.1002/hec.3647pmid: 29577493

This study used Monte Carlo simulations to examine the ability of the two‐stage least squares (2SLS) estimator and two‐stage residual inclusion (2SRI) estimators with varying forms of residuals to estimate the local average and population average treatment effect parameters in models with binary outcome, endogenous binary treatment, and single binary instrument. The rarity of the outcome and the treatment was varied across simulation scenarios. Results showed that 2SLS generated consistent estimates of the local average treatment effects (LATE) and biased estimates of the average treatment effects (ATE) across all scenarios. 2SRI approaches, in general, produced biased estimates of both LATE and ATE under all scenarios. 2SRI using generalized residuals minimized the bias in ATE estimates. Use of 2SLS and 2SRI is illustrated in an empirical application estimating the effects of long‐term care insurance on a variety of binary health care utilization outcomes among the near‐elderly using the Health and Retirement Study.

Peng, Lizhong; Meyerhoefer, Chad; Chou, Shin‐Yi

doi: 10.1002/hec.3649pmid: 29532974

We investigate the health impacts of unconventional natural gas development of Marcellus shale in Pennsylvania between 2001 and 2013 by merging well permit data from the Pennsylvania Department of Environmental Protection with a database of all inpatient hospital admissions. After comparing changes in hospitalization rates over time for air pollution‐sensitive diseases in counties with unconventional gas wells to changes in hospitalization rates in nonwell counties, we find a significant association between shale gas development and hospitalizations for pneumonia among the elderly, which is consistent with higher levels of air pollution resulting from unconventional natural gas development. We note that the lack of any detectable impact of shale gas development on younger populations may be due to unobserved factors contemporaneous with drilling, such as migration.

Park, Sungchul; Basu, Anirban

doi: 10.1002/hec.3657pmid: 29577489

Risk adjustment is instituted to counter risk selection by accurately equating payments with expected expenditures. Traditional risk‐adjustment methods are designed to estimate accurate payments at the group level. However, this generates residual risks at the individual level, especially for high‐expenditure individuals, thereby inducing health plans to avoid those with high residual risks. To identify an optimal risk‐adjustment method, we perform a comprehensive comparison of prediction accuracies at the group level, at the tail distributions, and at the individual level across 19 estimators: 9 parametric regression, 7 machine learning, and 3 distributional estimators. Using the 2013–2014 MarketScan database, we find that no one estimator performs best in all prediction accuracies. Generally, machine learning and distribution‐based estimators achieve higher group‐level prediction accuracy than parametric regression estimators. However, parametric regression estimators show higher tail distribution prediction accuracy and individual‐level prediction accuracy, especially at the tails of the distribution. This suggests that there is a trade‐off in selecting an appropriate risk‐adjustment method between estimating accurate payments at the group level and lower residual risks at the individual level. Our results indicate that an optimal method cannot be determined solely on the basis of statistical metrics but rather needs to account for simulating plans' risk selective behaviors.

Winters, John V.

doi: 10.1002/hec.3648pmid: 29484777

This paper used microdata from the 2013–2015 American Community Survey to examine differences in federal government, state and local government, private sector, and self‐employment among employed veterans and nonveterans. The U.S. federal and state governments have hiring preferences to benefit veterans, especially disabled veterans. Other factors may also push veterans toward public sector employment. I found that veteran status substantially increased the likelihood of federal employment, with the largest magnitudes for severely disabled veterans. Differences in state and local government employment were modest and exhibited heterogeneity by disability severity.

Claxton, Karl; Lomas, James; Martin, Stephen

doi: 10.1002/hec.3650pmid: 29607571

Several recent studies have estimated the responsiveness of mortality to English National Health Service spending. Although broadly similar, the studies differ in how they identify the outcome equation. One approach uses conventional socio‐economic variables as instruments for endogenous health care expenditure, whereas the other exploits exogenous elements in the resource allocation formula for local budgets. The former approach has usually been applied to specific disease areas (e.g., for cancer and circulatory disease), whereas the other has only been applied to all‐cause mortality. In this letter, we compare the two approaches by using them to estimate the direct all‐cause elasticity as well as disease‐specific elasticities. We also calculate the implied all‐cause elasticity associated with the disease‐specific results. We find that the “funding rule” approach to identification can be successfully replicated and applied to disease area models. This is important because disease area models reduce the danger of aggregation bias present in all‐cause analysis, and they offer the opportunity to link estimated mortality effects to more complete measures of health outcome that reflect what is currently known about the survival and morbidity disease burden in different programmes.

Leurent, Baptiste; Gomes, Manuel; Carpenter, James R.

doi: 10.1002/hec.3654pmid: 29573044

Cost‐effectiveness analyses (CEA) conducted alongside randomised trials provide key evidence for informing healthcare decision making, but missing data pose substantive challenges. Recently, there have been a number of developments in methods and guidelines addressing missing data in trials. However, it is unclear whether these developments have permeated CEA practice. This paper critically reviews the extent of and methods used to address missing data in recently published trial‐based CEA.