Moral Hazard in Health Insurance: How Important Is Forward Looking Behavior?

We investigate whether individuals exhibit forward looking behavior in their response to the non-linear pricing common in health insurance contracts. Our empirical strategy exploits the fact that employees who join an employer-provided health insurance plan later in the calendar year face the same initial ("spot") price of medical care but a higher expected end-of-year ("future") price than employees who join the same plan earlier in the year. Our results reject the null of completely myopic behavior; medical utilization appears to respond to the future price, with a statistically significant elasticity of medical utilization with respect to the future price of -0.4 to -0.6. To try to quantify the extent of forward looking behavior, we develop a stylized dynamic model of individual behavior and calibrate it using our estimated behavioral response and additional data from the RAND Health Insurance Experiment. Our calibration suggests that the elasticity estimate may be substantially smaller than the one implied by fully forward-looking behavior, yet it is sufficiently high to have an economically significant effect on the response of annual medical utilization to a non-linear health insurance contract. Overall, our results point to the empirical importance of accounting for dynamic incentives in analyses of the impact of health insurance on medical utilization.

A Mass-Magnitude Relation for Low-mass Stars Based on Dynamical Measurements of Thousands of Binary Star Systems

Stellar mass is a fundamental parameter that is key to our understanding of stellar formation and evolution, as well as the characterization of nearby exoplanet companions. Historically, stellar masses have been derived from long-term observations of visual or spectroscopic binary star systems. While advances in high-resolution imaging have enabled observations of systems with shorter orbital periods, stellar mass measurements remain challenging, and relatively few have been precisely measured. We present a new statistical approach to measuring masses for populations of stars. Using Gaia astrometry, we analyze the relative orbital motion of $>3,800$ wide binary systems comprising low-mass stars to establish a Mass-Magnitude relation in the Gaia $G_\mathrm{RP}$ band spanning the absolute magnitude range $14.5>M_{G_\mathrm{RP}}>4.0$, corresponding to a mass range of $0.08$~M$_{\odot}\lesssim M\lesssim1.0$~M$_{\odot}$. This relation is directly applicable to $>30$ million stars in the Gaia catalog. Based on comparison to existing Mass-Magnitude relations calibrated for 2MASS $K_{s}$ magnitudes, we estimate that the internal precision of our mass estimates is $\sim$10$\%$. We use this relation to estimate masses for a volume-limited sample of $\sim$18,200 stars within 50~pc of the Sun and the present-day field mass function for stars with $M\lesssim 1.0$~M$_{\odot}$, which we find peaks at 0.16~M$_{\odot}$. We investigate a volume-limited sample of wide binary systems with early K dwarf primaries, complete for binary mass ratios $q>0.2$, and measure the distribution of $q$ at separations $>100$~au. We find that our distribution of $q$ is not uniformly distributed, rather decreasing towards $q=1.0$.Comment: 13 pages, 8 figure

ESTIMATING WELFARE IN INSURANCE MARKETS USING VARIATION IN PRICES

We provide a graphical illustration of how standard consumer and producer theory can be used to quantify the welfare loss associated with inefficient pricing in insurance markets with selection. We then show how this welfare loss can be estimated empirically using identifying variation in the price of insurance. Such variation, together with quantity data, allows us to estimate the demand for insurance. The same variation, together with cost data, allows us to estimate how insurer s costs vary as market participants endogenously respond to price. The slope of this estimated cost curve provides a direct test for both the existence and nature of selection, and the combination of demand and cost curves can be used to estimate welfare. We illustrate our approach by applying it to data on employer-provided health insurance from one specific company. We detect adverse selection but estimate that the quantitative welfare implications associated with inefficient pricing in our particular application are small, in both absolute and relative terms.National Institute of Aging (R01 AG032449

Estimating Welfare in Insurance Markets Using Variation in Prices

We show how standard consumer and producer theory can be used to estimate welfare in insurance markets with selection. The key observation is that the same price variation needed to identify the demand curve also identifies how costs vary as market participants endogenously respond to price. With estimates of both the demand and cost curves, welfare analysis is straightforward. We illustrate our approach by applying it to the employee health insurance choices at Alcoa, Inc. We detect adverse selection in this setting but estimate that its quantitative welfare implications are small, and not obviously remediable by standard public policy tools.

Particle Size Distribution in Aluminum Manufacturing Facilities.

As part of exposure assessment for an ongoing epidemiologic study of heart disease and fine particle exposures in aluminum industry, area particle samples were collected in production facilities to assess instrument reliability and particle size distribution at different process areas. Personal modular impactors (PMI) and Minimicro-orifice uniform deposition impactors (MiniMOUDI) were used. The coefficient of variation (CV) of co-located samples was used to evaluate the reproducibility of the samplers. PM2.5 measured by PMI was compared to PM2.5 calculated from MiniMOUDI data. Mass median aerodynamic diameter (MMAD) and concentrations of sub-micrometer (PM1.0) and quasi-ultrafine (PM0.56) particles were evaluated to characterize particle size distribution. Most of CVs were less than 30%. The slope of the linear regression of PMI_PM2.5 versus MiniMOUDI_PM2.5 was 1.03 mg/m3 per mg/m3 (± 0.05), with correlation coefficient of 0.97 (± 0.01). Particle size distribution varied substantively in smelters, whereas it was less variable in fabrication units with significantly smaller MMADs (arithmetic mean of MMADs: 2.59 μm in smelters vs. 1.31 μm in fabrication units, p = 0.001). Although the total particle concentration was more than two times higher in the smelters than in the fabrication units, the fraction of PM10 which was PM1.0 or PM0.56 was significantly lower in the smelters than in the fabrication units (p < 0.001). Consequently, the concentrations of sub-micrometer and quasi-ultrafine particles were similar in these two types of facilities. It would appear, studies evaluating ultrafine particle exposure in aluminum industry should focus on not only the smelters, but also the fabrication facilities

Ischemic Heart Disease Incidence in Relation to Fine versus Total Particulate Matter Exposure in a U.S. Aluminum Industry Cohort.

Ischemic heart disease (IHD) has been linked to exposures to airborne particles with an aerodynamic diameter <2.5 μm (PM2.5) in the ambient environment and in occupational settings. Routine industrial exposure monitoring, however, has traditionally focused on total particulate matter (TPM). To assess potential benefits of PM2.5 monitoring, we compared the exposure-response relationships between both PM2.5 and TPM and incidence of IHD in a cohort of active aluminum industry workers. To account for the presence of time varying confounding by health status we applied marginal structural Cox models in a cohort followed with medical claims data for IHD incidence from 1998 to 2012. Analyses were stratified by work process into smelters (n = 6,579) and fabrication (n = 7,432). Binary exposure was defined by the 10th-percentile cut-off from the respective TPM and PM2.5 exposure distributions for each work process. Hazard Ratios (HR) comparing always exposed above the exposure cut-off to always exposed below the cut-off were higher for PM2.5, with HRs of 1.70 (95% confidence interval (CI): 1.11-2.60) and 1.48 (95% CI: 1.02-2.13) in smelters and fabrication, respectively. For TPM, the HRs were 1.25 (95% CI: 0.89-1.77) and 1.25 (95% CI: 0.88-1.77) for smelters and fabrication respectively. Although TPM and PM2.5 were highly correlated in this work environment, results indicate that, consistent with biologic plausibility, PM2.5 is a stronger predictor of IHD risk than TPM. Cardiovascular risk management in the aluminum industry, and other similar work environments, could be better guided by exposure surveillance programs monitoring PM2.5

County-Level Hispanic Ethnic Density and Cardiovascular Disease Mortality.

Background Hispanics are the fastest growing ethnic group in the United States, and little is known about how Hispanic ethnic population density impacts cardiovascular disease ( CVD ) mortality. Methods and Results We examined county-level deaths for Hispanics and non-Hispanic whites from 2003 to 2012 using data from the National Center for Health Statistics' Multiple Cause of Death mortality files. Counties with more than 20 Hispanic deaths (n=715) were included in the analyses. CVD deaths were identified using International Classification of Diseases, Tenth Revision (ICD-10), I00 to I78, and population estimates were calculated using linear interpolation from 2000 and 2010 census data. Multivariate linear regression was used to examine the association of Hispanic ethnic density with Hispanic and non-Hispanic white age-adjusted CVD mortality rates. County-level age-adjusted CVD mortality rates were adjusted for county-level demographic, socioeconomic, and healthcare factors. There were a total of 4 769 040 deaths among Hispanics (n=382 416) and non-Hispanic whites (n=4 386 624). Overall, cardiovascular age-adjusted mortality rates were higher among non-Hispanic whites compared with Hispanics (244.8 versus 189.0 per 100 000). Hispanic density ranged from 1% to 96% in each county. Counties in the highest compared with lowest category of Hispanic density had 60% higher Hispanic mortality (215.3 versus 134.2 per 100 000 population). In linear regression models, after adjusting for county-level demographic, socioeconomic, and healthcare factors, increasing Hispanic ethnic density remained strongly associated with mortality for Hispanics but not for non-Hispanic whites. Conclusions CVD mortality is higher in counties with higher Hispanic ethnic density. County-level characteristics do not fully explain the higher CVD mortality among Hispanics in ethnically concentrated counties

Incident Ischemic Heart Disease After Long-Term Occupational Exposure to Fine Particulate Matter: Accounting for 2 Forms of Survivor Bias.

Little is known about the heart disease risks associated with occupational, rather than traffic-related, exposure to particulate matter with aerodynamic diameter of 2.5 µm or less (PM2.5). We examined long-term exposure to PM2.5 in cohorts of aluminum smelters and fabrication workers in the United States who were followed for incident ischemic heart disease from 1998 to 2012, and we addressed 2 forms of survivor bias. Left truncation bias was addressed by restricting analyses to the subcohort hired after the start of follow up. Healthy worker survivor bias, which is characterized by time-varying confounding that is affected by prior exposure, was documented only in the smelters and required the use of marginal structural Cox models. When comparing always-exposed participants above the 10th percentile of annual exposure with those below, the hazard ratios were 1.67 (95% confidence interval (CI): 1.11, 2.52) and 3.95 (95% CI: 0.87, 18.00) in the full and restricted subcohorts of smelter workers, respectively. In the fabrication stratum, hazard ratios based on conditional Cox models were 0.98 (95% CI: 0.94, 1.02) and 1.17 (95% CI: 1.00, 1.37) per 1 mg/m(3)-year in the full and restricted subcohorts, respectively. Long-term exposure to occupational PM2.5 was associated with a higher risk of ischemic heart disease among aluminum manufacturing workers, particularly in smelters, after adjustment for survivor bias

How general are risk preferences? Choices under uncertainty in different domains.

We examine the extent to which an individual's actual insurance and investment choices display a stable ranking in willingness to bear risk, relative to his peers, across different contexts. We do so by examining the same individuals' decisions regarding their 401(k) asset allocations and their choices in five different employer-provided insurance domains, including health and disability insurance. We reject the null that there is no domain-general component of preferences. Among the five insurance domains, the magnitude of the domain-general component of preferences appears substantial; we find for example that one's choices in other insurance domains are substantially more predictive of one's choice in a given insurance domain than either one's detailed demographic characteristics or one's claims experience in that domain. However, we find considerably less predictive power between one's insurance choices and the riskiness of one's 401(k) asset allocations, suggesting that the common element of an individual's preferences may be stronger among domains that are "closer" in context. We also find that the relationship between insurance and investment choices appears considerably larger for employees who may be associated with better "financial sophistication." Overall, we view our findings as largely consistent with an important domain-general component of risk preferences.

Retromer-dependent lysosomal stress in Parkinson's disease

While causative mutations in complex disorders are rare, they can be used to extract a biological pathway whose pathogenicity can generalize to common forms of the disease. Here we begin by relying on the biological consequences of mutations in LRRK2 and VPS35, genetic causes of autosomal-dominant Parkinson's disease, to hypothesize that 'Retromer-dependent lysosomal stress' represents a pathway that can generalize to idiopathic Parkinson's disease. Next, we outline a series of studies that can test this hypothesis, including the development of biomarkers of pathway dysfunction. If validated, the hypothesis can suggest a unified mechanism of disease and might inform future diagnostic and therapeutic investigations. This article is part of a discussion meeting issue 'Understanding the endo-lysosomal network in neurodegeneration'.</p