Private Insurance,Selection, and Health Care Use

Observed differences in medical utilization between the privately insured and uninsured reflect the combined effects of self-selection and insurance incentives (moral hazard). This article provides a Bayesian framework for decomposing the disparity into incentive and selection components. The effect of self-selection in private insurance on the number of doctor visits is estimated using a multiyear sample of the U.S. adult non-Medicare population obtained from the Medical Expenditure Panel Survey. We use a flexible econometric framework based on the “Roy model” and develop a Markov chain Monte Carlo algorithm. We estimate the distribution of treatment effects and find strong evidence indicating selection, which accounts, on average, for 50% or more of the observed disparity in doctor visits.     

医保统筹地区城乡医疗服务利用差异的因素分解

在使用回归分解方法的基础上,采用Heckman二步法缓解样本选择偏误,采用Neumark法消除指数基准问题,并以此对中国医保统筹地区城乡间医疗服务利用的差异进行分解.研究表明:描述统计上的城乡差异,很可能低估了城乡间医疗服务利用的不公平现象；导致城乡医疗差异最主要的因素是收入因素和医保政策因素.     

The demand to initiate medical care: A morbidity specific analysis

The dependence of the one year probability of utilizing preventive services and the one year probability of utilizing visits for acute micro-organism disease in the Kaiser-Oregon Prepaid Medical Care System on ten (10) explanatory factors has been investigated using a multiple logistic function analysis.Our results demonstrate the importance of disaggregating the population and the type of medical care when investigating the determinants of utilizing service. This is especially important for understanding the underlying structure of medical care utilization decisions.The study also illustrates a potentially fruitful application of the multivariate logistic analysis to the fields of health care planning and policy analysis. The use of specific types of medical care in the short-term is probabilistic and depends on many factors. For most groups, the multivariate logistic approach produces an analysis reasonably consistent with the actual data.Further research is needed to test the predictive ability of these types of utilization models and to investigate the determinants of other morbidity specific types of medical care utilization. The problem will then be to develop a model of the quantity of alternative types of services utilized-conditioned on the number of persons initiating service for alternative health reasons.     

Zero-Inflated Poisson Regression for Longitudinal Data

Medical and public health research often involve the analysis of repeated or longitudinal count data that exhibit excess zeros such as the number of yearly doctor visits by a group of individuals over a number of years. Zero-inflated Poisson (ZIP) regression models can be used to account for excess zeros in count data. We propose an extension of the ZIP model that is appropriate for longitudinal data. Our extension includes a non stationary, observation-driven time series model based correlation structure. We discuss estimation of the model parameters and the inefficiency of the estimators when the correlation structure is mis-specified. The model's application to the analysis of health care utilization data is also discussed.     

Three-part model for fractional response variables with application to Chinese household health insurance coverage

A survey on health insurance was conducted in July and August of 2011 in three major cities in China. In this study, we analyze the household coverage rate, which is an important index of the quality of health insurance. The coverage rate is restricted to the unit interval [0, 1], and it may differ from other rate data in that the “two corners” are nonzero. That is, there are nonzero probabilities of zero and full coverage. Such data may also be encountered in economics, finance, medicine, and many other areas. The existing approaches may not be able to properly accommodate such data. In this study, we develop a three-part model that properly describes fractional response variables with non-ignorable zeros and ones. We investigate estimation and inference under two proportional constraints on the regression parameters. Such constraints may lead to more lucid interpretations and fewer unknown parameters and hence more accurate estimation. A simulation study is conducted to compare the performance of constrained and unconstrained models and show that estimation under constraint can be more efficient. The analysis of household health insurance coverage data suggests that household size, income, expense, and presence of chronic disease are associated with insurance coverage.     

ESTIMATING THE NUMBER OF VISITS TO THE DOCTOR

The frequency of doctor consultations has direct consequences for health care budgets, yet little statistical analysis of the determinants of doctor visits has been reported. We consider the distribution of the number of visits to the doctor and, in particular, we model its dependence on a number of demographic factors. Examination of the Australian 1995 National Health Survey data reveals that generalized linear Poisson or negative binomial models are inadequate for modelling the mean as a function of covariates, because of excessive zero counts, and a mean‐variance relationship that varies enormously over covariate values. A negative binomial model is used, with parameter values estimated in subgroups according to the discrete combinations of the covariate values. Smoothing splines are then used to smooth and interpolate the parameter values. In effect the mean and the shape parameters are each modelled as (different) functions of gender, age and geographical factors. The estimated regressions for the mean have simple and intuitive interpretations. However, the dependence of the (negative binomial) shape parameter on the covariates is more difficult to interpret and is subject to influence by extreme observations. We illustrate the use of the model by estimating the distribution of the number of doctor consultations in the Statistical Local Area of Ryde, based on population numbers from the 1996 census.     

Time-consistent investment and reinsurance under relative performance concerns

This article investigates the optimal time-consistent investment and reinsurance for two mean–variance insurance managers who take into account the relative performance by comparison to their peers. The unique time-consistent Nash equilibrium policies and the corresponding value functions are derived for asset concentration and diversification. No matter which case is chosen, when the two insurance managers are sensitive to each other’s wealth, they chase each other’s trading behaviors leading to under-reinsurance and overinvestment and lower utility relative to the standard case without relative concerns. The cost–benefit from asset diversification to asset concentration and economic implications of parameters are illustrated by numerical examples.     

“新农保”影响了老人的健康绩效吗?——来自中国老年健康影响因素跟踪调查的证据

基于"中国老年健康影响因素跟踪调查"2008-2011/2012年两期面板数据,使用PSMDD方法和中介效应模型系统评估"新农保"对老人健康的影响。结果表明,新农保政策的实施对老人的健康既发挥了积极作用,又带来了消极影响。"新农保"能够显著改善老人的自理能力和减少患慢性病数,提高了老人的生理健康,但是却降低了老人的幸福感,使老人患抑郁症的可能性显著提升,导致老人的心理健康状况恶化。提高老人健康绩效的重点应在于对老人心理健康的照料和饮食条件的改善。同时,政府应注意到新农保制度实施过程中的消极影响,提早预防和改进。     

Analysis of recurrent events with non-negligible event duration,with application to assessing hospital utilization

In an attempt to provide tools for assessing hospital utilization, this paper extends well-known models for recurrent events to address non-negligible event duration and presents a procedure for estimating the model parameters. The model extension is natural and easy to understand. Asymptotic properties of the associated inferences are derived adapting the well-developed methods based on the counting process formulation. Several specifications of the proposed modeling are illustrated with the hospitalization records of childhood cancer survivors from a health care insurance system that motivated this research. The usefulness and robustness of the proposed approach is demonstrated numerically via simulation.     

Mean-Minimum Exact Confidence Intervals

This article introduces mean-minimum (MM) exact confidence intervals for a binomial probability. These intervals guarantee that both the mean and the minimum frequentist coverage never drop below specified values. For example, an MM 95[93]% interval has mean coverage at least 95% and minimum coverage at least 93%. In the conventional sense, such an interval can be viewed as an exact 93% interval that has mean coverage at least 95% or it can be viewed as an approximate 95% interval that has minimum coverage at least 93%. Graphical and numerical summaries of coverage and expected length suggest that the Blaker-based MM exact interval is an attractive alternative to, even an improvement over, commonly recommended approximate and exact intervals, including the Agresti–Coull approximate interval, the Clopper–Pearson (CP) exact interval, and the more recently recommended CP-, Blaker-, and Sterne-based mean-coverage-adjusted approximate intervals.     

Education-related inequity in healthcare with heterogeneous reporting of health

Reliance on self-rated health to proxy medical need can bias estimation of education-related inequity in healthcare utilization. We correct this bias both by instrumenting self-rated health with objective health indicators and by purging self-rated health of reporting heterogeneity that is identified from health vignettes. Using data on elderly Europeans, we find that instrumenting self-rated health shifts the distribution of visits to a doctor in the direction of inequality favouring the better educated. There is a further, and typically larger, shift in the same direction when correction is made for the tendency of the better educated to rate their health more negatively.     

Regression analysis of health care charges with heteroscedasticity

We examine the effects of a prospective drug utilization review and patients' characteristics on total in-patient and out-patient health care charges. Our analysis of charges is complicated by the fact that the total health care charges are skewed. A log-transformation of these charges can normalize their distribution but may not stabilize their variance. To handle these problems, we propose a linear regression model with a non-constant variance (heteroscedasticity). Using results from a fitted linear regression model for log-transformed charges, we also discuss interpreting the regression coefficients in the original scale and estimating the total health care charges to individual patients. Employing these methods, we analyse total health care charges for drug utilization review patients with hypertension and identify patients' factors that are related to their total health care charges.     

On familial Poisson mixed models with multi-dimensional random effects

When a generalized linear mixed model with multiple (two or more) sources of random effects is considered, the inferences may vary depending on the nature of the random effects. In this paper, we consider a familial Poisson mixed model where each of the count responses of a family are influenced by two independent unobservable familial random effects with two distinct components of dispersion. A generalized quasilikelihood (GQL) approach is discussed for the estimation of the dispersion components as well as the regression effects of the model. A simulation study is conducted to examine the relative performance of the GQL approach as opposed to a simpler method of moments. Furthermore, the GQL estimation methodology is illustrated by using health care utilization data that follow a Poisson mixed model with one component of dispersion and by using simulated asthma data that follow a Poisson mixed model with two sources of random effects with two distinct components of dispersion.     

Bootstrapping the Hausman Test in Panel Data Models

In the article, it is shown that in panel data models the Hausman test (HT) statistic can be considerably refined using the bootstrap technique. Edgeworth expansion shows that the coverage of the bootstrapped HT is second-order correct.

The asymptotic versus the bootstrapped HT are compared also by Monte Carlo simulations. At the null hypothesis and a nominal size of 0.05, the bootstrapped HT reduces the coverage error of the asymptotic HT by 10–40% of nominal size; for nominal sizes less than or equal to 0.025, the coverage error reduction is between 30% and 80% of nominal size. For the nonnull alternatives, the power of the asymptotic HT fictitiously increases by over 70% of the correct power for nominal sizes less than or equal to 0.025; the bootstrapped HT reduces overrejection to less than one fourth of its value. The advantages of the bootstrapped HT increase with the number of explanatory variables.

Heteroscedasticity or serial correlation in the idiosyncratic part of the error does not hamper advantages of the bootstrapped version of HT, if a heteroscedasticity robust version of the HT and the wild bootstrap are used. But, the power penalty is not negligible if a heteroscedasticity robust approach is used in the homoscedastic panel data model.     

Copula regression spline models for binary outcomes

We introduce a framework for estimating the effect that a binary treatment has on a binary outcome in the presence of unobserved confounding. The methodology is applied to a case study which uses data from the Medical Expenditure Panel Survey and whose aim is to estimate the effect of private health insurance on health care utilization. Unobserved confounding arises when variables which are associated with both treatment and outcome are not available (in economics this issue is known as endogeneity). Also, treatment and outcome may exhibit a dependence which cannot be modeled using a linear measure of association, and observed confounders may have a non-linear impact on the treatment and outcome variables. The problem of unobserved confounding is addressed using a two-equation structural latent variable framework, where one equation essentially describes a binary outcome as a function of a binary treatment whereas the other equation determines whether the treatment is received. Non-linear dependence between treatment and outcome is dealt using copula functions, whereas covariate-response relationships are flexibly modeled using a spline approach. Related model fitting and inferential procedures are developed, and asymptotic arguments presented.     

中国农村老年人生活满意度研究

基于2013年"中国健康与养老追踪调查"(CHARLS)数据,对中国农村老年人生活满意度的影响因素进行分析,重点关注新农保和新农合对生活满意度的影响。研究发现,性别、婚姻状况、是否有医疗保险、是否有亲人(除了配偶以外)或朋友能长期照顾对农村老年人生活满意度有显著影响。年龄和社交活动对农村老年人生活满意度的影响部分显著。根据研究结论,建议老龄问题立法层面应考虑性别差异,适度提升基础养老金水平,进一步发挥新农合的医疗保障作用并加强农村社区居家养老模式的建设。     

基于生存分析法的中国政策性农作物保险费率研究

为探索生存分析法对中国政策性农业保险纯费率厘定的适用性,采用辽宁省盘锦市1987—2008年的水稻、玉米和高粱作物单产数据,分别运用生存分析模型计算出75％产量保障水平的基础风险与巨灾风险概率,进而厘定出三种作物保险纯费率分别为3．0％、3．7％、3．7％。结果表明：该方法可以有效解决国内作物保险纯费率厘定过程中的巨灾风险厘定难题,将对中国政策性农业保险定价与补贴政策提供科学的研究依据。     

Nonparametric Testing for Asymmetric Information

Asymmetric information is an important phenomenon in many markets and in particular in insurance markets. Testing for asymmetric information has become a very important issue in the literature in the last two decades. Almost all testing procedures that are used in empirical studies are parametric, which may yield misleading conclusions in the case of misspecification of either functional or distributional relationships among the variables of interest. Motivated by the literature on testing conditional independence, we propose a new nonparametric test for asymmetric information, which is applicable in a variety of situations. We demonstrate that the test works reasonably well through Monte Carlo simulations and apply it to an automobile insurance dataset and a long-term care insurance (LTCI) dataset. Our empirical results consolidate Chiappori and Salanié’s findings that there is no evidence for the presence of asymmetric information in the French automobile insurance market. While Finkelstein and McGarry found no positive correlation between risk and coverage in the LTCI market in the United States, our test detects asymmetric information using only the information that is available to the insurance company, and our investigation of the source of asymmetric information suggests some sort of asymmetric information that is related to risk preferences as opposed to risk types and thus lends support to Finkelstein and McGarry.     

Parental partnership and joblessness in childhood and their influence on young people's outcomes

Summary.  The paper estimates the relationship between several outcomes in early adulthood (education, inactivity, early birth, distress and smoking) and experiences of life in a single-parent family and with jobless parent(s) during childhood. The analysis is performed using a sample of young adults, who are selected from the first nine waves of the British Household Panel Survey (1991–1999) and can be matched with at least one parent and one sibling over the same period. This sample allows us to estimate the relationship of interest by using sibling differences. We also use another sample of young adults from the British Household Panel Survey, matched to at least one parent, to estimate more conventional level models and to compute nonparametric bounds and point estimates. The estimates based on sibling differences require weaker assumptions (compared with the assumptions that are imposed by nonparametric estimators under conditional independence and level estimators) for the identification of the effects of family structure and parental joblessness on the outcomes under analysis. We find that experiences of life in a single-parent family and with jobless parents during childhood are usually associated with disadvantageous outcomes for young adults, the effect of family structure is in general significantly greater (in absolute value) than the effect of parental worklessness and most of the unfavourable outcomes are linked to an early family disruption, when the child was aged 0–5 years, whereas the timing of parental joblessness during childhood has more complex effects, with different outcomes being more strongly influenced by parental worklessness at different ages of the child.     

Modelling postfledging survival and age-specific breeding probabilities in species with delayed maturity: A case study of Roseate Terns at Falkner Island,Connecticut

We modelled postfledging survival and age-specific breeding probabilities in endangered Roseate Terns ( Sterna dougallii ) at Falkner Island, Connecticut, USA using capture-recapture data from 1988-1998 of birds ringed as chicks and as adults. While no individuals bred as 2-year-olds during this period, about three-quarters of the young that survived and returned as 3-year-olds nested, and virtually all surviving birds had begun breeding by the time they reached 5 years of age. We found no evidence of temporal variation age of first breeding of birds from different cohorts. There was significant temporal variation in the annual survival of adults and the survival over the typical 3-year maturation period of prebreeding birds, with extremely low values for both groups from the 1991 breeding season. The estimated overwinter survival rate (0.62) for adults from 1991-1992 was about three-quarters the usual rate of about 0.83, but the low survival of fledglings from 1991 resulted in less than 25% of the otherwise expected number of young from that cohort returning as breeding birds; this suggests that fledglings suffered a greater proportional decrease in survival than did adults. The survival estimates of young from 1989 and 1990 show that these cohorts were not negatively influenced by the events that decimated the young from 1991, and the young from 1992 and 1993 had above-average survival estimates. The apparent decrease since 1996 in development of fidelity of new recruits to this site is suspected to be due mainly to nocturnal disturbance and predation of chicks causing low productivity.