Generalized Modeling Approaches to Risk Adjustment of Skewed Outcomes Data

There are two broad classes of models used to address the econometric problems caused by skewness in data commonly encountered in health care applications: (1) transformation to deal with skewness (e.g., OLS on ln(y)); and (2) alternative weighting approaches based on exponential conditional models (ECM) and generalized linear model (GLM) approaches. In this paper, we encompass these two classes of models using the three parameter generalized gamma (GGM) distribution, which includes several of the standard alternatives as special cases OLS with a normal error, OLS for the log normal, the standard gamma and exponential with a log link, and the Weibull. Using simulation methods, we find the tests of identifying distributions to be robust. The GGM also provides a potentially more robust alternative estimator to the standard alternatives. An example using inpatient expenditures is also analyzed.

Estimating Log Models: To Transform or Not to Transform?

Data on health care expenditures, length of stay, utilization of health services, consumption of unhealthy commodities, etc. are typically characterized by: (a) nonnegative outcomes; (b) nontrivial fractions of zero outcomes in the population (and sample); and (c) positively-skewed distributions of the nonzero realizations. Similar data structures are encountered in labor economics as well. This paper provides simulation-based evidence on the finite-sample behavior of two sets of estimators designed to look at the effect of a set of covariates x on the expected outcome, E(y|x), under a range of data problems encountered in every day practice: generalized linear models (GLM), a subset of which can simply be viewed as differentially weighted nonlinear least-squares estimators, and those derived from least-squares estimators for the ln(y). We consider the first- and second- order behavior of these candidate estimators under alternative assumptions on the data generating processes. Our results indicate that the choice of estimator for models of ln(E(x|y)) can have major implications for empirical results if the estimator is not designed to deal with the specific data generating mechanism. Garden-variety statistical problems - skewness, kurtosis, and heteroscedasticity - can lead to an appreciable bias for some estimators or appreciable losses in precision for others.

Covering the uninsured: What is it worth?

One out of six Americans under age sixty-five lacks health insurance, a situation that imposes sizable hidden costs upon society. The poorer health and shorter lives of those without coverage account for most of these costs. Other impacts are manifested by Medicare and disability support payments, demands on the public health infrastructure, and losses of local health service capacity. We conclude that the estimated value of health forgone each year because of uninsurance ($65?$130 billion) constitutes a lower-bound estimate of economic losses resulting from the present level of uninsurance nationally

Use of Propensity Scores in Non-Linear Response Models: The Case for Health Care Expenditures

Under the assumption of no unmeasured confounders, a large literature exists on methods that can be used to estimating average treatment effects (ATE) from observational data and that spans regression models, propensity score adjustments using stratification, weighting or regression and even the combination of both as in doubly-robust estimators. However, comparison of these alternative methods is sparse in the context of data generated via non-linear models where treatment effects are heterogeneous, such as is in the case of healthcare cost data. In this paper, we compare the performance of alternative regression and propensity score-based estimators in estimating average treatment effects on outcomes that are generated via non-linear models. Using simulations, we find that in moderate size samples (n= 5000), balancing on estimated propensity scores balances the covariate means across treatment arms but fails to balance higher-order moments and covariances amongst covariates, raising concern about its use in non-linear outcomes generating mechanisms. We also find that besides inverse-probability weighting (IPW) with propensity scores, no one estimator is consistent under all data generating mechanisms. The IPW estimator is itself prone to inconsistency due to misspecification of the model for estimating propensity scores. Even when it is consistent, the IPW estimator is usually extremely inefficient. Thus care should be taken before naively applying any one estimator to estimate ATE in these data. We develop a recommendation for an algorithm which may help applied researchers to arrive at the optimal estimator. We illustrate the application of this algorithm and also the performance of alternative methods in a cost dataset on breast cancer treatment.

Surveyor spacecraft system. Volume I - Final sterilization report, Apr. 1961 - Mar. 1963

Surveyor sterilization study - spacecraft material and component compatibility to sterilization process, sterile techniques for aseptic assembly, and operation pla

Factors Affecting Laboratory Test Use and Prices

The use of clinical laboratory tests has more than doubled during the past decade. Some observers of the health system feel that this growth is excessive and is a result of current payment systems. This article examines the effects of current reimbursement policies with regard to the use of laboratory tests and prices charged for tests. The results suggest the following: The method of financing medical care, including cost sharing and prepaid group practice arrangements, affects the volume of laboratory testing through the number of patient contacts with the medical care system rather than through the number of tests used per patient contact. Fee ceilings on physician time appear to be partially offset by higher test prices. Cost-based reimbursement for hospital services is associated with higher charges in hospital laboratories

The Health Effects of Medicare for the Near-Elderly Uninsured

We study how the trajectory of health for the near-elderly uninsured changes upon enrolling into Medicare at the age of 65. We find that Medicare increases the probability of the previously uninsured having excellent or very good health, decreases their probability of being in good health, and has no discernable effects at lower health levels. Surprisingly, we found Medicare had a similar effect on health for the previously insured. This suggests that Medicare helps the relatively healthy 65 year olds, but does little for those who are already in declining health once they reach the age of 65. The improvement in health between the uninsured and insured were not statistically different from each other. The stability of insurance coverage afforded by Medicare may be the source of the health benefit suggesting that universal coverage at other ages may have similar health effects.