Attrition Bias in Panel Data: A Sheep in Wolf's Clothing? A Case Study Based on the Mabel Survey.

This paper investigates the nature and consequences of sample attrition in a unique longitudinal survey of medical doctors. We describe the patterns of non-response and examine if attrition affects the econometric analysis of medical labour market outcomes using the estimation of physician earnings equations as a case study. We compare the econometric gestimates obtained from a number of different modelling strategies, which are as follows: balanced versus unbalanced samples; an attrition model for panel data based on the classic sample selection model; and a recently developed copula-based selection model.

Adverse and Advantageous Selection in the Medicare Supplemental Market: A Bayesian Analysis of Prescription drug Expenditure.

This paper develops an extended specification of the two-part model, which controls for unobservable self-selection and heterogeneity of health insurance, and analyzes the impact of Medicare supplemental plans on the prescription drug expenditure of the elderly, using a linked data set based on the Medicare Current Beneficiary Survey data for 2003-2004. The econometric analysis is conducted using a Bayesian econometric framework. We estimate the treatment effects for different counterfactuals and find significant evidence of endogeneity in plan choice and the presence of both adverse and advantageous selections in the supplemental insurance market.

Microvascular free flap reconstruction for head and neck cancer in a resource-constrained environment in rural India.

Introduction: Reconstruction with free flaps has significantly changed the outcome of patients with head and neck cancer. Microsurgery is still considered a specialised procedure and is not routinely performed in the resource-constrained environment of certain developing parts of India.

Materials And Methods: This article focuses on the practice environment in a cancer clinic in rural India.

Cost-offsets of prescription drug expenditures: data analysis via a copula-based bivariate dynamic hurdle model.

In this paper, we estimate a copula-based bivariate dynamic hurdle model of prescription drug and nondrug expenditures to test the cost-offset hypothesis, which posits that increased expenditures on prescription drugs are offset by reductions in other nondrug expenditures. We apply the proposed methodology to data from the Medical Expenditure Panel Survey, which have the following features: (i) the observed bivariate outcomes are a mixture of zeros and continuously measured positives; (ii) both the zero and positive outcomes show state dependence and inter-temporal interdependence; and (iii) the zeros and the positives display contemporaneous association. The point mass at zero is accommodated using a hurdle or a two-part approach.

Alternative approaches for econometric analysis of panel count data using dynamic latent class models (with application to doctor visits data).

Cross-sectional latent class regression models, also known as switching regressions or hidden Markov models, cannot identify transitions between classes that may occur over time. This limitation can potentially be overcome when panel data are available. For such data, we develop a sequence of models that combine features of the static cross-sectional latent class (finite mixture) models with those of hidden Markov models.

Disentangling incentives effects of insurance coverage from adverse selection in the case of drug expenditure: a finite mixture approach.

This paper takes a finite mixture approach to model heterogeneity in incentive and selection effects of drug coverage on total drug expenditure among the Medicare elderly US population. Evidence is found that the positive drug expenditures of the elderly population can be decomposed into two groups different in the identified selection effects and interpreted as relatively healthy with lower average expenditures and relatively unhealthy with higher average expenditures, accounting for approximately 25 and 75% of the population, respectively. Adverse selection into drug insurance appears to be strong for the higher expenditure component and weak for the lower expenditure group.

Provider networks and primary-care signups: do they restrict the use of medical services?

This article analyzes the effect of gatekeeper and network restrictions on use of health-care services using simulation-based estimation methods. Data from the Community Tracking Survey (1996-1997) show significant evidence of selection into plans with gatekeeper and/or network restrictions. Enrollees in plans with networks of physicians have fewer office-based visits to non-physician medical professionals, but more emergency room visits and hospital stays.

Restrictions on provider access in health plans and socioeconomic status.

Objective: To model the socioeconomic determinants of restrictions on provider access and choices in health plans.

Data Sources: Data from the 1996-97 Community Tracking Study are used. Publicly available enrollee data including enrollee reports of health care plan characteristics are linked with restricted use data with insurer reports of health plan characteristics.

The effect of managed care on use of health care services: results from two contemporaneous household surveys.

This paper estimates treatment effects of managed care plans on the utilization of health care services using data from two contemporaneous, nationally representative household surveys from the USA. The paper exploits recent advances in simulation-based econometrics to take the endogeneity of enrollment into managed care plans into account and identify the causal relationship between managed care enrollment and utilization. Overall, results from the two surveys are remarkably similar, lending credibility to their external validity and to the econometric model and estimation methods.

Economics of self-medication: theory and evidence.

A pervasive phenomenon in developing countries is that self-prescribed medications are purchased from drug vendors without professional supervision. In this article we develop a model of self-medicating behavior of a utility-maximizing consumer who balances the benefits and risks of self-medication. The empirical investigation focuses on the role of income and health insurance on the use of self-medication.

The structure of demand for health care: latent class versus two-part models.

We contrast the two-part model (TPM) that distinguishes between users and non-users of health care, with a latent class model (LCM) that distinguishes between infrequent and frequent users. In model comparisons using data on counts of utilization from the RAND Health Insurance Experiment (RHIE), we find strong evidence in favor of the LCM. We show that individuals in the infrequent and frequent user latent classes may be described as being healthy and ill, respectively.