Jacob Nielsen Arendt

Exploring the relationship between costs and quality: Does the joint evaluation of costs and quality alter the ranking of Danish hospital departments?

ObjectiveThe purpose is to evaluate the relationship between costs and quality and to assess whether the joint evaluation of costs and quality affects the ranking of hospital departments relative to comparison based on costs alone.MethodsUsing patient level data for 3,754 patients in six vascular departments, we estimate fixed effect models for costs (linear) and quality (logistic). We consider two quality measures; mortality and wound complications. To assess whether the joint evaluation of costs and quality affects the ranking of departments, we construct joint confidence regions for each pair of departmental effects for costs and quality using a bootstrap method and rank departments according to their cost-effectiveness ratio. The findings are used to evaluate a theory of a U-shaped cost/quality relationship.ResultsThe association between cost and quality differs depending on how quality is measured. Lower costs are associated with higher mortality, implying a cost/quality trade-off. In contrast, there is no clear association between costs and wound complications among vascular departments.ConclusionsCompared to benchmarking of departments based solely on their costs, we show that the ranking of departments may be altered considerably when quality is taken into account. Consequently, it is important to have a well-rounded view of departmental objectives when undertaking performance evaluation. The results for mortality may lend some support to the theory of a U-shaped cost/quality relationship. However, the results for wound complications do not support the theory of a U-shaped cost/quality relationship.

Lavindkomst og antal kontakter med almen praksis

Introduction: The purpose of this study is to examine whether there exist differences in utilization of general practice between low income groups and citizens with higher income after adjustment for social and health-related factors Material and methods: Various registers from Statistics Denmark with information on all adult citizensof Odense Municipality in 2003-2005 are applied. Income is measured as equivalent disposable income and adjustment for social and health-related personal characteristics are conducted using Poisson regression. Results: Poor with income below 73.000 DKK have 2-3 fewer contacts with general practice annually than those with an income of 73.000-146.000 DKK, depending upon age groups. The difference in contacts with general practice between the poor and those with a higher income is statistically significant. The difference is reduced to 1.5 fewer contacts after adjustment for differences in social and health-related characteristics. Conclusion: Poor adults have significantly fewer contacts with general practice than those with higher income. The difference is not explained by social and health-related factors. This is in contrast to results from earlier studies and may be an indication of underutilization of general practice. Other explanations may be that the poor enter the primary health sector through other channels, that the type and quality of contacts vary between income groups or that adjustment for health or other need indicators are not sufficient.

Evaluating the performance of simple estimators for probit models with two dummy endogenous regressors

This study considers the small sample performance of approximate but simple two-stage estimators for probit models with two endogenous binary covariates. Monte Carlo simulations show that all the considered estimators, including the simulated maximum-likelihood (SML) estimation, of the trivariate probit model are biased in very small samples (N=100). With moderately small samples (N=500), some of the approximations perform as well as the SML estimator when the degree of endogeneity is not very large. Some of the approximations seem robust with higher correlations and are also promising for testing the exogeneity of binary covariates. The methods are used to estimate the impact of employment-based health insurance and health care (HC) on HC use, where the approximations seem to work at least as well as the SML and in some cases better.