Generalized approaches to partitioning the attributable risk of interacting risk factors can remedy existing pitfalls.

OBJECTIVE: To clarify the properties of different approaches to estimate the contribution of single-risk factors to the disease load in a population. STUDY DESIGN AND SETTING: Three methods of partitioning attributable risks are reviewed and two additional procedures as modifications of the existing algorithms are introduced. Basis properties of the approaches are outlined in the simplest setting with two exposure variables. The extension to more complex settings is illustrated by an example involving three risk factors. RESULTS: The quantification of the impact of single-risk factors can vary considerably according to the method used. Different orderings of the risk factors with respect to their impact can occur. Approaches can be classified according to two features: (i) inclusion or exclusion of partial interactions between risk factors, (ii) equal or proportional distribution of the surplus resulting from the combined action of risk factors. Practical applications have to carefully consider intrinsic limitations of all partitioning approaches. CONCLUSION: The decision on which concept to use when partitioning attributable risks on the population level should be based on the desired properties the solution ought to have. Arguments from game-theoretical reasoning can help to guide further research in this area, especially in exploring the methods using proportional division rules that are not yet fully understood.

Factors contributing to patient satisfaction with medical rehabilitation in German hospitals.

The objective of this study was to quantify overall patient satisfaction, through the identification of the particular aspects of patient satisfaction that were most likely to cause patients to recommend the rehabilitation hospital to others. The research entailed analysing secondary data from a quality improvement programme for medical rehabilitation, conducted from 1997 until 2004, in seven rehabilitation hospitals in Germany. Overall patient satisfaction and several potential predictors were examined in relation to 120,825 patients who had received inpatient medical rehabilitation. Recommending the rehabilitation hospital to others is a measure of overall patient satisfaction with the rehabilitation. Logistic regression was used to identify the factors that predicted patient satisfaction or dissatisfaction at discharge from the rehabilitation hospital. Overall satisfaction was mainly determined by the general atmosphere in the hospital, successful rehabilitation and the medical care. The general atmosphere was strongly associated with admission procedures, accommodation, catering, service, organisation and nursing care. In conclusion, the results suggest that in order to increase the rate of recommendation, rehabilitation hospitals should aim for not only high quality in medical care, but also the creation of a pleasant atmosphere.

Comparison of confidence intervals for adjusted attributable risk estimates under multinomial sampling.

The epidemiologic concept of the adjusted attributable risk is a useful approach to quantitatively describe the importance of risk factors on the population level. It measures the proportional reduction in disease probability when a risk factor is eliminated from the population, accounting for effects of confounding and effect-modification by nuisance variables. The computation of asymptotic variance estimates for estimates of the adjusted attributable risk is often done by applying the delta method. Investigations on the delta method have shown, however, that the delta method generally tends to underestimate the standard error, leading to biased confidence intervals. We compare confidence intervals for the adjusted attributable risk derived by applying computer intensive methods like the bootstrap or jackknife to confidence intervals based on asymptotic variance estimates using an extensive Monte Carlo simulation and within a real data example from a cohort study in cardiovascular disease epidemiology. Our results show that confidence intervals based on bootstrap and jackknife methods outperform intervals based on asymptotic theory. Best variants of computer intensive confidence intervals are indicated for different situations.