Comparing the importance of disease rate versus practice style variations in explaining differences in small area hospitalization rates for two respiratory conditions

UMMS Affiliation

Department of Quantitative Health Sciences

Publication Date


Document Type



Aged; *Bayes Theorem; Bronchitis; Chronic Disease; Diagnosis-Related Groups; Female; Hospitalization; Humans; Male; Massachusetts; Models, Statistical; Pneumonia, Bacterial; Poisson Distribution; Pulmonary Emphysema; *Small-Area Analysis


Biostatistics | Epidemiology | Health Services Research


Many studies have reported large variations in age- and sex-adjusted rates of hospitalizations across small geographic areas. These variations have often been attributed to differences in medical practice style which are not reflected in differences in health care outcomes. There is, however, another potentially important source of variation that has not been examined much in the literature: geographic differences in the age-sex adjusted size of the pool of patients who present with the disease and are candidates for hospitalization. Previous studies of small area variations in hospitalization rates have only used data on hospitalizations. Thus, it has not been possible to distinguish the extent to which differences in hospitalization rates are due to (i). differences in the chance that patients diagnosed with a disease are admitted to a hospital, which we refer to as the 'practice style effect,' versus (ii). geographic differences in the total amount of diagnosed disease, which we refer to as the 'disease effect.' Elementary methods for estimating the relative strength of the two effects directly from the data can be misleading, since equal amounts of variability in each effect result in unequal impacts on hospitalization rates. In this paper we describe a model-based approach for estimating the relative importance of the practice style effect and the disease effect in explaining variations in hospitalization rates. The key to our approach is the use of data on both inpatient and outpatient visits. We use 1997 Medicare data for two respiratory medical conditions across 71 small areas in Massachusetts: chronic bronchitis and emphysema, and bacterial pneumonia. Based on a Poisson model for the process generating hospitalizations and outpatient visits, we use a Bayesian framework and Gibbs sampling to compute and compare the correlation between the number of people hospitalized and each of these two sources of variation. Our results show that for the two conditions, disease rate variation explains at least as much of the variation in hospitalization rates as does practice style variation.

DOI of Published Version



Stat Med. 2003 May 30;22(10):1775-86. Link to article on publisher's site

Journal/Book/Conference Title

Statistics in medicine

PubMed ID


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Link to Article in PubMed