| Abstract: |
Objective To determine if obese patients have longer average length of stay
once they are admitted to hospital, across a range of specialties. This
contributes to measuring the impact of obesity on health care resource use.
Data Sources/Study Setting Administrative hospital data are used for the
financial year 2005/06 covering all episodes of patient care (1.3 million) in
122 public hospitals in the state of Victoria, Australia. The data are
collected as part of Diagnosis Related Group (DRG) case mix funding
arrangements by the state government. Study Design Statistical analysis are
undertaken using quantile regression analysis to determine differences in
average length of stay within different specialties for two groups of
patients, those classified as obese, and those not classified as obese.
Quantile regression allows a comparison of differences between the length of
stay of obese and non-obese patients across the whole distribution of length
of stay of inpatients, in contrast to more commonly used statistical methods
which use only the mean. We condition on a range of patient and hospital
characteristics such as age, sex, socioeconomic status, medical complexity of
patients, teaching status, size and location of hospitals. Data
Collection/Extraction Methods Data on inpatient episodes with at least one
overnight stay in hospital are used. We exclude episodes with missing
information on one or more of the explanatory variables and we exclude
specialties with less than 50 reported obese inpatients per financial year.
The final sample consists of just over 460,000 observations. Principal
Findings Large and significant differences in average length of stay are found
between obese and non-obese patients for nearly all specialties. In some
specialties, obese patients can stay up to 4 days longer. However, obesity
does not necessarily lead to longer hospital stays. In a range of specialties,
obese patients have shorter length of stay on average. In general, differences
between obese and non-obese patients are more pronounced at greater levels of
medical complexity. There is some evidence that differences may arise because
obese patients are more likely to be treated medically rather than surgically,
to be transferred to another hospital, thus shifting risks and costs, or to
die from higher complication rates. Conclusions Our study sheds new light on
the impact of obesity on health care costs. We demonstrate that an analysis
across the whole spectrum of medical complexity provides much better estimates
of resource use by obese patients than standard techniques. Future research
should focus on differences in the way obese patients are managed in hospital.
This will show where resource use is most intense, and help policy makers and
hospital managers increase efficiency and quality of care for obese patients. |