Centre for Health Economics Modelling the impact of being obese on hospital costs Katharina Hauck Bruce Hollingsworth A project funded by the NHMRC (grant number ) and the ARC (grant number DP )

2 Centre for Health Economics Background Cost of obesity (and related co-morbidities) to the health care system are a concern Studies may underestimate the economic cost of obesity Obesity directly causes illnesses which are costly to treat Obesity may also influence the progression or severity of other illnesses, including ones which are not directly caused by obesity

3 Centre for Health Economics Research Question and Approach Is it more costly to treat obese patients, once they are in hospital? Difference in cost irrespective of type of illness and procedure? Analyse impact on length of stay (LOS) of inpatients LOS is major determinant of hospital costs Generate different estimates over the whole distribution of LOS (from one night to very long)

4 Centre for Health Economics Data Australian administrative public hospital data ‘Victorian Admitted Episodes Data’ (VAED) for 2005/06 Analysis on patient level Patient defined as obese if one of 2nd to 12th diagnosis code falls within the range of ICD codes "E660“ to "E669“ Our sample: financial year 2005/06 with 461,563 inpatients, of which 6,086 (1%) are obese

5 Centre for Health Economics Model LOS = f (obese, age, gender, nonelective, private payer, index of social advantage, cost weight, number of diagnoses and procedures, total separations of hospital, type and location of hospital) Coefficient on dummy variable ‘obese’ is estimate of impact of obesity (+ more costly, - less costly) Analysis for selected hospital specialties, and for medical and surgical admissions

6 Centre for Health Economics Problem: Outliers Problem: upper and lower outliers with respect to LOS In VAED: 3.4% of Patients stay very long and 1.3% very short, conditional on observable characteristics Outlier status established with OLS regression of LOS on explanatory factors Observations are –Lower outliers if resO LS < Q(25) - 3*Inter Quartile Range –Upper outliers if resO LS > Q(75) + 3*Inter Quartile Range

7 Centre for Health Economics Estimation: Quantile Regression Problem: Large proportion of outliers violates assumptions of normality of Ordinary Least Squares Regression Solution: Quantile regressions on 19 quantiles of LOS Quantiles of the conditional distribution of LOS are expressed as functions of observed covariates Quantiles range from 0.05 (very short LOS) to 0.95 (very long LOS), including the median 0.5

8 Centre for Health Economics Estimation: Quantile Regression Quantile regression minimizes a sum of absolute residuals Residuals are weighed asymmetrically (for all quantiles except the median) –According to quantile, differing weights are given to positive and negative residuals Outliers do not bias estimates at other quantiles Quantile regressions allow for differing impact of being ‘obese’ at various points of the distribution of LOS

9 Centre for Health Economics Summary statistics Non-obeseObese Mean Standard Deviation Mean Standard Deviation Length of stay Cost weight Age Number of diagnoses Number of procedures Non-elective admissions56.88 %65.02 % Medical admissions % % Admissions to major teaching hospital % % Admissions to city or big rural hospital13.59 % % Privately paying patients8.13 % 6.58 %

10 Centre for Health Economics Results – Hospital Specialties

11 Centre for Health Economics Results – Hospital Specialties

12 Centre for Health Economics Results - Hospital Specialties

13 Centre for Health Economics Results - Hospital Specialties

14 Centre for Health Economics Results - Hospital Specialties

15 Centre for Health Economics Results - Hospital Specialties

16 Centre for Health Economics Results - Hospital Specialties

17 Centre for Health Economics Results - Hospital Specialties

18 Centre for Health Economics Results – Episode type

19 Centre for Health Economics Results – Episode type

20 Centre for Health Economics Why have obese different LOS? Why do obese stay longer in some specialties, but shorter in others? Possible answers: –Obese stay longer when they are treated as a medical case because they are more complex? –Obese stay shorter when they are treated as a surgical case because they are much more complex, and are transferred to another hospital (risk/cost shifting), or even die? Any ideas?

21 Centre for Health Economics Why have obese different LOS?

22 Centre for Health Economics Future Research Investigate reasons for cost differences Analyse reasons for different patterns across specialties Use data on: - Transfers to other hospitals - Readmissions (to the same, and different hospitals) - Complications and adverse events - Mortality rates (in-hospital, and 30 day after stay)

23 Centre for Health Economics Probit estimations Difference in probability of being transferred to another hospital when obese, conditional on other explanatory factors –Negative effect (?!) of ‘obese’ for Haematology, Respiratory and Endocrinology, insignificant for all other specialties Difference in probability of dying when obese, conditional on other explanatory factors –Negative effect (?!) of ‘obese’ for the whole sample, and a range of specialities including Orthopaedics, Cardiology, General Medicine, and General Surgery.