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Modelling the impact of being obese on hospital costs

Modelling the impact of being obese on hospital costs. Katharina Hauck Bruce Hollingsworth A project funded by the NHMRC (grant number 334114) and the ARC (grant number DP0772235 ). Background. Cost of obesity (and related co-morbidities) to the health care system are a concern

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Modelling the impact of being obese on hospital costs

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  1. Modelling the impact of being obese on hospital costs Katharina Hauck Bruce Hollingsworth A project funded by the NHMRC (grant number 334114) and the ARC (grant number DP0772235)

  2. 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. 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. 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. 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. 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 resOLS< Q(25) - 3*Inter Quartile Range • Upper outliers if resOLS > Q(75) + 3*Inter Quartile Range

  7. 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. 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. Summary statistics

  10. Results – Hospital Specialties

  11. Results – Hospital Specialties

  12. Results - Hospital Specialties

  13. Results - Hospital Specialties

  14. Results - Hospital Specialties

  15. Results - Hospital Specialties

  16. Results - Hospital Specialties

  17. Results - Hospital Specialties

  18. Results – Episode type

  19. Results – Episode type

  20. 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. Why have obese different LOS?

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

  23. 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.

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