Parallel_Session_2_Talk_5_Huber
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Talks of the Swiss Health Economics Workshop 2013

Talks of the Swiss Health Economics Workshop 2013

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Parallel_Session_2_Talk_5_Huber Presentation Transcript

  • 1. PREDICTION OF HEALTH CARE EXPENDITURES, UTILIZATION, AND MORTALITY IN SWITZERLAND USING HEALTH CARE CLAIMS DATA Carola A. Huber, PhD MPH,1 Sebastian Schneeweiss, MD ScD,2 Andri Signorell, MSc,1 Oliver Reich, PhD1,3 1Department of Health Sciences, Helsana Insurance Group; 2Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham & Women’s Hospital, Harvard Medical School; 3Department of Public Health and Health Technology Assessment, UMIT, University of Health Sciences, Medical Informatics and Technology 113.09.2013
  • 2. Significant medical and economic burden of chronic diseases 60% of deaths caused by chronic conditions (WHO) 75% of total health care expenditures in the U.S. Evaluation of population health status and health care cost is important… for health policy debates for decision on resource allocation 2 Background
  • 3. Little is known in Switzerland… Population-based data on clinical diagnosis and health care costs are scarce Administrative data as an useful source of information: Reliable Consistently available Practice-based High number cases Widely accepted in health services and health economic research 3 Background
  • 4. Currently: clinical diagnoses in outpatient settings are missing Prescription drug data as proxy for clinicial diagnoses Drug substances medication classes treatment of disease Pharmacy-based cost groups (PCGs) as frequently used method: Epidemiological studies Risk adjustment modeling 4 Background
  • 5. Pharmacy-based morbidity measures Based on various PCGs Method to incorporte health status of the individual patient into prediction models Chronic Disease Score (CDS) Originally from von Korff et al. (1992) One of the most commonly used morbidity indexes International Studies: CDS as a good predictor for health care use, costs and mortality not transferable to every health care system CDS based on ambiguous and outdated medication classifications 5 Background
  • 6. 1) To develop an updated, Swiss-adapted "Chronic Disease Score", a pharmacy-based morbidity index 2) To predict health care costs, health care utilisation and mortality, using the Chronic Disease Score 6 Aims of the study
  • 7. Health care claims data (Helsana) Swiss residents with mandatory health insurance At least 18 years Continuously insured 2009-2010 Population characteristics: Age Sex Language area Health insurance status 7 Data and study population
  • 8. Pharmacy-based cost group (PCG) model According to the WHO ATC (Anatomical Therapeutic Chemical) classification system Drug code treatment of chronic disease (e.g. insulin diabetes) Identification of 22 chronic conditions 8 Identification of chronic diseases
  • 9. 22 chronic conditions calculation of CDS CDS = overall disease severity = cost weights for each disease Defining cost weights by regression model: Total costs (2009; baseline) = disease + sex + age + language area + insurance status Each chronic disease cost weight ∑ CDS For two age groups: "18-65 years" and ">65 years" 9 Calculating the Chronic Disease Score (CDS)
  • 10. 10 Predicting future health outcomes 22 Diseases Helsana-insured persons (2009) Prescription drug data (ATC-code) CostsCDS Helsana-insured persons (2010) Costs Physician visits Hospitalization Mortality
  • 11. Construction of a three stepwise regression model: 1. Model: costs = f(sex, age, language area) 2. Model: costs = f(model 1 + insurance status) 3. Model: costs = f(model 2 + CDS) Also, prediction of health care utilization and mortality Model performance: R2, c-statistic 11 Predicting future health care costs
  • 12. N = 436'350 52% women Mean age: 55 years 12 Population characteristics (baseline, 2009) 38.5 15.9 14.1 31.5 0 10 20 30 40 50 0 1 2 ≥3 % Number of chronic conditions
  • 13. 13 (Cost-) weights for each disease
  • 14. 14 Density plot: CDS 18-65 years >65 years $
  • 15. 15 Predicted outcomes R2 Model 1 Model 2 Model 3 + sex, age, region + CDS+ health insurance status Regression results: explained variance by CDS on total health care costs 18-65 yr >65 yr 18-65 y >65 y 18-65 y >65 y Health care costs (total) 2.5 6.0 4.7 7.0 17.9 14.1
  • 16. 16 Predicted outcomes R2 Model 1 Model 2 Model 3 + sex, age, region + CDS+ health insurance status Total costs separated for inpatient/outpatient setting: explained variance by CDS on outpatient costs 18-65 yr >65 yr 18-65 y >65 y 18-65 y >65 y Outpatient costs 3.5 1.4 6.6 2.9 28.0 14.4 Physician care costs 3.8 1.1 6.7 2.7 18.4 9.1 Primary care costs 4.8 3.8 8.0 6.1 16.8 13.1
  • 17. 17 Predicted outcomes R2 Model 1 Model 2 Model 3 Inpatient costs (total) 0.4 5.9 0.8 6.4 1.3 8.3 Hospitalization costs 0.4 0.8 0.6 0.9 1.2 1.9 Results from the inpatient setting: explained variance by CDS on inpatient costs 18-65 yr >65 yr 18-65 y >65 y 18-65 y >65 y
  • 18. 18 Predicted outcomes R2 Model 1 Model 2 Model 3 Outpatient visits (total) 6.0 4.9 10.9 7.3 29.2 22.9 Primary care visits 4.0 4.5 7.8 6.9 15.8 15.1 Predicted outpatient visits: explained variance by CDS on health care use 18-65 yr >65 yr 18-65 y >65 y 18-65 y >65 y
  • 19. Hospitalization Mortality 19 Predicted outcome c-statistic Model 1 Model 2 Model 3 18-65 yr >65 yr 18-65 y >65 y 18-65 y >65 y Hospitalization 0.60 0.58 0.62 0.59 0.67 0.64 Goodness of fit (c-statistic), predicting hospitalization and mortality by CDS Predicted outcome c-statistic Model 1 Model 2 Model 3 18-65 yr >65 yr 18-65 y >65 y 18-65 y >65 y Mortality 0.75 0.74 0.78 0.75 0.79 0.77
  • 20. CDS as a Swiss-adapted, updated, pharmacy-based morbidity index: Based on updated medication classifications Comprising a large number of diseases Reliable and relatively easy to use morbidity measure 20 Conclusion
  • 21. + CDS improvement of explained variance in all predicted outcomes "up to a doubling of the R-square" Best prediction of: total health care costs outpatient costs outpatient visits Small improvement in hospitaltization and mortality 21 Summary
  • 22. Quantifying population health status and medical expenditures (e.g. by CDS) is important for future resource allocation CDS contributes to the understanding of the "burden of disease" in CH Pharmacy-based morbidity measures (CDS) should be seen as a valid method For predicting future medical expenditures Widely evaluated Used in different health care settings 22 Conclusion (in general)
  • 23. … made use of the new measure Two specific examples: Integration of the CDS in capitation models Calculation of the budget for MC-physician-networks Describing and comparing the morbidity of different populations within insurance schemes Enhancing the understanding of the MC-network performance 23 Helsana Implications