Talks of the Swiss Health Economics Workshop 2013

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CDS based on ambiguous and outdated medication classifications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