Chronic Disease Surveillance

using Administrative Data

Lisa M. Lix, PhD

Souradet Shaw, MA

MANITOBA CENTRE FOR HEALTH POLICY

University of Manitoba, Canada

Lecture Outline

1. What is chronic disease surveillance?

2. Why use administrative data for chronic disease surveillance?

3. Constructing case definitions

4. Validating case definitions

5. An example: Diabetes

6. Conclusions

7. References

1. What is Chronic Disease Surveillance?

• Chronic diseases are: “…not prevented by vaccines or generally cured by medication, nor do they just disappear. To a large degree, the major chronic disease killers…are an extension of what people do, or not do, as they go about the business of daily living.” (CDC, 2004)

• Surveillance is:

“…the ongoing systematic collection, analysis, and interpretation of outcome-specific data for use in planning, implementing, and evaluating public health practice…” (Thacker & Berkelman, 1988).

• Chronic disease surveillance involves activities related to the ongoing monitoring or tracking of chronic diseases.

2. Why Use Administrative Data for Chronic Disease Surveillance?

• Administrative data are usually collected by government for some administrative purpose (e.g., paying doctors or hospitals), but not primarily for research or surveillance.

• The databases are often population

based, so important population subgroups are not missed.

• Comparisons between disease cases and non-cases.

• Trends over time can often be monitored.

Advantages of Using Administrative Data for Surveillance

Limitations of Other Data Sources

• Vital statistics data

• Clinical registries • Survey data

Limitations of Administrative Data

Administrative data are collected for purposes of health system management and provider payment, and not for chronic disease surveillance. Thus, it is important to assess their validity for surveillance

3. Constructing Case Definitions• Diagnoses/Treatment - Diagnosis codes

–International Classification of Diseases (ICD)

–to identify diagnosed cases– Prescription drugs–Anatomic, Therapeutic, Chemical

(ATC) codes–to identify treated cases of chronic

disease

4. Validating Case Definitions

• Validation data source

• Measures of validity

Potential Validation Data Sources

• Population-based survey data

• Chart review

Measures of Validity

Case definition validation measures:

–Kappa statistic ()

–Sensitivity

–Specificity

–Positive predicted value (PPV)

–Negative predicted value (NPV)

Calculation of Validation Indices for Chronic Disease Case Definitions

Validation Data

Administrative

Data

Sensitivity =A/(A+C)*100

Specificity =D/(B+D)*100

PPV =A/(A+B)*100

NPV =D/(C+D)*100

Has Disease Does Not Have Disease

Has Disease A B

Does Not Have Disease

C D

5. An Example: Diabetes

Case Definitions• ICD-9-CM code 250 was used to

identify diabetes cases in hospital and medical data.

• ATC code A10 (drugs used in diabetes) was used to identify diabetes cases in prescription drug data.

Validating Diabetes Case Definitions

• Data from Canadian Community Health Survey (CCHS), Cycle 1.1, collected between September 2000 and November 2001 were used for the validation.

• 18 diabetes case definitions were tested.

Validation ResultsTable 1: Estimates of agreement, sensitivity, specificity, and predictive

values for diabetes case definitions

# Years

Case Definition Kappa Sens (%) Spec (%) PPV (%) NPV (%)

1 a: 1+H or 1+P 0.77 76.9 98.7 79.2 98.5

b: 1+H or 2+P 0.73 63.2 99.5 89.5 97.7

c: 1+H or 1+P or 1+Rx

0.81 85.8 98.6 79.4 99.1

2 d: 1+H or 1+P 0.78 85.2 98.1 74.0 99.0

e: 1+H or 2+P 0.82 79.5 99.3 87.9 98.7

f: 1+H or 1+P or 1+Rx

0.80 89.6 97.9 73.7 99.3

3 g: 1+H or 1+P 0.75 87.8 97.4 68.7 99.2

h: 1+H or 2+P 0.83 84.9 99.0 83.9 99.0

i: 1+H or 1+P or 1+Rx

0.76 90.5 97.3 68.2 99.4

Data are for fiscal years 2000/01 – 2002/03

Estimating Diabetes Prevalence

• Cross-sectional and longitudinal prevalence can be estimated using administrative data.

Manitoba Prevalence EstimatesTable 2: Crude prevalence estimates for diabetes case

definitions for Manitoba, Canada

# Years Case Definition Prevalence Estimate (%)

1 a: 1+H or 1+P 5.8

b: 1+H or 2+P 4.4

c: 1+H or 1+P or 1+Rx 6.5

2 d: 1+H or 1+P 7.1

e: 1+H or 2+P 4.6

f: 1+H or 1+P or 1+Rx 7.5

3 g: 1+H or 1+P 7.9

h: 1+H or 2+P 6.3

i: 1+H or 1+P or 1+Rx 8.2

Data are for fiscal years 2000/01 – 2002/03

6. Conclusions

• Administrative data appear to be a valid tool for identifying diabetes cases.

• No case definition is “the best”; there is usually a trade-off between choosing a sensitive or specific case definition.

Conclusions, cont’d

• There are advantages to using administrative data for chronic disease surveillance, including easy access in most jurisdictions.

7. References

1. Blanchard J.F., Ludwig S., Wajda A., Dean H., Anderson K., Kendall O., Depew N. Incidence and prevalence of diabetes in Manitoba, 1986-1991. Diabetes Care 1996;19:807-811.

2. CDC. The Burden of Chronic Diseases and Their Risk Factors: National and State Perspectives 2004. Atlanta: Department of Health and Human Services; 2004. Available at: http://www.cdc.gov/nccdphp/burdenbook2004.

3. Chronic Disease Prevention Alliance of Canada (CDPAC). The Case for Change. Available from: http://www.cdpac.ca/content/case_for_change.asp. Accessed on January 19, 2006.

4. Cricelli C., Mazzaglia G., Samani F., Marchi M., Sabatini A., Nardi R., Ventriglia G., Caputi A.P. Prevalence estimates for chronic diseases in Italy: exploring the differences between self-report and primary care databases. J Pub Health Med 2003;25:254-257.

5. Hux J.E., Ivis F., Flintoft V., Bica A. Diabetes in Ontario: determination of prevalence and incidence using a validated administrative data algorithm . Diabetes Care 2002;25:512-516.

6. Kue-Young T. 2005. Population Health: Concepts and Methods. 2nd Ed. Oxford University Press, New York.

7. Lix L., Yogendran M., Burchill C., Metge C., McKeen N., Bond R. Defining and Validating Chronic Diseases: An Administrative Data Approach. Winnipeg, MB: Manitoba Centre for Health Policy, 2006.

8. Maskarinec G. Diabetes in Hawaii: estimating prevalence from insurance claims data. Am J Pub Health 1997;87:1717-1720.

9. Powell K.E., Diseker R.A. III, Presley R.J., Tolsma D., Harris S., Mertz K.J., Viel K., Conn D.I., McClellan W. Administrative data as a tool for arthritis surveillance: estimating prevalence and utilization of services. J Pub Health Manag Pract 2003;9:291-298

7. References, cont’d

7. References, cont’d

10. Rector Rector T.S, Wickstrom S.L., Shah M, Thomas Greenlee N., Rheault P., Rogowski J. et al. Specificity and sensitivity of claims-based algorithms for identifying members of Medicare plus Choice health plans that have chronic medical conditions. HSR 2004;39:1839-1861.

11. Robinson J.R., Young T.K., Roos L.L., Gelskey D.E. Estimating the burden of disease. Comparing administrative data and self-reports. Med Care1997;35:932-947.

12. Thacker S.B., Berkelman R.L. Public health surveillance in the United States. Epidemiol Rev 1988;10:164-190.

13. WHO. Preventing Chronic Diseases: A Vital Investment. http://www.who.int/chp/chronic_disease_report/contents/en/index.html

14. Shultz S.E., Kopec J.A. Impact of chronic conditions. Health Reports 2003;14:41-53.

This presentation is based on a Manitoba Centre for Health Policy (MCHP) report, “Defining and Validating Chronic Diseases: An Administrative Data Approach”, published in 2006 (Manitoba Health project # 2004/05-01).

8. Acknowledgements