Deriving practice-level estimates from physician-level surveys

1. U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES Centers for Disease Control and Prevention National Center for Health Statistics Deriving practice-level estimates from physician-level surveys Catharine W. Burt , EdD and Esther Hing, MPH. Chief, Ambulatory Care Statistics Branch Session 32 June 20, 2007 ICES III, Montreal, Canada

2. Topics • Introduction • Multiplicity theory • Re-weighting methods • Application to NAMCS • Assumptions • Analytical example • Limitations

4. Multiplicity theory • Multiplicity occurs when the same observation unit can be counted multiple times among the selection units • eg., same patient is counted in multiple records of visits/discharges or same medical practice is counted in records of multiple physicians • Using principles of network sampling, you can adjust weights to estimate the observations of interest rather than the selection units

5. Desired observation units V v v V V V V Survey selection units

6. Greek for the Geeks = the selection probability of physician i (i = 1, …, N) and if physician i is affiliated with practice j, and if physician i is not affiliated with practice j.

7. Weight adjustment to estimate X  Observation weight =selection weight/ M where M is the multiplicity information for the selection unit

8. Re-weighting methodology • Assumptions and definitions • Use multiplicity information from the physician data to adjust physician-level estimates into practice-level estimates • Dividing the physician sampling weight by number of physicians in the practice provides a measure of practices

9. Physicians ► practices example… • Samples of physician records in medical practices • Physician data have the same practice included in multiple observations. • If we knew how many physicians were in the same practice as the sampled physicians, then we can adjust the estimator to account for the multiplicity.

10. Application to NAMCS • National Ambulatory Medical Care Survey • Annual survey of 3,000 nationally representative office-based physicians in patient care • Excludes radiologists, anesthesiologists, and pathologists and federally-employed physicians • Face-to-face induction interview asks physicians questions about his/her office practice • Records are weighted by the inverse of the probability of selection, adjusted for nonresponse (~60% RR), with a calibration ratio to annual totals

11. Induction interview content • Number of locations • Number of other physicians • Ownership • Type of office • Private, clinic, HMO, faculty practice plan, etc • EMR adoption • Revenue sources

12. Assumptions • Used the first location reported • Assumes practice information provided by sample physician is a constant for the practice • Does not account for multiplicity of practices within a physician • i.e., Ignores the fact that some physicians are affiliated with multiple practices (about 1% of physicians)

13. 3 medical practices with a total of 7 physicians V v v V V V V Solo practice Partner practice Group practice

14. Probability of selecting a practice V v v V V V V 1/7 2/7 4/7

15. Multiplicity factor V V V V V v v 1/7 2/7 4/7 1 .5 .25

16. Multiplicity information • How many other physicians practice with you at this location? • M= 1+ # of other physicians • Practice weight = physician weight / M

17. Re-weighting example Sum = 110 physicians 43 practices

18. Practice weight = physician weight / practice size • physician weight → 311,200 physicians ± 8,000 • practice weight → 161,200 practices ± 5,300

19. Percent distribution of office-based medical physicians and practices by size Physicians Practices

20. Computerized administrative and clinical support systems 80 74.2 Practices Physicians 69.2 70 60 50 40 30 19.0 15.0 20 9.2 6.5 10 0 Uses electronic billing Uses EMR Uses CPOE

21. Good National estimates of practices Characteristics that are common among physicians Bad Characterizing practices Underestimates larger practices Be careful how you define size First-listed location Location with most visits Location of the visit Limitations of NAMCS data…