Completeness of Physician Billing Claims for Diabetes Prevalence Estimation

Transcription

1 Completeness of Physician Billing Claims for Diabetes Prevalence Estimation Lisa M. Lix 1, John Paul Kuwornu 1, George Kephart 2, Khokan Sikdar 3, Hude Quan 4 1 University of Manitoba; 2 Dalhousie University; 3 Alberta Health Services; 4 University of Calgary CSEB Conference June 26, 2013

2 Background Administrative health data are widely used for chronic disease research and surveillance Example: Canadian Chronic Disease Surveillance System estimates chronic disease prevalence using caseascertainment algorithms based on diagnoses in provincial/territorial hospital records and physician billing claims

3 Background Physician billing claims ascertain more cases than hospital records because most chronic diseases are diagnosed and managed in an outpatient setting However, physician billing claims may be an incomplete source of data for chronic disease research and surveillance because: An increasing percentage of physicians are being remunerated on alternate payment plans instead of by feefor-service methods Physicians on alternate payment plans might not consistently shadow bill, the practice of submitting parallel billing claims

4 Full-Time Equivalent Physicians Receiving Alternate Clinical Payments by Province, 1999/00 & 2005/06

5 Study Aims To estimate the completeness of physician billing claims for Manitoba physicians remunerated by fee-for-service and alternate payment plans To examine the effects of completeness on estimates of diabetes prevalence

6 Methods Data Source: administrative data from Manitoba To ascertain diabetes cases: prescription drug records, hospital discharge abstracts, physician billing claims To ascertain demographics & health insurance coverage for diabetes cases: population registry To ascertain characteristics of physicians: provider registry Study Years 1995/96 to 2010/11

7 Methods Study Design Retrospective cohort design Inclusion criteria: Health insurance coverage during the study years At least one prescription with an ATC code of A10 (drugs for diabetes) between 1996/97 and 2010/11 The first prescription in this period is the index prescription 20+ years of age at index prescription date Exclusion criteria: No prescriptions with an ATC code of A10 in 1995/96

8 Flow Chart of Study Design* *Based on Alshammari et al., CJPH, 2010 Stratify this patient cohort into groups: (1) index prescription is from a fee-forservice physician (2) index prescription is from a non-fee-forservice physician (3) index prescription is from another provider Identify the date of diabetes diagnosis from hospital records or physician billing claims for patients in each cohort group Classify the patient cohort as: True positives: diagnosis date is 182 days after the prescription index date Late entries: diagnosis date is 183+ days after the prescription index date * Missed cases: no diagnosis date after prescription index date *adjusted for censoring

9 Study Variables Diabetes diagnosis: case definition used by the Canadian Chronic Disease Surveillance System (CCDSS), which is based on a two-year case ascertainment window Variables to describe prescribing physician: (defined at index prescription date) Method of remuneration: fee-for-service, non-fee-for-service, missing payment information Age, Speciality, health region of practice Variables to describe patient cohort: (defined at index prescription date) Age, sex, health region of residence

10 Statistical Analyses The diabetes cohort and prescribers were described using frequencies and percentages Diabetes prevalence and 95% confidence intervals were estimated with and without missed cases

11 Type of Prescriber for Incident Diabetes Cases (N = 80,188) % % % Fee-for-Service Physician Other/Missing Prescriber Non-Fee-for-Service Physician

12 Frequency of Physicians who Prescribed Diabetes Medications by Fiscal Year / / / / / / / / / / / / /11 Fee-for-service physician Non-fee-for-service physician

13 Characteristics of Physicians who Prescribe Diabetes Medications Characteristic Fee-for-Service Non-Fee-for-Service N % N % TOTAL Specialty GP Specialist Missing Practice Location Winnipeg Region Northern Rural Region Southern Rural Region Age, Mean (SD) 43.5 (11.9) 37.3 (8.7)

14 Percentage of Diabetes Cases by Type of Prescriber Other/Missing Prescriber Non Fee-for-Service Physician Fee-for-Service Physician 0% 20% 40% 60% 80% 100% True Positives Late Entries Missed Cases

15 Characteristics (%) of Missed Cases by Type of Prescriber Sex Fee-for-Service Physician (n = 5867) Non-Fee-for- Service Physician (n = 991) Other or Missing Prescriber (n = 835) Male Female Age Group < 45 years years years years years Health Region Winnipeg Rural North Rural South

16 Percent Crude Diabetes Prevalence Estimates for Manitoba Crude Prevalence (all cases) Crude Prevalence (without missed cases) Crude Prevalence (without non-fee-for-service missed cases)

17 Summary The completeness of physician billing claims, based on the proportion of cases with a prescribed diabetes medication who were identified as true cases, was similar for fee-for-service and non-fee-for-service physicians The percentage of cases classified as missed was only slightly higher (< 2%) for non-fee-for service than fee-for-service physicians

18 Conclusions Both fee-for-service and non-fee-for-service physicians are almost equally likely to miss reporting diabetes cases, based on comparisons of ascertainment rates from physician billing claims and prescription drug records Diabetes prevalence estimates increased by almost 11% when missed cases were included, but missed cases from non-fee-for-service physicians resulted in an increase in the crude diabetes prevalence estimate of only 1.4%.

19 Implications The case ascertainment method used in this study can be applied to other chronic diseases and over time, to assess potential for data loss The results are being used to develop parameters for simulation models, to develop different scenarios of surveillance data loss if physicians who are remunerated by alternate payment plans do not shadow bill

20 Limitations Physicians were classified as either fee-for-service or non-fee-for-service; some may receive both types of remuneration The results may be sensitive to the definitions used to ascertain true positives, late entries, and missed cases

21 Acknowledgements The research team was funded by an operating grant from the Canadian Institutes of Health Research. LML is funded by a Manitoba Research Chair. The authors acknowledge the Manitoba Centre for Health Policy for use of data contained in the Population Health Research Data Repository under project #2012/ The results and conclusions are those of the authors and no official endorsement by the Manitoba Centre for Health Policy, Manitoba Health, or other data providers is intended or should be inferred.

22 Contact Information Lisa Lix, University of Manitoba