1. Obesity Care Practices in the VHA: Documentation of Heights, Weights, & Obesity Diagnoses Polly Hitchcock Noël Veterans Evidence-based Research, Dissemination, & Implementation Center (VERDICT) VA HSR&D IIR 05-121-2

2. Collaborators VERDICT/UT Health Science Center San Antonio: Copeland L, Hazuda H, Pugh MJ, Wang CP, McCarthy A, Bollinger M VA National Ctr for Health Promotion & Disease Prevention: Kahwati L, Jones KR Site PIs: Nelson K, Hoffman V, Dundon P, Tsevat J, Arterburn D, Foulis P, Mossop PA

3. BMI = wt (kg)/[ht (m)] 2

4. Relevance MOVE! uses population-based approach to screening VHA indicator for screening introduced FY08 – national clinical reminder under discussion % of PC & MH patients screened with BMI & offered treatment if at-risk HEDIS/NCQA is developing & piloting obesity screening measure

5. Presentation Objectives A. What proportion of primary care patients had their hts & wts recorded in the EMR FY02- FY06? (screening) B. Among a cohort of primary care patients meeting BMI criteria for obesity in FY02: 1. What proportion had their hts/wts recorded in FY03-FY06? (monitoring) 2. What proportion received a diagnosis of obesity in FY02-FY06? (recognition)

6. Design inception cohort of primary care patients with BMI > 30 heights & weights recorded in EMR in FY02 followed FY03-FY06 6 regions (VISNs) (early & late adopters of MOVE!)

8. Data Sources Administrative Data: Sociodemographic, diagnostic, and utilization data from NPCD Pharmacy data from PBM Mortality data from MINI-VITALS Heights & weights obtained from VHAs new Corporate Data Warehouse (CDW)

9. Weight (& Height) Data Inherently variable over time changes in energy balance disease, surgery, injury, or aging Variety of sources/opportunities for error Measurement & reporting errors Data entry errors

10. Data Error Examples Among 847,976 primary care pts with multiple hts recorded in same year, 21,051 (2.5%) had hts differ by > 2 -10 inches Among 105,425 occurrences of pts > 2 wts recorded on same day, 10,054 (9.5%) had wts differ by >10-1,000 lbs

11. Obesity Screening > 1 PC visits each year for each VISN (where majority of care received) hts & wts recorded in EMR in their VISN for each FY02-FY06

12. *Primary care population from 1,053,228 in FY02 to 1,342,688 in FY06

13. Cohort Identification > 1 PC visits in the 6 VISNs in FY02 (N=1,053,228) wts & hts filtered to remove biologically implausible values wt & ht FY02: 844,066 (80.1%) wt FY02 & ht FY02-06: 89,018 ( 8.5%) Total: 933,088 (88.6%)

14. Cohort Identification: BMI Method 1 Maximum wt FY02 & Minimum ht FY02-FY06

15. Cohort Identification: BMI Method 1 Minimum ht/ Maximum wt N (%) BMI>30 371,842 39.9% BMI M (SD) 34.8 (4.8) BMI Range 30.0 – 161.8 # BMI > 70 252 # ht<60 in 7,078 # wt<170 lb 4,006 Among 933,088 PC patients with ht FY02 & wt FY02-FY06

16. Cohort Identification: BMI Method 2 median wt for each quarter of FY02, then median of median wts mode of all ht values FY02-FY06; in case of > 2 modes: if diff < 3 inches, averaged if diff > 3 inches, eliminated

17. Cohort Identification & Refinement Minimum ht/ Maximum wt Modal ht/Md of Median wts N (%) obese 371,842 39.9%330,802 35.5% BMI M (SD) 34.8 (4.8)34.5 (4.4) BMI Range 30.0 – 161.830.0 – 95.4 # BMI > 70 25270 # ht<60 in 7,0782,968 # wt<170 lb 4,0062,553 Among 933,088 PC patients with ht FY02 & wt FY02-FY06

18. Obesity Monitoring Cohort survivors For those with PC visit, determined proportion with wt & ht (or wt only) recorded each year FY03-FY06

19. Cohort Characteristics Survivors N=290,558 NonSurvivors N=40,244 BMI > 35 33% > 60 yrs 53%81% Male 94%97% Minority 16%15% Total Obese Cohort N=330,802

20. *Among 290,558 with PC visit each year (89.6% in FY02 to 82.3% in FY06)

22. Obesity Recognition Cohort survivors Proportion with ICD-9-CM codes 278, 278.00, 278.01, 259.9, V77.8 FY02 only and FY02-FY06

23. Cohort Survivors Diagnosed with Obesity Diagnosis in FY02? Diagnosis in FY02-06? Yes27%51% No73%49% Total Survivors 290,558

24. Obesity Dx in Cohort Survivors with Class I vs Class II+ Obesity Obesity Dx FY02-FY06?BMI < 35BMI > 35 Yes41% 72% No59%28% Total193,46297,096 p <.0001, Cohort Survivors N=290,558

25. Limitations administrative data hts, wts, & dxs may be entered in text fields & not captured by administrative data recording & data entry errors cohort may be misspecified obesity based on BMI not representative of entire VHA

26. Discussion Significant variation in screening & monitoring early MOVE! adopters performed better Characteristics of those not screened unknown Significant # do not have dxs or hts recorded – perceptions of importance? majority of cohort > 60 yrs BMI used to dose medications & calculate ventilation unit parameters implications for health services research

27. Next Steps Describe variations in other obesity care practices & factors that predict Examine the impact of obesity care practices on BMI and other important clinical outcomes Identify longitudinal patterns (latent classes) of BMI trends over time

28. Q U E S T I O N S ?