Understanding and Using NAMCS and NHAMCS Data: A Hands-On Workshop

1. Understanding and Using NAMCS and NHAMCS Data: A Hands-On Workshop Part II-Advanced Programming Techniques Esther Hing

2. Overview • Issues when trending NAMCS/NHAMCS data • CHC data & estimates • Provider-level estimates • Visit-level data aggregated to provider-level statistics • Visits vs. patient estimates • Summary

3. NAMCS/NHAMCS trend data

4. Survey content varies over time • Variables routinely rotate on and off survey • Be careful about trending diagnosis prior to 1979 because of ICDA (based on ICD-8) • Even after 1980- be careful about changes in ICD-9-CM • Number of medications varies over years 1980-81 – 8 medications 1985, 1989-94 – 5 medications 1995-2002 – 6 medications 2003 and after--8 medications • Medications coded according to MULTUM terminology in 2006, and according to the National Drug code Directory maintained by FDA in years before 2006 are not comparable. • Diagnostic & therapeutic service checkboxes vary

5. PDF of Survey Content for the NAMCS and NHAMCS is on webpagewww.cdc.gov/nchs/about/major/ahcd1.htm

6. Public Use Data File Documentation for each year is another source Documentation includes: • A description of the survey • Record format • Marginal data (summaries) • Various definitions • Reason for Visit classification codes • Medication & generic names • Therapeutic classes

7. Combining multiple years • 2 year combinations are best for subpopulation analysis • 3-4 year combinations for disease specific analysis • Keep adding years until you have at least 30 raw cases in important cells • RSE improves incrementally with the number of years combined

8. RSE improves incrementally with the number of years combined • RSE = SE/x • RSE for percent of visits by persons less than 21 years of age with diabetes • 1999 RSE = .08/.18 = .44 (44%) • 1998 & 1999 RSE = .06/.18 = .33 (33%) • 1998, 1999, & 2000 RSE = .05/.21 = .24 (24%)

9. Combining multiple settings • NAMCS, hospital emergency department (ED), and outpatient department (OPD) data can be combined in one or multiple years • NAMCS & OPD variables virtually identical, many ED variables are same • OPD and NAMCS should be combined to get estimates of ambulatory physician care especially for African-American, Medicaid or adolescent subpopulations • Only NAMCS has physician specialty

10. Variance computations • Survey design variables need to be identical across time and settings regardless of software used • SUDAAN 3 & 4-stage design variables available for survey years 1993 through 2001 • Starting in 2002, 1-stage design variables were released with PUF files, permitting use of SUDAAN 1-stage WR variances, STATA, SAS’s Complex Survey procedures and SPSS’s Complex Samples 12.0 module

11. Design Variables—Survey Years 2002 2001 1-Stage design variables 3- or 4-Stage design variables 3- or 4-Stage design variables 2003 1-Stage design variables only

12. Code to create design variables: survey years 2001 & earlier CPSUM=PSUM; CSTRATM = STRATM; IF CPSUM IN(1, 2, 3, 4) THEN DO; CPSUM = PROVIDER +100000; CSTRATM = (STRATM*100000) +(1000*(MOD(YEAR,100))) + (SUBFILE*100) + PROSTRAT; END; ELSE CSTRATM = (STRATM*100000);

13. 2006 NAMCS Community Health Center data

14. NAMCS sample of Community Health Centers (CHCs) • CHC physicians always included in NAMCS • Typically small n of CHC physicians precluded presentation of estimates (unreliable) • 2006 NAMCS included separate stratum of about 100 CHCs • Within CHCs, up to 3 physicians or mid-level providers (physician assistants or nurse practitioners) and their visits sampled

15. Comparison of primary care visits to community health centers and physician offices • 1/Difference between community health centers and physician offices is statistically significant (p<0.05). • SOURCE: Cherry DK, Hing E, Woodwell DA, Rechtsteiner EA. National Ambulatory Medical Care Survey: 2006 Summary. • National health statistics reports; no.3. Hyattsville, MD: National Center for Health Statistics. 2008.

16. NAMCS sample of Community Health Centers limitations • 2006 NAMCS PUF only includes CHC physician visits • Additional level of sampling for CHC providers increases sampling variability of estimates • CHC physician visits insufficient for detailed analysis of CHC physicians • 2006-07 CHC PUF file planned for release in 2009; will include visits to mid-level providers

17. NAMCS/NHAMCS provider-level estimates

18. Physician weight released on NAMCS PUF file • NAMCS physician weight (PHYSWT) first released on 2005 PUF • PHYSWT only on first visit record for physician • Physician file created by selecting records with PHYSWT>0 • Survey design variables same for physicians as visits

19. Physician characteristics on 2006 NAMCS PUF file Physician characteristics on PUF: • Physician specialty (SPECR) • Physician specialty group (SPECCAT) • Geographic region (REGION) • Metropolitan statistical area (MSA) • Solo practice (SOLO) • Other Induction interview variables on pages 62-73 of NAMCS PUF documentation

20. Other information on NAMCS Physician weight • Selected physician estimates presented on page 88 of 2006 NAMCS PUF documentation • See pages 27-28 for additional information about the physician-level weight

21. Exercise: compare visit estimates with physician estimates • Compare number of visits by physician specialty with number of physicians by specialty • Steps • Read NAMCS PUF • Estimate visits using PUF • Estimate physicians from physician file

22. Run Exercise 1 Reads NAMCS PUF and produces weighted frequency of visits by physician specialty

23. Output

24. Run Exercise 2: Creates physician file and produces weighted frequency of physicians by specialty • PHYSWT>0 cases n=1,268

25. Output

26. Run Exercise 3: Compute standard errors of physician percentages by specialty using SAS’s PROC SURVEYFREQ

27. Output

28. Physician weight caveatNAMCS PUF files • PUF physician estimates may differ slightly from published physician estimates (e.g. Physicians using electronic medical records in 2005 EStat report) • 2005 NAMCS PUF includes only physicians with visit records (n=1,058) • EStat estimates include additional 223 in-scope physicians unavailable during sample week (on vacation or conferences) who responded to Physician Induction Interview (n=1,281)

29. Provider weights released on NHAMCS PUF file • Hospital ED weight (EDWT) only on first ED visit record for department within sample hospital • Hospital OPD weight (OPDWT) only on first OPD visit record for that department within sample hospital • Create hospital file by selecting records with EDWT>0 or OPDWT>0 for more accurate variance estimates; use subpopulation option to select either ED or OPD data • Survey design variables same for hospital departments as visits

30. Provider weights released on NHAMCS PUF file (cont.) • Selected ED estimates (n=364) presented on page 112 of 2006 NHAMCS PUF documentation • Selected OPD estimates (n=235) presented page 116-117 of 2006 NHAMCS PUF documentation • See pages 23-24 for more details on use of ED and OPD weight

31. Provider weights released on 2006 NHAMCS PUF file (cont.) ED characteristics on PUF: • Hospital ownership (OWNER), • Receipt of Medicaid Disproportionate Share Program funds (MDSP), • Receipt of bioterrorism hospital preparedness funding (BIOTER), • Geographic region (REGION), • Metropolitan statistical area (MSA), and • Multiple variables on ED use of electronic medical records

32. Provider weights released on 2006 NHAMCS PUF file (cont.) OPD characteristics on PUF: • Hospital ownership (OWNER), • Receipt of Medicaid Disproportionate Share Program funds (MDSP), • Receipt of bioterrorism hospital preparedness funding (BIOTER), • Geographic region (REGION), • Metropolitan statistical area (MSA), and • Multiple variables on OPD use of electronic medical records

33. Aggregating visit statistics at the physician or facility level

34. Why aggregate visit data to provider level • Provides additional information about provider • Visit characteristic linked to providers can be compared across providers • Examples • Average caseload by expected payment source across EDs • Average visit duration in EDs by ED visit volume

35. Example • Note: Plus sign indicates median percentages across all emergency departments. • Box represents the middle 50 percent of emergency departments. • Lines represent emergency departments with extreme percentages. • SOURCE: Burt, McCaig. Staffing, Capacity, and ambulance diversion in emergency department: • United States, 2003-04. Advance data from vital and health statistics; no. 376. 2006.

36. Steps • Convert dichotomous analytic variables to 0/1 format (requires conversion to percentages afterwards) • Convert missing values on continuous variables to “.” • Use PROC SUMMARY to create one record per provider along with aggregate statistic for that provider • Run weighted average on provider file

37. Aggregate ED waiting time from visit file and estimate distribution across EDs by MSA status • Run Exercise 4: Read ED visit file and aggregate waiting time; print first 10 observations

38. Output

39. Aggregate ED waiting time from visit file and estimate distribution across EDs by MSA status (Cont.) • Run Exercise 5: Computes average waiting times in hospital EDs in MSAs and Non-MSAs

40. Output for MSAs

41. Histogram and Box plot for MSAs

42. Normal probability plot for MSAs

43. Histogram and Box plot for MSAs

44. Output for Non-MSAs

45. Histogram and Box plot for Non-MSAs

46. Normal probability plot for Non-MSAs

47. Distribution of average waiting time across EDs in MSAs and Non-MSAs MSA Non-MSA Percentile

48. NAMCS/NHAMCS patient-level estimates

49. Advantages & limitations of population-based surveys • Population-based surveys • Estimate persons, including those who never saw a health care provider during reference period (e.g., last 12 months) • Health care utilization data subject to recall or proxy reporting for children • Less likely to measure rare medical conditions

50. Advantages & limitations of encounter-based surveys • Encounter-based surveys • Estimate the number, kind, and characteristics of health care encounters • Useful in estimating the burden of illness on the health care system • Can estimate rare medical conditions • Characteristics not subject to recall since information found in medical record • Estimate visits not patients