1. 1 How to understand and use National Ambulatory Medical Care Survey (NAMCS) and National Hospital Ambulatory Medical Care Survey (NHAMCS) data for clinical research Yuwei Zhu 10-29-2004 Dept of Biostatistics

2. 2 Overview I. Survey Background I. Survey Background II. Survey Methodology II. Survey Methodology III. Technical Considerations III. Technical Considerations IV. Getting the Data – Using Raw Data Files IV. Getting the Data – Using Raw Data Files V. Example V. Example VI. Data Analysis – SAS, STATA, SUDAAN VI. Data Analysis – SAS, STATA, SUDAAN VII. Other Public Domain Data VII. Other Public Domain Data

3. 3 Performed by:  Centers for Disease Control and Prevention (CDC)  National Center for Health Statistics, Division of Health Care Statistics, and National Health Care Survey NAMCS and NHAMCS

4. 4 istory National Ambulatory Medical Care Survey (NAMCS) History Survey began in 1973 Survey began in 1973 Annual data collection through 1981 Annual data collection through 1981 Conducted in 1985 Conducted in 1985 Annual began again in 1989 Annual began again in 1989

5. 5 NAMCS Classified by the American Medical Association and the American Osteopathic Association as delivering “office-based, patient care” Healthcare providers within private, non–hospital-based clinics and health maintenance organizations (HMOs) are within the scope of the survey

6. 6 NAMCS Patient visits made to the offices of non– federally employed physicians Patient visits made to the offices of non– federally employed physicians –Excluding:  Anesthesiology  Radiology  Pathology

7. 7 In-Scope NAMCS locations Freestanding clinic Freestanding clinic Federally qualified health center Federally qualified health center Neighborhood and mental health centers Neighborhood and mental health centers Non-federal government clinic Non-federal government clinic Family planning clinic Family planning clinic HMO HMO Faculty practice plan Faculty practice plan Private solo or group practice Private solo or group practice

8. 8 Out-of-Scope NAMCS locations Hospital EDs and OPDs Hospital EDs and OPDs Ambulatory surgicenter Ambulatory surgicenter Institutional setting (schools, prisons) Institutional setting (schools, prisons) Industrial outpatient facility Industrial outpatient facility Federal Government operated clinic Federal Government operated clinic Laser vision surgery Laser vision surgery

9. 9 NAMCS NAMCS uses a multistage probability sample design to obtain –Primary sampling units (PSUs) –Physician practices within the PSUs –Patient visits within physician practices

10. 10 Sample design - NAMCS 112 PSUs (counties) 112 PSUs (counties) –Counties –Groups of counties –County equivalents (such as parishes or independent cities) –Towns –Townships Nonfederally employed, office-based physicians stratified by specialty, 3,000 physicians Nonfederally employed, office-based physicians stratified by specialty, 3,000 physicians About 30 visits per doctor over a randomly selected 1-week period, 25,000 visits About 30 visits per doctor over a randomly selected 1-week period, 25,000 visits

11. 11 NHAMCS) History National Hospital Ambulatory Medical Care Survey (NHAMCS) History Survey began in 1992 Survey began in 1992 Annual data collection Annual data collection

12. 12 National sample of visits to the EDs and outpatient departments of noninstitutional general and short-stay hospitals in the United States National sample of visits to the EDs and outpatient departments of noninstitutional general and short-stay hospitals in the United States Excluded hospitals: Excluded hospitals: –Federal –Military –Veterans Administration NHAMCS

13. 13 NHAMCS This survey uses a 4-stage probability design with samples –geographically defined areas –hospitals within these areas –clinics within the hospital –patient visits within clinics. The first stage is similar to NAMCS

14. 14 Sample design - NHAMCS 112 PSUs (counties) Panel of 600 non-Federal, general or short stay hospitals Clinics (OPDs) and emergency service areas (EDs), 400 EDs and 250 OPDs About 200 visits per OPD, 100 per ED over random 4-week period, 37,000 ED and 35,000 OPD visits

15. 15 NHAMCS Scope OPD was intended to be parallel to the NAMCS in the hospital setting OPD was intended to be parallel to the NAMCS in the hospital setting General medicine, surgery, pediatrics, ob/gyn, substance abuse, and “other” clinics are in- scope General medicine, surgery, pediatrics, ob/gyn, substance abuse, and “other” clinics are in- scope Ancillary services are out of scope Ancillary services are out of scope

16. 16 Data Items Patient characteristics Patient characteristics –Age, sex, race, ethnicity Visit characteristics Visit characteristics –Source of payment, continuity of care, reason for visit, diagnosis, treatment Provider characteristics Provider characteristics –Physician specialty, hospital ownership… Drug characteristics added in 1980 Drug characteristics added in 1980 –Class, composition, control status, etc.

17. 17 Repeating fields (from text entries) Up to 3 fields each… Up to 3 fields each… –Reason for visit –Physician’s diagnosis –Cause of injury Diagnostic services (6 fields) Diagnostic services (6 fields) Surgical procedures (2 fields) Surgical procedures (2 fields) Medications (6 fields) Medications (6 fields) –Drug ingredients (5 fields) –Therapeutic class (3 fields – 2002 on)

18. 18 Coding Systems Used Reason for Visit Classification (NCHS) Reason for Visit Classification (NCHS) ICD-9-CM for diagnoses, causes of injury and procedures ICD-9-CM for diagnoses, causes of injury and procedures Drug Classification System (NCHS) Drug Classification System (NCHS) National Drug Code Directory National Drug Code Directory

19. 19 Drug Data in NAMCS/ NHAMCS What is a “Drug Mention” ? What is a “Drug Mention” ? Any of up to 6 medications that were ordered, supplied, administered, or continued during the visit. Any of up to 6 medications that were ordered, supplied, administered, or continued during the visit. Respondents are asked to report trade names or generic names only (not dosage, administration, or regimen).

20. 20 Drug Characteristics Generic Name (for single ingredient drugs) Generic Name (for single ingredient drugs) Prescription Status Prescription Status Composition Status Composition Status Controlled Substance Status Controlled Substance Status Up to 3 NDC Therapeutic Classes (4-digit) Up to 3 NDC Therapeutic Classes (4-digit) Up to 5 Ingredients (for multiple ingredient drugs) Up to 5 Ingredients (for multiple ingredient drugs)

21. 21 Some User Considerations NAMCS/NHAMCS sample visits, not patients NAMCS/NHAMCS sample visits, not patients No estimates of incidence or prevalence No estimates of incidence or prevalence No state-level estimates No state-level estimates Not sampled by setting or by non- physician providers Not sampled by setting or by non- physician providers May capture different types of care for solo vs. group practice physicians May capture different types of care for solo vs. group practice physicians

22. 22 Data uses Understand health care practice Understand health care practice Examine the quality of care Examine the quality of care Track certain conditions Track certain conditions Find health disparities Find health disparities Measure Healthy People 2010 objectives Measure Healthy People 2010 objectives Serve as benchmark for states Serve as benchmark for states

23. 23 Data users Over 100 journal publications in last 2 years Over 100 journal publications in last 2 years Medical associations Medical associations Government agencies Government agencies Health services researchers Health services researchers University and medical schools University and medical schools Broadcast and print media Broadcast and print media

24. 24 Sample Weight Each NAMCS record contains a single weight, which we call Patient Visit Weight Each NAMCS record contains a single weight, which we call Patient Visit Weight Same is true for OPD records and ED records Same is true for OPD records and ED records This weight is used for both visits and drug mentions This weight is used for both visits and drug mentions

25. 25 Reliability of Estimates Estimates should be based on at least 30 sample records AND Estimates should be based on at least 30 sample records AND Estimates with a relative standard error (standard error divided by the estimate) greater than 30 percent are considered unreliable by NCHS standards Estimates with a relative standard error (standard error divided by the estimate) greater than 30 percent are considered unreliable by NCHS standards Both conditions should be met to obtain reliable estimates Both conditions should be met to obtain reliable estimates

26. 26 How Good are the Estimates? Depends on what you are looking at. In general, OPD estimates tend to be somewhat less reliable than NAMCS and ED. Depends on what you are looking at. In general, OPD estimates tend to be somewhat less reliable than NAMCS and ED. Since 1999, Advance Data reports include standard errors in every table so it is easy to compute confidence intervals around the estimates. Since 1999, Advance Data reports include standard errors in every table so it is easy to compute confidence intervals around the estimates.

27. 27 Sampling Error NAMCS and NHAMCS are not simple random samples NAMCS and NHAMCS are not simple random samples Clustering effects of visits within the physician’s practice, physician practices within PSUs, clinics within hospitals Clustering effects of visits within the physician’s practice, physician practices within PSUs, clinics within hospitals Must use some method to calculate standard errors for frequencies, percents, and rates Must use some method to calculate standard errors for frequencies, percents, and rates

28. 28 Ways to Improve Reliability of Estimates Combine NAMCS, ED and OPD data to produce ambulatory care visit estimates Combine NAMCS, ED and OPD data to produce ambulatory care visit estimates Combine multiple years of data Combine multiple years of data Aggregate categories of interest into broader groups. Aggregate categories of interest into broader groups.

29. 29 NAMCS vs. NHAMCS Consider what types of settings are best for a particular analysis Consider what types of settings are best for a particular analysis –Persons of color are more likely to visit OPD's and ED's than physician offices –Persons in some age groups make disproportionately larger shares of visits to ED's than offices and OPD's

30. 30 File Structure Download data and layout from website Download data and layout from website http://www.cdc.gov/nchs/about/major/ahcd/ ahcd1.htm http://www.cdc.gov/nchs/about/major/ahcd/ ahcd1.htm Flat ASCII files for each setting and year Flat ASCII files for each setting and year NAMCS: 1973-2002 NHAMCS: 1992-2002

31. 31 Trend considerations Variables routinely rotate on and off survey Variables routinely rotate on and off survey Be careful about trending diagnosis prior to 1979 because of ICDA (based on ICD-8) 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 Even after 1980- be careful about changes in ICD-9-CM Number of medications varies over years Number of medications varies over years 1980-81 – 8 medications 1985, 1989-94 – 5 medications 1995-2002 – 6 medications 2003+ – 8 medications Diagnostic & therapeutic checkboxes vary Diagnostic & therapeutic checkboxes vary Use spreadsheet for significance of trends Use spreadsheet for significance of trends

32. 32 Example Hypothesis -- Educational Efforts Targeted at Judicious Antibiotic Use Will Reduce Prescription Rates in all Treatment Settings Hypothesis -- Educational Efforts Targeted at Judicious Antibiotic Use Will Reduce Prescription Rates in all Treatment Settings

33. 33 Study Design Retrospective collection of data from Retrospective collection of data from –NAMCS –NHAMCS 1994-2000 study years 1994-2000 study years Antibiotic prescribing patterns and diagnoses Antibiotic prescribing patterns and diagnoses Children <5 years of age Children <5 years of age Clinic type -- Pediatric Clinic type -- Pediatric Physician type – Pediatrician or Family Medicine Physician type – Pediatrician or Family Medicine

34. 34 Data Stratification Race – White, Black and other Time period – 94 & 95, 96 & 97, 98 & 00 Antibiotics – Penicillin's, Cephalosporins, Erythromycin/lincosamide/macrolides,Tetracyclines, Chloramphenicol derivatives, Aminoglycosides, Sulfonamides and trimethoprim, Miscellaneous antibacterial agents, and Quinolone/derivatives Diagnoses -- Otitis media, Sinusitis, Pharyngitis,Bronchitis,Upper respiratory tract infection (URI)

35. 35

36. 36 Total Care YearsWhiteBlack Rate Ratio 95% CI Visit rates per 1000 children aged <5 years 1994- 1995 415031021.34 1.22, 1.47* 1996- 1997 452943201.05 1.02, 1.08* 1998- 2000 420443020.98 0.70, 1.34

37. 37 0% 20% 40% 60% 80% 100% 1994- 1995 1996- 1998 1999- 2000 1994- 1995 1996- 1998 1999- 2000 Years % Distribution health care visit site Hospital-based ED Office-based White childrenBlack Children

38. 38 Total Care YearsWhiteBlack Rate Ratio 95% CI Antibiotic prescription rates per 1000 children aged <5 years 1994- 1995 14949981.50 1.48, 1.51* 1996- 1997 142113201.08 0.96, 1.22 1998- 2000 111810741.04 0.86, 1.24

39. 39 Total Care YearsWhiteBlack Rate Ratio 95% CI Otitis media rates per 1000 children aged <5 years 1994- 1995 8165201.57 1.46, 1.69* 1996- 1997 7797391.06 1.04, 1.07* 1998- 2000 6306031.05 0.69, 1.58

40. 40 Results Decline in antibiotic prescribing in children <5 years; most notable in office-based and emergency department settings Decline in antibiotic prescribing in children <5 years; most notable in office-based and emergency department settings Penicillin's were common antibiotics used Penicillin's were common antibiotics used Most common diagnosis in all three settings was otitis media Most common diagnosis in all three settings was otitis media Natasha B. Halasa, Marie R. Griffin, Yuwei Zhu, and Kathryn M. Edwards. Difference in antibiotic prescribing patterns for children aged less than five years in the three major outpatient settings, Journal of Pediatrics. 2004; 144:200-205

41. 41 Code to create design variables: survey years 2001 & earlier 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);

42. 42 proc crosstab data=test1 design=WOR filetype=sas; Nest stratm psum subfile prostrat year provider dept su clinic/missunit; Totcnt poppsum _zero_ _zero_ _zero_ popprovm _zero_ popsum _zero_ popvism; Weight patwt; Tables sex*ager; run; SUDAAN version 8.0.2 example

43. 43 proc crosstab data=test1 filetype=sas; Nest stratm psum ; Weight patwt; Tables sex*ager; run; SUDAAN version 8.0.2 example

44. 44 Use http:// ***/test1 svyset [pweight=patwt], strata(cstratm) psu(cpsum) svytab sex ager svymean age STATA version 8. example

45. 45 proc surveyfreq data=test1; tables sex*ager; strata cstratm; cluster cpsum; weight patwt; run; SAS version 9.1 example

46. 46 Some considerations: SUDAAN vs. SAS Proc Surveymeans SUDAAN PROC Surveymeans design variables=cstratm, cpsum (1-stage design) nest=cstratm, cpsum nest=cstratm, cpsum strata cstratm strata cstratm cluster cpsum cluster cpsum Sort by design variables Sort by design variables Sort not needed Sort not needed Weight data: Patwt Weight data: Patwt Subgroup=identify categorical variables Subgroup=identify categorical variables Class=identify categorical variables Class=identify categorical variables Tables=analysis variables Tables=analysis variables Var=analysis variables Var=analysis variables

47. 47 If nothing else, remember…The Public Use Data File Documentation is YOUR FRIEND! Each booklet includes: Each booklet includes: –A description of the survey –Record format –Marginal data (summaries) –Various definitions –Reason for Visit classification codes –Medication & generic names –Therapeutic classes

48. 48 Other Public Domain Data CDC WONDER -- http://wonder.cdc.gov/ CDC WONDER -- http://wonder.cdc.gov/http://wonder.cdc.gov/ National Center for Health Statistics -- http://www.cdc.gov/nchs/ National Center for Health Statistics -- http://www.cdc.gov/nchs/ http://www.cdc.gov/nchs/ National Health and Nutrition Examination Survey (NHANES) -- National Health and Nutrition Examination Survey (NHANES) --http://www.cdc.gov/nchs/nhanes.htm National Health Interview Survey (NHIS) -- http://www.cdc.gov/nchs/nhis.htm National Health Interview Survey (NHIS) -- http://www.cdc.gov/nchs/nhis.htm National Survey of Family Growth (NSFG) -- http://www.cdc.gov/nchs/nsfg.htm National Survey of Family Growth (NSFG) -- http://www.cdc.gov/nchs/nsfg.htm Census -- http://www.census.gov/ Census -- http://www.census.gov/ http://www.census.gov/

49. 49 Other Public Domain Data (cont.) Dept. of Health, TN http://hitspot.state.tn.us/hitspot/hit/main/ SPOT/frames/SPOT/index.htm Dept. of Health, TN http://hitspot.state.tn.us/hitspot/hit/main/ SPOT/frames/SPOT/index.htm

50. 50 Thanks Natasha Halasha Natasha Halasha Susan Schappert - National Center for Health Statistics Susan Schappert - National Center for Health Statistics Linda McCaig & David Woodwell - National Center for Health Statistics Linda McCaig & David Woodwell - National Center for Health Statistics

51. 51 Questions?