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 be careful about changes in
ICD-9-CM
Number of medications varies over years
– 8 medications
1985, – 5 medications
– 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 webpage www. cdc
PDF of Survey Content for the NAMCS and NHAMCS is on webpage

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
2001
2002
1-Stage design
variables
3- or 4-Stage design
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 ;
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

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
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 of NAMCS PUF documentation

20. Other information on NAMCS Physician weight
Selected physician estimates presented on page 88 of NAMCS PUF documentation
See pages 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 caveat NAMCS 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 NHAMCS PUF documentation
Selected OPD estimates (n=235) presented page of 2006 NHAMCS PUF documentation
See pages 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, Advance data from vital and health statistics; no

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
Percentile

48. NAMCS/NHAMCS patient-level estimates
Now we turn to patient-level estimates. Patient-level estimates can be made using NAMCS data or the outpatient component of the NHAMCS data.

49. Advantages & limitations of 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
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

51. Advantages of translating NAMCS/NHAMCS encounter data to patient estimates
Describes patterns of care by frequency of visits to the doctor
Provides more information about patients from encounter-level data
Better describes quality of care to patients vs. describing content of encounter
-What benefits do we gain by adding a question assessing past visits?
We can describe patterns of care by frequency of visits to the doctor—Patients with condition X typically visit the physician more often in a year than patients with condition Y. (encounter-level data)
Provides more information about patients from encounter-level data.
Better describe quality of care to patients vs. describing content of encounter. Whereas in the past, we might report that in 2005 there were 142,622,000 visits where a diet/nutrition health education was ordered or provided, but we know that with the past visit item that this could, and probably does, represent patients being counted at least twice. So, a better indicator of care would be how many INDIVIDUAL patients received diet/nutrition counseling.

52. How are patients estimated from ambulatory encounter data?
Based on multiplicity estimator; component of network theory
Multiplicity inherent in ambulatory data
On average, patients see their physician about 3 times a year
Some patients see multiple physicians during year

53. References
Burt CW and Hing E. Making patient-level estimates from medical encounter records using a multiplicity estimator. Stat Med 2007; 26:
Sirken MG. Network Sampling. In Encyclopedia of Biostatistics, Armitage P, Colton T (eds). Wiley: West Sussex. 1998;
Birnbaum ZW, Sirken MG. Design of Sample Surveys to Estimate the Prevalence of Rare Diseases. Vital and Health Statistics, PHS Publication No. 1, Series 2 (1). U.S. Government Printing Office: Washington, 1965.

54. Multiplicity of patient visits to physician
v v
V V V V

55. Probability of selecting visit increases with number of patient visits
v v
V V V V
1/7
2/7
4/7

56. To count patient only once, adjust visit probability
v v
V V V V
1/1
1/2
1/4

57. Patients estimated using multiplicity estimator
(visit weight)jk
patient weight =
Sjk
Basically, a patient weight is a modification (reduction) of the visit weight. The visit rate is made up of 2 basic components & 2 adjustments: the inverse of the selection probability associated with a sampled physician, the inverse of the selection probability of the sampled visit, & 2 adjustments.
-The visit weight is reduced by Sjk, where Sjk is the number of visits in the past 12 months (j=1,….,r) by the patient who had sample visit k (k=1,….m) with provider j.
-For example, if a patient visited the physician only once during the year (the sample visit), the visit would get a multiplicity adjustment of 1. If the sample visit was made by a person with 4 total visits within the year, the visit would get a multiplicity adjustment ¼.
Number of visits in the past 12 months to sampled provider

58. Assumptions of patient estimate
Patient is relation between person and sampled doctor
Assumes previous visits by same patient have similar visit characteristics
One person can be different patients to different doctors

59. Limitations of patient estimator
Assumption of similar characteristics is not applicable to all analytical variables
Patient estimates not equivalent to person-level estimates (doesn’t count persons with no medical encounters)
Patient estimates limited to physician offices and hospital outpatient departments
Multiplicity information first collected in half samples of 2001 NAMCS and NHAMCS (OPD)
Question on multiplicity of visits available on PUF since 2002
Multiplicity information will be available for ED visits in 2007

60. Comparison of distributions for visits and patients: NAMCS 2001
2-3
4-6 7+ 10 20 30 40 50 60
Percent
-Let’s look at some quick results:
This slide
Visits
Patients

61. Percent distribution for people making any health care visits by number of visits made in one year: NHIS,
Percent of persons 60 50 40 30 20 10
1-3
4-9
10+
Number of visits
Rate of persons making no health care visit was 17.5.

62. Estimated Percentage of Patients Aged >45 Years Who Received Exercise Counseling from their Primary-Care Physicians, by Sex and Age Group—National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey, United States,
This chart is an example of visit data from the NAMCS and NHAMCS-OPD re-weighted to represent estimates of patients who received exercise counseling during their last visit. In this chart, the percentage of patients who received exercise counseling from their primary care physician decreased as patient age increased for both males and females 45 years and over.
SOURCE: Cherry, DK. QuickStat MMWR. November 2, 2007/ 56(43); 1142.

63. Patient weight summary
Visit records may be re-weighted to provide patient-level estimates
Re-weighted distribution more closely resembles population-based estimates
No change in sampling variance estimation procedure other than using the new weight
Past visits items provide depth to analysis of ambulatory care utilization

64. Exercise: Compare Visit and Patient estimates

65. Multiplicity measure
This is the question measuring multiplicity of visits. The question has not changed since first collected in Several issues need to be pointed out about this item:
First, information about past visit is collected only for established patients, that is , patients who’ve been previously seen by the physician.
Second, the number of past visits in the last 12 months excludes the sample visit. This was done to standardize how the sample visit is counted. Since the past visits item did not include the sample visit, however, it needs to be added back for each category of past visits

66. Creation of a re-weighting factor
Item categories
Annual visits
Sjk (Interval midpoint)
VR (visit ratio)
New 1
0 visits
1-2 visits
2-3
2.5 .4
3-5 visits
4-6 5 .2
6+ visits 7+ 8
.125
This spreadsheet illustrates how the sample visit is added back in the “Annual visits” (second) column.
-New patients by definition have no previous visits during the last 12 months to the sample provider, so their number of annual visits=1 .
-Although the remaining response categories were categorical, they were also adjusted upward by 1 in column 2.
-For example, a patient with 1-2 visits (row 3) becomes a patient with 2-3 total visits.
-To convert categorical annual visits to a single number, Sjk, the interval midpoint is assigned (column 3).
-The midpoint for interval 2-3 becomes 2.5.
-For interval 4-6 visits, the midpoint is 5.
-For the 7+ visits category, the conservative midpoint of 8 is assigned. We know that the midpoint of this distribution tail is more than 7, but have no good estimate for the likely midpoint.
Finally the visit ratio is the reciprocal of the interval midpoint column

67. Patients estimated using multiplicity estimator
(visit weight)jk
patient weight =
Sjk
Basically, a patient weight is a modification (reduction) of the visit weight. The visit rate is made up of 2 basic components & 2 adjustments: the inverse of the selection probability associated with a sampled physician, the inverse of the selection probability of the sampled visit, & 2 adjustments.
-The visit weight is reduced by Sjk, where Sjk is the number of visits in the past 12 months (j=1,….,r) by the patient who had sample visit k (k=1,….m) with provider j.
-For example, if a patient visited the physician only once during the year (the sample visit), the visit would get a multiplicity adjustment of 1. If the sample visit was made by a person with 4 total visits within the year, the visit would get a multiplicity adjustment ¼.
Number of visits in the past 12 months to sampled provider

68. SAS code-multiplicity estimator
if pastvis=8 then vr=1;
else if pastvis=1 then vr=1;
else if pastvis=2 then vr=.4;
else if pastvis=3 then vr=.2;
else if pastvis=4 then vr=.125;
vrpatwt=patwt*vr;
-This is the programming code that you will be using in your particular application. We use SAS-callable SUDAAN, so I would do this programming in SAS to create the variable VR & VRPATWT.
-This code mimics the table that we previously studied.
-PASTVIS=8 indicates the patient is “new”
-PASTVIS=1 links back to the 0 past visits category
-PASTVIS=2 links back to the 1-2 past visits category
-PASTVIS=3 links back to the 3-4 past visits category
-PASTVIS=4 links back to the 6+ visit category

69. Patient estimate exercise
Compare distribution of visits and patients with 7+ visits during past 12 months by patient age
Run exercise 6: Computes distribution of visits by age

70. Output

71. Patient estimate exercise
Run exercise 7: Computes distribution of patients with 7+ visits during past 12 months
Use patient weight (VRPATWT)

72. Output

73. Number of visits and patients with 7+ visits during past 12 months

74. We hope the topics covered in this session will be useful to you in future analyses of NAMCS and NHAMCS data.
Thank you for attending this session.