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

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

NAMCS/NHAMCS trend data

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

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 www.cdc.gov/nchs/about/major/ahcd1.htm

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

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

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%)

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

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

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

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);

2006 NAMCS Community Health Center data

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

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.

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

NAMCS/NHAMCS provider-level estimates

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

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

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

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

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

Output

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

Output

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

Output

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)

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

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

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

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

Aggregating visit statistics at the physician or facility level

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

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.

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

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

Output

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

Output for MSAs

Histogram and Box plot for MSAs

Normal probability plot for MSAs

Histogram and Box plot for MSAs

Output for Non-MSAs

Histogram and Box plot for Non-MSAs

Normal probability plot for Non-MSAs

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

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.

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

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

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.

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

References Burt CW and Hing E. Making patient-level estimates from medical encounter records using a multiplicity estimator. Stat Med 2007; 26:1762-1774. Sirken MG. Network Sampling. In Encyclopedia of Biostatistics, Armitage P, Colton T (eds). Wiley: West Sussex. 1998; 2977-2986. 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.

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

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

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

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

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

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

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

Percent distribution for people making any health care visits by number of visits made in one year: NHIS, 1999-2000 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.

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, 2003-2005 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.

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

Exercise: Compare Visit and Patient estimates

Multiplicity measure This is the question measuring multiplicity of visits. The question has not changed since first collected in 2001. 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

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

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

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

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

Output

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

Output

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

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.