Transcript NAMCS and NHAMCS - Vanderbilt University Medical Center

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
1
Overview
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I. Survey Background
II. Survey Methodology
III. Technical Considerations
IV. Getting the Data – Using Raw Data Files
V. Example
VI. Data Analysis – SAS, STATA, SUDAAN
VII. Other Public Domain Data
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NAMCS and NHAMCS
Performed by:
 Centers for Disease Control and
Prevention (CDC)
 National Center for Health
Statistics, Division of Health Care
Statistics, and National Health Care
Survey
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National Ambulatory Medical Care
Survey (NAMCS) History
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Survey began in 1973
 Annual data collection through 1981
 Conducted in 1985
 Annual began again in 1989
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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
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NAMCS
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Patient visits made to the offices of non–
federally employed physicians
– Excluding:
 Anesthesiology
 Radiology
 Pathology
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In-Scope NAMCS locations
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Freestanding clinic
Federally qualified health center
Neighborhood and mental health centers
Non-federal government clinic
Family planning clinic
HMO
Faculty practice plan
Private solo or group practice
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Out-of-Scope NAMCS locations
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Hospital EDs and OPDs
Ambulatory surgicenter
Institutional setting (schools, prisons)
Industrial outpatient facility
Federal Government operated clinic
Laser vision surgery
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NAMCS
NAMCS uses a multistage probability
sample design to obtain
–Primary sampling units (PSUs)
–Physician practices within the PSUs
–Patient visits within physician practices
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Sample design - NAMCS
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
About 30 visits per doctor over a randomly
selected 1-week period, 25,000 visits
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National Hospital Ambulatory Medical
Care Survey (NHAMCS) History
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Survey began in 1992
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Annual data collection
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NHAMCS
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National sample of visits to the EDs and
outpatient departments of noninstitutional
general and short-stay hospitals in the
United States
 Excluded hospitals:
– Federal
– Military
– Veterans Administration
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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
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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
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NHAMCS Scope
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OPD was intended to be parallel to the NAMCS
in the hospital setting
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General medicine, surgery, pediatrics, ob/gyn,
substance abuse, and “other” clinics are inscope
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Ancillary services are out of scope
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Data Items
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Patient characteristics
– Age, sex, race, ethnicity
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Visit characteristics
– Source of payment, continuity of care, reason
for visit, diagnosis, treatment
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Provider characteristics
– Physician specialty, hospital ownership…
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Drug characteristics added in 1980
– Class, composition, control status, etc.
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Repeating fields (from text entries)
Up to 3 fields each…
– Reason for visit
– Physician’s diagnosis
– Cause of injury
 Diagnostic services (6 fields)
 Surgical procedures (2 fields)
 Medications (6 fields)
– Drug ingredients (5 fields)
– Therapeutic class (3 fields – 2002 on)
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Coding Systems Used
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Reason for Visit Classification (NCHS)
 ICD-9-CM for diagnoses, causes of injury and
procedures
 Drug Classification System (NCHS)
 National Drug Code Directory
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Drug Data in NAMCS/ NHAMCS
What is a “Drug Mention” ?
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).
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Drug Characteristics
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Generic Name (for single ingredient drugs)
Prescription Status
Composition Status
Controlled Substance Status
Up to 3 NDC Therapeutic Classes (4-digit)
Up to 5 Ingredients (for multiple ingredient
drugs)
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Some User Considerations
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NAMCS/NHAMCS sample visits, not
patients
No estimates of incidence or prevalence
No state-level estimates
Not sampled by setting or by nonphysician providers
May capture different types of care for
solo vs. group practice physicians
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Data uses
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Understand health care practice
Examine the quality of care
Track certain conditions
Find health disparities
Measure Healthy People 2010 objectives
Serve as benchmark for states
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Data users
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Over 100 journal publications in last 2 years
Medical associations
Government agencies
Health services researchers
University and medical schools
Broadcast and print media
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Sample Weight
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Each NAMCS record contains a single
weight, which we call Patient Visit Weight
 Same is true for OPD records and ED records
 This weight is used for both visits and drug
mentions
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Reliability of Estimates
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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
 Both conditions should be met to obtain
reliable estimates
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How Good are the Estimates?
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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.
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Sampling Error
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NAMCS and NHAMCS are not simple random
samples
 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
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Ways to Improve Reliability of Estimates
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Combine NAMCS, ED and OPD data to produce
ambulatory care visit estimates
 Combine multiple years of data
 Aggregate categories of interest into broader
groups.
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NAMCS vs. NHAMCS
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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
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File Structure
Download data and layout from website
http://www.cdc.gov/nchs/about/major/ahcd/a
hcd1.htm
 Flat ASCII files for each setting and year
NAMCS: 1973-2002
NHAMCS: 1992-2002
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Trend considerations
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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+ – 8 medications
Diagnostic & therapeutic checkboxes vary
Use spreadsheet for significance of trends
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Example
Hypothesis -- Educational Efforts Targeted
at Judicious Antibiotic Use Will Reduce
Prescription Rates in all Treatment Settings
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Study Design
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Retrospective collection of data from
– NAMCS
– NHAMCS
1994-2000 study years
Antibiotic prescribing patterns and diagnoses
Children <5 years of age
Clinic type -- Pediatric
Physician type – Pediatrician or Family Medicine
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Data Stratification
Race – White, Black and other
 Time period – 94 & 95, 96 & 97, 98 & 00
 Antibiotics – Penicillin's, Cephalosporins,
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Erythromycin/lincosamide/macrolides,Tetracyclines,
Chloramphenicol derivatives, Aminoglycosides,
Sulfonamides and trimethoprim, Miscellaneous
antibacterial agents, and Quinolone/derivatives
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Diagnoses -- Otitis media, Sinusitis,
Pharyngitis,Bronchitis,Upper respiratory tract
infection (URI)
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Rates per 1000
children
Overall Antibiotic Rates in Children
<5 Based on Source of Care
2000
1500
1000
500
0
1994 1995 1996 1997 1998 1999 2000
Years
Hospital-based
ED
Office-based
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Total Care Years
White
Black Rate 95% CI
Ratio
Visit rates 1994per 1000 1995
children
aged <5
1996years
1997
4150
3102
1.34
1.22,
1.47*
4529
4320
1.05
1.02,
1.08*
19982000
4204
4302
0.98
0.70,
1.34
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% Distribution health care
visit site
White children
Black Children
100%
80%
Hospital-bas
ED
60%
Office-based
40%
20%
0%
1994- 1996- 19991995 1998 2000
1994- 1996- 19991995 1998 2000
Years
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Total Care
Years
White
Black
Rate 95% CI
Ratio
1.50 1.48,
1.51*
Antibiotic
prescription
rates per
1000
children
aged <5
years
19941995
1494
998
19961997
1421
1320
1.08
0.96,
1.22
19982000
1118
1074
1.04
0.86,
1.24
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Total Care
Years
19941995
Otitis media
rates per
19961000
1997
children
aged <5
1998years
2000
Rate
White Black Ratio
816
520
1.57
95% CI
1.46,
1.69*
779
739
1.06
1.04,
1.07*
630
603
1.05
0.69,
1.58
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Results
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Decline in antibiotic prescribing in children <5
years; most notable in office-based and
emergency department settings
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Penicillin's were common antibiotics used
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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
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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);
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SUDAAN version 8.0.2
example
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;
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SUDAAN version 8.0.2
example
proc crosstab data=test1 filetype=sas;
Nest stratm psum ;
Weight patwt;
Tables sex*ager;
run;
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STATA version 8. example
Use http:// ***/test1
svyset [pweight=patwt], strata(cstratm) psu(cpsum)
svytab sex ager
svymean age
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SAS version 9.1 example
proc surveyfreq data=test1;
tables sex*ager;
strata cstratm;
cluster cpsum;
weight patwt;
run;
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Some considerations: SUDAAN vs.
SAS Proc Surveymeans
SUDAAN
•design variables=cstratm,
cpsum (1-stage design)
PROC Surveymeans
•design variables=cstratm,
cpsum (1-stage design)
•nest=cstratm, cpsum
•strata cstratm
•cluster cpsum
•Sort by design variables
•Sort not needed
•Weight data: Patwt
•Weight data: Patwt
•Subgroup=identify
categorical variables
•Tables=analysis variables
•Class=identify categorical
variables
•Var=analysis variables
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If nothing else, remember…The Public
Use Data File Documentation is
YOUR FRIEND!
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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
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Other Public Domain Data
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CDC WONDER -- http://wonder.cdc.gov/
National Center for Health Statistics -http://www.cdc.gov/nchs/
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 Survey of Family Growth (NSFG) -http://www.cdc.gov/nchs/nsfg.htm
Census -- http://www.census.gov/
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Other Public Domain Data (cont.)
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Dept. of Health, TN
http://hitspot.state.tn.us/hitspot/hit/main/
SPOT/frames/SPOT/index.htm
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Thanks
Natasha Halasha
 Susan Schappert - National Center for
Health Statistics
 Linda McCaig & David Woodwell National Center for Health Statistics
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Questions?
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