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How to understand and use National Ambulatory Medical Care Survey (NAMCS) and National Hospital

Ambulatory Medical Care Survey (NHAMCS) data for clinical research

Yuwei Zhu10-29-2004

Dept of Biostatistics

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OverviewOverview I. Survey Background I. Survey Background II. Survey Methodology II. Survey Methodology III. Technical ConsiderationsIII. Technical Considerations IV. Getting the Data – Using Raw Data FilesIV. Getting the Data – Using Raw Data Files V. ExampleV. Example VI. Data Analysis – SAS, STATA, SUDAANVI. Data Analysis – SAS, STATA, SUDAAN VII. Other Public Domain DataVII. Other Public Domain Data

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Performed by:Performed by: Centers for Disease Control and Centers for Disease Control and

Prevention (CDC)Prevention (CDC) National Center for Health National Center for Health

Statistics, Division of Health Care Statistics, Division of Health Care Statistics, and National Health Care Statistics, and National Health Care SurveySurvey

NAMCS and NHAMCS

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National Ambulatory Medical Care Survey (NAMCS) Historyistory

Survey began in 1973 Survey began in 1973 Annual data collection through 1981Annual data collection through 1981 Conducted in 1985 Conducted in 1985 Annual began again in 1989 Annual began again in 1989

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NAMCSNAMCS

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

Patient visits made to the offices of non–Patient visits made to the offices of non–federally employed physicians federally employed physicians – Excluding:Excluding:

AnesthesiologyAnesthesiology RadiologyRadiology PathologyPathology

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In-Scope NAMCS locationsIn-Scope NAMCS locations Freestanding clinicFreestanding clinic Federally qualified health centerFederally qualified health center Neighborhood and mental health centersNeighborhood and mental health centers Non-federal government clinicNon-federal government clinic Family planning clinicFamily planning clinic HMOHMO Faculty practice planFaculty practice plan Private solo or group practicePrivate solo or group practice

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Out-of-Scope NAMCS locationsOut-of-Scope NAMCS locations

Hospital EDs and OPDsHospital EDs and OPDs Ambulatory surgicenterAmbulatory surgicenter Institutional setting (schools, prisons)Institutional setting (schools, prisons) Industrial outpatient facilityIndustrial outpatient facility Federal Government operated clinicFederal Government operated clinic Laser vision surgeryLaser vision surgery

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NAMCSNAMCS

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 - NAMCSSample design - NAMCS

112 PSUs (counties)112 PSUs (counties)– CountiesCounties– Groups of countiesGroups of counties– County equivalents (such as parishes or independent County equivalents (such as parishes or independent

cities)cities)– TownsTowns– TownshipsTownships

Nonfederally employed, office-based physicians Nonfederally employed, office-based physicians stratified by specialty, 3,000 physicians stratified by specialty, 3,000 physicians

About 30 visits per doctor over a randomly About 30 visits per doctor over a randomly selected 1-week period, 25,000 visitsselected 1-week period, 25,000 visits

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National Hospital Ambulatory Medical Care Survey (NHAMCS) HistoryNHAMCS) History

Survey began in 1992 Survey began in 1992

Annual data collectionAnnual data collection

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National sample of visits to the EDs and National sample of visits to the EDs and outpatient departments of noninstitutional outpatient departments of noninstitutional general and short-stay hospitals in the general and short-stay hospitals in the United StatesUnited States

Excluded hospitals:Excluded hospitals:– FederalFederal– MilitaryMilitary– Veterans AdministrationVeterans Administration

NHAMCS

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NHAMCSNHAMCS

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 - NHAMCSSample design - NHAMCS

112 PSUs (counties)112 PSUs (counties)

Panel of 600 non-Federal, general or short Panel of 600 non-Federal, general or short stay hospitalsstay hospitals

Clinics (OPDs) and emergency service Clinics (OPDs) and emergency service areas (EDs), 400 EDs and 250 OPDsareas (EDs), 400 EDs and 250 OPDs

About 200 visits per OPD, About 200 visits per OPD,

100 per ED over random 4-week period,100 per ED over random 4-week period,

37,000 ED and 35,000 OPD visits37,000 ED and 35,000 OPD visits

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NHAMCS ScopeNHAMCS Scope

OPD was intended to be parallel to the NAMCS OPD was intended to be parallel to the NAMCS in the hospital settingin the hospital setting

General medicine, surgery, pediatrics, ob/gyn, General medicine, surgery, pediatrics, ob/gyn, substance abuse, and “other” clinics are in-substance abuse, and “other” clinics are in-scopescope

Ancillary services are out of scopeAncillary services are out of scope

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Data ItemsData Items

Patient characteristics Patient characteristics – Age, sex, race, ethnicityAge, sex, race, ethnicity

Visit characteristicsVisit characteristics– Source of payment, continuity of care, reason for Source of payment, continuity of care, reason for

visit, diagnosis, treatmentvisit, diagnosis, treatment Provider characteristicsProvider characteristics

– Physician specialty, hospital ownership…Physician specialty, hospital ownership… Drug characteristics added in 1980Drug characteristics added in 1980

– Class, composition, control status, etc.Class, composition, control status, etc.

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Repeating fields (from text entries)Repeating fields (from text entries)

Up to 3 fields each…Up to 3 fields each…– Reason for visit Reason for visit – Physician’s diagnosisPhysician’s diagnosis– Cause of injuryCause 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)Drug ingredients (5 fields)– Therapeutic class (3 fields – 2002 on)Therapeutic class (3 fields – 2002 on)

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Coding Systems UsedCoding Systems Used

Reason for Visit Classification (NCHS)Reason for Visit Classification (NCHS) ICD-9-CM for diagnoses, causes of injury and ICD-9-CM for diagnoses, causes of injury and

proceduresprocedures Drug Classification System (NCHS)Drug Classification System (NCHS) National Drug Code DirectoryNational Drug Code Directory

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Drug Data in NAMCS/ NHAMCSDrug Data in NAMCS/ NHAMCS

What is a “Drug Mention” ?What is a “Drug Mention” ?

Any of up to 6 medications that were ordered, supplied, Any of up to 6 medications that were ordered, supplied, administered, or continued during the visit. administered, or continued during the visit.

Respondents are asked to report trade names or generic Respondents are asked to report trade names or generic names only (not dosage, administration, or regimen). names only (not dosage, administration, or regimen).

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Drug CharacteristicsDrug Characteristics

Generic Name (for single ingredient drugs)Generic Name (for single ingredient drugs) Prescription StatusPrescription Status Composition StatusComposition Status Controlled Substance StatusControlled 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 Up to 5 Ingredients (for multiple ingredient

drugs)drugs)

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Some User ConsiderationsSome User Considerations

NAMCS/NHAMCS sample visits, not NAMCS/NHAMCS sample visits, not patientspatients

No estimates of incidence or prevalenceNo estimates of incidence or prevalence No state-level estimatesNo state-level estimates Not sampled by setting or by non-Not sampled by setting or by non-

physician providersphysician providers May capture different types of care for May capture different types of care for

solo vs. group practice physicianssolo vs. group practice physicians

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Data usesData uses

Understand health care practiceUnderstand health care practice Examine the quality of careExamine the quality of care Track certain conditionsTrack certain conditions Find health disparitiesFind health disparities Measure Healthy People 2010 objectivesMeasure Healthy People 2010 objectives Serve as benchmark for statesServe as benchmark for states

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Data usersData users

Over 100 journal publications in last 2 yearsOver 100 journal publications in last 2 years Medical associationsMedical associations Government agenciesGovernment agencies Health services researchersHealth services researchers University and medical schoolsUniversity and medical schools Broadcast and print mediaBroadcast and print media

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Sample WeightSample Weight

Each NAMCS record contains a single Each NAMCS record contains a single weight, which we call Patient Visit Weightweight, which we call Patient Visit Weight

Same is true for OPD records and ED recordsSame is true for OPD records and ED records This weight is used for both visits and drug This weight is used for both visits and drug

mentionsmentions

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Reliability of EstimatesReliability of Estimates

Estimates should be based on at least 30 Estimates should be based on at least 30 sample records ANDsample records AND

Estimates with a relative standard error Estimates with a relative standard error (standard error divided by the estimate) (standard error divided by the estimate) greater than 30 percent are considered greater than 30 percent are considered unreliable by NCHS standardsunreliable by NCHS standards

Both conditions should be met to obtain Both conditions should be met to obtain reliable estimatesreliable estimates

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How Good are the Estimates?How Good are the Estimates?

Depends on what you are looking at. In general, Depends on what you are looking at. In general, OPD estimates tend to be somewhat less OPD estimates tend to be somewhat less reliable than NAMCS and ED. reliable than NAMCS and ED.

Since 1999, Advance Data reports include Since 1999, Advance Data reports include standard errors in every table so it is easy to standard errors in every table so it is easy to compute confidence intervals around the compute confidence intervals around the estimates.estimates.

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Sampling ErrorSampling Error

NAMCS and NHAMCS are not simple random NAMCS and NHAMCS are not simple random samplessamples

Clustering effects of visits within the Clustering effects of visits within the physician’s practice, physician practices within physician’s practice, physician practices within PSUs, clinics within hospitalsPSUs, clinics within hospitals

Must use some method to calculate standard Must use some method to calculate standard errors for frequencies, percents, and rateserrors for frequencies, percents, and rates

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Ways to Improve Reliability of EstimatesWays to Improve Reliability of Estimates

Combine NAMCS, ED and OPD data to produce Combine NAMCS, ED and OPD data to produce ambulatory care visit estimatesambulatory care visit estimates

Combine multiple years of dataCombine multiple years of data Aggregate categories of interest into broader Aggregate categories of interest into broader

groups.groups.

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NAMCS vs. NHAMCSNAMCS vs. NHAMCS

Consider what types of settings are best for a Consider what types of settings are best for a particular analysisparticular analysis– Persons of color are more likely to visit OPD's Persons of color are more likely to visit OPD's

and ED's than physician officesand ED's than physician offices– Persons in some age groups make Persons in some age groups make

disproportionately larger shares of visits to disproportionately larger shares of visits to ED's than offices and OPD'sED's than offices and OPD's

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File StructureFile Structure

Download data and layout from websiteDownload data and layout from website

http://www.cdc.gov/nchs/about/major/ahhttp://www.cdc.gov/nchs/about/major/ahcd/ahcd1.htmcd/ahcd1.htm

Flat ASCII files for each setting and yearFlat ASCII files for each setting and year

NAMCS: 1973-2002NAMCS: 1973-2002

NHAMCS: 1992-2002NHAMCS: 1992-2002

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Trend considerationsTrend considerations

Variables routinely rotate on and off surveyVariables routinely rotate on and off survey Be careful about trending diagnosis prior to Be careful about trending diagnosis prior to

1979 because of ICDA (based on ICD-8)1979 because of ICDA (based on ICD-8) Even after 1980- be careful about changes Even after 1980- be careful about changes

in ICD-9-CMin ICD-9-CM Number of medications varies over yearsNumber of medications varies over years

1980-81 – 8 medications1980-81 – 8 medications1985, 1989-94 – 5 medications1985, 1989-94 – 5 medications1995-2002 – 6 medications1995-2002 – 6 medications2003+ – 8 medications2003+ – 8 medications

Diagnostic & therapeutic checkboxes varyDiagnostic & therapeutic checkboxes vary Use spreadsheet for significance of trends Use spreadsheet for significance of trends

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Example Example

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

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Study DesignStudy Design

Retrospective collection of data fromRetrospective collection of data from– NAMCS NAMCS – NHAMCSNHAMCS

1994-2000 study years1994-2000 study years Antibiotic prescribing patterns and Antibiotic prescribing patterns and

diagnosesdiagnoses Children <5 years of ageChildren <5 years of age Clinic type -- PediatricClinic type -- Pediatric Physician type – Pediatrician or Family Physician type – Pediatrician or Family

MedicineMedicine

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Data StratificationData 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)

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Overall Antibiotic Rates in Children <5 Based on Source of Care

0

500

1000

1500

2000

1994 1995 1996 1997 1998 1999 2000

Years

Rat

es p

er 1

000

chil

dre

n

Hospital-based ED Office-based

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Total CareTotal Care YearsYears White White BlackBlack Rate Rate RatioRatio

95% CI 95% CI

Visit rates Visit rates per 1000 per 1000 children children aged <5 aged <5 yearsyears

1994-1994-19951995

41504150 31023102 1.341.34 1.22, 1.22, 1.47*1.47*

1996-1996-19971997

45294529 43204320 1.051.05 1.02, 1.02, 1.08*1.08*

1998-1998-20002000

42044204 43024302 0.980.98 0.70, 0.70, 1.341.34

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0%

20%

40%

60%

80%

100%

1994-1995

1996-1998

1999-2000

1994-1995

1996-1998

1999-2000

Years

% D

istr

ibu

tio

n h

ealt

h c

are

visi

t si

te

Hospital-based

ED

Office-based

White children Black Children

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Total CareTotal Care YearsYears White White BlackBlack Rate Rate RatioRatio

95% CI 95% CI

Antibiotic Antibiotic prescriptioprescription rates per n rates per 1000 1000 children children aged <5 aged <5 yearsyears

1994-1994-19951995

14941494 998998 1.501.50 1.48, 1.48, 1.51*1.51*

1996-1996-19971997

14211421 13201320 1.081.08 0.96, 0.96, 1.221.22

1998-1998-20002000

11181118 10741074 1.041.04 0.86, 0.86, 1.241.24

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Total CareTotal Care YearsYears White White BlackBlackRate Rate RatioRatio 95% CI 95% CI

Otitis media Otitis media rates per rates per 1000 1000 children children aged <5 aged <5 yearsyears

1994-1994-19951995

816816 520520 1.571.57 1.46, 1.46, 1.69*1.69*

1996-1996-19971997

779779 739739 1.061.06 1.04, 1.04, 1.07*1.07*

1998-1998-20002000

630630 603603 1.051.05 0.69, 0.69, 1.581.58

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ResultsResults Decline in antibiotic prescribing in children <5 Decline in antibiotic prescribing in children <5

years; most notable in office-based and years; most notable in office-based and emergency department settings emergency department settings

Penicillin's were common antibiotics usedPenicillin's were common antibiotics used

Most common diagnosis in all three settings Most common diagnosis in all three settings was otitis mediawas otitis media

Natasha B. Halasa, Marie R. Griffin, Natasha B. Halasa, Marie R. Griffin, Yuwei ZhuYuwei Zhu, and Kathryn , and Kathryn M. Edwards. Difference in antibiotic prescribing patterns for M. Edwards. Difference in antibiotic prescribing patterns for children aged less than five years in the three major outpatient children aged less than five years in the three major outpatient settings, Journal of Pediatrics. 2004; 144:200-205settings, Journal of Pediatrics. 2004; 144:200-205

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Code to create design variables: Code to create design variables: survey years 2001 & earlier 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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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

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proc crosstab data=test1 filetype=sas;

Nest stratm psum ;

Weight patwt;

Tables sex*ager;

run;

SUDAAN version 8.0.2 example

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Use http:// ***/test1

svyset [pweight=patwt], strata(cstratm) psu(cpsum)

svytab sex ager

svymean age

STATA version 8. example

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proc surveyfreq data=test1;tables sex*ager;strata cstratm;cluster cpsum;weight patwt;run;

SAS version 9.1 example

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Some considerations: SUDAAN vs. Some considerations: SUDAAN vs. SAS Proc SurveymeansSAS Proc Surveymeans

SUDAANSUDAAN PROC SurveymeansPROC Surveymeans

••design design variables=cstratm, variables=cstratm, cpsum (1-stage design)cpsum (1-stage design)

••design variables=cstratm, design variables=cstratm, cpsum (1-stage design)cpsum (1-stage design)

••nest=cstratm, cpsumnest=cstratm, cpsum ••strata cstratm strata cstratm

••cluster cpsumcluster cpsum

••Sort by design variablesSort by design variables ••Sort not neededSort not needed

••Weight data: PatwtWeight data: Patwt ••Weight data: PatwtWeight data: Patwt

••Subgroup=identify Subgroup=identify categorical categorical variablesvariables

••Class=identify categorical Class=identify categorical variablesvariables

••Tables=analysis Tables=analysis variablesvariables

••Var=analysis variablesVar=analysis variables

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If nothing else, remember…The Public If nothing else, remember…The Public Use Data File Documentation is Use Data File Documentation is

YOUR FRIEND!YOUR FRIEND! Each booklet includes:Each booklet includes:

– A description of the surveyA description of the survey– Record formatRecord format– Marginal data (summaries)Marginal data (summaries)– Various definitionsVarious definitions– Reason for Visit classification codesReason for Visit classification codes– Medication & generic namesMedication & generic names– Therapeutic classesTherapeutic classes

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Other Public Domain Data Other Public Domain Data CDC WONDER -- CDC WONDER -- http://wonder.cdc.gov/http://wonder.cdc.gov/ National Center for Health Statistics -- National Center for Health Statistics --

http://www.cdc.gov/nchs/http://www.cdc.gov/nchs/ National Health and Nutrition Examination National Health and Nutrition Examination

Survey (NHANES) --Survey (NHANES) --http://www.cdc.gov/nchs/nhanes.htmhttp://www.cdc.gov/nchs/nhanes.htm

National Health Interview Survey (NHIS) -- National Health Interview Survey (NHIS) -- http://www.cdc.gov/nchs/nhis.htmhttp://www.cdc.gov/nchs/nhis.htm

National Survey of Family Growth (NSFG) -- National Survey of Family Growth (NSFG) -- http://www.cdc.gov/nchs/nsfg.htmhttp://www.cdc.gov/nchs/nsfg.htm

Census -- Census -- http://www.census.gov/http://www.census.gov/

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Other Public Domain Data (cont.)Other Public Domain Data (cont.)

Dept. of Health, TN Dept. of Health, TN http://hitspot.state.tn.us/hitspot/hit/http://hitspot.state.tn.us/hitspot/hit/main/SPOT/frames/SPOT/index.htm main/SPOT/frames/SPOT/index.htm

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Thanks Thanks

Natasha HalashaNatasha Halasha Susan Schappert - Susan Schappert - National Center for National Center for

Health StatisticsHealth Statistics Linda McCaig & David Woodwell - Linda McCaig & David Woodwell -

National Center for Health StatisticsNational Center for Health Statistics

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Questions?Questions?