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1
Understanding and Using
NAMCS and NHAMCS Data:
A Hands-On Workshop
Part II-Using Raw Data Files
Donald CherryU.S. DEPARTMENT OF HEALTH AND HUMAN SERVICESCenters for Disease Control and PreventionNational Center for Health Statistics
U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICESCenters for Disease Control and PreventionNational Center for Health Statistics
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Session Goals
A the end of this session I would like you A the end of this session I would like you to:to: Be able to successfully download data Be able to successfully download data
files and create a SAS dataset for analysisfiles and create a SAS dataset for analysis Understand some of the limitations and Understand some of the limitations and
advantages of using NAMCS/NHAMCS advantages of using NAMCS/NHAMCS downloadable datadownloadable data
Perform simple analysesPerform simple analyses
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Using raw data files File structureFile structure Exercises using SUDAAN & SAS Proc SurveymeansExercises using SUDAAN & SAS Proc Surveymeans
Downloading data & creating a SAS datasetDownloading data & creating a SAS dataset Simple frequencies with/without standard errors Simple frequencies with/without standard errors Creating a new variable-AsthmaCreating a new variable-Asthma Visit rates-male/femaleVisit rates-male/female Total number of drug mentionsTotal number of drug mentions Antidepressant drug mentionsAntidepressant drug mentions Time spent with physicianTime spent with physician
Trend considerations Trend considerations Other issues--multiple years/settingsOther issues--multiple years/settings SummarySummary
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File Structure
Download data and layout from websiteDownload data and layout from website
http://www.cdc.gov/nchs/about/major/ahcd/ahcd1.htmhttp://www.cdc.gov/nchs/about/major/ahcd/ahcd1.htm
Flat ASCII files for each setting and yearFlat ASCII files for each setting and yearNAMCS: 1973-2002NAMCS: 1973-2002
NHAMCS: 1992-2002NHAMCS: 1992-2002
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Structure organizationProvider
provider infopractice info
geographic info
Visit patient & visit info
treatment & outcome infomedications
Medication 1 Medication 2 Medication 6Medication 5Medication 4Medication 3
Drug ingredient 1-5 Therapeutic class 1-3
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Double-click: My Computer\Local Disk Double-click: My Computer\Local Disk C:\DUC_04C:\DUC_04
Double-click: SAS file: ExercisesDouble-click: SAS file: Exercises
Hands-on Exercises
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proc surveyfreq data=namtest1;tables sex*ager;strata cstratm;cluster cpsum;weight patwt2;run;
SAS version 9.1 example
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Visit estimates
PhycodePhycode SexSex PatwtPatwt (Patwt/(Patwt/Pop)*100Pop)*100
SexwtSexwt
14011401 11 100100 (100/800)*100(100/800)*100 12.512.5
15221522 11 300300 (300/800)*100(300/800)*100 37.537.5
10011001 11 5050 (50/800)*100(50/800)*100 6.256.25
500500 11 120120 (120/800)*100(120/800)*100 1515
71.2571.25
Female population=800 New variableCalculation*
*Note: Rate=est/pop=Σ patwt/pop=1/pop*Σ patwt.
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Arrays
PhycodePhycode PatcodePatcode Med1Med1 Med2Med2 Med3Med3 Med4Med4 Med5Med5 Med6Med6 TotalTotal
14311431 11 3290532905 9000090000 9000090000 9000090000 9000090000 9000090000 11
14311431 22 9500595005 0299502995 9000090000 9000090000 9000090000 9000090000 22
14311431 33 2674026740 9000090000 9000090000 9000090000 9000090000 9000090000 11
14311431 44 9000090000 9000090000 9000090000 9000090000 9000090000 9000090000 00
14311431 55 2122821228 3546535465 4029540295 9000090000 9000090000 9000090000 33
Note: 90000=No mention.
Total drug mentions: 7
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Some considerations: SUDAAN vs. SAS Proc Surveymeans
SUDAANSUDAAN PROC SurveymeansPROC Surveymeans
••design variables=cstratm, design 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: Patwt2Weight data: Patwt2 ••Weight data: Patwt2Weight data: Patwt2
••Subgroup=identify Subgroup=identify categorical variables categorical variables
••Class=identify categorical Class=identify categorical variablesvariables
••Tables=analysis variablesTables=analysis variables ••Var=analysis variablesVar=analysis variables
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Trend considerations Variables routinely rotate on and off surveyVariables routinely rotate on and off survey Be careful about trending diagnosis prior to 1979 Be careful about trending diagnosis prior to 1979
because of ICDA (based on ICD-8)because of ICDA (based on ICD-8)
Even after 1980- be careful about changes in Even after 1980- be careful about changes in ICD-9-CMICD-9-CM
Number of medications varies over yearsNumber of medications varies over years1980-81 – 8 medications1980-81 – 8 medications
1985, 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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Combining multiple years
2 year combinations are best for 2 year combinations are best for subpopulation analysissubpopulation analysis
3-4 year combinations for disease specific 3-4 year combinations for disease specific analysisanalysis
Keep adding years until you have at least 30 Keep adding years until you have at least 30 raw cases in important cellsraw cases in important cells
RSE improves incrementally with the RSE improves incrementally with the number of years combinednumber of years combined
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RSE improves incrementally with the number of years combined
RSE = SE/RSE = SE/xx
RSE for percent of visits by persons less RSE for percent of visits by persons less than 21 years of age with diabetesthan 21 years of age with diabetes 1999 RSE = .08/.18 = .44 (44%)1999 RSE = .08/.18 = .44 (44%) 1998 & 1999 RSE = .06/.18 = .33 (33%)1998 & 1999 RSE = .06/.18 = .33 (33%) 1998, 1999, & 2000 RSE = .05/.21 = .24 1998, 1999, & 2000 RSE = .05/.21 = .24
(24%)(24%)
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NAMCS, ED, and OPD can be combined in one NAMCS, ED, and OPD can be combined in one or multiple yearsor multiple years
NAMCS & OPD variables virtually identical, NAMCS & OPD variables virtually identical, many ED variables are samemany ED variables are same
OPD and NAMCS should be combined to get OPD and NAMCS should be combined to get estimates of ambulatory physician care especially estimates of ambulatory physician care especially for African-American, Medicaid or adolescent for African-American, Medicaid or adolescent subpopulationssubpopulations
Only NAMCS has physician specialtyOnly NAMCS has physician specialty
Combining multiple settings
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2001*
3- & 4-Stage
design variables
2003
2002
1-Stage design
variables only
1-Stage design
variables
3- & 4-Stage design
variables
Design Variables—Survey Years
*Plan to re-release years with 1-stage design variables.
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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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If nothing else, remember…The Public Use Data File Documentation is
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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Where to get more information?
www.cdc.gov/nchs/about/major/ahcd1.htmwww.cdc.gov/nchs/about/major/ahcd1.htm
Call Ambulatory Care Statistics Branch at Call Ambulatory Care Statistics Branch at 301-458-4600301-458-4600