Understanding and Using NAMCS and NHAMCS Data

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Understanding and Using NAMCS and NHAMCS Data

• Data Tools and Basic Programming Techniques
• Donald Cherry
• Ambulatory and Hospital Care Statistics Branch
• Division of Health Care Statistics

2
Overview

• Some important features of NAMCS NHAMCS
• File structure
• SETS
• Exercises using SAS Proc Surveyfreq/Proc
  Surveymeans, SUDAAN, STATA
• Downloading data creating a SAS dataset
• Simple frequencies with/without standard errors
• Creating a new variable-Asthma
• Visit rates for asthma-male/female
• Total number of digestive write-in
• procedures
• Time spent with physician
• Considerations
• Summary

3
NAMCS and NHAMCS

• National Ambulatory Medical Care Survey (NAMCS)
• Visits to nonfederal, office-based physicians
• CHCs sampled beginning in 2006
• National Hospital Ambulatory Medical Care Survey
  (NHAMCS)
• Visits to hospital outpatient and emergency
  departments

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NAMCS Sample Design

• Three stage design
• 112 PSUs
• Physician practices within PSUs
• Patient visits within practices
• One-week reporting period
• About 30 visits per doctor are typically sampled
• For 20063,350 doctors sampled
• 104 CHCs sampled physician visits included in
  sample
• Total visits 29,392

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Scope of the NAMCS

• Basic unit of sampling is the physician-patient
  visit
• In scope visits
• Must occur in physicians office
• Must be for medical purposes
• Administrative visits not sampled
• House calls, emails, phone calls not sampled

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Scope of the NAMCS (cont.)

• Physicians must be
• Classified by AMA or AOA as primarily engaged in
  office-based patient care
• nonfederally employed
• not in anesthesiology, radiology, or pathology
• 64 percent unweighted response rate in 2006
• CHCs are Federally Qualified or look alike

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NHAMCS Sample Design

• Multistage probability design
• First stage sample of 112 PSUs
• Hospitals within PSUs
• Clinics within OPDs, Emergency Service Area (ESA)
  within EDs
• Patient visits within clinics, ESAs
• 4-week reporting period
• 382 hospitals sampled in 2006 35,849 ED visits
  and 35,105 OPD visits

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Scope of the NHAMCS

• Basic unit of sampling is patient visit
• Emergency and outpatient departments of
  noninstitutional general and short-stay hospitals
• Not Federal, military, or Veterans Administration
  facilities
• Located in 50 states and D.C.

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Sample Weight

• 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/procedure mentions

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Data Items

• Patient characteristics
• Age, sex, race, ethnicity
• Visit characteristics
• Source of payment, continuity of care, reason for
  visit, diagnosis, treatment
• Provider characteristics
• Physician specialty, hospital ownership
• MULTUM drug characteristics added in 2006

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Coding Systems Used

• Reason for Visit Classification (NCHS)
• ICD-9-CM for diagnoses, causes of injury and
  procedures
• Drug Classification System-MULTUM

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File Structure

• Download data and layout from website
• http//www.cdc.gov/nchs/about/major/ahcd/ahcd1.ht
  m
• Flat ASCII files for each setting and year
• NAMCS 1973-2006
• NHAMCS 1992-2006
• STATA files on Web
• NAMCS 2003-2005
• NHAMCS 2003-2005

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Creating a usable STATA dataset

• Two options
• Use the self-extracting file in STATA folder to
  open a complete dataset for the 2003-2005 NAMCS,
  NHAMCS-ED, NHAMCS-OPD
• Use the DO file (.do) and the dictionary file
  (.dct) along with the flat data file (.exe) to
  create a dataset
• StatTransfer

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Organizational structure-NAMCS data
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SETS-Statistical Export and Tabulation System
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Hands-on Exercises

• STATA Users
• Double-click My Computer\Local Disk C\DUC_08
• Open STATA
• In the command window type
• Set mem 1000m
• Set matsize 5000
• Under the File icon-double-click namcs05.dta
• Under New Do File Editor-double-click STATA
  exercises.do

• SAS/SUDAAN Users
• Double-click My Computer\Local Disk C\DUC_08
• Double-click Final Exercises

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Visit rate estimates
Female population800
New variable
Calculation
Phycode Sex Patwt (Patwt/Pop)100 Sexwt
1401 1 100 (100/800)100 12.5
1820 1 300 (300/800)100 37.5
1001 1 50 (50/800)100 6.25
500 1 120 (120/800)100 15
71.25 visits per 100 persons
Sample size4
Visits570
Note Rateest/popS patwt/pop1/popS patwt.
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Calculating Total Number of Write-in Procedures
Record Proc1 Proc2 Proc3 Proc4 Proc5 Proc6 Proc7 Proc8 Totproc
1 1911 0000 0000 0000 0000 0000 0000 0000 1
2 2182 2186 0000 0000 0000 0000 0000 0000 2
3 5490 0000 0000 0000 0000 0000 0000 0000 1
4 0000 0000 0000 0000 0000 0000 0000 0000 0
5 8192 0000 0000 0000 0000 0000 0000 8200 2
Note 0000No procedure recorded.
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Data Considerations
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NAMCS vs. NHAMCS

• Consider what types of settings are best for a
  particular analysis
• Persons of color are more likely to visit OPDs
  and EDs than physician offices
• Persons in some age groups make
  disproportionately larger shares of visits to
  EDs than offices and OPDs

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Procedures
Program Categorical Variables Continuous Variables
SAS PROC SURVEYFREQ PROC SURVEYMEANS
STATA SVY TAB SVY MEAN
SUDAAN PROC CROSSTAB PROC DESCRIPT
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How Good are the Estimates?

• Depends In general, OPD estimates tend to be
  somewhat less reliable than NAMCS and ED.
• Since 1999, our Advance Data reports include
  standard errors in every table so it is easy to
  compute confidence intervals around the
  estimates.

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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)

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Some User Considerations

• NAMCS/NHAMCS sample visits, not patients
• No estimates of incidence or prevalence
• No state-level estimates
• May capture different types of care for solo vs.
  group practice physicians
• Data is only as good as what is documented in the
  medical record

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If nothing else, rememberThe Public Use Data
File Documentation is YOUR FRIEND!

• 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