NHANES 1999-2004

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NHANES 1999-2004 Analytic Strategies
Deanna Kruszon-Moran, MS
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Analyzing Data NHANES 1999-2004Preparing your
data files

• Downloading demographic, questionnaire, exam and
  lab files.
• Files are no longer available as self-extracting
  zip files.
• Documentation and procedure files are now in
  Adobe PDF format and can be viewed or accessed
  directly via the web link
• Clicking on the data link will allow you to store
  the data file or open it directly with SAS.
• Data files are in SAS transport (.xpt) format.

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Know your data

• Read the documentation !!
• Read the documentation !!
• Read the documentation !!
• Read the documentation!!

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Preparing your data files

• Merging
• Merge all files by sequence number to the
  demographic file.
• Verify the numbers of records merged and the
  final sample number against the published
  frequencies on the web.
• Be sure they are what you expected and all merges
  worked correctly.

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Know your data

• Run basic frequencies.
• Know your target population.
• Understand how item was measured
• (how is the item defined, topcoded, recoded)
• Recode variables as necessary
• (example age groups, positive/negative lab
  tests, high/low BP, high/low cholesterol etc.).
• Recode unknown/refusals as missing data
• (77, 99 recode to missing).
• Check your coding run frequencies in SAS.

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Know your data

• Continuous Outcome Data
• Look for outliers in your measure.
• Run Proc Univariate.
• Look for outliers among the weights.
• Use Proc Univariate on the weight variable.
• Outlying variables especially those with large
  weights can really influence your estimates.
• Look at normality.
• Consider transformations.
• Log, square root, power.

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NHANES Sample Design

• NHANES is a complex, multistage,
• probability cluster design of the civilian,
• noninstitutionalized US population.

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Sample Weights

• To analyze NHANES data you must use the sample
  weights to account for

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1. The base probability of selection
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2. Over sampling

• NHANE 1999-2004 - Oversampled
• African Americans
• Mexican Americans
• Persons with low income
• Adolescents aged 12-19
• Persons aged 60

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Non-response to the interview exam Sample
persons age 20
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Non-response issues for NHANES

• Non-response
• Most components have some level of individual
  item or component non-response.
• ONLY non-response to the interview and exam has
  already been accounted for in the weights.
• All additional non-response to the outcome
  measure of interest should be examined against
  all possible predictors.
• Potential biases should be discussed.
• If non-response is high, re-weighting should be
  considered.

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Why weight?
Sample Subdomain US Population sample unweighted sample weighted
Non-Hispanic Blacks 13 25 12
Mexican Americans 9 28 9
12-19 year olds 12 24 12
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Sample weights Which weights?
Weight Variables to Use Household Interview Data ONLY ANY Data from Exam/Lab/MEC Interview
Any 2 yrs of data (1999-2000 or 2001-2002 or 2003-2004) WTINT2YR WTMEC2YR
4 yrs of data (1999-2002) WTINT4YR WTMEC4YR
4 or 6 yrs of data (1999-2004) or (2001-2004) Combine appropriate 2 or 4 year weights as follows Combine appropriate 2 or 4 year weights as follows
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Two, Four, Six, Eight - How can we estimate?

• For 4 years of data from 2001-2004 -
• MEC4YR 1/2 WTMEC2YR
• For 6 years of data from 1999-2004
• if sddsrvyr1 or sddsrvyr2 then
• MEC6YR 2/3 WTMEC4YR / for 1999-2002 /
• If sddsrvyr3 then
• MEC6YR 1/3 WTMEC2YR / for 2003-2004 /
• Only when analyzing years 1999-2002, you should
  not combined 2 year weights but use the 4 year
  weights provided.

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Two, Four, Six, Eight - How can we estimate?

• Future years of data will be combined similarly
  
• For 6 years of data from 2001-2006 -
• if sddsrvyr in (1,2,3) then
• MEC6YR 1/3 WTMEC2YR
• For 8 years of data from 1999-2006
• if sddsrvyr1 or sddsrvyr2 then
• MEC8YR 1/2 WTMEC4YR / for 1999-2002 /
• if sddsrvyr3 or sddsrvyr4 then
• MEC8YR 1/4 WTMEC2YR etc / for 2003-2006 /

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Sample Weights - Subsamples

• Subsamples and appropriate weights
• Look at your primary variable of interest and the
  corresponding weight.
• Look at all other variables you want to combine
  with it.
• Are all from the interview? Exam? Subsample (i.e.
  fasting, audiometry, dioxin, VOCs ) ?
• Use the weight from the smallest subsample for
  your analysis.
• Be consistent!

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Sample Weights - Subsamples

• Subsamples and appropriate weights
• Be careful about combining subsamples beyond MEC
  VOCs, Interview Dioxin etc.
• Combining subsamples such as Environmental AM
  fasting could be problematic.
• Some subsamples are mutually exclusive.
• Weights were not designed for combining
  subsamples and may not produce good estimates.

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Preparing for Analyses

• Subsetting the data for SUDAAN
• If using MEC exam weights - SUBSET the data on
  those MEC EXAMINED in SAS before using SUDAAN.
• If using other subsample weights subset the
  data on those in the subsample corresponding to
  the weights you are using.
• Then use the SUBPOPN statement in the SUDAAN
  procedure to further subset your data by age,
  gender etc. to reflect the target population you
  are interested in analyzing.

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Sample Weights

• Example
• You are interested in examining the association
  of high triglycerides, blood pressure, and body
  mass index (BMI) controlling for race/ethnicity
  on females age 20-59 from the 6 years of data
  from 1999-2004.

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Sample Weights

• Step 1 Determine the smallest sample
  population for the analysis to determine the
  correct weight to use.
• Race/ethnicity, gender and age are in the
  interview.
• Blood pressure and weight come from the MEC exam
  a subset of those interviewed.
• Triglycerides were measured on a subsample of
  those MEC examined who fasted for 8 hours and
  came to the AM MEC exam.
• Therefore, the fasting subsample is the smallest
  subsample in the analysis and you would use the
  AM fasting weights (WTSAF2YR and WTSAF4YR).

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Sample Weights

• Step 2 Combine weights in SAS prior to the
  SUDAAN procedure for the 6 years from 1999-2004
  
• If sddsrvyr in (1,2) then
• WEIGHT6 2/3WTSAF4YR / 1999-2002 /
• If sddsrvyr3 then
• WEIGHT6 1/3WTSAF2YR / 2003-2004/

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Sample Weights

• Step 3 Subset your data set in SAS to reflect
  the weight being used (AM fasting weights
  WTSAF2YR or WTSAF4YR)
• SAS Code
• IF WTSAF2YR ne . or WTSAF4YR ne .

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Sample Weights

• Step4 Last specify the correct weight to use
  using the weight statement in SUDAAN
• and subset your data to obtain the subpopulation
  of interest using the SUBPOPN statement in SUDAAN
  (females age 20-59)
• WEIGHT WEIGHT6
• SUBPOPN riagendr2 and ridageyr gt 19 and
  ridageyr lt 60

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NHANES 1999-2000Variance Estimation

• Why must you use the sample design to estimate
  the variance?
• NHANES is a cluster design
• Individual within a cluster are more similar than
  those in other clusters.
• This homogeneity or clustering results in a
  reduction of our effective sample size because we
  choose individuals within cluster vs randomly
  throughout the population.

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NHANES 1999-2004Variance Estimation

• Why must you use the sample design to estimate
  the variance?
• Variance estimates that do not account for this
  intra cluster correlation are too low and biased.
• Survey software such as SUDAAN or SAS Survey
  procedures must be used to account for the
  complex design and produce unbiased variance
  estimates
• These procedures require information on the
  sample design (i.e. identification of the PSU and
  strata) for each sample person.

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NHANES 1999-2000Variance Estimation

• For the initial 1999-2000 data release we
  recommended
• Using JK-1/Jackknife/leave-one-out procedure.
• Required 52 replicate weights for each of 52
  groups created. Only provided for 1999-2000.
• Can still be used if you have software that can
  produce the replicate weights.
• Replicate weights for this procedure will no
  longer be created on the data set.
• Too cumbersome

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NHANES 1999-2004Variance Estimation

• We now recommend
• Using the Taylor series (linearization) method
• Same as that used in NHANES III.
• We now provide Masked Variance Units (MVUs) in
  place of primary sampling units (PSUs) to
  maintain confidentiality.
• Design variables are called - SDMVSTRA and
  SDMVPSU.

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Design Variables

• SDMVSTRA and SDMVPSU
• Found in the demographic file.
• Found in all two year data sets and can be
  combined for 4 or 6 or year data sets.
• Can be used the same as the actual stratum and
  PSU variables.
• Produce variance estimates close to those using
  the true design.
• Data MUST be sorted by SDMVSTRA and SDMVPSU
  first, before using SUDAAN.

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Sample SUDAAN Code
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Preparing for AnalysisSetting up the procedure
in SAS Surveymeans
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Other data analysis issues from NHANES

• Calculating Population Totals
• Estimates of the number of persons in the U.S.
  population with a particular condition must be
  done carefully.
• Recommended procedure is to
• First, estimate the proportion with the condition
  for each subdomain of interest.
• Mutliply that by the population control totals
  for that subdomain.
• Tables are available on the NCHS web site with
  the current March 2001 CPS control totals as part
  of the analytic guidelines.

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Other data analysis issues from NHANES

• Calculating Population Totals
• Estimates of number of persons with a condition
  can be obtained by summing the weights of those
  positive.
• These estimates will be less reliable due to
• item non response
• and sampling error
• Not the recommended method.

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Analyzing within NHANES 1999-2004

• Things to consider
• Data released in two year cycles.
• We STRONGLY RECOMMEND using two or more cycles (4
  or more years )to produce reliable estimates.
• Verify data items collected were comparable in
  wording and methods.
• When combining years remember to use correct
  combined weights.

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Analyzing trends with NHANES NHANES III to
NHANES 1999-2004

• Things to consider
• What is your sample from each surveyage?
• How different was the question worded or the
  interview methods ?
• How different were the lab or exam methodologies
  ? Cutoffs used? Definitions?
• For current NHANES 1999-2004 sample sizes may be
  smaller depending on number of years measured -
  especially in sub domains
• Larger sampling variation.
• May need to limit comparisons.

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Race/Ethnicity NHANES 1999-2004

• Two variables available
• RIDRETH1
• RIDRETH2

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Race/Ethnicity NHANES 1999-2004

• Ridreth1- Use for analyses of 1999-2004 data
  alone.
• 1Mexican American
• 2other Hispanic
• 3non-Hispanic white
• 4non-Hispanic black
• 5other races including multiracial.
• For 2 and 4 years of data we know there is
  insufficient sample size to analyze other
  Hispanics (group 2) alone or to analyze all
  Hispanics.
• Analyses to evaluate whether 6 years of data
  (1999-2004) are sufficient to analyze these
  Hispanic groups are ongoing.
• Groups 2 and 5 can AND should continue to be
  combined to represent all other races.

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Race/Ethnicity NHANES 1999-2004

• Ridreth2
• Use for analyzing trends from NHANES III to
  NHANES 1999-2004.
• Most comparable to race/ethnicity variable
  collected in NHANES III.
• Coded as
• 1non-Hispanic white
• 2non-Hispanic black
• 3Mexican American
• 4other including Multi-Racial
• 5other Hispanic

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Analyzing data from NHANES 1999-2004

• Crude versus Age Standardized Estimates
• Age distributions within survey samples vary by
  racial/ethnic group.
• Age distributions also vary by survey NHANES
  III vs. NHANES 1999-2004.
• When comparing estimates across racial/ethnic
  groups or between surveys you may need to age
  standardize.
• Also present all age specific estimates!

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Analyzing data from NHANES 1999-2004

• When Age Standardizing
• Use the 2000 U.S. Census Population for
  consistency for both NHANES III and all NHANES
  1999-2000 or above.
• For guidelines and population proportions see the
  website below for the Klein and Schoenborn HP2010
  Statistical Notes on Age Adjustment using the
  2000 Projected U.S. Population.
• http//www.cdc.gov/nchs/data/statnt/statnt20.pdf

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Analyzing data from NHANES 1999-2004

• When Age Standardizing
• In SUDAAN, use the STDVAR and STDWGT statements.
• STDVAR variable name for the age groups.
• STDWGT corresponding proportion of the 2000
  U.S. Census population for that age subgroup.

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Age standardization for NHANES

• Crude vs. Age Standardized Estimates Example

Hepatitis B NHANES III Non-Hispanic White Non-Hispanic Black Mexican American
Crude Prevalence 3.1 (2.6-3.6) 11.9 (10.6-13.2) 3.6 (2.8-4.6)
Age Standardized 2.6 (2.2-3.1) 11.9 (10.7-13.3) 4.4 (3.4-5.6)
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Analyzing Data from NHANES 1999-2004

• Analytic Guidelines
• Detailed guidelines for working with NHANES data
  can be found at
• http//www.cdc.gov/nchs/nhanes.htm
• This document contains everything discussed today
  and will continue to grow to include guidelines
  for statistical tests, multivariate analyses,
  modeling and more!
• Web based tutorial also currently in creation.
• Target date for release is Dec 31st 2006.