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The Effects of Raking and Cell Phone Integration on BRFSS Outcome s. Machell Town, M.S. Carol Pierannunzi, Ph.D. . Division of Behavioral Surveillance. Office of Surveillance, Epidemiology, and Laboratory Services. Division of Behavioral Surveillance. Brief Agenda. Weighting procedures

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the effects of raking and cell phone integration on brfss outcome s
The Effects of Raking and Cell Phone Integration on BRFSS Outcome s

Machell Town, M.S.

Carol Pierannunzi, Ph.D.

Division of Behavioral Surveillance

Office of Surveillance, Epidemiology, and Laboratory Services

Division of Behavioral Surveillance

brief agenda
Brief Agenda
  • Weighting procedures
    • Design weights
    • Post stratification
    • Iterative proportional fitting
  • Why change weighting procedures now?
    • Cell phone
    • Computer capacity
  • Impact of changes on estimation
    • BRFSS
    • Examples of small and large impact
    • Changes when cell phones are incorporated
  • Conclusions
  • Brief look at state level phone use data (preliminary)
design and geostrata weighting
Design and GeoStrata Weighting
  • Takes into account the geographic region/strata of the sample.
  • Design weight uses number of adults in household and number of phones in household for landline sample.
  • BRFSS landline sample is drawn using low/high density strata within each of the regions (usually smaller than states)
  • Stratum weight (_STRWT) = NRECSTR/ NRECSEL
calculating the design weight
Calculating the Design Weight
  • Design Weight = _STRWT* (1/NUMPHON2) * NUMADULT
    • NUMPHON2= number of phones within the household
    • NUMADULT = number of adults eligible for the survey within the household
  • Questions for the design weights are asked in screening questions and in demographic sections of the survey
data weighting
Data Weighting
  • Data weights take the design weighting and incorporate characteristics of the population and the sample
  • Final Weights (_FINALWT) = Design Weight * some form of data weighting
    • In past BRFSS used post stratification
    • In 2008 Iterative Proportional Fitting was first used
    • In 2011 Iterative Proportional Fitting will be only method of data weighting for BRFSS
where we have been post stratification
Where We Have Been---Post Stratification
  • Post Stratification is based on known demographics of the population.
    • For BRFSS Post stratification included:

·Regions within states

·Race/ Ethnicity (in detailed categories)

·Gender

·Age (in 7 categories)

  • Post-stratification forces the sum of the weighted frequencies to equal the population estimates for the region or state by race, age ,and gender.
  • Post stratification weights are applied to the responses, allowing for estimates of how groups of non-respondents would have answered survey questions.
post stratification
Post-stratification
  • Post-stratification Adjustment Factor is calculated for each race/ethnicity, gender, and age group combination.
  • _POSTSTR = Population/Design weight within the weighting class cell.
weight trimming
Weight Trimming
  • Sometimes post-stratification resulted in very small or disproportionately large weightswithin age/race/gender/region categories.
  • Weight trimming or category collapsing would be done if categories were disproportionately large or too small (< 50 responses).
iterative proportional fitting
Iterative Proportional Fitting

Rather than adjusting weights to categories, IPF adjusts for each dimension separately in an iterative process.

The process will continue up to 75 times, or until data converges to Census estimates.

new variables introduced as controls with ipf
New Variables Introduced as Controls With IPF
  • Education
  • Marital status
  • Home ownership/renter
  • Telephone source (cell phone or landline)
post stratification vs iterative proportional fitting
Post Stratification vs. Iterative Proportional Fitting

Operates with less computer time

Allows for incorporation of new variables.

Allows for incorporation of cell phone data.

Seems to more accurately represent population data (reduces bias).

why incorporate ipf now
Why Incorporate IPF Now?
  • Computer capacity has increased.
  • Cell phones are becoming larger percentage of the total number of calls.
  • Noncoverage with declining response rates makes weighting more important than ever.
brfss 2010 combined states a data difference in weighted percentages
BRFSS 2010 Combined States a DataDifference In Weighted Percentages

A Excludes AK, DC, TN, SD

brfss 2010 combined states data difference in weighted percentages of health outcomes
BRFSS 2010 Combined States DataDifference In Weighted Percentages of Health Outcomes

A Excludes AK, DC, TN, SD

weighted prevalence estimates for current smoker by year weighting method
Weighted Prevalence Estimates for Current Smoker by Year, Weighting Method

NOTE: All US states and territories except SD and TN

conclusions 1
Conclusions (1)
  • New weighting procedures are needed to keep pace with changes in personal communications.
  • The inclusion of new variables and more complex weighting procedures for large scale survey data are now feasible, because of improvements in computer capacity.
  • There will be some differences in estimates when weighting procedures change and when new variables for weighting are introduced.
  • Examples shown here are only depictions of potential outcomes of changes at the BRFSS.
conclusions 2
Conclusions (2)
  • Good news: demographic characteristics adjusted to more closely match Census data.
  • Most health outcomes indicate increases in risk behaviors (especially when state data are aggregated).
  • Some increases in chronic conditions, but uneven across states.
thank you
Thank You

For more information please contact Centers for Disease Control and Prevention

1600 Clifton Road NE, Atlanta, GA 30333

Telephone: 1-800-CDC-INFO (232-4636)/TTY: 1-888-232-6348

E-mail: [email protected] Web: http://www.cdc.gov

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Office of Surveillance, Epidemiology, and Laboratory Services

Division of Behavioral Surveillance

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