The effects of raking and cell phone integration on brfss outcome s
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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 Outcome s

  • 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 Outcome sGeoStrata 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 Outcome s

  • 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


Weighting

Post -Stratification Outcome s

Weighting


Data weighting
Data Weighting Outcome s

  • 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--- Outcome sPost 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 Outcome s

  • 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 Outcome s

  • 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 Outcome s

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 Outcome s

  • Education

  • Marital status

  • Home ownership/renter

  • Telephone source (cell phone or landline)


Post stratification vs iterative proportional fitting
Post Stratification vs. Iterative Proportional Fitting Outcome s

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? Outcome s

  • 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.












Impact of changing to raking ipv on the brfss
Impact of changing to RAKING ( Outcome sipv) on the BRFSS


Brfss 2010 combined states a data difference in weighted percentages
BRFSS 2010 Combined States Outcome sa DataDifference In Weighted Percentages

A Excludes AK, DC, TN, SD


Marginal changes weighted percentages for demographic characteristics brfss 2010
Marginal Changes Outcome sWeighted Percentages for Demographic Characteristics, BRFSS 2010


Brfss 2010 combined states data difference in weighted percentages of health outcomes
BRFSS 2010 Combined States Data Outcome sDifference In Weighted Percentages of Health Outcomes

A Excludes AK, DC, TN, SD


Marginal changes for in weighted percentage s health outcomes brfss 2010
Marginal Changes for Outcome sin Weighted Percentage s Health Outcomes, BRFSS 2010


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


State level outcomes
State level outcomes Weighting Method


In some cases small changes landline only
In Some Cases, Small Changes Weighting Method(Landline Only)


In some cases larger differences but not consistent differences landline only
In Some Cases, Larger Differences– Weighting Method But Not Consistent Differences(Landline Only)


In some cases consistent differences landline only
In Some Cases, Consistent Differences Weighting Method(Landline Only)


But differences go away sometimes when cell phones are added
But Differences Go Away Sometimes Weighting MethodWhen Cell Phones Are Added



Conclusions
Conclusions Responses


Conclusions 1
Conclusions (1) Responses

  • 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) Responses

  • 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 Responses

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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