The Effects of Raking and Cell Phone Integration on BRFSS Outcome s. Brief Agenda
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1 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 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) 1
2 WEIGHTING PROCEDURES 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 sample is drawn using low/high density strata within each of the regions (usually smaller than states) Stratum weight (_STRWT) = NRECSTR/ NRECSEL 2
3 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 Post -Stratification WEIGHTING 3
4 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 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 nonrespondents would have answered survey questions. 4
5 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 Sometimes poststratification resulted in very small or disproportionately large samples within age/race/gender/region categories. Weight trimming or category collapsing would be done if categories were disproportionately large or too small (< 50 responses). 5
6 Iterative Proportional Fitting (Raking) WEIGHTING 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. Regions within states Phone source Detailed race/ethnicity Martial status Gender by race/ethnicity Education level Age by gender Age by race/ethnicity Renter/owner 6
7 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 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). 7
8 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. Raking Iteration 1 Output Weight Sum of % of Sum of Target Weights Output Target % of Difference First Control Variable Weights Total Difference Weights Weights in % Age 18-24,Male Age 18-24,Female Age 25-34,Male Age 25-34,Female Age 35-44,Male Age 35-44,Female Age 45-54,Male Age 45-54,Female Age 55-64,Male Age 55-64,Female Age 65-74,Male Age 65-74,Female Age 75+,Male Age 75+,Female
9 Raking Iteration 1 Output Sum of % of Target % Weight Sum Target Weights Output of Difference Second Control Variable of Weights Total Difference Weights Weights in % WH NH OT NH HISP BL NH,AS NH,AI NH Raking - Iteration 1 Input Weight Sum Target Sum of Weights % of Input Target % Difference Third Control Variable of Weights Total Difference Weights of Weights in % Less than HS HS Grad Some College College Grad
10 Raking Iteration 1 Output Weight Sum of % of Sum of Target Weights Output Target % of Difference Fourth Control Variable Weights Total Difference Weights Weights in % Married Never married, member unmarried couple Divorced, Widowed, Separated Output Sum of % of Weight Sum Target Weights Output Target % of Difference Fifth Control Variable of Weights Total Difference Weights Weights in % Phone interruption No Phone Interruption Raking Iteration 1 Output Weight Sum of % of Sum of Target Weights Output Target % of Difference Sixth Control Variable Weights Total Difference Weights Weights in % Male, WH NH Male, BL NH,AS NH,AI NH,OT NH,HISP Female, WH NH Female, HISP Female, BL NH,AS NH,AI NH,OT NH
11 Raking Iteration 1 Output Weight Sum of % of Sum of Target Weights Output Target % of Difference Seventh Control Variable Weights Total Difference Weights Weights in % 18-34, WH NH , BL NH,AS NH,AI NH,OT NH,HISP , WH NH , BL NH,AS NH,AI NH,OT NH,HISP , WH NH , BL NH,AS NH,AI NH,OT NH,HISP Raking Iteration 1 Output Weight Sum of % of Sum of Target Weights Output Target % of Difference Eighth Control Variable Weights Total Difference Weights Weights in % Cell Phone Only Only and Cell Phone
12 Raking Iteration 2 Output Weight Sum of Target % of Output Difference Target % in % from Difference First Control Variable Weights Total Weights of Weights Iteration1 in % Age 18-24,Male Age 18-24,Female Age 25-34,Male Age 25-34,Female Age 35-44,Male Age 35-44,Female Age 45-54,Male Age 45-54,Female Age 55-64,Male Age 55-64,Female Age 65-74,Male Age 65-74,Female Age 75+,Male Age 75+,Female Raking - Iteration 7 Output Weight % of Target Difference Sum of Target Output % of in % from Difference First Control Variable Weights Total Weights Weights Iteration1 in % Age 18-24,Male Age 18-24,Female Age 25-34,Male Age 25-34,Female Age 35-44,Male Age 35-44,Female Age 45-54,Male Age 45-54,Female Age 55-64,Male Age 55-64,Female Age 65-74,Male Age 65-74,Female Age 75+,Male Age 75+,Female
13 Raking - Iteration 7 Output % of Target % Difference in % at Eighth Control Weight Sum Target Output of Iteration Difference Variable of Weights Total Weights Weights 1 in % Cell Phone Only Only and Cell Phone **** Program terminated at iteration 7 because all current percents differ from target percents by less than 0.025***** 25 IMPACT OF CHANGING TO RAKING (IPV) ON THE BRFSS 13
14 Combined States a Data Difference In Demographics LL Post stratified LL Raking LLCP Raking A Excludes AK, DC, TN, SD Marginal Changes Demographic Characteristics
15 Combined States Data Difference In Health Outcomes LL Post stratified LL Raking LLCP Raking A Excludes AK, DC, TN, SD Marginal Changes for Health Outcomes
16 STATE LEVEL OUTCOMES In Some Cases, Small Changes ( Only) Table 1 State-level Responses to Question: Has a doctor, nurse or other healthcare provider ever told you that you have diabetes? By Type Of Weighting Procedure for Data Response Weighted frequency with Post- Stratification Percent With Post- Stratification Weighted frequency with Raking Percent With Raking Differences in Percentages (Post- Stratification- Raking) Yes 434, , Yes, but only during pregnancy 26, , No 3,031, ,029, No, Pre-diabetes/ borderline diabetes 55, ,
17 In Some Cases, Larger Differences But Not Consistent Differences ( Only) Response Table 2 State-level Responses to Question: Would you say that in general your health is excellent, very good, good, fair or poor? By Type Of Weighting Procedure for Data Weighted Frequency With Post- Stratification Percent With Post- Stratification Weighted Frequency With Raking Percent With Raking Differences In Percentages (Post-Stratification - Raking) Excellent 631, , Very Good 1,037, , Good 1,107, ,111, Fair 519, , Poor 247, , In Some Cases, Consistent Differences ( Only) Table 3 State-level Responses to Question: During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise? By Type Of Weighting Procedure for Data Response Weighted Frequency With Post- Stratification Percent With Post- Stratification Weighted Frequency With Raking Percent With Raking Differences In Percentages (Post-Stratification - Raking) Yes 2,448, ,342, No 1,101, ,207,
18 But Differences Go Away Sometimes When Cell Phones Are Added Table 4 State-level Responses to Question: During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise? By Type Of Weighting Procedure for and Cell Phone Data Response Weighted Frequency With Post- Stratification Percent With Post- Stratification Weighted Frequency With Raking Percent With Raking Differences In Percentages (Post- Stratification - Raking) And Cell Phone Weighted Frequency With Raking And Cell Phone Percent And Cell Phone Differences In Percentages (Post- Stratification - Raking) Yes 2,448, ,342, ,447, No 1,101, ,207, ,102, Persistent Differences May Exist Even When Adding Cell Phone Responses Table 5 State-level Responses to Question: Do you smoke cigarettes every day, some days or not at all? By Type Of Weighting Procedure for and Cell Phone Data Response Every day Some Days Not At All Weighted Frequency With Post- Stratification Percent With Post- Stratification Weighted Frequency With Raking Percent With Raking Differences In Percentages (Post- Stratification - Raking) And Cell Phone Weighted Frequency With Raking And Cell Phone Percent And Cell Phone Differences In Percentages (Post- Stratification - Raking) 581, , , , , , , , ,
19 CONCLUSIONS 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. 19
20 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. State level phone use data (1) LAND LINE AND CELL PHONE STATUS-COMBINE LL & CP DATA, BRFSS 2010 Total Cell Phone Only CP and LL Land Line Only State Name Sample N % N % N % Total Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Dist. of Col Florida
21 State level phone use data (2) LAND LINE AND CELL PHONE STATUS-COMBINE LL & CP DATA, BRFSS 2010 Total Cell phone only LL and CP only State Name Sample N % N % N % Georgia Hawaii Total Cell Phone CP and LL Land Line Idaho Only Only Illinois State Name 5628 Sampl 436 N 27.7 % N 4163 % N 63.0 % e Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts State level phone use data (3) LAND LINE AND CELL PHONE STATUS-COMBINE LL & CP DATA, BRFSS 2010 Cell phone only LL and CP only State Name Sample N % N % N % Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio
22 State level phone use data (4) LAND LINE AND CELL PHONE STATUS-COMBINE LL & CP DATA, BRFSS 2010 Cell phone only LL and CP only State Name Sample N % N % N % Oklahoma Oregon Pennsylvania Rhode Island South Carolina Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming Thank You For more information please contact Centers for Disease Control and Prevention 1600 Clifton Road NE, Atlanta, GA Telephone: CDC-INFO ( )/TTY: cdcinfo@cdc.gov Web: 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 22
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