Transcript of Smoking data from BRFSS surveys, 2000 - 2008 Robert Delongchamp, MPH, PhD Professor of Epidemiology,...
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- Smoking data from BRFSS surveys, 2000 - 2008 Robert
Delongchamp, MPH, PhD Professor of Epidemiology, CoPH Consultant,
ADH Tobacco Master Settlement Tobacco Master Settlement
9/21/20101Health Research, Policy and Health Promotion
Conference
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- To construct credible estimates of smoking prevalence for
specific sexes, ages, locations and times from data collected in
BRFSS surveys That is, I want the 2008 smoking prevalence among 40
year old males living in Mount Ida! 9/21/20102Health Research,
Policy and Health Promotion Conference
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- Outline Behavioral Risk Factor Surveillance System Direct
estimates of smoking prevalence Simulated smoking prevalence in a
cohort State trends in smoking prevalence Geographically weighted
regression (GWR) Regional trends in smoking prevalence
39/21/2010Health Research, Policy and Health Promotion
Conference
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- Behavioral Risk Factor Surveillance System
www.cdc.gov/brfss/about.htm 9/21/20104Health Research, Policy and
Health Promotion Conference
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- BRFSS Annual telephone survey (nation-wide) probability sample
of Arkansans with landlines self-reporting of several risk factors
selected the surveys from years, 2000 through 2008 9/21/20105Health
Research, Policy and Health Promotion Conference
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- Relevant BRFSS Data Annual telephone survey Smoking Questions
respondents assigned to 3 categories (_smoke3) Current smoker (>
100 cigarettes & smoke: every day or some days) Former smoker
(> 100 cigarettes & smoke: not at all) Never smoked (<
100 cigarettes) doesnt deal with smokeless tobacco, pipes &
cigars 9/21/20106Health Research, Policy and Health Promotion
Conference
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- Relevant BRFSS Data Annual telephone survey Smoking Questions
Demographic Information age: selected 35 through 84 sex
9/21/20107Health Research, Policy and Health Promotion
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- Relevant BRFSS Data Annual telephone survey Smoking Questions
Demographic Information zip code convert to a region a.k.a. Zip
Code Tabulation Area (ZCTA) deleted respondents w/o ZCTA
9/21/20108Health Research, Policy and Health Promotion
Conference
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- Records 30,457 from Arkansas 47 from Mount Ida 18 From Huttig
9/21/20109Health Research, Policy and Health Promotion
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- Estimates of smoking prevalence based on the survey design Some
can be downloaded from the web site 9/21/201010Health Research,
Policy and Health Promotion Conference
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- Age-sex-year specific estimates direct estimates (design-based
approach) 180 parameters (5 x 9 x 2 x 2) 5 age groups: 35 44, , 75
84 9 years: 2000 to 2008 Both sexes trinomial response (never,
current, former) 9/21/201011Health Research, Policy and Health
Promotion Conference
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- Arkansas estimates direct estimates 180 parameters / 270
estimates large CIs small numbers of respondents Mt Ida? 47
respondents 9/21/201012Health Research, Policy and Health Promotion
Conference
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- Working toward regional estimates Major problem: sample size
limits the precision of direct estimates 180 parameters are too
many for precise estimates of Arkansas rates let alone Mt. Ida Need
to reduce number of parameters or increase sample sizes Approach:
reduce parameters by modeling the main trends in these data
9/21/201013Health Research, Policy and Health Promotion
Conference
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- No Interaction!!! Patterns with age and sex in smoking
prevalence are stable across the years. Can collate the information
about age and sex patterns across years. What are the patterns?
Type 3 Analysis of Effects EffectDF Wald Chi-SquarePr > ChiSq
year1615.790.4675 agegrp81271.92
- Annual Change Brown regions had increasing prevalence of
current smokers (APC > 100) Prevalence of current smokers
declined in the remaining regions; highest declines in shades of
blue. 9/21/201040
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- Prevalence of Current Smokers This shows the decline with age
as well as the decline with year. Thus, Mena has fewer current
smokers among the cohort which was 40 years old in 2000.
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- Conclusions Are ZCTA estimates credible? data-based established
methodologies population weighted survey sample (weights
respondents) multinomial logistic regression (trinomial responses)
geographically weighted regression (weights locations) statistical
properties essentially moving averages of state-wide estimates
state-wide estimates are good 9/21/201042Health Research, Policy
and Health Promotion Conference
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- Conclusions Are ZCTA estimates credible? GWR, as defined
herein, always gives estimates shrinkage estimate (to state-wide
estimates) even where direct estimation is unreliable even at
locations w/o data (e.g. White River Refuge or Oklahoma)
9/21/201043Health Research, Policy and Health Promotion
Conference
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- Nothins free direct estimation is unbiased but may have large
variance model-based estimates achieve more precision but may have
large bias only as accurate as the model is valid 9/21/201044Health
Research, Policy and Health Promotion Conference
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- 9/21/201045Health Research, Policy and Health Promotion
Conference