RESEARCH OBJECTIVE: To create prevalence estimates of asthma symptoms for California legislative districts. DATA SOURCES: Three main data sources were used for this study: 2001 California Health Interview Survey, 2000 Census, and 2000-2002 March Current Population Surveys. STUDY DESIGN: Secondary data analyses were conducted from cross-sectional data to distribute the joint probability of ever having an asthma diagnosis and symptoms in the last 12 months within an Assembly district. We applied hierarchical logistic regressions to estimate the parameters for selected survey and census data that predicted the probabilities of diagnosed asthmatics with asthma symptoms. Predictors included individual-level variables and contextual variables at zip code levels. PRINCIPAL FINDINGS: Asthma symptom prevalence geographically varied by age within and across Assembly districts throughout California. CONCLUSIONS: With modest investments in establishing analytic data files and estimating regression parameters for target conditions, small area estimation (SAE) procedures can create health data estimates not otherwise available at the sub-county level. Applying SAE procedures to asthma symptom prevalence suggest that these data can become essential reference tools for advocates and policy makers currently addressing this and other public health concerns in the state.
RESEARCH OBJECTIVE: To create prevalence estimates of asthma symptoms for California legislative districts. DATA SOURCES: Three main data sources were used for this study: 2001 California Health Interview Survey, 2000 Census, and 2000-2002 March Current Population Surveys. STUDY DESIGN: Secondary data analyses were conducted from cross-sectional data to distribute the joint probability of ever having an asthma diagnosis and symptoms in the last 12 months within an Assembly district. We applied hierarchical logistic regressions to estimate the parameters for selected survey and census data that predicted the probabilities of diagnosed asthmatics with asthma symptoms. Predictors included individual-level variables and contextual variables at zip code levels. PRINCIPAL FINDINGS: Asthma symptom prevalence geographically varied by age within and across Assembly districts throughout California. CONCLUSIONS: With modest investments in establishing analytic data files and estimating regression parameters for target conditions, small area estimation (SAE) procedures can create health data estimates not otherwise available at the sub-county level. Applying SAE procedures to asthma symptom prevalence suggest that these data can become essential reference tools for advocates and policy makers currently addressing this and other public health concerns in the state.
Authors: Carolyn A Mendez; Steven P Wallace; Hongjian Yu; Ying-Ying Meng; Jenny Chia; E Richard Brown Journal: Policy Brief UCLA Cent Health Policy Res Date: 2003-05
Authors: Carolyn A Mendez-Luck; Hongjian Yu; Ying-Ying Meng; Jenny Chia; Beth L Newman; Aleck Sripipatana; Steven P Wallace Journal: Policy Brief UCLA Cent Health Policy Res Date: 2004-06
Authors: Hongjian Yu; Ying-Ying Meng; Carolyn A Mendez-Luck; Mona Jhawar; Steven P Wallace Journal: Am J Public Health Date: 2007-02-28 Impact factor: 9.308
Authors: Yan Cui; Susie B Baldwin; Amy S Lightstone; Margaret Shih; Hongjian Yu; Steven Teutsch Journal: J Urban Health Date: 2012-06 Impact factor: 3.671
Authors: Marc N Elliott; Daniel F McCaffrey; Brian K Finch; David J Klein; Nate Orr; Megan K Beckett; Nicole Lurie Journal: Health Serv Res Date: 2009-07-27 Impact factor: 3.402
Authors: Mary Kreger; Katherine Sargent; Abigail Arons; Marion Standish; Claire D Brindis Journal: Am J Public Health Date: 2011-08-11 Impact factor: 9.308
Authors: Carl D Stevens; David L Schriger; Brian Raffetto; Anna C Davis; David Zingmond; Dylan H Roby Journal: Health Aff (Millwood) Date: 2014-08 Impact factor: 6.301
Authors: Emanuel Alcala; Paul Brown; John A Capitman; Mariaelena Gonzalez; Ricardo Cisneros Journal: Int J Environ Res Public Health Date: 2019-07-27 Impact factor: 3.390