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Estimating Health Conditions for Small Areas: Asthma Symptom Prevalence for State Legislative Districts

Authors

  • Carolyn A. Mendez-Luck,

    1. UCLA School of Public Health, Department of Community Health Sciences, UCLA Center for Health Improvement in Minority Elders/Resource Centers for Minority Aging Research, Box 951772, 21-275B CHS, Los Angeles, CA 90095-1772
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    • Address correspondence to Carolyn A. Mendez-Luck, Ph.D., M.P.H., UCLA School of Public Health, Department of Community Health Sciences, UCLA Center for Health Improvement in Minority Elders/Resource Centers for Minority Aging Research, Box 951772, 21-275B CHS, Los Angeles, CA 90095-1772. Hongjian Yu, Ph.D., and Ying-Ying Meng, Dr. P.H., are with the UCLA Center for Health Policy Research, Los Angeles, CA. Mona Jhawar, M.P.H., is with the Los Angeles County Department of Public Health, Los Angeles, CA. Steven P. Wallace, Ph.D., is with the UCLA School of Public Health, Department of Community Health Sciences, UCLA Center for Health Improvement in Minority Elders/Resource Centers for Minority Aging, UCLA Center for Health Policy Research, Los Angeles, CA.

  • Hongjian Yu,

    1. UCLA Center for Health Policy Research, Los Angeles, CA
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  • Ying-Ying Meng,

    1. UCLA Center for Health Policy Research, Los Angeles, CA
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  • Mona Jhawar,

    1. Los Angeles County Department of Public Health, Los Angeles, CA
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  • Steven P. Wallace

    1. UCLA School of Public Health, Department of Community Health Sciences, UCLA Center for Health Improvement in Minority Elders/Resource Centers for Minority Aging, UCLA Center for Health Policy Research, Los Angeles, CA
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Abstract

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.

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