Estimating health conditions for small areas: asthma symptom prevalence for state legislative districts.
OBJECTIVE
To create prevalence estimates of asthma symptoms for California legislative districts.


METHODS
Three main data sources were used for this study: 2001 California Health Interview Survey, 2000 Census, and 2000-2002 March Current Population Surveys.


METHODS
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.


RESULTS
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.
Mendez-Luck
Carolyn A
CA
UCLA School of Public Health, Department of Community Health Sciences, UCLA Center for Health Improvement in Minority Elders/Resource Centers for Minority Aging Research, Los Angeles, CA 90095-1772, USA.
Yu
Hongjian
H
Meng
Ying-Ying
YY
Jhawar
Mona
M
Wallace
Steven P
SP
D000293:Adolescent; D000328:Adult; D000368:Aged; D001249:Asthma / Q000453:epidemiology*; D002140:California / Q000453:epidemiology; D002648:Child; D002675:Child, Preschool; D003430:Cross-Sectional Studies; D005260:Female; D006304:Health Status; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007802:Language; D008297:Male; D008875:Middle Aged; D015995:Prevalence; D012111:Residence Characteristics / Q000706:statistics & numerical data; D012737:Sex Factors; D017062:Small-Area Analysis; D012959:Socioeconomic Factors