Estimation of asthma incidence among low-income children in Texas: a novel approach using Medicaid claims data
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2012/10/15
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Description:Few recent estimates of childhood asthma incidence exist in the literature, although the importance of incidence surveillance for understanding asthma risk factors has been recognized. Asthma prevalence, morbidity, and mortality reports have repeatedly shown that low-income children are disproportionately impacted by the disease. The aim of this study was to demonstrate the utility of Medicaid claims data for providing statewide estimates of asthma incidence. Medicaid analytic extract (MAX) data for Texas children aged 0-17 years enrolled in Medicaid between 2004 and 2007 were used to estimate incidence overall and by age group, gender, race, and county of residence. A =13-month period of continuous enrollment was required in order to distinguish incident from prevalent cases identified in the claims data. The age-adjusted incidence of asthma was 4.26/100 person-years during 2005-2007, higher than reported in other populations. Incidence rates decreased with age, were higher for males than females, differed by race, and tended to be higher in rural than urban areas. This study demonstrates the utility of Medicaid analytic extract data for estimating asthma incidence and describes the methodology required for a population with unstable enrollment. [Description provided by NIOSH]
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ISSN:0002-9262
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Volume:176
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Issue:8
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NIOSHTIC Number:nn:20041989
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Citation:Am J Epidemiol 2012 Oct; 176(8):744-750
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Contact Point Address:Dr. Elaine Symanski, University of Texas School of Public Health, 1200 Herman Pressler Drive, RAS E643, Houston, TX 77030
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Email:Elaine.Symanski@uth.tmc.edu
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Federal Fiscal Year:2013
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Performing Organization:University of Texas Health Science Center, Houston
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Peer Reviewed:True
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Start Date:20050701
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Source Full Name:American Journal of Epidemiology
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End Date:20250630
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Main Document Checksum:urn:sha-512:7ce969902be6f8a35e116e809e53d8b623000682f86c778464f8a9b9983a10175fa60968f21dfbb2c777d1e9304cca08aa45eef48fc9f0ad8408526343ce1ed9
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