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Optimizing community-level surveillance data for pediatric asthma management
Community-level approaches for pediatric asthma management rely on locally collected information derived primarily from two sources: claims records and school-based surveys. We combined claims and school-based surveillance data, and examined the asthma-related risk patterns among adolescent students...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5984210/ https://www.ncbi.nlm.nih.gov/pubmed/29868356 http://dx.doi.org/10.1016/j.pmedr.2018.02.004 |
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author | Benka-Coker, Wande O. Gale, Sara L. Brandt, Sylvia J. Balmes, John R. Magzamen, Sheryl |
author_facet | Benka-Coker, Wande O. Gale, Sara L. Brandt, Sylvia J. Balmes, John R. Magzamen, Sheryl |
author_sort | Benka-Coker, Wande O. |
collection | PubMed |
description | Community-level approaches for pediatric asthma management rely on locally collected information derived primarily from two sources: claims records and school-based surveys. We combined claims and school-based surveillance data, and examined the asthma-related risk patterns among adolescent students. Symptom data collected from school-based asthma surveys conducted in Oakland, CA were used for case identification and determination of severity levels for students (high and low). Survey data were matched to Medicaid claims data for all asthma-related health care encounters for the year prior to the survey. We then employed recursive partitioning to develop classification trees that identified patterns of demographics and healthcare utilization associated with severity. A total of 561 students had complete matched data; 86.1% were classified as high-severity, and 13.9% as low-severity asthma. The classification tree consisted of eight subsets: three indicating high severity and five indicating low severity. The risk subsets highlighted varying combinations of non-specific demographic and socioeconomic predictors of asthma prevalence, morbidity and severity. For example, the subset with the highest class-prior probability (92.1%) predicted high-severity asthma and consisted of students without prescribed rescue medication, but with at least one in-clinic nebulizer treatment. The predictive accuracy of the tree-based model was approximately 66.7%, with an estimated 91.1% of high-severity cases and 42.3% of low-severity cases correctly predicted. Our analysis draws on the strengths of two complementary datasets to provide community-level information on children with asthma, and demonstrates the utility of recursive partitioning methods to explore a combination of features that convey asthma severity. |
format | Online Article Text |
id | pubmed-5984210 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-59842102018-06-04 Optimizing community-level surveillance data for pediatric asthma management Benka-Coker, Wande O. Gale, Sara L. Brandt, Sylvia J. Balmes, John R. Magzamen, Sheryl Prev Med Rep Regular Article Community-level approaches for pediatric asthma management rely on locally collected information derived primarily from two sources: claims records and school-based surveys. We combined claims and school-based surveillance data, and examined the asthma-related risk patterns among adolescent students. Symptom data collected from school-based asthma surveys conducted in Oakland, CA were used for case identification and determination of severity levels for students (high and low). Survey data were matched to Medicaid claims data for all asthma-related health care encounters for the year prior to the survey. We then employed recursive partitioning to develop classification trees that identified patterns of demographics and healthcare utilization associated with severity. A total of 561 students had complete matched data; 86.1% were classified as high-severity, and 13.9% as low-severity asthma. The classification tree consisted of eight subsets: three indicating high severity and five indicating low severity. The risk subsets highlighted varying combinations of non-specific demographic and socioeconomic predictors of asthma prevalence, morbidity and severity. For example, the subset with the highest class-prior probability (92.1%) predicted high-severity asthma and consisted of students without prescribed rescue medication, but with at least one in-clinic nebulizer treatment. The predictive accuracy of the tree-based model was approximately 66.7%, with an estimated 91.1% of high-severity cases and 42.3% of low-severity cases correctly predicted. Our analysis draws on the strengths of two complementary datasets to provide community-level information on children with asthma, and demonstrates the utility of recursive partitioning methods to explore a combination of features that convey asthma severity. Elsevier 2018-02-08 /pmc/articles/PMC5984210/ /pubmed/29868356 http://dx.doi.org/10.1016/j.pmedr.2018.02.004 Text en © 2018 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Regular Article Benka-Coker, Wande O. Gale, Sara L. Brandt, Sylvia J. Balmes, John R. Magzamen, Sheryl Optimizing community-level surveillance data for pediatric asthma management |
title | Optimizing community-level surveillance data for pediatric asthma management |
title_full | Optimizing community-level surveillance data for pediatric asthma management |
title_fullStr | Optimizing community-level surveillance data for pediatric asthma management |
title_full_unstemmed | Optimizing community-level surveillance data for pediatric asthma management |
title_short | Optimizing community-level surveillance data for pediatric asthma management |
title_sort | optimizing community-level surveillance data for pediatric asthma management |
topic | Regular Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5984210/ https://www.ncbi.nlm.nih.gov/pubmed/29868356 http://dx.doi.org/10.1016/j.pmedr.2018.02.004 |
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