Enbal Shacham,Stephen E Scroggins,Adam Gilmore,Jane Cheng,Rosalinda Nava
{"title":"Predictors of Pediatric Asthma Management: Identifying Actionable Results With Geographic Determinants.","authors":"Enbal Shacham,Stephen E Scroggins,Adam Gilmore,Jane Cheng,Rosalinda Nava","doi":"10.1097/phh.0000000000001982","DOIUrl":null,"url":null,"abstract":"BACKGROUND\r\nPediatric asthma remains one of the most prominent chronic health conditions among US youth. Geographic determinants such as air pollutants have been identified as playing a role in asthma development and exacerbation. The purpose of this study was to determine geospatial predictors of pediatric asthma exacerbation events and to prioritize housing remediation resources.\r\n\r\nMETHODS\r\nElectronic medical records were abstracted from a health plan in Southern California. The inclusion criteria that created a sample of 51 557 members were those aged 21 years and younger, who had at least 1 asthma-related encounter between January 2019 and December 2021. Diagnoses, age, number of clinic and emergency department visits, and home addresses were included. The air quality index from the closest monitoring station during the study period, residential distance from a primary roadway, and residential distance from manufacturing sites were included in the spatial analysis.\r\n\r\nRESULTS\r\nThe average number of asthma-related clinic visits was 2 across the sample. Individuals with more asthma-related clinic visits residing in public housing were more likely to live within 4 km of industrial manufacturing locations (P < .001), reside closer to a major roadway (P < .001), and experience a higher number of poor air quality days (P < .001). Modeling results show these factors were also significantly predictive of an increase of asthma-related health care encounters.\r\n\r\nCONCLUSIONS\r\nThe findings of this study were consistent with previous studies linking asthma and poor air quality and further highlighted some of the additive and potentially exponential challenges that public housing, major roadways, and manufacturing sites provide communities in their proximity. This research can guide environmental interventions, including the frequency of public housing inspections, community outreach, and the development of communication strategies, to reduce asthma-related experiences across neighborhoods.","PeriodicalId":520109,"journal":{"name":"Journal of Public Health Management & Practice","volume":"2 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Public Health Management & Practice","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1097/phh.0000000000001982","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
BACKGROUND
Pediatric asthma remains one of the most prominent chronic health conditions among US youth. Geographic determinants such as air pollutants have been identified as playing a role in asthma development and exacerbation. The purpose of this study was to determine geospatial predictors of pediatric asthma exacerbation events and to prioritize housing remediation resources.
METHODS
Electronic medical records were abstracted from a health plan in Southern California. The inclusion criteria that created a sample of 51 557 members were those aged 21 years and younger, who had at least 1 asthma-related encounter between January 2019 and December 2021. Diagnoses, age, number of clinic and emergency department visits, and home addresses were included. The air quality index from the closest monitoring station during the study period, residential distance from a primary roadway, and residential distance from manufacturing sites were included in the spatial analysis.
RESULTS
The average number of asthma-related clinic visits was 2 across the sample. Individuals with more asthma-related clinic visits residing in public housing were more likely to live within 4 km of industrial manufacturing locations (P < .001), reside closer to a major roadway (P < .001), and experience a higher number of poor air quality days (P < .001). Modeling results show these factors were also significantly predictive of an increase of asthma-related health care encounters.
CONCLUSIONS
The findings of this study were consistent with previous studies linking asthma and poor air quality and further highlighted some of the additive and potentially exponential challenges that public housing, major roadways, and manufacturing sites provide communities in their proximity. This research can guide environmental interventions, including the frequency of public housing inspections, community outreach, and the development of communication strategies, to reduce asthma-related experiences across neighborhoods.