A Risk Stratification Algorithm for Asthma Identification and Prioritization in a Low-Income Urban School

Autor/inn/en	Rabner, Marc; Bissett, Katherine; Johnson, Sara B.; Connor, Katherine A.
Titel	A Risk Stratification Algorithm for Asthma Identification and Prioritization in a Low-Income Urban School
Quelle	In: Journal of School Health, 90 (2020) 7, S.538-544 (7 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Connor, Katherine A.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-4391
DOI	10.1111/josh.12903
Schlagwörter	Diseases; Chronic Illness; Urban Schools; Elementary School Students; Middle School Students; Screening Tests; Incidence; At Risk Students; Student Needs; School Health Services; School Nurses; Attendance; Low Income Students + Suchen Sie Ihr Suchwort? Disease; Krankheit; Chronic disease; Chronische Krankheit; Urban area; Urban areas; School; Schools; Stadtregion; Stadt; Schule; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Screening-Verfahren; Vorkommen; Schuleingangsuntersuchung; Anwesenheit
Abstract	Background: Asthma can interfere with school attendance and engagement. School health programs are central to asthma management. Case identification is limited by reliance on parent-completed forms, which are often missing. This project tested a low-burden screening algorithm to stratify students based on priority for nurse outreach at 2 large, urban schools with high asthma prevalence. Methods: Students in grades 1-8 completed a 4-item asthma screener. Two-stage stratification incorporated screener responses, school nurse records, and absenteeism. Students were assigned low, medium, or high priority for follow up. Asthma prevalence in the high priority group was calculated for substantiated asthma. Whether stratification was more likely than chance to identify new cases of asthma in the high-priority group was evaluated using chi-square tests. Results: Of 1397 students, 69.7% were screened. Secondary stratification decreased the number of students in the high and medium priority groups. New asthma cases were identified in 46.4% of high-priority families reached for follow up. High-priority students were more likely to be identified as having asthma than chance alone (p < 0.001). Conclusions: A low-burden screening algorithm appropriately placed students with asthma in the high priority group. This approach may allow efficient, targeted follow up of the highest need students in high prevalence populations. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01