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

J Sch Health. 2020 Jul;90(7):538-544. doi: 10.1111/josh.12903. Epub 2020 May 7.


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 < .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.

Keywords: asthma; asthma screening; school health; school nursing.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Absenteeism
  • Algorithms
  • Asthma* / diagnosis
  • Asthma* / epidemiology
  • Humans
  • Risk Assessment
  • School Health Services
  • Schools*
  • Urban Population