Logo image
Back
Identification of temporal condition patterns associated with pediatric obesity incidence using sequence mining and big data
Journal article   Open access   Peer reviewed

Identification of temporal condition patterns associated with pediatric obesity incidence using sequence mining and big data

Elizabeth A. Campbell, Ting Qian, Jeffrey M. Miller, Ellen J. Bass and Aaron J. Masino
INTERNATIONAL JOURNAL OF OBESITY, v 44(8), pp 1753-1765
01 Aug 2020
DOI: https://doi.org/10.1038/s41366-020-0614-7
PMID: 32494036
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1038/s41366-020-0614-7View
Published, Version of Record (VoR)CC BY V4.0,  Open

Abstract

Endocrinology & Metabolism Life Sciences & Biomedicine Nutrition & Dietetics Science & Technology
Background Electronic health records (EHRs) are potentially important components in addressing pediatric obesity in clinical settings and at the population level. This work aims to identify temporal condition patterns surrounding obesity incidence in a large pediatric population that may inform clinical care and childhood obesity policy and prevention efforts. Methods EHR data from healthcare visits with an initial record of obesity incidence (index visit) from 2009 through 2016 at the Children's Hospital of Philadelphia, and visits immediately before (pre-index) and after (post-index), were compared with a matched control population of patients with a healthy weight to characterize the prevalence of common diagnoses and condition trajectories. The study population consisted of 49,694 patients with pediatric obesity and their corresponding matched controls. The SPADE algorithm was used to identify common temporal condition patterns in the case population. McNemar's test was used to assess the statistical significance of pattern prevalence differences between the case and control populations. Results SPADE identified 163 condition patterns that were present in at least 1% of cases; 80 were significantly more common among cases and 45 were significantly more common among controls (p < 0.05). Asthma and allergic rhinitis were strongly associated with childhood obesity incidence, particularly during the pre-index and index visits. Seven conditions were commonly diagnosed for cases exclusively during pre-index visits, including ear, nose, and throat disorders and gastroenteritis. Conclusions The novel application of SPADE on a large retrospective dataset revealed temporally dependent condition associations with obesity incidence. Allergic rhinitis and asthma had a particularly high prevalence during pre-index visits. These conditions, along with those exclusively observed during pre-index visits, may represent signals of future obesity. While causation cannot be inferred from these associations, the temporal condition patterns identified here represent hypotheses that can be investigated to determine causal relationships in future obesity research.

Metrics

5 Record Views
5 citations in Web of Science
5 citations in Scopus

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
Web of Science research areas
Endocrinology & Metabolism
Nutrition & Dietetics
Logo image