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Identification of temporal condition patterns associated with pediatric obesity incidence using sequence mining and
Elizabeth A Campbell1,2, Ting Qian3, Jeffrey M Miller2
1Department of Information Science, College of Computing and Informatics, Drexel University, Philadelphia, PA, USA.
International Journal of Obesity (2005)
|June 5, 2020
Summary
Electronic health records reveal temporal patterns linked to childhood obesity. Conditions like asthma and allergies appearing before obesity diagnosis may signal future risk, informing prevention strategies.
Area of Science:
- Pediatric Health
- Health Informatics
- Obesity Research
Background:
- Electronic Health Records (EHRs) offer potential for addressing pediatric obesity.
- Identifying temporal condition patterns surrounding obesity incidence is crucial for clinical care and policy.
Purpose of the Study:
- To identify temporal condition patterns associated with obesity incidence in a large pediatric population.
- To inform clinical care, policy, and prevention efforts for childhood obesity.
Main Methods:
- Utilized EHR data from 2009-2016 for 49,694 pediatric obesity cases and matched controls.
- Applied the SPADE algorithm to identify temporal condition patterns and McNemar's test for significance.
Main Results:
- Identified 163 condition patterns; 80 were more common in cases, 45 in controls.
- Asthma and allergic rhinitis showed strong associations with obesity incidence, especially pre-index.
- Seven conditions, including ENT disorders, were exclusively diagnosed pre-index in cases.
Conclusions:
- SPADE analysis revealed temporally dependent condition associations with obesity incidence.
- Pre-index allergic rhinitis and asthma may indicate future obesity risk.
- Identified patterns warrant further investigation for causal relationships in obesity research.
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