Validated methods for identifying individuals with obesity in health care administrative databases: A systematic review

Sékou Samadoulougou; Leanne Idzerda; Roxane Dault; Alexandre Lebel; Anne-Marie Cloutier; Alain Vanasse

doi:10.1002/osp4.450

Validated methods for identifying individuals with obesity in health care administrative databases: A systematic review

Obes Sci Pract. 2020 Sep 4;6(6):677-693. doi: 10.1002/osp4.450. eCollection 2020 Dec.

Authors

Sékou Samadoulougou^{1

2}, Leanne Idzerda^{1

2}, Roxane Dault³, Alexandre Lebel^{1

2

4}, Anne-Marie Cloutier³, Alain Vanasse^{5

6}

Affiliations

¹ Centre for Research on Planning and Development (CRAD) Laval University Québec Canada.
² Evaluation Platform on Obesity Prevention Quebec Heart and Lung Institute Research Center Québec Canada.
³ Research Group in Health Informatics (GRIIS) Université de Sherbrooke Sherbrooke Canada.
⁴ Graduate School of Land Management and Regional Planning, Faculty of Planning, Architecture, Art and Design Laval University Québec Canada.
⁵ Département de médecine de famille et médecine d'urgence, Faculté de médecine et des sciences de la santé Université de Sherbrooke Sherbrooke Canada.
⁶ Centre de recherche CIUSSS de l'Estrie-CHUS Sherbrooke Canada.

Abstract

Background: Health care administrative databases are increasingly used for health studies and public health surveillance. Cases of individuals with obesity are selected using case-identification methods. However, the validity of these methods is fragmentary and particularly challenging for obesity case identification.

Objective: The objectives of this systematic review are to (1) determine the case-identification methods used to identify individuals with obesity in health care administrative databases and (2) to summarize the validity of these case-identification methods when compared with a reference standard.

Methods: A systematic literature search was conducted in six bibliographic databases for the period January 1980 to June 2019 for all studies evaluating obesity case-identification methods compared with a reference standard.

Results: Seventeen articles met the inclusion criteria. International Classification of Diseases (ICD) codes were the only case-identification method utilized in selected articles. The performance of obesity-identification methods varied widely across studies, with positive predictive value ranging from 19% to 100% while sensitivity ranged from 3% to 92%. The sensitivity of these methods was usually low while the specificity was higher.

Conclusion: When obesity is reported in health care administrative databases, it is usually correctly reported; however, obesity tends to be highly underreported in databases. Therefore, case-identification methods to monitor the prevalence and incidence of obesity within health care administrative databases are not reliable. In contrast, the use of these methods remains relevant for the selection of individuals with obesity for cohort studies, particularly when identifying cohorts of individuals with severe obesity or cohorts where obesity is associated with comorbidities.

Keywords: algorithm; case‐identification; databases; validation.

Publication types

Review