Positive predictive value between medical-chart body-mass-index category and obesity versus codes in a claims-data warehouse

Caplan, Eleanor O.; Kamble, Pravin; Harvey, Raymond A.; Smolarz, B. Gabriel; Renda, Andrew; Bouchard, Jonathan; Huang, Jie

doi:10.1080/03007995.2017.1366302

Cited by 2 publications

(6 citation statements)

References 5 publications

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“…Among the articles included in this review, six studies evaluated the statistical validity of methods in people suffering from severe obesity 10,15,19,20,22,31 . In individuals with BMI between 35 and 39 kg/m 2 , sensitivity varied (depending on the studies and association with other comorbidities) from 22.8% to 58.8%, whereas specificity always exceeded 93%.…”

Section: Resultsmentioning

confidence: 99%

“…The specificity of case-identification methods for the identification of individuals with obesity was generally higher than the sensitiv- with comorbidities associated with obesity and in cases of severe obesity. 10,15,19,20,22,23,[31][32][33] This may be explained by the fact that subjects with severe obesity or comorbidities are more likely to be coded as such in administrative databases because the patient's weight has direct relevance to the disease of interest. This may lead to a positive bias in the coding of obesity for these groups.…”

Section: Discussionmentioning

confidence: 99%

“…Overall, several factors that are likely to positively affect the accuracy of the case‐identification methods were identified. Case‐identification methods tended to be more accurate when reported with comorbidities associated with obesity and in cases of severe obesity 10,15,19,20,22,23,31–33 . This may be explained by the fact that subjects with severe obesity or comorbidities are more likely to be coded as such in administrative databases because the patient's weight has direct relevance to the disease of interest.…”

Section: Discussionmentioning

confidence: 99%

“…12,[14][15][16][17] In addition, the caseidentification methods used in recent studies have led to variable performance depending on the existence of other comorbidities, the severity of obesity and the type of health care administrative data. 12,[18][19][20][21][22][23] Accurate case-identification methods for health care administrative databases is essential to minimize classification errors, which can represent a significant form of bias related to the use of administrative databases.…”

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confidence: 99%

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

Samadoulougou

Idzerda

Dault

et al. 2020

Obesity Science & Practice

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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 caseidentification 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 caseidentification 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.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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

Samadoulougou

Idzerda

Dault

et al. 2020

Obesity Science & Practice

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“…All utilized ICD codes are included in the supplement (Table S1). Restriction of our cohort to those with a diagnosis of obesity, either as a condition or supporting code, has been validated as approximately 95% specific for true prevalent obesity (12,13) (Table S1). This was chosen to reduce confounding by body mass, to establish more uniform indications for HbA1c testing, and to select participants likely to have similar pathophysiologic processes giving rise to hyperglycemia (14).…”

Section: Cohort Definitionmentioning

confidence: 99%

Association Between Prediabetes/Diabetes and Asthma Exacerbations in a Claims-Based Obese Asthma Cohort

Brigham

Keet

et al. 2019

The Journal of Allergy and Clinical Immunology: In Practice

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Background: Metabolic dysfunction may contribute to worsened asthma in obesity. The relationship between pre-diabetes and diabetes, metabolic conditions more common in obesity, with asthma outcomes is not well-characterized. Objective: We sought to determine the association between pre-diabetes/diabetes and asthma exacerbations in an obese asthma cohort. Methods: A retrospective cohort of US adults with obese asthma, age 18-64, was created from a claims-based health services database spanning 2010-2015. Individuals with a hemoglobin A1c (HbA1c) measurement were identified, categorized as within normal (<5.6%), pre-diabetes (5.7-6.4%), and diabetes (≥6.5%) ranges. Exacerbations, defined as asthma-related hospitalization, emergency department visit, or corticosteroid prescription ±14 days of an asthma-related outpatient visit, were ascertained. Associations were fit with zero-inflated negative binomial models, adjusted for age, sex, region, smoking, medication use, and comorbidities. Results: 5,722 individuals were identified. Higher HgbA1c was associated with higher asthma exacerbation rates. In the fully-adjusted model, compared to individuals with normal HbA1c, those

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Positive predictive value between medical-chart body-mass-index category and obesity versus codes in a claims-data warehouse

Abstract: BMI categories derived from administrative claims, when available, can be used successfully particularly in the context of obesity research.

Cited by 2 publications

References 5 publications

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

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

Association Between Prediabetes/Diabetes and Asthma Exacerbations in a Claims-Based Obese Asthma Cohort

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