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Prevalence and recognition of obesity and its associated comorbidities: cross-sectional analysis of electronic health record data from a large US integrated health system

OBJECTIVE: To determine the prevalence of obesity and its related comorbidities among patients being actively managed at a US academic medical centre, and to examine the frequency of a formal diagnosis of obesity, via International Classification of Diseases, Ninth Revision (ICD-9) documentation amo...

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Autores principales: Pantalone, Kevin M, Hobbs, Todd M, Chagin, Kevin M, Kong, Sheldon X, Wells, Brian J, Kattan, Michael W, Bouchard, Jonathan, Sakurada, Brian, Milinovich, Alex, Weng, Wayne, Bauman, Janine, Misra-Hebert, Anita D, Zimmerman, Robert S, Burguera, Bartolome
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5702021/
https://www.ncbi.nlm.nih.gov/pubmed/29150468
http://dx.doi.org/10.1136/bmjopen-2017-017583
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author Pantalone, Kevin M
Hobbs, Todd M
Chagin, Kevin M
Kong, Sheldon X
Wells, Brian J
Kattan, Michael W
Bouchard, Jonathan
Sakurada, Brian
Milinovich, Alex
Weng, Wayne
Bauman, Janine
Misra-Hebert, Anita D
Zimmerman, Robert S
Burguera, Bartolome
author_facet Pantalone, Kevin M
Hobbs, Todd M
Chagin, Kevin M
Kong, Sheldon X
Wells, Brian J
Kattan, Michael W
Bouchard, Jonathan
Sakurada, Brian
Milinovich, Alex
Weng, Wayne
Bauman, Janine
Misra-Hebert, Anita D
Zimmerman, Robert S
Burguera, Bartolome
author_sort Pantalone, Kevin M
collection PubMed
description OBJECTIVE: To determine the prevalence of obesity and its related comorbidities among patients being actively managed at a US academic medical centre, and to examine the frequency of a formal diagnosis of obesity, via International Classification of Diseases, Ninth Revision (ICD-9) documentation among patients with body mass index (BMI) ≥30 kg/m(2). DESIGN: The electronic health record system at Cleveland Clinic was used to create a cross-sectional summary of actively managed patients meeting minimum primary care physician visit frequency requirements. Eligible patients were stratified by BMI categories, based on most recent weight and median of all recorded heights obtained on or before the index date of 1July 2015. Relationships between patient characteristics and BMI categories were tested. SETTING: A large US integrated health system. RESULTS: A total of 324 199 active patients with a recorded BMI were identified. There were 121 287 (37.4%) patients found to be overweight (BMI ≥25 and <29.9), 75 199 (23.2%) had BMI 30–34.9, 34 152 (10.5%) had BMI 35–39.9 and 25 137 (7.8%) had BMI ≥40. There was a higher prevalence of type 2 diabetes, pre-diabetes, hypertension and cardiovascular disease (P value<0.0001) within higher BMI compared with lower BMI categories. In patients with a BMI >30 (n=134 488), only 48% (64 056) had documentation of an obesity ICD-9 code. In those patients with a BMI >40, only 75% had an obesity ICD-9 code. CONCLUSIONS: This cross-sectional summary from a large US integrated health system found that three out of every four patients had overweight or obesity based on BMI. Patients within higher BMI categories had a higher prevalence of comorbidities. Less than half of patients who were identified as having obesity according to BMI received a formal diagnosis via ICD-9 documentation. The disease of obesity is very prevalent yet underdiagnosed in our clinics. The under diagnosing of obesity may serve as an important barrier to treatment initiation.
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spelling pubmed-57020212017-11-27 Prevalence and recognition of obesity and its associated comorbidities: cross-sectional analysis of electronic health record data from a large US integrated health system Pantalone, Kevin M Hobbs, Todd M Chagin, Kevin M Kong, Sheldon X Wells, Brian J Kattan, Michael W Bouchard, Jonathan Sakurada, Brian Milinovich, Alex Weng, Wayne Bauman, Janine Misra-Hebert, Anita D Zimmerman, Robert S Burguera, Bartolome BMJ Open Diabetes and Endocrinology OBJECTIVE: To determine the prevalence of obesity and its related comorbidities among patients being actively managed at a US academic medical centre, and to examine the frequency of a formal diagnosis of obesity, via International Classification of Diseases, Ninth Revision (ICD-9) documentation among patients with body mass index (BMI) ≥30 kg/m(2). DESIGN: The electronic health record system at Cleveland Clinic was used to create a cross-sectional summary of actively managed patients meeting minimum primary care physician visit frequency requirements. Eligible patients were stratified by BMI categories, based on most recent weight and median of all recorded heights obtained on or before the index date of 1July 2015. Relationships between patient characteristics and BMI categories were tested. SETTING: A large US integrated health system. RESULTS: A total of 324 199 active patients with a recorded BMI were identified. There were 121 287 (37.4%) patients found to be overweight (BMI ≥25 and <29.9), 75 199 (23.2%) had BMI 30–34.9, 34 152 (10.5%) had BMI 35–39.9 and 25 137 (7.8%) had BMI ≥40. There was a higher prevalence of type 2 diabetes, pre-diabetes, hypertension and cardiovascular disease (P value<0.0001) within higher BMI compared with lower BMI categories. In patients with a BMI >30 (n=134 488), only 48% (64 056) had documentation of an obesity ICD-9 code. In those patients with a BMI >40, only 75% had an obesity ICD-9 code. CONCLUSIONS: This cross-sectional summary from a large US integrated health system found that three out of every four patients had overweight or obesity based on BMI. Patients within higher BMI categories had a higher prevalence of comorbidities. Less than half of patients who were identified as having obesity according to BMI received a formal diagnosis via ICD-9 documentation. The disease of obesity is very prevalent yet underdiagnosed in our clinics. The under diagnosing of obesity may serve as an important barrier to treatment initiation. BMJ Publishing Group 2017-11-16 /pmc/articles/PMC5702021/ /pubmed/29150468 http://dx.doi.org/10.1136/bmjopen-2017-017583 Text en © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
spellingShingle Diabetes and Endocrinology
Pantalone, Kevin M
Hobbs, Todd M
Chagin, Kevin M
Kong, Sheldon X
Wells, Brian J
Kattan, Michael W
Bouchard, Jonathan
Sakurada, Brian
Milinovich, Alex
Weng, Wayne
Bauman, Janine
Misra-Hebert, Anita D
Zimmerman, Robert S
Burguera, Bartolome
Prevalence and recognition of obesity and its associated comorbidities: cross-sectional analysis of electronic health record data from a large US integrated health system
title Prevalence and recognition of obesity and its associated comorbidities: cross-sectional analysis of electronic health record data from a large US integrated health system
title_full Prevalence and recognition of obesity and its associated comorbidities: cross-sectional analysis of electronic health record data from a large US integrated health system
title_fullStr Prevalence and recognition of obesity and its associated comorbidities: cross-sectional analysis of electronic health record data from a large US integrated health system
title_full_unstemmed Prevalence and recognition of obesity and its associated comorbidities: cross-sectional analysis of electronic health record data from a large US integrated health system
title_short Prevalence and recognition of obesity and its associated comorbidities: cross-sectional analysis of electronic health record data from a large US integrated health system
title_sort prevalence and recognition of obesity and its associated comorbidities: cross-sectional analysis of electronic health record data from a large us integrated health system
topic Diabetes and Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5702021/
https://www.ncbi.nlm.nih.gov/pubmed/29150468
http://dx.doi.org/10.1136/bmjopen-2017-017583
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