Table of Contents
ISRN Obesity
Volume 2014, Article ID 517694, 7 pages
http://dx.doi.org/10.1155/2014/517694
Research Article

A Single Institution’s Overweight Pediatric Population and Their Associated Comorbid Conditions

1Department of Pediatric Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
2Division of Endocrinology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
3Pediatric Surgical Associates, Children's Hospitals and Clinics of Minnesota, 2530 Chicago Avenue, South, Suite 550, Minneapolis, MN 55404, USA

Received 3 December 2013; Accepted 30 December 2013; Published 13 February 2014

Academic Editors: E. Rodríguez and C. Schmidt

Copyright © 2014 Sigrid Bairdain et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background. Obesity studies are often performed on population data. We sought to examine the incidence of obesity and its associated comorbidities in a single freestanding children’s hospital. Methods. We performed a retrospective analysis of all visits to Boston Children’s Hospital from 2000 to 2012. This was conducted to determine the incidence of obesity, morbid obesity, and associated comorbidities. Each comorbidity was modeled independently. Incidence rate ratios were calculated, as well as odds ratios. Results. A retrospective review of 3,185,658 person-years in nonobese, 26,404 person-years in obese, and 25,819 person-years in the morbidly obese was conducted. Annual rates of all major comorbidities were increased in all patients, as well as in our obese and morbidly obese counterparts. Incidence rate ratios (IRR) and odds ratios (OR) were also significantly increased across all conditions for both our obese and morbidly obese patients. Conclusions. These data illustrate the substantial increases in obesity and associated comorbid conditions. Study limitations include (1) single institution data, (2) retrospective design, and (3) administrative undercoding. Future treatment options need to address these threats to longevity and quality of life.