Dataset Information

Prevalence of toddler, child and adolescent overweight and obesity derived from primary care electronic medical records: an observational study.

ABSTRACT: Population monitoring and surveillance of objectively measured child weight data in Canada is limited to national surveys with poor regional applicability, and no healthy weight data are available for children less than 2 years of age. We aimed to determine the prevalence of childhood overweight and obesity using objective measures derived from primary care electronic medical records.Observational data included all height and weight records for children less than 20 years of age, between 2004 and 2013, from 3 Ontario primary care research networks. We calculated body mass index (BMI)-for-age and weight-for-length using the World Health Organization Growth Standards and Reference to assign growth status indicator categories by age group. Descriptive data and prevalence estimates were generated for 2013. We also compared weight-for-length for children less than 2 years of age with a corresponding billing code for known well-child visits.Our study included 8261 children with a corresponding growth status indicator, a sample close to 4 times larger than the national survey sample. In 2013, 28.4% of children aged 5-19 years, and 6% of children aged 0-5 years, were categorized as overweight or obese. Between 2008 and 2013, the total number of 18-month well baby visit billing codes was 1152; 6.9% of this group were categorized as overweight or obese; 19.2% were categorized as having risk of overweight.Primary care electronic medical records show good potential for ongoing population monitoring of overweight and obesity, particularly for very young children for whom early intervention is likely to show the greatest positive health impact.

SUBMITTER: Biro S

PROVIDER: S-EPMC5047841 | biostudies-other | 2016 Jul-Sep

REPOSITORIES: biostudies-other

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Prevalence of toddler, child and adolescent overweight and obesity derived from primary care electronic medical records: an observational study.

Biro Suzanne S Barber Dave D Williamson Tyler T Morkem Rachael R Khan Shahriar S Janssen Ian I

CMAJ open 20160701 3

<h4>Background</h4>Population monitoring and surveillance of objectively measured child weight data in Canada is limited to national surveys with poor regional applicability, and no healthy weight data are available for children less than 2 years of age. We aimed to determine the prevalence of childhood overweight and obesity using objective measures derived from primary care electronic medical records.<h4>Methods</h4>Observational data included all height and weight records for children less th ...[more]

PMID: 27730118

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Project description:Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real-world evidence for assessing device safety and tracking device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements-one of the most common implantable devices-as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.8-53.9% over rule-based methods, and detected over six times as many complication events compared to using structured data alone. Using these additional events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t?=?5.14; p?<?0.001). Some implant models were associated with higher or lower rates of hip pain mentions. Our methods complement existing surveillance mechanisms by requiring orders of magnitude less hand-labeled training data, offering a scalable solution for national medical device surveillance using electronic health records.

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Dataset Information

Prevalence of toddler, child and adolescent overweight and obesity derived from primary care electronic medical records: an observational study.

Publications

Prevalence of toddler, child and adolescent overweight and obesity derived from primary care electronic medical records: an observational study.

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