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

Biro, Suzanne; Barber, Dave; Williamson, Tyler; Morkem, Rachael; Khan, Shahriar; Janssen, Ian

doi:10.9778/cmajo.20150108

Cited by 23 publications

(21 citation statements)

References 26 publications

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“…Some authors believe that the best place for measuring height and weight is in primary care settings during wellchild visits, ensuring the appropriate equipment is used, continuous training of the staff, and the use of measurement protocols [33]. Furthermore, these settings minimize unintended negative consequences associated to monitoring growth in other environments (e.g., schools), such as stigmatization.…”

Section: Discussionmentioning

confidence: 99%

Inverse-probability weighting and multiple imputation for evaluating selection bias in the estimation of childhood obesity prevalence using data from electronic health records

Sayón-Orea

Moreno‐Iribas

Delfrade

et al. 2020

BMC Med Inform Decis Mak

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Background and objectives: Height and weight data from electronic health records are increasingly being used to estimate the prevalence of childhood obesity. Here, we aim to assess the selection bias due to missing weight and height data from electronic health records in children older than five. Methods: Cohort study of 10,811 children born in Navarra (Spain) between 2002 and 2003, who were still living in this region by December 2016. We examined the differences between measured and non-measured children older than 5 years considering weight-associated variables (sex, rural or urban residence, family income and weight status at 2-5 yrs). These variables were used to calculate stabilized weights for inverse-probability weighting and to conduct multiple imputation for the missing data. We calculated complete data prevalence and adjusted prevalence considering the missing data using inverse-probability weighting and multiple imputation for ages 6 to 14 and group ages 6 to 9 and 10 to 14. Results: For 6-9 years, complete data, inverse-probability weighting and multiple imputation obesity age-adjusted prevalence were 13.

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Section: Discussionmentioning

confidence: 99%

Inverse-probability weighting and multiple imputation for evaluating selection bias in the estimation of childhood obesity prevalence using data from electronic health records

Sayón-Orea

Moreno‐Iribas

Delfrade

et al. 2020

BMC Med Inform Decis Mak

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“…The rate of childhood obesity appears to have stabilized in recent years, yet approximately one in six children (or 17%) ages 2–19 in the United States still have obesity in 2013–2014 (Ogden et al., ). Obesity during childhood often persists into adulthood (Biro et al., ). In addition, obesity is associated with numerous diseases such as asthma, cardiovascular metabolic diseases, musculoskeletal diseases, sleep apnea, dental caries, as well as psychological comorbidities, such as attention deficit hyperactive disorders, and internalized and externalized disorders (Pulgaron, ).…”

Section: Introductionmentioning

confidence: 99%

Factors associated with Ohio nurse practitioners’ childhood obesity preventive practice patterns

Tanda

Beverly

Hughes

2017

Journal of the American Association of Nurse Practitioners

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Background and purpose More nurse practitioners (NPs) infiltrate into primary care settings, and play a pivotal role in prevention of childhood obesity. However, minimal research has been published about their practice patterns. The current study uses a sample of Ohio NPs and describes NPs’ childhood obesity prevention practice patterns, knowledge of the guidelines, their personal physical activity in relation to childhood obesity prevention practices, and identification of perceived barriers for practice. Method Mail surveys were sent to a random selection of Ohio NPs. Quantitative and qualitative analyses methods were used to analyze the association between NPs’ personal physical activity and their practice patterns and barriers to preventive practices related to childhood obesity. Conclusions Ohio NPs who reported engaging in higher physical activity levels were more likely to report providing frequent assessment and counseling of healthy diet and physical activity. Frequency of weight tracking and referral to specialists were less frequent regardless of physical activity engagement by the NPs. Parental resistance was the most significant barrier to lifestyle counseling. Implications for practice Findings from the current study may offer new insight into innovative approaches for treatment and prevention of childhood obesity. These may include simple quality improvement projects to advocacy for community and school programs.

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“…Given the paucity of Canadian data in this age group, we were unable to directly compare our estimates to national surveys (Roberts et al, 2012;Rodd & Sharma, 2016;Shields & Tremblay, 2010). However, our prevalence estimates were similar to those observed in a study of children 0-2 years of age using primary care electronic medical records in Ontario (Biro et al, 2016). The prevalence of severe obesity observed in our study was substantially lower than US estimates of 2.07% for children aged 2-4 years (Pan et al, 2012) and 4.6-6.4% for youth aged 2 to 19 years old (using the CDC > 120% of the 95th percentile) (Skinner et al, 2016;Koebnick et al, 2010).…”

Section: Discussionmentioning

confidence: 51%

Severe obesity in children 17 to 24 months of age: a cross-sectional study of TARGet Kids! and Better Outcomes Registry & Network (BORN) Ontario

Satkunam

Anderson²,

Carsley

et al. 2018

Can J Public Health

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

Cited by 23 publications

References 26 publications

Inverse-probability weighting and multiple imputation for evaluating selection bias in the estimation of childhood obesity prevalence using data from electronic health records

Inverse-probability weighting and multiple imputation for evaluating selection bias in the estimation of childhood obesity prevalence using data from electronic health records

Factors associated with Ohio nurse practitioners’ childhood obesity preventive practice patterns

Severe obesity in children 17 to 24 months of age: a cross-sectional study of TARGet Kids! and Better Outcomes Registry & Network (BORN) Ontario

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