9 research outputs found
Inverse-probability weighting and multiple imputation for evaluating selection bias in the estimation of childhood obesity prevalence using data from electronic health records
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 13.18% (95% CI: 12.54–13.85), 13.22% (95% CI: 12.57–13.89) and 13.02% (95% CI: 12.38–13.66) and
for 10–14 years 8.61% (95% CI: 8.06–9.18), 8.62% (95% CI: 8.06–9.20) and 8.24% (95% CI: 7.70–8.78), respectively.
Conclusions: Ages at which well-child visits are scheduled and for the 6 to 9 and 10 to 14 age groups, weight
status estimations are similar using complete data, multiple imputation and inverse-probability weighting. Readily
available electronic health record data may be a tool to monitor the weight status in children
Validity of type 2 diabetes diagnosis in a population-based electronic health record database
Validity of type 2 diabetes diagnosis in a population-based electronic health record database
Abstract Background The increasing burden of type 2 diabetes mellitus makes the continuous surveillance of its prevalence and incidence advisable. Electronic health records (EHRs) have great potential for research and surveillance purposes; however the quality of their data must first be evaluated for fitness for use. The aim of this study was to assess the validity of type 2 diabetes diagnosis in a primary care EHR database covering more than half a million inhabitants, 97% of the population in Navarra, Spain. Methods In the Navarra EPIC-InterAct study, the validity of the T90 code from the International Classification of Primary Care, Second Edition was studied in a primary care EHR database to identify incident cases of type 2 diabetes, using a multi-source approach as the gold standard. The sensitivity, specificity, positive predictive value, negative predictive value and the kappa index were calculated. Additionally, type 2 diabetes prevalence from the EHR database was compared with estimations from a health survey. Results The sensitivity, specificity, positive predictive value and negative predictive value of incident type 2 diabetes recorded in the EHRs were 98.2, 99.3, 92.2 and 99.8%, respectively, and the kappa index was 0.946. Overall prevalence of type 2 diabetes diagnosed in the EHRs among adults (35–84 years of age) was 7.2% (95% confidence interval [CI] 7.2–7.3) in men and 5.9% (95% CI 5.8–5.9) in women, which was similar to the prevalence estimated from the health survey: 8.5% (95% CI 7.1–9.8) and 5.5% (95% CI 4.4–6.6) in men and women, respectively. Conclusions The high sensitivity and specificity of type 2 diabetes diagnosis found in the primary care EHRs make this database a good source for population-based surveillance of incident and prevalent type 2 diabetes, as well as for monitoring quality of care and health outcomes in diabetic patients
Inverse-probability weighting and multiple imputation for evaluating selection bias in the estimation of childhood obesity prevalence using data from electronic health records
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 13.18% (95% CI: 12.54–13.85), 13.22% (95% CI: 12.57–13.89) and 13.02% (95% CI: 12.38–13.66) and
for 10–14 years 8.61% (95% CI: 8.06–9.18), 8.62% (95% CI: 8.06–9.20) and 8.24% (95% CI: 7.70–8.78), respectively.
Conclusions: Ages at which well-child visits are scheduled and for the 6 to 9 and 10 to 14 age groups, weight
status estimations are similar using complete data, multiple imputation and inverse-probability weighting. Readily
available electronic health record data may be a tool to monitor the weight status in children