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

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

Residential proximity to green spaces and breast cancer risk: The multicase-control study in Spain (MCC-Spain)

Background: Breast cancer is the main cause of cancer mortality among women. Green spaces have been recently associated with reduced cancer mortality among women. Mechanisms explaining the beneficial effect of green spaces include increased levels of physical activity and reduced exposure to air pollution, which have been both associated with cancer development. Objectives: To investigate the associations between presence of urban green areas, presence of agricultural areas and surrounding greenness and risk of breast cancer, and to assess whether these associations are mediated by physical activity and/or air pollution levels. Methods: We geocoded the current residence of 1129 breast cancer cases and 1619 controls recruited between 2008 and 2013 in ten provinces of Spain, as part of the MCC-Spain study. We assigned different indicators of exposure to green spaces in a buffer of 300 m, and in nested buffers of 100 m and 500 m around the residence: presence of urban green areas according to Urban Atlas, presence of agricultural areas according to CORINE Land Cover 2006, and surrounding greenness according to the average of the Normalized Difference Vegetation Index. We used logistic mixed-effects regression models with a random effect for hospital adjusting for potential confounders. We explored the effect of several potential effect modifiers. We assessed mediation effect by physical activity and levels of air pollution. Results: Presence of urban green areas was associated with reduced risk of breast cancer after adjusting for age, socio-economic status at individual and at area level, education, and number of children [OR (95%CI) = 0.65 (0.49–0.86)]. There was evidence of a linear trend between distance to urban green areas and risk of breast cancer. On the contrary, presence of agricultural areas and surrounding greenness were associated with increased risk of breast cancer [adjusted OR (95%CI) = 1.33 (1.07–1.65) and adjusted OR (95%CI) = 1.27 (0.92–1.77), respectively]. None of the associations observed were mediated by levels of physical activity or levels or air pollution. Conclusions: The association between green spaces and risk of breast cancer is dependent on land-use. The confirmation of these results in other settings and the study of potential mechanisms for the associations observed are needed to advance the understanding on the potential effects of green spaces on health