No abstract

Missing data from air quality datasets is a common problem, but it is much more severe in small cities or localities. This poses a great challenge for environmental epidemiology as high exposures to pollutants worldwide occur in these settings and gaps in datasets hinder health studies that could later inform local and international policies. Here, we propose the use of imputation methods as a tool to reconstruct air quality datasets and applied this approach to an air quality dataset in Temuco, a mid-size city in Chile as a case-study. We attempted to reconstruct the database comparing five approaches: mean imputation, conditional mean imputation, K-Nearest Neighbor imputation, multiple imputation and Bayesian Principal Component Analysis imputation. As a base for the imputation methods, linear regression models were fitted for PM 2.5 against other air quality and meteorological variables. Methods were challenged against validation sets where data was removed artificially. Imputation methods were able to reconstruct the dataset with good performance in terms of completeness, errors, and bias, even when challenged against the validations sets. The performance improved when including covariates from a second monitoring station in Temuco. K-Nearest Neighbor imputation showed slightly better performance than multiple imputation for error (25% vs. 27%) and bias (2.1% vs. 3.9%), but presented lower completeness (70% vs. 100%). In summary, our results show that the imputation methods can be to a certain extent successful in reconstructing air quality dataset in a real-life situation.

Objective. To estimate the point prevalence and likely ranges of pregnancy-induced hypertension, pre-eclampsia, gestational diabetes, low birth weight and preterm delivery in Latin America and the Caribbean, and evaluate the heterogeneity of the estimates. Methods. We conducted a systematic review and meta-analysis of observational studies reporting the prevalence of maternal and perinatal adverse outcomes in populations in Latin American and the Caribbean published between 2000 and 2019 in English, Spanish, or Portuguese. We searched PubMed, Embase, and LILACS. We estimated the point prevalence and evaluated overall heterogeneity and, in sub-group analyses, heterogeneity by study design and level of bias. Results. Of 1087 records retrieved, 50 articles were included in the review: two on hypertensive disorders of pregnancy, 14 on pre-eclampsia, six on gestational diabetes, nine on low birth weight and 19 on preterm birth. No meta-analysis for hypertensive disorders of pregnancy could be done because of the small number of studies. Point prevalence estimates and 95% confidence intervals (CIs) for pre-eclampsia, gestational diabetes, low birth weight, and preterm birth were: 6.6% (95% CI: 4.9%, 8.6%), 8.5% (95% CI: 3.9%, 14.7%), 8.5% (95% CI: 7.2%, 9.8%), and 10.0% (95% CI: 8.0%, 12.0%), respectively. We observed substantial heterogeneity overall and by study design. No major differences in estimates were observed by level of bias. Conclusions. The results of this study provide updated estimates of some of the most prevalent adverse pregnancy and perinatal outcomes in Latin America and the Caribbean. They highlight that important heterogeneity exists in prevalence estimates, which may reflect the diversity of populations in the region.

No abstract

No abstract