The pharmacist and health promoter team management of uncontrolled diabetes among Latinos appears to be a feasible approach to improving medication management.

Adults and older adults from the Maule Region showed severe dental damage from caries. Although rurality and use of services do not seem to affect caries experience, they are associated with differences in fillings and missing teeth.

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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.

Automatic geocoding methods have become popular in recent years, facilitating the study of the association between health outcomes and the place of living. However, rather few studies have evaluated geocoding quality, with most of them being performed in the US and Europe. This article aims to compare the quality of three automatic online geocoding tools against a reference method. A subsample of 300 handwritten addresses from hospital records was geocoded using Bing, Google Earth, and Google Maps. Match rates were higher (> 80%) for Google Maps and Google Earth compared with Bing. However, the accuracy of the addresses was better for Bing with a larger proportion (> 70%) of addresses with positional errors below 20m. Generally, performance did not vary for each method for different socioeconomic status. Overall, the methods showed an acceptable, but heterogeneous performance, which may be a warning against the use of automatic methods without assessing quality in other municipalities, particularly in Chile and Latin America.

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