Qualitative studies are needed to better understand the attitude of health professionals when prescribing PPIs.

A relationship between vitamin D deficiency (VDD) and gestational diabetes mellitus (GDM) has been described. Considering that GDM prevalence depends on body mass index (BMI), our main objective was to determine if VDD is associated with GDM, independent of BMI. A cross-sectional study with 886 pregnant women was conducted in Elda (Spain) from September 2019 to June 2020. To assess the association, Poisson regression models with robust variance were used to estimate the prevalence ratio (PR). The observed GDM prevalence was 10.5%, while the VDD prevalence was 55.5%. In the crude model, both VDD and obesity were associated with GDM, but in the adjusted model, only VDD was statistically significant (PR = 1.635, p = 0.038). A secondary event analysis did not detect differences in VDD, but BMI yielded a higher frequency of births by cesarean section and newborns with a >90 percentile weight in the obesity group. In conclusion, VDD is associated with GDM, independent of BMI. Future longitudinal studies could provide information on causality.

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BackgroundOther studies have assessed nonadherence to proton pump inhibitors (PPIs), but none has developed a screening test for its detection.ObjectivesTo construct and internally validate a predictive model for nonadherence to PPIs.MethodsThis prospective observational study with a one-month follow-up was carried out in 2013 in Spain, and included 302 patients with a prescription for PPIs. The primary variable was nonadherence to PPIs (pill count). Secondary variables were gender, age, antidepressants, type of PPI, non-guideline-recommended prescription (NGRP) of PPIs, and total number of drugs. With the secondary variables, a binary logistic regression model to predict nonadherence was constructed and adapted to a points system. The ROC curve, with its area (AUC), was calculated and the optimal cut-off point was established. The points system was internally validated through 1,000 bootstrap samples and implemented in a mobile application (Android).ResultsThe points system had three prognostic variables: total number of drugs, NGRP of PPIs, and antidepressants. The AUC was 0.87 (95% CI [0.83–0.91], p < 0.001). The test yielded a sensitivity of 0.80 (95% CI [0.70–0.87]) and a specificity of 0.82 (95% CI [0.76–0.87]). The three parameters were very similar in the bootstrap validation.ConclusionsA points system to predict nonadherence to PPIs has been constructed, internally validated and implemented in a mobile application. Provided similar results are obtained in external validation studies, we will have a screening tool to detect nonadherence to PPIs.