The Mediterranean diet (MD), despite its multiple benefits, presents low levels of adherence among children. Moreover, childhood is a key stage in the acquisition of healthy habits. The aim of this study was to describe adherence to MD in school-age children from Asturias, Spain, and to evaluate the association with weight status and several lifestyle behaviors. A cross-sectional study was conducted on 309 children aged between 8 and 13 years old. The level of adherence to MD was evaluated through the KIDMED questionnaire. Descriptive analysis and logistic regression models were used to analyze the association between adherence to MD and weight status, frequency of out-of-school exercise, frequency of school canteen attendance, and sleep habits. We found that 54.4% of children had optimal adherence to MD and 29.9% of the sample was overweight or obese. Frequency of exercise practice was positively associated with optimal adherence to MD (95% CI: 1.02, 1.33). A positive association was found between some KIDMED items and frequency of out-of-school exercise practice and attendance at the school canteen. This study shows the need for an improvement in the adherence to MD in youth considering the concomitant occurrence of other related healthy behaviors.

Smoking by women is associated with adverse pregnancy outcomes such as spontaneous abortion, preterm delivery, low birth weight, infertility, and prolonged time to pregnancy. Anogenital distance (AGD) is a sensitive biomarker of prenatal androgen and antiandrogen exposure. We investigated the effect of smoking and passive smoke exposure during pregnancy on anogenital distance in offspring at 4 years in the INMA-Asturias cohort (Spain). Women were interviewed during pregnancy to collect information on tobacco consumption, and anogenital distance was measured in 381 children: Anoscrotal distance in boys and anofourchetal distance in girls. We also measured maternal urinary cotinine levels at 32 weeks of pregnancy. We constructed linear regression models to analyze the association between prenatal smoke exposure and anogenital distance and adjusted the models by relevant covariates. Reported prenatal smoke exposure was associated with statistically significant increased anogenital index (AGI), both at week 12 of pregnancy (β = 0.31, 95% confidence interval: 0.00, 0.63) and at week 32 of pregnancy (β = 0.31, 95% confidence interval: 0.00, 0.63) in male children, suggesting altered androgenic signaling.

Modern science is frequently based on the exploitation of large volumes of information storage in datasets and involving complex computational architectures. The statistical analyses of these datasets have to cope with specific challenges and frequently involve making informed but arbitrary decisions. Epidemiological papers have to be concise and focused on the underlying clinical or epidemiological results, not reporting the details behind relevant methodological decisions. In this work, we used an analysis of the cardiovascular-related measures tracked in 4–8-year-old children, using data from the INMA-Asturias cohort for illustrating how the decision-making process was performed and its potential impact on the obtained results. We focused on two particular aspects of the problem: how to deal with missing data and which regression model to use to evaluate tracking when there are no defined thresholds to categorize variables into risk groups. As a spoiler, we analyzed the impact on our results of using multiple imputation and the advantage of using quantile regression models in this context.

Quantile regression (QR) provides a flexible framework for characterizing conditional distributions and can offer useful insights for quantile estimation. In this paper, we provide intuition for the interpretation of QR by highlighting the approach that it is a reparameterization of more popular conditional cumulative distribution function regression. From this approach, we naturally explore the use of the proportional hazards Cox regression model in the QR context. Therefore, we propose a framework based on the Cox regression model for estimating conditional quantiles, obtained through a reparameterization of the conditional cumulative distribution function under a proportional hazards structure. We establish the large-sample properties of the resulting estimator and investigate its behavior in finite samples through a Monte Carlo simulation study. An empirical application illustrates its practical implementation in a real-world context. Furthermore, we introduce the average quantile effect, a summary measure for quantifying the effects of covariates across conditional quantiles. The proposed estimator behaves adequately under the required assumptions, although inferences on the associated parameters strongly depend on these premises.

Cerebrovascular diseases are among the leading causes of mortality and disability worldwide. This study aimed to identify geographic patterns and sex differences in unplanned hospital admissions for cerebrovascular diseases, and to assess their association with ambient air pollution within small areas of Asturias, a northern region of Spain that exhibits elevated rates of mortality and morbidity from these pathologies. The study included 20,305 unplanned hospital admissions recorded between 2016 and 2023. Standardized admission ratios, smoothed relative risks, and posterior risk probabilities were estimated using reparameterized Besag–York–Mollié models at the census tract level. Global and Local Moran’s I statistics were computed to evaluate the spatial correlation of admissions. Subsequently, estimates of PM₁₀ and PM_2.5 concentrations were incorporated into the models, adjusting for average net income per person, to assess their association with admission risk and their contribution to the geographical patterns observed. Spatial analysis revealed marked inequalities in cerebrovascular admissions both between sexes and across small areas. High-risk clusters were consistently identified in the north-central coastal area for both sexes. For men, higher PM₁₀ and PM_2.5 levels were statistically significantly associated with hospitalizations (PM₁₀: RR = 1.08, 95% CI: 1.02–1.14; PM_2.5: RR = 1.22, 95% CI: 1.07–1.40, per 5 µg/m³ increase) and explained part of the spatial variability observed. For women, neither pollutant showed statistically significant associations nor appeared to be associated with the geographic patterns. Integrating both geographic and sex-related factors is essential for identifying high-risk areas and potential risk factors in the burden of cerebrovascular disease. In men, exposure to PM₁₀ and PM_2.5 was associated with cerebrovascular hospitalizations and explained part of the spatial variability observed, while no such association was found in women.