BODYFAT: a new calculator to determine the risk of being overweight validated in Spanish children between 11 and 17 years of age.

The assessment of body fat of children in primary care requires consideration of the dynamic changes in height, weight, lean mass, and fat mass during childhood growth. To achieve this, we aim to develop a predictive equation based on anthropometric values, with optimal diagnostic utility. This is a cross-sectional observational study, involving schoolgoers aged 11-17 years in the Vigo metropolitan area. Out of 10,747 individuals, 577 were randomly recruited. VARIABLES: age, sex, ethnicity/country of origin, weight, height, 8 skinfolds, 3 diameters, 7 perimeters, and 85% percentile of body fat mass as the gold standard. Generalized additive regression was selected by cross-validation and compared using receiver operating characteristic curves (ROC curves). Sensitivity, specificity, positive and negative predictive values, true positive and true negative values, false positive and false negative values, accuracy, and positive and negative likelihood ratios were calculated. Two models were identified. The optimal model includes sex, weight, height, leg perimeter, and arm perimeter, with sensitivity of 0.93 (0.83-1.00), specificity of 0.91 (0.83-0.96), accuracy of 0.91 (0.84-0.96), and area under the curve (AUC) of 0.957 (0.928-0.986). The second model includes sex, age, and body mass index, with sensitivity of 0.93 (0.81-1.00), specificity of 0.90 (0.80-0.97), accuracy of 0.90 (0.82-0.96), and an AUC of 0.944 (0.903-0.984). CONCLUSION: Two predictive models, with the 85th percentile of fat mass as the gold standard, built with basic anthropometric measures, show very high diagnostic utility parameters. Their calculation is facilitated by a complementary online calculator. WHAT IS KNOWN: • In routine clinical practice, mainly in primary care, BMI is used to determine overweight and obesity. This index has its weaknesses in the assessment of children. WHAT IS NEW: • We provide a calculator whose validated algorithm, through the determination of fat mass by impedanciometry, makes it possible to determine the risk of overweight and obesity in the community setting, through anthropometric measurements, providing a new practical, accessible and reliable model that improves the classification of overweight and obesity in children with respect to that obtained by determining BMI.

Effectiveness and safety of tetanus vaccine administration by intramuscular vs. subcutaneous route in anticoagulated patients: Randomized clinical trial in primary care.

Design: Prospective, double-blind clinical trial comparing tetanus-diphtheria vaccine administration routes, intramuscular (IM) vs. subcutaneous (SC) injection, in patients with oral anticoagulants. ISRCTN69942081. Study population: Patients treated with oral anticoagulants, 15 health centers, Vigo (Spain). Sample size, 117 in each group. Outcome variables: Safety analysis: systemic reactions and, at the vaccine administration site, erythematic, swelling, hematoma, granuloma, pain.Effectiveness analysis: differences in tetanus toxoid antibody titers.Independent variables: route, sex, age, baseline serology, number of doses administered. Analysis: Following the CONSORT guidelines, we performed an intention-to-treat analysis. We conducted a descriptive study of the variables included in both groups (117 in each group) and a bivariate analysis. Fewer than 5% of missing values. Imputation in baseline and final serology with the median was performed. Lost values were assumed to be values missing at random. We conducted a descriptive study of the variables and compared routes. For safety, multivariate logistic regression was applied, with each safety criterion as outcome and the independent variables. Odds ratios (ORs) were calculated. For effectiveness, a generalized additive mixed model, with the difference between final and initial antibody titers as outcome. Due to the bimodal distribution of the outcome, the normal mixture fitting with gamlssMX was used. All statistical analyses were performed with the gamlss.mx and texreg packages of the R free software environment. Results: A previously published protocol was used across the 6-year study period. The breakdown by sex and route showed: 102 women and 132 men; and 117 IM and 117 SC, with one dose administered in over 80% of participants. There were no differences between groups in any independent variable. The second and third doses administered were not analyzed, due to the low number of cases. In terms of safety, there were no severe general reactions. Locally, significant adjusted differences were observed: in pain, by sex (male, OR: 0.39) and route (SC, OR: 0.55); in erythema, by sex (male, OR: 0.34) and route (SC, OR: 5.21); and in swelling, by sex (male, OR: 0.37) and route (SC, OR: 2.75). In terms of effectiveness, the model selected was the one adjusted for baseline serology.

Analysis of the impact of social determinants and primary care morbidity on population health outcomes by combining big data: A research protocol.

Background: In recent years, different tools have been developed to facilitate analysis of social determinants of health (SDH) and apply this to health policy. The possibility of generating predictive models of health outcomes which combine a wide range of socioeconomic indicators with health problems is an approach that is receiving increasing attention. Our objectives are twofold: (1) to predict population health outcomes measured as hospital morbidity, taking primary care (PC) morbidity adjusted for SDH as predictors; and (2) to analyze the geographic variability of the impact of SDH-adjusted PC morbidity on hospital morbidity, by combining data sourced from electronic health records and selected operations of the National Statistics Institute (Instituto Nacional de Estadística/INE). Methods: The following will be conducted: a qualitative study to select socio-health indicators using RAND methodology in accordance with SDH frameworks, based on indicators published by the INE in selected operations; and a quantitative study combining two large databases drawn from different Spain's Autonomous Regions (ARs) to enable hospital morbidity to be ascertained, i.e., PC electronic health records and the minimum basic data set (MBDS) for hospital discharges. These will be linked to socioeconomic indicators, previously selected by geographic unit. The outcome variable will be hospital morbidity, and the independent variables will be age, sex, PC morbidity, geographic unit, and socioeconomic indicators. Analysis: To achieve the first objective, predictive models will be used, with a test-and-training technique, fitting multiple logistic regression models. In the analysis of geographic variability, penalized mixed models will be used, with geographic units considered as random effects and independent predictors as fixed effects. Discussion: This study seeks to show the relationship between SDH and population health, and the geographic differences determined by such determinants. The main limitations are posed by the collection of data for healthcare as opposed to research purposes, and the time lag between collection and publication of data, sampling errors and missing data in registries and surveys. The main strength lies in the project's multidisciplinary nature (family medicine, pediatrics, public health, nursing, psychology, engineering, geography).