Associated factors with mycotoxin exposure in Spanish population.

Human exposure to mycotoxins is a global concern since filamentous fungi can contaminate food and feed from crops to ready-to-eat meals. Human urine biomonitoring is a widely used technique to evaluate mycotoxins exposure, as an alternative to food correlation studies. The aim of this study is to describe human exposure to mycotoxins and to investigate the associated sociodemographic, lifestyle and dietary variables. Participants were 540 women from the Valencia (Spain) cohort of the Spanish Childhood and Environment Project (INMA). A validated multi-mycotoxin method using HPLC-Q-TOF-MS was applied to determine the concentration of ten selected mycotoxins: Enniatin A, Enniatin B, Enniatin A1, Enniatin B1, Beauvericine, Aflatoxin B1, Aflatoxin B2, Aflatoxin G1, Aflatoxin G2 and Ochratoxin A. A simultaneous untargeted screening of mycotoxins and their metabolites has been performed. Mycotoxins associations were assessed by bivariate and multivariate regression models using participants' sociodemographic, lifestyle and dietary data collected through questionnaires. Mycotoxins were detected in 81% of urine samples. The method quantified mycotoxins concentrations in up to 151 samples. Most quantified mycotoxins were: Enniatin B [% of detection (concentration range)] = 26% (1.0-39.7 ng/mg) and Enniatin B1 = 7% (0.5-14.4 ng/mg). Besides the ten-targeted mycotoxins, other mycotoxins and metabolites were studied, and higher incidence was observed for Deepoxy-deoxynivalenol (45%), Ochratoxin B (18%) and Ochratoxin α (17%). Higher mycotoxins concentrations were associated with rural areas as well as with participants belonged to lower social class, beer, light sodas and fruit juice consumers. On the contrary, higher processed meat intake was related to lower mycotoxins' levels. Studies are required to better evaluate the exposure to mycotoxins from food and their environmental relationships.

Clinical characteristics of COVID-19 hospitalized patients associated with mortality: A cohort study in Spain.

Background: The heterogeneity of patients with COVID-19 may explain the wide variation of mortality rate due to the population characteristics, presence of comorbidities and clinical manifestations. Methods: In this study, we analyzed 5342 patients' recordings and selected a cohort of 177 hospitalized patients with a poor prognosis at an early stage. We assessed during 6 months their symptomatology, coexisting health conditions, clinical measures and health assistance related to mortality. Multiple Cox proportional hazards models were built to identify the associated factors with mortality risk. Results: We observed that cough and kidney failure triplicate the mortality risk and both bilirubin levels and oncologic condition are shown as the most associated with the demise, increasing in four and ten times the risk, respectively. Other clinical characteristics such as fever, diabetes mellitus, breathing frequency, neutrophil-lymphocyte ratio, oxygen saturation, and troponin levels, were also related to mortality risk of in-hospital death. Conclusions: The present study shows that some symptomatology, comorbidities and clinical measures could be the target of prevention tools to improve survival rates.

Adherence to dietary treatment and clinical factors associated with anti-transglutaminase antibodies in celiac disease during the follow-up.

INTRODUCTION: In clinical practice, celiac disease (CD) is monitored through anti-transglutaminase (TGA-IgA) antibody levels. The normalization of serum levels in successive periodic measurements indicates good response and adherence to dietary treatment. OBJECTIVES: To evaluate the factors associated with the evolution of TGA-IgA antibodies and their association with dietary non-compliance and diseases related to CD. METHODS: This prospective observational study was carried out in 254 participants, who were recruited from patients from a hospital in southern Spain. Information about sex, age, serological test results, HLA DQ2/DQ8 haplotypes, mucosal atrophy, gastrointestinal and extra-intestinal symptoms, as well as diagnosis of diseases related to CD, was collected. RESULTS: Clinical manifestations, such as diarrhoea, abdominal pain and weight loss, showed differences according to sex and age. Children under 18 years of age presented a degree of total or severe atrophy of the intestinal villi. TGA-IgA antibodies concentrations were directly associated with the number of digestive disorders manifested by the patient and the record of dietary non-compliance and inversely related to the number of extra-digestive disorders. CONCLUSIONS: Adolescents between 12 and 18 years old were the least monitored as well as the group with more extra-intestinal symptoms reported. Therefore, it is necessary to develop strategies in clinical practice aimed at this population group and continuous monitoring should be implemented to improve life quality and reduce complications that may arise in the long term.

Professional counseling in women with serious mental illness: achieving a shift toward a more effective contraceptive method.

OBJECTIVES: Mental disorders in reproductive-aged women have significant implications for the risk of unintended pregnancies. The objective of this study is to assess the professional counseling in clinical practice based on motivational interview in women with serious mental illness (SMI) in order to achieve a change to a more effective contraceptive method. STUDY DESIGN: A prospective observational cohort study (2012-2017) was conducted in a convenience sample of women with severe-moderate psychiatric disorders (n = 91). Information related to psychiatric health, contraceptive use, sexual and reproductive health and socio-demographics was collected. To assess the variation in the contraceptive method, follow-up visits were planned before and after medical counseling. All participants underwent an evidence-based individual motivational interview for contraception counseling. A multivariate logistic model was carried out to identify the factors involved in changing to a more effective contraceptive method. RESULTS: After evidence-based counseling, 51.6% of participants changed their contraceptive method to a more effective one. This change was associated with gender violence (ß coefficient = 1.58, p value = .006). The relation between changing to a more effective contraceptive method and both previous abortions and having children was also positive, although the coefficients did not reach statistical significance. CONCLUSIONS: Evidence-based contraception counseling in clinical practice, based on an adapted protocol to patients with SMI, has shown, in this study, to be adequate to promote the shift to more effective contraceptive methods, avoiding the need of daily compliance in this population. Gender violence has been significantly associated with the shift to very high effectiveness methods as well as previous abortions and having children, not significantly.

Effect of cost on early removal of contraceptive implants: a prospective cohort study.

OBJECTIVES: To evaluate the effect of the cost of subdermal etonogestrel implant (SEI) on the continuation rate one year after insertion, and to assess the reasons given by users to remove the implant before the expiration date. METHODS: Prospective cohort study conducted among 265 women who chose the SEI as a contraceptive method in a sexual and reproductive health center in the eastern region of Spain, between October/2012 and October/2017. The sample was divided into two cohorts depending on the cost of the implant for the user (free-of-charge or requiring partial payment). Kaplan-Meier survival curves were used to compare the cumulative removal rates of free implants with partially paid implants within the first year of insertion. Cox proportional hazards models were used to control for confounders. RESULTS: After adjusting for confounders, no significant associations were found between the cost of the implant and its removal within a year of insertion. No significant associations were found in the reasons given for implant removal and for the duration of implant use. CONCLUSIONS: Cost was not associated with SEI continuation rates within the first year of use. No other significant variables were found to explain implant removal within one year of use.

Geographic conditioning in dietary, social, and health patterns in elderly population with disabilities.

OBJECTIVE: The aim of this study was to identify the most relevant variables defining the dietary, social, and health patterns of elderly populations with disabilities, considering their geographic profile. METHODS: A cross-sectional study was carried out in a sample of 354 disabled, free-living elderly adults from three different geographic profiles (metropolitan, rural, and mixed profile). The dietary data were obtained through a validated food habit questionnaire. The data regarding health status, cohabitation unit, and social benefits were obtained through the public social services. A standardized principal component analysis was used to select the most relevant variables, by considering their contributions to each principal component and their relation with the geographic factor. RESULTS: From 131 variables, we highlighted 27 (57.37% of variability explained). The variables with more contribution are, in order, the calorie intake (especially from lipids), absence of home assistance, and the difference between intake and recommended calories. The procedure was validated by assessing the prediction using a multinomial logistic regression model (88.2% and 66.7% of success rate regarding the metropolitan and rural profiles, respectively). There is a differentiated behavior based on the geographic origin of individuals, specifically regarding caloric intake, number of diseases, and the requirement for home assistance. CONCLUSIONS: Older adults living in a metropolitan are tend to have a greater number of diseases as well as a lower caloric intake. The increased rural caloric consumption comes from lipids. Better health status in rural areas is associated with a lower need for home assistance.

Nutrients associated with diseases related to aging: a new healthy aging diet index for elderly population / Nutrientes asociados a las enfermedades relacionadas con el envejecimiento: un nuevo índice de dieta saludable para personas mayores

Introduction: several indexes are used to measure the quality of nutrition in advanced ages. None of them were designed to evaluate nutrition to avoid disabilities in elderly population. Objectives: to retrieve from literature "nutrients and intakes" showing to be involved in aging, and propose a new index, considering this information, to evaluate the quality of nutrition for preventing diseases related to aging. Methods: a bibliographic review was performed, retrieving information on nutrients associated with aging. All these nutrients were incorporated into a new Healthy Aging Diet Index (HADI). Next, a cross-sectional study was carried out with two convenience samples of elderly, collecting the nutritional and dietary data, calculating different validated indexes and comparing them with HADI to validate the results. Results: forty-eight manuscripts were retrieved for full-text analysis. Associations were found between cardiovascular diseases and macronutrients, dietary fibre, sodium and vitamin D; cancer and fatty acids; diabetes and fatty acids, fibre and simple sugars; osteopenia/osteoporosis and calcium and vitamin D; sarcopenia and proteins, calcium, and vitamin D; and between cognitive impairment and fatty acids and folates. Sample 2, associated with rural areas, obtained lower indexes’ scores. The behavior of HADI is similar to the other indexes (6.24/14 and 6.10/14 in samples 1 and 2, respectively). Conclusions: the presented collection of nutrients adds useful evidence for the design of diets that allow healthy aging. The new index proposed is a tool of specific nutritional measurement in studies aimed to prevent diseases related to aging

Introducción: suelen usarse diversos índices dietéticos para medir la calidad de la nutrición en edades avanzadas. Sin embargo, ninguno de ellos fue diseñado para evaluar la nutrición con el objetivo de evitar discapacidades en la población anciana. Objetivos: identificar en la literatura científica los "nutrientes e ingestas" involucrados en el envejecimiento, así como proponer un nuevo índice, considerando esta información, que sirva para evaluar la calidad de la nutrición con objeto de prevenir enfermedades relacionadas con el envejecimiento. Métodos: se realizó una revisión bibliográfica, obteniendo información sobre los nutrientes asociados con el envejecimiento. Todos estos nutrientes fueron considerados en el diseño de un nuevo Índice de Dieta de Envejecimiento Saludable (HADI). A continuación, se realizó un estudio transversal con dos muestras de ancianos (muestreo de conveniencia), recogiendo datos nutricionales y dietéticos, calculando las puntuaciones de diferentes índices dietéticos validados y comparándolos con HADI para validar los resultados. Resultados: cuarenta y ocho manuscritos fueron finalmente utilizados para el análisis de texto completo. Se encontraron asociaciones entre enfermedades cardiovasculares y macronutrientes, fibra dietética, sodio y vitamina D; cáncer y ácidos grasos; diabetes y ácidos grasos, fibra y azúcares simples; osteopenia/osteoporosis y calcio y vitamina D; sarcopenia y proteínas, calcio y vitamina D; y entre el deterioro cognitivo y los ácidos grasos y los folatos. La muestra 2, asociada a áreas rurales, obtuvo puntuaciones en los índices más bajas. El comportamiento de HADI es similar al de los otros índices (6.24/14 y 6.10/14 en las muestras 1 y 2, respectivamente). Conclusiones: la recopilación de nutrientes presentada agrega evidencia científica útil para el diseño de dietas que faciliten un envejecimiento saludable. El nuevo índice propuesto es una herramienta de medición nutricional específica en estudios dirigidos a prevenir enfermedades relacionadas con el envejecimiento

Strategic procedure in three stages for the selection of variables to obtain balanced results in public health research.

Multidisciplinary research in public health is approached using methods from many scientific disciplines. One of the main characteristics of this type of research is dealing with large data sets. Classic statistical variable selection methods, known as "screen and clean", and used in a single-step, select the variables with greater explanatory weight in the model. These methods, commonly used in public health research, may induce masking and multicollinearity, excluding relevant variables for the experts in each discipline and skewing the result. Some specific techniques are used to solve this problem, such as penalized regressions and Bayesian statistics, they offer more balanced results among subsets of variables, but with less restrictive selection thresholds. Using a combination of classical methods, a three-step procedure is proposed in this manuscript, capturing the relevant variables of each scientific discipline, minimizing the selection of variables in each of them and obtaining a balanced distribution that explains most of the variability. This procedure was applied on a dataset from a public health research. Comparing the results with the single-step methods, the proposed method shows a greater reduction in the number of variables, as well as a balanced distribution among the scientific disciplines associated with the response variable. We propose an innovative procedure for variable selection and apply it to our dataset. Furthermore, we compare the new method with the classic single-step procedures.

Strategic procedure in three stages for the selection of variables to obtain balanced results in public health research / Procedimiento estratégico en tres fases para la selección de variables, con el fin de obtener resultados equilibrados en investigación sobre salud pública / Procedimento estratégico em três estágios de seleção de variáveis para a obtenção de resultados equilibrados na pesquisa em saúde pública

Multidisciplinary research in public health is approached using methods from many scientific disciplines. One of the main characteristics of this type of research is dealing with large data sets. Classic statistical variable selection methods, known as "screen and clean", and used in a single-step, select the variables with greater explanatory weight in the model. These methods, commonly used in public health research, may induce masking and multicollinearity, excluding relevant variables for the experts in each discipline and skewing the result. Some specific techniques are used to solve this problem, such as penalized regressions and Bayesian statistics, they offer more balanced results among subsets of variables, but with less restrictive selection thresholds. Using a combination of classical methods, a three-step procedure is proposed in this manuscript, capturing the relevant variables of each scientific discipline, minimizing the selection of variables in each of them and obtaining a balanced distribution that explains most of the variability. This procedure was applied on a dataset from a public health research. Comparing the results with the single-step methods, the proposed method shows a greater reduction in the number of variables, as well as a balanced distribution among the scientific disciplines associated with the response variable. We propose an innovative procedure for variable selection and apply it to our dataset. Furthermore, we compare the new method with the classic single-step procedures.

La investigación multidisciplinaria en salud pública se enfoca usando métodos de muchas disciplinas científicas. Una de las principales características de este tipo de investigación es lidiar con conjuntos voluminosos de datos. Los métodos clásicos estadísticos de selección de variables, conocidos como "screen and clean", y utilizados en un solo paso, seleccionan las variables con mayor peso explicativo en su modelo. Estos métodos, comúnmente usados en investigación pública en salud, pueden inducir a enmascarar la multicolinealidad, excluyendo variables relevantes para los expertos en cada disciplina y sesgando el resultado. Se usan algunas técnicas específicas para resolver este problema, como las regresiones penalizadas y estadísticas bayesianas, que ofrecen resultados más equilibrados entre subconjuntos de variables, pero con umbrales menos restrictivos de selección. Usando la combinación de métodos clásicos, se propone en este trabajo un tercer paso en el procedimiento, recogiendo variables relevantes de cada disciplina científica, minimizando la selección de variables en cada una de ellas y obteniendo una distribución equilibrada que explica la mayor parte de la variabilidad. Este procedimiento fue aplicado en un conjunto de datos de una investigación en salud pública. Comparando los resultados con los métodos de un solo paso, el método propuesto expone una gran reducción en el número de variables, así como la distribución equilibrada entre las disciplinas científicas asociadas con la variable de respuesta. Proponemos un procedimiento innovador para la selección de variables y aplicarlo a nuestro conjunto de datos. Asimismo, comparamos el nuevo método con los procedimientos clásicos de un solo paso.

A pesquisa multidisciplinar em saúde pública emprega métodos provenientes de diversas disciplinas científicas. Uma das principais características desse tipo de pesquisa é o fato de lidar com conjuntos de dados grandes. Os métodos clássicos de seleção de variáveis estatísticas, conhecidos como "screen and clean" (filtrar e limpar), e aplicados a partir de um passo único, selecionam as variáveis com o maior peso explanatório no modelo. Esses métodos, amplamente disseminados na pesquisa em saúde pública, podem induzir ao mascaramento e à multi-colinearidade, excluindo variáveis que seriam relevantes para os especialistas em cada disciplina e enviesando os resultados. Algumas técnicas específicas usadas para resolver esse problema, como regressões penalizadas e estatísticas Bayesianas, oferecem resultados mais equilibrados entre subconjuntos de variáveis, porém com limiares de seleção menos restritivos. O artigo propõe um procedimento com três passos, usando uma combinação de métodos clássicos, captando as variáveis relevantes de cada disciplina científica, minimizando a seleção de variáveis em cada disciplina e obtendo uma distribuição equilibrada que explica a maior parte da variabilidade. O procedimento foi aplicado a um conjunto de dados de uma pesquisa em saúde pública. Ao comparar os resultados com os métodos que utilizam um único passo, o método proposto demonstra maior redução no número de variáveis, assim como, uma distribuição equilibrada entre as disciplinas científicas relacionadas à variável dependente. Propomos um procedimento inovador para a seleção de variáveis, que aplicamos depois ao nosso conjunto de dados. Além disso, comparamos o método novo com os procedimentos clássicos de apenas um estágio.