The Self-Reported Quality of Sleep and Its Relationship with the Development of Arterial Hypertension: Perspectives from the Tlalpan 2020 Cohort.

Background/Objectives: A well-established association exists between the development of hypertension and sleep quality. The connection between self-reported sleep quality and the onset of hypertension is particularly significant in populations with metabolic deterioration, such as in Mexico. Methods: The Tlalpan 2020 Cohort was analyzed to explore this association. Clinical and anthropometric characteristics, along with the Medical Outcomes Study Sleep Scale (MOS-SS), were compared between participants who developed hypertension and those who did not over a follow-up period of 30.8 months. The potential role of poor sleep quality in the development of hypertension was assessed. Results: Among 1520 participants, 12% developed hypertension. These individuals had higher anthropometric and laboratory values and reported poorer sleep quality. An elevated sleep problems index was associated with a 50% higher relative risk of developing hypertension (OR: 1.5; 95% CI: 1.087 to 2.069). Additionally, self-reported snoring was associated with hypertension onset (36.3 vs. 43.3; p = 0.019). Conclusions: Poor sleep quality and respiratory disturbances during sleep increase the risk of developing hypertension. Furthermore, hypertension was associated with snoring, highlighting the importance of early interventions to improve sleep quality.

Sleep Quality, Nutrient Intake, and Social Development Index Predict Metabolic Syndrome in the Tlalpan 2020 Cohort: A Machine Learning and Synthetic Data Study.

This study investigated the relationship between Metabolic Syndrome (MetS), sleep disorders, the consumption of some nutrients, and social development factors, focusing on gender differences in an unbalanced dataset from a Mexico City cohort. We used data balancing techniques like SMOTE and ADASYN after employing machine learning models like random forest and RPART to predict MetS. Random forest excelled, achieving significant, balanced accuracy, indicating its robustness in predicting MetS and achieving a balanced accuracy of approximately 87%. Key predictors for men included body mass index and family history of gout, while waist circumference and glucose levels were most significant for women. In relation to diet, sleep quality, and social development, metabolic syndrome in men was associated with high lactose and carbohydrate intake, educational lag, living with a partner without marrying, and lack of durable goods, whereas in women, best predictors in these dimensions include protein, fructose, and cholesterol intake, copper metabolites, snoring, sobbing, drowsiness, sanitary adequacy, and anxiety. These findings underscore the need for personalized approaches in managing MetS and point to a promising direction for future research into the interplay between social factors, sleep disorders, and metabolic health, which mainly depend on nutrient consumption by region.

[Social, demographic and morbimortality characteristics of the cases treated for COVID-19 at the Ignacio Chávez National Institute of Cardiology. A descriptive cross-sectional study]. / Características sociales, demográficas y de morbimortalidad de los casos atendidos por COVID-19 en el Instituto Nacional de Cardiología Ignacio Chávez. Un estudio transversal descriptivo.

Introduction: The COVID-19 pandemic brought with it a large number of adverse consequences for public health with serious socioeconomic repercussions. In this study we characterize the social, demographic, morbidity and mortality conditions of individuals treated for COVID-19 in one of the SARS-CoV-2 reference hospitals in Mexico City. Method: A descriptive cross-sectional study was carried out in 259 patients discharged from the Instituto Nacional de Cardiología Ignacio Chávez, between April 11, 2020 and March 14, 2021. A multivariate logistic regression model was used to identify the association between sociodemographic and clinical variables. An optimization was performed using maximum likelihood calculations to choose the best model compatible with the data. The maximum likelihood model was evaluated using ROC curves, goodnessof-fit estimators, and multicollinearity analysis. Statistically significant patterns of comorbidities were inferred by evaluating a hypergeometric test over the frequencies of co-occurrence of pairs of conditions. A network analysis was implemented to determine connectivity patterns based on degree centrality, between comorbidities and outcome variables. Results: The main social disadvantages of the studied population are related to the lack of social security (96.5%) and the lag in housing conditions (81%). Variables associated with the probability of survival were being younger (p < 0.0001), having more durable material goods (p = 0.0034) and avoiding: pneumonia (p = 0.0072), septic shock (p < 0.0001) and acute respiratory failure (p < 0.0001); (AUROC: 91.5%). The comorbidity network for survival cases has a high degree of connectivity between conditions such as cardiac arrhythmias and essential arterial hypertension (Degree Centrality = 90 and 78, respectively). Conclusions: Given that among the factors associated with survival to COVID-19 there are clinical, sociodemographic and social determinants of health variables, in addition to age; It is imperative to consider the various factors that may affect or modify the health status of a population, especially when addressing emerging epidemic phenomena such as the current COVID-19 pandemic.

Introducción: La pandemia de enfermedad por coronavirus 2019 (COVID-19) trajo aparejadas una gran cantidad de consecuencias adversas para la salud pública con serias repercusiones socioeconómicas. En este estudio caracterizamos las condiciones sociales, demográficas y de morbimortalidad de los casos atendidos por COVID-19 en uno de los hospitales de referencia de coronavirus 2 del síndrome respiratorio agudo grave (SARS-CoV-2) en la Ciudad de México. Método: Se llevó a cabo un estudio transversal descriptivo en 259 pacientes egresados del Instituto Nacional de Cardiología Ignacio Chávez, entre el 11 de abril de 2020 y el 14 de marzo de 2021. Se utilizó un modelo de regresión logística multivariante para identificar la asociación entre variables sociodemográficas y clínicas. Se realizó una optimización mediante cálculos de máxima verosimilitud para elegir el mejor modelo compatible con los datos. El modelo de máxima verosimilitud fue evaluado mediante curvas ROC, estimadores de bondad de ajuste y análisis de multicolinealidad. Se infirieron patrones de comorbilidades estadísticamente significativos mediante la evaluación de una prueba hipergeométrica en las frecuencias de coocurrencia de pares de condiciones. Se implementó un análisis de redes para determinar los patrones de conectividad basado en la centralidad de grado, entre algunas comorbilidades y las variables de desenlace. Resultados: Las principales desventajas sociales de la población estudiada se relacionan con la falta de seguridad social (96.5%) y el rezago en las condiciones de vivienda (81%). Las variables asociadas a la probabilidad de sobrevivir fueron tener una menor edad (p < 0.0001), contar con más bienes materiales durables (p = 0.0034) y evitar: la neumonía (p = 0.0072), el choque séptico (p < 0.0001) y la insuficiencia respiratoria aguda (p < 0.0001); (AUROC: 91.5%). Las red de comorbilidades para los casos de supervivencia tienen un alto grado de conectividad entre padecimientos como las arritmias cardiacas e hipertensión arterial esencial (centralidad de grado: 90 y 78 respectivamente). Conclusiones: En vista de que entre los factores asociados a supervivencia existen variables clínicas, sociodemográficas y determinantes sociales de la salud, además de la edad, resulta imperativo considerar los diversos factores que puedan incidir o modificar el estado de salud de una población, sobre todo al abordar los fenómenos epidémicos emergentes como es el caso de la actual pandemia de COVID-19.

Effects of social confinement during the first wave of COVID-19 in Mexico City.

Background: The COVID-19 pandemic led to global social confinement that had a significant impact on people's lives. This includes changes such as increased loneliness and isolation, changes in sleep patterns and social habits, increased substance use and domestic violence, and decreased physical activities. In some cases, it has increased mental health problems, such as anxiety, depression, and post-traumatic stress disorder. Objective: The objective of this study is to analyze the living conditions that arose during social confinement in the first wave of COVID-19 within a group of volunteers in Mexico City. Methods: This is a descriptive and cross-sectional analysis of the experiences of volunteers during social confinement from 20 March 2020 to 20 December 2020. The study analyzes the impact of confinement on family life, work, mental health, physical activity, social life, and domestic violence. A maximum likelihood generalized linear model is used to determine the association between domestic violence and demographic and health-related factors. Results: The findings indicate that social confinement had a significant impact on the participants, resulting in difficulties within families and vulnerable conditions for individuals. Gender and social level differences were observed in work and mental health. Physical activity and social life were also modified. We found that suffering from domestic violence was significantly associated with being unmarried (OR = 1.4454, p-value = 0.0479), lack of self-care in feeding habits (OR = 2.3159, p-value = 0.0084), and most notably, having suffered from a symptomatic COVID-19 infection (OR = 4.0099, p-value = 0.0009). Despite public policy to support vulnerable populations during confinement, only a small proportion of the studied population reported benefiting from it, suggesting areas for improvement in policy. Conclusion: The findings of this study suggest that social confinement during the COVID-19 pandemic had a significant impact on the living conditions of people in Mexico City. Modified circumstances on families and individuals, included increased domestic violence. The results can inform policy decisions to improve the living conditions of vulnerable populations during times of social confinement.

Relationship between the Social Development Index and Self-Reported Periodontal Conditions.

Inequalities in oral health are influenced by the social strata of the population. Few studies have focused on the multitude of factors related to social development as indicators of living conditions and periodontal health status. The aim of this study is to evaluate the association between self-reported periodontal conditions and the Social Development Index (SDI). A cross-sectional validated questionnaire was carried out among 1294 Mexican adults. Descriptive statistics and multivariate logistic regression models were used to identify the best predictors of self-reported periodontal conditions. Bone loss reporting was used as a proxy for the presence of periodontal disease. We found that higher global scores on the SDI and quality and available space in the home (QASH) increase the probability of having bone loss. Global SDI (OR = 7.27) and higher QASH (OR = 3.66) were indeed the leading societal factors related to periodontal disease. These results have pointed out how SDI and its indicators, in particular QASH, can be used to further explore inequities related to privileged access to dental care in the context of periodontal diseases.

A machine learning approach to personalized predictors of dyslipidemia: a cohort study.

Introduction: Mexico ranks second in the global prevalence of obesity in the adult population, which increases the probability of developing dyslipidemia. Dyslipidemia is closely related to cardiovascular diseases, which are the leading cause of death in the country. Therefore, developing tools that facilitate the prediction of dyslipidemias is essential for prevention and early treatment. Methods: In this study, we utilized a dataset from a Mexico City cohort consisting of 2,621 participants, men and women aged between 20 and 50 years, with and without some type of dyslipidemia. Our primary objective was to identify potential factors associated with different types of dyslipidemia in both men and women. Machine learning algorithms were employed to achieve this goal. To facilitate feature selection, we applied the Variable Importance Measures (VIM) of Random Forest (RF), XGBoost, and Gradient Boosting Machine (GBM). Additionally, to address class imbalance, we employed Synthetic Minority Over-sampling Technique (SMOTE) for dataset resampling. The dataset encompassed anthropometric measurements, biochemical tests, dietary intake, family health history, and other health parameters, including smoking habits, alcohol consumption, quality of sleep, and physical activity. Results: Our results revealed that the VIM algorithm of RF yielded the most optimal subset of attributes, closely followed by GBM, achieving a balanced accuracy of up to 80%. The selection of the best subset of attributes was based on the comparative performance of classifiers, evaluated through balanced accuracy, sensitivity, and specificity metrics. Discussion: The top five features contributing to an increased risk of various types of dyslipidemia were identified through the machine learning technique. These features include body mass index, elevated uric acid levels, age, sleep disorders, and anxiety. The findings of this study shed light on significant factors that play a role in dyslipidemia development, aiding in the early identification, prevention, and treatment of this condition.

Individual Factors Associated With COVID-19 Infection: A Machine Learning Study.

The fast, exponential increase of COVID-19 infections and their catastrophic effects on patients' health have required the development of tools that support health systems in the quick and efficient diagnosis and prognosis of this disease. In this context, the present study aims to identify the potential factors associated with COVID-19 infections, applying machine learning techniques, particularly random forest, chi-squared, xgboost, and rpart for feature selection; ROSE and SMOTE were used as resampling methods due to the existence of class imbalance. Similarly, machine and deep learning algorithms such as support vector machines, C4.5, random forest, rpart, and deep neural networks were explored during the train/test phase to select the best prediction model. The dataset used in this study contains clinical data, anthropometric measurements, and other health parameters related to smoking habits, alcohol consumption, quality of sleep, physical activity, and health status during confinement due to the pandemic associated with COVID-19. The results showed that the XGBoost model got the best features associated with COVID-19 infection, and random forest approximated the best predictive model with a balanced accuracy of 90.41% using SMOTE as a resampling technique. The model with the best performance provides a tool to help prevent contracting SARS-CoV-2 since the variables with the highest risk factor are detected, and some of them are, to a certain extent controllable.

Cardiovascular Risk Factors and Social Development Index.

Cardiovascular diseases (CVD) are the leading causes of morbidity and mortality worldwide. The complex etiology of CVD is known to be significantly affected by environmental and social factors. There is, however, a lag in our understanding of how population level components may be related to the onset and severity of CVD, and how some indicators of unsatisfied basic needs might be related to known risk factors. Here, we present a cross-sectional study aimed to analyze the association between cardiovascular risk factors (CVRF) and Social Development Index (SDI) in adult individuals within a metropolitan urban environment. The six components of SDI as well as socioeconomic, anthropometric, clinical, biochemical, and risk behavior parameters were explored within the study population. As a result, several CVRF (waist circumference, waist-to-height ratio, body mass index, systolic blood pressure, glucose, lower high-density lipoprotein cholesterol, triglycerides, and sodium) were found in a higher proportion in the low or very low levels of the SDI, and this pattern occurs more in women than in men. Canonical analysis indicates a correlation between other socioeconomic features and anthropometric, clinical, and biochemical factors (canonical coefficient = 0.8030). Further studies along these lines are needed to fully establish how to insert such associations into the design of health policy and interventions with a view to lessen the burden of cardiovascular diseases, particularly in metropolitan urban environments.