The Islamic Republic of Iran reported its first COVID-19 cases by 19th February 2020, since then it has become one of the most affected countries, with more than 73,000 cases and 4,585 deaths to this date. Spatial modeling could be used to approach an understanding of structural and sociodemographic factors that have impacted COVID-19 spread at a province-level in Iran. Therefore, in the present paper, we developed a spatial statistical approach to describe how COVID-19 cases are spatially distributed and to identify significant spatial clusters of cases and how socioeconomic and climatic features of Iranian provinces might predict the number of cases. The analyses are applied to cumulative cases of the disease from February 19th to March 18th. They correspond to obtaining maps associated with quartiles for rates of COVID-19 cases smoothed through a Bayesian technique and relative risks, the calculation of global (Moran’s I) and local indicators of spatial autocorrelation (LISA), both univariate and bivariate, to derive significant clustering, and the fit of a multivariate spatial lag model considering a set of variables potentially affecting the presence of the disease. We identified a cluster of provinces with significantly higher rates of COVID-19 cases around Tehran (p-value< 0.05), indicating that the COVID-19 spread within Iran was spatially correlated. Urbanized, highly connected provinces with older population structures and higher average temperatures were the most susceptible to present a higher number of COVID-19 cases (p-value < 0.05). Interestingly, literacy is a factor that is associated with a decrease in the number of cases (p-value < 0.05), which might be directly related to health literacy and compliance with public health measures. These features indicate that social distancing, protecting older adults, and vulnerable populations, as well as promoting health literacy, might be useful to reduce SARS-CoV-2 spread in Iran. One limitation of our analysis is that the most updated information we found concerning socioeconomic and climatic features is not for 2020, or even for a same year, so that the obtained associations should be interpreted with caution. Our approach could be applied to model COVID-19 outbreaks in other countries with similar characteristics or in case of an upturn in COVID-19 within Iran.

Background Depression is a well-recognised problem in the elderly. The aim of this study was to determine the factors associated with predictors of change in depressive symptoms, both in subjects with and without baseline significant depressive symptoms. Methods Longitudinal study of community-dwelling elderly people (>60 years or older), baseline evaluations, and two additional evaluations were reported. Depressive symptoms were measured using a 30-item Geriatric Depression Scale, and a score of 11 was used as cutoff point for significant depressive symptoms in order to stratify the analyses in two groups: with significant depressive symptoms and without significant depressive symptoms. Sociodemographic data, social support, anxiety, cognition, positive affect, control locus, activities of daily living, recent traumatic life events, physical activity, comorbidities, and quality of life were evaluated. Multi-level generalised estimating equation model was used to assess the impact on the trajectory of depressive symptoms. Results 7,882 subjects were assessed, with 29.42% attrition. At baseline assessment, mean age was 70.96 years, 61.15% were women. Trajectories of depressive symptoms had a decreasing trend. Stronger associations in those with significant depressive symptoms, were social support (OR .971, p<.001), chronic pain (OR 2.277, p<.001) and higher locus of control (OR .581, p<.001). In contrast for those without baseline significant depressive symptoms anxiety and a higher locus of control were the strongest associations. Conclusions New insights into late-life depression are provided, with special emphasis in differentiated factors influencing the trajectory when stratifying regarding basal status of significant depressive symptoms. Limitations The study has not included clinical evaluations and nutritional assessments

BackgroundOlder emergency department patients are more vulnerable than younger patients, yet many risk factors that contribute to the mortality of older patients remain unclear and under investigation. This study endeavored to determine mortality and factors associated with mortality in patients over 60 years of age who were admitted to the emergency departments of two general hospitals in Mexico City.MethodsThis is a hospital cohort study involving adults over 60 years of age admitted to the emergency department and who are beneficiaries of the Mexican Institute of Social Security and residents of Mexico City.All causes of mortality from the time of emergency department admission until a follow-up home visit after discharge were measured. Included risk factors were: socio-demographic, health-care related, mental and physical variables, and in-hospital care-related. Survival functions were estimated using Kaplan-Meier curves. Hazard ratios (HR) were derived from Cox regression models in a multivariate analysis.ResultsFrom the 1406 older adults who participated in this study, 306 (21.8%) did not survive. Independent mortality risk factors found in the last Cox model were age (HR = 1.02, 95% CI, 1.005–1.04; p = 0.01), length of stay in the ED (HR = 1.003, 95% CI = 0.99, 1.04; p = 0.006), geriatric care trained residents model in Hospital A (protective factor) (HR = 0.66, 95% CI = 0.46, 0.96; p = 0.031), and the FRAIL scale (HR of 1.34 95% CI, 1.02–1.76; p = 0.033).ConclusionsRisk factors for mortality in patients treated at Mexican emergency departments are length of stay and variables related to frailty status.

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COVID-19 is a respiratory disease caused by SARS-CoV-2, which has significantly impacted economic and public healthcare systems worldwide. SARS-CoV-2 is highly lethal in older adults (>65 years old) and in cases with underlying medical conditions, including chronic respiratory diseases, immunosuppression, and cardio-metabolic diseases, including severe obesity, diabetes, and hypertension. The course of the COVID-19 pandemic in Mexico has led to many fatal cases in younger patients attributable to cardio-metabolic conditions. Thus, in the present study, we aimed to perform an early spatial epidemiological analysis for the COVID-19 outbreak in Mexico. Firstly, to evaluate how mortality risk from COVID-19 among tested individuals (MRt) is geographically distributed and secondly, to analyze the association of spatial predictors of MRt across different states in Mexico, controlling for the severity of the disease. Among health-related variables, diabetes and obesity were positively associated with COVID-19 fatality. When analyzing Mexico as a whole, we identified that both the percentages of external and internal migration had positive associations with early COVID-19 mortality risk with external migration having the second-highest positive association. As an indirect measure of urbanicity, population density, and overcrowding in households, the physicians-to-population ratio has the highest positive association with MRt. In contrast, the percentage of individuals in the age group between 10 to 39 years had a negative association with MRt. Geographically, Quintana Roo, Baja California, Chihuahua, and Tabasco (until April 2020) had higher MRt and standardized mortality ratios, suggesting that risks in these states were above what was nationally expected. Additionally, the strength of the association between some spatial predictors and the COVID-19 fatality risk varied by zone.

Background Social disadvantages that start during childhood and continue into the later stages in life may be linked to the presence of diabetes during adulthood. Objective . To analyze whether the presence of social disadvantages in childhood and in the present affects the presence of diabetes in older adults. Methods The present study was based on longitudinal data from the third and fourth Mexican Health and Aging Study (MHAS) waves (2012 and 2015). Data on diabetes diagnosis, past (e.g. “no shoes during childhood”) and present (e.g. self-perception of economic status) social disparities, and other covariables were analyzed. Results From 8,848 older adults, 21.5% ( n = 1903) were classified as prevalent cases (PG), 5.2% ( n = 459) as incident cases (IG) and 77.4% ( n = 6,486) were free of disease (NDG). The predictor variable “no shoes during childhood” was statistically significant in the model incident versus no diabetes group. Hypertension and body mass index (BMI) were the most relevant covariates as they were statistically significant in the three groups (PG, IG and NDG). Conclusions Not having shoes during childhood, an indicator of social disadvantages, is associated with the incidence and prevalence of diabetes in older adults. This suggests that social disadvantages can be a determinant for the presence of chronic diseases in adulthood.

Frailty is an age-associated condition, characterized by an inappropriate response to stress that results in a higher frequency of adverse outcomes (e.g., mortality, institutionalization and disability). Some light has been shed over its genetic background, but this is still a matter of debate. In the present study, we used network biology to analyze the interactome of frailty-related genes at different levels to relate them with pathways, clinical deficits and drugs with potential therapeutic implications. Significant pathways involved in frailty: apoptosis, proteolysis, muscle proliferation, and inflammation; genes as FN1, APP, CREBBP, EGFR playing a role as hubs and bottlenecks in the interactome network and epigenetic factors as HIST1H3 cluster and miR200 family were also involved. When connecting clinical deficits and genes, we identified five clusters that give insights into the biology of frailty: cancer, glucocorticoid receptor, TNF-α, myostatin, angiotensin converter enzyme, ApoE, interleukine-12 and −18. Finally, when performing network pharmacology analysis of the target nodes, some compounds were identified as potentially therapeutic (e.g., epigallocatechin gallate and antirheumatic agents); while some other substances appeared to be toxicants that may be involved in the development of this condition.