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To analyse the relationship between the Frailty Index and 10 oral conditions controlling for nutritional status among Mexican community-dwelling older people.Studies suggest that the association between frailty and oral conditions are mediated by nutrition.This cross-sectional analysis includes 487 community-dwelling men and women aged ≥70 years old. Interview and clinical examinations were performed at participants' homes. Objective (number of natural teeth, root remnants, dental condition, utilisation and functionality of removable dental prostheses and periodontitis) and subjective (utilisation of dental services, self-rated oral health, chewing difficulties and xerostomia) oral variables were collected by trained personnel. The Frailty Index was calculated considering 35 deficits. Nutritional status measured with the Mini-Nutritional assessment (MNA), age, sex, education, and marital status were included as covariates. We fitted 11 multivariate generalised linear models (one for each oral condition), assuming gamma distribution for Frailty Index as the outcome.Participants average age was 78.1 years, 52.1% were women. We observed a higher Frailty Index among those rating their oral health as worse than others their age (5.1%), reporting chewing difficulties often (4.9%) and fairly and very often (7.0%), and xerostomia (4.8%). Age, gender and MNA were consistently associated with the Frailty Index.Subjective oral conditions are compatible with the Frailty Index after controlling for older people's nutritional status and covariates.

Aging has been defined as a biological process characterized by the functional decline of organs and systems leading to vulnerability and a susceptible state for developing neuronal, musculoskeletal, behavioral, and cardiovascular diseases and disorders. Sleep disorders, including insomnia, are common during aging, and these conditions have been associated with memory and cognitive decline in older adults. However, the molecular bases of such phenomenon have not been thoroughly described. Moreover, during the aging process, neurotransmitters, neurohormones, and neurotrophins decrease significantly, leading to the impairment of cognitive functions. In this sense, BDNF, the most abundant neurotrophic factor in the human brain, has been suggested as a potential target for the prevention and improvement of cognitive decline during aging; however, the current evidence demonstrates the exogenous administration of BDNF does not improve cognitive function. Hence, in the present study, we quantified pro-BDNF (inactive) and BDNF (active) concentrations in serum samples derived from older individuals with insomnia and/or cognitive decline. We used linear regression to analyze whether clinical or sociodemographic variables impacted the levels of BNDF concentration. We observed that insomnia, rather than cognitive decline, is significantly associated with BDNF concentration, and these effects are independent of other variables. To our knowledge, this is the first study that points to the impact of insomnia on improving the levels of BDNF during aging and suggests that opportune treatment of insomnia may be more beneficial to prevent cognitive decline during aging.

Objetivo. Estudiar las transiciones de la discapacidad de acuerdo con la afiliación a servicios de salud en adultos mayores de 50 años en México, así como el conjunto de variables socioeconómicas y de salud asociadas. Material y métodos. La información proviene de la Encuesta Nacional de Salud y Envejecimiento en México (Enasem-encuesta). Se ajustaron 20 modelos de regresión para las transiciones de la discapacidad medida a través de actividades básicas de la vida diaria en el periodo 2012-15, usando variables socioe­conómicas y de salud. Resultados. Los afiliados al Seguro Popular tuvieron la mayor frecuencia de transición hacia la discapacidad con 13.26%. La afiliación a instituciones de sa­lud no tuvo relación con ninguna de las cuatro transiciones estudiadas. Las enfermedades asociadas con el desarrollo de discapacidad varían de acuerdo con la afiliación. Conclu­siones. Los factores socioeconómicos y de salud asociados con las transiciones de la discapacidad son diferentes entre afiliaciones.

We aimed to determine the association between the duration of widowhood and cognition decline. We compared the decline observed in widowed people compared with married, single, or separated persons using the scores obtained in the cognitive assessment of memory, learning, and visual exploration by adults and older adults in Mexico. The Mexican Health and Aging Study (MHAS) provides the base for this paper. This study is an analysis of the fourth data wave (2015), except for the independent variable: marital status. Marital status was built longitudinally with information from the four surveys (2001, 2003, 2012, and 2015). The sample comprised 6898 adults aged 50 and over. Cognition was assessed with an adapted Cross-Cultural Cognitive Examination (CCCE). Confounders include sociodemographic characteristics (sex, age, schooling, self-perception of economic status, and whether the individual worked or not), multimorbidity, functionality, support networks, and psychological characteristics. Of the total sample, 4094 (59.3%) were women. The mean age was 70.86 years (SD = 7.4). The baseline of the study is 2001. In 2001, 8.7% (n = 600) were widows or widowers. People widowed by 2001 scored −0.158 points in cognition while divorced participants scored - 0.095 points.

Identifying relationships between components of an index helps to gain a better understanding of the condition they define. The Frailty Index (FI) measures the global health of individuals and can be used to predict outcomes as mortality. Previously, we modelled the relationship between the FI components (deficits) and death through an undirected graphical model and a social network analysis framework. Here, we model the FI components and death through an averaged Bayesian network obtained through a structural learning process and resampling, in order to understand how the FI components and death are causally related. We identified that components are not similarly related between them and that deficits are related according to their type. Two deficits were the most relevant in terms of their connections, and two others were directly associated with death. We obtained the strength of the relationships in order to identify the most plausible, identifying clusters of deficits. Finally, we propagated evidence and studied how FI components predict mortality, obtaining a correct assignation of almost 74% and a true positive rate (TPR) of 56%. Values were obtained after changing the model threshold (via Youden’s Index maximization) whose possible values are represented in a Receiving Operating Characteristic (ROC) curve (TPR vs. 1-True Negative Rate). The greater number of deficits included for the evidence, the best performances; nevertheless, the FI does not seem to be quite efficient to correctly differentiate between dead and living people.

Background: Acute ischemic stroke is among the main causes of mortality worldwide; a rapid and opportune diagnosis is crucial to improve a patient's outcome. MicroRNAs are quite useful for a rapid and accurate diagnosis.Methods: We perform both structural networks approach and a meta-analysis (using a random-effect model to evaluate the heterogeneity and risk bias, according to the PRISMA statement) to analyze the feasibility to develop a microRNA-based biomarker panel for an opportune AIS diagnosis. Results: Structural networks identify a set of eight miRNAs (miR-16, miR-124-3p, miR-484, miR-15a, miR-4454, miR-107, miR-125b-5p and miR-320b) as preliminary microRNA-based biomarker panel, from these only three microRNAs are significantly associated with the main risk factors of AIS, (miR-107: hypertension, 95% CI 9.74-53.24 p

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.

To study the transitions of disability according to health insurance in the population of adults over 50 in Mexico, as well as the set of socioeconomic and health variables associated with these transitions.Data comes from the Mexican Health and Aging Study (MHAS). Twenty regression models were fitted for the transitions of disability measured through activities of daily living in the period 2012-15, using socioeconomic and health as explanatory variables.Enrollees with Seguro Popular had the highest frequency of transition to disability with 13.26%. Health insurance was not found to be related to any of the four transitions studied. Diseases associated with the development of disability vary according to health insurance.The socioeconomic and health factors associated with disability transitions are different between health insurance enrollees.

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., Author summary Iran was among the first countries reporting a rapid increase in the number of COVID-19 cases. Spatial epidemiology is useful to study the spatial distribution of a disease and to identify factors associated with the number of cases of such disease. By applying these methods, we aimed to identify whether there are clusters of regions in Iran with high or low number of COVID-19 cases and the association of different factors with these numbers, considering spatial relationships and maps representing these associations. Interestingly, we found regions of high number of cases and that more COVID-19 cases were present in provinces with more urbanization, aging population, number of physicians, efficient communications, and greater average temperatures, whereas less COVID-19 cases were present in provinces with more literacy. This study allowed us to understand the spatial behavior of the disease and the importance of having adequate health policies, literacy campaigns, and disseminating health information to the population.

ObjectivesTo analyse the transitions of disability onset and recovery, estimate life expectancy (LE) with and without disability and explore the relation between insurance and disability patterns in the population aged 50+ in Mexico.DesignMultistate life table analysis of data from a longitudinal cohort survey.SettingData came from the Mexican Health and Aging Study, a longitudinal and representative survey of older adults in Mexico.Participants10 651 individuals aged 50+ interviewed in 2012 and 2015, including those who died between waves.Primary and secondary outcome measuresDisability is measured using the activities of daily living (ADL) scale. Transition rates between disability free, ADL disabled and death were employed to estimate total life expectancy (TLE) and disability-free life expectancy (DFLE).Results46% of the individuals who reported an ADL limitation in 2012 recover from disability by 2015. TLE at age 60 for people without ADL limitations is 30 years (95% CI 28.9 to 31), out of which 4.7 years (95% CI 4.1 to 5.4) are lived with ADL limitations, while TLE at age 60 in the initially disabled is 18.7 years (95% CI 17.3 to 20), with 9.4 years (95% CI 8.4 to 10.3) lived with disability. DFLE at age 60 in people with social security is 24.2 years (95% CI 23.3 to 25.2), in people with public insurance is 24.6 years (95% CI 23.7 to 25.4) and in uninsured people is 26.9 years (95% CI 25.9 to 27.9).ConclusionsIn Mexico, a substantial proportion of ADL disabled individuals recover from disability. Nevertheless, initially disabled individuals have a considerably lower DFLE regardless of age when compared with initially active individuals. There appeared to be no differences in terms of disability and LEs between the individuals with social security and public insurance.

Random intercept models are linear mixed models (LMM) including error and intercept random effects. Sometimes heteroscedasticity is included and the response variable is transformed into a logarithmic scale, while inference is required in the original scale; thus, the response variable has a log-normal distribution. Hence, correction terms should be included to predict the response in the original scale. These terms multiply the exponentiated predicted response variable, which subestimates the real values. We derive the correction terms, simulations and real data about the income of elderly are presented to show the importance of using them to obtain more accurate predictions. Generalizations for any LMM are also presented.

We introduce a new type of graphical log-linear model called restricted graphical log-linear model. This model is obtained by imposing equality restrictions on subsets of main effects and of first-order interactions. These restrictions are obtained through partitions of the variable and first-order interaction sets. The vertices or variables in the same class have the same main effects in all their categories and the first-order interactions in the same class are equal. We study its properties and derive its associated likelihood equations and give some applications. A graphical representation is possible through a coloured graph.

Background Frailty remains a challenge in the aging research area with a number of gaps in knowledge still to be filled. Frailty seems to behave as a network, and in silico evidence is available on this matter. Having in vivo evidence that frailty behaves as a complex network was the main purpose of our study. Methods Data from the Mexican Health and Aging Study (main data 2012, mortality 2015) was used. Frailty was operationalized with a 35-deficit frailty index (FI). Analyzed nodes were the deficits plus death. The edges, linking those nodes were obtained through structural learning, and an undirected graph associated with a discrete probabilistic graphical model (Markov network) was derived. Two algorithms, hill-climbing (hc) and Peter and Clark (PC), were used to derive the graph structure. Analyses were performed for the whole population and tertiles of the total FI score. Results From the total sample of 10,983 adults aged 50 or older, 43.8% were women, and the mean age was 64.6 years (SD = 9.3). The number of connections increased according to the tertile level of the FI score. As the FI score raised, groups of interconnected deficits increased and how the nodes are connected changed. Conclusions Frailty phenomenon can be modeled using a Bayesian network. Using the full sample, the most central nodes were self-report of health (most connected node) and difficulty walking a block, and all deficits related to mobility were very interconnected. When frailty levels are considered, the most connected nodes differ, but are related with vitality, mainly at lower frailty levels. We derived that not all deficits are equally related since clusters of very related deficits and non-connected deficits were obtained, which might be considered in the construction of the FI score. Further research should aim to identify the nature of all observed interactions, which might allow the development of specific interventions to mitigate the consequences of frailty in older adults.

Population aging has a direct impact on the increasing demand of health services and on medical care costs. The objective was to carry out a cost analysis of health care costs in older adults in a regional general hospital of the Instituto Mexicano del Seguro Social.A calculation of the costs was done based on a retrospective collection of health care data. Unit prices were used to estimate costs. These were reported in 2016 Mexican pesos. A cost analysis was carried out by means of a regression model. Explanatory variables were sex, age and comorbidity level, the latter measured by using the Charlson index.The average cost of all the 509 patients was 34 769 Mexican pesos (SD = 2869 pesos). Age variable explains the costs; however, sex and comorbidity variables were not significant. Cost predictions with the statistical model show differences mainly by age. In the case of females, the model predicts greater costs compared with those of males. Costs for older adults of 85 years or more were greater than those for the group of younger people (75-84).The hospitalization costs estimated are high and they differ according to the age group. We suggest to make further research in order to know the factors associated with high hospital costs for this age group.el envejecimiento tiene como consecuencia el aumento de la demanda de servicios y los costos de la atención médica. El objetivo fue realizar un análisis de costos de la atención médica en población adulta mayor en un hospital general regional del Instituto Mexicano del Seguro Social.se realizó la estimación de costos por medio de la recolección retrospectiva de bienes y servicios. Los precios unitarios oficiales fueron usados para valorar los costos. Los costos se reportan en pesos de 2016. Se realizó un análisis de costos por medio de un modelo de regresión. Las variables explicativas fueron la edad, el sexo y el nivel de comorbilidad medido por el índice de Charlson.el costo promedio para 509 pacientes fue de 34 769 pesos mexicanos (DE = 2869 pesos). La variable de edad explica los costos pero las de sexo y nivel de comorbilidad no fueron significativas. Las predicciones de costos con el modelo estadístico muestran diferencias entre grupos de edad principalmente. Los costos promedio fueron más altos para mujeres que para hombres. Los costos para mayores de 85 años fueron superiores que para los del grupo de 75-84 años.los costos estimados por hospitalizaciones son elevados y son distintos por nivel de edad. Se sugiere investigar más sobre los factores que pueden explicar el crecimiento de los costos de hospitalización.

"el envejecimiento tiene como consecuencia el aumento de la demanda de servicios y los costos de la atención médica. El objetivo fue realizar un análisis de costos de la atención médica en población adulta mayor en un hospital general regional del Instituto Mexicano del Seguro Social. Métodos: se realizó la estimación de costos por medio de la recolección retrospectiva de bienes y servicios. Los precios unitarios oficiales fueron usados para valorar los costos. Los costos se reportan en pesos de 2016. Se realizó un análisis de costos por medio de un modelo de regresión. Las variables explicativas fueron la edad, el sexo y el nivel de comorbilidad medido por el índice de Charlson. Resultados: el costo promedio para 509 pacientes fue de 34 769 pesos mexicanos (DE = 2869 pesos). La variable de edad explica los costos pero las de sexo y nivel de comorbilidad no fueron significativas. Las predicciones de costos con el modelo estadístico muestran diferencias entre grupos de edad principalmente. Los costos promedio fueron más altos para mujeres que para hombres. Los costos para mayores de 85 años fueron superiores que para los del grupo de 75-84 años. Conclusiones: los costos estimados por hospitalizaciones son elevados y son distintos por nivel de edad. Se sugiere investigar más sobre los factores que pueden explicar el crecimiento de los costos de hospitalización."

Frailty remains a challenge in the aging research area with a number of gaps in knowledge still to be filled. New approaches to its study have been proposed, including the one discussed in this article. We tested frailty as through the use of graphical probabilistic models (bayesian networks) with empirical data. Data from the Mexican Health and Aging Study (main data 2012, mortality 2015) was used. Frailty was operationalized with a 35-deficit frailty index (FI). Analyzed nodes were the deficits, plus death and the total score of the FI. The edges, or ties, linking those nodes (set E) were obtained through structural learning, and an undirected discrete graph G (V, E) associated with a discrete graphical probabilistic model (Markov network) was derived. Structural learning was possible through hill-climbing (hc) and PC algorithms. Analyses were performed for the whole population and tertiles of the total FI score. The number of connections within nodes increased according to the tertile level of the total FI score. Groups of interconnected deficits increased as the FI score raised. Almost all deficits related to mobility were interconnected and death was not the most connected node. Frailty behaves as a nonlinear system under the looks of a complex network. Further research should aim to identify the nature of the interactions observed. This could contribute to the development of a conceptual framework that would allow specific interventions to mitigate the consequences of frailty in older adults to be developed.

Background: The number of older adults expected to increase over the coming decades, the public health impact in this population may be substantial, and a greater understanding of the structure underlying risk factor presentation as a potential source of heterogeneity is critical. Objective: Identify and characterize profiles of dependency status in a population of dependent elderly individuals. Methods: The present study is based on the first wave of the Mexican Health Aging Study (MHAS). We included subjects aged 50 or older (n = 13,463 respondents interviewed in 2001). We performed Latent Class Analysis on four domains in older adults’ indicators (physical, psychological, economic and social) to identify distinct classes of dependency profiles. We used LCA to group individuals into homogenous categories of dependency based on observed domains of multidimensional dependency. Multivariable logistic regression was conducted to examine the sociodemographic characteristics associated with each profile. Results: A 4-class solution based on cognitive performance at baseline was the best-fitting model. We characterized the four distinct classes of dependency profiles: active older adult, low, moderate and severe dependency that encompassed multiple dimensions of dependence. Using the “active older adult” class as the reference group, severe dependency, low dependency, and moderate dependency class were more likely to contain females, low education level and poor quality of life 3) the moderate dependency class was less likely to contain cigarette smoking and alcohol user. Conclusions: This study suggests that dependency do not follow a uniform adjustment pattern during the aging process, which reconciles inconsistent previous findings.

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.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 cut-off 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.A number of 7882 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.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.The study has not included clinical evaluations and nutritional assessments.