Analysis of hospital dental care for patients with special needs in Brazil.

This analytical cross-sectional study aimed to analyze the access of patients with special needs (PSN) in Brazilian municipalities to hospital dental care of the Unified Health System (Sistema Único de Saúde - SUS), based on data from the Hospital Information System of the Unified Health System (Sistema de Informações Hospitalares do SUS- SIH/SUS - SIH), from 2010 to 2018. The Kolmogorov-Smirnov normality test was performed; the Poisson regression was used to verify factors associated with the variable total number of hospitalization authorizations with the main procedure of dental treatment for PSN ("Total de Autorizações de Internação Hospitalar" - AIH), the Spearman correlation test with a significance level of 5% was used to characterize the relationships between the Municipal Human Development Index per municipality - (Índice de Desenvolvimento Humano Municipal - HDI) and the Oral Health Coverage in the Family Health Strategy by municipality (Cobertura de saúde bucal na estratégia saúde da família por município - SBSF Coverage), and the relationship of the AIH with SBSF Coverage. A total of 127,691 procedures were performed, of which 71,517 (56%) were clinical procedures, such as restorations, endodontic treatments, supra and subgingival scaling, among others. Municipalities in the Midwest (PR=5.117) and Southeast (RP = 4.443) regions had more precedures than the others. A weak correlation was found between AIH and SBSF Coverage (r = -0.2, p < 0.001) and HDI and SBSF Coverage (r = -0.074, p < 0.001). Population size, region, health coverage, oral hygiene, and number of dentists in hospitals affected the availability of dental procedures in PSN.

Geographical variation in hotspots of stunting among under-five children in Ethiopia: A geographically weighted regression and multilevel robust Poisson regression analysis.

INTRODUCTION: Childhood stunting is a global public health concern, associated with both short and long-term consequences, including high child morbidity and mortality, poor development and learning capacity, increased vulnerability for infectious and non-infectious disease. The prevalence of stunting varies significantly throughout Ethiopian regions. Therefore, this study aimed to assess the geographical variation in predictors of stunting among children under the age of five in Ethiopia using 2019 Ethiopian Demographic and Health Survey. METHOD: The current analysis was based on data from the 2019 mini Ethiopian Demographic and Health Survey (EDHS). A total of 5,490 children under the age of five were included in the weighted sample. Descriptive and inferential analysis was done using STATA 17. For the spatial analysis, ArcGIS 10.7 were used. Spatial regression was used to identify the variables associated with stunting hotspots, and adjusted R2 and Corrected Akaike Information Criteria (AICc) were used to compare the models. As the prevalence of stunting was over 10%, a multilevel robust Poisson regression was conducted. In the bivariable analysis, variables having a p-value < 0.2 were considered for the multivariable analysis. In the multivariable multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval is presented to show the statistical significance and strength of the association. RESULT: The prevalence of stunting was 33.58% (95%CI: 32.34%, 34.84%) with a clustered geographic pattern (Moran's I = 0.40, p<0.001). significant hotspot areas of stunting were identified in the west and south Afar, Tigray, Amhara and east SNNPR regions. In the local model, no maternal education, poverty, child age 6-23 months and male headed household were predictors associated with spatial variation of stunting among under five children in Ethiopia. In the multivariable multilevel robust Poisson regression the prevalence of stunting among children whose mother's age is >40 (APR = 0.74, 95%CI: 0.55, 0.99). Children whose mother had secondary (APR = 0.74, 95%CI: 0.60, 0.91) and higher (APR = 0.61, 95%CI: 0.44, 0.84) educational status, household wealth status (APR = 0.87, 95%CI: 0.76, 0.99), child aged 6-23 months (APR = 1.87, 95%CI: 1.53, 2.28) were all significantly associated with stunting. CONCLUSION: In Ethiopia, under-five children suffering from stunting have been found to exhibit a spatially clustered pattern. Maternal education, wealth index, birth interval and child age were determining factors of spatial variation of stunting. As a result, a detailed map of stunting hotspots and determinants among children under the age of five aid program planners and decision-makers in designing targeted public health measures.

Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions.

Many imaging techniques for biological systems-like fixation of cells coupled with fluorescence microscopy-provide sharp spatial resolution in reporting locations of individuals at a single moment in time but also destroy the dynamics they intend to capture. These snapshot observations contain no information about individual trajectories, but still encode information about movement and demographic dynamics, especially when combined with a well-motivated biophysical model. The relationship between spatially evolving populations and single-moment representations of their collective locations is well-established with partial differential equations (PDEs) and their inverse problems. However, experimental data is commonly a set of locations whose number is insufficient to approximate a continuous-in-space PDE solution. Here, motivated by popular subcellular imaging data of gene expression, we embrace the stochastic nature of the data and investigate the mathematical foundations of parametrically inferring demographic rates from snapshots of particles undergoing birth, diffusion, and death in a nuclear or cellular domain. Toward inference, we rigorously derive a connection between individual particle paths and their presentation as a Poisson spatial process. Using this framework, we investigate the properties of the resulting inverse problem and study factors that affect quality of inference. One pervasive feature of this experimental regime is the presence of cell-to-cell heterogeneity. Rather than being a hindrance, we show that cell-to-cell geometric heterogeneity can increase the quality of inference on dynamics for certain parameter regimes. Altogether, the results serve as a basis for more detailed investigations of subcellular spatial patterns of RNA molecules and other stochastically evolving populations that can only be observed for single instants in their time evolution.

A Genuinely Hybrid, Multiscale 3D Cancer Invasion and Metastasis Modelling Framework.

We introduce in this paper substantial enhancements to a previously proposed hybrid multiscale cancer invasion modelling framework to better reflect the biological reality and dynamics of cancer. These model updates contribute to a more accurate representation of cancer dynamics, they provide deeper insights and enhance our predictive capabilities. Key updates include the integration of porous medium-like diffusion for the evolution of Epithelial-like Cancer Cells and other essential cellular constituents of the system, more realistic modelling of Epithelial-Mesenchymal Transition and Mesenchymal-Epithelial Transition models with the inclusion of Transforming Growth Factor beta within the tumour microenvironment, and the introduction of Compound Poisson Process in the Stochastic Differential Equations that describe the migration behaviour of the Mesenchymal-like Cancer Cells. Another innovative feature of the model is its extension into a multi-organ metastatic framework. This framework connects various organs through a circulatory network, enabling the study of how cancer cells spread to secondary sites.

High-dimensional covariate-augmented overdispersed poisson factor model.

The current Poisson factor models often assume that the factors are unknown, which overlooks the explanatory potential of certain observable covariates. This study focuses on high dimensional settings, where the number of the count response variables and/or covariates can diverge as the sample size increases. A covariate-augmented overdispersed Poisson factor model is proposed to jointly perform a high-dimensional Poisson factor analysis and estimate a large coefficient matrix for overdispersed count data. A group of identifiability conditions is provided to theoretically guarantee computational identifiability. We incorporate the interdependence of both response variables and covariates by imposing a low-rank constraint on the large coefficient matrix. To address the computation challenges posed by nonlinearity, two high-dimensional latent matrices, and the low-rank constraint, we propose a novel variational estimation scheme that combines Laplace and Taylor approximations. We also develop a criterion based on a singular value ratio to determine the number of factors and the rank of the coefficient matrix. Comprehensive simulation studies demonstrate that the proposed method outperforms the state-of-the-art methods in estimation accuracy and computational efficiency. The practical merit of our method is demonstrated by an application to the CITE-seq dataset. A flexible implementation of our proposed method is available in the R package COAP.

Optimizing spatio-temporal correlation structures for modeling food security in Africa: a simulation-based investigation.

This study investigates the impact of spatio- temporal correlation using four spatio-temporal models: Spatio-Temporal Poisson Linear Trend Model (SPLTM), Poisson Temporal Model (TMS), Spatio-Temporal Poisson Anova Model (SPAM), and Spatio-Temporal Poisson Separable Model (STSM) concerning food security and nutrition in Africa. Evaluating model goodness of fit using the Watanabe Akaike Information Criterion (WAIC) and assessing bias through root mean square error and mean absolute error values revealed a consistent monotonic pattern. SPLTM consistently demonstrates a propensity for overestimating food security, while TMS exhibits a diverse bias profile, shifting between overestimation and underestimation based on varying correlation settings. SPAM emerges as a beacon of reliability, showcasing minimal bias and WAIC across diverse scenarios, while STSM consistently underestimates food security, particularly in regions marked by low to moderate spatio-temporal correlation. SPAM consistently outperforms other models, making it a top choice for modeling food security and nutrition dynamics in Africa. This research highlights the impact of spatial and temporal correlations on food security and nutrition patterns and provides guidance for model selection and refinement. Researchers are encouraged to meticulously evaluate the biases and goodness of fit characteristics of models, ensuring their alignment with the specific attributes of their data and research goals. This knowledge empowers researchers to select models that offer reliability and consistency, enhancing the applicability of their findings.

Digital colloid-enhanced Raman spectroscopy by single-molecule counting.

Quantitative detection of various molecules at very low concentrations in complex mixtures has been the main objective in many fields of science and engineering, from the detection of cancer-causing mutagens and early disease markers to environmental pollutants and bioterror agents1-5. Moreover, technologies that can detect these analytes without external labels or modifications are extremely valuable and often preferred6. In this regard, surface-enhanced Raman spectroscopy can detect molecular species in complex mixtures on the basis only of their intrinsic and unique vibrational signatures7. However, the development of surface-enhanced Raman spectroscopy for this purpose has been challenging so far because of uncontrollable signal heterogeneity and poor reproducibility at low analyte concentrations8. Here, as a proof of concept, we show that, using digital (nano)colloid-enhanced Raman spectroscopy, reproducible quantification of a broad range of target molecules at very low concentrations can be routinely achieved with single-molecule counting, limited only by the Poisson noise of the measurement process. As metallic colloidal nanoparticles that enhance these vibrational signatures, including hydroxylamine-reduced-silver colloids, can be fabricated at large scale under routine conditions, we anticipate that digital (nano)colloid-enhanced Raman spectroscopy will become the technology of choice for the reliable and ultrasensitive detection of various analytes, including those of great importance for human health.

Socio-environmental predictors of diabetes incidence disparities in Tanzania mainland: a comparison of regression models for count data.

BACKGROUND: Diabetes is one of the top four non-communicable diseases that cause death and illness to many people around the world. This study aims to use an efficient count data model to estimate socio-environmental factors associated with diabetes incidences in Tanzania mainland, addressing lack of evidence on the efficient count data model for estimating factors associated with disease incidences disparities. METHODS: This study analyzed diabetes counts in 184 Tanzania mainland councils collected in 2020. The study applied generalized Poisson, negative binomial, and Poisson count data models and evaluated their adequacy using information criteria and Pearson chi-square values. RESULTS: The data were over-dispersed, as evidenced by the mean and variance values and the positively skewed histograms. The results revealed uneven distribution of diabetes incidence across geographical locations, with northern and urban councils having more cases. Factors like population, GDP, and hospital numbers were associated with diabetes counts. The GP model performed better than NB and Poisson models. CONCLUSION: The occurrence of diabetes can be attributed to geographical locations. To address this public health issue, environmental interventions can be implemented. Additionally, the generalized Poisson model is an effective tool for analyzing health information system count data across different population subgroups.

A beta-Poisson model for infectious disease transmission.

Outbreaks of emerging and zoonotic infections represent a substantial threat to human health and well-being. These outbreaks tend to be characterised by highly stochastic transmission dynamics with intense variation in transmission potential between cases. The negative binomial distribution is commonly used as a model for transmission in the early stages of an epidemic as it has a natural interpretation as the convolution of a Poisson contact process and a gamma-distributed infectivity. In this study we expand upon the negative binomial model by introducing a beta-Poisson mixture model in which infectious individuals make contacts at the points of a Poisson process and then transmit infection along these contacts with a beta-distributed probability. We show that the negative binomial distribution is a limit case of this model, as is the zero-inflated Poisson distribution obtained by combining a Poisson-distributed contact process with an additional failure probability. We assess the beta-Poisson model's applicability by fitting it to secondary case distributions (the distribution of the number of subsequent cases generated by a single case) estimated from outbreaks covering a range of pathogens and geographical settings. We find that while the beta-Poisson mixture can achieve a closer to fit to data than the negative binomial distribution, it is consistently outperformed by the negative binomial in terms of Akaike Information Criterion, making it a suboptimal choice on parsimonious grounds. The beta-Poisson performs similarly to the negative binomial model in its ability to capture features of the secondary case distribution such as overdispersion, prevalence of superspreaders, and the probability of a case generating zero subsequent cases. Despite this possible shortcoming, the beta-Poisson distribution may still be of interest in the context of intervention modelling since its structure allows for the simulation of measures which change contact structures while leaving individual-level infectivity unchanged, and vice-versa.

Variation vs. specialization: the dose-time-effect of technical and physiological variety in the development of elite swimmers.

OBJECTIVE: It is heavily discussed whether larger variety or specialization benefit elite performance at peak age. Therefore, this study aimed to determine technical (number of different swimming strokes) and physiological (number of different race distances) variety required to become an international-class swimmer (> 750 swimming points) based on 1'522'803 race results. RESULTS: Correlation analyses showed lower technical variety in higher ranked swimmers (P < 0.001), yet with small effects (0.11-0.30). However, Poisson distribution revealed dose-time-effects and specified number of swimming strokes required during each age group. Specifically, freestyle swimmers showed highest chances when starting to compete in three to four swimming strokes but reduced their variety to three swimming strokes at the ages of 12/13yrs with another transition to two swimming strokes at the ages of 19/21yrs (female/male swimmers, respectively). Although both sexes showed similar specialization pattern throughout their career, earlier specialization was generally evident in female compared to male swimmers. At peak performance age, freestyle was most frequently combined with butterfly. Swimmers who either kept competing in all five swimming strokes or focused on only one at the beginning of their careers showed lowest probability of becoming an international-class swimmer. Physiological variety increased during junior age but declined again to three race distances towards elite age.

Testing latent classes in gut microbiome data using generalized Poisson regression models.

Human microbiome research has gained increasing importance due to its critical roles in comprehending human health and disease. Within the realm of microbiome research, the data generated often involves operational taxonomic unit counts, which can frequently present challenges such as over-dispersion and zero-inflation. To address dispersion-related concerns, the generalized Poisson model offers a flexible solution, effectively handling data characterized by over-dispersion, equi-dispersion, and under-dispersion. Furthermore, the realm of zero-inflated generalized Poisson models provides a strategic avenue to simultaneously tackle both over-dispersion and zero-inflation. The phenomenon of zero-inflation frequently stems from the heterogeneous nature of study populations. It emerges when specific microbial taxa fail to thrive in the microbial community of certain subjects, consequently resulting in a consistent count of zeros for these individuals. This subset of subjects represents a latent class, where their zeros originate from the genuine absence of the microbial taxa. In this paper, we introduce a novel testing methodology designed to uncover such latent classes within generalized Poisson regression models. We establish a closed-form test statistic and deduce its asymptotic distribution based on estimating equations. To assess its efficacy, we conduct an extensive array of simulation studies, and further apply the test to detect latent classes in human gut microbiome data from the Bogalusa Heart Study.

Application of marginalized zero-inflated models when mediators have excess zeroes.

Mediation analysis has become increasingly popular over the last decade as researchers are interested in assessing mechanistic pathways for intervention. Although available methods have increased, there are still limited options for mediation analysis with zero-inflated count variables where the distribution of response has a "cluster" of data at the zero value (i.e. distribution of number of cigarettes smoked per day, where nonsmokers cluster at zero cigarettes). The currently available methods do not obtain unbiased population average effects of mediation effects. In this paper, we propose an extension of the counterfactual approach to mediation with direct and indirect effects to scenarios where the mediator is a count variable with excess zeroes by utilizing the Marginalized Zero-Inflated Poisson Model (MZIP) for the mediator model. We derive direct and indirect effects for continuous, binary, and count outcomes, as well as adapt to allow mediator-exposure interactions. Our proposed work allows straightforward calculation of direct and indirect effects for the overall population mean values of the mediator, for scenarios in which researchers are interested in generalizing direct and indirect effects to the population. We apply this novel methodology to an application observing how alcohol consumption may explain sex differences in cholesterol and assess model performance via a simulation study comparing the proposed MZIP mediator framework to existing methods for marginal mediator effects.

Unsupervised learning of stationary and switching dynamical system models from Poisson observations.

Objective. Investigating neural population dynamics underlying behavior requires learning accurate models of the recorded spiking activity, which can be modeled with a Poisson observation distribution. Switching dynamical system models can offer both explanatory power and interpretability by piecing together successive regimes of simpler dynamics to capture more complex ones. However, in many cases, reliable regime labels are not available, thus demanding accurate unsupervised learning methods for Poisson observations. Existing learning methods, however, rely on inference of latent states in neural activity using the Laplace approximation, which may not capture the broader properties of densities and may lead to inaccurate learning. Thus, there is a need for new inference methods that can enable accurate model learning.Approach. To achieve accurate model learning, we derive a novel inference method based on deterministic sampling for Poisson observations called the Poisson Cubature Filter (PCF) and embed it in an unsupervised learning framework. This method takes a minimum mean squared error approach to estimation. Terms that are difficult to find analytically for Poisson observations are approximated in a novel way with deterministic sampling based on numerical integration and cubature rules.Main results. PCF enabled accurate unsupervised learning in both stationary and switching dynamical systems and largely outperformed prior Laplace approximation-based learning methods in both simulations and motor cortical spiking data recorded during a reaching task. These improvements were larger for smaller data sizes, showing that PCF-based learning was more data efficient and enabled more reliable regime identification. In experimental data and unsupervised with respect to behavior, PCF-based learning uncovered interpretable behavior-relevant regimes unlike prior learning methods.Significance. The developed unsupervised learning methods for switching dynamical systems can accurately uncover latent regimes and states in population spiking activity, with important applications in both basic neuroscience and neurotechnology.

Comparison of different statistical models for the analysis of fracture events: findings from the Prevention of Falls Injury Trial (PreFIT).

BACKGROUND: Fractures are rare events and can occur because of a fall. Fracture counts are distinct from other count data in that these data are positively skewed, inflated by excess zero counts, and events can recur over time. Analytical methods used to assess fracture data and account for these characteristics are limited in the literature. METHODS: Commonly used models for count data include Poisson regression, negative binomial regression, hurdle regression, and zero-inflated regression models. In this paper, we compare four alternative statistical models to fit fracture counts using data from a large UK based clinical trial evaluating the clinical and cost-effectiveness of alternative falls prevention interventions in older people (Prevention of Falls Injury Trial; PreFIT). RESULTS: The values of Akaike information criterion and Bayesian information criterion, the goodness-of-fit statistics, were the lowest for negative binomial model. The likelihood ratio test of no dispersion in the data showed strong evidence of dispersion (chi-square = 225.68, p-value < 0.001). This indicates that the negative binomial model fits the data better compared to the Poisson regression model. We also compared the standard negative binomial regression and mixed effects negative binomial models. The LR test showed no gain in fitting the data using mixed effects negative binomial model (chi-square = 1.67, p-value = 0.098) compared to standard negative binomial model. CONCLUSIONS: The negative binomial regression model was the most appropriate and optimal fit model for fracture count analyses. TRIAL REGISTRATION: The PreFIT trial was registered as ISRCTN71002650.

Multivariate control charts for monitoring a bivariate correlated count process with application to meningococcal disease.

In recent years, with the increasing number and complexity of infectious diseases, the idea of using control charts to monitor public health and disease has been proposed. In this paper, we study multivariate control charts for monitoring a bivariate integer-valued autocorrelation process with bivariate Poisson distribution and select the optimal control scheme by comparing the performance of control charts. Furthermore, the meningococcal patient event in two states in Australia serves as an example to illustrate the application of these methods. The results show that the D exponentially weighted moving average control scheme can detect the changes in the mean value faster, which is a significant advantage.

[The Validity of the Poisson Distribution to Analyze Microbial Colony Counts on Agar Plates for Food Samples].

Microbial colony counts of food samples in microbiological examinations are one of the most important items. The probability distributions for the colony counts per agar plate at the dilution of counting had not been intensively studied so far. Recently we analyzed the colony counts of food samples with several probability distributions using the Pearson's chi-square value by the "traditional" statistics as the index of fit [Fujikawa and Tsubaki, Food Hyg.Saf.Sc., 60, 88-95 (2019)]. As a result, the selected probability distributions depended on the samples. In this study we newly selected a probability distribution, namely a statistical model, suitable for the above data with the method of maximum likelihood from the probabilistic point of view. The Akaike's Information Criterion (AIC) was used as the index of fit. Consequently, the Poisson model were better than the negative binomial model for all of four food samples. The Poisson model was also better than the binomial for three of four microbial culture samples. With Baysian Information Criterion (BIC), the Poisson model was also better than these two models for all the samples. These results suggested that the Poisson distribution would be the best model to estimate the colony counts of food samples. The present study would be the first report on the statistical model selection for the colony counts of food samples with AIC and BIC.

Classical and Bayesian inference for the discrete Poisson Ramos-Louzada distribution with application to COVID-19 data.

The present study is based on the derivation of a new extension of the Poisson distribution using the Ramos-Louzada distribution. Several statistical properties of the new distribution are derived including, factorial moments, moment-generating function, probability moments, skewness, kurtosis, and dispersion index. Some reliability properties are also derived. The model parameter is estimated using different classical estimation techniques. A comprehensive simulation study was used to identify the best estimation method. Bayesian estimation with a gamma prior is also utilized to estimate the parameter. Three examples were used to demonstrate the utility of the proposed model. These applications revealed that the PRL-based model outperforms certain existing competing one-parameter discrete models such as the discrete Rayleigh, Poisson, discrete inverted Topp-Leone, discrete Pareto and discrete Burr-Hatke distributions.

A Poisson distribution-based general model of cancer rates and a cancer risk-dependent theory of aging.

This article presents a formula for modeling the lifetime incidence of cancer in humans. The formula utilizes a Poisson distribution-based "np" model to predict cancer incidence, with "n" representing the effective number of cell turnover and "p" representing the probability of single-cell transformation. The model accurately predicts the observed incidence of cancer in humans when a reduction in cell turnover due to aging is taken into account. The model also suggests that cancer development is ultimately inevitable. The article proposes a theory of aging based on this concept, called the "np" theory. According to this theory, an organism maintains its order by balancing cellular entropy through continuous proliferation. However, cellular "information entropy" in the form of accumulated DNA mutations increases irreversibly over time, restricting the total number of cells an organism can generate throughout its lifetime. When cell division slows down and fails to compensate for the increased entropy in the system, aging occurs. Essentially, aging is the phenomenon of running out of predetermined cell resources. Different species have evolved separate strategies to utilize their limited cell resources throughout their life cycle.

A flexible quasi-likelihood model for microbiome abundance count data.

In this article, we present a flexible model for microbiome count data. We consider a quasi-likelihood framework, in which we do not make any assumptions on the distribution of the microbiome count except that its variance is an unknown but smooth function of the mean. By comparing our model to the negative binomial generalized linear model (GLM) and Poisson GLM in simulation studies, we show that our flexible quasi-likelihood method yields valid inferential results. Using a real microbiome study, we demonstrate the utility of our method by examining the relationship between adenomas and microbiota. We also provide an R package "fql" for the application of our method.

Dependência funcional dentro e fora de casa em pessoas idosas com 75 anos ou mais: diferenças entre os sexos / Functional disability inside and outside of home of older adults aged 75 years and older: differences between males and females / Dependencia funcional dentro y fuera del hogar en ancianos de 75 años o más: diferencias entre sexos

O sexo é um importante fator a ser considerado na compreensão da dependência de cuidados na velhice. Objetivo:Verificar fatores associados à dependência, dentro e fora de casa, em pessoas idosas com 75 anos ou mais, com ênfase na diferença entre os sexos. Metodologia:Pesquisa transversal com dados do estudo FIBRA. A capacidade funcional nas Atividades Instrumentais de Vida Diária (AIVD) foi dividida em atividades realizadas dentro de casa (uso do telefone, manejo da medicação, tarefas domésticas e preparo da refeição) e atividades realizadas fora de casa (fazer compras, utilizar transporte e manejo do dinheiro). As variáveis independentes incluíram aspectos sociodemográficas e de saúde. Foram estimadas razões de prevalência por meio de modelos de regressão múltipla de Poisson a fim de verificar as variáveis associadas com dependência dentro e fora de casa. Resultados:A amostra foi composta por 804 idosos. Dentro de casa, não houve fatores associados à dependência para o sexo masculino. Enquanto para o sexo feminino, os fatores associados foram fragilidade (RP = 1,99; 95%IC: 1,26-3,15) e 80 anos e mais (RP = 1,41; 95%IC: 1,05-1,89). Quanto à dependência fora de casa, a fragilidade destacou-se como um fator associado a ambos os sexos, masculino (RP = 2,80 95%IC: 1,17-6,64) e feminino (RP = 1,98 95%IC: 1,24-3,17). Conclusões:Para o sexo feminino, a idade avançada e a fragilidade foram os fatores de maior associação com dependência, tanto para o ambiente dentro quanto fora de casa. Para o sexo masculino, a fragilidade foi o único e grande determinante de dependência nas atividades fora de casa, apresentando prevalência maior do que a encontrada na amostra do sexo feminino (AU).

Sexis an important factor to be considered tocomprehendoldage care dependencyObjective:Verify associated factors to dependency, in and out of home, in persons with 75 years or more, with emphasis on sexdifferences. Methodology:Cross-sectional research with data from the FIBRA Study. The functional dependence in Instrumental Activities of Daily Living (IADL) was divided in activities performed inside home (using telephone, managing medicine, housework and meal preparation) and activities performed outside home (shopping, transportation and managing finances).The independent variable included health and sociodemographic aspects. Estimates on prevalence ratios were made using multiple Poisson regression models to verify the many variables associated with dependency inside and outside home. Results:The sample was composed of 804 older people. Inside home there were not any factors associated with dependency in the males. However, in the females the associated factors were frailty (PR = 1.99; 95%CI: 1.26-3.15) and 80 and older (PR = 1.41; 95%CI: 1.05-1.89). As to dependency outside home, frailty was a factor that stood out in both sexes, male (PR = 2.80 95%CI: 1.17-6.64) and female (PR = 1.98 95%CI: 1.24-3.17). Conclusions:To women, older age and frailty were the strongest factors of dependency, to both inside and outside home. To men, frailty was the strongest and single dependency factor for dependency in outside activities, showing a higher prevalence than that of the female sex (AU).

El sexo es un factor importante queconsiderar en la comprensión de la dependencia del cuidado en la vejez. Objetivo:Verificar los factores vinculados a la dependencia, dentro y fuera del hogar, en ancianos de 75 años o más, con énfasis en la diferencia entre los sexos. Metodología:Investigación transversal con datos del estudio FIBRA. La capacidad funcional en las Actividades Instrumentales de la Vida Diaria (AIVD) se dividió en actividades realizadas dentro del hogar (uso del teléfono, administración de medicamentos, tareas domésticas y preparación de comidas) y actividades realizadas fuera del hogar (hacer compras, uso del transporte y manejo del dinero). Las variables independientes incluyeron aspectos sociodemográficos y de salud. Las razones de prevalencia se estimaron utilizando modelos de regresión múltiple de Poisson con el fin de verificarlas variables vinculadas con la dependencia dentro y fuera del hogar. Resultados:El muestreofue constituidopor 804 ancianos. Dentro del hogar, no hubo factores asociados con la dependencia de los hombres. Mientras que, para las mujeres, los factores asociados fueron fragilidad (RP = 1,99; IC95%: 1,26-3,15) y 80 años y más (RP = 1,41; IC95%: 1,05-1,89). En cuanto a la dependencia fuera del hogar, la fragilidad se destacó como un factor asociado a ambos sexos, masculino (RP = 2,80 IC95%: 1,17-6,64) y femenino (RP = 1,98 IC95%: 1,24-3,17). Conclusiones: Para el sexo femenino, la edad avanzada y la fragilidad fueron los factores más vinculados a la dependencia, tanto para el ambiente dentro como fuera del hogar. Para los varones, la fragilidad fue el único determinante importante de dependencia en actividades fuera del hogar, con una prevalencia mayor que la encontrada en elmuestreofemenino (AU).