Functional mobility and 10-year all-cause and cause-specific mortality in older people from São Paulo, Brazil.

Background: A better understanding of performance in functional mobility tasks related to the mortality patterns for the different causes of death for the Brazilian older population is still a challenge.

Objective: To analyze if gait speed and chair stand test performance are associated with mortality in older adults, and if the overall mobility status changes the effect of other mortality risk factors.

Methods: The data were from SABE (Health, Well-being and Aging Study), a multiple-cohort study conducted in São Paulo, Brazil, with a representative sample of people aged 60 and more.

[Factors associated with limitations in functional mobility in elderly in the city of São Paulo, Brazil: a comparative analysis of 15 years].

The study aimed to analyze the prevalence of self-reported limitation of functional mobility and associated factors from 2000 to 2015 in elderly residing in the city of São Paulo, Brazil. The analyses used data from the four waves (2000, 2006, 2010, and 2015) in the Health, Well-Being, and Aging Study (SABE). Regression models were conducted to analyze the demographic, socioeconomic, behavioral, and health-related characteristics of individuals associated with limitations of mobility in each wave of the study, and multilevel analysis was performed for comparison between the four waves.

Early identification of older individuals at risk of mobility decline with machine learning.

Background: The early identification of individuals at risk of mobility decline can improve targeted strategies of prevention.

Aims: To evaluate the predictive performance of machine learning (ML) algorithms in identifying older individuals at risk of future mobility decline.

Methods: We used data from the SABE Study (Health, Well-being and Aging Study), a representative sample of people aged 60 years and more, living in the Municipality of São Paulo, Brazil.

Cause-specific mortality prediction in older residents of São Paulo, Brazil: a machine learning approach.

Background: Populational ageing has been increasing in a remarkable rate in developing countries. In this scenario, preventive strategies could help to decrease the burden of higher demands for healthcare services. Machine learning algorithms have been increasingly applied for identifying priority candidates for preventive actions, presenting a better predictive performance than traditional parsimonious models.

Gender Differences between Multimorbidity and All-Cause Mortality among Older Adults.

Objectives: This study seeks to determine the prevalence of chronic diseases and analyze the association between multimorbidity and all-cause mortality by sex.

Methods: This is a 16-year longitudinal study of follow-up. We used sample data of the SABE (Health, Well-Being and Aging) study cohort and mortality data obtained through the Mortality Information Improvement Program of the City of São Paulo (PRO-AIM) from the 2000-2016 period.

Blurred lines: racial misclassification in death certificates in Brazil.

Objectives: To analyze the agreement between self-reported race and race reported on death certificates for older (≥ 60 years) residents of São Paulo, Brazil (from 2000 to 2016) and to estimate weights to correct mortality data by race.

Methods: We used data from the Health, Well-Being and Aging Study (SABE) and from Brazil's Mortality Information System. Misclassification was identified by comparing individual self-reported race with the corresponding race on the death certificate (n = 1012).

Reproductive factors and age at natural menopause: A systematic review and meta-analysis.

Objectives: To systematically review the evidence on the association between age at natural menopause (NM) and reproductive factors such as age at menarche, parity and ever use of oral contraceptives.

Study Design: A literature search was carried out in PubMed, Scielo, Scopus and LILACS databases, without restriction of publication year until July 6, 2017. We excluded clinical trials, case-control studies, case reports and studies using statistical methods other than Cox proportional hazard models to assess the factors associated with age at NM.

[Machine learning for predictive analyses in health: an example of an application to predict death in the elderly in São Paulo, Brazil].

This study aims to present the stages related to the use of machine learning algorithms for predictive analyses in health. An application was performed in a database of elderly residents in the city of São Paulo, Brazil, who participated in the Health, Well-Being, and Aging Study (SABE) (n = 2,808). The outcome variable was the occurrence of death within five years of the elder's entry into the study (n = 423), and the predictors were 37 variables related to the elder's demographic, socioeconomic, and health profile.

Age at natural menopause and mortality: A survival analysis of elderly residents of São Paulo, Brazil.

Objective: To conduct a survival analysis according to age at natural menopause (NM) in a representative sample of elderly women from the municipality of São Paulo, Brazil.

Study Design: We analyzed data from the Health, Well-Being and Aging study (SABE), a cohort that started in 2000. Mortality data up to September 2016 were obtained by linkage from the Program for Mortality Information of São Paulo (PRO-AIM).

Overachieving Municipalities in Public Health: A Machine-learning Approach.

Background: Identifying successful public health ideas and practices is a difficult challenge towing to the presence of complex baseline characteristics that can affect health outcomes. We propose the use of machine learning algorithms to predict life expectancy at birth, and then compare health-related characteristics of the under- and overachievers (i.e.

Individual and contextual characteristics of indoor and outdoor falls in older residents of São Paulo, Brazil.

Purpose Of The Study: To analyze a representative sample of older individuals of São Paulo, Brazil, according to outdoor fallers, indoor fallers and non-fallers, and to identify biological and socioeconomic (individual and contextual) factors associated with the occurrence and place of falls.

Materials And Methods: A cross-sectional study was conducted using data (n = 1345) from the 2010 wave of the Health, Wellbeing and Aging (SABE) Study, a representative sample of older residents (60 years and older) of São Paulo, Brazil. Multinomial logistic analysis was performed to identify individual factors associated with the occurrence and place of falls, and multilevel multinomial analysis to identify contextual effects (green areas, violence, presence of slums and income inequality).

Individual and Neighborhood Factors Associated With Functional Mobility and Falls in Elderly Residents of São Paulo, Brazil: A Multilevel Analysis.

Objective: To identify socioeconomic and contextual factors associated with functional mobility and falls in elderly residents of São Paulo, Brazil.

Method: We used data from the Health, Well-Being, and Aging ( Saúde, Bem-estare Envelhecimento [SABE]) Study. The dependent variables were falling in the last year and functional mobility impairment.