Mortality, survival, and causes of death in mental disorders: comprehensive prospective analyses of the UK Biobank cohort.

BACKGROUND: The UK Biobank is a large middle-aged cohort recruited in 2006-2010. We used data from its participants to analyze mortality, survival, and causes of death associated with mental disorders. METHODS: Our exposures were mental disorders identified using (1) symptom-based outcomes derived from an online Mental Health Questionnaire (n = 157 329), including lifetime/current depression, lifetime/current generalized anxiety disorder, lifetime/recent psychotic experience, lifetime bipolar disorder, current alcohol use disorder, and current posttraumatic stress disorder and (2) hospital data linkage of diagnoses within the International Classification of Diseases, 10th revision (ICD-10) (n = 502 422), including (A) selected diagnoses or groups of diagnoses corresponding to symptom-based outcomes and (B) all psychiatric diagnoses, grouped by ICD-10 section. For all exposures, we estimated age-adjusted mortality rates and hazard ratios, as well as proportions of deaths by cause. RESULTS: We found significantly increased mortality risk associated with all mental disorders identified by symptom-based outcomes, except for lifetime generalized anxiety disorder (with hazard ratios in the range of 1.08-3.0). We also found significantly increased mortality risk associated with all conditions identified by hospital data linkage, including selected ICD-10 diagnoses or groups of diagnoses (2.15-7.87) and ICD-10 diagnoses grouped by section (2.02-5.44). Causes of death associated with mental disorders were heterogeneous and mostly natural. CONCLUSIONS: In a middle-aged cohort, we found a higher mortality risk associated with most mental disorders identified by symptom-based outcomes and with all disorders or groups of disorders identified by hospital data linkage of ICD-10 diagnoses. The majority of deaths associated with mental disorders were natural.

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. METHODS: Data were collected from the Health, Well Being and Aging (SABE) Study, a representative sample of older residents of São Paulo, Brazil. Machine learning algorithms were applied to predict death by diseases of respiratory system (DRS), diseases of circulatory system (DCS), neoplasms and other specific causes within 5 years, using socioeconomic, demographic and health features. The algorithms were trained in a random sample of 70% of subjects, and then tested in the other 30% unseen data. RESULTS: The outcome with highest predictive performance was death by DRS (AUC-ROC = 0.89), followed by the other specific causes (AUC-ROC = 0.87), DCS (AUC-ROC = 0.67) and neoplasms (AUC-ROC = 0.52). Among only the 25% of individuals with the highest predicted risk of mortality from DRS were included 100% of the actual cases. The machine learning algorithms with the highest predictive performance were light gradient boosted machine and extreme gradient boosting. CONCLUSION: The algorithms had a high predictive performance for DRS, but lower for DCS and neoplasms. Mortality prediction with machine learning can improve clinical decisions especially regarding targeted preventive measures for older individuals.

The relationship between neighborhood-level socioeconomic characteristics and individual mental disorders in five cities in Latin America: multilevel models from the World Mental Health Surveys.

PURPOSE: Our understanding of community-level predictors of individual mental disorders in large urban areas of lower income countries is limited. In particular, the proportion of migrant, unemployed, and poorly educated residents in neighborhoods of these urban areas may characterize group contexts and shape residents' health. METHODS: Cross-sectional household interviews of 7251 adults were completed across 83 neighborhoods of Buenos Aires, Argentina; Medellín, Colombia; São Paulo, Brazil; Lima, Peru; and Mexico City, Mexico as part of the World Mental Health Survey Initiative. Past-year internalizing and externalizing mental disorders were assessed, and multilevel models were used. RESULTS: Living in neighborhoods with either an above-average or below-average proportion of migrants and highly educated residents was associated with lower odds of any internalizing disorder (for proportion migrants: OR 0.75, 95% CI 0.62-0.91 for the bottom tertile and OR 0.79, 95% CI 0.67-0.94 for the top tertile compared to the middle tertile; for proportion highly educated: OR 0.76, 95% CI 0.64-0.90 for the bottom tertile and OR 0.58, 95% CI 0.37-0.90 for the top tertile compared to the middle tertile). Living in neighborhoods with an above-average proportion of unemployed individuals was associated with higher odds of having any internalizing disorder (OR 1.49, 95% CI 1.14-1.95 for the top tertile compared to the middle tertile). The proportion of highly educated residents was associated with lower odds of externalizing disorder (OR 0.54, 95% CI 0.31-0.93 for the top tertile compared to the middle tertile). CONCLUSIONS: The associations of neighborhood-level migration, unemployment, and education with individual-level odds of mental disorders highlight the importance of community context for understanding the burden of mental disorders among residents of rapidly urbanizing global settings.

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., municipalities that have a worse and better outcome than predicted, respectively). METHODS: Our outcome was life expectancy at birth for Brazilian municipalities, and we used as predictors 60 local characteristics that are not directly controlled by public health officials (e.g., socioeconomic factors). RESULTS: The highest predictive performance was achieved by an ensemble of machine learning algorithms (cross-validated mean squared error of 0.168), including a 35% gain in comparison with standard decision trees. Overachievers presented better results regarding primary health care, such as higher coverage of the massive multidisciplinary program Family Health Strategy. On the other hand, underachievers performed more cesarean deliveries and mammographies and had more life-support health equipment. CONCLUSIONS: The findings suggest that analyzing the predicted value of a health outcome may bring insights about good public health practices.

Zika-Associated Microcephaly Epidemic and Birth Rate Reduction in Brazilian Cities.

OBJECTIVES: To estimate birth reduction potentially in response to Zika virus-associated microcephaly among the 36 largest Brazilian cities. METHODS: We analyzed the number of live births per month on the basis of information on approximately 8.2 million births from all of Brazil's state capitals and cities that had more than 10 000 annual births. RESULTS: In the second half of 2016, the live birth rate was reduced by 7.78% (95% confidence interval [CI] = 6.64%, 8.89%; P < .001). This reduction was correlated with the Zika virus-associated microcephaly rate. In the cities with the highest microcephaly rate in 2015 (> 1 case per 1000 live births), the reduction in the live birth rate was 10.84% (95% CI = 8.58%, 13.04%). CONCLUSIONS: The birth rate in the largest Brazilian cities during the second half of 2016 was significantly reduced, which is potentially the effect of a birth control recommendation prompted by an epidemiological alert. Public Health Implications. The effects of population-based interventions should be weighed by considering the actual risk of disease and the sociodemographic impact of strategies such as birth control.

Incremental health expenditure and lost days of normal activity for individuals with mental disorders: results from the São Paulo Megacity Study.

BACKGROUND: With the recent increase in the prevalence of mental disorders in developing countries, there is a growing interest in the study of its consequences. We examined the association of depression, anxiety and any mental disorders with incremental health expenditure, i.e. the linear increase in health expenditure associated with mental disorders, and lost days of normal activity. METHODS: We analyzed the results from a representative sample survey of residents of the Metropolitan Region of São Paulo (n = 2,920; São Paulo Megacity Mental Health Survey), part of the World Mental Health (WMH) Survey Initiative, coordinated by the World Health Organization and performed in 28 countries. The instrument used for obtaining the individual results, including the assessment of mental disorders, was the WMH version of the Composite International Diagnostic Interview 3.0 (WMH-CIDI 3.0) that generates psychiatric diagnoses according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria. Statistical analyses were performed by multilevel generalized least squares (GLS) regression models. Sociodemographic determinants such as income, age, education and marital status were included as controls. RESULTS: Depression, anxiety and any mental disorders were consistently associated with both incremental health expenditure and missing days of normal activity. Depression was associated with an incremental annual expenditure of R$308.28 (95% CI: R$194.05-R$422.50), or US$252.48 in terms of purchasing power parity (PPP). Anxiety and any mental disorders were associated with a lower, but also statistically significant, incremental annual expenditure (R$177.82, 95% CI: 79.68-275.97; and R$180.52, 95% CI: 91.13-269.92, or US$145.64 and US$147.85 in terms of PPP, respectively). Most of the incremental health costs associated with mental disorders came from medications. Depression was independently associated with higher incremental health expenditure than the two most prevalent chronic diseases found by the study (hypertension and diabetes). CONCLUSIONS: The fact that individuals with mental disorders had a consistent higher health expenditure is notable given the fact that Brazil has a universal free-of-charge healthcare and medication system. The results highlight the growing importance of mental disorders as a public health issue for developing countries.

Racial disparities in life expectancy in Brazil: challenges from a multiracial society.

OBJECTIVES: We calculated life expectancy at birth for Whites, Blacks, and mixed races in Brazil, and decomposed the differences by causes of death. METHODS: We used Ministry of Health death records and 2010 Census population data (190,755,799 residents and 1,136,947 deaths). We applied the Arriaga methodology to calculate decomposition of life expectancy by cause of death. We performed sensitivity analyses for underreporting of deaths, missing data, and numerator-denominator bias. RESULTS: Using standard life table methods, female life expectancy was highest for mixed races (78.80 years), followed by Whites (77.54 years), then Blacks (76.32 years). Male life expectancy was highest for Whites (71.10 years) followed closely by mixed races (71.08 years), and lower for Blacks (70.11 years). Homicides contributed the most to the relative life expectancy increase for Whites, and cancer decreased the gap. After adjustment for underreporting, missing data, and numerator-denominator bias, life expectancy was higher for Whites than for Blacks and mixed races. CONCLUSIONS: Despite wide socioeconomic differences between Whites and mixed races, standard life table methods showed that mixed races had higher life expectancy than Whites for women, and similar for men. With the increase of multiracial populations, measuring racial disparities in life expectancy will be a fast-growing challenge.

Improving the performance of machine learning algorithms for health outcomes predictions in multicentric cohorts.

Machine learning algorithms are being increasingly used in healthcare settings but their generalizability between different regions is still unknown. This study aims to identify the strategy that maximizes the predictive performance of identifying the risk of death by COVID-19 in different regions of a large and unequal country. This is a multicenter cohort study with data collected from patients with a positive RT-PCR test for COVID-19 from March to August 2020 (n = 8477) in 18 hospitals, covering all five Brazilian regions. Of all patients with a positive RT-PCR test during the period, 2356 (28%) died. Eight different strategies were used for training and evaluating the performance of three popular machine learning algorithms (extreme gradient boosting, lightGBM, and catboost). The strategies ranged from only using training data from a single hospital, up to aggregating patients by their geographic regions. The predictive performance of the algorithms was evaluated by the area under the ROC curve (AUROC) on the test set of each hospital. We found that the best overall predictive performances were obtained when using training data from the same hospital, which was the winning strategy for 11 (61%) of the 18 participating hospitals. In this study, the use of more patient data from other regions slightly decreased predictive performance. However, models trained in other hospitals still had acceptable performances and could be a solution while data for a specific hospital is being collected.

Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach.

Objectives: Our aim was to test if machine learning algorithms can predict cancer mortality (CM) at an ecological level and use these results to identify statistically significant spatial clusters of excess cancer mortality (eCM). Methods: Age-standardized CM was extracted from the official databases of Brazil. Predictive features included sociodemographic and health coverage variables. Machine learning algorithms were selected and trained with 70% of the data, and the performance was tested with the remaining 30%. Clusters of eCM were identified using SatScan. Additionally, separate analyses were performed for the 10 most frequent cancer types. Results: The gradient boosting trees algorithm presented the highest coefficient of determination (R 2 = 0.66). For total cancer, all algorithms overlapped in the region of Bagé (27% eCM). For esophageal cancer, all algorithms overlapped in west Rio Grande do Sul (48%-96% eCM). The most significant cluster for stomach cancer was in Macapá (82% eCM). The most important variables were the percentage of the white population and residents with computers. Conclusion: We found consistent and well-defined geographic regions in Brazil with significantly higher than expected cancer mortality.

Emergency department use and Artificial Intelligence in Pelotas: design and baseline results.

OBJETIVO: To describe the initial baseline results of a population-based study, as well as a protocol in order to evaluate the performance of different machine learning algorithms with the objective of predicting the demand for urgent and emergency services in a representative sample of adults from the urban area of Pelotas, Southern Brazil. METHODS: The study is entitled "Emergency department use and Artificial Intelligence in PELOTAS (RS) (EAI PELOTAS)" (https://wp.ufpel.edu.br/eaipelotas/). Between September and December 2021, a baseline was carried out with participants. A follow-up was planned to be conducted after 12 months in order to assess the use of urgent and emergency services in the last year. Afterwards, machine learning algorithms will be tested to predict the use of urgent and emergency services over one year. RESULTS: In total, 5,722 participants answered the survey, mostly females (66.8%), with an average age of 50.3 years. The mean number of household people was 2.6. Most of the sample has white skin color and incomplete elementary school or less. Around 30% of the sample has obesity, 14% diabetes, and 39% hypertension. CONCLUSION: The present paper presented a protocol describing the steps that were and will be taken to produce a model capable of predicting the demand for urgent and emergency services in one year among residents of Pelotas, in Rio Grande do Sul state.

Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study.

Artificial intelligence (AI) algorithms are transforming several areas of the digital world and are increasingly being applied in healthcare. Mobile apps based on predictive machine learning models have the potential to improve health outcomes, but there is still no consensus on how to inform doctors about their results. The aim of this study was to investigate how healthcare professionals prefer to receive predictions generated by machine learning algorithms. A systematic search in MEDLINE, via PubMed, EMBASE and Web of Science was first performed. We developed a mobile app, RandomIA, to predict the occurrence of clinical outcomes, initially for COVID-19 and later expected to be expanded to other diseases. A questionnaire called System Usability Scale (SUS) was selected to assess the usability of the mobile app. A total of 69 doctors from the five regions of Brazil tested RandomIA and evaluated three different ways to visualize the predictions. For prognostic outcomes (mechanical ventilation, admission to an intensive care unit, and death), most doctors (62.9%) preferred a more complex visualization, represented by a bar graph with three categories (low, medium, and high probability) and a probability density graph for each outcome. For the diagnostic prediction of COVID-19, there was also a majority preference (65.4%) for the same option. Our results indicate that doctors could be more inclined to prefer receiving detailed results from predictive machine learning algorithms.

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. Cox regression models were used to analyze 10-year all-cause and cause-specific mortality with consideration for gait speed and the chair stand test. RESULTS: Of the 1411 participants, 26% died during the follow-up. The performance in the chair stand test had a more consistent association with mortality (hazard ratio (HR)=1.03, 95%CI: 1.00, 1.05) than gait speed. Being unable to perform the test also increased the risk to die by all-cause (HR=1.71, 95%CI: 1.21, 2.42) and by diseases of the circulatory system (HR=2.14, 95%CI: 1.25, 3.65). The stratified analysis of mobility performance changed the effects of some of the mortality risk factors, such as cognitive impairment and multimorbidity. CONCLUSIONS: The chair stand test could be a better choice than 3-meters walking test as a mortality predictor. In addition, the impact of cognitive decline and multimorbidity were greater among those with reduced mobility, supporting the development of preventive interventions and public policies targeted at more vulnerable groups of older adults.

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. Mobility decline was assessed 5 years after admission in the study by self-reported difficulty to walk a block, climb steps, being able to stoop, crouch and kneel, or lifting or carrying weights greater than 5 kg. Popular machine learning algorithms were trained in 70% of the sample with 10-fold cross-validation, and predictive performance metrics were obtained from applying the trained algorithms to the other 30% (test set). RESULTS: Of the 1,615 individuals, 48% developed difficulty in at least one of the four tasks, 32% in stooping, crouching and kneeling, and 30% in carrying weights. The random forest algorithm had the best predictive performance for most outcomes. The tasks that the algorithm was able to predict with better performance were crouching and kneeling (AUC-ROC: 0.81[0.76-0.85]), and lifting or carrying weights (AUC-ROC: 0.80[0.75-0.84]). Age was the most important variable for the algorithms, followed by education and back pain, according to the SHAP (SHapley Additive exPlanations) values. CONCLUSION: Applications of ML algorithms are a promising tool to identify older patients at risk of mobility decline, with the potential of improving targeted preventive programs.

Clusters of Pregnant Women with Severe Acute Respiratory Syndrome Due to COVID-19: An Unsupervised Learning Approach.

COVID-19 has been widely explored in relation to its symptoms, outcomes, and risk profiles for the severe form of the disease. Our aim was to identify clusters of pregnant and postpartum women with severe acute respiratory syndrome (SARS) due to COVID-19 by analyzing data available in the Influenza Epidemiological Surveillance Information System of Brazil (SIVEP-Gripe) between March 2020 and August 2021. The study's population comprised 16,409 women aged between 10 and 49 years old. Multiple correspondence analyses were performed to summarize information from 28 variables related to symptoms, comorbidities, and hospital characteristics into a set of continuous principal components (PCs). The population was segmented into three clusters based on an agglomerative hierarchical cluster analysis applied to the first 10 PCs. Cluster 1 had a higher frequency of younger women without comorbidities and with flu-like symptoms; cluster 2 was represented by women who reported mainly ageusia and anosmia; cluster 3 grouped older women with the highest frequencies of comorbidities and poor outcomes. The defined clusters revealed different levels of disease severity, which can contribute to the initial risk assessment of the patient, assisting the referral of these women to health services with an appropriate level of complexity.

Prediction of absenteeism in public schools teachers with machine learning.

OBJECTIVE: To predict the risk of absence from work due to morbidities of teachers working in early childhood education in the municipal public schools, using machine learning algorithms. METHODS: This is a cross-sectional study using secondary, public and anonymous data from the Relação Anual de Informações Sociais, selecting early childhood education teachers who worked in the municipal public schools of the state of São Paulo between 2014 and 2018 (n = 174,294). Data on the average number of students per class and number of inhabitants in the municipality were also linked. The data were separated into training and testing, using records from 2014 to 2016 (n = 103,357) to train five predictive models, and data from 2017 to 2018 (n = 70,937) to test their performance in new data. The predictive performance of the algorithms was evaluated using the value of the area under the ROC curve (AUROC). RESULTS: All five algorithms tested showed an area under the curve above 0.76. The algorithm with the best predictive performance (artificial neural networks) achieved 0.79 of area under the curve, with accuracy of 71.52%, sensitivity of 72.86%, specificity of 70.52%, and kappa of 0.427 in the test data. CONCLUSION: It is possible to predict cases of sickness absence in teachers of public schools with machine learning using public data. The best algorithm showed a better result of the area under the curve when compared with the reference model (logistic regression). The algorithms can contribute to more assertive predictions in the public health and worker health areas, allowing to monitor and help prevent the absence of these workers due to morbidity.

Income Inequality and Self-Reported Health Among Older Adults in Brazil.

This study analyzes the association between income inequality and self-reported health (SRH) in older adults, and separately for the young-old and very-old groups, residing in each of the 27 Brazilian capitals. The sample consisted of 4,912 individuals aged 60 or older residing in Brazilian capitals in 2013. Bayesian multilevel models were applied to the whole sample and separately for individuals aged 60 to 79 (young-old), and 80 or more (very-old). Our results show significant associations between income inequality and SRH, even after controlling for individual and contextual factors. We found greater odds of poor SRH among older adults living in areas with medium (odds ratio [OR] = 1.66, 95% confidence interval [CI]: 1.49-1.86) and high-income inequality (OR = 2.21, 95% CI: 2.05-2.38). The negative association between income inequality and health, independently of the individual and contextual characteristics, suggests that living in unequal areas can have a detrimental effect on the health of older adults.

A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil.

The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing countries. This study proposes to predict the risk of developing critical conditions in COVID-19 patients by training multipurpose algorithms. We followed a total of 1040 patients with a positive RT-PCR diagnosis for COVID-19 from a large hospital from São Paulo, Brazil, from March to June 2020, of which 288 (28%) presented a severe prognosis, i.e. Intensive Care Unit (ICU) admission, use of mechanical ventilation or death. We used routinely-collected laboratory, clinical and demographic data to train five machine learning algorithms (artificial neural networks, extra trees, random forests, catboost, and extreme gradient boosting). We used a random sample of 70% of patients to train the algorithms and 30% were left for performance assessment, simulating new unseen data. In order to assess if the algorithms could capture general severe prognostic patterns, each model was trained by combining two out of three outcomes to predict the other. All algorithms presented very high predictive performance (average AUROC of 0.92, sensitivity of 0.92, and specificity of 0.82). The three most important variables for the multipurpose algorithms were ratio of lymphocyte per C-reactive protein, C-reactive protein and Braden Scale. The results highlight the possibility that machine learning algorithms are able to predict unspecific negative COVID-19 outcomes from routinely-collected data.

Machine learning analysis to predict health outcomes among emergency department users in Southern Brazil: a protocol study.

OBJECTIVE: Emergency services are essential to the organization of the health care system. Nevertheless, they face different operational difficulties, including overcrowded services, largely explained by their inappropriate use and the repeated visits from users. Although a known situation, information on the theme is scarce in Brazil, particularly regarding longitudinal user monitoring. Thus, this project aims to evaluate the predictive performance of different machine learning algorithms to estimate the inappropriate and repeated use of emergency services and mortality. METHODS: To that end, a study will be conducted in the municipality of Pelotas, Rio Grande do Sul, with around five thousand users of the municipal emergency department. RESULTS: If the study is successful, we will provide an algorithm that could be used in clinical practice to assist health professionals in decision-making within hospitals. Different knowledge dissemination strategies will be used to increase the capacity of the study to produce innovations for the organization of the health system and services. CONCLUSION: A high performance predictive model may be able to help decisionmaking in the emergency services, improving quality of care.

Basic sanitation and self-reported health in Brazilian capitals: a multilevel analysis. / Saneamento básico e saúde autoavaliada nas capitais brasileiras: uma análise multinível.

OBJECTIVE: This study aimed to analyze the association between the contextual determinants related to basic sanitation and self-reported health in Brazilian capitals. METHODS: The sample consisted of 27,017 adults (≥18 years) residing in the 27 Brazilian capitals in 2013, from the National Health Survey (PNS). The association between self-reported health and sanitation (sewage system, water supply and garbage collection) was analyzed using Bayesian multilevel models, controlling for individual factors (first level of the model) and area-level socioeconomic characteristics (second level). RESULTS: We found a consistent association between better self-reported health and better sanitation levels, even after controlling for individual and contextual characteristics. At the contextual level, lower odds of poor self-reported health was observed among those living in areas with medium (OR = 0.59, 95%CI 0.57 - 0.61) or high (OR = 0.61, 95%CI 0.57 - 0.66) sewage system level; medium (OR = 0.77, 95%CI 0.71 - 0.83) coverage of water supply; and high (OR = 0.78, 95%CI 0.69 - 0.89) garbage collection level. CONCLUSION: The positive association between better sanitation conditions and health, independently of the individual factors and the socioeconomic characteristics of the place of residence, confirms the need to consider sanitation in the planning of health policies.

OBJETIVO: Analisar a associação entre os determinantes contextuais referentes ao saneamento básico e a autoavaliação de saúde nas capitais brasileiras. MÉTODOS: Analisaram-se 27.017 adultos (≥ 18 anos) residentes nas 27 capitais brasileiras em 2013, utilizando dados da Pesquisa Nacional de Saúde (PNS). Ajustaram-se modelos multiníveis logísticos bayesianos para analisar a associação entre a autoavaliação de saúde e a cobertura dos serviços de saneamento básico (rede de esgoto, abastecimento de água e coleta de lixo), controlando a análise por fatores individuais (primeiro nível do modelo) e renda per capita da cidade de residência (segundo nível). RESULTADOS: A maior cobertura de serviços de saneamento básico esteve consistentemente associada à melhor percepção da saúde, mesmo após o controle pelas características individuais e contextuais. Observou-se menor chance de autoavaliação ruim de saúde entre indivíduos que viviam em capitais com média (odds ratio - OR = 0,59; intervalo de confiança - IC95% = 0,57 - 0,61) e alta (OR = 0,61; IC95% = 0,57 - 0,66) cobertura da rede de coleta de esgoto; média (OR = 0,77; IC95% = 0,71 - 0,83) cobertura de serviço de abastecimento de água; e alta (OR = 0,78; IC95% = 0,69 - 0,89) proporção de coleta de lixo. CONCLUSÃO: A associação positiva entre melhores condições de saneamento básico e a autoavaliação da saúde, independentemente dos fatores individuais e das condições socioeconômicas do local de residência, confirma a necessidade de se considerar o saneamento básico na elaboração de políticas de saúde.

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). Racial agreement was analyzed by performing sensitivity and Cohen's Kappa tests. Multinomial logistic regressions were adjusted to identify characteristics associated with misclassification. Correction weights were applied to race-specific mortality rates. RESULTS: Total racial misclassification was 17.3% (13.1% corresponded to whitening, and 4.2% to blackening). Racial misclassification was higher for self-reported pardos/mixed (63.5%), followed by blacks (42.6%). Official vital statistics suggest highest elderly mortality rates for whites, but after applying correction weights, black individuals had the highest rate (45.85/1000 population), followed by pardos/mixed (42.30/1000 population) and whites (37.91/1000 population). CONCLUSIONS: Official Brazilian data on race-specific mortality rates may be severely misclassified, resulting in biased estimates of racial inequalities.