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Exposure to particulate matter (PM) pollution is a significant health risk, driving the search for innovative metrics that more accurately reflect the potential harm to human health. Among these, oxidative potential (OP) has emerged as a promising health-based metric, yet its application and relevance across different environments remain to be further explored. This study, set in two high-altitude Bolivian cities, aims to identify the most significant sources of PM-induced oxidation in the lungs and assess the utility of OP in assessing PM health impacts. Utilizing two distinct assays, OPDTT and OPDCFH, we measured the OP of PM samples, while also examining the associations between PM mass, OP, and black carbon (BC) concentrations with hospital visits for acute respiratory infections (ARI) and pneumonia over a range of exposure lags (0-2 weeks) using a Poisson regression model adjusted for meteorological conditions. The analysis also leveraged Positive Matrix Factorization (PMF) to link these health outcomes to specific PM sources, building on a prior source apportionment study utilizing the same dataset. Our findings highlight anthropogenic combustion, particularly from traffic and biomass burning, as the primary contributors to OP in these urban sites. Significant correlations were observed between both OPDTT and PM2.5 concentration exposure and ARI hospital visits, alongside a notable association with pneumonia cases and OPDTT levels. Furthermore, PMF analysis demonstrated a clear link between traffic-related pollution and increased hospital admissions for respiratory issues, affirming the health impact of these sources. These results underscore the potential of OPDTT as a valuable metric for assessing the health risks associated with acute PM exposure, showcasing its broader application in environmental health studies.
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Poluentes Atmosféricos , Altitude , Cidades , Material Particulado , Material Particulado/análise , Bolívia/epidemiologia , Humanos , Poluentes Atmosféricos/análise , Adulto , Infecções Respiratórias/epidemiologia , Oxirredução , Masculino , Pessoa de Meia-Idade , Feminino , Pneumonia/epidemiologia , Pneumonia/induzido quimicamente , Adulto Jovem , Adolescente , Poluição do Ar/análise , Poluição do Ar/efeitos adversos , Criança , Monitoramento Ambiental/métodos , Pré-EscolarRESUMO
Introducción: La enfermedad por coronavirus (COVID-19) se ha extendido entre la población de todo el país y ha tenido un gran impacto a nivel mundial. Sin embargo, existen diferencias geográficas importantes en la mortalidad de COVID-19 entre las diferentes regiones del mundo y en Costa Rica. Objetivo: Explorar el efecto de algunos de los factores sociodemográficos en la mortalidad de COVID-19 en pequeñas divisiones geográficas o cantones de Costa Rica. Métodos: Usamos registros oficiales y aplicamos un modelo de regresión clásica de Poisson y un modelo de regresión ponderada geográficamente. Resultados: Obtuvimos un criterio de información de Akaike (AIC) más bajo con la regresión ponderada (927.1 en la regresión de Poison versus 358.4 en la regresión ponderada). Los cantones con un mayor riesgo de mortalidad por COVID-19 tuvo una población más densa; bienestar material más alto; menor proporción de cobertura de salud y están ubicadas en el área del Pacífico de Costa Rica. Conclusiones: Una estrategia de intervención de COVID-19 específica debería concentrarse en áreas de la costa pacífica con poblaciones más densas, mayor bienestar material y menor población por unidad de salud.
Introduction: The coronavirus disease (COVID-19) has spread among the population of Costa Rica and has had a great global impact. However, there are important geographic differences in mortality from COVID-19 among world regions and within Costa Rica. Objective: To explore the effect of some sociodemographic factors on COVID-19 mortality in the small geographic divisions or cantons of Costa Rica. Methods: We used official records and applied a classical epidemiological Poisson regression model and a geographically weighted regression model. Results: We obtained a lower Akaike Information Criterion with the weighted regression (927.1 in Poisson regression versus 358.4 in weighted regression). The cantons with higher risk of mortality from COVID-19 had a denser population; higher material well-being; less population by health service units and are located near the Pacific coast. Conclusions: A specific COVID-19 intervention strategy should concentrate on Pacific coast areas with denser population, higher material well-being and less population by health service units.
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Introduction: As the studies predicting mortality in severe acute respiratory illness (SARI) have inferred associations either from dichotomous outcomes or from time-event models, we identified some clinical-epidemiological characteristics and predictors of mortality by comparing and discussing two multivariate models. Methods: To identify factors associated with death among all SARI hospitalizations occurred in Botucatu (Brazil)/regardless of the infectious agent, and among the COVID-19 subgroup, from March 2020 to 2022, we used a multivariate Poisson regression model with binomial outcomes and Cox proportional hazards (time-event). The performance metrics of both models were also analyzed. Results: A total of 3,995 hospitalized subjects were included, of whom 1338 (33%) tested positive for SARS-CoV-2. We identified 866 deaths, of which 371 (43%) were due to the COVID-19. In the total number of SARI cases, using both Poisson and Cox models, the predictors of mortality were the presence of neurological diseases, immunosuppression, obesity, older age, and need for invasive ventilation support. However, the Poisson test also revealed that admission to an intensive care unit and the COVID-19 diagnosis were predictors of mortality, with the female gender having a protective effect against death. Likewise, Poisson proved to be more sensitive and specific, and indeed the most suitable model for analyzing risk factors for death in patients with SARI/COVID-19. Conclusion: Given these results and the acute course of SARI and COVID-19, to compare the associations and their different meanings is essential and, therefore, models with dichotomous outcomes are more appropriate than time-to-event/survival approaches.
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COVID-19 , Humanos , Feminino , COVID-19/epidemiologia , SARS-CoV-2 , Pandemias , Teste para COVID-19 , Fatores de RiscoRESUMO
Dengue is an arbovirus transmitted by mosquitoes of the genus Aedes and is one of the 15 main public health problems in the world, including Colombia. Where limited financial resources create a problem for management, there is a need for the department to prioritize target areas for public health implementation. This study focuses on a spatio-temporal analysis to determine the targeted area to manage the public health problems related to dengue cases. To this end, three phases at three different scales were carried out. First, for the departmental scale, four risk clusters were identified in Cauca (RR ≥ 1.49) using the Poisson model, and three clusters were identified through Getis-Ord Gi* hotspots analysis; among them, Patía municipality presented significantly high incidence rates in the time window (2014-2018). Second, on the municipality scale, altitude and minimum temperature were observed to be more relevant than precipitation; considering posterior means, no spatial autocorrelation for the Markov Chain Monte Carlo was found (Moran test Ë 1.0), and convergence was reached for b1-b105 with 20,000 iterations. Finally, on the local scale, a clustered pattern was observed for dengue cases distribution (nearest neighbour index, NNI = 0.202819) and the accumulated number of pupae (G = 0.70007). Two neighbourhoods showed higher concentrations of both epidemiological and entomological hotspots. In conclusion, the municipality of Patía is in an operational scenario of a high transmission of dengue.
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The level of clustering and the adjustment by cluster-robust standard errors have yet to be widely considered and reported in cross-sectional studies of tuberculosis (TB) in prisons. In two cross-sectional studies of people deprived of liberty (PDL) in Medellin, we evaluated the impact of adjustment versus failure to adjust by clustering on prevalence ratio (PR) and 95% confidence interval (CI). We used log-binomial regression, Poisson regression, generalized estimating equations (GEE), and mixed-effects regression models. We used cluster-robust standard errors and bias-corrected standard errors. The odds ratio (OR) was 20% higher than the PR when the TB prevalence was >10% in at least one of the exposure factors. When there are three levels of clusters (city, prison, and courtyard), the cluster that had the strongest effect was the courtyard, and the 95% CI estimated with GEE and mixed-effect models were narrower than those estimated with Poisson and binomial models. Exposure factors lost their significance when we used bias-corrected standard errors due to the smaller number of clusters. Tuberculosis transmission dynamics in prisons dictate a strong cluster effect that needs to be considered and adjusted for. The omission of cluster structure and bias-corrected by the small number of clusters can lead to wrong inferences.
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Prisões , Tuberculose , Humanos , Estudos Transversais , Tuberculose/epidemiologia , Modelos Estatísticos , Análise por ConglomeradosRESUMO
Dengue fever is a tropical disease transmitted mainly by the female Aedes aegypti mosquito that affects millions of people every year. As there is still no safe and effective vaccine, currently the best way to prevent the disease is to control the proliferation of the transmitting mosquito. Since the proliferation and life cycle of the mosquito depend on environmental variables such as temperature and water availability, among others, statistical models are needed to understand the existing relationships between environmental variables and the recorded number of dengue cases and predict the number of cases for some future time interval. This prediction is of paramount importance for the establishment of control policies. In general, dengue-fever datasets contain the number of cases recorded periodically (in days, weeks, months or years). Since many dengue-fever datasets tend to be of the overdispersed, long-tail type, some common models like the Poisson regression model or negative binomial regression model are not adequate to model it. For this reason, in this paper we propose modeling a dengue-fever dataset by using a Poisson-inverse-Gaussian regression model. The main advantage of this model is that it adequately models overdispersed long-tailed data because it has a wider skewness range than the negative binomial distribution. We illustrate the application of this model in a real dataset and compare its performance to that of a negative binomial regression model.
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Aedes aegypti is the main vector of dengue in the Americas and is also a transmitter of urban yellow fever arboviruses, Zika, and Chikungunya, all of which have substantial economic impacts on the affected countries. Through mathematical models, the influence of climatic factors on the oviposition of Ae. aegypti was determined. The data were collected in the city of Apucarana, Paraná State, using oviposition traps. Daily data were submitted to a negative binomial regression model (p < 0.05). The analyses were performed using the R statistical program to determine the climatic factors that most influenced oviposition. A Poisson regression showed that the variables temperature, atmospheric pressure, humidity, and precipitation significantly increased the number of eggs. However, using the semi-normal probability graph with a simulation envelope, it was determined that the Poisson regression model was not adequate to explain the relationships between the variables. Thus, a negative binomial regression model was used, which overcame the problem of overdispersion, and showed that only temperature affected the increase in the number of eggs, where an increase of 1 °C was expected to result in a 54.03% increase in the number of Ae. aegypti eggs.
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Objectives: To understand and forecast the evolution of COVID-19 (Coronavirus disease 2019) in Chile, and analyze alternative simulated scenarios to better predict alternative paths, in order to implement policy solutions to stop the spread and minimize damage. Methods: We have specified a novel multi-parameter generalized logistic growth model, which does not only look at the trend of the data, but also includes explanatory covariates, using a quasi-Poisson regression specification to account for overdispersion of the count data. We fitted our model to data from the onset of the disease (February 28) until September 15. Estimating the parameters from our model, we predicted the growth of the epidemic for the evolution of the disease until the end of October 2020. We also evaluated via simulations different fictional scenarios for the outcome of alternative policies (those analyses are included in the Supplementary Material). Results and Conclusions: The evolution of the disease has not followed an exponential growth, but rather, stabilized and moved downward after July 2020, starting to increase again after the implementation of the Step-by-Step policy. The lockdown policy implemented in the majority of the country has proven effective in stopping the spread, and the lockdown-relaxation policies, however gradual, appear to have caused an upward break in the trend.
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COVID-19 , Epidemias , Chile/epidemiologia , Controle de Doenças Transmissíveis , Humanos , SARS-CoV-2RESUMO
Inadequate food and nutrition affect human well-being, particularly for many poor subpopulations living in rural areas. The purpose of this research was to analyze the factors that determine the Household Dietary Diversity Score (HDDS) in the rural area of the Paute River Basin, Azuay Province, Ecuador. The sample size of 383 surveys was determined by a stratified random sampling method with proportional affixation. Dietary diversity was measured through the HDDS, with 12 food groups (cereals; roots and tubers; fruits; sugar/honey; meat and eggs; legumes or grains; vegetables; oils/fats; milk and dairy products; meats; miscellaneous; fish and shellfish) over a recall period of 7 days. A Poisson regression model was used to determine the relationship between the HDDS and sociodemographic variables. The results show that the average HDDS of food consumption is 10.89 foods. Of the analyzed food groups, the most consumed are cereals; roots and tubers; fruits; sugar/honey. In addition, the determinants that best explain the HDDS in the predictive model were housing size, household size, per capita food expenditure, area of cultivated land, level of education, and marital status of the head of household. The tools used in this research can be used to analyze food and nutrition security interventions. Furthermore, the results allow policymakers to identify applicable public policies in the fight against hunger.
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Abastecimento de Alimentos , Rios , Animais , Dieta , Equador , Características da Família , Humanos , População RuralRESUMO
Estimation of excess deaths due to a natural disaster is an important public health problem. The CDC provides guidelines to fill death certificates to help determine the death toll of such events. But, even when followed by medical examiners, the guidelines cannot guarantee a precise calculation of excess deaths. We propose two models to estimate excess deaths due to an emergency. The first model is simple, permitting excess death estimation with little data through a profile likelihood method. The second model is more flexible, incorporating temporal variation, covariates, and possible population displacement while allowing inference on how the emergency's effect changes with time. The models are implemented to build confidence intervals estimating Hurricane Maria's death toll.
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Causas de Morte , Tempestades Ciclônicas/mortalidade , Modelos Estatísticos , Intervalos de Confiança , Atestado de Óbito , Guias como Assunto , Humanos , Funções Verossimilhança , Vigilância da População , Saúde Pública/métodos , Porto RicoRESUMO
Resumo O estudo analisou a associação entre posição socioeconômica (renda), depressão materna e saúde da criança no Brasil, utilizando informações da Pesquisa Nacional por Amostra de Domicílios 2008 (PNAD/IBGE). A análise considerou o delineamento amostral da pesquisa e incluiu 46.874 indivíduos com idade até 9 anos. Modelos Poisson foram estimados para três desfechos de saúde da criança: saúde reportada pelos pais ou responsáveis, restrição das atividades habituais por motivo de saúde e episódios de acamamento nas duas semanas anteriores à entrevista. Os resultados apontaram associação entre a depressão da mãe e os três desfechos, mesmo após o ajuste para posição socioeconômica, características maternas (saúde autorreferida, idade, escolaridade e tabagismo), idade, sexo e cor da pele da criança, além de região geográfica, situação censitária e número de moradores do domicílio. Constatou-se ainda que a associação entre depressão materna e saúde da criança independe da posição socioeconômica. Assim, políticas de saúde pública que objetivem atenuar os efeitos adversos da depressão materna sobre a saúde da criança, precisam considerar também os estratos mais elevados de renda da sociedade.
Abstract The study analyzed the association between socioeconomic position (income), maternal depression and the health of children in Brazil, using information from the 2008 National Household Survey (PNAD/IBGE). The analysis considered the sampling design for the research and included 46,874 individuals up to the age of nine. The Poisson models were estimated for three health outcomes for children: health as reported by the parents or the responsible person, restrictions on habitual activities for health reasons and periods when they were confined to bed two weeks before the interviews in the study. The results showed an association between the mothers' depression and the three health outcomes, even after taking into account the following: socioeconomic position, maternal characteristics (health self-referral, age, level of education and smoking), age, gender, the child's race, geographical region, the situation as noted in the census and the number of residents in a household. It was found that there still exists an association between maternal depression and children's health irrespective of socioeconomic position. Therefore public policies that aim to reduce the adverse effects of maternal depression on the health of children need to also take into account the higher income segments of society.
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Humanos , Masculino , Feminino , Recém-Nascido , Lactente , Pré-Escolar , Criança , Adolescente , Adulto , Adulto Jovem , Distribuição de Poisson , Filho de Pais com Deficiência/estatística & dados numéricos , Depressão/epidemiologia , Renda , Política Pública , Fatores Socioeconômicos , Brasil/epidemiologia , Nível de Saúde , Inquéritos e Questionários , Mães/psicologiaRESUMO
Generalized Linear Models (GLM) with negative binomial distribution for errors, have been widely used to estimate safety at the level of transportation planning. The limited ability of this technique to take spatial effects into account can be overcome through the use of local models from spatial regression techniques, such as Geographically Weighted Poisson Regression (GWPR). Although GWPR is a system that deals with spatial dependency and heterogeneity and has already been used in some road safety studies at the planning level, it fails to account for the possible overdispersion that can be found in the observations on road-traffic crashes. Two approaches were adopted for the Geographically Weighted Negative Binomial Regression (GWNBR) model to allow discrete data to be modeled in a non-stationary form and to take note of the overdispersion of the data: the first examines the constant overdispersion for all the traffic zones and the second includes the variable for each spatial unit. This research conducts a comparative analysis between non-spatial global crash prediction models and spatial local GWPR and GWNBR at the level of traffic zones in Fortaleza/Brazil. A geographic database of 126 traffic zones was compiled from the available data on exposure, network characteristics, socioeconomic factors and land use. The models were calibrated by using the frequency of injury crashes as a dependent variable and the results showed that GWPR and GWNBR achieved a better performance than GLM for the average residuals and likelihood as well as reducing the spatial autocorrelation of the residuals, and the GWNBR model was more able to capture the spatial heterogeneity of the crash frequency.
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Acidentes de Trânsito/estatística & dados numéricos , Modelos Estatísticos , Regressão Espacial , Meios de Transporte/estatística & dados numéricos , Brasil , Planejamento Ambiental , Humanos , Modelos Lineares , Análise de Regressão , Segurança/estatística & dados numéricos , Fatores SocioeconômicosRESUMO
In recent years, Guangxi has become one of the most severely affected provinces by epidemics of avian cholera in China. To date, the major determinant climatic factors of the disease in the region have remained largely unknown, making it difficult to effectively target countermeasures for avian cholera surveillance and control. This study aimed to quantify the relationship between climatic variables and cases of avian cholera in subtropical areas of China. Data relating to local meteorological variables and notified cases of avian cholera were supplied by the relevant authorities between January 2006 and December 2015. Spearman correlation, co-linearity statistics and cross-correlation analysis were applied to the data, controlling for co-linearity and lag effects. A time-series Poisson regression analysis was conducted to examine the degree of correlation between the climate variables and avian cholera transmission. The results indicated that monthly mean temperature, relative humidity, rainfall and the multivariate El Niño Southern Oscillation Index, with 2-3 months lag, were correlated with avian cholera incidence. The final model had good predictive ability for the occurrence of avian cholera. Overall, the findings indicate that climate variability plays an important role in avian cholera transmission in Guangxi province. Adoption of the model presented in this study could usefully inform avian cholera surveillance strategies, making them significantly simpler and more effective. The model could also serve as a decision support tool for veterinary professionals and health authorities.
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Animais , Aves Domésticas/anormalidades , Aves Domésticas/crescimento & desenvolvimento , Cólera/diagnóstico , Cólera/veterinária , Distribuição de Poisson , Zonas ClimáticasRESUMO
In recent years, Guangxi has become one of the most severely affected provinces by epidemics of avian cholera in China. To date, the major determinant climatic factors of the disease in the region have remained largely unknown, making it difficult to effectively target countermeasures for avian cholera surveillance and control. This study aimed to quantify the relationship between climatic variables and cases of avian cholera in subtropical areas of China. Data relating to local meteorological variables and notified cases of avian cholera were supplied by the relevant authorities between January 2006 and December 2015. Spearman correlation, co-linearity statistics and cross-correlation analysis were applied to the data, controlling for co-linearity and lag effects. A time-series Poisson regression analysis was conducted to examine the degree of correlation between the climate variables and avian cholera transmission. The results indicated that monthly mean temperature, relative humidity, rainfall and the multivariate El Niño Southern Oscillation Index, with 2-3 months lag, were correlated with avian cholera incidence. The final model had good predictive ability for the occurrence of avian cholera. Overall, the findings indicate that climate variability plays an important role in avian cholera transmission in Guangxi province. Adoption of the model presented in this study could usefully inform avian cholera surveillance strategies, making them significantly simpler and more effective. The model could also serve as a decision support tool for veterinary professionals and health authorities.(AU)
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Animais , Aves Domésticas/anormalidades , Aves Domésticas/crescimento & desenvolvimento , Cólera/diagnóstico , Cólera/veterinária , Zonas Climáticas , Distribuição de PoissonRESUMO
Abstract High variability in recorded vital events creates serious problems for small-area mortality estimation by age and sex. Many existing approaches to fitting local mortality schedules, including those most often used in Brazil, estimate rates by making rigid mathematical assumptions about local age patterns. Such methods assume that all areas within a larger area (for example, microregions within a mesoregion) have identically-shaped log mortality schedules by age. We propose a more flexible statistical estimation method that combines Poisson regression with the TOPALS relational model (DE BEER, 2012). We use the new method to estimate age-specific mortality rates in Brazilian small areas (states, mesoregions, microregions, and municipalities) in 2010. Results for Minas Gerais show notable differences in the age patterns of mortality between adjacent small areas, demonstrating the advantages of using a flexible functional form in regression models.
Resumo A alta variabilidade dos dados nos registros vitais, em razão do baixo número de pessoas expostas, impõe sérios problemas para estimação da mortalidade por idade e sexo em pequenas áreas. Muitas abordagens atuais, incluindo as mais utilizadas no Brasil, estimam as taxas específicas de mortalidade assumindo pressupostos matemáticos rígidos sobre o verdadeiro padrão etário da mortalidade. Padronização indireta, por exemplo, assume que todas as áreas dentro de uma área maior (microrregiões em uma mesorregião, por exemplo) possuem um padrão de mortalidade idêntico, com diferença constante no nível das taxas logarítmicas por idade. Propomos um método estatístico mais flexível que combina regressão Poisson com um modelo relacional denominado TOPALS (DE BEER, 2012). Usamos o novo método para estimar as taxas específicas de mortalidade em pequenas áreas no Brasil (estados, mesorregiões, microrregiões e municípios) em 2010. Resultados para o estado de Minas Gerais mostram diferenças notáveis no padrão de mortalidade por idade entre pequenas áreas adjacentes, demonstrando as vantagens do uso de um método de estimação mais flexível.
Resumen La alta variabilidad de los datos en los registros vitales, debida al bajo número de personas expuestas al riesgo de morir, plantea serios problemas para la estimación de la mortalidad por edad y sexo en pequeñas áreas. Muchos enfoques recientes, incluyendo los más utilizados en Brasil, estiman las tasas de mortalidad por edad con presupuestos matemáticos rígidos acerca del verdadero padrón etario de la mortalidad. La estandarización indirecta, por ejemplo, asume que todas las áreas dentro de una área mayor (microrregiones de una mesorregión) tengan una idéntica estructura de la mortalidad, con diferencia constante en los niveles de las tasas logarítmicas por edad. Proponemos un método estadístico más flexible que combina la regresión de Poisson con un modelo relacional llamado TOPALS. Utilizamos el nuevo método para estimar las tasas de mortalidad específicas en pequeñas áreas en Brasil (estados, mesorregiones, microrregiones y municipios) en 2010. Los resultados para el estado de Minas Gerais muestran diferencias notables en la estructura de mortalidad entre áreas pequeñas adyacentes, lo que demuestra las ventajas de usar un método de estimación más flexible.
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Humanos , Masculino , Feminino , Lactente , Pré-Escolar , Criança , Adolescente , Adulto , Pessoa de Meia-Idade , Idoso , Distribuição por Idade e Sexo , Expectativa de Vida , Mortalidade/tendências , Brasil , Distribuição de PoissonRESUMO
En este manuscrito se revisan algunos aspectos básicos de la utilización de regresiones en los estudios epidemiológicos, haciendo énfasis en aquellas aplicadas al estudio de eventos discretos. De esta manera se hace una introducción a los modelos lineales generalizados, cuya estructura es una extensión de una ecuación lineal para analizar desenlaces discretos. De este modo podemos estimar medidas de asociación como la razón de tasas usando la regresión de Poisson, o bien, el riesgo relativo (o la razón de prevalencias) usando la regresión log-binomial. En cada caso es esencial conocer la naturaleza de la variable dependiente, su distribución y reconocer las limitaciones de cada una de las herramientas de análisis.
Some basic aspects about using regressions in epidemiological studies are reviewed. Particularly, this manuscript focused on those applied to the study of discrete events. Generalized lineal models, such as Poisson and log-binomial, have a structure that is an extension of a lineal equation to analyze discrete outcomes. Thus, we can estimate association measures as the incidence rate ratio, using the Poisson regression, or the relative risk (or prevalence ratio), using log-binomial regression. In each case it is essential to know the nature of the dependent variable, as well as, its distribution and recognize the limitations of each analysis tool.
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Humanos , Modelos Lineares , Distribuição Binomial , Distribuição de Poisson , Risco , Razão de PrevalênciasRESUMO
Este artigo objetiva verificar a evolução temporal da mortalidade por suicídio em pessoas com 60 anos ou mais segundo a unidade da federação no período de 1980 a 2009. Na construção das séries históricas empregaram-se dados da mortalidade por suicídio (CID-9 códigos E950 a E959 e CID-10 códigos X60 a X84 e Y87.0) obtidos do Sistema de Informação sobre Mortalidade (SIM/MS). Dados referentes à contagem populacional foram obtidos do Instituto Brasileiro de Geografia e Estatística. Na avaliação da tendência temporal empregou-se o modelo de regressão de Poisson, no qual a variável resposta foi o número de óbitos e a variável explanatória o ano calendário centralizado. Foram consideradas tendências estatisticamente significativas aquelas cujo p-valor < 0,05. Os resultados mostram a presença de tendência estatisticamente significativa de aumento para quatro estados e de queda para dois (população geral; 60 anos ou mais). Na população masculina houve aumento em cinco e redução em dois. As taxas femininas exibiram aumento em um estado e queda em três. Verificou-se tendência de aumento no Piauí, Ceará e Rio Grande do Norte e de redução no Amazonas, São Paulo e Roraima para aqueles com idades entre 60 e 69 anos. Observaram-se taxas crescentes na população de 70 a 79 anos do Piauí e decrescentes em Roraima.
The scope of this paper is to determine the temporal evolution of mortality by suicide in people aged 60 or more per State in Brazil between 1980 and 2009. Historical mortality by suicide data (ICD-9 codes E950 to E959 and ICD-10 codes X60 to X84 and Y87.0) were obtained from the Mortality Information System (SIM / MS). Data regarding population counts were obtained from the Brazilian Institute of Geography and Statistics. In the assessment of temporal trends the Poisson regression model was used, in which the dependent variable was the number of deaths and the centralized calendar year was the explanatory variable. Statistically significant trends were considered those whose p-value was d" 0.05. The results revealed the presence of a statistically significant increasing trend in four states and a decrease in two (general population; 60 years or more). In the male population there was an increase in five states and a reduction in two. The female rate showed an increase in one state and a decrease in three. There was an increasing trend in Piauí, Ceará and Rio Grande do Norte and a reduction in Amazonas, Roraima, and São Paulo for people aged between 60 and 69. Increasing rates were observed in the population aged 70-79 in Piauí and decreasing trends in Roraima.
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Idoso , Feminino , Humanos , Pessoa de Meia-Idade , Suicídio/estatística & dados numéricos , Suicídio/tendências , Brasil/epidemiologia , Fatores de TempoRESUMO
No Brasil, o aborto está entre as principais causas de mortalidade materna. Pesquisas mostram que o aborto é praticado clandestinamente por mulheres de todas as classes sociais; no entanto, tem consequências desiguais, dependendo da inserção social, produzindo riscos à vida de mulheres pobres. Embora o tema venha sendo amplamente explorado nos últimos 20 anos, observou-se escassez de dados sobre mulheres de baixa renda. Desta forma, o presente estudo tem por objetivo estimar a prevalência de mulheres com aborto provocado. Arrolaram-se mulheres por inquérito domiciliar de base populacional em setores de baixa renda de São Vicente, São Paulo. Eram elegíveis as mulheres em idade fértil de 15 a 49 anos. A avaliação das razões de prevalência de mulheres com aborto provocado foi realizada por meio de modelos lineares generalizados, usando-se a regressão de Poisson com função de ligação logarítmica e variância robusta para aproximar a binomial. As variáveis que demonstraram ter maior influência no relato de aborto foram: "aceitar sempre esta prática" (IC95 por cento 2,98 - 11,02), seguida de "não ter filho nascido vivo" (IC95 por cento 1,35 - 19,78), ter de "dois a cinco nascidos vivos" (IC95 por cento 1,42 - 14,40) e ter de "seis ou mais nascidos vivos" (IC 95 por cento 1,35 - 19,78), "idade no momento da entrevista" (IC 95 por cento 1,01 - 1,07) e "renda" < R$ 484,97 (IC 95 por cento 1,04 - 2,96). É necessário campanha de grande abrangência sobre a prática do aborto, que consiga sensibilizar para esta causa todas as mulheres, sobretudo as de baixa renda, evitando assim mortes desnecessárias.
In Brazil, abortion is among the leading causes of maternal mortality. Research has shown that abortion is practiced clandestinely by women of all social classes, but has unequal consequences depending on social inclusion, producing risks to poor women. Although the issue has been widely explored in the past 20 years, there is a lack of data about low-income women. Thus, the present study aims to estimate the prevalence of women with induced abortion. Women from a population-based household survey in low-income sectors of São Vicente, São Paulo were recruited. Women of childbearing age from 15 to 49 years were eligible. The evaluation of the prevalence ratios for women with induced abortion was performed by using generalized linear models, with Poisson log-link function and robust variance to approximate the binomial. The most frequent variables that influenced reporting of abortion were: "always accept this practice" (95 percent CI 2.98 - 11.02), followed by "not having a child born alive" (95 percent CI 1.35 - 19.78), having "two to five live births" (95 percent CI 1.42 - 14.40 ), "having 'six or more live births" (95 percent CI 1.35 - 19.78), "age at interview" (95 percent CI 1.01 - 1.07) and "income" < R$ 484.97' (95 percent CI 1.04 - 2.96). A widespread campaign about the practice of abortion, which can raise awareness among women in favor of the cause, especially among those in low-income strata is necessary to prevent unnecessary deaths.
Assuntos
Adolescente , Adulto , Feminino , Humanos , Pessoa de Meia-Idade , Gravidez , Adulto Jovem , Aborto Induzido/estatística & dados numéricos , Brasil , Estudos Transversais , Áreas de Pobreza , Fatores SocioeconômicosRESUMO
Introducción. Las leucemias son el cáncer más frecuente durante la infancia. El estudio pretende describir la mortalidad por leucemias en menores de 20 años en México. Material y métodos. A partir del Sistema Estadístico y Epidemiológico de las Defunciones se calcularon tasas específicas por edad, género y entidad federativa. Se estimó la tasa media de mortalidad anual (TMMA) por estado, y la tasa truncada estandarizada por edad de mortalidad. La estandarización fue por el método directo y el error estándar por la aproximación de Poisson, los intervalos de confianza (IC) fueron de 95%. En la elaboración de la razón estandarizada de mortalidad (REM) se utilizó la tasa nacional como referencia. Se calculó la proporción de cambio anual estatal y nacional con IC al 95%, además se estimaron las tendencias nacionales y estatales de 1998 a 2002 por medio de la regresión de Poisson. Resultados. La mortalidad por leucemias representó 51.1%. La razón hombre/mujer fue de 1.3. Los grupos de edad más afectados fueron los de 5-9 y 10-14 años, ambas con TMMA de 27.7 por 10(6) habitantes. La REM para Quintana Roo y Puebla fueron significativas. En cuanto a la tendencia Tlaxcala presentó un incremento y Baja California Sur un decremento, ambos fueron estadísticamente significativos. Conclusiones. La mortalidad por leucemias en menores de 20 años representa un problema de salud pública nacional, por lo que el diagnóstico temprano y tratamiento específico deben ser de alta prioridad.
Introduction. Leukemias are the most frequent form of cancer in childhood and adolescence. This study describes the mortality rate for individuals under 20 years of age with a primary diagnosis of leukemia in Mexico over a 15 year period, from 1988-2002. Material and methods. Specific mortality rates were calculated according to age, gender and state of origin based on data provided by a National Epidemiological Mortality Reporting System (SEED). The median annual mortality rate and age adjusted mortality rate were estimated for each state in Mexico. The direct method was used for standardization and standard error with 95% confidence intervals were also calculated. The national mortality rate was used as a reference to estimate the standardized mortality rate. State annual change and trends were calculated from 1988 to 2002 by Poisson regression. Results. The leukemia mortality rate during the study period was 51.1%; the male/female ratio was 1.3 and the predominant age group ranged from 10 to 14 years of age. The median annual mortality rate of 27.7 per 10(6) inhabitants. Conclusions. Leukemia mortality in children and adolescents under 20 years of age represents a major public health problem in Mexico, early diagnosis and specific treatment must be considered high priority.