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      Fatores associados à mortalidade perinatal em uma capital do Nordeste brasileiro Translated title: Factors associated with perinatal mortality in a Brazilian Northeastern capital

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          Abstract

          Resumo O objetivo do estudo foi avaliar os fatores sociodemográficos, maternos e do recém-nascido associados à mortalidade perinatal em São Luís, Maranhão. Os óbitos perinatais foram identificados na coorte e pelo Sistema de Informações sobre Mortalidade. Foram incluídos 5.236 nascimentos, sendo 70 óbitos fetais e 36 neonatais precoces. Para investigar os fatores associados utilizou-se análise de regressão logística com modelo hierarquizado. O coeficiente de mortalidade perinatal foi 20,2 por mil nascimentos. A baixa escolaridade materna e a ausência de companheiro foram associadas a maior chance de óbito perinatal. A família ser chefiada por outros familiares foi fator de proteção. Tiveram maior chance de óbito perinatal filhos de mães que não realizaram pelo menos seis consultas de pré-natal (OR=4,61; IC95%:2,43-8,74) e com gravidez múltipla (OR=9,15; IC95%:4,08-20,53). Presença de malformações congênitas (OR=4,13; IC95%:1,23-13,82), nascimento pré-termo (OR= 3,36; IC95%: 1,56-7,22) e baixo peso ao nascer (BPN) (OR=11,87; IC95%:5,46-25,82) se associaram ao óbito perinatal. A mortalidade perinatal foi associada à vulnerabilidade social, não realização do número de consultas pré-natal recomendado, malformações congênitas, nascimento pré-termo e BPN.

          Translated abstract

          Abstract This study investigated factors associated with perinatal mortality in São Luís, Maranhão, Northeastern Brazil. Data on perinatal mortality were obtained from the BRISA birth cohort and from the Mortality Information System, including records of 5,236 births, 70 of which referred to fetal deaths and 36 to early neonatal deaths. Factors associated with mortality were investigated using a hierarchical logistic regression model, resulting in a perinatal mortality coefficient equal to 20.2 per thousand births. Mothers with low education level and without a partner were associated with an increased risk of perinatal death. Moreover, children of mothers who did not have at least six antenatal appointments and with multiple pregnancies (OR= 9.15; 95%CI:4.08-20.53) were more likely to have perinatal death. Perinatal death was also associated with the presence of congenital malformations (OR= 4.13; 95%CI:1.23-13.82), preterm birth (OR= 3.36; 95%CI:1.56-7.22), and low birth weight (OR=11.87; 95%CI:5.46-25.82). In turn, families headed by other family members (OR= 0.29; 95%CI: 0.12 - 0.67) comprised a protective factor for such condition. Thus, the results indicate an association between perinatal mortality and social vulnerability, non-compliance with the recommended number of prenatal appointments, congenital malformations, preterm birth, and low birthweight.

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          Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis

          Summary Background Preterm birth is the leading cause of death in children younger than 5 years worldwide. Although preterm survival rates have increased in high-income countries, preterm newborns still die because of a lack of adequate newborn care in many low-income and middle-income countries. We estimated global, regional, and national rates of preterm birth in 2014, with trends over time for some selected countries. Methods We systematically searched for data on preterm birth for 194 WHO Member States from 1990 to 2014 in databases of national civil registration and vital statistics (CRVS). We also searched for population-representative surveys and research studies for countries with no or limited CRVS data. For 38 countries with high-quality data for preterm births in 2014, data are reported directly. For countries with at least three data points between 1990 and 2014, we used a linear mixed regression model to estimate preterm birth rates. We also calculated regional and global estimates of preterm birth for 2014. Findings We identified 1241 data points across 107 countries. The estimated global preterm birth rate for 2014 was 10·6% (uncertainty interval 9·0–12·0), equating to an estimated 14·84 million (12·65 million–16·73 million) live preterm births in 2014. 12· 0 million (81·1%) of these preterm births occurred in Asia and sub-Saharan Africa. Regional preterm birth rates for 2014 ranged from 13·4% (6·3–30·9) in North Africa to 8·7% (6·3–13·3) in Europe. India, China, Nigeria, Bangladesh, and Indonesia accounted for 57·9 million (41×4%) of 139·9 million livebirths and 6·6 million (44×6%) of preterm births globally in 2014. Of the 38 countries with high-quality data, preterm birth rates have increased since 2000 in 26 countries and decreased in 12 countries. Globally, we estimated that the preterm birth rate was 9×8% (8×3–10×9) in 2000, and 10×6% (9×0–12×0) in 2014. Interpretation Preterm birth remains a crucial issue in child mortality and improving quality of maternal and newborn care. To better understand the epidemiology of preterm birth, the quality and volume of data needs to be improved, including standardisation of definitions, measurement, and reporting. Funding WHO and the March of Dimes.
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            Assistência pré-natal no Brasil

            O estudo tem por objetivo analisar a assistência pré-natal oferecida às gestantes usuárias de serviços de saúde públicos e/ou privados utilizando dados da pesquisa Nascer no Brasil, realizada em 2011 e 2012. As informações foram obtidas por meio de entrevista com a puérpera durante a internação hospitalar e dados do cartão de pré- natal. Os resultados mostram cobertura elevada da assistência pré-natal (98,7%) tendo 75,8% das mulheres iniciado o pré-natal antes da 16a semana gestacional e 73,1% compareceram a seis ou mais consultas. O pré-natal foi realizado, sobretudo, em unidades básicas (89,6%), públicas (74,6%), pelo mesmo profissional (88,4%), em sua maioria médicos (75,6%), e 96% receberam o cartão de pré-natal. Um quarto das gestantes foi considerado de risco. Do total das entrevistadas, apenas 58,7% foram orientadas sobre a maternidade de referência, e 16,2% procuraram mais de um serviço para a admissão para o parto. Desafios persistem para a melhoria da qualidade dessa assistência, com a realização de procedimentos efetivos para a redução de desfechos desfavoráveis.
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              Common pitfalls in statistical analysis: Logistic regression

              Logistic regression analysis is a statistical technique to evaluate the relationship between various predictor variables (either categorical or continuous) and an outcome which is binary (dichotomous). In this article, we discuss logistic regression analysis and the limitations of this technique.
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                Author and article information

                Journal
                csc
                Ciência & Saúde Coletiva
                Ciênc. saúde coletiva
                ABRASCO - Associação Brasileira de Saúde Coletiva (Rio de Janeiro, RJ, Brazil )
                1413-8123
                1678-4561
                April 2022
                : 27
                : 4
                : 1513-1524
                Affiliations
                [2] Teresina Piauí orgnameUniversidade Federal do Piauí orgdiv1Departamento de Nutrição Brazil
                [1] São Luís orgnameUniversidade Federal do Maranhão orgdiv1Programa de Pós-Graduação em Saúde Coletiva Brazil carolina.carvalho@ 123456ufma.br
                Article
                S1413-81232022000401513 S1413-8123(22)02700401513
                10.1590/1413-81232022274.07882021
                d4d96032-5e5d-42d7-adfb-1268348a595e

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 04 October 2020
                : 22 April 2021
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 36, Pages: 12
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                SciELO Public Health

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                Temas Livres

                Mortalidade perinatal,Fatores de risco,Recém-nascido,Perinatal mortality,Risk factors,Newborn

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