The aim of this study was to validate a definition to identify cases of early neonatal near miss using data from health information systems (SIS in Portuguese). This was a concurrent validation study focusing on three definitions for identification of cases of early neonatal near miss among live births in a university hospital in 2012. Three different definitions were applied to this live birth cohort using the criteria birth weight, gestational age, 5-minute Apgar score, admission to the neonatal intensive care unit, mechanical ventilation, and congenital malformations, in different combinations, considering the proposals in two Brazilian articles (Silva et al.; Pillegi-Castro et al.) and a third (SIS definition) with available data from health information systems. Cases were defined as infants that had survived the risk conditions as of the 7th day of life. For concurrent validation, the study adopted early neonatal deaths as the reference. Of the 2,097 live births studied, 33 died in the early neonatal period, and the number of cases of early neonatal near miss varied according to the definition used: 153 (Silva definition), 194 (Pileggi-Castro definition), and 304 (SIS definition). Sensitivity and specificity were 97% and 92.6%, respectively, according to the Silva definition, 90.9% and 90.6% according to the Pileggi-Castro definition, and 93.9% and 85.3% according to the SIS definition. The results show that the SIS definition has sensitivity and specificity close to the other definitions and suggest that it is possible to monitor early neonatal near miss using only data that are available in official health information systems.

Objective: To characterize near miss neonatal morbidity in tertiary hospitals in a capital city of Northeast Brazil based on Health Information Systems, and to identify differences regarding indicators of near miss cases, allowing the surveillance of newborns with risk of death.Methods: A cross-sectional study carried out in hospitals with neonatal intensive care unit, whose neonatal near miss cases in 2012 were identified from a deterministic linkage between the Mortality Information System and the Live Birth Information System. The biological variables of children, variables related to maternal characteristics and indicators of near miss were calculated by type of service and hospital. Biological variables of children, variables related to maternal characteristics and near miss indicators were calculated by service type and hospital and then compared by ratio difference test, parametric and non-parametric tests for measures of central tendency.Results: Of 24,254 live births, 2,098 cases of neonatal morbidity near miss were identified, most of them concentrated in the public hospitals (89.9%). The combination of birth weight and gestational age had the largest number of cases in both segments, public (43.5%) and private (46%). Variations in neonatal near miss indicators were observed between hospitals, which suggests assistance problems.Conclusions: The concept of neonatal near miss, its applicability with data from Health Information Systems, and its indicators are a preliminary tool to monitor hospital care for newborns by signaling health services that require in-depth evaluation and investments for quality improvement.

O objetivo do artigo foi descrever e comparar indicadores de near miss neonatal em hospitais de referência para gestação e parto de alto risco. É um estudo exploratório, transversal, realizado em dois hospitais gerais localizados na cidade do Recife, Pernambuco, Brasil. Considerou-se casos de near miss neonatal os recém-nascidos do ano de 2016 com idade gestacional < 33 semanas ou peso ao nascer < 1.750g ou Apgar no 5º minuto de vida < 7 ou internação em unidade de terapia intensiva (UTI) neonatal, e que permaneceram vivos até 7 dias de vida. Os dados foram extraídos do Sistema de Informações sobre Nascidos Vivos e sobre Mortalidade, do Sistema de Informações Hospitalares e do Cadastro Nacional de Estabelecimentos de Saúde, para caracterizar todos os nascidos vivos das instituições, os casos de near miss e a disponibilidade de tecnologia. Calculou-se os indicadores de near miss neonatal e a taxa de mortalidade neonatal precoce. O Instituto de Medicina Integral Professor Fernando Figueira acolheu a clientela de maior gravidade, apresentou maior taxa de near miss neonatal (119,21 por mil nascidos vivos; p = 0,009) e de mortalidade neonatal precoce (35,22 por mil nascidos vivos; p < 0,001). O Hospital das Clínicas registrou a maior proporção de internações em UTI neonatal (76% dos casos; p < 0,001). Os indicadores de near miss neonatal demonstraram diferenças entre os hospitais analisados, sendo úteis para a vigilância da assistência neonatal em instituições de saúde, mas necessitam de atenção ao perfil e contexto local quando a intenção é realizar avaliações classificatórias. Os achados mostram a complexidade de avaliar diferentes serviços de saúde.

Objective: To compare 2012 and 2016 data on early neonatal near miss indicators from Health Information Systems at a university hospital. Methods: This is a cross-sectional study conducted in 2012 and 2016. We considered early neonatal near misses the live births that presented one of the following risk conditions at birth: gestational age <33 weeks, birth weight <1,750g or 5-minute Apgar score <7, or Neonatal Intensive Care Unit (NICU) admission, and were alive until the 7th day of life. Data were collected from the Live Birth Information System, Hospital Information System, and Mortality Information System. We calculated the early neonatal mortality rate, neonatal near miss rate, severe neonatal outcome rate, early neonatal survival index, and early neonatal mortality index, compared by year of birth. Results: In 2012, 304 early neonatal near misses were registered, with a higher proportion of cases with very low birth weight and mothers who had zero to three prenatal visits. In 2016, the number of cases was 243, with a predominance of more NICU admissions. The incidence of early neonatal deaths and early neonatal near misses was higher in 2012 than in 2016. Conclusions: Neonatal near miss indicators identified difference between years. The cases were more severe in 2012 and there were more NICU admissions in 2016.

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