Dissertação Felipe Camilo.pdf
Dissertação Felipe Camilo.pdf
Documento PDF (926.3KB)
Documento PDF (926.3KB)
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UNIVERSIDADE FEDERAL DE ALAGOAS
FACULDADE DE MEDICINA
PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIAS MÉDICAS
Felipe Camilo Santiago Veloso
Escore de predição de óbito neonatal no quinto minuto de vida
Maceió – AL
2022
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FELIPE CAMILO SANTIAGO VELOSO
Escore de predição de óbito neonatal no quinto minuto de vida
Dissertação de Mestrado apresentada ao Programa de
Pós-Graduação
em
Ciências
Médicas
da
Universidade Federal de Alagoas (UFAL), como
parte das exigências para a obtenção do título de
Mestre em Ciências Médicas.
Área de Concentração:
degenerativas.
Doenças
crônicas
e
Orientador: Prof. Dr. Samir Buainain Kassar.
Coorientador: Prof. Dr. Jorge Artur Peçanha de
Miranda Coelho.
Maceió – AL
2022
3
4
FELIPE CAMILO SANTIAGO VELOSO
Escore de predição de óbito neonatal no quinto minuto de vida
Dissertação de Mestrado apresentada ao Programa de
Pós-Graduação
em
Ciências
Médicas
da
Universidade Federal de Alagoas (UFAL), como
parte das exigências para a obtenção do título de
Mestre em Ciências Médicas.
Área de Concentração:
degenerativas.
Doenças
crônicas
e
Orientador: Prof. Dr. Samir Buainain Kassar.
Coorientador: Prof. Dr. Jorge Artur Peçanha de
Miranda Coelho.
Prof. Dr. Samir Buainain Kassar, CPF: 384.145.544-15, Universidade Federal de
Alagoas. (Orientador)
BANCA EXAMINADORA
Prof. Dr. Samir Buainain Kassar, CPF: 384.145.544-15, Universidade Federal de
Alagoas. (Orientador)
Prof. Dr. Ricardo Queiroz Gurgel, CPF: 154.049.605-82, Universidade Federal de
Sergipe. (Avaliador Externo)
Profa. Dra. Auxiliadora Damianne Pereira Vieira da Costa, CPF: 034.537.184-45,
Universidade Federal de Alagoas. (Avaliador Interno)
Prof. Dr. Alysson Wagner Fernandes Duarte, CPF: 056.482.824-60, Universidade
Federal de Alagoas. (Avaliador Interno)
RESUMO
O estudo objetiva elaborar um escore de predição de óbito neonatal. Trata-se de um estudo
caso-controle que utilizou dados secundários públicos coletados a partir do Sistema de
Informação sobre Nascidos Vivos (SINASC) e do Sistema de Informações sobre
Mortalidade (SIM). O espaço temporal foi cinco anos (2016 – 2020). Considerou-se
controle os dois nascidos-vivos registrados imediatamente após a ocorrência do óbito
(caso). As variáveis foram idade gestacional, peso ao nascer, número de consultas prénatais, Apgar no 5º minuto e anomalias congênitas. As análises foram compostas pela
frequência absoluta e relativa, Odds Ratio Bruto (ORB) e Odds Ratio Ajustado (ORA).
Para a construção do escore de predição, calculou-se o ponto de corte, sensibilidade,
especificidade, razão de probabilidade positiva (RPP), razão de probabilidade negativa
(RPN) e área sob a curva (ASC). O peso < 2500 gramas obteve um ORA de 7.56 (5.86,
9.74), a presença de malformação ORA 28.15 (16.73, 47.36), a idade gestacional < 37
semanas ORA 6.06 (4.72, 7.78), o Apgar < 7 no 5º minuto ORA 61.61 (38.01, 99.88) e
número de consultas pré-natais < 7 ORA 1.29 (1.04, 1.59). O ponto de corte do escore de
predição foi cinco pontos, sensibilidade 70.21%, especificidade 96.65%, RPP 20.95, RPN
0.30 e AUC 0.896. O escore de predição mostra-se promissor para predizer o óbito
neonatal, apresentando uma grande influência na probabilidade quando maior que cinco
pontos, obtendo melhor pontuação quando positiva.
PALAVRAS-CHAVE: Mortalidade neonatal. Fatores de risco. Regras de predição
clínica.
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ABSTRACT
The study aims to develop a neonatal death prediction score. This is a case-control study
that used public secondary data collected from the Live Births Information System (LBIS)
and the Mortality Information System (MIS). The time span was five years (2016 – 2020).
The two live births registered immediately after the occurrence of death (case) were
considered control. The variables were gestational age, birth weight, number of prenatal
consultations, 5th minute APGAR and congenital anomalies. The analyzes consisted of
absolute and relative frequency, Crude Odds Ratio (COR) and Adjusted Odds Ratio
(AOR). To construct the prediction score, the cutoff point, sensitivity, specificity, positive
probability ratio (PPR), negative probability ratio (NPR) and area under the curve (AUC)
were calculated. Weight < 2500 grams had an AOR of 7.56 (5.86, 9.74), the presence of
malformation AOR 28.15 (16.73, 47.36), gestational age < 37 weeks AOR 6.06 (4.72,
7.78), the Apgar < 7 at the 5th minute AOR 61.61 (38.01, 99.88) and number of prenatal
consultations < 7 AOR 1.29 (1.04, 1.59). The prediction score cutoff was five points,
sensitivity 70.21%, specificity 96.65%, PPR 20.95, NPR 0.30 and AUC 0.896. The
prediction score is promising to predict neonatal death, showing a great influence on the
probability when greater than five points, obtaining a better score when positive.
KEYWORDS: Neonatal mortality. Risk factors. Clinical Decision Rules.
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SUMÁRIO
1 INTRODUÇÃO ........................................................................................................... 09
2 OBJETIVOS ................................................................................................................ 10
2.1 Objetivo geral ........................................................................................................... 10
2.2 Objetivos específicos ................................................................................................ 10
3 REVISÃO DE LITERATURA .................................................................................... 11
3.1 Definição de óbito neonatal ...................................................................................... 11
3.2 Fatores relacionados ao óbito neonatal ..................................................................... 11
3.3 Epidemiologia .......................................................................................................... 12
3.4 Escores de Predição ................................................................................................. 12
4 JUSTIFICATIVA ........................................................................................................ 15
5 METODOLOGIA ....................................................................................................... 16
5.1 Tipo de estudo ........................................................................................................... 16
5.2 Local e Amostra do estudo ........................................................................................ 16
5.3 Critérios de elegibilidade .......................................................................................... 16
5.3.1 Critérios de inclusão .............................................................................................. 16
5.3.2 Critérios de exclusão .............................................................................................. 16
5.4 Coleta e Armazenamento dos dados ......................................................................... 16
5.5 Variáveis ................................................................................................................... 17
5.5.1 Primária ................................................................................................................. 17
5.5.2 Secundárias ............................................................................................................ 17
5.5.2.1 Caso .................................................................................................................... 17
5.5.2.2 Controle .............................................................................................................. 17
5.7 Análise estatística ..................................................................................................... 18
5.7.1 Escore de predição ................................................................................................. 18
5.8. Aspectos éticos ........................................................................................................ 19
5.8.1 Autorização ........................................................................................................... 19
5.8.2 Riscos .................................................................................................................... 19
5.8.3 Benefícios .............................................................................................................. 20
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6 PRODUTO .................................................................................................................. 21
6.1 Produto I ................................................................................................................... 22
7 CONCLUSÕES ........................................................................................................... 40
8 LIMITAÇÕES E PERSPECTIVAS ............................................................................ 41
REFERÊNCIAS .......................................................................................................... 42
APÊNDICE ................................................................................................................. 47
ANEXO ....................................................................................................................... 49
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1 INTRODUÇÃO
O óbito neonatal consiste na morte entre o nascimento e o 27º dia de vida
(PATHIRANA et al, 2016). Tal período é comumente dividido em óbito neonatal precoce
e tardio, cada um com suas particularidades causais e importância epidemiológica
(VELOSO et al, 2019).
No primeiro período, entre o nascimento e o 7º dia de vida, estão incluídas as
causas evitáveis, sendo a prematuridade, baixo peso ao nascer e complicações intraparto
os motivos mais prevalentes (VELOSO et al, 2019; VALLELY et al, 2021). Já no
segundo período, são as anomalias congênitas as causas mais prevalentes (VALLELY et
al, 2021).
A primeira semana de vida é a mais crítica para o recém-nascido, principalmente
nas primeiras 24 horas de vida (LEHTONEN, 2017). Estima-se que, anualmente, 2.7
milhões de óbitos neonatais ocorram em todo mundo, sendo 1 milhão deles acontecendo
na primeira semana (LEHTONEN, 2017; VALLELY et al, 2021).
Diante disso, é importante a utilização de ferramentas para auxiliar na prevenção
desses óbitos, uma vez que são, em sua maioria, causas evitáveis (DORLING; FIELD;
MANKTELOW, 2005; PEDROSA et al, 2007; VELOSO et al, 2019). Há, na literatura,
inúmeras propostas de escores de predição, sejam para países de alta, média ou baixa
renda, sejam para ambientes hospitalares ou em unidades intensivas neonatais
(INTERNATIONAL
NEONATAL
NETWORK,
1993;
DORLING;
FIELD;
MANKTELOW, 2005; ROSENBERG et al., 2008; MEDVEDEV et al., 2020).
O Brasil, um país de dimensões continentais, possui diversos contextos sociais e
econômicos. O Nordeste encontra-se nesse contexto como uma região com dificuldades
assistenciais e isso se reflete nos altos números de óbitos neonatais. É, sim, possível evitar
esses óbitos por meio da assistência e da prevenção, as quais podem ser realizadas tanto
por meio de políticas públicas, quanto por ferramentas capazes de predizer o óbito.
Há várias escores de predição disponíveis e específicos para as diversas situações
e realidades. Porém, um escore de predição com dados brasileiros auxiliaria a anteceder
os desfechos desfavoráveis no Brasil, tornando-se, assim, interessante a elaboração de um
escore de prevenção de óbito neonatal a fim de auxiliar os profissionais de saúde a
entender e prever o óbito neonatal no Brasil.
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2 OBJETIVOS
2.1 Objetivo geral
Elaborar um escore de predição de óbito neonatal utilizando dados coletados a
partir do Sistema de Informação sobre Nascidos Vivos (SINASC) e do Sistema de
Informações sobre Mortalidade (SIM).
2.2 Objetivos específicos
Analisar a ocorrência de óbitos neonatais a partir das variáveis idade gestacional;
peso ao nascer; número de consultas pré-natais; Apgar no 5º minuto e anomalias
congênitas.
Analisar o risco de ocorrência de óbito neonatal a partir das variáveis idade
gestacional; peso ao nascer; número de consultas pré-natais; Apgar no 5º minuto e
anomalias congênitas.
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3 REVISÃO DE LITERATURA
3.1 Definição de óbito neonatal
O óbito neonatal ocorre entre o nascimento e o 27º dia de vida (ISLAM; BISWAS,
2021). Há dois momentos bem definidos que auxiliam o profissional de saúde a entender
a dinâmica desse período (BELUZO et al., 2020). O óbito neonatal precoce,
compreendido entre o nascimento e o 7º dia de vida, é o período mais importante dessa
divisão (LIMA et al., 2020). É nesse momento que, geralmente, são encontrados os
fatores evitáveis, cuja ação dos profissionais de saúde na prevenção é essencial para
diminuir as chances de óbito (PEDROSA et al. 2007; VELOSO et al., 2019). Já no óbito
neonatal tardio, período entre o 7º e o 27º dia de vida, estão situadas as causas mais
prolongadas, cuja prevenção não é o fator essencial na redução dos números, mas sim o
tratamento eficaz (PEDROSA et al. 2007; LANSKY et al, 2014; LIMA et al., 2020).
3.2 Fatores relacionados ao óbito neonatal
Os fatores relacionados ao óbito neonatal podem ser divididos de acordo com a
evitabilidade causal (PEDROSA et al. 2007). As causas evitáveis são fatores modificáveis
a partir de mudanças individuais e governamentais (VELOSO et al., 2019). O óbito
evitável é um reflexo da saúde pública de um país, sinônimo de uma falha na prevenção
ou da assistência ao recém-nascido (PEDROSA et al. 2007; LANSKY et al, 2014;
VELOSO et al., 2019).
3.3 Epidemiologia
Entre 2000 e 2020, a média global geral foi de 65.267.392 óbitos neonatais
(UNICEF, 2020). Apesar desse número expressivo, ao comparar os números no início e
no término do período, houve uma redução de, aproximadamente, 40% nos óbitos
neonatais, mostrando que houve uma importante mudança no contexto da prevenção e da
assistência a essa problemática (UNICEF, 2020).
Entre 1999 e 2019, o Brasil registrou 659.543 óbitos neonatais em 20 anos, sendo
506.655 óbitos neonatais precoces e 152.888 óbitos neonatais tardios (BRASIL, 2021).
A região Sudeste foi a região mais afetada com 236.636 óbitos neonatais, sendo 176.241
precoces e 60.395 tardios (BRASIL, 2021). Logo em seguida, a região Nordeste registrou
222.518 óbitos neonatais, sendo 177.203 precoces e 45.315 tardios (BRASIL, 2021).
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Em Alagoas, baseando-se nas regiões de saúde, de 1999 a 2019, a 1ª Região de
Saúde, na qual a capital Maceió está situada, foi registrado 5.439 óbitos neonatais, sendo
4.186 precoces e 1.253 tardios (BRASIL, 2021).
3.4 Escores de Predição
Os escores de predição são ferramentas de pontuação que envolvem o uso de
dados demográficos, fisiológicos e clínicos para quantificar uma determinada questão
investigada (DORLING; FIELD; MANKTELOW, 2005).
Há, classicamente, seis escores que avaliam o risco de óbito neonatal: Clinical
Risk Index for Babies (CRIB); Clinical Risk Index for Babies II (CRIB II); Score for
Neonatal Acute Phisiology (SNAP); Score for Neonatal Acute Phisiology II (SNAP II);
Score for Neonatal Acute Phisiology – Perinatal Extension (SNAPPE); e Score for
Neonatal Acute Phisiology – Perinatal Extension II (SNAPPE II). (MCLEOD et al,
2020).
O CRIB, descrito em 1993, consiste em uma coorte composta por 812 recémnascidos de muito baixo peso ao nascer ou prematuros extremos tratados em uma Unidade
de Terapia Intensiva Neonatal (UTIN) no Reino Unido, e combinou o peso ao nascer, a
idade gestacional, a presença de malformação congênita, o valor máximo do base excess
nas primeiras 12 horas de vida e o valor mínimo e máximo do FiO2 nas primeiras 12
horas de vida, sendo classificados como um escore de boa qualidade discriminatória
(ASC = 0.90). (INTERNATIONAL NEONATAL NETWORK, 1993).
O SNAP, uma coorte de 1643 recém-nascidos tratados em três UTINs em Boston
(EUA), foi desenvolvido no mesmo ano do CRIB, e utilizou 26 variáveis escolhidas
arbitrariamente e analisadas nas primeiras 24 horas. Tais variáveis consistiram na pressão
arterial, frequência cardíaca, frequência respiratória, temperatura, PO2 e razão PO2/FiO2.
(RICHARDSON et al., 1993). O SNAPPE, por sua vez, ampliou a análise do SNAP,
adicionando fatores perinatais, a exemplo do peso ao nascer, idade gestacional e Apgar
no 5º minuto. (RICHARDSON et al., 1993). Da mesma forma que o CRIB, tanto o SNAP
quanto o SNAPPE consistiram em um escore de boa qualidade discriminatória (ASC
SNAP = 0.87; ASC SNAPPE = 0.90). (DORLING; FIELD; MANKTELOW, 2005).
O SNAP II e o SNAPPE II foram coortes lançadas em 2001 com o objetivo de
simplificar a abordagem dos escores originais (RICHARDSON et al., 2001). O SNAP II
utilizou a pressão arterial média, a menor temperatura aferida, a razão PO2/FiO2, o menor
pH sérico, a presença de múltiplos desmaios e a diurese nas primeiras 12 horas de vida
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(RICHARDSON et al., 2001). Já o SNAPPE II, além das variáveis do SNAP II, utilizou
o peso menor que 749 gramas, o Apgar menor que 7 no 5º minuto e a relação peso – idade
gestacional. (RICHARDSON et al., 2001).
O CRIB II, coorte construída em 2003, surgiu a partir de críticas em relação à
primeira versão, as quais se resumem à influência do cuidador no fornecimento das
informações a serem coletadas e contemplam as variáveis peso ao nascer
(ultrassonografia gestacional), sexo, temperatura na admissão e o valor do base excess
nas primeiras 12 horas de vida (PARRY et al., 2003)
No decorrer do tempo, surgiram outros escores para ampliar os parâmetros dos
escores clássicos, bem como solucionar problemas relacionados à localidade ou ao acesso
à saúde, a exemplo do Neonatal Mortality Score from Ethiopia (NMSE), Neonatal del
Cono Sur Score (NEOCOSUR) e Neonatal Mortality Score-9 Mexico (EMN-9 Mex).
O NMSE, foi conduzido a partir de um caso-controle, sendo 124 casos (óbitos
neonatais) e 122 controles (sobreviventes). Os parâmetros utilizados foram a idade
materna, a paridade, o sexo, a idade gestacional, o peso ao nascer, o tipo de parto, além
da frequência cardíaca, respiratória, temperatura, presença de desconforto respiratório,
nível de consciência, relação peso – idade gestacional e perímetro cefálico, produzindo
um ASC de 0.88. (MEDIRATTA et al., 2020)
O NEOCOSUR, uma coorte prospectiva, a qual utilizou dados de 16 unidades
neonatais na América do Sul, lançou-se como uma boa proposta para países de média e
baixa renda, utilizando como parâmetros a idade materna, o peso ao nascer, idade
gestacional, Apgar no 1º minuto, malformações congênitas, sexo, uso de corticoide no
período perinatal e a relação peso – idade gestacional. O escore em questão encontrou um
ASC de 0.85. (MARSHALL et al., 2005).
O EMN-9 Mex é um caso-controle aninhado, o qual utilizou 22 casos e 132
controles e selecionou como parâmetros: peso ao nascer, presença de acidose metabólica,
nível de lactato, razão PaO2 / FiO2, contagem de plaquetas e glicemia, encontrando um
ASC de 0.92. (MÁRQUEZ-GONZÁLEZ et al., 2015). Seguindo a mesma tendência e
proposta dos escores, surgiram o Simplified Age-Weight-Sex (SAWS) e o Neonatal
Mortality Risk among neonates weighing 2000 g or less (NMR-2000).
O SAWS, uma variável do CRIB-II, foi construída a partir de uma coorte
retrospectiva utilizando 467 recém-nascidos no Cairo (Egito). Com a proposta de ser
utilizado principalmente em países de baixa renda, o SAWS utiliza a peso ao nascer, idade
gestacional e sexo, com um ASC de 0.71 (ROSENBERG et al., 2008). Já o NMR-2000,
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também construída a partir de uma coorte retrospectiva, utilizou os dados de 187 unidades
neonatais do Reino Unido e da Gâmbia. Com uma proposta de ser utilizado em países de
média e baixa renda, o escore utiliza o peso ao nascer, a saturação de oxigênio e o suporte
de oxigênio nas primeiras 24 horas de vida, produzindo um ASC de 0.85 (MEDVEDEV
et al., 2020).
A tabela 1 resume as principais características dos 11 escores de predição de óbito
neonatal.
Tabela 1 - Principais características dos 11 escores de predição de óbito neonatal.
ESCORE
ANO
TIPO DE
ESTUDO
VARIÁVEIS PRINCIPAIS
Peso ao nascer; Idade gestacional; Presença de malformação congênita;
CRIB
1993
Coorte
Valor máximo do base excess nas primeiras 12 horas de vida; Valor
mínimo e máximo do FiO2 nas primeiras 12 horas de vida
CRIB II
2003
Coorte
SNAP
1993
Coorte
Peso ao nascer (Ultrassonografia gestacional); Sexo; Temperatura na
admissão; Valor do base excess nas primeiras 12 horas de vida
Pressão arterial; Frequência cardíaca; Frequência respiratória;
Temperatura; PO2 e Razão PO2/FiO2
Pressão arterial média; Menor temperatura aferida; Razão PO2/FiO2;
SNAP II
2001
Coorte
Menor pH sérico; Presença de múltiplos desmaios; Diurese nas
primeiras 12 horas de vida
SNAPPE
1993
Coorte
SNAPPE II
2001
Coorte
SNAP + Peso ao nascer; Idade gestacional; Apgar no 5º minuto
SNAP II + Presença de peso menor que 749 gramas; Apgar menor que 7
no 5º minuto; Relação peso – idade gestacional
Idade materna; Paridade; Sexo; Idade gestacional; Peso ao nascer; Tipo
NMSE
2020
Caso-
de parto; Frequência cardíaca; Frequência Respiratória; Temperatura;
Controle
Presença de desconforto respiratório; Nível de consciência; Relação
peso – idade gestacional; perímetro cefálico
Idade materna; Peso ao nascer; Idade gestacional; Apgar no 1º minuto;
NEOCOSUR
2005
Coorte
Malformações congênitas; Sexo; Uso de corticoide no período perinatal;
Relação peso – idade gestacional
EMN-9MEX
Caso2015
Controle
Aninhado
SAWS
2008
Coorte
NMR-2000
2020
Coorte
Peso ao nascer; Presença de acidose metabólica; Nível de lactato; Razão
PaO2/FiO2; Contagem de plaquetas; Glicemia
Peso ao nascer; Idade gestacional; Sexo
Peso ao nascer; Saturação de oxigênio; Suporte de oxigênio nas
primeiras 24 horas de vida
Fonte: elaborado pelos autores.
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4 JUSTIFICATIVA
O Brasil, um país de dimensões continentais, possui diversos contextos sociais e
econômicos. Tal fato reflete na condição assistencial ao recém-nascido, sendo mais
favorável a sobrevivência em regiões mais desenvolvidas. O Nordeste encontra-se nesse
contexto, como uma região com dificuldades assistenciais.
O óbito neonatal precoce é o fator causal predominante do óbito neonatal
brasileiro, indicando que é possível evitar esses óbitos por meio da assistência e da
prevenção, as quais podem ser realizadas tanto por meio de políticas públicas, quanto por
ferramentas capazes de predizer o óbito.
Há várias escores de predição disponíveis e específicos para as diversas situações
e realidades. O escore mais completo atualmente é o NMR-2000, o qual utiliza três
parâmetros e possui uma alta acurácia (ASC 0.85). Há também o SAWS, específico para
países de média e baixa renda, o qual poderia ser utilizado no Brasil. Apesar dessas
possibilidades, é visto a necessidade de ampliar as variáveis utilizadas no SAWS, porém
com uma metodologia semelhante ao NMR-2000. Por isso, diante desse contexto, a
proposta desse estudo é elaborar um escore de predição de óbito neonatal a fim de auxiliar
os profissionais de saúde a entender e predizer o óbito neonatal no Brasil.
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5 METODOLOGIA
5.1 Tipo de estudo
Estudo caso-controle o qual utilizou dados secundários públicos coletados do
Sistema de Informação sobre Nascidos Vivos (SINASC) e do Sistema de Informações
sobre Mortalidade (SIM).
5.2 Local e Amostra do estudo
Mediante autorização da Superintendência de Vigilância em Saúde (SUVISA) da
Secretaria de Saúde do Estado de Alagoas (SESAU), número do processo
02000.0000025417/2020, os pesquisadores coletaram os dados do Estado de Alagoas in
locu, diminuindo, assim, a possibilidade de erros ao coletar estas informações via
Departamento de Informática do Sistema Único de Saúde do Brasil (DATASUS).
Considerou-se caso os óbitos neonatais e os controles os dois nascidos-vivos
registrados imediatamente após a ocorrência do óbito.
5.3 Critérios de elegibilidade
5.3.1 Critérios de inclusão
Todos os óbitos neonatais ocorridos em Alagoas (Brasil) entre janeiro de 2016 e
outubro de 2020 foram incluídos, bem como os dois nascidos-vivos registrados
imediatamente após esses óbitos neonatais.
5.3.2 Critérios de exclusão
Excluíram-se os registros de óbitos neonatais, bem como de nascidos-vivos, os
quais não possuíam informações a respeito do peso ao nascer, da idade gestacional, do
Apgar no 5º minuto, do número de consultas pré-natais ou da presença de anomalia
congênita.
5.4 Coleta e Armazenamento de dados
A coleta de dados foi realizada por cinco pesquisadores utilizando um software
desenvolvido pelo Departamento de Estatística da Universidade Federal de Alagoas,
construído exclusivamente para este projeto. O software utilizou um script em linguagem
Python versão 3.8.8 (Python Software Foundation, Delaware, EUA) e uma biblioteca de
manipulação de dados tabulares pandas versão 1.2.3 (Python Software Foundation,
Delaware, EUA).
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O processo de acesso aos dados consistiu na realização de um Linkage. A partir
do número de registro da DNV, encontrou-se a DO correspondente, bem como as duas
DNV subsequentes a hora do óbito registrada na DO anteriormente coletada.
Após o encontro das informações, os dados foram importados para uma planilha
construída a partir do software Microsoft Excel 365 (Microsoft Corporation, Redmond –
Washington, EUA).
Szwarcwald et al. (2019), analisando as informações do Sistema de Informações
sobre Nascidos Vivos (SINASC), principal base de dados utilizada em nosso estudo,
mostraram que houve concordância em todas as variáveis testadas, sendo a variável de
maior fragilidade a idade gestacional com valor de kappa de 0.461, reforçando a
confiabilidade desses dados.
5.6 Variáveis
5.6.1 Primária
A variável primária foi o óbito neonatal, uma variável qualitativa dicotômica.
5.6.2 Secundárias
5.6.2.1 Caso
a) semanas de gestação (menor que 37 semanas, maior ou igual a 37 semanas),
variável qualitativa dicotômica;
b) peso ao nascer (menor que 2.500 g, maior ou igual a 2.500 g), variável
qualitativa dicotômica;
c) números de consultas pré-natais (número de consultas menor que 7, número
de consultar maior ou igual a 7), variável qualitativa dicotômica;
d) valor do Apgar no 5º minuto (Apgar menor que 7, Apgar maior ou igual a 7),
variável qualitativa dicotômica;
e) presença de anomalia (presença, ausência ou ignorado), variável qualitativa
nominal.
5.6.2.2 Controle
a) semanas de gestação (menor que 37 semanas, maior ou igual a 37 semanas),
variável qualitativa dicotômica;
b) peso ao nascer (menor que 2.500 g, maior ou igual a 2.500 g), variável
qualitativa dicotômica;
18
c) números de consultas pré-natais, (número de consultas menor que 7, número
de consultar maior ou igual a 7), variável qualitativa dicotômica;
d) valor do Apgar no 5º minuto (Apgar menor que 7, Apgar maior ou igual a 7),
variável qualitativa dicotômica;
e) presença de anomalia (presença ou ausência), variável qualitativa dicotômica.
5.7 Análise estatística
O software estatístico utilizado foi o IBM SPSS Statistics versão 25.0 (IBM,
Armonk – New York, EUA).
A análise descritiva foi composta pelo encontro das frequências absolutas e
relativas, bem como pela construção de gráficos e tabelas.
A análise inferencial foi composta pela análise bivariada e multivariada. O Odds
Ratio foi a medida de risco avaliada. Considerou-se estatisticamente significante um p <
0.05.
5.7.1 Escore de predição
O processo de seleção consistiu no uso das variáveis identificadas como fatores
de risco para óbito neonatal, cujo Odds Ratio ajustado foi maior que 2.0, em uma revisão
sistemática com meta-análise sobre mortalidade neonatal, a qual envolveu várias cidades
brasileiras (VELOSO et al., 2019).
Para cada uma das variáveis foram atribuídas, de forma arbitrária, pontuações de
acordo com a importância das variáveis no contexto do óbito neonatal. Deu-se três pontos
para o peso < 2500 gramas, dois pontos para a presença de malformação congênita, dois
pontos para a idade gestacional < 37 semanas, dois pontos para o Apgar < 7 no 5º minuto
e um ponto para número de consultas pré-natais < 7 (Imagem 7). O ponto de corte,
também escolhido de forma arbitrária, foi cinco pontos.
19
Imagem 1 - Distribuição do sistema de pontuação.
Fonte: elaborado pelos autores.
Calculou-se, a sensibilidade, especificidade, razão de probabilidade positiva
(RPP), razão de probabilidade negativa (RPN), área sob a curva (AUC). Por fim, foi
construída a curva ROC. O valor de p estatisticamente significativo adotado foi 0.05.
Considerou-se como uma grande influência na probabilidade de ocorrência do óbito um
RPP > 10 e um RPN < 0.1.
5.8. Aspectos éticos
5.8.1 Autorização
A Superintendência de Vigilância em Saúde (SUVISA) da Secretaria de Saúde do
Estado de Alagoas (SESAU), número do processo 02000.0000025417/2020, autorizou os
pesquisadores a coletarem os dados do Estado de Alagoas in locu, mediante concordância
em não coletar os dados individuais dos participantes, a exemplo do nome da mãe, nome
do pai e data de nascimento.
Por ser uma coleta de dados secundários, os quais podem ser encontrados via
Departamento de Informática do Sistema Único de Saúde do Brasil (DATASUS), não
houve a necessidade de submissão ao Comitê de Ética em Pesquisa (CEP/UFAL).
5.8.2 Riscos
Os participantes correram o risco de ter seus dados individuais divulgados ou
estigmatizados por qualquer conteúdo revelado. Para minimizar esse risco, os
pesquisadores criaram uma função no software desenvolvido pela equipe, a qual excluiu
tais dados sigilosos. Além disso, os responsáveis pela SUVISA, os quais acompanharam
os pesquisadores na coleta de dados, controlaram todo o processo, a fim de garantir o nãovazamento de dados do setor.
20
5.8.3 Benefícios
A população, por meio dos resultados deste estudo, conhecerá a realidade do
Estado de Alagoas, bem como os fatores que mais apresentam risco ao óbito neonatal.
Além disso, outro benefício, mais direcionado aos profissionais de saúde, é a proposta de
uma escala de prevenção de óbito neonatal, auxiliando esses profissionais a preparar as
gestantes e o ambiente de trabalho que atuam a receber um recém-nascido conforme
classificado.
.
21
6 PRODUTO
1. NEONATAL DEATH PREDICTION SCORE IN THE FIFTH MINUTE OF
LIFE, submetido segundo as normas da PAEDIATRIC AND PERINATAL
EPIDEMIOLOGY.
22
6.1 Produto I
NEONATAL DEATH PREDICTION SCORE IN THE FIFTH
MINUTE OF LIFE
NEONATAL DEATH PREDICTION SCORE
Felipe Camilo Santiago Veloso1, Carine Ramos Accioly de Barros1, Karin Melo
Araújo1, José Pedro Cassemiro Micheleto1, Hellena Almeida Canuto2, Miriã da
Silva Moreira2, Jorge Artur Peçanha de Miranda Coelho2, Samir Buainain
Kassar1.
1 Faculty of Medicine, Federal University of Alagoas, Maceió, Alagoas, Brazil.
2 Interdisciplinary Group for Discovery of Knowledge in Large Volumes of Data (IGDKD), Federal
University of Alagoas, Maceió, Alagoas, Brazil.
Corresponding Author: Prof. Dr. Samir Buainain Kassar. Faculdade de
Medicina. Universidade Federal de Alagoas, Maceió, Alagoas, Brasil. Av.
Lourival Melo Mota, S/N, Tabuleiro do Martins, Maceió, Alagoas, Brasil. CEP:
57.072-900.
23
SYNOPSIS
1 Research Question
What is the difference between a neonatal death prediction score
constructed from Brazilian data and other neonatal death prediction scores?
2 What is already known?
The Clinical Risk Index for Babies (CRIB) had an AUC of 90%, the Score
for Neonatal Acute Physiology (SNAP) an AUC of 87%. the Simplified AgeWeight and Sex mortality risk classification (SAWS) an AUC of 71% and the
Neonatal Mortality Risk among neonates weighting 2000 g or less (NMR-2000)
an AUC of 85.2%.
3 What does this study help?
Analyzing the database of one of the states of Brazil, the study allows us
to understand the causal dynamics of neonatal deaths, in addition to proposing
the elaboration of a neonatal death prediction score to be used by health
professionals.
24
ABSTRACT E KEYWORDS
Context. The first week of life is the most critical for the newborn, especially in
the first 24 hours of life. It is estimated that, annually, 2.7 million neonatal deaths
occur worldwide, with 1 million of them occurring in the first week. In the literature,
there are numerous proposals for prediction scores, whether for developed or
developing countries, or for hospital or intensive care units.
Objective. Develop a neonatal death prediction score.
Methods. A case-control study that used public secondary data collected from
the Live Births Information System (LBIS) and the Mortality Information System
(MIS). The time span was five years (2016 – 2020). The two live births registered
immediately after the occurrence of death (case) were considered control. The
variables were gestational age, birth weight, number of prenatal consultations,
APGAR at 5 minutes and congenital anomalies. The analyzes consisted of
absolute and relative frequency, Crude Odds Ratio (COR) and Adjusted Odds
Ratio (AOR). To construct the prediction score, the cutoff point, sensitivity,
specificity, positive probability ratio (PPR), negative probability ratio (NPR) and
area under the curve (AUC) were calculated.
Results. Weight < 2500 grams had an AOR of 7.56 (5.86, 9.74), the presence of
malformation AOR 28.15 (16.73, 47.36), gestational age < 37 weeks AOR 6.06
(4.72, 7.78), the Apgar < 7 at the 5th minute AOR 61.61 (38.01, 99.88) and
number of prenatal consultations < 7 AOR 1.29 (1.04, 1.59). The prediction score
cutoff was five points, sensitivity 70.21%, specificity 96.65%, PPR 20.95, NPR
0.30 and AUC 0.896.
Conclusion. The prediction score is promising to predict neonatal death,
showing a great influence on the probability when greater than five points,
obtaining a better score when positive.
KEYWORDS – Neonatal mortality. Risk factors. Clinical Decision Rules.
25
MAIN TEXT
Context
Neonatal death consists of death between birth and the 27th day of life 1. This
period is commonly divided into early and late neonatal death, each with its
causal particularities and epidemiological importance2.
In the first period, between birth and the 7th day of life, avoidable causes are
included, with prematurity, low birth weight and intrapartum complications being
the most prevalent reasons2,3. In the second period, congenital anomalies are the
most prevalent causes3.
The first week of life is the most critical for the newborn, especially in the first 24
hours of life4. It is estimated that, annually, 2.7 million neonatal deaths occur
worldwide, with 1 million of them occurring in the first week 3,4.
Therefore, it is important to use tools to help prevent these deaths, since they are
mostly preventable causes2,5,6. In the literature, there are numerous proposals for
prediction scores, whether for high, medium or low-income countries, or for
hospital or intensive care units5,7-9.
The purpose of this study is to develop a neonatal death prediction score in order
to help health professionals understand and predict neonatal death.
Methods
A case-control study which used public secondary data collected from the Live
Birth Information System (LBIS) and the Mortality Information System (MIS).
Neonatal deaths were considered cases and controls were the two live births
recorded immediately after the death occurred. All neonatal deaths that occurred
in the State of Alagoas (Brazil) between January 2016 and October 2020 were
included, as well as the two live births recorded immediately after these neonatal
deaths.
26
The primary variable was neonatal death. Secondary variables were gestational
age, birth weight, prenatal consultations, Apgar at 5 minutes and congenital
anomalies.
Data collection was performed using proprietary software developed in Python
language version 3.8.8 (Python Software Foundation, Delaware, USA) and a
pandas tabular data manipulation library version 1.2.3 (Python Software
Foundation, Delaware, USA). Access to data consisted of performing a Linkage,
that is, from the Birth Registration Number (BRN), the corresponding Death
Registration Number (DRN) was found, as well as the two BRN subsequent to
the time of death recorded in the DRN previously collected.
The statistical software used was IBM SPSS Statistics version 25.0 (IBM, Armonk
– New York, USA). The descriptive analysis was composed by finding the
absolute and relative frequencies, as well as the construction of graphs and
tables. The inferential analysis consisted of bivariate and multivariate analysis.
The Crude Odds Ratio (COR) and Adjusted Odds Ratio (AOR) were the
measures of risk assessed. A p < 0.05 was considered statistically significant.
The process of elaborating the prediction score consisted of using the variables
identified as risk factors for neonatal death, whose AOR was greater than 2.0,
found in the study by Veloso et al (2019).
For each of the variables, scores were arbitrarily assigned according to the
importance of the variables in the context of neonatal death. Three points were
given for weight < 2500 grams, two points for the presence of congenital anomaly,
two points for gestational age < 37 weeks, two points for Apgar < 7 points in the
5th minute and one point for the number of prenatal consultations < 7 times. The
cut-off point was five points.
Sensitivity, Specificity, Positive Probability Ratio (PPR), Negative Probability
Ratio (NPR), Area Under the Curve (AUC) were calculated and the ROC curve
was constructed. A p < 0.05 was considered statistically significant. An PPR > 10
27
and an NPR < 0.1 were considered as a major influence on the probability of
death.
Approval by the Research Ethics Committee was not necessary, as the data
collected and used are in the public domain.
Results
Between 2016 and 2020, 6189 records were collected, of which 2089 were
neonatal deaths. After applying the eligibility criteria, 5198 records remained, with
1138 deaths and 3860 live births (Image 1)
We found 1317 (25.3%) premature infants, 1180 (22.7%) low birth weight
newborns, 677 (13%) with inadequate prenatal care, 555 (10.7%) with Apgar < 7
at the 5th minute and 229 (4.4%) with some congenital anomaly (Table 1).
Of the deaths, 931 (63.9%) newborns were low birth weight, 205 (15.3%) had
some congenital anomaly, 957 (72.2%) were premature, 532 (40.5%) had Apgar
< 7 at the 5th minute and 883 (68.2%) had inadequate prenatal care (Table 2).
Low birth weight was 33 times more likely to die (COR 33.17 [27.89,39.45]), the
presence of congenital anomaly 28 times (COR 28.79 [18.77,44.18]), prematurity
25 times (COR 25.27 [21.49,29.71]), Apgar < 7 in the 5th minute 113 times (COR
113.49 [74.23,173.50]) and inadequate prenatal care 3 times (COR 3.65
[3.18,4.18]) (Table 2).
When adjusting the variables, low birth weight was 7 times more likely to die (AOR
7.56 [5.86,9.74]), the presence of congenital anomaly 28 times (AOR 28.15
[16.73,47.36]), prematurity 6 times (AOR 6.06 [4.72,7.78]), Apgar < 7 in the 5th
minute 61 times (AOR 61.61 [38.01,99.88]) and inadequate prenatal care 1 time
(AOR 1.29 [1.04,1.59]) (Table 2).
In the elaboration of the prediction score, sensitivity of 70.21%, specificity of
96.65%, PPR of 20.95, NPR of 0.30 and AUC 0.896 (0.883, 0.908) were found
(Table 3; Image 2).
28
Comments
The study showed that 72.2% of deaths recorded between 2016 and 2020 in
Alagoas were premature newborns, 68.2% had inadequate prenatal care, 63.9%
were underweight, 40.5% had Apgar < 7 in the 5th minute and 15.3% had some
congenital anomaly.
In addition, it was evidenced that Apgar < 7 in the 5th minute was 61 times more
likely to die, followed by the presence of congenital anomaly with 28 times, low
birth weight with 7 times, prematurity with 6 times and inadequate prenatal care
with a turn.
Lansky et al (2014), when analyzing the profile of neonatal deaths in Brazil,
showed that 82% of registered deaths were related to low birth weight and 81.7%
to prematurity. Kassar et al (2013), when identifying risk factors for neonatal
mortality, also showed that the main cause of neonatal death was low birth
weight, with 69.9% of deaths recorded. Such findings corroborate the findings in
our study, which pointed to prematurity and low birth weight as one of the main
causes.
Sleutjes et al (2018), analyzing the risk factors for neonatal mortality in the State
of São Paulo (Brazil), showed that 76.7% of registered deaths were caused by
prematurity, 75% by low birth weight and 75% by a Apgar < 7. On the other hand,
Vallely et al (2021), using data from the Papua New Guinea Perinatal Problems
Identification System to describe the causes and avoidable factors associated
with early neonatal death, showed that 23% of registered deaths were caused by
prematurity , corroborating the findings of our study.
Cnattingius et al (2020), when using the Swedish database to investigate the
association between the risk of neonatal death, showed that, of the 1,986 deaths
recorded, 71.65% corresponded to extreme prematurity. In addition, while being
born with a gestational age of 30 weeks is 10.9 times more likely to die, a
gestational age of 22 weeks is 641.9 times more likely to die. On the other hand,
Veloso et al (2019), when analyzing the risk of neonatal death in Brazil, showed
29
that the gestational age of 37 weeks or less is 5.74 times more likely to die, a
number close to the finding in our study. Sleutjes et al (2018) pointed out that a
gestational age less than 38 weeks has a 168 times greater chance of death.
Andegiorgish et al (2020), evaluating neonatal mortality and associated factors in
Asmara (Eritrea), showed that being born with a gestational age of less than 37
weeks is 1.46 times more likely to die. On the other hand, Demitto et al (2016),
when identifying the factors associated with neonatal mortality, pointed out that
being premature at 28 weeks or less is 682.47 times more likely to progress to
death.
Garcia et al (2017), analyzing the risk factors for neonatal mortality in Santa
Catarina (Brazil), showed that an Apgar score < 7 in the 5th minute has a 19.08
times greater chance of death and low birth weight has a chance of 9.46 times of
progressing to death. Lona Reys et al (2018), in turn, when evaluating the factors
associated with neonatal mortality in Guadalajara (Mexico), pointed out that
Apgar < 7 in the 5th minute has a 9.40 times greater chance of death, while low
birth weight 6.30 times. Weddih et al (2019), when evaluating the factors
associated with neonatal death in Nuaquexote (Mauritania), showed that a low
birth weight newborn is 3.91 times more likely to die. Such findings showed the
importance of Apgar in the risk of neonatal death, corroborating the findings of
our study.
The study, when elaborating the prediction score, found sensitivity of 70.21%,
specificity of 96.65%, PPR of 20.95, NPR of 0.30 and AUC of 89.6%. The
International Neonatal Network (1993) published the Clinical Risk Index for
Babies (CRIB) and, using six parameters and a result in the first 12 hours, found
an AUC of 90%. In contrast, using 28 items to be collected in the first 24 hours of
life, Richardson et al (1993) developed the Score for Neonatal Acute Physiology
(SNAP), with an AUC of 87%. Our study, using five parameters, found an AUC
of 89.6%, proving to be an alternative to be considered.
Rosenberg et al (2008) developed the Simplified Age-Weight and Sex mortality
risk classification (SAWS) and, using the variables sex, gestational age and birth
30
weight, found an AUC of 71%. Also using three parameters collected in the first
24 hours, Medvedev et al (2020) developed the Neonatal Mortality Risk among
neonates weighting 2000 g or less (NMR-2000), reaching an AUC of 85.2%. The
scale proposed by this study proved to be superior to the proposals presented
above, with an AUC of 89.6%.
It should be noted that our study used secondary data to develop a neonatal
death prediction score. Szwarcwald et al (2019), when analyzing the quality of
the data recorded in the Live Birth Information System (LBIS), showed agreement
in all the variables tested, with the most fragile variable being the gestational age
with a kappa value of 0.461, evidencing a quality of the data that make up the
analyzed database.
Finally, a point to be highlighted is the great positive influence that our score has
when the score is greater than 5 (PPR > 10), showing a high probability of death.
On the other hand, the same cannot be said when the score is less than 5 (NPR
> 0.1).
Conclusion
The prediction score is promising to predict neonatal death, showing a great
influence on the probability when greater than five points, obtaining a better score
when positive.
31
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et al. Analysis of neonatal mortality risk factors in Brazil: a systematic review and
meta-analysis of observational studies. J Pediatr (Rio J) 2019; 95:519-530.
[3] Vallely LM, Smith R, Laman M, Riddell MA, Mengi A, Au L et al. Early neonatal
death review from two provinces in Papua New Guinea: A retrospective analysis.
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[4] Lehtonen L, Gimeno A, Parra-Llorca A, Vento M. Early neonatal death: A
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[6] Pedrosa LDCO, Sarinho SW, Ordonha MR. Quality of information analysis on
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23:2385-2395.
[7] International Neonatal Network. The CRIB (Clinical Risk Index for Babies)
Score: a tool for assessing initial neonatal risk and comparing performance of
neonatal intensive-care units. Lancet 1993; 342:193-198.
[8] Rosenberg RE, Ahmed S, Saha SK, Ahmed NU, Chowdhury A, Law PA et al.
Simplified Age-Weight Mortality Risk Classification for Very Low Birth Weight
Infants in Low-Resource Settings. J Pediatr 2008; 153:519-524.
32
[9] Medvedev MM, Brotherton H, Gai A, Tann C, Gale C, Waiswa P et al.
Development and validation of a simplified score to predict neonatal mortality risk
among neonates weighing 2000 g or less (NMR-2000): an analysis using data
from the UK and The Gambia. Lancet Child Adolesc Health 2020; 4:299-311.
[10] Lansky S, Friche AAL, da Silva AAM, Campos D, Bittencourt DAS, de
Carvalho ML et al. Birth in Brazil survey: neonatal mortality, pregnancy and
childbirth quality of care. Cad Saude Publica 2014; 30:S1-S15.
[11] Kassar SB, Melo AMC, Coutinho SB, Lima MC, Lira PIC. Determinants of
neonatal death with emphasis on health care during pregnancy, childbirth and
reproductive history. J Pediatr (Rio J) 2013; 89:269-277.
[12] Sleutjes FCM, Parada CMGL, Carvalhaes MABL, Temer MJ. Risk factors for
neonatal death in na inland region in the State of São Paulo Brazil. Cien Saude
Colet 2018; 23:2713-2720.
[13] Cnattingius S, Johansson S, Razaz N. Apgar Score and Risk of Neonatal
Death among Preterm Infants. N Engl J Med 2020; 383:49-57.
[14] Andegiorgish AM, Andemariam M, Temesghen S, Ogbai L, Ogbe Z, Zeng L.
Neonatal mortality and associated factors in the specialized neonatal care unit
Asmara, Eritrea. BMC Public Health 2020; 20: 10.
[15] Demitto MO, Gravena AAF, Dell’Agnolo CM, Antunes MB, Pelloso SM. High
risk pregnancies and factors associated with neonatal death. Rev Esc Enferm
USP 2017; 51:e03208.
[16] Garcia LP, Fernandes CM, Traebert J. Risk factors for neonatal death in the
capital city with the lowest infant mortality rate in Brazil. J Pediatr (Rio J) 2019;
95:194-200.
33
[17] Lona Reyes JC, Ramírez ROP, Ramos LL, Ruiz LMG, Vázquez EAB, Patino
VR. Neonatal mortality and associated factors in newborn infants admitted to a
Neonatal Care Unit. Arch Argent Pediatr 2018; 116:42-48.
[18] Weddih A, Ahmed MLCB, Sidatt M, Abdelghader N, Abdelghader F, Ahmed
A et al. Prevalence and factors associated with neonatal mortality among
neonates hospitalized at the National Hospital Nouakchott, Mauritania. Pan Afr
Med J 2019; 34:152.
[19] Richardson DK, Gray JE, McCormick MC, Workman-Daniels K, Goldmann
DA. Score for Neonatal Acute Physiology: a physiologic severity index for
neonatal intensive care. Pediatrics 1993; 91:617-623.
[20] Szwarcwald CL, Leal MC, Esteves-Pereira AP, de Almeida WS, de Frias PG,
Damacena GN et al. Evaluation of data from the Brazilian Information System on
Live Births (SINASC). Cad Saude Publica 2019; 35:e00214918.
34
FIGURE LEGENDS
Image 1. Data selection flowchart.
Image 2. ROC curve.
35
TABLES
Table 1. Descriptive analysis of the data.
5198 records
Gestational age
≥ 37 weeks
3867
74.4%
< 37 weeks
1317
25.3%
Ignored
14
0.3%
≥ 2500 grams
4018
77.3%
< 2500 grams
1180
22.7%
None
105
2%
1a3
572
11%
4a6
1627
31.3%
≥7
2837
54.6%
Ignored
57
1.1%
≥7
4619
88.9%
<7
555
10.7%
Ignored
24
0.5%
Presence
229
4.4%
Absence
4953
95.3%
Ignored
16
0.3%
Birth weight
Number of prenatal
consultations
APGAR in the 5th minute
Congenital anomalies
36
Table 2. Bivariate and multivariate analysis.
Death
Yes
No
931 (69.60%)
249 (6.50%)
COR (95% IC)
AOR (95% IC)
33.17 (27.89 –
7.56 (5.86 –
39.45)
9.74)
28.79 (18.77 –
28.15 (16.73 –
44.18)
47.36)
25.27 (21.49 –
6.06 (4.72 –
29.71)
7.78)
113.49 (74.23 –
61.61 (38.01 –
173.50)
99.88)
3.65 (3.18 –
1.29 (1.04 –
4.18)
1.59)
Weight
< 2500 grams
≥ 2500 grams
407 (30.40%)
3611 (93.50%)
Congenital anomalies
Yes
No
205 (15.32%)
24 (0.63%)
1133 (84.68%)
3820 (99.37%)
957 (72.20%)
360 (9.30%)
Gestational age
< 37 weeks
≥ 37 weeks
368 (27.80%)
3499 (90.70%)
APGAR in the 5th minute
<7
≥7
532 (40.50%)
231 (0.60%)
782 (59.50%)
3837 (99.40%)
883 (68.20%)
1421 (36.90%)
Prenatal
<7
consultations
≥7
412 (31.80%)
consultations
2425 (63.10%)
37
Table 3. Sensitivity, specificity, positive probability ratio, negative
probability ratio and area under the curve.
DEATH
YES
NO
Score ≥ 5
884
128
Score < 5
375
3701
Sensitivity
70.21%
Specificity
96.65%
Positive Probability Ratio
20.95
Negative Probability Ratio
0.30
Area under the curve.
0.896
38
FIGURES
Image 1. Data selection flowchart.
39
Image 2. ROC curve.
40
7 CONCLUSÕES
O estudo mostrou que 72.2% dos óbitos registrados entre 2016 e 2020 em Alagoas
eram recém-nascidos prematuros, 68.2% apresentaram pré-natal inadequado, 63.9% eram
de baixo peso, 40.5% apresentaram Apgar < 7 no 5º minuto e 15.3% possuíam alguma
anomalia congênita. Além disso, o Apgar < 7 no 5º minuto apresentou 61 vezes mais chance
de óbito, seguido da presença de anomalia congênita com 28 vezes, baixo peso ao nascer
com 7 vezes, prematuridade com 6 vezes e pré-natal inadequado com uma vez.
O escore de predição proposto obteve uma sensibilidade de 70.21%, especificidade
de 96.65%, RPP de 20.95, RPN de 0.30 e AUC de 89.6%. Diante disso, tal escore mostra-se
promissor para predizer o óbito neonatal, apresentando uma grande influência na
probabilidade de ocorrência de óbito neonatal quando maior que cinco pontos.
41
8 LIMITAÇÕES E PERSPECTIVAS
A nossa proposta de escore utilizou dados secundários, o que pode levantar uma
desconfiança dos dados. SZWARCWALD et al (2019) publicaram um estudo analisando a
qualidade dos dados registrados no Sistema de Informações sobre Nascidos Vivos
(SINASC), uma das bases utilizadas em nosso estudo e que está presente juntamente com o
Sistema de Informações sobre Mortalidade (SIM) no Departamento de Informática do
Sistema Único de Saúde do Brasil (DATASUS). Tal estudo mostrou que houve concordância
em todas as variáveis testadas, sendo a variável de maior fragilidade a idade gestacional com
valor de kappa de 0.461, evidenciando a boa qualidade dos dados que compõem o SINASC
(SZWARCKWALD et al., 2019).
Por fim, um ponto a ser destacado é a grande influência que o escore proposto possui
quando a pontuação é maior que 5, pois o RPP foi maior que 10. Isso mostra que há uma
probabilidade alta de que o recém-nascido irá evoluir para o óbito. Em contrapartida, o
mesmo não pode ser dito quando o escore proposto for menor que 5, pois o RPN foi maior
que 0.3, mostrando que há uma pequena influência na probabilidade.
O escore de predição proposto é um dispositivo de rápido e fácil acesso, podendo ser
construído em 5 minutos. Por isso, a intenção dessa proposta é difundir esse escore,
principalmente nos países de média e baixa renda, onde os sistemas de saúde são deficientes
em vários aspectos. Sendo um escore construído a partir de dados do Brasil, onde o perfil
causal é semelhante a outros países de média e baixa renda, o escore proposto mostra-se
promissor e uma opção interessante a ser utilizada em estudos prospectivos no contexto da
mortalidade neonatal.
42
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47
APÊNDICE
48
49
ANEXO
50
PAEDIATRIC AND PERINATAL EPIDEMIOLOGY
Author Guidelines
Contents
1. Submission
2. Aims and Scope
3. Manuscript Categories and Requirements
4. Preparing Your Submission
5. Editorial Policies and Ethical Considerations
6. Author Licensing
7. Publication Process After Acceptance
8. Post Publication
9. Editorial Office Contact Details
1. SUBMISSION
Authors should kindly note that submission implies that the content has not been published
or submitted for publication elsewhere except as a brief abstract in the proceedings of a
scienti c meeting or symposium.
Once the submission materials have been prepared in accordance with the Author
Guidelines,
manuscripts
should
be
submitted
online
at
https://mc.manuscriptcentral.com/ppe Click here for more details on how to use
ScholarOne.
Data protection:
By submitting a manuscript to or reviewing for this publication, your name, email address,
and a liation, and other contact details the publication might require, will be used for the
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recognise the importance of protecting the personal information collected from users in the
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You
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protectionpolicy.html.
more
at
https://authorservices.wiley.com/statements/data-
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Preprint Policy:
Please review Wiley’s preprint policy here.
However, Wiley also knows that the use of preprint servers is not universally accepted and
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Please see below for the speci c policy language.
This journal will consider for reviews previously available as preprints on non-commercial
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requested to update any pre-publication versions with a link to the nal published article.
Social Media Quote:
When submitting an Original article you will be asked to also include a social media
information
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We will post the quote on both Twitter and Facebook.
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We require that you provide a Tweetable quote of 280 characters or less, (in the
manuscript and at the submission online portal), summarising the main ndings of the
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For help with submissions, please contact: PPEoffice@wiley.com.
We look forward to your submission.
2. AIMS AND SCOPE
Paediatric and Perinatal Epidemiology welcomes original research, brief reports, reviews
(including systematic reviews and meta-analysis), letters to the editor, and debates, as well
as papers describing the methods of large epidemiological studies or novel cohort or
longitudinal study designs. Topics of interest include the application of epidemiologic
methods to studies of fertility, pregnancy and obstetrical complications, birth outcomes,
child health and development, and the in uence of the foetal and early environment on child
or adult health. We also encourage submissions on the development and applications of new
and innovative methods.
52
All authors are expected to meet the International Committee of Medical Journal Editors
Uniform Criteria for Authorship (http://www.icmje.org/), which they con rm by their
signature on the letter of submission.
3. MANUSCRIPT CATEGORIES AND REQUIREMENTS
Editor's Note papers and Debate papers are by invite only. If you are invited, you
should state your approval to submit by the Editor-in-chief in the cover letter.
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The journal does not accept case reports. Authors of case reports are encouraged to submit
to the Wiley Open Access journal, Clinical Case Reports (www.clinicalcasesjournal.com),
which aims to directly improve health outcomes by identifying and disseminating examples
of best clinical practice.
Manuscript submission
Paediatric and Perinatal Epidemiology requires all manuscripts to be submitted
electronically at https://mc.manuscriptcentral.com/ppe. Login or click the “Create Account”
option if you are a rst-time user of the ScholarOne system. Full instructions and support for
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support@scholarone.com or at
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PPE’s Editorial Assistant (PPEoffice@wiley.com) will be able to assist.
Word and references limits
Except where noted below, all manuscripts are to include a structured abstract and uniform
section and subsection headings. The structured abstract (no more than 300 words) should
include the following: Background, Objectives, Methods, Results, and Conclusions.
•
Original articles: Submissions may not exceed 3500 words, with a maximum of 6
tables and gures combined, and up to 60 references. They should include a structured
abstract (no more than 300 words; see “original submission” format below).
•
Systematic review and meta-analysis articles: Submissions may not exceed 4500
words, with a maximum of 8 tables and gures combined and up to 75 references.
They should include a
•
structured abstract (no more than 300 words; see “Systematic reviews and metaanalysis” format below).
53
•
Research protocol: Submissions should not exceed 4500 words, with a maximum of
8 tables and gures combined and up to 60 references. They should include a
structured abstract (no more than 300 words; see “Study Design” format below).
•
Methodology: Submissions should not exceed 3500 words, with a maximum of 6
tables and gures combined and up to 60 references. They should include a structured
abstract (no more than 300 words; see “original submission” format below).
•
Brief report: Submissions should not exceed 1500 words, with a maximum of 3 tables
and gures combined, and up to 30 references. They should include a structured
abstract (no more than 300 words; see “Original submission” format below).
•
Debate: Submissions should not exceed 1500 words with a maximum of 1 table or
gure and no more than 10 references. The title should be in the following format: e.g.
“Point: The value of descriptive series analysis: The case of Portugal” or
“Counterpoint: The value of descriptive series analysis: The case of Portugal”. An
abstract is not required.
•
Letters to the editor: Submissions should not exceed 500 words with a maximum of
6 references, including the original manuscript to which the letter is responding. An
abstract is not required.
4. PREPARING YOUR SUBMISSION
General style conventions and formatting requirements
All manuscripts should be submitted in English using United Kingdom spelling and
grammar conventions. Manuscripts should be typed with double spacing in Calibri font, 12
points. Pages should be numbered consecutively in the bottom centre. Do not fully justify
the text.
Style conventions
In an e ort to standardise language use throughout the journal, Paediatric and Perinatal
Epidemiology has adopted the following style conventions:
•
Birthweight not birth weight; stillbirth not still birth.
•
Breast feeding (noun) not breastfeeding; and breast-feeding mothers (adjective).
•
Preterm or low birthweight never premature.
•
Confidence intervals; not confidence limits.
•
Multivariable not multivariate, for regression models with a single outcome variable.
•
Parity to refer to the number of prior livebirth or stillbirth delivered at ≥20 weeks.
Use parity zero if the pregnant or delivering woman has had no previous livebirths
54
or stillbirths and refer to her as a primipara (plural primiparae). A woman who has
had at least one prior viable pregnancy is a multipara (plural multiparae).
Parts of the Manuscript
The manuscript should be submitted in separate les: main text le; gures.
Main Text File
The text le should be presented in the following order:
i. A short informative title containing the major key words. The title should not
contain abbreviations
(see Wiley's best practice SEO tips); ii. A short running title of less than 40
characters;
iii. The full names of the authors; iv. The author's institutional a
liations where the
work was conducted, with a footnote for the author’s present address if di erent from
where the work was conducted; v. Abstract and keywords;
vi.
Main text;
vii.
Acknowledgments;
viii.
References;
ix.
Tables (each table complete with title and footnotes);
x.
Figure legends;
xi.
Appendices (if relevant).
Figures and supporting information should be supplied as separate les.
Body of text
•
Do NOT indent paragraphs. Instead separate paragraphs with a blank extra line
between paragraphs.
•
Con dence intervals should be put in round brackets, separated by a comma (not a
dash). For example, RR 2.31, 95% CI 1.90, 2.74; or RR 2.31 (95% CI 1.90, 2.74).
•
Do not insert line numbers in the document.
•
Ethics/human subjects statement (e.g, institutional review board approval) is
required; it should be included as the last sentence of the rst paragraph under
Methods.
Reporting of numerical data
•
Report percentages and risks with one digit, and risk estimates and CIs to two
significant digits. Round accordingly, reporting numbers appearing more than once
consistently.
55
•
Confidence intervals should be put in round brackets, separated by a comma (see
example above).
P-values and con dence intervals
•
We strongly discourage the use of P-values or statements that re ect “statistical
significance” testing. The use of P-values is permitted for the following three
scenarios only: (a) tests for linear and non-linear trends; (b) tests of interactions; and
(c) multiple degrees of freedom tests (e.g, ANOVA).
•
All ratio (OR, RR, HR) and di erence measures should be accompanied by a 95%
con dence interval.
Title page
•
Title: Be concise; declaring the type of study design is encouraged; do not specify
the study (sample) size.
•
List of authors (do not list quali cations or academic titles), with full names, each
followed by a superscript number (not letter) to link with the institution at which the
authors were affiliated when the work was completed.
•
For the corresponding author, please list: Full name, department, institution, city and
state of location, and country and email address only; do not list the full mailing
address, telephone, or fax numbers.
Authorship
The journal follows the ICMJE de nition of authorship, which indicates that authorship be
based on the following 4 criteria:
•
Substantial contributions to the conception or design of the work; or the acquisition,
analysis, or interpretation of data for the work; AND
•
Drafting the work or revising it critically for important intellectual content; AND
•
Final approval of the version to be published; AND
•
Agreement to be accountable for all aspects of the work in ensuring that questions
related to the accuracy or integrity of any part of the work are appropriately
investigated and resolved.
In addition to being accountable for the parts of the work he or she has done, an author
should be able to identify which co-authors are responsible for speci c other parts of the
work. In addition, authors should have con dence in the integrity of the contributions of their
co-authors.
56
All those designated as authors should meet all four criteria for authorship, and all who meet
the four criteria should be identi ed as authors. Those who do not meet all four criteria should
be acknowledged. These authorship criteria are intended to reserve the status of authorship
for those who deserve credit and can take responsibility for the work. The criteria are not
intended for use as a means to disqualify colleagues from authorship who otherwise meet
authorship criteria by denying them the opportunity to meet criterion #s 2 or 3. Therefore,
all individuals who meet the rst criterion should have the opportunity to participate in the
review, drafting, and nal approval of the manuscript.
Conflict of Interest Statement
Authors will be asked to provide a con ict of interest statement during the submission
process. For details on what to include in this section, see the ‘Con ict of Interest’ section in
the Editorial Policies and Ethical Considerations section below. Submitting authors should
ensure they liaise with all co-authors to con rm agreement with the nal statement.
Abstract
1. Original submissions and brief reports, follow this structure.
•
Background: Brie y state the reason(s) or justi cation for undertaking the
study. Objectives: Spell out the primary objective of the study. A hypothesis
statement can also accompany an objective.
•
Methods: Begin by declaring the type of study design, time frame of study,
population, and data source. Describe the primary exposure and outcome.
Provide a brief description of analytic method, and how threats to study
validity, including but not limited to, confounding, were addressed (if
applicable). If space permits, declare alternate exposure de nitions and
secondary outcome(s).
•
Results: Begin by providing the study size, exposure and outcome prevalence
(or other appropriate descriptive measure). Statement of e ect measures (for
the primary outcome) must be preceded by the outcome prevalence
conditional on the exposure. Do not report Pvalues (see exceptions “P-value”
section below); instead di erence and ratio measures must be accompanied by
95% con dence intervals.
•
Conclusion(s): Declare the primary nding of the study—if you have declared
a hypothesis earlier, state if the study supports or does not support the
57
hypothesis. Conclusions should not be overstated, and do not present any new
ndings here without declaring them in the “Results” section. Do not declare
any policy-based implications or recommendations unless the study and/or
the objective is policy-related. Causal language should be avoided unless
fully supported by the design and statistical analysis.
2. Systematic reviews and meta-analysis, follow this structure.
•
Background: Brie y state the reason(s) or justi cation for undertaking the
study.
•
Objectives: Spell out the primary and secondary objectives of the study.
•
Data sources: List all data sources that were accessed to undertake the
systematic review and/or meta-analysis.
•
Study selection and data extraction: State explicitly the inclusion and
exclusion criteria. How were data extracted from every study?
•
Synthesis: Describe how the systematic review was performed and/or how
the meta-analysis was accomplished. State how heterogeneity was assessed,
and how data pooling was accomplished.
•
Results: Begin by providing the number of eligible studies, total study size,
as well as the prevalence of the exposure, and outcome. Statement of e ect
measures (for the primary outcome) must be preceded by the outcome
prevalence conditional on the exposure. Do not report P-values (see
exceptions in the “P-value” section below); instead di erence and ratio
measures must be accompanied by 95% con dence intervals. Where
appropriate, provide an assessment of the inter- study heterogeneity (I2
statistic); and discuss the potential for associations to be a ected by
publication bias.
•
Conclusion(s): Declare the primary nding of the study; if you have declared
a hypothesis earlier, state if the study supports or does not support the
hypothesis. Conclusions should not be overstated, and do not present any new
ndings here without declaring them in the “Results” section. Causal language
should be avoided.
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3. Study design papers, follow this structure.
•
Background: Brie y state the reason(s) or justi cation for undertaking the
study.
•
Objectives: Spell out the primary and secondary objectives of the study.
•
Population: State the population from which subjects were recruited
(sampling base).
•
Design: State the study design; be as explicit as possible.
•
Methods: Describe the exposure(s) and primary and secondary outcome(s),
and other relevant details.
•
Preliminary results: Begin by stating the time frame of study, and provide a
description of the cohort. Describe the primary exposure(s) and outcome(s).
Provide details regarding recruitment and follow-up, and comment on loss to
follow-up. If space permits, declare alternate exposure de nitions and
secondary outcome(s).
•
Conclusion(s): Declare the primary nding of the study. Conclusions should
not be overstated, and do not present any new ndings here without declaring
them in the “Results” section. Causal language should be avoided.
Synopsis
We require that you provide a brief synopsis of the paper of no more than 125 words,
organised under the following headings.
•
Study question.
•
What’s already known.
•
What this study adds.
Keywords
Insert a set of 4-6 key words, separated by semicolons, on a new page after the abstract.
Keywords should be taken from those recommended by the US National Library of
Medicine's Medical Subject Headings (MeSH) browser list at www.nlm.nih.gov/mesh.
Word count
Provide a word count not including the abstract, tables, gures, or references after the
keywords.
Main text
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The required section headings (shown in bold) are as follows: Background, Methods,
Results, Comment, and Conclusions.
The Methods section should include the following sub-sections:
•
Cohort or case-control selection: Preferably with a ow chart describing all
exclusions.
•
Exposure (both primary and secondary).
•
Outcomes (both primary and secondary).
•
Statistical analysis: Clearly describe the general approach to statistical analyses and
including the following to sub-sections.
o Missing data (see below).
o Sensitivity analyses (see below).
•
Ethics approval: A sentence noting the institution(s) where ethics approval was
obtained.
Manuscript Structure
The manuscript should contain the following sections, in the following order, with each
section beginning on a new page: Title page, Synopsis, Abstract, Key words, Main text,
References, Acknowledgements, Funding, Figure legends, Tables, Table legends, Figures,
Supplemental tables, and Supplemental gures.
Acknowledgments
Contributions from anyone who does not meet the criteria for authorship should be listed,
with permission from the contributor, in an Acknowledgments section. Any
acknowledgements should be placed at the end of the text before the references. Authors
should be sure that they have obtained permission to mention any individual acknowledged
by name. Financial and material support should also be mentioned. Thanks to anonymous
reviewers are not appropriate.
References
References in the text should be referred to by a superscript number after the punctuation.
The list of references at the end of the manuscript should be listed in the order in which they
appear in the text. Note that journal names should be spelt out in full, and both the beginning
and the ending page numbers should be listed in full. References to personal
communications, unpublished data or manuscripts “in preparation” should not be included.
If essential, such material may be incorporated at the appropriate place in the text. The style
should be as follows:
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•
For articles, give authors’ names followed by initials, full title of the article, name of
journal, year of publication, volume number, rst and last relevant page numbers. List
all authors and if the number exceeds six give the rst six, followed by et al.
Example: Sophist J, Paradigm K. The variation in infant sex ratio according
to degree of maternal pedantry. International Journal of Perinatal Variation 1979;
7:143-152.
•
For books, give authors’ names followed by initials, title of chapter/article, title of
book preceded by “In:,” “Editor(s):” followed by name(s) and initial(s), place of
publication, publisher’s name, year of publication, rst and last relevant page numbers.
Example: Cart A. Patterns of illness in children living in an area of heavy
pollution. In: Horse Sense. Editors: Loh J, Mee K, Soh AH. Solihull: Khyber Press,
1984; pp. 14-83.
•
We strongly recommend the use of a tool such as EndNote for reference management
and
formatting.
EndNote
reference
styles
can
be
found
at:
https://endnote.com/downloads/styles.
Tables
•
Tables should only be prepared in Microsoft Word using the table function and
created in a manner such that it is clear what is being shown.
•
Tables should be clearly labelled and able to be understood apart from the text.
•
Each table should begin on a separate sheet, numbered consecutively with Arabic
numerals, containing only horizontal lines (one each at the top and bottom of the
table and with additional lines to divide table sections only as needed), and with a
concise legend. Table footnotes should be denoted with superscript lowercase letters.
•
Aside from the column headings, none of the table entries should be in bold.
•
Con dence intervals should be put in round brackets, separated by a comma not a
dash.
•
The reference category for relative measures of e ect should always be labelled as
“1.00
•
(Reference)” (not “ref”); for absolute measures, the reference category should be
labelled as “0.00 (Reference).”
Figure Legends
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Legends should be concise but comprehensive – the gure and its legend must be
understandable without reference to the text. Include de nitions of any symbols used and de
ne/explain all abbreviations and units of measurement.
Figures
•
Authors’ original artwork will be used; labelling should be in Calibri typeface, 12
points so that after reduction it is no smaller than eight points.
•
All gures must be at least 300 × 300 DPI.
•
Symbols and lines should be distinct after reduction; histograms should be black,
white or hatched in distinctive ways; background lines should not be used. Legends
for gures should be typed on a separate sheet.
•
In the full-text online edition of the journal, gure legends may be truncated in
abbreviated links to the full-screen version. Therefore, the rst 100 characters of any
legend should inform the reader of key aspects of the gure.
•
Complete guidance regarding the preparation and preferred le formats for gures and
images are available at http://author services.wiley.com/bauthor/illustration.asp.
Supplemental Material
•
We accept supplemental tables and gures that support the main analyses.
•
All supplemental tables and gures must be referenced in the text as “eTable x” or
“eFigure x.”
Other Points to Consider
•
Presenting a DAG to highlight the pathways amongst variables is highly
recommended.
•
Cohort, cross-sectional studies, or randomised controlled trials: always present
relative risk/risk ratio orrate ratio (never odds ratios) or risk di erences, derived from
log-linear regression models (see Spiegelman D, Hertzmark E. Easy SAS
calculations for risk or prevalence ratios and di erences. American Journal of
Epidemiology 2005 Aug 1;162 (3):199-200).
•
Case-control studies: odds ratios are ne, but if sampling fractions of cases and
controls are available,then present e ect measures from a weighted analysis, and so a
relative risk/risk ratio or rate ratio is preferred.
•
Standard deviations are preferred over standard errors for sample descriptions.
•
Avoid statements such as “This was the rst study to…” or “We were the rst to…”
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•
When race, ethnicity, or nationality (de ned as place of birth) is identi ed as research
variables, authorsshould make clear the purpose for using such variables. Authors
should describe their methods of de nition and classi cation of racial, ethnic, or
nationality groupings. Ethnocentricity should be avoided. For example, in choosing
a reference group, it should not be assumed that the majority racial, or ethnic group
is necessarily the best choice. Care should be taken to explain the choice of referent.
Limitations of race, ethnicity, and nationality data and measurement should be
clearly stated. Known or potential causes of the observed di erences between groups
should be explored and discussed.
•
Sex versus gender: We are cognisant of the fact that some people do not identify their
gender as thebiological sex they were born with. We ask that authors be clear whether
they are talking about biological sex or self-identi ed gender.
Data Citation
Please review Wiley’s data citation policy here.
Additional Files
Appendices
Appendices will be published after the references. For submission they should be supplied
as separate les but referred to in the text.
Supporting Information
Supporting information is information that is not essential to the article, but provides greater
depth and background. It is hosted online and appears without editing or typesetting. It may
include tables, gures, videos, datasets, etc.
Click here for Wiley’s FAQs on supporting information.
Note: if data, scripts, or other artefacts used to generate the analyses presented in the paper
are available via a publicly available data repository, authors should include a reference to
the location of the material within their paper.
Wiley Author Resources
Manuscript Preparation Tips: Wiley has a range of resources for authors preparing
manuscripts for submission available here. In particular, we encourage authors to consult
Wiley’s best practice tips on Writing for Search Engine Optimization.
Article Preparation Support
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Wiley Editing Services o ers expert help with English Language Editing, as well as
translation, manuscript formatting, gure illustration, gure formatting, and graphical abstract
design – so you can submit your manuscript with con dence.
Also, check out our resources for Preparing Your Article for general guidance about writing
and preparing your manuscript.
5. EDITORIAL POLICIES AND ETHICAL CONSIDERATIONS
Editorial Review and Acceptance
The acceptance criteria for all papers are the quality and originality of the research and its
signi cance to our readership. Except where otherwise stated, manuscripts are single-blind
peer reviewed. Papers will only be sent to review if the Editors-in-Chief determine that the
paper meets the appropriate quality and relevance requirements.
Wiley's policy on con dentiality of the review process is available here.
Guidelines on Publishing and Research Ethics in Journal Articles Please review Wiley’s
policies surrounding human studies, animal studies, clinical trial registration, biosecurity,
and research reporting guidelines here.
Con ict of Interest
The journal requires that all authors disclose any potential sources of con ict of interest. Any
interest or relationship, nancial or otherwise that might be perceived as in uencing an author's
objectivity is considered a potential source of con ict of interest. These must be disclosed
when directly relevant or directly related to the work that the authors describe in their
manuscript. Potential sources of con ict of interest include but are not limited to: patent or
stock ownership, membership of a company board of directors, membership of an advisory
board or committee for a company, and consultancy for or receipt of speaker's fees from a
company. The existence of a con ict of interest does not preclude publication. If the authors
have no con ict of interest to declare, they must also state this at submission. It is the
responsibility of the corresponding author to review this policy with all authors and
collectively to disclose with the submission ALL pertinent commercial and other
relationships.
Funding
Authors should list all funding sources in the Acknowledgments section. Authors are
responsible for the accuracy of their funder designation. If in doubt, please check the Open
Funder Registry for the correct nomenclature: https://www.crossref.org/services/funder-
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registry/. Please list all funding sources, including the grant or contract number and the
funding agency
Missing data
•
Multiple imputation methods are required so long as the pattern of missing data satis
es the assumptions required for imputations, with a minimum of 50 imputations.
•
Please describe exactly the proportion of missing data for individual variables, how
multiple imputation was performed and all other relevant details. Providing citations
will be preferred.
•
Multiple imputation is generally not necessary when missing data are <5%.
Sensitivity analysis
Most observational studies su er from two common biases: selection bias and unmeasured
confounding. We ask that authors undertake and report additional sensitivity analysis that
addresses the following biases.
•
Selection bias: Authors should provide a ow diagram to describe the exclusion
categories and loss to follow-up. Authors must explicitly address selection bias by
describing the characteristics of included versus excluded groups and the potential
impact on results, including using statistical techniques, such as inverse probability
weighting, when appropriate.
•
Unmeasured confounding: An additional requirement for the estimation of causal e
ects requires that the associations remain una ected by unmeasured confounding. We
ask authors to undertake a sensitivity analysis to address unmeasured confounding
through the “E-value” method, described in VanderWeele TJ, Ding P. Sensitivity
analysis in observational research: Introducing the “E-Value.” Annals of Internal
Medicine 2017;167(4):268-274.
Material and Methods
If a method or tool is introduced in the study, including software, questionnaires, and scales,
the author should state the license this is available under and any requirement for permission
for use. If an existing method or tool is used in the research, the authors are responsible for
checking the license and obtaining the permission. If permission was required, a statement
con rming permission should be included in the Material and Methods section.
The Comment section should include the following sub-headings:
•
Principal ndings
•
Strengths of the study
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•
Limitations of the data
•
Interpretation
•
Conclusions
Data Sharing and Data Accessibility
Please review Wiley’s policy here.This journal encourages data sharing.
The journal encourages authors to share the data and other artefacts supporting the results in
the paper by archiving it in an appropriate public repository. Authors should include a data
accessibility statement, including a link to the repository they have used, in order that this
statement can be published alongside their paper.
Human subject information in databases
The journal refers to the World Health Medical Association Declaration of Taipei on Ethical
Considerations Regarding Health Databases and Biobanks.
Publication Ethics
This journal is a member of the ,a href="https://publicationethics.org/">Committee on
Publication Ethics (COPE). Note this journal uses iThenticate’s CrossCheck software to
detect instances of overlapping and similar text in submitted manuscripts. Read Wiley’s Top
10 Publishing Ethics Tips for Authors here. Wiley’s Publication Ethics Guidelines can be
found here.
ORCID
Please see Wiley’s resources on ORCID here.
As part of our commitment to supporting authors at every step of the publishing process,
Paediatric and Perinatal Epidemiology requires the submitting author (only) to provide an
ORCID iD when submitting a manuscript. This takes around 2 minutes to complete. Find
more information.
6. AUTHOR LICENSING
If your paper is accepted, the author identi ed as the formal corresponding author will receive
an email prompting them to log in to Author Services, where via the Wiley Author Licensing
Service (WALS) they will be required to complete a copyright license agreement on behalf
of all authors of the paper.
Authors may choose to publish under the terms of the journal’s standard copyright
agreement, or Open Access under the terms of a Creative Commons License.
General information regarding licensing and copyright is available here. To review the
Creative Commons License options o ered under Open Access, please click here. (Note that
66
certain funders mandate that a particular type of CC license has to be used; to check this
please click here.)
Self-Archiving de nitions and policies. Note that the journal’s standard copyright
agreement allows for self-archiving of di erent versions of the article under speci c
conditions. Please click here for more detailed information about self-archiving de nitions
and policies.
Open Access fees: If you choose to publish using Open Access you will be charged a fee. A
list of Article Publication Charges for Wiley journals is available here.
Funder Open Access: Please click here for more information on Wiley’s compliance with
speci c Funder Open Access Policies.
7. PUBLICATION PROCESS AFTER ACCEPTANCE
Accepted article received in production
When your accepted article is received by Wiley’s production team, you (corresponding
author) will receive an email asking you to login or register with Author Services. You will
be asked to sign a publication license at this point.
Accepted Articles
The journal o ers Wiley’s Accepted Articles service for all manuscripts. This service ensures
that accepted
‘in press’ manuscripts are published online very soon after acceptance, prior to copy-editing
or typesetting. Accepted Articles are published online a few days after nal acceptance, appear
in PDF format only, are given a Digital Object Identi er (DOI), which allows them to be
cited and tracked, and are indexed by PubMed. After publication of the nal version article
(the article of record), the DOI remains valid and can continue to be used to cite and access
the article.
Accepted Articles will be indexed by PubMed; submitting authors should therefore carefully
check the names and a
liations of all authors provided in the cover page of the
manuscript so it is correct for indexing. Subsequently the nal copyedited and proofed articles
will appear in an issue on Wiley Online Library; the link to the article in PubMed will
automatically be updated.
Proofs
Authors will receive an e-mail noti cation with a link and instructions for accessing HTML
page proofs online. Page proofs should be carefully proofread for any copyediting or
typesetting errors. Online guidelines are provided within the system. No special software is
67
required, all common browsers are supported. Authors should also make sure that any
renumbered tables, gures, or references match text citations and that gure legends correspond
with text citations and actual gures. Proofs must be returned within 48 hours of receipt of
the email. Return of proof via e-mail is possible in the event that the online system cannot
be used or accessed.
Major alterations to the text, tables, and gures are only allowed in exceptional circumstances,
and the additional cost may be charged to the author. Such changes must be approved by the
Editor-in- Chief.
Early View
The journal o ers rapid publication via Wiley’s Early View service. Early View (Online
Version of Record) articles are published on Wiley Online Library before inclusion in an
issue. Note there may be a delay after corrections are received before your article appears
online, as Editors also need to review proofs. Once your article is published on Early View
no further changes to your article are possible. Your Early View article is fully citable and
carries an online publication date and DOI for citations.
8. POST PUBLICATION
Access and sharing
When your article is published online:
•
You receive an email alert (if requested).
•
You can share a link to your published article through social media.
•
As the author, you will have free access to your paper (after accepting the Terms &
Conditions of use,you can view your article).
•
The corresponding author and co-authors can nominate up to ten colleagues to
receive a publicationalert and free online access to your article. You can now order
print copies of your article (instructions are sent at proo ng stage or use the website
indicated below). www.sheridan.com/wiley/eoc
Wiley’s Author Name Change Policy
In cases where authors wish to change their name following publication, Wiley will update
and republish the paper and redeliver the updated metadata to indexing services. Our
editorial and production teams will use discretion in recognizing that name changes may be
of a sensitive and private nature for various reasons including (but not limited to) alignment
with gender identity, or as a result of marriage, divorce, or religious conversion.
Accordingly, to protect the author’s privacy, we will not publish a correction notice to the
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paper, and we will not notify co-authors of the change. Authors should contact the journal’s
Editorial O
ce with their name change request.
Article Promotion Support
Wiley Editing Services o ers professional video, design, and writing services to create
shareable video abstracts, infographics, conference posters, lay summaries, and research
news stories for your research – so you can help your research get the attention it deserves.
Cover Image Submissions
This journal accepts artwork submissions for Cover Images. This is an optional service you
can use to help increase article exposure and showcase your research. For more information,
including artwork guidelines, pricing, and submission details, please visit the Journal Cover
Image page.
Measuring the Impact of your Work
Wiley also helps you measure the impact of your research through our specialist partnerships
with Kudos and Altmetric.
Archiving Services
Portico and CLOCKSS are digital archiving/preservation services we use to ensure that
Wiley content will be accessible to customers in the event of a catastrophic event such as
Wiley going out of business or the platform not being accessible for a signi cant period of
time. Member libraries participating in these services will be able to access content after
such an event. Wiley has licenses with both Portico and CLOCKSS, and all journal content
gets delivered to both services as it is published on Wiley Online Library. Depending on
their integration mechanisms, and volume loads, there is always a delay between content
being delivered and showing as “preserved” in these products.
9. EDITORIAL OFFICE CONTACT DETAILS
For queries about submissions, please contact PPEo
ce@wiley.com
Transferable Review: Health Science Reports
This journal works together with Wiley’s Open Access Journal, Health Science Reports to
enable rapid publication of good quality research that is unable to be accepted for publication
by our journal. Authors may be o ered the option of having the paper, along with any related
peer reviews, automatically transferred for consideration by the Editor of Health Science
Reports. Authors will not need to reformat or rewrite their manuscript at this stage, and
publication decisions will be made a short time after the transfer takes place. The Editor of
Health Science Reports will accept submissions that report wellconducted research that
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reaches the standard acceptable for publication. Health Science Reports is a Wiley Open
Access journal which is indexed on PubMed/MEDLINE and Scopus. For more information
please go to www.healthsciencereports.org.