Abstract

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

Key words:
Perinatal mortality; Risk factors; Newborn

Introduction

Perinatal mortality comprises the death of a child during pregnancy (fetal mortality) or up to seven days after birth (early neonatal mortality). Fighting perinatal mortality has been a major challenge for the care of pregnant women and their children worldwide, especially in the middle- and low-income countries¹1 World Health Organization (WHO). Maternal and perinatal health. Geneva: WHO; 2013. [cited 2020 maio 6]. Available from: https://www.who.int/maternal_child_adolescent/topics/maternal/maternal_perinatal/en/.

In Brazil, regional socioeconomic inequalities influence perinatal mortality rates, which rise as socioeconomic vulnerability increases²2 Jacinto E, Aquino EML, Mota MELA. Perinatal mortality in the municipality of salvador, Northeastern Brazil: Evolution from 2000 to 2009. Rev Saude Publica 2013; 47(5):846-853.^,33 Brasil. Ministério da Saúde (MS). Secretaria de Ciência, Tecnologia e Insumos Estratégicos. Departamento de Ciência e Tecnologia. Síntese de evidências para políticas de saúde. Brasília: MS; 2012.. Moreover, despite its reduction, this mortality indicator decreased slower than other indicators, such as infant mortality⁴4 Carvalho CA, Silva AAM, Victora C, Goldani M, Bettiol H, Thomaz EBAF, Barros F, Horta BL, Menezes A, Cardoso V, Cavalli RC, Santos I, Batista RFL, Simões VM, Barbieri M, Barros A. Changes in Infant and Neonatal Mortality and Associated Factors in Eight Cohorts from Three Brazilian Cities. Sci Rep 2020; 10(1):1-8..

Studies chose perinatal mortality as the most appropriate indicator of the quality of prenatal and neonatal care and health service use³3 Brasil. Ministério da Saúde (MS). Secretaria de Ciência, Tecnologia e Insumos Estratégicos. Departamento de Ciência e Tecnologia. Síntese de evidências para políticas de saúde. Brasília: MS; 2012.. Fails to detect and treat gestational diseases early and prevent complications during pregnancy, childbirth, and the puerperium cause preventable deaths, which contribute to maintain perinatal mortality as a public health issue in Brazil, despite its decreasing rates⁵5 Almeida MF, Alencar GP, Schoeps D, Novaes HMD, Campbell O, Rodrigues LC. Sobrevida e fatores de risco para mortalidade neonatal em uma coorte de nascidos vivos de muito baixo peso ao nascer, na região sul do município de São Paulo, Brasil. Cad Saude Publica 2011; 27(6):1088-1098..

Brazil still underreports perinatal deaths. Thus, analyzing this indicator in information obtained in population-based surveys can provide more accurate estimates⁶6 Vieira MSM, Vieira FM, Fröde TS, d'Orsi E. Fetal Deaths in Brazil: Historical Series Descriptive Analysis 1996-2012. Matern Child Health J 2016; 20(8):1634-1650.^,77 Almeida MF, Alencar GP, Schoeps D, Minuci EG, Silva ZP, Ortiz LP, Novaes HMD, Alencar AP, Raspantini, Santos PC. Qualidade das informações registradas nas declarações de óbito fetal em São Paulo, SP. Rev Saude Publica 2011; 45(5):845-853.. Moreover, many important variables associated with perinatal mortality remain unavailable, and their study could improve the effectiveness of perinatal and prenatal care policies⁸8 Barbeiro FMS, Fonseca SC, Tauffer MG, Ferreira MSS, Silva FP, Ventura PM, Quadros JI. Fetal deaths in Brazil: A systematic review. Rev Saude Publica 2015; 49.^,99 Fonseca SC, Coutinho ESF. Perinatal mortality research in Brazil: review of methodology and results. Cad Saude Publica 2004; 20(Supl. 1):S7-S19.. This explains the low number of Brazilian publications on the factors associated with perinatal mortality. Studies conducted in Brazil show that low socioeconomic status, late maternal ages, low birth weight, and prematurity relate to perinatal mortality²2 Jacinto E, Aquino EML, Mota MELA. Perinatal mortality in the municipality of salvador, Northeastern Brazil: Evolution from 2000 to 2009. Rev Saude Publica 2013; 47(5):846-853.^,1010 Aquino TA, Guimarães MJB, Wanick-Sarinho S, Ferreira LOC. Fatores de risco para a mortalidade perinatal no Recife, Pernambuco, Brasil, 2003 Risk factors for perinatal mortality in Recife, Pernambuco State, Brazil, 2003. Cad Saude Publica 2007; 23(12):2853-2861.^,1111 Menezes AMB, Barros FC, Victora CG, Tomasi E, Halpern R, Oliveira ALB. Fatores de risco para mortalidade perinatal em Pelotas, RS, 1993. Rev Saude Publica 1998; 32(3):209-216.. It remains unclear whether these factors can vary significantly according to local socioeconomic development and health service accessibility, which differ considerably among Brazilian regions¹²12 Stopa SR, Malta DC, Monteiro CN, Szwarcwald CL, Goldbaum M, Cesar CLG. Use of and access to health services in Brazil, 2013 National Health Survey. Rev Saude Publica 2017; 51(Supl. 1):3s..

Studying the factors associated with perinatal mortality allows strategies for the more effective reduction of one of the most resilient mortality indicators. Given this context, this study aims to evaluate the sociodemographic factors associated with perinatal mortality in São Luís, Maranhão.

Methods

This is a cross-sectional study, part of a population-based cohort initiated in 2010 entitled “Fatores etiológicos do nascimento pré-termo e consequências dos fatores perinatais na saúde da criança: coorte de nascimento em duas cidades brasileiras” (Etiological factors of preterm births and consequences of perinatal factors on children’s health: a birth cohort from two Brazilian cities) - BRISA, which analyzes births in São Luís, in the state of Maranhão, and Ribeirão Preto, in the state of São Paulo, Brazil. This study aims to evaluate the perinatal deaths in São Luís identified in the birth cohort.

São Luís is the capital city of the state of Maranhão, inhabited by 1.014.837 citizens in 2010. It is in the northeastern region of Brazil, one of the poorest in the country. Its human development index (HDI) is 0.768, 14^th among Brazilian capitals, behind all southern, southeastern, and midwestern capitals of the country¹³13 Programa das Nações Unidas para o Desenvolvimento (PNUD). Perfil - São Luís, MA. Atlas do Desenvolvimento Humano no Brasil. Brasília: PNUD; 2010. [acessado 2020 maio 7]. Disponível em: http://www.atlasbrasil.org.br/2013/pt/perfil_m/sao-luis_ma.

The São Luís birth cohort was conducted from January 1 to December 31, 2010, and included births in both public and private services, whose institutions performed at least 100 deliveries per year. In 2010, 98% of deliveries occurred in hospitals; and only 3.3% of births in the city were excluded from this study. Our sample was systematically stratified by maternity, proportional to the number of deliveries performed. Each surveyed hospital had an initial causal number (drawn from 1 to 3) with a sampling interval of three, i.e., one in three women were interviewed. An interview and birth control form was prepared in which deliveries were registered chronologically and included live and dead newborns. There was a 4.6% loss due to refusals by mothers to participate, or early discharge, resulting in a final sample of 5,236 births¹⁴14 Silva AAM, Batista RFL, Simões VMF, Thomaz EBAF, Ribeiro CCC, Lamy-Filho F, Lamy ZC, Brito e Alves MTSS, Loureiro FHF, Cardoso VC, Bettiol H, Barbieri MA. Changes in perinatal health in two birth cohorts (1997/1998 and 2010) in São Luís, Maranhão State, Brazil. Cad Saude Publica 2015; 31(7):1437-1450..

Only newborns whose mothers lived in São Luís in the last three months were included in the sample. In 2010, SINASC (the Information System on Live Births) registered 17,544 live and dead births in São Luís (by place of residence). Thus, our final cohort sample accounted for 29.8% of all deliveries in the city.

Interviews were conducted in the first 24 hours after delivery, based on two standardized questionnaires on the pregnancy, mother, and newborn. Birth weight was obtained from maternal medical records. Gestational age was collected from maternal reports of their last menstrual period and their medical records. Both sources were compared, and mothers were prioritized in case of discrepancies. All questionnaires were applied by trained professionals after the informed consent form was signed.

The dependent variable was perinatal death, defined as fetal or neonatal deaths occurring between 22 weeks of gestation and less than seven days of postnatal life¹1 World Health Organization (WHO). Maternal and perinatal health. Geneva: WHO; 2013. [cited 2020 maio 6]. Available from: https://www.who.int/maternal_child_adolescent/topics/maternal/maternal_perinatal/en/. These were identified in the BRISA cohort and confirmed by the 2010 Mortality Information System (SIM). To detect early neonatal mortality, the BRISA and SIM databases were cross-referenced. The Maranhão State Health Department provided the latter upon our formal request. Linkage was used via the Data Link software. To identify perinatal deaths, the information was filtered for age (code < 400) and São Luís (code 211130). After filtering, the data was used to cross-reference the databases (mothers’ names, newborns’ sex, dates of birth, and birth weight). Subsequently, the software generated a table with possible links to be evaluated. To identify stillbirths, only the age was modified in the filter. Type of death = 1 was reported; the code for fetal deaths in death certificates. Cross-referencing and verification followed the same procedure as that for early neonatal deaths.

SIM registered 398 perinatal deaths in São Luís in 2010. By cross-referencing the databases, 106 perinatal (26.6% of the total in the city), 70 fetal, and 36 early neonatal deaths were identified. Of these, 46 fetal deaths were identified in hospital interviews and, subsequently, 24 by SIM. All 36 early neonatal deaths were identified via the SIM database after the interviews.

Variables were divided into three levels of a hierarchical theoretical model (Figure 1), aiming to prioritize the theoretical plausibility of the complex interrelations between variables and not only the statistical associations among them. Variables were interpreted at their levels, rather than at later ones, to prevent underestimating their effect due to the presence of mediators¹⁵15 Victora CG, Huttly SR, Fuchs SC, Olinto MTA. The role of conceptual frameworks in epidemiological analysis: A hierarchical approach. Int J Epidemiol 1997; 26(1):224-227.. In our hierarchical model, outcomes were affected by newborns’ biological characteristics (proximal level), which, in turn, were influenced by maternal and reproductive factors (intermediate level), impacted by socioeconomic and demographic variables (distal level).

Socioeconomic and demographic data were included in level 1: newborns’ sex, maternal education (0-4 years, 5-8 years, 9-11 years, > 12 years), family income in Reais (divided into tertiles - high, medium, and low), mothers’ marital status (with or without a partner), the head of the family (i.e., the one with the highest income: mother, partner, or other), and ethnicity (white or other). The recorded ethnicity was self-reported. Women who were married or living in consensual unions were considered with a partner, whereas those who reported being single, divorced, or widowed, without a partner.

Maternal and reproductive characteristics were included in Level 2 as intermediate variables: smoking during pregnancy (yes or no), maternal age (< 20 years, 20-34 years, and ≥ 35 years), parity (1 delivery, 2-4 deliveries, or ≥ 5 deliveries), previous miscarriages (yes or no), previous preterm births (yes or no), attended prenatal consultations (≥ 6 or < 6), type of delivery (vaginal or cesarean), pregnancy type (single or multiple) and hospital where the delivery occurred (public or private).

Congenital malformations, preterm births, and newborns’ birth weight were included in Level 3 as proximal variables. Congenital malformations were reported by the mothers. Newborns under 2500g were classified as low-weight births. Newborns whose gestational ages were under 37 weeks were considered preterm births.

For the statistical analysis, the SPSS 14.0 software was used. After categorizing the variables of interest, data were described via relative and absolute frequencies. Two models were adjusted to associate independent variables with perinatal deaths: a simple logistic regression, and subsequently, a hierarchical one. A 5% significance level was adopted.

The multiple logistic regression analysis analyzed the factors associated with perinatal mortality, with variables inserted in levels following the hierarchical theoretical model. Variables showing a p-value < 0.1 in their level were included in the next level. This strategy was used to verify which variables in the theoretical model were potential mortality predictors since spurious associations may be made, and true associations, diluted by the many variables in the multiple model, leading to imprecise confidence intervals¹⁶16 Ranganathan P, Pramesh CS, Aggarwal R. Common pitfalls in statistical analysis: Logistic regression. Perspect Clin Res 2017; 8(3):148-151.. The clinical and scientific plausibility of the associations found was considered in all stages. The effect of each variable on the outcome was only evaluated at its level for those variables showing a p-value < 0.05.

This study met the fundamental requirements of Resolution No. 196/96 and its complementary ones of the National Health Council, being approved by the Research Ethics Committee in the University Hospital at the Universidade Federal do Maranhão under protocol no. 4771/2008-30.

Results

The perinatal mortality rate in São Luís was 20.2/1000 births, 66% of which were fetal deaths (Table 1).

The bivariate analysis shows that chances of perinatal death were lower among families headed by a partner or another family member (Table 2) but higher among women with less than four years of schooling (OR: 4.9; 95% CI: 1.8 - 13.32), without a partner (OR: 2.16; 95% CI: 1.36 - 3.43), and in families whose income were in the medium (OR: 2.75; 95% CI: 1.33 - 5.73), or low tertiles (OR: 2.69; 95% CI: 1.30 - 5.59)

The maternal and reproductive characteristics analyzed show perinatal death was more likely among mothers who smoked during pregnancy (OR: 2.6; 95% CI: 1.24 - 5.46), who attended less than six prenatal consultations (OR: 6; 95% CI: 3.53 - 10.18), had more than five deliveries (OR: 2; 95% CI: 1.23 - 5.77), with multiple pregnancies (OR: 9.6; 95% CI: 5.03 - 18.33) delivered in public hospitals (OR: 5.01; 95% CI: 1.58 - 15.91). On the other hand, mothers who underwent cesarean sections (OR: 0.45; 95% CI: 0.28 - 0.73) (Table 3) experienced perinatal deaths. The bivariate analysis shows that congenital malformations, preterm births, and low birth weights characterized the higher chance of perinatal death (Table 3).

Once adjusted for the variables of each level, the multiple analysis showed that perinatal death was almost four times more likely among newborns of mothers with less than four years of schooling (OR: 3.86; 95% CI: 1.14 - 13.03), and 2.44 times as high for those without a partner (OR: 2.44; 95% CI: 1 - 5.93). Families headed by another family member had a lower chance to experience perinatal death than mother-headed families (OR: 0.29; 95% CI: 0.12 - 0.67).

Children whose mothers attended less than six prenatal consultations (OR: 4.61; 95% CI: 2.43 - 8.74) and with multiple pregnancies (OR: 9.15; 95% CI: 4.08 - 20.53) had a higher chance of perinatal death. The final model showed that congenital malformations (OR: 4.13; 95% CI: 1.23 - 13.82), preterm births (OR: 3.36; 95% CI: 1.56 - 7.22) and low birth weight (OR: 11.87; 95% CI: 5.46 - 25.82) characterized newborns with a higher chance of perinatal death (Table 4).

Discussion

The perinatal mortality rate in São Luís was 20.2/1000 births, associated with mothers with low educational attainment, heading families, without a partner, who attended less than six prenatal consultations whose children had either congenital malformations, preterm births, or low birth weights.

Our coefficient resembles the average for the Brazilian Northeast (20.9/1000) in 2009, above the national average (17.3/1000), and the developed regions of the country, such as the South (13.9/1000). Other studies observed similar coefficients in other northeastern capitals, such as Recife (16.6/1000)¹⁰10 Aquino TA, Guimarães MJB, Wanick-Sarinho S, Ferreira LOC. Fatores de risco para a mortalidade perinatal no Recife, Pernambuco, Brasil, 2003 Risk factors for perinatal mortality in Recife, Pernambuco State, Brazil, 2003. Cad Saude Publica 2007; 23(12):2853-2861. and Salvador (19.2/1000)²2 Jacinto E, Aquino EML, Mota MELA. Perinatal mortality in the municipality of salvador, Northeastern Brazil: Evolution from 2000 to 2009. Rev Saude Publica 2013; 47(5):846-853..

Carvalho et al.⁴4 Carvalho CA, Silva AAM, Victora C, Goldani M, Bettiol H, Thomaz EBAF, Barros F, Horta BL, Menezes A, Cardoso V, Cavalli RC, Santos I, Batista RFL, Simões VM, Barbieri M, Barros A. Changes in Infant and Neonatal Mortality and Associated Factors in Eight Cohorts from Three Brazilian Cities. Sci Rep 2020; 10(1):1-8. analyzed the changes in infant mortality indicators in the Ribeirão Preto, Pelotas, and São Luís birth cohorts. They showed a significant reduction in the perinatal mortality in Ribeirão Preto (from 42.1/1000 in 1978/79 to 10.6/1000 in 2010) and Pelotas (from 32.2/1000 in 1982 to 18/1000 in 1993). However, more recently, Ribeirão Preto had a lower reduction, and Pelotas, a stagnation. São Luís had a 44.8% reduction in its perinatal mortality coefficient from 1997/98 to 2010, from 36.6 to 20.2/1000 births, the highest perinatal mortality out of the three cities in the last period studied⁴4 Carvalho CA, Silva AAM, Victora C, Goldani M, Bettiol H, Thomaz EBAF, Barros F, Horta BL, Menezes A, Cardoso V, Cavalli RC, Santos I, Batista RFL, Simões VM, Barbieri M, Barros A. Changes in Infant and Neonatal Mortality and Associated Factors in Eight Cohorts from Three Brazilian Cities. Sci Rep 2020; 10(1):1-8.. Thus, despite the reduction, the coefficients remain very high, especially when compared with those from Brazilian southern and southeastern cities, such as Curitiba, São Paulo, and Ribeirão Preto⁴4 Carvalho CA, Silva AAM, Victora C, Goldani M, Bettiol H, Thomaz EBAF, Barros F, Horta BL, Menezes A, Cardoso V, Cavalli RC, Santos I, Batista RFL, Simões VM, Barbieri M, Barros A. Changes in Infant and Neonatal Mortality and Associated Factors in Eight Cohorts from Three Brazilian Cities. Sci Rep 2020; 10(1):1-8.^,1717 Sobieray NLE da C, Urbanetz AA, Tristão EGT. Estudo da Mortalidade Materna no Município de Dourados Mato Grosso do Sul de 2002 a 2005. Arq Med Hosp Fac Cienc Med St Casa São Paulo 2015; (60):47-53.^,1818 Camargo ABM. A natimortalidade e a mortalidade perinatal em São Paulo. Sao Paulo em Perspect 2008; 22(1):30-47..

We noted that coefficients vary according to socioeconomic development. Underdeveloped African countries, like Ethiopia, have very high perinatal mortality rates (41/1000 births). On the other hand, high-income countries presented rates of around 6/1000 births¹⁹19 Kamal R, Hudman J, McDermott D. What do we know about infant mortality in the U.S. and comparable countries? Peterson-Kaiser Health System Tracker; 2019 [cited 2020 maio 7]. Available from: https://www.healthsystemtracker.org/chart-collection/infant-mortality-u-s-compare-countries/#item-perinatal-mortality-u-s-slightly-lower-comparable-countries
https://www.healthsystemtracker.org/char... . These great differences may relate to socioeconomic and health service inequalities, suggesting different accesses to prenatal and perinatal care¹⁹19 Kamal R, Hudman J, McDermott D. What do we know about infant mortality in the U.S. and comparable countries? Peterson-Kaiser Health System Tracker; 2019 [cited 2020 maio 7]. Available from: https://www.healthsystemtracker.org/chart-collection/infant-mortality-u-s-compare-countries/#item-perinatal-mortality-u-s-slightly-lower-comparable-countries
https://www.healthsystemtracker.org/char... . Thus, the perinatal mortality rate in São Luís is much higher when compared with that of high-income countries.

We attested the influence of socioeconomic inequalities since mothers with fewer schooling years were more likely to experience perinatal deaths. Kale et al.²⁰20 Kale PL, Fonseca SC, Willian P, Oliveira M. Tendência da mortalidade fetal e infantil segundo evitabilidade das causas de morte e escolaridade materna. Rev Bras Epidemiol 2021; 24(Supl. 1): e210008. analyzed fetal and neonatal mortality evolution in Rio de Janeiro from 2000 to 2018. They noted that the low-schooling group was the only one with high and increasing mortality rates, evidencing how social inequalities influence healthcare. Low schooling can compromise the acquisition and understanding of important care information, especially about prenatal care. Moreover, women belonging to extreme categories of low schooling form a group with higher concentrations of risk factors as education levels rise ²¹.

We observed a greater vulnerability to perinatal deaths among mothers heading families and those without partners. These conditions probably expose these women to an overload of domestic functions, childcare, home support, and the lack of emotional support that may entail psychosocial risks²²22 Garcia ÉM, Martinelli KG, Gama SGN, Oliveira AE, Esposti CDD, Santos Neto ET. Risco gestacional e desigualdades sociais: uma relação possível? Cien Saude Colet 2019; 24(12):4633-4642..

We considered attending less than six prenatal consultations a factor for perinatal mortality. Berhan & Berhan²³23 Berhan Y, Berhan A. A meta-analysis of selected maternal and fetal factors for perinatal mortality. Ethiop J Health Sci 2014; 24(Supl.):55-68. and Wondemagegn et al.²⁴24 Wondemagegn AT, Alebel A, Tesema C, Abie W. The effect of antenatal care follow-up on neonatal health outcomes: a systematic review and meta-analysis. Public Health Rev 2018; 39(1):33. showed in meta-analyses that women who had adequate prenatal care were less prone to perinatal mortality, were more likely to diagnose early gestational diseases, fetal alterations, and help to reduce the barriers between pregnant women and specialized health services²⁵25 Viellas EF, Domingues RMSM, Dias MAB, Gama SGN, Filha MMT, Costa JV, Bastos MH, Leal MC. Assistência pré-natal no Brasil. Cad Saude Publica 2014; 30(supl. 1):S85-S100.. Moreover, prenatal consultations are learning experiences in which healthcare providers can intervene, disseminating information on risk warnings during pregnancy, adequate postpartum health, and breastfeeding²⁴24 Wondemagegn AT, Alebel A, Tesema C, Abie W. The effect of antenatal care follow-up on neonatal health outcomes: a systematic review and meta-analysis. Public Health Rev 2018; 39(1):33.. Thus, adequately developed prenatal care can positively influence maternal and child health, increasing newborns’ chances of survival²⁶26 Chou VB, Walker N, Kanyangarara M. Estimating the global impact of poor quality of care on maternal and neonatal outcomes in 81 low- and middle-income countries: A modeling study. Persson LÅ, ed. PLOS Med 2019; 16(12):e1002990..

Besides complying with the recommended number of prenatal consultations, other aspects are also important, especially regarding the quality of prenatal care provided. Martins²⁷27 Martins EF. Mortalidade perinatal e avaliação da assistencia ao pré-natal, ao parto e ao recém-nascido em Belo Horizonte, Minas Gerais [tese]. Belo Horizonte: Universidade Federal de Minas Gerais; 2010., in Belo Horizonte, State of Minas Gerais, showed that failures in prenatal care were among the main causes of perinatal mortality — especially regarding its late beginning; the non-compliance with municipal protocols on consultation frequency; performance of tests, procedures, and recommended referrals; and failure to control diseases and infections during pregnancy. In recent years, Brazil has virtually universalized prenatal care, so we must invest in improving its quality, which might help further reduce perinatal mortality²⁸28 Victora CG, Aquino EML, Leal MC, Monteiro CA, Barros FC, Szwarcwald CL. Saúde de mães e crianças no Brasil: progressos e desafios. Lancet 2011:32-46..

Multiple pregnancies are also an important risk factor for perinatal mortality. They increase the risk of intrauterine growth restriction, premature membrane rupture, and preterm births, increasing perinatal morbidity and mortality²⁹29 Santana DS, Silveira C, Costa ML, Souza RT, Surita FG, Souza JP, Mazhar SB, Jayaratne K, Qureshi Z, Sousa MH, Vogel JP, Cecatti JG. Perinatal outcomes in twin pregnancies complicated by maternal morbidity: Evidence from the WHO Multicountry Survey on Maternal and Newborn Health. BMC Pregnancy Childbirth 2018; 18(1):1-11.^,3030 Santana DS, Surita FG, Cecatti JG. Multiple pregnancy: Epidemiology and association with maternal and perinatal morbidity. Rev Bras Ginecol Obstet 2018; 40(9):554-562.. Thus, these pregnancies require careful prenatal monitoring, good-quality delivery care, and timely postnatal support.

This study also associates preterm births and congenital malformations with perinatal mortality. Preterm birth complications are the leading cause of infant mortality worldwide. Respiratory distress syndrome, bronchopulmonary dysplasia, necrotizing enterocolitis, periventricular sepsis, and leukomalacia are some conditions that may compromise the life of newborns who survive preterm delivery, decreasing their chances of survival³¹31 Chawanpaiboon S, Vogel JP, Moller A-B, Lumbiganon P, Petzold M, Hogan D, Landoulsi S, Jampathong N, Kongwattanakul K, Laopaiboon M, Lewis C, Rattanakanokchai S, Teng DN, Thinkhamrop J, Watananirun K, Zhang J, Zhou W, Gulmezoglu AM. Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis. Lancet Glob Heal 2019; 7(1):e37-e46.. A Dutch study found preterm births to be the greatest risk factor for perinatal mortality, followed by congenital abnormalities and intrauterine growth restriction³²32 Ravelli ACJ, Eskes M, van der Post JAM, Abu-Hanna A, de Groot CJM. Decreasing trend in preterm birth and perinatal mortality, do disparities also decline? BMC Public Health 2020; 20(1):783.. Brazil must further reduce preterm birth rates, as recent interventions have had a limited influence on reducing this indicator. In Europe, some countries have changed the active management of preterm deliveries and improved the quality and efficacy of medical care, increasing survival without increasing hospital morbidity rates³³33 Bonet M, Cuttini M, Piedvache A, Boyle EM, Jarreau PH, Kollée L, Maier RF, Milligan D, Reempts PV, Weber T, Barros H, Gadzinowki J, Draper ES, Zeitlin J, MOSAIC and EPICE research groups. Changes in management policies for extremely preterm births and neonatal outcomes from 2003 to 2012: two population-based studies in ten European regions. BJOG An Int J Obstet Gynaecol 2017; 124(10):1595-1604..

Congenital malformations are the second cause of infant mortality in Brazil, following only preterm births. They cause structural, functional abnormalities, and metabolic disorders, which can provoke miscarriages or preclude postnatal lives. Moreover, they may relate to preterm births and low birth weights, increasing the risk of perinatal death³⁴34 Amorim MMR, Vilela PC, Santos ARVD, Lima ALMV, Melo EFP, Bernardes HF, Filho PFBM, Guimarães VB. Impacto das malformações congênitas na mortalidade perinatal e neonatal em uma maternidade-escola do Recife. Rev Bras Saúde Matern Infant 2006; 6(Supl. 1):s19-s25..

We expected low birth weight to increase perinatal mortality since it is one of its most important determinant factors¹⁰10 Aquino TA, Guimarães MJB, Wanick-Sarinho S, Ferreira LOC. Fatores de risco para a mortalidade perinatal no Recife, Pernambuco, Brasil, 2003 Risk factors for perinatal mortality in Recife, Pernambuco State, Brazil, 2003. Cad Saude Publica 2007; 23(12):2853-2861.^,3535 Vilanova CS, Hirakata VN, Souza Buriol VC, Nunes M, Goldani MZ, Silva CH. The relationship between the different low birth weight strata of newborns with infant mortality and the influence of the main health determinants in the extreme south of Brazil. Popul Health Metr 2019; 17(1):15.. Low birth weight relates to other clinical risks, such as preterm births and restricted intrauterine growth. Unfavorable socioeconomic conditions and failures in prenatal care may also cause low birth weight³⁶36 Buriol VCS, Hirakata V, Goldani MZ, Silva CH. Temporal evolution of the risk factors associated with low birth weight rates in Brazilian capitals (1996-2011). Popul Health Metr 2016; 14(1):15., increasing the risk of infant mortality³⁵35 Vilanova CS, Hirakata VN, Souza Buriol VC, Nunes M, Goldani MZ, Silva CH. The relationship between the different low birth weight strata of newborns with infant mortality and the influence of the main health determinants in the extreme south of Brazil. Popul Health Metr 2019; 17(1):15..

This study’s strengths are data from a large, systematic, population-based birth cohort providing information on many variables that could be risk factors for perinatal mortality. As a limitation, we cite the lack of information on 24 perinatal deaths recorded in the SIM database. Moreover, since mothers reported most of the information obtained, there might be a memory bias. The exclusion of maternities with less than 100 births per year from the sample may have led to the underreporting of perinatal deaths. However, we believe this effect is minimal since only 3.3% of deliveries in 2010 in São Luís occurred in these maternities. Our results indicate risk factors for perinatal mortality, one of the most resilient infant mortality indicators. Although the literature reports reduced rates in Brazil and São Luís, we found that the perinatal mortality rate in the city is higher than that of other cities in the country and even higher when compared with the rates in high-income countries. Knowing the factors associated with this indicator may guide public policies seeking more effective actions to reduce perinatal mortality.

We highlight the importance of improving socioeconomic factors (which require structural changes in human and social development), prenatal care, and characteristics of pregnancies and newborns, such as multiple pregnancies, congenital malformations, preterm births, and low birth weight. Therefore, the minimum schedule of prenatal visits must be monitored and be of sufficient quality to ensure early detection of gestational morbidities and congenital malformations. Adequate prenatal follow-ups may intervene in behavioral risk factors, infection control, and maternal morbidities, helping to reduce adverse outcome rates, such as preterm births and low birth weight. Moreover, we must reinforce the need for rational medical interventions to avoid iatrogenic prematurity.

References

Publication Dates

History