Sociodemographic Determinants of Caesarean Delivery in the Largest Public Maternity Hospital in Angola

Abstract

Introduction: This study aimed to describe demographic, socioeconomic and pregnancy-related characteristics associated with a caesarean delivery in Luanda.Material and Methods: We conducted a cross-sectional study which included 995 puerperal women and who were assessed between December 2012 and February 2013 at Lucrécia Paím maternity hospital in Luanda, Angola. Data was collected using a structured questionnaire administered by trained interviewers. Logistic regression models were fitted to estimate the magnitude of associations (odds ratios—95% confidence intervals) between the type of delivery and variables showing a significant association in the bivariate analysis.Results: The prevalence of caesarean section was 44%. Women with caesarean delivery were less educated, resided more often in a periurban area and were more likely to disclose lower family income when compared with women who had vaginal delivery. Living in a periurban area was significantly associated with a caesarean delivery regardless of all covariates considered, for primiparous women (adjusted odds ratios, 95% confidence intervals = 2.14, 1.27 – 3.62) and for multiparous women (1.78, 1.26 – 2.51). Among multiparous women, a lower family income was also significantly associated with caesarean delivery. Hypertensive disorders during the current pregnancy were associated with a caesarean delivery in the multivariate models fitted for primiparous (3.96, 1.57 – 9.98) and for multiparous women (1.68, 1.03 – 2.74).Discussion: The associations between low socioeconomic position and high risk of caesarean delivery demonstrated in this study are consistent results in previous researches curried out in African contexts. The poorer and less educated women, who live far from health facilities, have few antenatal care visits and often arrive with complicated conditions, justifying a caesarean delivery. Lack of qualified human and material resources to manage emergencies in peripheral health centers and delaying in the reference system also leads to an increase in the number of caesareans performed in this particular maternity hospital.Conclusion: Sociodemographic differences were observed according to the type of delivery. Cesarean section was more prevalent among women with lower income and residents in periurban areas.

Cesarianas em mulheres imigrantes segundo classificação de Robson, características maternas e adequação da assistência pré-natal [Cesarean sections in immigrant women, by Robson's classification, the mother’s characteristics, and adequacy of antenatal care] [Cesáreas en mujeres inmigrantes según la clasificación de Robson, características maternas y adecuación de la atención prenatal]

“…No estudo em tela, a maior chance de cesárea ocorreu em mulheres imigrantes com nível mais baixo de instrução (OR:1,58 [1,38-1,81]), o que corrobora resultado encontrado na maior maternidade de Angola 13 . Ressalta-se que neste país, mulheres com baixa escolaridade, independentemente de serem nativas ou imigrantes, residem longe das unidades de saúde, recebem poucos cuidados pré-natais e muitas vezes chegam nestes serviços já com alguma complicação, o que justificaria o parto cirúrgico 13 . Este fato também poderia explicar esta via de parto entre as imigrantes no Brasil, uma vez que a maior chance de cesárea ocorreu entre as que possuíam o pré-natal considerado intermediário ou inadequado.…”

“…Os dados encontrados mostram que o número de partos cesáreos, à exemplo do que ocorre com mulheres brasileiras, foram excessivos, pois muitas gestantes tinham condições clínicas favoráveis para o parto vaginal. É importante ressaltar que a Classificação de Robson contribui para o monitoramento e análise das modalidades de parto, com o intuito de diminuir as taxas de cesáreas 11,13 . Inclusive, a Organização Mundial de Saúde propõe que esta classificação seja usada como instrumento padrão em todo o mundo para avaliar, monitorar e comparar taxas de cesáreas ao longo do tempo em um mesmo hospital e entre diferentes hospitais.…”

“…O desfecho da via de parto foi categorizado segundo a classificação de Robson, uma vez que permite a categorização por grupos segundo o tipo de parto e o perfil obstétrico das parturientes, possibilitando a identificação dos excessos de cesariana 13 . A análise considerou o número de partos observados e esperados em cada grupo dessa classificação.…”

An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers

“…Many of the concepts identified in the related research works can be linked to maternal interaction diagram in Fig 1 . Concepts involving labor interventions such as identifying predictors of childbirth delivery, healthcare personnel interactions with patients, knowledge and skills of healthcare personnel in administering labor interventions, medicalization processes (identified medical conditions) that requires care and support, impact of variations for childbirth intervention for both multiparous and nulliparous women have been examined in various studies. Some of the problems identified and addressed in related works above include; prediction of vaginal delivery outcome after a caesarean section [27], Estimation of labor risk scores for maternal and neonatal delivery outcome [23],…”

“…Concepts involving labor interventions such as identifying predictors of childbirth delivery, healthcare personnel interactions with patients, knowledge and skills of healthcare personnel in administering labor interventions, medicalization processes (identified medical conditions) that requires care and support, impact of variations for childbirth intervention for both multiparous and nulliparous women have been examined in various studies. Some of the problems identified and addressed in related works above include; prediction of vaginal delivery outcome after a caesarean section [27], Estimation of labor risk scores for maternal and neonatal delivery outcome [23], A systematic review [24] of pregnancy outcome with machine learning for optimal delivery mode, The adoption of entropy feature selection strategy as a means of overcoming the challenges of use of important variable features[22], determining sub-types of women at higher risk of caesarean section delivery [19], predictive modeling [20] of emergency cesarean section as a delivery outcome, psychological and other negative effects associated with labor interventions[16], identifying pregnancy related complications [17, understanding women’s expectations with respect to healthcare personnel interactions during labor [10], impact of variations in childbirth interventions in high-income countries for multiparous and nulliparous women[11]and many others using various features both demographic and medical records of patients. In determining childbirth delivery outcome for an expectant woman either by healthcare personnel or through maternal or patient request, an understanding of pregnancy history relating to number of unsuccessful pregnancies (spontaneous abortions or otherwise), number of stillbirths (fetal deaths if any) in addition to other known factors such as maternal age, blood pressure, haemoglobin count, maternal pulse, fetal heart rate etc may provide useful insight into critical decisions and the underlying reasons for which patient requests are made.…”