Pregnancy-related morbidity and risk factors for fatal foetal outcomes in the Taabo health and demographic surveillance system, Côte d’Ivoire

BMC Pregnancy and Childbirth, Volume 18, No. 1, Year 2018

Interprétation

Background: Reliable, population-based data on pregnancy-related morbidity and mortality, and risk factors for fatal foetal outcomes are scarce for low- and middle-income countries. Yet, such data are essential for understanding and improving maternal and neonatal health and wellbeing. Methods: Within the 4-monthly surveillance rounds of the Taabo health and demographic surveillance system (HDSS) in south-central Côte d’Ivoire, all women of reproductive age identified to be pregnant between 2011 and 2014 were followed-up. A questionnaire pertaining to antenatal care, pregnancy-related morbidities, delivery circumstances, and birth outcome was administered to eligible women. Along with sociodemographic information retrieved from the Taabo HDSS repository, these data were subjected to penalized maximum likelihood logistic regression analysis, to determine risk factors for fatal foetal outcomes. Results: A total of 2976 pregnancies were monitored of which 118 (4.0%) resulted in a fatal outcome. Risk factors identified by multivariable logistic regression analysis included sociodemographic factors of the expectant mother, such as residency in a rural area (adjusted odds ratio (aOR)=2.87; 95% confidence interval (CI) 1.31-6.29) and poorest wealth tertile (aOR=1.79; 95% CI 1.02-3.14), a history of miscarriage (aOR=23.19; 95% CI 14.71-36.55), non-receipt of preventive treatment such as iron/folic acid supplementation (aOR=3.15; 95% CI 1.71-5.80), only two doses of tetanus vaccination (aOR=2.59; 95% CI 1.56-4.30), malaria during pregnancy (aOR=1.94; 95% CI 1.21-3.11), preterm birth (aOR=4.45; 95% CI 2.82-7.01), and delivery by caesarean section (aOR=13.03; 95% CI 4.24-40.08) or by instrumental delivery (aOR=5.05; 95% CI 1.50-16.96). Women who paid for delivery were at a significantly lower odds of a fatal foetal outcome (aOR=0.39; 95% CI 0.25-0.74). Conclusions: We identified risk factors for fatal foetal outcomes in a mainly rural HDSS site of Côte d’Ivoire. Our findings call for public health action to improve access to, and use of, quality services of ante- and perinatal care. This study was conducted within the Taabo HDSS [20, 21]. The Taabo HDSS includes one small town (Taabo Cité), which is the centre of the department of Taabo that also holds the only small hospital for the surveillance zone. There are 13 main villages with more than 100 associated hamlets. The latter are settlements usually consisting of a group of households constructed close to agricultural exploitation sites, rather isolated, and not yet officially considered a village by the territorial administrative authority mainly due to its small population size (< 500 inhabitants). Meanwhile, there is a primary health care centre in all of the 13 villages, 10 of which were fully operational with an assigned nurse and three are managed by trained community-health workers (CHWs) not yet being entirely functional. In the hamlets, no basic primary care is available but CHWs that are part of the hamlet’s population may be approached for advice before seeking formal care. Basic antenatal care is provided at the general hospital of Taabo and all nurse-led operational health centres; thereof four villages also host professional midwives who offer their service. With regard to emergency obstetric care (EOC), such as caesarean section and instrumental delivery (e.g. forceps delivery), the first is supposed to be only done at the general hospital of Taabo where an operating block is available, while instrumental deliveries may also being performed as emergency measure by midwives. For women delivering in a health facility, referral to a better equipped medical centre in case of complications is decided and an official transfer statement provided by the respective midwives. However, the actual transport remains to be organised by the women’s relatives due to a lack of ambulances (only available in Taabo-Cité and the village of Kotiéssou). The costs for antenatal care and birth assistance are not standardised and thus difficult to be estimated. Certainly the costs increase with the level of proficiency of the service providers and depend on whether facilities are public or private [17]. Since 2011 antenatal care and delivery are by national policy free of charge, however additional costs for health seeking by expectant mothers are common [22]. Of note, in primarily rural areas such as the Taabo HDSS many women, especially when it is their first child, still tend to spend the last trimester of their pregnancy close to their relatives that may live in more remote areas. The place of labour and child birth may thus differ in many cases from the actual residence of the expectant mothers. The objective of this study was to assess pregnancy-related morbidities and risk factors for a fatal foetal outcome. All women of reproductive age (15–49 years) whose pregnancy started and ended between January 1, 2011 and December 31, 2014 were included. Each household of the Taabo HDSS is visited at least three times a year for detailed surveillance of vital events (i.e. birth, death, in-migration, out-migration, and pregnancy). New pregnancies were systematically listed and followed-up longitudinally. The status and potential negative events related to pregnancy were registered by trained field-enumerators. Miscarriage, stillbirth, and live birth were registered as pregnancy outcomes. Each woman identified with a new pregnancy was interviewed with a pre-tested questionnaire with an emphasis on pregnancy status, estimated date of last menstrual period (LMP), and number of earlier pregnancies and births. Furthermore, in relation to potential negative consequences of a pregnancy, a standardised INDEPTH questionnaire on pregnancy-related morbidity was administered by field-enumerators to expectant mothers [23]. Sociodemographic information from women becoming pregnant during the 4-year observational study was readily available from the Taabo HDSS database [20]. Data were double-entered, cross-checked, and managed using a household registration system implemented in Windev version 12.0 (PC Soft; Montpellier, France) [24]. All statistical analyses were performed in Stata version 12.0 (StataCorp; College Station, TX, USA). Data records from pregnant women with complete sociodemographic, pregnancy-related morbidity, and birth circumstances information that were not lost to follow-up were considered for analysis (Fig. 1). Flow chart indicating all pregnancies registered and monitored between 2011 and 2014 in the Taabo HDSS and final study sample comprising complete data for analysis The primary outcome variable was defined as fatal foetal outcome and applied to all pregnancies that resulted in stillbirth, miscarriage, or early neonatal death using WHO definitions (i.e. dead born with gestational age higher or lower than 28 weeks, or death within the first 7 days after birth) [7]. Assessed explanatory variables for relationship analysis included (i) sociodemographic characteristics of the expectant mother; (ii) antenatal care sought; (iii) pregnancy-related morbidities and concomitant health conditions; and (iv) circumstances of delivery reported. Socioeconomic status was determined using a household-based asset approach and principal component analysis (PCA) with stratification into wealth tertiles (i.e. poorest, poor, and least poor) [25]. Gestational age at birth was calculated based on the first day of LMP and date of birth. Preterm birth was defined as gestational age < 37 weeks [26] or as perceived “shorter than normal” (i.e. estimated duration of < 8 months) preterm birth in women not able to provide reliable information on LMP (about 8% of all expectant mothers). χ2 test statistics were used to investigate significant univariate differences between mothers whose pregnancy resulted in live birth compared to a fatal outcome for the aforementioned explanatory variables. Univariate and multivariable logistic regression analyses were performed to identify significant relationships between fatal foetal outcome and covariates. In order to address estimation bias from fatal foetal outcome being a rare event, penalized maximum likelihood logistic regression models, as proposed by Firth, were used [27, 28]. Results were presented as odds ratios (ORs) and 95% confidence interval (CI). Differences and relationships with a p-value below 0.05 were considered as statistically significant. The multivariable regression model was built using a stepwise elimination approach, excluding explanatory variables at a significance level of 0.20 or higher. Sociodemographic factors known to have negative consequences on birth outcome (e.g. age, socioeconomic status, and residency of the mother) from earlier studies were included in the final model [29].

Résumé de l’IA

Study Justification:

– Reliable data on pregnancy-related morbidity and mortality, as well as risk factors for fatal foetal outcomes, are lacking in low- and middle-income countries.
– Understanding these factors is crucial for improving maternal and neonatal health and well-being.

Study Highlights:

– The study was conducted in the Taabo health and demographic surveillance system (HDSS) in Côte d’Ivoire.
– A total of 2976 pregnancies were monitored, with 4.0% resulting in a fatal outcome.
– Risk factors for fatal foetal outcomes were identified, including sociodemographic factors, history of miscarriage, non-receipt of preventive treatment, malaria during pregnancy, preterm birth, and delivery by caesarean section or instrumental delivery.
– Women who paid for delivery had a significantly lower odds of a fatal foetal outcome.

Study Recommendations:

– Public health action is needed to improve access to and use of quality ante- and perinatal care services.
– Efforts should be made to address the identified risk factors, such as improving access to preventive treatments and ensuring appropriate delivery practices.

Key Role Players:

– Health authorities and policymakers
– Healthcare providers (nurses, midwives, doctors)
– Community health workers
– Ambulance services (for transportation in case of complications)
– Researchers and data analysts

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers
– Provision of preventive treatments (iron/folic acid supplementation, tetanus vaccination)
– Improvement of healthcare facilities and equipment
– Ambulance services for transportation
– Research and data analysis expenses

Force de la preuve

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a population-based surveillance system and includes a large number of pregnancies. The study also uses multivariable logistic regression analysis to identify risk factors for fatal foetal outcomes. However, to improve the evidence, the abstract could provide more information on the sample size and characteristics of the study population, as well as the specific methods used for data collection and analysis.

Abstrait

This study was conducted within the Taabo HDSS [20, 21]. The Taabo HDSS includes one small town (Taabo Cité), which is the centre of the department of Taabo that also holds the only small hospital for the surveillance zone. There are 13 main villages with more than 100 associated hamlets. The latter are settlements usually consisting of a group of households constructed close to agricultural exploitation sites, rather isolated, and not yet officially considered a village by the territorial administrative authority mainly due to its small population size (< 500 inhabitants). Meanwhile, there is a primary health care centre in all of the 13 villages, 10 of which were fully operational with an assigned nurse and three are managed by trained community-health workers (CHWs) not yet being entirely functional. In the hamlets, no basic primary care is available but CHWs that are part of the hamlet’s population may be approached for advice before seeking formal care. Basic antenatal care is provided at the general hospital of Taabo and all nurse-led operational health centres; thereof four villages also host professional midwives who offer their service. With regard to emergency obstetric care (EOC), such as caesarean section and instrumental delivery (e.g. forceps delivery), the first is supposed to be only done at the general hospital of Taabo where an operating block is available, while instrumental deliveries may also being performed as emergency measure by midwives. For women delivering in a health facility, referral to a better equipped medical centre in case of complications is decided and an official transfer statement provided by the respective midwives. However, the actual transport remains to be organised by the women’s relatives due to a lack of ambulances (only available in Taabo-Cité and the village of Kotiéssou). The costs for antenatal care and birth assistance are not standardised and thus difficult to be estimated. Certainly the costs increase with the level of proficiency of the service providers and depend on whether facilities are public or private [17]. Since 2011 antenatal care and delivery are by national policy free of charge, however additional costs for health seeking by expectant mothers are common [22]. Of note, in primarily rural areas such as the Taabo HDSS many women, especially when it is their first child, still tend to spend the last trimester of their pregnancy close to their relatives that may live in more remote areas. The place of labour and child birth may thus differ in many cases from the actual residence of the expectant mothers. The objective of this study was to assess pregnancy-related morbidities and risk factors for a fatal foetal outcome. All women of reproductive age (15–49 years) whose pregnancy started and ended between January 1, 2011 and December 31, 2014 were included. Each household of the Taabo HDSS is visited at least three times a year for detailed surveillance of vital events (i.e. birth, death, in-migration, out-migration, and pregnancy). New pregnancies were systematically listed and followed-up longitudinally. The status and potential negative events related to pregnancy were registered by trained field-enumerators. Miscarriage, stillbirth, and live birth were registered as pregnancy outcomes. Each woman identified with a new pregnancy was interviewed with a pre-tested questionnaire with an emphasis on pregnancy status, estimated date of last menstrual period (LMP), and number of earlier pregnancies and births. Furthermore, in relation to potential negative consequences of a pregnancy, a standardised INDEPTH questionnaire on pregnancy-related morbidity was administered by field-enumerators to expectant mothers [23]. Sociodemographic information from women becoming pregnant during the 4-year observational study was readily available from the Taabo HDSS database [20]. Data were double-entered, cross-checked, and managed using a household registration system implemented in Windev version 12.0 (PC Soft; Montpellier, France) [24]. All statistical analyses were performed in Stata version 12.0 (StataCorp; College Station, TX, USA). Data records from pregnant women with complete sociodemographic, pregnancy-related morbidity, and birth circumstances information that were not lost to follow-up were considered for analysis (Fig. 1). Flow chart indicating all pregnancies registered and monitored between 2011 and 2014 in the Taabo HDSS and final study sample comprising complete data for analysis The primary outcome variable was defined as fatal foetal outcome and applied to all pregnancies that resulted in stillbirth, miscarriage, or early neonatal death using WHO definitions (i.e. dead born with gestational age higher or lower than 28 weeks, or death within the first 7 days after birth) [7]. Assessed explanatory variables for relationship analysis included (i) sociodemographic characteristics of the expectant mother; (ii) antenatal care sought; (iii) pregnancy-related morbidities and concomitant health conditions; and (iv) circumstances of delivery reported. Socioeconomic status was determined using a household-based asset approach and principal component analysis (PCA) with stratification into wealth tertiles (i.e. poorest, poor, and least poor) [25]. Gestational age at birth was calculated based on the first day of LMP and date of birth. Preterm birth was defined as gestational age < 37 weeks [26] or as perceived “shorter than normal” (i.e. estimated duration of < 8 months) preterm birth in women not able to provide reliable information on LMP (about 8% of all expectant mothers). χ2 test statistics were used to investigate significant univariate differences between mothers whose pregnancy resulted in live birth compared to a fatal outcome for the aforementioned explanatory variables. Univariate and multivariable logistic regression analyses were performed to identify significant relationships between fatal foetal outcome and covariates. In order to address estimation bias from fatal foetal outcome being a rare event, penalized maximum likelihood logistic regression models, as proposed by Firth, were used [27, 28]. Results were presented as odds ratios (ORs) and 95% confidence interval (CI). Differences and relationships with a p-value below 0.05 were considered as statistically significant. The multivariable regression model was built using a stepwise elimination approach, excluding explanatory variables at a significance level of 0.20 or higher. Sociodemographic factors known to have negative consequences on birth outcome (e.g. age, socioeconomic status, and residency of the mother) from earlier studies were included in the final model [29].

Matériaux

N/A

Innovations

Based on the information provided, here are some potential innovations that could improve access to maternal health:

1. Mobile health (mHealth) interventions: Develop mobile applications or text messaging services to provide pregnant women with information and reminders about antenatal care, pregnancy-related morbidities, and delivery circumstances. This can help improve knowledge and adherence to recommended care.

2. Community health worker (CHW) training and support: Strengthen the capacity of CHWs in rural areas to provide basic antenatal care and support pregnant women. This can include training on identifying risk factors, providing preventive treatments, and referring women to higher-level care when necessary.

3. Improving access to emergency obstetric care (EOC): Enhance the availability and accessibility of EOC services, such as caesarean sections and instrumental deliveries, especially in rural areas. This can involve improving infrastructure, ensuring the availability of skilled healthcare providers, and establishing effective referral systems.

4. Financial support for maternal healthcare: Implement policies or programs that reduce financial barriers to accessing antenatal care and delivery services. This can include providing free or subsidized care, reimbursing transportation costs, and addressing additional costs associated with seeking care.

5. Health education and awareness campaigns: Conduct community-based education programs to raise awareness about the importance of antenatal care, preventive treatments, and early recognition of pregnancy-related morbidities. This can help empower women to seek timely and appropriate care.

6. Strengthening data collection and analysis: Invest in robust health information systems to collect and analyze reliable population-based data on pregnancy-related morbidity and mortality, as well as risk factors for adverse outcomes. This can inform evidence-based decision-making and targeted interventions.

It is important to note that these recommendations are based on the information provided and may need to be adapted to the specific context and resources available in Côte d’Ivoire.

AI Innovations Description

Based on the description provided, here is a recommendation that can be developed into an innovation to improve access to maternal health:

1. Strengthen Antenatal Care Services: Enhance the availability and quality of antenatal care services in the Taabo health and demographic surveillance system (HDSS) in Côte d’Ivoire. This can be achieved by training healthcare providers to provide comprehensive antenatal care, including regular check-ups, iron/folic acid supplementation, tetanus vaccination, and prevention and management of malaria during pregnancy.

2. Improve Access to Emergency Obstetric Care: Enhance access to emergency obstetric care, such as caesarean sections and instrumental deliveries, in the Taabo HDSS. This can be done by ensuring that the general hospital in Taabo has the necessary facilities and resources to perform these procedures. Additionally, efforts should be made to improve transportation options for pregnant women in need of emergency care, such as ambulances.

3. Address Socioeconomic Factors: Address socioeconomic factors that contribute to poor maternal health outcomes. This can be achieved by implementing policies and programs that aim to reduce poverty and improve access to healthcare services for women in rural areas. Additionally, efforts should be made to ensure that antenatal care and delivery services are affordable and accessible to all women, regardless of their socioeconomic status.

4. Enhance Community Engagement: Engage community health workers (CHWs) and other community members in promoting maternal health and encouraging women to seek timely and appropriate care. This can be done through community awareness campaigns, training programs for CHWs, and the establishment of support networks for pregnant women.

5. Strengthen Data Collection and Analysis: Improve the collection and analysis of data on maternal health outcomes and risk factors in the Taabo HDSS. This can help identify trends, monitor progress, and inform evidence-based decision-making for improving maternal health services.

By implementing these recommendations, it is expected that access to maternal health services will be improved, leading to better health outcomes for pregnant women and their babies in the Taabo HDSS in Côte d’Ivoire.

AI Innovations Methodology

Based on the provided description, here are some potential recommendations to improve access to maternal health:

1. Strengthening Antenatal Care: Increase the availability and accessibility of antenatal care services, particularly in rural areas. This can be done by establishing more primary health care centers and ensuring that they are fully operational with trained healthcare professionals.

2. Improving Preventive Treatment: Ensure that all pregnant women receive essential preventive treatments such as iron/folic acid supplementation and tetanus vaccination. This can be achieved through targeted education and awareness campaigns, as well as ensuring the availability of these treatments in healthcare facilities.

3. Enhancing Emergency Obstetric Care: Improve access to emergency obstetric care, including caesarean sections and instrumental deliveries. This can be done by equipping more healthcare facilities with operating blocks and trained healthcare professionals. Additionally, efforts should be made to improve transportation infrastructure and availability of ambulances for timely referrals.

4. Addressing Socioeconomic Factors: Address socioeconomic factors that contribute to poor maternal health outcomes, such as poverty and rural residency. This can be achieved through targeted interventions that aim to improve the overall living conditions and economic opportunities in these areas.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define the indicators: Identify key indicators that reflect access to maternal health, such as the percentage of pregnant women receiving antenatal care, the percentage of women receiving preventive treatments, and the availability of emergency obstetric care services.

2. Collect baseline data: Gather data on the current status of these indicators in the target population. This can be done through surveys, interviews, and data collection from healthcare facilities.

3. Develop a simulation model: Create a simulation model that incorporates the baseline data and the potential impact of the recommendations. This model should consider factors such as population size, geographic distribution, and healthcare infrastructure.

4. Simulate the impact: Run the simulation model to estimate the potential impact of the recommendations on the selected indicators. This can be done by adjusting the relevant parameters in the model based on the expected changes resulting from the recommendations.

5. Analyze the results: Analyze the simulation results to determine the potential improvements in access to maternal health. This can include assessing changes in the selected indicators, identifying areas of improvement, and evaluating the overall effectiveness of the recommendations.

6. Refine and iterate: Based on the analysis, refine the simulation model and repeat the simulation process to further optimize the recommendations and assess their long-term impact.

It is important to note that the methodology for simulating the impact may vary depending on the specific context and available data. Therefore, it is recommended to consult with experts in the field of maternal health and data analysis to ensure the accuracy and validity of the simulation results.

Auteur

Dima Health

Statistics:

Citations: 5

Identifiers:

DOI: 10.1186/s12884-018-1858-2

Research Areas:

Community Interventions, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Workforce

Study Countries:

Ivory Coast

Study Design:

Case-Control Study, Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female

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