The high burden of infant deaths in rural Burkina Faso: A prospective community-based cohort study

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Study Justification:
– Infant mortality rates (IMR) remain high in many sub-Saharan African countries, especially in rural areas with limited access to health services.
– This study aimed to measure IMR and explore risk factors for infant death in a rural area of Burkina Faso.
– The findings of this study can provide relevant data on the burden of and risk factors for infant death in rural communities.
Study Highlights:
– The study included 866 live born children in Banfora Health District, a rural area in South-West Burkina Faso.
– The infant mortality rate (IMR) was found to be 113 per 1000 live births, with over 75% of infant deaths occurring by 6 months of age.
– Infections (35%) and preterm birth complications (23%) were the most common probable causes of death by 6 months.
– Risk factors for infant death included maternal history of child death, polygyny, twin births, and poor anthropometric z-scores at week-3.
Study Recommendations:
– Community-based health interventions targeting mothers and children at high risk are urgently needed to reduce the high burden of infant deaths in rural areas.
– These interventions should focus on improving access to healthcare services, promoting proper nutrition, and preventing infections.
– Education and awareness programs should also be implemented to increase knowledge about infant health and care practices.
Key Role Players:
– Health professionals and organizations: Doctors, nurses, midwives, and community health workers are needed to provide healthcare services and education to mothers and children.
– Government agencies: The Ministry of Health and other relevant government departments should support and coordinate efforts to address infant mortality.
– Non-governmental organizations (NGOs): NGOs working in the healthcare sector can contribute resources and expertise to implement community-based interventions.
Cost Items for Planning Recommendations:
– Healthcare facilities and equipment: Budget should include costs for establishing or improving healthcare facilities in rural areas, as well as purchasing necessary medical equipment.
– Training and capacity building: Funds should be allocated for training healthcare professionals and community health workers to provide quality care and education.
– Outreach and awareness programs: Budget should cover costs for organizing and implementing education and awareness programs targeting mothers and communities.
– Monitoring and evaluation: Resources should be allocated for monitoring and evaluating the effectiveness of interventions and making necessary adjustments.
Please note that the cost items provided are general categories and not actual cost estimates. Actual costs will vary depending on the specific context and implementation strategies.

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 prospective community-based cohort study with a large sample size (866 live born children). The study provides descriptive statistics, multivariable analyses, and identifies risk factors for infant death. However, to improve the evidence, the abstract could include more details about the study design, methodology, and limitations.

Background: Infant mortality rates (IMR) remain high in many sub-Saharan African countries, especially in rural settings where access to health services may be limited. Studies in such communities can provide relevant data on the burden of and risk factors for infant death. We measured IMR and explored risk factors for infant death in a cohort of children born in Banfora Health District, a rural area in South-West Burkina Faso. Methods. A prospective community-based cohort study was nested within the PROMISE-EBF trial (NCT00397150) in 24 villages of the study area. Maternal and infant baseline characteristics were collected at recruitment and after birth, respectively. Home visits were conducted at weeks 3, 6, 12, 24 and 52 after birth. Descriptive statistics were calculated using robust standard errors to account for cluster sampling. Cox multivariable regression was used to investigate potential risk factors for infant death. Results: Among the 866 live born children included in the study there were 98 infant deaths, yielding an IMR of 113 per 1000 live births (95% CI: 89-143). Over 75% of infant deaths had occurred by 6months of age and the post neonatal infant mortality rate was 67 per 1000 live births (95% CI: 51-88). Infections (35%) and preterm births complications (23%) were the most common probable causes of death by 6months. Multivariable analyses identified maternal history of child death, polygyny, twin births and poor anthropometric z-scores at week-3 as factors associated with increased risk of infant death. Conclusions: We observed a very high IMR in a rural area of Burkina Faso, a country where 75% of the population lives in rural settings. Community-based health interventions targeting mothers and children at high risk are urgently needed to reduce the high burden of infant deaths in these areas. © 2012Diallo et al.; licensee BioMed Central Ltd.

The study took place in Banfora Health District, in the south-west of Burkina Faso close to the border with Cote d“Ivoire. The district covers an area of 6,300 km2, and had an estimated population of 282,000 in 2010 with three major ethnic groups, the Gouin, the Karoboro and the Dioula [11]. The area experiences an annual rainfall of 950 to 1250 mm during a 6-month rainy season (May-Oct). Farming and animal husbandry are the main activities in the rural areas while the town of Banfora with a population of 75,000 is a flourishing trading centre [11]. The study was conducted in three subcounties, Banfora, Soubakénédougou and Sidéradougou (Figure ​(Figure11). Overview of the study area. In 2006, the district health system consisted of 60 primary health facilities and one regional hospital in the town of Banfora. The ratios of health personnel to population in 2010 were estimated to be approximately 1:5300 for nurses, 1:5200 for midwives, and 1:40000 for physicians [12]. Based on official reports for the study area from the Ministry of Health in Burkina Faso, over 94% of pregnant women attended antenatal care (ANC), 77% were reported to deliver in a health facility and 66% of children aged 12–23 months were reported to have received the full set of EPI vaccines in 2010 [13]. HIV-prevalence is low in the rural areas of Banfora region and was estimated to be of 0.6% among the 15–49 years-old in 2010 [13]. The 2006-national census in Burkina Faso reported Banfora to have an IMR of 101 and a U5MR of 165 per 1000 live births [11]. A cohort study was nested within the PROMISE-EBF trial ( http://www.clinicaltrials.govNCT00397150 ), a community-based, cluster-randomized trial to promote exclusive breastfeeding (EBF) through individual peer-counselling, which was implemented in 24 villages of Banfora Health District as reported elsewhere [14,15]. Children born to all pregnant women enrolled in both arms of the main PROMISE-EBF trial formed a prospective cohort that was followed until 12 months of age. The PROMISE-EBF trial sample size was calculated using prevalence of EBF and diarrhea at 12 weeks of child age as primary outcomes [14,15]. No sample size estimation was done for infant deaths. However, post-hoc analyses showed that the sample of 866 newborns enrolled would enable us to estimate the IMR with a precision of  ± 2% based on estimates from the 2006-national census in Burkina Faso [11] and a confidence level of 95%. The details of participants’ enrolment and follow-up for the first 6 months are reported elsewhere [14,16]. In summary, pregnant women were identified in each study village by female “recruiters” over a one year period (June 2006 to May 2007) through weekly household visits. In 23 villages with a mean population of 1330, a random sample of up to 4 pregnant women per village was selected monthly. In the 24th village (Siniéna) with a population of nearly 5000, we sampled 8 women per month instead of 4. Women were recruited into the EBF-trial if they met the study inclusion criteria which were as follows: pregnancy of 7 months or more, intention to remain in the village for the next 12 months, plan to breastfeed the child, absence of any severe maternal disease or mental handicap which could prevent either breastfeeding or cooperation and provision of individually written and informed consent. While the main EBF-trial included only singleton live births and planned a follow-up for 6 months, we report here on all live born children of enrolled mothers, including those who had multiple births. Children were followed-up by trained data collectors, irrespective of trial arm until they were 12 months or older. Data collection visits were scheduled at recruitment and after birth at day 7 and at weeks 3, 6, 12, 24 (± 7 days for each visit) and at 12 months. Mothers who were not at home for a scheduled home visit were revisited by data collectors three times before the visit was considered as missed. Data collection lasted from March 2006 to November 2008. Maternal baseline data (age, parity, medical history, household assets, etc.) were recorded at enrolment. Pregnancy outcomes and newborn baseline data were collected during the day-7 visit or at the earliest completed visit after birth. Newborn birth weight was recorded from the child health card when available. From week-3, we recorded information on the child’s feeding pattern and growth. Deaths at any time after birth were recorded. Infant weight and height were measured at each home visit using Seca®872 scales and a Seca®210 infantometer ( http://www.seca.com), respectively. Weight was recorded to the nearest 0.10 kg and height was measured to the nearest 0.5 cm. All interviews were conducted in the mother’s local language to improve comprehension and cooperation. A standard verbal autopsy (VA) questionnaire [17] was used to capture information on the circumstances surrounding infant deaths and was filled within 4–6 weeks. However, narrative items describing the causes of death were completed only for infants who died before 6 months of age and so cause of death was only assessed for deaths before 6 months of age. Two independent physicians reviewed the VAs to assign probable causes of death using a hierarchical grouping adapted from the Child Health Epidemiology Reference Group Classification [18] and ICD-10. Deaths during the neonatal period were classified into the following sequential cause groups: congenital defects, tetanus, trauma/surgical, preterm birth complications, birth asphyxia, sepsis/pneumonia, diarrhoea/gastroenteritis, other/unknown. Postneonatal deaths were classified into the following causes: diarrhoea/gastroenteritis, pertussis, measles, injury/surgical, meningitis, pneumonia/acute respiratory tract infection, malaria, malnutrition, other/unknown. Multiple causes were allowed, although only the primary cause of death is reported here. The opinion of a senior paediatrician was sought in cases of disagreement between the two physicians. Data collection was done using handheld computers (PDAs) with the Epihandy software ( http://www.openXdata.org) for visits up to 6 months, and with paper forms for the 12 month visit. We used the WHO’s standard definitions of neonatal (i.e. death of a live born newborn within 28 days), post-neonatal (i.e. death of an infant between 1–12 months) and infant death (i.e. the death of any live born infant before 12 months of age). The main exposures included in analyses were maternal baseline characteristics (age, parity, education, socioeconomic status, use of health services and medical history) and newborn characteristics (season of birth, sex, twinship and anthropometry). Children with birth weight <2500 g were considered as low birth weight. Anthropometric status was assessed using WHO’s standards ( http://www.who.int/childgrowth/en/). Children were classified as wasted, stunted or underweight if their relevant z-score was below −2. A child with any z-score < −2 at 3 weeks of age was defined as having a “low anthropometric score”. Data collected on paper questionnaires were entered by two independent clerks using Epidata 3.1 ( http://www.epidata.dk), cleaned and merged with the cleaned datasets from the electronic questionnaires. Data were analyzed with STATA/SE 11.0 (Statacorp, College Station, Texas). Summary statistics of continuous and discrete variables of mothers and infants were produced. The 95% confidence intervals (CI) of proportions were calculated using robust standard errors to account for the cluster sampling of the PROMISE-EBF trial. Risk of death by one year of age (commonly known as IMR) was calculated as the proportion of infant deaths per 1000 live births and a 95% CI calculated using a robust standard error. Mortality rates were estimated using survival analysis and are reported per 1000 person-years of observation (PYO). A Kaplan-Meier plot was produced to show cumulative risk of death until 12 months. Between-cluster variation in mortality rates was assessed using a likelihood ratio test (LR test) with a random-effects Cox regression model. Potential risk factors for infant deaths were screened through univariable Cox regression models for three age ranges (0–6 months, 1–12 months and 0–12 months). These analyses took account of possible clustering (fitting Cox Gamma shared frailty models in STATA/SE 11.0) and only variables with a p <0.25 in Wald-statistic tests were retained for further exploration. We explored interactions between polygyny and several maternal baseline variables including distance to nearest health facility, education, parity, ANC visits, and health facility delivery. We also looked at interactions between health facility delivery and maternal education or parity. Based on Mosley and Chen’s model [19] for risk factors assessment in child mortality, we conducted multivariable Cox regression models adjusting for distance to nearest health facility, maternal history of child death, newborn’s season of birth and sex considered as potential confounders. Covariates that remained associated with infant death risk (p < 0.05) in the adjusted models and that met major criteria for causal inference [20] were considered as risk factors. The study was approved by the Institutional Review Board of Centre MURAZ in Burkina Faso (N°013/2005/CE-CM) and by the Western Regional Committee for Medical and Health Research Ethics in Norway (Sak No05/8197). Administrative clearances were sought from the national and regional health authorities of Burkina Faso. All study participants were requested to provide individually written and informed consent prior to enrolment. All mothers and infants included in the study were offered free care and medication in local health facilities for the duration of the study, for illnesses related to lactational problems (mastitis, breast abscess) and infections (pneumonia, diarrhoea and malaria).

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

1. Mobile Health Clinics: Implementing mobile health clinics that can travel to remote areas, providing prenatal care, vaccinations, and other essential maternal health services.

2. Telemedicine: Introducing telemedicine services that allow pregnant women in rural areas to consult with healthcare professionals remotely, reducing the need for travel and improving access to medical advice.

3. Community Health Workers: Training and deploying community health workers who can provide basic prenatal care, education, and support to pregnant women in rural communities.

4. Maternal Health Education Programs: Developing and implementing educational programs that focus on maternal health, including prenatal care, nutrition, and hygiene practices, to empower women with knowledge and improve their health outcomes.

5. Improving Infrastructure: Investing in the improvement of healthcare infrastructure in rural areas, including the construction of health facilities and the provision of medical equipment and supplies.

6. Transportation Support: Establishing transportation support systems, such as ambulances or transportation vouchers, to ensure that pregnant women in rural areas can access healthcare facilities in a timely manner.

7. Maternal Health Hotlines: Setting up hotlines or helplines that pregnant women can call for immediate assistance or advice regarding their maternal health concerns.

8. Partnerships with NGOs: Collaborating with non-governmental organizations (NGOs) that specialize in maternal health to provide resources, funding, and expertise to improve access to maternal healthcare services in rural areas.

9. Maternal Health Incentives: Introducing incentives, such as financial rewards or vouchers, to encourage pregnant women in rural areas to seek prenatal care and deliver in healthcare facilities.

10. Data Collection and Analysis: Implementing systems for collecting and analyzing data on maternal health outcomes in rural areas, which can help identify areas of improvement and inform evidence-based interventions.

It’s important to note that the specific innovations to be implemented should be tailored to the local context and needs of the community.
AI Innovations Description
Based on the information provided, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Implement community-based health interventions: Develop and implement community-based health interventions specifically targeting mothers and children at high risk in rural areas of Burkina Faso. These interventions should focus on improving access to maternal health services, including antenatal care, skilled birth attendance, and postnatal care.

2. Strengthen healthcare infrastructure: Invest in improving healthcare infrastructure in rural areas, including increasing the number of primary health facilities and trained health personnel. This will help ensure that pregnant women have access to quality maternal health services closer to their communities.

3. Enhance maternal education and awareness: Implement programs to enhance maternal education and awareness about the importance of antenatal care, skilled birth attendance, and postnatal care. This can be done through community health education sessions, mobile health clinics, and the use of community health workers.

4. Improve transportation and logistics: Address transportation and logistics challenges by providing reliable and affordable transportation options for pregnant women to access healthcare facilities. This can include establishing transportation networks, providing subsidies for transportation costs, and improving road infrastructure.

5. Strengthen collaboration and coordination: Foster collaboration and coordination between different stakeholders, including government agencies, non-governmental organizations, and community-based organizations, to ensure a comprehensive and integrated approach to improving access to maternal health services.

By implementing these recommendations, it is expected that access to maternal health services will be improved, leading to a reduction in infant mortality rates and improved maternal and child health outcomes in rural areas of Burkina Faso.
AI Innovations Methodology
Based on the information provided, here are some potential recommendations for improving access to maternal health in rural areas:

1. Strengthening Antenatal Care (ANC) Services: Enhance the quality and availability of ANC services in rural areas to ensure that pregnant women receive comprehensive care, including regular check-ups, health education, and screenings for potential complications.

2. Mobile Health Clinics: Implement mobile health clinics that can travel to remote areas, providing essential maternal health services such as prenatal care, vaccinations, and postnatal care. This can help overcome geographical barriers and reach women who have limited access to healthcare facilities.

3. Community Health Workers: Train and deploy community health workers who can provide basic maternal health services, education, and support to pregnant women and new mothers in rural communities. These workers can also serve as a link between the community and healthcare facilities.

4. Telemedicine: Utilize telemedicine technologies to connect rural healthcare providers with specialists in urban areas. This can enable remote consultations, diagnosis, and treatment for pregnant women, reducing the need for travel and improving access to specialized care.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the indicators: Identify key indicators to measure the impact of the recommendations, such as the number of pregnant women receiving ANC services, the percentage of women delivering in healthcare facilities, and the infant mortality rate.

2. Collect baseline data: Gather data on the current status of maternal health in the target area, including the number of ANC visits, facility-based deliveries, and infant mortality rates.

3. Implement the recommendations: Introduce the recommended interventions, such as strengthening ANC services, deploying mobile health clinics, training community health workers, and implementing telemedicine.

4. Monitor and evaluate: Continuously monitor the implementation of the interventions and collect data on the indicators identified in step 1. This can be done through surveys, interviews, and health facility records.

5. Analyze the data: Analyze the collected data to assess the impact of the interventions on the selected indicators. Compare the baseline data with the post-intervention data to determine any improvements in access to maternal health.

6. Adjust and refine: Based on the analysis, make adjustments and refinements to the interventions as needed. This could involve scaling up successful interventions, addressing any challenges or barriers identified, and continuously improving the strategies to maximize impact.

By following this methodology, it would be possible to simulate the impact of the recommendations on improving access to maternal health in rural areas and make evidence-based decisions for further interventions.

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