Prevalence and Factors Associated with Maternal Group B Streptococcus Colonization in Madagascar and Senegal

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Study Justification:
– Maternal group B Streptococcus (GBS) colonization is a major risk factor for neonatal GBS infection.
– Data on GBS in low- and middle-income countries are scarce.
– This study aimed to estimate the prevalence of GBS colonization among pregnant women in Madagascar and Senegal.
Study Highlights:
– The prevalence of GBS colonization was 5.0% in Madagascar and 16.1% in Senegal.
– No factors among sociodemographic characteristics, living conditions, and obstetric history were found to be associated independently with GBS colonization in both countries.
– This community-based study provides one of the first estimates of maternal GBS colonization among pregnant women from Madagascar and Senegal.
Study Recommendations:
– Further research is needed to understand the factors contributing to GBS colonization in low- and middle-income countries.
– Strategies should be developed to prevent and manage GBS colonization in pregnant women to reduce the risk of neonatal GBS infection.
Key Role Players:
– Researchers and scientists specializing in maternal and neonatal health.
– Healthcare workers, including nurses and midwives, for implementing prevention and management strategies.
– Matrons and influential women within local communities for promoting awareness and education about GBS colonization.
Cost Items for Planning Recommendations:
– Research funding for conducting further studies and implementing prevention strategies.
– Training programs for healthcare workers and matrons.
– Awareness campaigns and educational materials for pregnant women and the general public.
– Diagnostic tests and treatments for GBS colonization and neonatal GBS infection.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are some areas for improvement. The study provides estimates of maternal Group B Streptococcus (GBS) colonization among pregnant women in Madagascar and Senegal, which is valuable information for low- and middle-income countries. The study design includes a large sample size and data collection from multiple sites. However, the abstract does not provide details on the methodology used for data collection and analysis, which could be improved. Additionally, the abstract does not mention any limitations of the study, which should be addressed to provide a more comprehensive evaluation of the evidence. To improve the evidence, the authors could provide more information on the study design, including the sampling strategy and statistical methods used. They should also discuss any limitations of the study, such as potential biases or confounding factors. This would enhance the transparency and reliability of the findings.

Maternal group B Streptococcus (GBS) colonization is a major risk factor for neonatal GBS infection. However, data on GBS are scarce in low- and middle-income countries. Using sociodemographic data and vaginal swabs collected from an international cohort of mothers and newborns, this study aimed to estimate the prevalence of GBS colonization among pregnant women in Madagascar (n = 1, 603) and Senegal (n = 616). The prevalence was 5.0% (95% CI, 3.9-6.1) and 16.1% (95% CI, 13.1-19.0) in Madagascar and Senegal, respectively. No factors among sociodemographic characteristics, living conditions, and obstetric history were found to be associated independently with GBS colonization in both countries. This community-based study provides one of the first estimates of maternal GBS colonization among pregnant women from Madagascar and Senegal.

BIRDY is a multicenter cohort study launched to address the lack of epidemiological data concerning drug-resistant neonatal and infantile bacterial infections in LMICs.14 As part of the project, our study included pregnant women in Antananarivo (an urban setting with three districts near Institut Pasteur de Madagascar; 4,100 women of reproductive age according to a local census) and Moramanga (a rural setting with six districts; 3,800 women of reproductive age) in Madagascar, and in Guédiawaye (an urban setting within the Wakhinane Nimzatt district; 4,000 births/year) and Sokone (a rural setting with14,500 inhabitants) in Senegal (Figure 1). At each study site, pregnant women were recruited consecutively via the local primary health-care center—which covers the selected districts, door-to-door home visits by “matrons” (influential women within the local communities who play a role in pregnancy follow-up and delivery), and investigators within the selected locations—during their third trimester of pregnancy or at delivery, at which point a lower vaginal swab was collected to screen for GBS colonization. Health-care workers (nurses and midwives) and collaborating matrons were trained for the project. Recruitment occurred from September 2012 to December 2016 in Madagascar. In Senegal, women were recruited from October 2013 to December 2018. In our study, we only included women from the cohort who underwent an effective GBS screening. Study sites maps. This figure appears in color at www.ajtmh.org. Collected variables were as follows: 1) sociodemographic factors (age, marital status, education, and employment), 2) living conditions (access to electricity, type of sanitation [indoor latrines and outdoor pour-flush latrines were considered as improved sanitation], number of people living under the same roof), 3) brachial circumference to determine nutritional status (undernutrition if < 24 cm15), 4) obstetric history (gravidity/parity, history of child death/stillbirth/miscarriage), 5) pregnancy follow-up information (number of antenatal consultations, professional pregnancy follow-up [physician, midwife, or nurse], and 6) medication during the current pregnancy (antibiotic consumption; iron/folate supplementation; intermittent preventive treatment of malaria in pregnancy with sulfadoxine-pyrimethamine [IPTp-SP], defined as an intake of at least one dose during pregnancy; number of doses of IPTp-SP; mosquito net use). The case report form is presented in Supplemental Figure 1. Collected data underwent quality assessment initiated both locally and centralized by the project’s data manager (Institut Pasteur). Paper data entry forms were reviewed, and errors and inconsistencies were corrected by clarifying with the source. Trained personnel collected the samples using sterile dry swabs in health-care facilities or at home. All samples were transported without transport medium to Institut Pasteur laboratories then stored in refrigerators before GBS screening was conducted. Isolates were plated onto a selective growth medium—BD Group B Streptococcus Differential agar (Granada Medium, Becton Dickinson, Franklin Lakes, NJ, a proteose peptone starch agar with 3-[N-morpholino]propanesulfonic acid, which is a buffering agent] and phosphate, and supplemented with methotrexate and antibiotics) in Senegal and BD Colombia colistin and nalidixic acid (CNA) agar (Becton Dickinson, Franklin Lakes, NJ) with 5% sheep blood in Madagascar—and incubated for 24 to 48 hours at 37°C in 5% carbon dioxide (anaerobic incubation for Granada Medium). In Senegal, the colonies were identified by morphological determination and a latex agglutination test (SLIDEX® Strepto Plus, bioMérieux, Marcy-l’Étoile, France). In Madagascar, the colonies were identified by morphological determination, beta hemolysis, and directly by matrix-assisted laser desorption/ionization–time of flight mass spectrometry (MALDI Biotyper®, Bruker Daltonics, Billerica, MA). Because Senegalese and Malagasy study population characteristics—demographics, socioeconomic background, cultural practices, and habits—and prevalence of maternal GBS colonization differed substantially, separate analyses were carried out. Quantitative variables were expressed as median (interquartile range); qualitative variables were expressed as a percentage and a 95% CI for GBS colonization prevalence. The GBS-positive versus GBS-negative groups were compared using the χ2 test or Fisher’s exact test for qualitative variables, and Wilcoxon rank-sum test for quantitative variables. Factors associated potentially with maternal GBS colonization were selected based on prior knowledge.16–18 Because of the important number of missing values concerning antibiotic consumption during pregnancy, this variable was not included in the analysis. The selected variables were first assessed in a univariate analysis and were then included in a logistic regression with backward elimination if the P value was less than 0.25. When two or more variables were correlated, the variables with the smaller P value were retained (age, gravidity, and parity). The significance threshold was fixed at 0.05. Statistical analyses were performed using Stata 14.0 (StataCorp LLC, College Station, TX). The BIRDY protocol was approved by the relevant national ethics committees for health research of Madagascar (068-MSANP/CE), Senegal (SEN 14-20) and France (IRB/2016/08/03, Institut Pasteur). Women were included after receiving information about the project, agreeing to biological sampling on themselves and their newborns, and signing an informed consent form. BIRDY provided free-of-charge tests and treatments of infantile infections during the follow-up period. BIRDY data collection has been declared to the Commission nationale de l’informatique et des libertés (a French national data protection authority), in accordance with French law.

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

1. Telemedicine: Implementing telemedicine services can help overcome geographical barriers and provide remote access to maternal health services. This would allow pregnant women in remote areas to consult with healthcare professionals and receive necessary care without having to travel long distances.

2. Mobile health (mHealth) applications: Developing mobile applications specifically designed for maternal health can provide pregnant women with access to important information, reminders for prenatal appointments, and educational resources. These apps can also facilitate communication between pregnant women and healthcare providers.

3. Community health workers: Training and deploying community health workers who can provide basic maternal health services and education within their communities can improve access to care, especially in rural areas where healthcare facilities may be limited.

4. Maternal health clinics: Establishing dedicated maternal health clinics in underserved areas can ensure that pregnant women have access to comprehensive prenatal care, including screenings for conditions such as Group B Streptococcus colonization.

5. Health financing schemes: Implementing innovative health financing schemes, such as microinsurance or conditional cash transfer programs, can help reduce financial barriers to accessing maternal health services. This can ensure that pregnant women can afford the necessary care without facing financial hardship.

6. Mobile clinics: Setting up mobile clinics that travel to remote areas can bring maternal health services directly to communities that lack access to healthcare facilities. These clinics can provide prenatal care, screenings, and vaccinations to pregnant women who may otherwise have difficulty accessing these services.

7. Public-private partnerships: Collaborating with private sector organizations, such as pharmaceutical companies or technology companies, can help leverage their resources and expertise to improve access to maternal health services. This can involve initiatives such as providing free or subsidized medications, medical supplies, or technological solutions.

It is important to note that the specific context and needs of each country or region should be considered when implementing these innovations.
AI Innovations Description
Based on the provided description, the recommendation to improve access to maternal health would be to implement routine screening and treatment for Group B Streptococcus (GBS) colonization among pregnant women in Madagascar and Senegal. This would involve the following steps:

1. Establish a standardized screening protocol: Develop guidelines and protocols for healthcare providers to screen pregnant women for GBS colonization using lower vaginal swabs.

2. Train healthcare workers: Provide training to healthcare workers, including nurses, midwives, and matrons, on how to collect and handle vaginal swabs for GBS screening. Ensure they are knowledgeable about the importance of GBS screening and the potential risks associated with GBS colonization.

3. Increase awareness among pregnant women: Conduct educational campaigns to raise awareness among pregnant women about the importance of GBS screening and the potential risks to their newborns. This can be done through community health centers, antenatal clinics, and home visits by influential community members.

4. Improve laboratory capacity: Strengthen laboratory facilities to ensure accurate and timely GBS screening. This may involve providing necessary equipment, reagents, and training for laboratory technicians.

5. Provide treatment for GBS-positive women: Develop treatment protocols for GBS-positive pregnant women, including the administration of antibiotics during labor to reduce the risk of neonatal GBS infection. Ensure that healthcare providers have access to appropriate antibiotics and are trained in their proper use.

6. Monitor and evaluate the program: Establish a system for monitoring and evaluating the implementation of GBS screening and treatment. This can help identify any challenges or gaps in the program and inform future improvements.

By implementing routine GBS screening and treatment, access to maternal health can be improved by reducing the risk of neonatal GBS infection and its associated complications.
AI Innovations Methodology
To improve access to maternal health, here are some potential recommendations:

1. Strengthening healthcare infrastructure: Investing in healthcare facilities, equipment, and trained healthcare professionals in both urban and rural areas can improve access to maternal health services.

2. Mobile health (mHealth) interventions: Utilizing mobile technology to provide information, reminders, and access to healthcare services can help overcome geographical barriers and improve access to maternal health.

3. Community-based interventions: Engaging local communities and training community health workers to provide maternal health services can increase access, especially in remote areas.

4. Transportation support: Providing transportation services or vouchers for pregnant women to reach healthcare facilities can address transportation barriers and improve access to maternal health services.

5. Financial incentives: Offering financial incentives, such as cash transfers or insurance schemes, can help overcome financial barriers and encourage pregnant women to seek maternal health services.

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 access to maternal health, such as the number of pregnant women receiving antenatal care, the number of deliveries attended by skilled birth attendants, and the number of postnatal check-ups.

2. Collect baseline data: Gather data on the current status of access to maternal health services in the target areas, including the number of healthcare facilities, healthcare professionals, and utilization rates of maternal health services.

3. Model the interventions: Develop a simulation model that incorporates the potential recommendations mentioned above. This model should consider factors such as population demographics, geographical distribution, and existing healthcare infrastructure.

4. Input data and assumptions: Input relevant data into the simulation model, such as the number of healthcare facilities to be built or upgraded, the number of community health workers to be trained, and the estimated impact of mHealth interventions or transportation support.

5. Run simulations: Run multiple simulations using different scenarios and assumptions to estimate the potential impact of the recommendations on improving access to maternal health. This could include variations in the scale of interventions, geographical coverage, and population coverage.

6. Analyze results: Analyze the simulation results to determine the potential impact of the recommendations on access to maternal health. This could include assessing changes in key indicators, such as increased utilization rates of maternal health services or reduced travel time to healthcare facilities.

7. Refine and validate the model: Continuously refine the simulation model based on feedback, additional data, and validation exercises. This will improve the accuracy and reliability of the simulation results.

By following this methodology, policymakers and stakeholders can gain insights into the potential impact of different recommendations on improving access to maternal health and make informed decisions on implementing the most effective interventions.

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