Association between prenatal provision of lipid-based nutrient supplements and caesarean delivery: Findings from a randomised controlled trial in Malawi

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Study Justification:
The study aimed to investigate whether the prenatal provision of small-quantity lipid-based nutrient supplements (SQ-LNS) was associated with an increased risk of caesarean section (CS) or other delivery complications. This was important because in populations with a high prevalence of childhood and adolescent undernutrition, supplementation during pregnancy to improve maternal nutritional status and prevent fetal growth restriction could potentially lead to delivery complications.
Highlights:
– The study enrolled 1391 pregnant women in Malawi and analyzed the associations between SQ-LNS, CS, and other delivery complications.
– The incidence of CS was higher in the SQ-LNS group compared to the iron-folic acid (IFA) and multiple micronutrient (MMN) groups.
– The relative risk of CS was 2.2 times higher in the SQ-LNS group compared to the IFA group.
– There were no significant differences in other delivery complications among the three groups.
Recommendations:
Based on the findings, the provision of SQ-LNS to pregnant women may have increased the incidence of caesarean section. However, it is unclear whether the higher CS incidence in the SQ-LNS group resulted from increased obstetric needs or more active health seeking and better access to services. Further research is needed to understand the underlying factors and implications of these findings.
Key Role Players:
– Researchers and scientists involved in maternal and child health
– Obstetricians and gynecologists
– Public health officials and policymakers
– Healthcare providers and nurses
– Community health workers
Cost Items for Planning Recommendations:
– Research funding for further investigations and studies
– Training and capacity building for healthcare providers and community health workers
– Implementation of interventions to improve access to obstetric care and services
– Monitoring and evaluation of interventions and outcomes
– Health education and awareness campaigns for pregnant women and their families
– Infrastructure and equipment for healthcare facilities
– Supply of nutrient supplements and other necessary resources for pregnant women
Please note that the cost items provided are general suggestions and may vary depending on the specific context and implementation strategies.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study is a randomized controlled trial, which is a strong study design. The sample size is adequate, and the statistical analyses are appropriate. However, there are some limitations that could be addressed to improve the evidence. First, the abstract does not provide information on the blinding of participants and researchers, which could introduce bias. Second, the abstract does not mention any potential confounding factors that were controlled for in the analysis. It would be helpful to know if the groups were balanced in terms of important characteristics such as maternal age, education, and socioeconomic status. Finally, the abstract does not provide information on the generalizability of the findings. It would be useful to know if the study population is representative of the broader population of pregnant women in Malawi. To improve the evidence, future studies could consider addressing these limitations and providing more detailed information on the study design, participant characteristics, and potential confounding factors.

In populations with a high prevalence of childhood and adolescent undernutrition, supplementation during pregnancy aiming at improving maternal nutritional status and preventing fetal growth restriction might theoretically lead to cephalopelvic disproportion and delivery complications. We investigated whether the prenatal provision of small-quantity lipid-based nutrient supplements (SQ-LNS) was associated with an increased risk of caesarean section (CS) or other delivery complications. Pregnant Malawian women were randomised to receive daily i) iron–folic acid (IFA) capsule (control), ii) multiple micronutrient (MMN) capsule of 18 micronutrients (second control), or iii) SQ-LNS with similar micronutrients as MMN, plus four minerals and macronutrients contributing 118 kcal. We analysed the associations of SQ-LNS, CS, and other delivery complications using log-binomial regressions. Among 1391 women enrolled, 1255 had delivery information available. The incidence of CS and delivery complications was 6.3% and 8.2%, respectively. The incidence of CS was 4.0%, 6.0%, and 8.9% (p = 0.017) in the IFA, MMN, and LNS groups, respectively. Compared to the IFA group, the relative risk (95% confidence interval) of CS was 2.2 (1.3–3.8) (p = 0.006) in the LNS group and 1.5 (0.8–2.7) (p = 0.200) in the MMN group. We found no significant differences for other delivery complications. Provision of SQ-LNS to pregnant women may have increased the incidence of CS. The baseline rate was, however, lower than recommended. It is unclear if the higher CS incidence in the SQ-LNS group resulted from increased obstetric needs or more active health seeking and a better supply of services. Trial registered at clinicaltrials.gov, NCT01239693.

This was a secondary analysis of data prospectively collected as part of a dietary intervention trial, iLiNS‐DYAD‐M, in Malawi (ClinicalTrials.gov, Identifier {“type”:”clinical-trial”,”attrs”:{“text”:”NCT01239693″,”term_id”:”NCT01239693″}}NCT01239693), in which mother–child pairs in the intervention group received SQ‐LNS whereas mothers in the control groups received either IFA or MMN. The main outcomes of the study included birth weight, newborn length, and length for age Z‐score (LAZ). In the current study, we analysed the association between maternal SQ‐LNS supplementation, compared to IFA and MMN, and the incidence of CS and other delivery complications. The enrolment in the study took place in one public district hospital (Mangochi), one semiprivate hospital (Malindi), and two public health centres (Lungwena and Namwera) in Mangochi district, Southern Malawi. In total, the clinics provided health care to approximately 190,000 people. Recruitment for the trial was open to pregnant women who came for antenatal care at any of the clinics and met the following criteria: ultrasound‐confirmed pregnancy of under 20 completed gestation weeks, at least 15 years of age, and without any chronic health conditions. Enrolled participants were randomly assigned into three groups that were provided with daily nutrient supplements. Women in the first control group, the IFA group, received standard Malawian antenatal care, including supplementation with micronutrient capsules containing 60 mg iron and 400 μg folic acid. Women in the second control, the MMN group, received capsules that contained IFA and 16 additional micronutrients. MMN was chosen as the second control because of the benefits it might have over IFA (Smith et al., 2017). Participants in the intervention group, the LNS group, received 20 g SQ‐LNS sachets containing 118 kcal, protein, carbohydrates, essential fatty acids, sucrose, and 22 micronutrients. IFA and MMN looked and tasted identical, but the SQ‐LNS sachets looked different from the control supplements. Data collectors delivered 15 supplement doses (IFA or MMN capsules or LNS sachets) fortnightly to each participant, at their home, until delivery. There was no direct observation of the consumption of the supplements. As a measure of compliance to the interventions, at each visit, the data collectors collected any leftover supplements or empty packaging from the participants. The mean adherence to the intervention (proportion of days when the supplements were consumed) was comparable and higher than 80% in all three groups (Ashorn et al., 2015). All three groups also received intermittent preventive malaria treatment. Details of the interventions can be found elsewhere (Ashorn et al., 2015). Participants were enroled between 14 and 20 gestation weeks. At the enrolment visit, trained anthropometrists measured the participating women’s weight, height, and mid‐upper arm circumference. Research nurses assessed the duration of pregnancy by measuring fetal biparietal diameter, femur length, and abdominal circumference with ultrasound imagers that used inbuilt Hadlock tables to estimate the duration of gestation. The same nurses measured the women’s peripheral blood malaria parasitemia with rapid tests (Clearview Malaria Combo; British Biocell International Ltd.) and haemoglobin concentration with a finger prick. Health facility nurses tested for HIV infection in all participants, except for those who opted out or were already known to be HIV infected, by using a whole‐blood antibody rapid test (Alere Determine HIV‐1/2; Alere Medical Co., Ltd.). All participants were invited for follow‐up visits at the study clinic at 32 and 36 gestational weeks. During these visits, standardised obstetric examinations were conducted and anthropometric measurements were taken again to examine maternal weight gain during pregnancy. The mean maternal weight gain during the second and third trimesters of pregnancy was comparable between all three groups (Ashorn et al., 2017). The delivery information was collected by a clinic data collector (trained study nurses, laboratory technicians, study monitor, or study coordinator) either at the clinic or at home within 48 h after delivery (the newborn visit). A clinic data collector filled the delivery information form based on the health passport and delivery charts. We defined CS either as a planned CS or an emergency CS. In a planned CS, the woman was informed during the antenatal period that she would have to deliver by CS due to identified complications that would make vaginal delivery unsafe. In an emergency CS, the decision of the procedure was made immediately before or during labour because of a life‐threatening situation either to the mother or the child. Any delivery complication was defined as a condition of a planned CS, emergency CS, vacuum extraction, prolonged labour, large perineal tear, or symphysiotomy. The child’s length, weight, and head circumference were measured at the first clinic visit after the birth (the postnatal visit) by trained anthropometrists. We considered newborn anthropometric measurements missing if they were collected more than 6 weeks after delivery. We calculated age‐ and sex‐standardised anthropometric indices (Z‐scores) by using the World Health Organisation (WHO) Child Growth Standards (WHO Multicentre Growth Reference Study Group 2006). We calculated the duration of pregnancy by adding the time interval between enrolment and delivery determined by ultrasound gestational age at enrolment. The sample size was originally calculated in accordance with the main objective of the iLiNS‐ DYAD‐M trial (Ashorn et al., 2015) and was based on an assumption of an effect size of at least 0.3 (difference between groups, divided by the pooled SD) for each continuous outcome, a power of 80%, and a two‐sided type I error rate of 5%. We carried out the statistical analyses with Stata 15.1 (StataCorp) based on the analysis plan written and published at ilins.org. We based the analysis on the principle of intention to treat. We excluded twin pregnancies and abortions from the analyses. We estimated the incidence of delivery complications in the three groups and calculated relative risks (RR) for the comparison of binary endpoints. To prevent inflated type I errors caused by multiple comparisons, we used a closed testing procedure. Null hypotheses for pairwise comparisons could only be rejected if the global null hypotheses of all three groups being identical had also been rejected (Cheung, 2013). We tested the global null hypotheses for binary endpoints either with Fisher’s exact test or the log‐binomial regression model. We tested quantitative endpoints with an analysis of variance. With the log‐binomial regression models for the binary endpoints, we used a set of Newton–Raphson maximisation of the log‐likelihood. If the algorithm failed to converge in the estimation, we used an alternative estimation algorithm with iterated reweighted least squares (Zou, 2004). With the same setting, we calculated RRs from bivariate analysis for single variables and we created cumulative stepwise multivariate log‐binomial regression models for an association attenuation analysis. For the first multivariable model, we included the child’s sex, gestational age, and variables with p < 0.05 from bivariate analysis, excluding the child's anthropometric measurements. For the second multivariable model, we included variables that were considered intermediate outcomes (i.e., maternal weekly gestational weight gain, LAZ, weight‐for‐age Z‐score [WLZ], and head circumference Z‐score [HCZ]) with variables from the first multivariable model. We performed likelihood ratio tests for the interaction between intervention and maternal characteristics. Maternal variables were specified in the statistical analysis plan before data analysis. Variables that were tested for interaction and analysed as stratified included maternal age, education, number of previous pregnancies, height, body mass index (BMI), alpha‐1‐acid glycoprotein, C‐reactive protein (CRP), HIV, peripheral blood malaria parasitemia, and anaemia at enrolment as well as the season of enrolment, gestational age at enrolment, food insecurity status, and child's sex. We provided stratified analyses in case of positive interaction (p < 0.10) or if either of the stratified comparisons of binary endpoints resulted in p < 0.05 and thus suggested a difference between intervention groups within a stratified subgroup. For the final analyses, each analysis was adjusted to the site of enrolment (i.e., hospitals and health clinics, to control for access to health services) and to other variables that were included in the provided stratified analyses (to control for participants with multiple classifications on the selected variables).

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women with information on nutrition, prenatal care, and potential complications. These apps can also send reminders for prenatal appointments and provide access to telemedicine consultations.

2. Community Health Workers: Train and deploy community health workers to provide education, support, and basic prenatal care to pregnant women in remote or underserved areas. These workers can also help identify high-risk pregnancies and refer women to appropriate healthcare facilities.

3. Telemedicine Services: Establish telemedicine services that allow pregnant women to consult with healthcare providers remotely. This can help overcome geographical barriers and provide access to specialized care for high-risk pregnancies.

4. Maternal Health Vouchers: Implement voucher programs that provide pregnant women with financial assistance to cover the costs of prenatal care, delivery, and postnatal care. This can help reduce financial barriers and increase access to quality maternal healthcare services.

5. Maternal Health Clinics: Set up dedicated maternal health clinics that offer comprehensive prenatal care, delivery services, and postnatal care in underserved areas. These clinics can be staffed by skilled healthcare providers and equipped with necessary medical equipment.

6. Transportation Support: Develop transportation support programs that provide pregnant women with affordable and reliable transportation to healthcare facilities. This can help overcome transportation barriers, especially in rural areas with limited public transportation options.

7. Maternal Health Education Programs: Implement community-based education programs that focus on improving maternal health knowledge and practices. These programs can include workshops, support groups, and outreach activities to raise awareness about the importance of prenatal care and healthy behaviors during pregnancy.

8. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve leveraging private healthcare providers, facilities, and resources to expand service coverage and reduce wait times.

9. Maternal Health Hotlines: Establish toll-free hotlines staffed by trained healthcare professionals who can provide information, support, and guidance to pregnant women. These hotlines can be available 24/7 and offer multilingual services to cater to diverse populations.

10. Maternal Health Monitoring Systems: Develop innovative monitoring systems that use technology, such as wearable devices or remote monitoring tools, to track maternal health indicators during pregnancy. This can help identify potential complications early and facilitate timely interventions.

It’s important to note that the implementation of these innovations should be context-specific and consider the local healthcare infrastructure, resources, and cultural factors.
AI Innovations Description
The study mentioned in the description investigated the association between the provision of small-quantity lipid-based nutrient supplements (SQ-LNS) during pregnancy and the risk of caesarean section (CS) or other delivery complications in Malawian women. The study enrolled pregnant women who were randomly assigned to receive either iron-folic acid (IFA) capsules (control group), multiple micronutrient (MMN) capsules, or SQ-LNS. The results showed that the incidence of CS was higher in the SQ-LNS group compared to the IFA and MMN groups. The relative risk of CS was 2.2 in the SQ-LNS group compared to the IFA group. However, there were no significant differences in other delivery complications among the three groups. The study suggests that the provision of SQ-LNS to pregnant women may have increased the incidence of CS, but further research is needed to understand the underlying reasons for this increase.
AI Innovations Methodology
Based on the provided description, the study investigated the association between prenatal provision of small-quantity lipid-based nutrient supplements (SQ-LNS) and the risk of caesarean section (CS) and other delivery complications in pregnant Malawian women. The study compared three groups: one group received iron-folic acid (IFA) capsules, the second group received multiple micronutrient (MMN) capsules, and the third group received SQ-LNS. The incidence of CS was found to be higher in the SQ-LNS group compared to the IFA and MMN groups. However, there were no significant differences in other delivery complications.

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

1. Define the recommendations: Identify specific innovations or interventions that can improve access to maternal health. For example, these could include increasing the availability of prenatal care services, improving transportation infrastructure to facilitate access to healthcare facilities, implementing telemedicine or mobile health solutions, or training and deploying more skilled birth attendants in underserved areas.

2. Identify key indicators: Determine the key indicators that will be used to measure the impact of the recommendations on improving access to maternal health. These indicators could include the number of pregnant women receiving prenatal care, the number of deliveries attended by skilled birth attendants, the distance and travel time to the nearest healthcare facility, and the number of maternal deaths or complications.

3. Collect baseline data: Gather baseline data on the selected indicators to establish the current state of access to maternal health. This data can be obtained from existing health records, surveys, or other relevant sources.

4. Develop a simulation model: Create a simulation model that incorporates the baseline data and the potential impact of the recommendations. The model should consider factors such as population demographics, geographic distribution, healthcare infrastructure, and resource availability. It should also account for potential barriers or challenges that may affect the implementation and effectiveness of the recommendations.

5. Simulate the impact: Run the simulation model to assess the potential impact of the recommendations on improving access to maternal health. The model should generate projections or estimates of the changes in the selected indicators based on the implementation of the recommendations.

6. Analyze the results: Analyze the simulated results to evaluate the effectiveness of the recommendations in improving access to maternal health. Assess the changes in the selected indicators and identify any potential trade-offs or unintended consequences.

7. Refine and iterate: Based on the analysis of the simulated results, refine the recommendations and the simulation model if necessary. Repeat the simulation process to further assess the impact of the refined recommendations.

By following this methodology, policymakers and healthcare stakeholders can gain insights into the potential impact of innovations or interventions on improving access to maternal health. This information can inform decision-making and resource allocation to prioritize and implement effective strategies.

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