Changes in women’s dietary diversity before and during pregnancy in Southern Benin

Maternal and Child Nutrition, Volume 16, No. 2, Year 2020

Interpretação

Dietary diversity before and during pregnancy is crucial to ensure optimal foetal health and development. We carried out a cohort study of women of reproductive age living in the Sô-Ava and Abomey-Calavi districts (Southern Benin) to investigate women’s changes in dietary diversity and identify their determinants both before and during pregnancy. Nonpregnant women were enrolled (n = 1214) and followed up monthly until they became pregnant (n = 316), then every 3 months during pregnancy. One 24-hr dietary recall was administered before conception and during each trimester of pregnancy. Women’s dietary diversity scores (WDDS) were computed, defined as the number of food groups out of a list of 10 consumed by the women during the past 24 hr. The analysis included 234 women who had complete data. Mixed-effects linear regression models were used to examine changes in the WDDS over the entire follow-up, while controlling for the season, subdistrict, socio-demographic, and economic factors. At preconception, the mean WDDS was low (4.3 ± 1.1 food groups), and the diet was mainly composed of cereals, oils, vegetables, and fish. The mean WDDS did not change during pregnancy and was equally low at all trimesters. Parity and household wealth index were positively associated with the WDDS before and during pregnancy in the multivariate analysis. Additional research is needed to better understand perceptions of food consumption among populations, and more importantly, efforts must be made to encourage women and communities in Benin to improve the diversity of their diets before and during pregnancy. This study was part of the Retard de croissance intra‐utérin et paludisme (RECIPAL) cohort study, which has been fully described elsewhere (Accrombessi et al., 2018). Nonpregnant women of reproductive age were recruited at a community level from Sô‐Ava and Abomey‐Calavi, two semiurban districts of Benin, and followed up with monthly until they became pregnant; these women constituted the primary cohort (preconceptional follow‐up). The subsample of women who became pregnant was then tracked monthly at the maternity clinic from early pregnancy to delivery; they constituted the secondary cohort (gestational follow‐up). The present study collected dietary intakes of women from both the primary and secondary cohorts between November 2014 and December 2017. The RECIPAL study was approved in Benin by the ethical committees of the Institute of Applied Biomedical Sciences and the Ministry of Public Health, and in France by the French National Research Institute for Sustainable Development (IRD). The study was conducted according to the Helsinki Declaration for medical research. Before data collection, written informed consent was obtained from each participant after ensuring their understanding of the purpose, objectives, confidentiality rules, benefits, and risks of taking part in the study. The study took place in four subdistricts in Southern Benin as follows: So‐Ava, Houedo‐Aguekon, Vekky in the district of So‐Ava, and Akassato in the district of Abomey‐Calavi. Both districts are semiurban areas, but Sô‐Ava has the distinction of being a lake area mainly occupied by natives, whereas Abomey‐Calavi is more heterogeneous in terms of population. The climate is subequatorial and characterised by a long rainy season (April–July), a short dry season (August–September), a short rainy season (September–October), and a long dry season (November–March). Women were enrolled in the primary cohort when they met the following criteria: being 18–45 years old, married, nonpregnant, apparently healthy, not known to be sterile, using no current contraception, having no travel plans of more than 2 months during the 18 months after inclusion, willing to become pregnant, and planning to deliver in either the Sô‐Ava or Abomey‐Calavi districts. These women were visited at home every month and tested for pregnancy. Women with positive pregnancy tests were enrolled in the secondary cohort. Women who did not conceive after 1 year of follow‐up were invited to the district maternal care centre for a medical examination. In cases of genital infection, they received medical advice and were referred to a gynaecologist. Follow‐up stopped after 2 years when women did not become pregnant. Demographic and socio‐economic characteristics of both women and their households were collected once upon inclusion in the primary cohort via a structured questionnaire. Data included household size, assets, housing type, women’s ages, parity (number of children, alive or dead), type of union (polygamous/monogamous), ethnic group, education, and main activities. A multiple correspondence analysis (Sourial et al., 2010; Traissac & Martin‐Prevel, 2012) using socio‐economic data was performed to compute a wealth index and to classify households into low, middle, and high wealth levels according to tertiles. Dietary assessments of women were conducted before conception and at each trimester of pregnancy. The minimum number of dietary assessments per woman considered in this analysis was two, and the maximum was four. One quantitative 24‐hr dietary recall (Gibson, Charrondiere, & Bell, 2017; Gibson & Ferguson, 2008) was performed through face‐to‐face interviews. Women were asked to describe all foods, drinks, and snacks consumed over the last 24 hr, including a detailed description of the recipes. Food items consumed were classified into 10 food groups according to recommended classifications (Food and Agriculture Organization [FAO] & Family Health International 360, 2016): (a) grains, white roots and tubers, and plantains (also known as starchy staples); (b) pulses (beans, peas, and lentils); (c) nuts and seeds; (d) dairy; (e) meat, poultry, and fish; (f) eggs; (g) dark green leafy vegetables; (h) other Vitamin A‐rich fruits and vegetables; (i) other fruits; and (j) other vegetables. The number of food groups consumed was summed up and dichotomised using a cut‐off at five food groups to compute the minimum dietary diversity for women (MDD‐W) indicator, which has been recently developed and validated as a proxy of micronutrient adequacy (FAO & Family Health International 360, 2016; Martin‐Prevel et al., 2017). We also used the number of food groups consumed as a continuous variable, namely the women’s dietary diversity score (WDDS‐10), which ranged from 0 to 10 food groups. Four additional food groups—red palm oil, other oils and fats, sugar and sugary drinks, and alcoholic beverages—were used for the purpose of describing women’s dietary patterns. These groups were not used in the calculation of WDDS or MDD‐W. Women’s height was measured at inclusion. Women’s weight was measured twice during the preconceptional follow‐up, then once a month during the gestational follow‐up. Both weight and height were measured according to World Health Organization (WHO) standard procedures (Norgan, 1988). Height was measured to the nearest millimetre with a SECA 206 gauge (Hamburg, Germany). Weight was measured with calibrated electronic scales (Tefal, France) with a precision of 100 g. Body mass index (BMI) was calculated before pregnancy and women were classified as underweight (BMI < 18.5 kg/m2), normal (18.5 ≤ BMI ≤ 24.9 kg/m2), or overweight or obese (BMI ≥ 25 kg/m2) based on WHO classification (WHO, 2018). Data were collected by seven enumerators (five nurses and two nutritionists) holding at least bachelor's degrees, with experience in field data collection. They were trained over 6 days on the 24‐hr recall technique, the questionnaire and tools, and anthropometric measurements. The questionnaire was pretested by the enumerators during the training and was adjusted where needed. During data collection, the enumerators were supervised daily by an experienced nutritionist doubling as the principal investigator and supported by a team of experts in nutritional epidemiology. The supervisor checked the proper completion of the questionnaires daily as well as consistency of the answers. Data from dietary recalls were entered and cross‐checked by repeated entry using the Epidata entry 3.1 software (Lauritsen & Bruus, 2004), whereas anthropometric, socio‐economic, and demographic data were entered using ACCESS 2007. Statistical analyses were performed using Stata 13 (College Station, TX, USA). We first described the basic characteristics of the sample from the primary cohort and examined whether women who became pregnant during the project (n = 316) differed from women who did not (n = 581). We presented mean (SD) for continuous variables and frequencies (%) for categorical variables. The main analysis was restricted to women who had one assessment at preconception and at least one assessment during pregnancy (n = 234). The mean WDDS, the proportion of women who consumed different food groups, and the proportion of women reaching the MDD‐W were compared over the entire follow‐up using a linear mixed model (for continuous variables) or a logistic mixed model (for categorical variables) including a random intercept (the individual) to take into account repeated measurements for the same subject. In bivariate analyses, we examined factors that were associated with women's dietary diversity before pregnancy, using the WDDS as the continuous response variable in linear regression models and using. Factors tested included subdistricts (geographical factor); women's ages, household size, parity, type of union, ethnic groups, women's and their husband's education levels (socio‐demographic factors); women's and their husbands' professional activities and wealth index of the household (economic factors); women's body mass index (nutritional factor). Variables associated with the WDDS with a level of statistical significance of 0.20 were considered for the multivariate analysis. Blocks of factors were constituted based on conceptual reasons (factors belonging to a same dimension); these blocks were successively entered in the model using a manual ascending method. The final multivariate model was used to test if the WDDS changed between the visits of follow‐up (preconception, trimester 1, 2, or 3 of pregnancy). Interaction terms factor*visit were also tested in the final model to examine whether any change in the WDDS differed according to the modality of the factors. Univariate and multivariate analyses were systematically controlled for the season because of its known effect on food availability and hence on dietary diversity. Statistical level of significance was set at p < .05. This study has some limitations. There was only one 24‐hr recall administered at each time point; for this reason, we could not survey women's habitual dietary intake. We also focused on dietary diversity, a single dimension of diet quality, and did not take into account other dimensions or food quantities. Another limitation is the lack of a qualitative survey on the socio‐cultural component for objective measurement of attitudes, behaviours, and beliefs regarding diets before and during pregnancy. Such data would have helped us gain more insight into the trends of our results. However, this was beyond the primary focus of our study, which was to investigate whether there are changes in women's dietary diversity before and during pregnancy. Further research will focus on this purpose. Nevertheless, the cohort design of the study was a real strength and constituted a unique source of data in West Africa. As this study was part of a larger study for which biological samples were collected, we believe that the high rate of lost to follow‐up was precisely due to very strong endogenic belief and mistrust towards the research team.

AI Digest

Study Justification:

– Dietary diversity before and during pregnancy is crucial for optimal fetal health and development.
– The study aimed to investigate changes in women’s dietary diversity and identify determinants before and during pregnancy.
– The study provides valuable insights into the dietary habits of women in Southern Benin and highlights the need for interventions to improve diet quality.

Study Highlights:

– The mean women’s dietary diversity score (WDDS) was low both before and during pregnancy.
– The diet mainly consisted of cereals, oils, vegetables, and fish.
– The WDDS did not change during pregnancy and remained low at all trimesters.
– Parity and household wealth index were positively associated with the WDDS.

Recommendations for Lay Reader:

– Women and communities in Benin should be encouraged to improve the diversity of their diets before and during pregnancy.
– Efforts should be made to raise awareness about the importance of dietary diversity for optimal fetal health and development.
– Additional research is needed to better understand perceptions of food consumption among populations.

Recommendations for Policy Maker:

– Develop and implement interventions to promote dietary diversity among women in Southern Benin.
– Provide education and resources to support women in making healthier food choices before and during pregnancy.
– Collaborate with community leaders and healthcare providers to raise awareness about the importance of a diverse diet for maternal and child health.

Key Role Players:

– Researchers and scientists to conduct further research on perceptions of food consumption and develop evidence-based interventions.
– Community leaders and influencers to promote dietary diversity and raise awareness among women and communities.
– Healthcare providers to provide education and support to women regarding healthy eating during pregnancy.

Cost Items for Planning Recommendations:

– Development and implementation of educational materials and resources.
– Training programs for healthcare providers and community leaders.
– Outreach and awareness campaigns.
– Monitoring and evaluation of interventions.
– Research funding for additional studies on perceptions of food consumption and effectiveness of interventions.

Força da prova

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study design is a cohort study, which is generally considered to provide reliable evidence. The study collected data from a large sample size and used statistical analysis to examine changes in dietary diversity. However, there are some limitations mentioned, such as the lack of habitual dietary intake data and qualitative survey on socio-cultural components. To improve the strength of the evidence, future research could consider including multiple dietary recalls to capture habitual intake and conducting qualitative surveys to gain more insight into attitudes and beliefs regarding diets before and during pregnancy.

Resumo

This study was part of the Retard de croissance intra‐utérin et paludisme (RECIPAL) cohort study, which has been fully described elsewhere (Accrombessi et al., 2018). Nonpregnant women of reproductive age were recruited at a community level from Sô‐Ava and Abomey‐Calavi, two semiurban districts of Benin, and followed up with monthly until they became pregnant; these women constituted the primary cohort (preconceptional follow‐up). The subsample of women who became pregnant was then tracked monthly at the maternity clinic from early pregnancy to delivery; they constituted the secondary cohort (gestational follow‐up). The present study collected dietary intakes of women from both the primary and secondary cohorts between November 2014 and December 2017. The RECIPAL study was approved in Benin by the ethical committees of the Institute of Applied Biomedical Sciences and the Ministry of Public Health, and in France by the French National Research Institute for Sustainable Development (IRD). The study was conducted according to the Helsinki Declaration for medical research. Before data collection, written informed consent was obtained from each participant after ensuring their understanding of the purpose, objectives, confidentiality rules, benefits, and risks of taking part in the study. The study took place in four subdistricts in Southern Benin as follows: So‐Ava, Houedo‐Aguekon, Vekky in the district of So‐Ava, and Akassato in the district of Abomey‐Calavi. Both districts are semiurban areas, but Sô‐Ava has the distinction of being a lake area mainly occupied by natives, whereas Abomey‐Calavi is more heterogeneous in terms of population. The climate is subequatorial and characterised by a long rainy season (April–July), a short dry season (August–September), a short rainy season (September–October), and a long dry season (November–March). Women were enrolled in the primary cohort when they met the following criteria: being 18–45 years old, married, nonpregnant, apparently healthy, not known to be sterile, using no current contraception, having no travel plans of more than 2 months during the 18 months after inclusion, willing to become pregnant, and planning to deliver in either the Sô‐Ava or Abomey‐Calavi districts. These women were visited at home every month and tested for pregnancy. Women with positive pregnancy tests were enrolled in the secondary cohort. Women who did not conceive after 1 year of follow‐up were invited to the district maternal care centre for a medical examination. In cases of genital infection, they received medical advice and were referred to a gynaecologist. Follow‐up stopped after 2 years when women did not become pregnant. Demographic and socio‐economic characteristics of both women and their households were collected once upon inclusion in the primary cohort via a structured questionnaire. Data included household size, assets, housing type, women’s ages, parity (number of children, alive or dead), type of union (polygamous/monogamous), ethnic group, education, and main activities. A multiple correspondence analysis (Sourial et al., 2010; Traissac & Martin‐Prevel, 2012) using socio‐economic data was performed to compute a wealth index and to classify households into low, middle, and high wealth levels according to tertiles. Dietary assessments of women were conducted before conception and at each trimester of pregnancy. The minimum number of dietary assessments per woman considered in this analysis was two, and the maximum was four. One quantitative 24‐hr dietary recall (Gibson, Charrondiere, & Bell, 2017; Gibson & Ferguson, 2008) was performed through face‐to‐face interviews. Women were asked to describe all foods, drinks, and snacks consumed over the last 24 hr, including a detailed description of the recipes. Food items consumed were classified into 10 food groups according to recommended classifications (Food and Agriculture Organization [FAO] & Family Health International 360, 2016): (a) grains, white roots and tubers, and plantains (also known as starchy staples); (b) pulses (beans, peas, and lentils); (c) nuts and seeds; (d) dairy; (e) meat, poultry, and fish; (f) eggs; (g) dark green leafy vegetables; (h) other Vitamin A‐rich fruits and vegetables; (i) other fruits; and (j) other vegetables. The number of food groups consumed was summed up and dichotomised using a cut‐off at five food groups to compute the minimum dietary diversity for women (MDD‐W) indicator, which has been recently developed and validated as a proxy of micronutrient adequacy (FAO & Family Health International 360, 2016; Martin‐Prevel et al., 2017). We also used the number of food groups consumed as a continuous variable, namely the women’s dietary diversity score (WDDS‐10), which ranged from 0 to 10 food groups. Four additional food groups—red palm oil, other oils and fats, sugar and sugary drinks, and alcoholic beverages—were used for the purpose of describing women’s dietary patterns. These groups were not used in the calculation of WDDS or MDD‐W. Women’s height was measured at inclusion. Women’s weight was measured twice during the preconceptional follow‐up, then once a month during the gestational follow‐up. Both weight and height were measured according to World Health Organization (WHO) standard procedures (Norgan, 1988). Height was measured to the nearest millimetre with a SECA 206 gauge (Hamburg, Germany). Weight was measured with calibrated electronic scales (Tefal, France) with a precision of 100 g. Body mass index (BMI) was calculated before pregnancy and women were classified as underweight (BMI < 18.5 kg/m2), normal (18.5 ≤ BMI ≤ 24.9 kg/m2), or overweight or obese (BMI ≥ 25 kg/m2) based on WHO classification (WHO, 2018). Data were collected by seven enumerators (five nurses and two nutritionists) holding at least bachelor's degrees, with experience in field data collection. They were trained over 6 days on the 24‐hr recall technique, the questionnaire and tools, and anthropometric measurements. The questionnaire was pretested by the enumerators during the training and was adjusted where needed. During data collection, the enumerators were supervised daily by an experienced nutritionist doubling as the principal investigator and supported by a team of experts in nutritional epidemiology. The supervisor checked the proper completion of the questionnaires daily as well as consistency of the answers. Data from dietary recalls were entered and cross‐checked by repeated entry using the Epidata entry 3.1 software (Lauritsen & Bruus, 2004), whereas anthropometric, socio‐economic, and demographic data were entered using ACCESS 2007. Statistical analyses were performed using Stata 13 (College Station, TX, USA). We first described the basic characteristics of the sample from the primary cohort and examined whether women who became pregnant during the project (n = 316) differed from women who did not (n = 581). We presented mean (SD) for continuous variables and frequencies (%) for categorical variables. The main analysis was restricted to women who had one assessment at preconception and at least one assessment during pregnancy (n = 234). The mean WDDS, the proportion of women who consumed different food groups, and the proportion of women reaching the MDD‐W were compared over the entire follow‐up using a linear mixed model (for continuous variables) or a logistic mixed model (for categorical variables) including a random intercept (the individual) to take into account repeated measurements for the same subject. In bivariate analyses, we examined factors that were associated with women's dietary diversity before pregnancy, using the WDDS as the continuous response variable in linear regression models and using. Factors tested included subdistricts (geographical factor); women's ages, household size, parity, type of union, ethnic groups, women's and their husband's education levels (socio‐demographic factors); women's and their husbands' professional activities and wealth index of the household (economic factors); women's body mass index (nutritional factor). Variables associated with the WDDS with a level of statistical significance of 0.20 were considered for the multivariate analysis. Blocks of factors were constituted based on conceptual reasons (factors belonging to a same dimension); these blocks were successively entered in the model using a manual ascending method. The final multivariate model was used to test if the WDDS changed between the visits of follow‐up (preconception, trimester 1, 2, or 3 of pregnancy). Interaction terms factor*visit were also tested in the final model to examine whether any change in the WDDS differed according to the modality of the factors. Univariate and multivariate analyses were systematically controlled for the season because of its known effect on food availability and hence on dietary diversity. Statistical level of significance was set at p < .05. This study has some limitations. There was only one 24‐hr recall administered at each time point; for this reason, we could not survey women's habitual dietary intake. We also focused on dietary diversity, a single dimension of diet quality, and did not take into account other dimensions or food quantities. Another limitation is the lack of a qualitative survey on the socio‐cultural component for objective measurement of attitudes, behaviours, and beliefs regarding diets before and during pregnancy. Such data would have helped us gain more insight into the trends of our results. However, this was beyond the primary focus of our study, which was to investigate whether there are changes in women's dietary diversity before and during pregnancy. Further research will focus on this purpose. Nevertheless, the cohort design of the study was a real strength and constituted a unique source of data in West Africa. As this study was part of a larger study for which biological samples were collected, we believe that the high rate of lost to follow‐up was precisely due to very strong endogenic belief and mistrust towards the research team.

Materiais

N/A

Inovações

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women with information on nutrition, dietary diversity, and healthy eating during pregnancy. These apps can also send reminders and notifications to encourage women to consume a diverse range of foods.

2. Community-based Education Programs: Implement community-based education programs that focus on raising awareness about the importance of dietary diversity during pregnancy. These programs can include workshops, cooking demonstrations, and nutrition counseling sessions to empower women with the knowledge and skills to make healthier food choices.

3. Telemedicine Services: Establish telemedicine services that allow pregnant women in remote or underserved areas to access prenatal care and nutritional guidance through virtual consultations with healthcare professionals. This can help overcome geographical barriers and improve access to maternal health services.

4. Public-Private Partnerships: Foster collaborations between public health agencies, private sector organizations, and non-profit organizations to develop and implement initiatives that promote dietary diversity during pregnancy. This can involve partnerships with food producers, distributors, and retailers to ensure the availability and affordability of diverse and nutritious foods for pregnant women.

5. Maternal Health Vouchers: Introduce maternal health vouchers that provide pregnant women with financial assistance to access prenatal care, including nutrition counseling and support. These vouchers can be distributed to vulnerable populations to reduce financial barriers and improve access to maternal health services.

6. Behavior Change Communication Campaigns: Launch behavior change communication campaigns that use various media channels (e.g., radio, television, social media) to promote the importance of dietary diversity during pregnancy. These campaigns can provide evidence-based information, success stories, and testimonials to motivate and inspire pregnant women to adopt healthier eating habits.

7. Integration of Maternal Health Services: Integrate maternal health services with existing healthcare systems, such as antenatal care clinics, to ensure that nutrition and dietary diversity are addressed as part of routine prenatal care. This can involve training healthcare providers on the importance of nutrition during pregnancy and providing them with tools and resources to support pregnant women in making healthy food choices.

8. Empowerment of Women: Implement programs that empower women to take control of their own health and nutrition during pregnancy. This can include initiatives that promote women’s education, income generation, and decision-making power within their households, enabling them to prioritize their own health and the health of their unborn child.

These innovations can help improve access to maternal health by addressing barriers related to knowledge, affordability, availability, and cultural beliefs surrounding dietary diversity during pregnancy.

AI Innovations Description

Based on the description provided, the recommendation to improve access to maternal health is to focus on promoting and encouraging women and communities in Benin to improve the diversity of their diets before and during pregnancy. This recommendation is based on the findings of the study, which showed that women’s dietary diversity scores (WDDS) were low both before and during pregnancy, and the diet mainly consisted of cereals, oils, vegetables, and fish.

To implement this recommendation, the following actions can be taken:

1. Education and awareness: Conduct educational campaigns and workshops to raise awareness about the importance of dietary diversity for maternal health. Provide information on the specific food groups that should be included in a balanced diet before and during pregnancy.

2. Nutritional counseling: Offer individual or group counseling sessions to pregnant women and women of reproductive age on improving their dietary diversity. Provide practical tips and guidance on meal planning, food preparation, and incorporating a variety of food groups into their diets.

3. Community engagement: Involve community leaders, local organizations, and healthcare providers in promoting the importance of dietary diversity for maternal health. Organize community events, cooking demonstrations, and nutrition workshops to engage and empower women and their families to make healthier food choices.

4. Access to nutritious foods: Address barriers to accessing diverse and nutritious foods by improving availability, affordability, and accessibility. This can be done through initiatives such as promoting local food production, supporting farmers’ markets, and advocating for policies that prioritize the availability of diverse food options.

5. Integration with existing maternal health services: Integrate nutrition education and counseling on dietary diversity into existing maternal health services, such as antenatal care visits and postnatal support. This ensures that women receive comprehensive care that addresses both their nutritional needs and overall maternal health.

6. Research and monitoring: Conduct further research to better understand the perceptions and behaviors related to food consumption among the population. This will help tailor interventions and strategies to effectively promote dietary diversity. Additionally, monitor and evaluate the impact of interventions on improving access to maternal health and dietary diversity to inform future efforts.

By implementing these recommendations, it is expected that access to maternal health will be improved by promoting better dietary diversity among women in Benin. This, in turn, can contribute to optimal fetal health and development, reducing the risk of maternal and infant complications.

AI Innovations Methodology

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

1. Nutrition Education Programs: Implement targeted nutrition education programs that focus on improving women’s dietary diversity before and during pregnancy. These programs can provide information on the importance of a diverse diet, offer guidance on healthy food choices, and promote the consumption of a variety of food groups.

2. Community-Based Interventions: Engage with local communities to raise awareness about the importance of maternal nutrition and encourage behavior change. This can be done through community workshops, support groups, and the involvement of community leaders and influencers.

3. Mobile Health (mHealth) Solutions: Utilize mobile technology to deliver maternal health information and reminders directly to women. This can include text messages or mobile applications that provide nutrition tips, reminders for prenatal appointments, and access to resources and support.

4. Integration of Maternal Health Services: Ensure that maternal health services, including nutrition counseling and support, are integrated into existing healthcare systems. This can involve training healthcare providers on maternal nutrition and incorporating nutrition assessments and counseling into routine prenatal care.

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

1. Define Key Indicators: Identify key indicators that measure access to maternal health, such as the percentage of women receiving prenatal care, the percentage of women with adequate nutrition during pregnancy, or the percentage of women with positive health outcomes during childbirth.

2. Baseline Data Collection: Collect baseline data on the identified indicators before implementing the recommendations. This can involve surveys, interviews, or analysis of existing data sources.

3. Implement Recommendations: Implement the recommended interventions, such as nutrition education programs, community-based interventions, mHealth solutions, and integration of maternal health services.

4. Monitor and Evaluate: Continuously monitor and evaluate the impact of the interventions on the identified indicators. This can involve collecting data at regular intervals, conducting surveys or interviews with program participants, and analyzing the data to assess changes in access to maternal health.

5. Compare Results: Compare the post-intervention data with the baseline data to determine the impact of the recommendations on improving access to maternal health. This can be done by calculating the percentage change in the identified indicators or conducting statistical analyses to assess the significance of the changes.

6. Refine and Scale-Up: Based on the evaluation results, refine the interventions as needed and scale them up to reach a larger population. This can involve expanding the reach of nutrition education programs, increasing community engagement efforts, or enhancing the functionality of mHealth solutions.

By following this methodology, it will be possible to simulate the impact of the recommendations on improving access to maternal health and make informed decisions on how to further enhance maternal health services in Southern Benin.

Autor

Dima Health

Statistics:

Citations: 7

Identifiers:

DOI: 10.1111/mcn.12906

ISSN: 17408695

Research Areas:

Community Interventions, Disparities, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Sexual and Reproductive Health, Social Determinants

Study Countries:

Benin, Multi-Countries

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Mixed-methods, Qualitative, Quantitative

Participants Gender:

Female

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