Seasonality of antenatal care attendance, maternal dietary intake, and fetal growth in the VHEMBE birth cohort, South Africa

PLoS ONE, Volume 14, No. 9, Year 2019

Interpretation

Background Seasonality of food availability, physical activity, and infections commonly occurs within rural communities in low and middle-income countries with distinct rainy seasons. To better understand the implications of these regularly occurring environmental stressors for maternal and child health, this study examined seasonal variation in nutrition and health care access of pregnant women and infants in rural South Africa. Methods We analyzed data from the Venda Health Examination of Mothers, Babies and their Environment (VHEMBE) birth cohort study of 752 mother-infant pairs recruited at delivery from August 2012 to December 2013 in the Vhembe District of Limpopo Province, the northernmost region of South Africa. We used truncated Fourier series regression to assess seasonality of antenatal care (ANC) attendance, dietary intake, and birth size. We additionally regressed ANC attendance on daily rainfall values. Models included adjustment for sociodemographic characteristics. Results Maternal ANC attendance, dietary composition, and infant birth size exhibited significant seasonal variation in both unadjusted and adjusted analyses. Adequate frequency of ANC attendance during pregnancy (≥ 4 visits) was highest among women delivering during the gardening season and lowest during the lean (rainy) season. High rainfall during the third trimester was also negatively associated with adequate ANC attendance (adjusted OR = 0.59, 95% CI: 0.40, 0.86). Carbohydrate intake declined during the harvest season and increased during the vegetable gardening and lean seasons, while fat intake followed the opposite trend. Infant birth weight, length, and head circumference z-scores peaked following the gardening season and were lowest after the harvest season. Maternal protein intake and ANC ≤ 12 weeks did not significantly vary by season or rainfall. Conclusions Seasonal patterns were apparent in ANC utilization, dietary intake, and fetal growth in rural South Africa. Interventions to promote maternal and child health in similar settings should consider seasonal factors. We analyzed data from the Venda Health Examination of Mothers, Babies and their Environment (VHEMBE) study conducted in the Vhembe District of Limpopo Province, the northernmost region of South Africa. Many households in this area engage in subsistence crop production as a source of food, growing primarily maize, fruits, and vegetables in backyards or on small plots of land [12]. The climate is subtropical with rainfall concentrated during a four-month period (November–February) [13]. The annual farming cycle revolves around rainfall: planting of staple crops occurs at the onset of the rainy season, followed by harvesting (March–June) after rainfall ends and vegetable gardening (July–October) until rainfall commences again [14,15]. As food stocks from the harvest become depleted over the year and market prices rise, a period of widespread food insecurity known as the “lean” season (November–February) coincides with rainfall, labor-intensive land preparation and planting, and an increase in infectious and parasitic diseases [1,15]. The VHEMBE study is a prospective birth cohort that aims to evaluate the determinants and impacts of environmental exposures on child growth, health, and development. This analysis used data collected at the time of delivery, including abstracted medical records that were recorded throughout the pregnancy. Participants were recruited among women who presented for delivery at Tshilidzini hospital, in the town of Thohoyandou, between August 2012 and December 2013. All women delivering or still in the hospital on a weekday were screened (n = 1,649). Women were eligible for inclusion if they were at least 18 years old, had contractions at least five minutes apart, spoke primarily TshiVenda (the most commonly spoken language in the area), lived within 20 km of the hospital and intended to remain in the area for at least two years, had not been diagnosed with malaria during the pregnancy and gave birth to a viable singleton. Of 920 eligible women, 152 declined to participate and 16 were lost to follow-up before full enrolment data were collected, yielding a study sample of 752 mother-infant pairs. For this analysis, sample sizes ranged from 605 to 751 due to missing outcomes data including first ANC visit date (n = 147), total number of ANC visits (n = 140), birth length (n = 6), head circumference (n = 6), birth weight (n = 1), and dietary intake (n = 1). Following recruitment at the time of delivery, trained bilingual Venda interviewers administered a structured questionnaire to collect sociodemographic characteristics and abstracted medical records. Participants were also visited at home one week after delivery, where Global Positioning System (GPS) coordinates were captured using a Garmin Etrex30 device outside the front door of the building where the mother slept. Coordinates were taken in duplicate (midway through the visit and at the end) and averaged to assign the final coordinates of each home. The University of California, Berkeley, the University of Pretoria, the Limpopo Department of Health and Social Development, Tshilidzini Hospital and McGill University granted ethics approval for the VHEMBE study. The Committee for Protection of Human Subjects at the University of California, Berkeley further approved the use of these data for the present study. Antenatal care (ANC) attendance records were collected at delivery from the mother’s ANC card, a form of medical record which women carry with them to all visits to any ANC clinic during pregnancy. We characterized ANC attendance by adequate frequency (≥4 visits) and early initiation (≤12 weeks), according to WHO recommendations at the time of the study [16]. Records of ANC attendance were missing for 140 (19%) of mothers. Field staff reported that mothers often forgot or did not have time to retrieve their ANC card from home and bring it to delivery. Although it is possible that some of these mothers never attended ANC, this proportion is expected to be small (nationally, 97% of mothers attend at least one ANC visit [17]). However, to limit potential bias from missing ANC records, we applied inverse probability of censoring weighting to ANC analyses (see Statistical Analysis) [18]. During the interview at the time of delivery, mothers completed a detailed quantitative food frequency questionnaire designed and validated in a population residing in the study area, which asked about food consumption in the previous month [19]. Total and per macronutrient energy intake in kilojoules (kJ) was estimated by a South African expert nutritionist using FoodFinder 3 software (South Africa Medical Research Council/WAMTechnology CC). For this analysis, we calculated carbohydrate, fat, and protein intakes as percentages of total energy intake. Child birthweight was assessed by hospital nurses immediately after delivery—with a Tanita BD-815U neonatal scale (Arlington Heights, IL, USA) provided by the study which measured grams to two decimal places—and was retrospectively abstracted from hospital records. Study nurses measured birth length and head circumference within the first 24 to 48 hours of birth. Birth length was measured using a Seca 417 portable infantometer (Chino, CA, USA), with the infant laid on his or her back, legs aligned and fully extended, and toes pointing directly upward. Head circumference was assessed to the nearest 0.1 cm using a tape measure positioned just above the eyebrows, above the ears, and around the largest part of the back of the head. Measurements were taken in triplicate and averaged. For each size outcome, growth z-scores for gestational age and sex were constructed according to WHO Child Growth Standards [20]. Gestational age was ascertained by last menstrual period (LMP) reported at delivery, with unlikely values (above the 1st or below the 99th birthweight-for-age percentiles) replaced by gestational age indicated in medical records for 10% of participants. Birth size measurements were complete for all but one missing birth weight and six missing length and head circumference measurements. We used logistic regression to model ANC outcomes (adequate attendance and early initiation) and linear regression for dietary intake (carbohydrate, fat, and protein) and birth size outcomes (birth weight, birth length, and head circumference z-scores) in relation to season. Seasonal trends in ANC attendance, dietary intake, and birth size were examined using truncated Fourier series terms [21], a method previously used and recommended by Rayco Solon et al. [5] and Fulford et al. [22] who have conducted extensive work on seasonality of birth size. This method avoids the pitfalls of using categorical seasons or month-of-year predictors, including arbitrary cutoffs and overparameterization. Delivery dates were transformed into cyclical data using sine and cosine functions parameterized by Fourier coefficients, allowing for modeling of continuous trends in seasonality. Borrowing from previous notation [5,22], we defined seasonal terms as follows: where p is the number of Fourier term pairs assessed and θi is the point in the annual cycle when the ith infant is born, calculated in radians (starting with the harvest, we set March 1 ~ 0; February 28 ~ 2π). Seasonality is modeled by adding the first p pairs of Fourier terms to the regression model, parameterized by βr and γr. Separate models for each ANC attendance, dietary intake, and infant birth size outcome were fitted with an increasing number of Fourier pairs. Seasonality was assessed using likelihood ratio (LR) tests or F tests for multiply imputed data to compare models and select the model with the best fit for each outcome. For each outcome, we selected the model that satisfied α = 0.10 for both comparison to the null model (no Fourier terms) as well as to the nested model with fewer Fourier terms. As a secondary analysis, we explored the role of rainfall in ANC attendance. Rainfall during each pregnancy was constructed with the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily data at 0.05° resolution [23]. CHIRPS is a publicly available quasi-global rainfall dataset that uses satellite imagery along with in-situ station data to create gridded rainfall data spanning 50°S to 50°N from 1981 to near-present. We averaged precipitation values within a 0.25° radius (~30 km) of a given woman’s home for each day of her pregnancy, summed across each trimester, and divided by the number days in each trimester (S1 Fig). Adequate ANC attendance was regressed on the average daily amount of rainfall during each trimester. Early ANC initiation was regressed on rainfall during the first trimester only. Average daily rainfall per trimester was modeled both as a continuous variable and in separate models as a categorical indicator for above or below the sample average. All regression models were adjusted for covariates identified a priori from the literature [5,6,24,25]. Models of ANC attendance included maternal parity, HIV status, education, marital status, and pregnancy desire; father’s supportiveness of the pregnancy; household income and distance from the home to the nearest main road (calculated as the Euclidian distance to a primary or secondary road obtained from OpenStreetMap [26]); and duration of pregnancy. Models of dietary intake included maternal parity, HIV status, height, education, marital status, household income, and duration of pregnancy. Models of birth size z-score included maternal parity, HIV status, height, education, marital status, and household income. Covariates were coded as shown in Table 1 except for income, which was log transformed. Data are presented as mean (SD) or median (IQR) for continuous measures, and n (%) for categorical measures. Variables with missing data: distance to a main road (n = 30), maternal height (n = 12), household income (n = 4), maternal HIV status (n = 3), and maternal education, marital status, pregnancy desire and father’s supportiveness (n = 1 for each). To limit potential bias from missing ANC data, we used inverse probability of censoring weighting (IPCW) to adjust for the propensity of missing ANC records [18]. We generated weights from a logistic model of missing versus observed ANC records regressed on the characteristics shown in S1 Table. This model incorporated multiple imputation by chained equations (MICE) with 20 iterations for predictors with missing data [27, 28], using the same variables shown in S1 Table. We also used these multiple imputation estimates in our adjusted outcomes regression models to include participants with missing covariates that would otherwise drop out of the model (n = 21 for ANC models; n = 15 for dietary intake models and birth weight; n = 17 for birth length and head circumference). Results were combined according to Rubin’s rules using the “mi estimate” command in Stata [27]. All statistical analyses were conducted using Stata 14.2 (College Station, TX, USA). Rainfall variables were constructed using MATLAB Release 2018b (Natick, MA, USA) and road distance was calculated using R Version 3.5.1 (Vienna, Austria).

AI Digest

Study Justification:

– The study aims to understand the implications of seasonal environmental stressors on maternal and child health in rural South Africa.
– It examines the seasonal variation in nutrition and healthcare access of pregnant women and infants in a region with distinct rainy seasons.
– By analyzing data from the VHEMBE birth cohort study, the study provides insights into the relationship between seasonality, antenatal care attendance, dietary intake, and fetal growth.

Study Highlights:

– Maternal antenatal care (ANC) attendance, dietary composition, and infant birth size showed significant seasonal variation.
– Adequate frequency of ANC attendance was highest during the gardening season and lowest during the lean (rainy) season.
– High rainfall during the third trimester was negatively associated with adequate ANC attendance.
– Carbohydrate intake declined during the harvest season and increased during the vegetable gardening and lean seasons, while fat intake followed the opposite trend.
– Infant birth weight, length, and head circumference z-scores peaked following the gardening season and were lowest after the harvest season.
– Maternal protein intake and ANC initiation ≤ 12 weeks did not significantly vary by season or rainfall.

Recommendations for Lay Reader and Policy Maker:

– Interventions to promote maternal and child health in similar settings should consider seasonal factors.
– Policy makers should prioritize strategies to ensure adequate ANC attendance during the lean season, when attendance is lowest.
– Efforts should be made to address the seasonal variations in dietary intake, particularly the decline in carbohydrate intake during the harvest season.
– Programs should focus on improving infant birth size during the post-harvest season, when z-scores are lowest.

Key Role Players:

– Researchers and scientists involved in maternal and child health studies.
– Healthcare providers, including doctors, nurses, and midwives.
– Community health workers and educators.
– Government health departments and policymakers.
– Non-governmental organizations (NGOs) working in the field of maternal and child health.

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers and community health workers.
– Development and implementation of educational programs for pregnant women and their families.
– Provision of adequate healthcare facilities and resources.
– Monitoring and evaluation of interventions.
– Research and data collection to assess the effectiveness of interventions.
– Collaboration and coordination among stakeholders.
– Communication and awareness campaigns.
– Infrastructure development, if necessary, to improve access to healthcare services.

Strength of Evidence

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 well-designed study with a large sample size. The study analyzed data from the VHEMBE birth cohort study, which included 752 mother-infant pairs. The study used truncated Fourier series regression to assess seasonality of antenatal care attendance, dietary intake, and birth size. The results showed significant seasonal variation in maternal ANC attendance, dietary composition, and infant birth size. The study also found that high rainfall during the third trimester was negatively associated with adequate ANC attendance. The findings suggest that interventions to promote maternal and child health in similar settings should consider seasonal factors. To improve the evidence, future studies could consider including a control group for comparison and conducting follow-up assessments to evaluate the long-term effects of seasonal factors on maternal and child health.

Abstract

We analyzed data from the Venda Health Examination of Mothers, Babies and their Environment (VHEMBE) study conducted in the Vhembe District of Limpopo Province, the northernmost region of South Africa. Many households in this area engage in subsistence crop production as a source of food, growing primarily maize, fruits, and vegetables in backyards or on small plots of land [12]. The climate is subtropical with rainfall concentrated during a four-month period (November–February) [13]. The annual farming cycle revolves around rainfall: planting of staple crops occurs at the onset of the rainy season, followed by harvesting (March–June) after rainfall ends and vegetable gardening (July–October) until rainfall commences again [14,15]. As food stocks from the harvest become depleted over the year and market prices rise, a period of widespread food insecurity known as the “lean” season (November–February) coincides with rainfall, labor-intensive land preparation and planting, and an increase in infectious and parasitic diseases [1,15]. The VHEMBE study is a prospective birth cohort that aims to evaluate the determinants and impacts of environmental exposures on child growth, health, and development. This analysis used data collected at the time of delivery, including abstracted medical records that were recorded throughout the pregnancy. Participants were recruited among women who presented for delivery at Tshilidzini hospital, in the town of Thohoyandou, between August 2012 and December 2013. All women delivering or still in the hospital on a weekday were screened (n = 1,649). Women were eligible for inclusion if they were at least 18 years old, had contractions at least five minutes apart, spoke primarily TshiVenda (the most commonly spoken language in the area), lived within 20 km of the hospital and intended to remain in the area for at least two years, had not been diagnosed with malaria during the pregnancy and gave birth to a viable singleton. Of 920 eligible women, 152 declined to participate and 16 were lost to follow-up before full enrolment data were collected, yielding a study sample of 752 mother-infant pairs. For this analysis, sample sizes ranged from 605 to 751 due to missing outcomes data including first ANC visit date (n = 147), total number of ANC visits (n = 140), birth length (n = 6), head circumference (n = 6), birth weight (n = 1), and dietary intake (n = 1). Following recruitment at the time of delivery, trained bilingual Venda interviewers administered a structured questionnaire to collect sociodemographic characteristics and abstracted medical records. Participants were also visited at home one week after delivery, where Global Positioning System (GPS) coordinates were captured using a Garmin Etrex30 device outside the front door of the building where the mother slept. Coordinates were taken in duplicate (midway through the visit and at the end) and averaged to assign the final coordinates of each home. The University of California, Berkeley, the University of Pretoria, the Limpopo Department of Health and Social Development, Tshilidzini Hospital and McGill University granted ethics approval for the VHEMBE study. The Committee for Protection of Human Subjects at the University of California, Berkeley further approved the use of these data for the present study. Antenatal care (ANC) attendance records were collected at delivery from the mother’s ANC card, a form of medical record which women carry with them to all visits to any ANC clinic during pregnancy. We characterized ANC attendance by adequate frequency (≥4 visits) and early initiation (≤12 weeks), according to WHO recommendations at the time of the study [16]. Records of ANC attendance were missing for 140 (19%) of mothers. Field staff reported that mothers often forgot or did not have time to retrieve their ANC card from home and bring it to delivery. Although it is possible that some of these mothers never attended ANC, this proportion is expected to be small (nationally, 97% of mothers attend at least one ANC visit [17]). However, to limit potential bias from missing ANC records, we applied inverse probability of censoring weighting to ANC analyses (see Statistical Analysis) [18]. During the interview at the time of delivery, mothers completed a detailed quantitative food frequency questionnaire designed and validated in a population residing in the study area, which asked about food consumption in the previous month [19]. Total and per macronutrient energy intake in kilojoules (kJ) was estimated by a South African expert nutritionist using FoodFinder 3 software (South Africa Medical Research Council/WAMTechnology CC). For this analysis, we calculated carbohydrate, fat, and protein intakes as percentages of total energy intake. Child birthweight was assessed by hospital nurses immediately after delivery—with a Tanita BD-815U neonatal scale (Arlington Heights, IL, USA) provided by the study which measured grams to two decimal places—and was retrospectively abstracted from hospital records. Study nurses measured birth length and head circumference within the first 24 to 48 hours of birth. Birth length was measured using a Seca 417 portable infantometer (Chino, CA, USA), with the infant laid on his or her back, legs aligned and fully extended, and toes pointing directly upward. Head circumference was assessed to the nearest 0.1 cm using a tape measure positioned just above the eyebrows, above the ears, and around the largest part of the back of the head. Measurements were taken in triplicate and averaged. For each size outcome, growth z-scores for gestational age and sex were constructed according to WHO Child Growth Standards [20]. Gestational age was ascertained by last menstrual period (LMP) reported at delivery, with unlikely values (above the 1st or below the 99th birthweight-for-age percentiles) replaced by gestational age indicated in medical records for 10% of participants. Birth size measurements were complete for all but one missing birth weight and six missing length and head circumference measurements. We used logistic regression to model ANC outcomes (adequate attendance and early initiation) and linear regression for dietary intake (carbohydrate, fat, and protein) and birth size outcomes (birth weight, birth length, and head circumference z-scores) in relation to season. Seasonal trends in ANC attendance, dietary intake, and birth size were examined using truncated Fourier series terms [21], a method previously used and recommended by Rayco Solon et al. [5] and Fulford et al. [22] who have conducted extensive work on seasonality of birth size. This method avoids the pitfalls of using categorical seasons or month-of-year predictors, including arbitrary cutoffs and overparameterization. Delivery dates were transformed into cyclical data using sine and cosine functions parameterized by Fourier coefficients, allowing for modeling of continuous trends in seasonality. Borrowing from previous notation [5,22], we defined seasonal terms as follows: where p is the number of Fourier term pairs assessed and θi is the point in the annual cycle when the ith infant is born, calculated in radians (starting with the harvest, we set March 1 ~ 0; February 28 ~ 2π). Seasonality is modeled by adding the first p pairs of Fourier terms to the regression model, parameterized by βr and γr. Separate models for each ANC attendance, dietary intake, and infant birth size outcome were fitted with an increasing number of Fourier pairs. Seasonality was assessed using likelihood ratio (LR) tests or F tests for multiply imputed data to compare models and select the model with the best fit for each outcome. For each outcome, we selected the model that satisfied α = 0.10 for both comparison to the null model (no Fourier terms) as well as to the nested model with fewer Fourier terms. As a secondary analysis, we explored the role of rainfall in ANC attendance. Rainfall during each pregnancy was constructed with the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily data at 0.05° resolution [23]. CHIRPS is a publicly available quasi-global rainfall dataset that uses satellite imagery along with in-situ station data to create gridded rainfall data spanning 50°S to 50°N from 1981 to near-present. We averaged precipitation values within a 0.25° radius (~30 km) of a given woman’s home for each day of her pregnancy, summed across each trimester, and divided by the number days in each trimester (S1 Fig). Adequate ANC attendance was regressed on the average daily amount of rainfall during each trimester. Early ANC initiation was regressed on rainfall during the first trimester only. Average daily rainfall per trimester was modeled both as a continuous variable and in separate models as a categorical indicator for above or below the sample average. All regression models were adjusted for covariates identified a priori from the literature [5,6,24,25]. Models of ANC attendance included maternal parity, HIV status, education, marital status, and pregnancy desire; father’s supportiveness of the pregnancy; household income and distance from the home to the nearest main road (calculated as the Euclidian distance to a primary or secondary road obtained from OpenStreetMap [26]); and duration of pregnancy. Models of dietary intake included maternal parity, HIV status, height, education, marital status, household income, and duration of pregnancy. Models of birth size z-score included maternal parity, HIV status, height, education, marital status, and household income. Covariates were coded as shown in Table 1 except for income, which was log transformed. Data are presented as mean (SD) or median (IQR) for continuous measures, and n (%) for categorical measures. Variables with missing data: distance to a main road (n = 30), maternal height (n = 12), household income (n = 4), maternal HIV status (n = 3), and maternal education, marital status, pregnancy desire and father’s supportiveness (n = 1 for each). To limit potential bias from missing ANC data, we used inverse probability of censoring weighting (IPCW) to adjust for the propensity of missing ANC records [18]. We generated weights from a logistic model of missing versus observed ANC records regressed on the characteristics shown in S1 Table. This model incorporated multiple imputation by chained equations (MICE) with 20 iterations for predictors with missing data [27, 28], using the same variables shown in S1 Table. We also used these multiple imputation estimates in our adjusted outcomes regression models to include participants with missing covariates that would otherwise drop out of the model (n = 21 for ANC models; n = 15 for dietary intake models and birth weight; n = 17 for birth length and head circumference). Results were combined according to Rubin’s rules using the “mi estimate” command in Stata [27]. All statistical analyses were conducted using Stata 14.2 (College Station, TX, USA). Rainfall variables were constructed using MATLAB Release 2018b (Natick, MA, USA) and road distance was calculated using R Version 3.5.1 (Vienna, Austria).

Materials

Innovations

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

1. Mobile Clinics: Implementing mobile clinics that can travel to rural areas, especially during the lean season when access to healthcare is limited, can provide antenatal care services to pregnant women who may not have easy access to healthcare facilities.

2. Telemedicine: Using telemedicine technology, healthcare providers can remotely monitor and provide consultations to pregnant women in rural areas. This can help overcome geographical barriers and ensure that women receive the necessary care and guidance throughout their pregnancy.

3. Community Health Workers: Training and deploying community health workers who are familiar with the local context and language can help bridge the gap between healthcare facilities and pregnant women in rural areas. These workers can provide education, support, and referrals for antenatal care services.

4. Weather Forecasting: Integrating weather forecasting data into healthcare systems can help anticipate and plan for seasonal variations in healthcare access. This can enable healthcare providers to allocate resources and services more effectively during periods of increased demand or limited access.

5. Nutrition Programs: Implementing nutrition programs that specifically address seasonal variations in dietary intake can help improve maternal and fetal health. These programs can provide education, resources, and support to pregnant women, ensuring they have access to a balanced diet throughout the year.

6. Transportation Support: Providing transportation support, such as subsidized or free transportation services, can help pregnant women in rural areas overcome barriers to accessing healthcare facilities. This can include arranging transportation for antenatal care visits, delivery, and postnatal care.

7. Health Education Campaigns: Conducting targeted health education campaigns that focus on the importance of antenatal care and the potential impact of seasonal variations on maternal and fetal health can help raise awareness and encourage pregnant women to seek timely care.

It is important to note that these recommendations are based on the information provided and may need to be tailored to the specific context and needs of the Vhembe District in South Africa.

AI Innovations Description

The study titled “Seasonality of antenatal care attendance, maternal dietary intake, and fetal growth in the VHEMBE birth cohort, South Africa” explores the seasonal variation in nutrition and healthcare access for pregnant women and infants in rural South Africa. The study found significant seasonal patterns in antenatal care attendance, dietary intake, and infant birth size.

Based on the findings of this study, a recommendation to improve access to maternal health would be to implement targeted interventions that take into account the seasonal factors affecting healthcare utilization and dietary intake. These interventions could include:

1. Seasonal awareness campaigns: Raise awareness among pregnant women and their families about the importance of regular antenatal care visits and maintaining a balanced diet throughout the year. Emphasize the specific challenges and risks associated with different seasons, such as the lean (rainy) season, and provide information on how to mitigate these risks.

2. Mobile healthcare services: Establish mobile healthcare clinics that can reach remote areas during the lean season when access to healthcare facilities may be limited. These clinics can provide antenatal care services, nutritional counseling, and support to pregnant women in underserved communities.

3. Community-based support groups: Create community-based support groups for pregnant women where they can share experiences, receive guidance, and access information on nutrition and healthcare. These support groups can be facilitated by trained healthcare professionals or community health workers.

4. Agricultural support programs: Collaborate with agricultural organizations and local communities to promote sustainable agriculture practices and increase food security during the lean season. This can include providing training on crop diversification, irrigation techniques, and storage methods to ensure a steady supply of nutritious food throughout the year.

5. Integration of weather forecasts: Incorporate weather forecasts and climate information into maternal health programs to help pregnant women and healthcare providers anticipate and prepare for seasonal challenges. This can include providing early warnings about extreme weather events and guidance on adapting healthcare services and dietary practices accordingly.

By implementing these recommendations, it is possible to improve access to maternal health services and promote better nutrition for pregnant women in rural areas, ultimately leading to improved maternal and child health outcomes.

AI Innovations Methodology

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

1. Mobile Clinics: Implement mobile clinics that can travel to rural areas, especially during the lean season when access to healthcare is limited. These clinics can provide antenatal care services, including check-ups, vaccinations, and health education.

2. Community Health Workers: Train and deploy community health workers who can provide basic maternal healthcare services, such as prenatal check-ups, health education, and referrals to healthcare facilities when necessary. These workers can bridge the gap between communities and healthcare facilities, improving access to care.

3. Telemedicine: Utilize telemedicine technologies to connect pregnant women in rural areas with healthcare professionals. Through video consultations, healthcare providers can assess the health of pregnant women, provide advice, and monitor their progress remotely. This can help overcome geographical barriers and improve access to specialized care.

4. Health Education Programs: Develop and implement health education programs that focus on maternal health and nutrition. These programs can be delivered through community workshops, radio broadcasts, or mobile phone applications. By increasing knowledge and awareness, women can make informed decisions about their health and seek appropriate care.

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

1. Define the target population: Identify the specific population group that will be impacted by the recommendations, such as pregnant women in rural areas of South Africa.

2. Collect baseline data: Gather data on the current state of access to maternal health services in the target population. This can include information on ANC attendance rates, dietary intake, birth size, and other relevant indicators.

3. Develop a simulation model: Create a mathematical or statistical model that simulates the impact of the recommendations on the target population. This model should consider factors such as the number of mobile clinics or community health workers deployed, the reach of telemedicine services, and the effectiveness of health education programs.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations. Vary the parameters of the model to explore different scenarios and outcomes.

5. Analyze results: Analyze the results of the simulations to determine the potential impact of the recommendations on improving access to maternal health. This can include assessing changes in ANC attendance rates, dietary intake, birth size, and other relevant indicators.

6. Validate and refine the model: Validate the simulation model by comparing the simulated results with real-world data, if available. Refine the model based on feedback and additional data to improve its accuracy and reliability.

7. Communicate findings: Present the findings of the simulation study to stakeholders, policymakers, and healthcare professionals. Highlight the potential benefits of the recommendations and provide evidence-based recommendations for implementation.

By following this methodology, stakeholders can gain insights into the potential impact of innovations and make informed decisions about improving access to maternal health.

Author

Dima Health

Statistics:

Citations: 4

Identifiers:

DOI: 10.1371/journal.pone.0222888

Research Areas:

Community Interventions, Disparities, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Technology and Innovations

Study Countries:

South Africa

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female