Maternal iron-deficiency is associated with premature birth and higher birth weight despite routine antenatal iron supplementation in an urban South African setting: The NuPED prospective study

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

Interpretation

Background Recent studies are suggesting a U-shaped relationship between antenatal iron exposure and birth outcomes. Little is known about the iron status and associated birth outcomes of pregnant women in South Africa. Our aim was to assess iron status at early, mid- and late pregnancy, and to determine associations with gestational age and birth weight in women in Johannesburg, South Africa. Methods In this prospective study of 250 pregnant women, we measured haemoglobin, biomarkers of iron status and inflammation at <18, 22 and 36 weeks of gestation, plus birth weight and gestational age at delivery. Associations of anaemia and iron status with birth outcomes were determined using regression models adjusted for confounders. Results At enrolment, the prevalence of anaemia, iron depletion (ID) and iron deficiency erythropoiesis (IDE) was 29%, 15% and 15%, respectively, and increased significantly with pregnancy progression. Anaemia and ID at 22 weeks, as well as IDE at 36 weeks were associated with higher birth weight (β = 135.4; 95% CI: 4.8, 266.1 and β = 205.4; 95% CI: 45.6, 365.1 and β = 178.0; 95% CI: 47.3, 308.7, respectively). Women in the lowest ferritin quartile at 22 weeks gave birth to babies weighing 312 g (95% CI: 94.8, 528.8) more than those in the highest quartile. In contrast, IDE at 22 weeks was associated with a higher risk for premature birth (OR: 3.57, 95% CI: 1.24, 10.34) and women in lower haemoglobin quartiles at <18 weeks had a shorter gestation by 7 days (β = -6.9, 95% CI: -13.3, -0.6) compared to those in the highest quartile. Conclusion Anaemia, ID and IDE prevalence increased during pregnancy despite routine iron supplementation. ID and anaemia at mid-pregnancy were associated with higher birth weight, while IDE was associated with premature birth. These results suggest that current antenatal screening and supplementation practices in South Africa need to be revisited. This study formed part of the Nutrition during Pregnancy and Early Development (NuPED) study, which is a prospective study conducted in South Africa’s largest city, Johannesburg. The NuPED study protocol has been published previously [14]. Briefly, generally healthy pregnant women were recruited from primary healthcare clinics in Johannesburg between March 2016 and November 2017. Women were eligible for inclusion if they were aged 18–39 years, <18 weeks of gestation with singleton pregnancies, proficient in local languages, born in South Africa or neighbouring countries, and if they have been residing in Johannesburg for at least 12 months. Women were excluded if they reported using illicit drugs, were smoking, or had been diagnosed with a non-communicable disease (namely diabetes, renal disease, high cholesterol, and hypertension), an infectious disease (namely tuberculosis and hepatitis), or a serious illness (namely cancer, lupus or psychosis). Due to South Africa’s high prevalence of HIV infection (36% of women aged 30–34 years [15]), women who were HIV positive were included in the study in order for it to be a better representation of the general population. The volunteering women who agreed to participate were followed-up at the antenatal clinic of an academic hospital until June 2018. Data were collected at early pregnancy (<18 weeks of gestation), mid-pregnancy (±22 weeks), late pregnancy (±36 weeks) and at birth. The primary outcome measures were birth weight and gestational age at birth. At birth, four trained study nurses obtained neonatal weight (to the nearest 10g) using calibrated digital infant scales within 12 hours of birth [16]. In case the study nurse could not obtain the birth weight herself, it was obtained from the medical record (measured using the same calibrated scales). Low birth weight (LBW) was defined as birth weight <2500 g [17]. Date and time of birth were recorded from maternal records. Women who delivered elsewhere were followed-up telephonically to obtain baby’s date of birth and sex of the baby. Gestational age at birth was calculated in days using gestational age determined at the first visit, which was before 18 weeks gestation (minimum -maximum range: 6–17 weeks), by means of foetal ultrasonography examination using international recommendations [18]. Preterm birth was defined as birth <37 + 0 weeks of gestation (259 days) [19]. Maternal dietary intake data were obtained at the first visit (<18 weeks of gestation) by means of an interviewer administered quantified food frequency questionnaire (QFFQ) using standardised probing questions [20]. The QFFQ was validated for a previous South African study [21], and its reproducibility was proven in similar study populations [22,23]. Women were asked according to the ~140 food items listed in the QFFQ, cooking methods, the type/brand, frequency and the amount of all food and beverages consumed in the past four weeks. To assist in portion size quantification, standard measuring equipment, two- and three-dimensional food models and common size containers (e.g. cups, bowls and glasses) were used. Three registered dietitians/nutritionists converted reported intakes to grams per week per food item using the Condensed Food Composition Tables for South Africa [24] and the South African Medical Research Council (SAMRC) Food Quantities Manual [25]. Analyses were done by the SAMRC by linking dietary intake data to the most recent food composition database to determine total daily dietary iron intake levels. The database includes the iron content values of fortified foods as per the food fortification programme. The Estimated Average Requirement (EAR) cut-point method was used to determine the proportion of subjects with intake below the EAR, indicative of inadequate intake of iron in this population. Supplement use was determined from participants’ daily recorded supplement use (yes/no) on a supplied calendar from enrolment until birth. In addition, at each visit, the women were asked the type/brand, frequency and the amount of all dietary supplements used in the past week, taking into consideration supplementation supplied as part of routine care as well as store bought supplements. From these data, average daily iron intake from routine supplements and total supplements during pregnancy were calculated. Percentage compliance with routine supplementation was calculated as total reported routine supplemented iron intake divided by total routine iron supplied X 100. Routine iron supplementation included 55 mg elemental iron per day provided as 170 mg dried ferrous sulphate. Maternal venous blood was drawn into labelled EDTA-coated and serum evacuated tubes at each visit during pregnancy. Haemoglobin concentrations were determined in whole blood (20μL) using calibrated HemoCue haemoglobin meters (Hb 201+, Ängelholm, Sweden). Haemoglobin values were adjusted for altitude as Johannesburg is located at 1753 meters above sea level [1]. Anaemia was defined as haemoglobin <11 g/dL at <18 weeks of gestation and haemoglobin <10.5 g/dL for mid- and late pregnancy based on cut-offs per trimester [26,27]. In addition, for the purpose of comparability, the prevalence of anaemia is reported according to the WHO [1] haemoglobin cut-off (<11 g/dL) throughout pregnancy. In cases where severe anaemia (haemoglobin <7 g/dL) was detected, the women were referred to the medical doctor on site and treated according to maternity care guidelines [11]. The treatment entailed higher doses of oral iron supplementation and these cases were therefore retained in analyses. In this study no women received parenteral iron therapy or blood transfusion. Serum was separated within 1h after blood draw and stored at -20°C for a maximum of 14 days until transportation for storage at -80°C until analysis. Ferritin and soluble transferrin receptor (sTfR) concentrations were determined using the Q-Plex Human Micronutrient Array (7-plex, Quansys Bioscience, Logan, UT, USA) [28]. This fully quantitative chemiluminescent multiplex assay also includes the acute phase proteins C-reactive protein (CRP) and α1-acid glycoprotein (AGP). Ferritin concentrations were adjusted for inflammation using the correction factors recommended by Thurnham et al. [29]. Iron depletion (ID) was defined as adjusted ferritin <15 μg/L [30]. Iron deficient erythropoiesis (IDE) was defined as sTfR >8.3 mg/L [31]. Iron deficiency anaemia (IDA) was defined as ferritin <15 μg/L plus haemoglobin <11 g/dL [1]. Socio-economic and -demographic data, including maternal age and living standards measurements (reflective of socio-economic status) [32], were collected at the first visit early in pregnancy by means of an interviewer-administered questionnaire. Maternal anthropometric measurements (height and weight) were obtained using standardised methods from the International Society for the Advancement of Kinanthropometry [33] at each study visit. To determine body mass index (BMI) weight (kg) was divided by height (m) squared. An obstetrician conducted foetal ultrasonography examination to confirm gestational age and singleton pregnancy at the first visit [34,35]. A 2-hour 75 g oral glucose tolerance test was performed between 24 and 28 weeks of gestation using standard procedures [36]. Medical files were inspected to obtain data on maternal medical history, including parity, HIV status, mode of delivery, labour induction, as well as sex of the baby. During analyses, women were considered HIV positive irrespective of date of HIV contraction (prior to or during pregnancy). Sample size calculation was done using the G*Power 3.1.9.2 statistical programme [37]. The calculation was based on multiple linear regression analysis (fixed model, single regression coefficient); a small effect size F2 of 0.05; probability of error (alpha) of 5%; a power of 80% and 10 predictors with birth weight as outcome. The result indicated a required sample size of 196 pregnant women. Considering an attrition rate of 25%, a minimum of 245 women were required. The sample size for this study was 250. Data processing and statistical analysis of data were performed using SPSS version 25 (SPSS Inc, Chicago, IL, USA). Raw data were captured in Microsoft Access (Microsoft Corporation, Washington, USA) and 20% of all captured data were randomly checked for correctness. Dietary data were captured in Microsoft Excel (Microsoft Corporation, Washington, USA) and all electronic entries were double checked for the correct food code and a realistic amount captured. Data were tested for outliers and normality by means of Q-Q plots, histograms and Shapiro-Wilk test. Normally distributed data are expressed as means ± SD; non-normally distributed data are expressed as medians (25th percentile - 75th percentile), except in the second results table which displays medians with minimum–maximum ranges. Descriptive statistics were conducted to describe iron intake at early pregnancy. To examine the longitudinal trajectory of the iron status parameters with pregnancy progression, median concentrations were determined at each visit. Univariable analyses per outcome were performed using Mann-Whitney U-test for continuous variables and Chi-square test for categorical variables. To test for significance of change in haematological biomarkers (haemoglobin, ferritin, sTfR, CRP and AGP) over time we used the 2-tailed paired t test. For the significance of change in proportions for the conditions (anaemia, ID, IDE, IDA and inflammation) over time we used the McNemar test. Next we used logistic regression analyses to investigate the relationship between the exposure (haemoglobin, ferritin, sTfR) and outcome variables (low birth weight and preterm birth) as binary outcomes with odds ratios (OR) and 95% confidence intervals (CI). Multiple linear regression analyses were conducted for continuous outcome variables (birth weight in grams and gestational age at birth in days). The β coefficient was reported with 95% CIs. In both regression analyses, 3 models were applied and different sets of covariates for the two outcome variables. For birth weight, model 1 adjusted for maternal age, gestational age at birth and sex of the baby. Model 2 included the covariates of model 1 plus parity and socio-economic status. Model 3 included the covariates of models 1 and 2 plus HIV status, maternal BMI at enrolment and glucose tolerance. For gestational age at birth, model 1 adjusted for maternal age, baby sex and delivery intervention (induction or caesarean section). Model 2 adjusted additionally for parity and socio-economic status. Model 3 adjusted in addition to models 1 and 2 for HIV status, maternal BMI at enrolment and glucose tolerance. Lastly, univariate comparisons were done between quartiles of each iron biomarker adjusted for the same covariates as with the regression analyses. P values of <0.05 were considered significant. During recruitment, an informed consent form was supplied to potentially eligible women who were interested in being part of the study. Written informed consent was obtained at the first visit from all the women before data collection. This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by both the Human Research Ethics Committees of the North-West University, Potchefstroom (NWU-00186-15-A1 and NWU-00049-16-A1) and the University of the Witwatersrand, Johannesburg (M150968 and M161045). The Gauteng Health Department, City of Johannesburg District Research Committee and Clinical Manager of Rahima Moosa Mother and Child Hospital gave permission to conduct research at the indicated clinical setting.

AI Digest

Study Justification:

This study aimed to assess the iron status of pregnant women in Johannesburg, South Africa, and determine its association with gestational age and birth weight. The study is important because recent research suggests a U-shaped relationship between antenatal iron exposure and birth outcomes. However, little is known about iron status and its impact on birth outcomes in South Africa. Understanding these associations can help inform antenatal screening and supplementation practices in the country.

Study Highlights:

– The study included 250 pregnant women in Johannesburg, South Africa.
– Iron status and inflammation biomarkers were measured at different stages of pregnancy, along with birth weight and gestational age at delivery.
– The prevalence of anaemia, iron depletion, and iron deficiency erythropoiesis increased significantly with pregnancy progression.
– Anaemia and iron depletion at 22 weeks, as well as iron deficiency erythropoiesis at 36 weeks, were associated with higher birth weight.
– Women in the lowest ferritin quartile at 22 weeks gave birth to babies weighing 312 g more than those in the highest quartile.
– Iron deficiency erythropoiesis at 22 weeks was associated with a higher risk of premature birth.
– Women in lower haemoglobin quartiles at

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is relatively strong, but there are some areas for improvement. The study design is prospective and includes a sample size calculation, which adds to the validity of the findings. The study also includes multiple measurements of iron status throughout pregnancy, which strengthens the evidence. However, the abstract does not provide information on the statistical significance of the associations between iron status and birth outcomes, which would further support the findings. Additionally, the abstract does not mention any potential limitations of the study, such as confounding factors or biases. To improve the evidence, it would be helpful to include the statistical significance of the associations and discuss potential limitations in the abstract.

Abstract

This study formed part of the Nutrition during Pregnancy and Early Development (NuPED) study, which is a prospective study conducted in South Africa’s largest city, Johannesburg. The NuPED study protocol has been published previously [14]. Briefly, generally healthy pregnant women were recruited from primary healthcare clinics in Johannesburg between March 2016 and November 2017. Women were eligible for inclusion if they were aged 18–39 years, <18 weeks of gestation with singleton pregnancies, proficient in local languages, born in South Africa or neighbouring countries, and if they have been residing in Johannesburg for at least 12 months. Women were excluded if they reported using illicit drugs, were smoking, or had been diagnosed with a non-communicable disease (namely diabetes, renal disease, high cholesterol, and hypertension), an infectious disease (namely tuberculosis and hepatitis), or a serious illness (namely cancer, lupus or psychosis). Due to South Africa’s high prevalence of HIV infection (36% of women aged 30–34 years [15]), women who were HIV positive were included in the study in order for it to be a better representation of the general population. The volunteering women who agreed to participate were followed-up at the antenatal clinic of an academic hospital until June 2018. Data were collected at early pregnancy (<18 weeks of gestation), mid-pregnancy (±22 weeks), late pregnancy (±36 weeks) and at birth. The primary outcome measures were birth weight and gestational age at birth. At birth, four trained study nurses obtained neonatal weight (to the nearest 10g) using calibrated digital infant scales within 12 hours of birth [16]. In case the study nurse could not obtain the birth weight herself, it was obtained from the medical record (measured using the same calibrated scales). Low birth weight (LBW) was defined as birth weight <2500 g [17]. Date and time of birth were recorded from maternal records. Women who delivered elsewhere were followed-up telephonically to obtain baby’s date of birth and sex of the baby. Gestational age at birth was calculated in days using gestational age determined at the first visit, which was before 18 weeks gestation (minimum -maximum range: 6–17 weeks), by means of foetal ultrasonography examination using international recommendations [18]. Preterm birth was defined as birth <37 + 0 weeks of gestation (259 days) [19]. Maternal dietary intake data were obtained at the first visit (<18 weeks of gestation) by means of an interviewer administered quantified food frequency questionnaire (QFFQ) using standardised probing questions [20]. The QFFQ was validated for a previous South African study [21], and its reproducibility was proven in similar study populations [22,23]. Women were asked according to the ~140 food items listed in the QFFQ, cooking methods, the type/brand, frequency and the amount of all food and beverages consumed in the past four weeks. To assist in portion size quantification, standard measuring equipment, two- and three-dimensional food models and common size containers (e.g. cups, bowls and glasses) were used. Three registered dietitians/nutritionists converted reported intakes to grams per week per food item using the Condensed Food Composition Tables for South Africa [24] and the South African Medical Research Council (SAMRC) Food Quantities Manual [25]. Analyses were done by the SAMRC by linking dietary intake data to the most recent food composition database to determine total daily dietary iron intake levels. The database includes the iron content values of fortified foods as per the food fortification programme. The Estimated Average Requirement (EAR) cut-point method was used to determine the proportion of subjects with intake below the EAR, indicative of inadequate intake of iron in this population. Supplement use was determined from participants’ daily recorded supplement use (yes/no) on a supplied calendar from enrolment until birth. In addition, at each visit, the women were asked the type/brand, frequency and the amount of all dietary supplements used in the past week, taking into consideration supplementation supplied as part of routine care as well as store bought supplements. From these data, average daily iron intake from routine supplements and total supplements during pregnancy were calculated. Percentage compliance with routine supplementation was calculated as total reported routine supplemented iron intake divided by total routine iron supplied X 100. Routine iron supplementation included 55 mg elemental iron per day provided as 170 mg dried ferrous sulphate. Maternal venous blood was drawn into labelled EDTA-coated and serum evacuated tubes at each visit during pregnancy. Haemoglobin concentrations were determined in whole blood (20μL) using calibrated HemoCue haemoglobin meters (Hb 201+, Ängelholm, Sweden). Haemoglobin values were adjusted for altitude as Johannesburg is located at 1753 meters above sea level [1]. Anaemia was defined as haemoglobin <11 g/dL at <18 weeks of gestation and haemoglobin <10.5 g/dL for mid- and late pregnancy based on cut-offs per trimester [26,27]. In addition, for the purpose of comparability, the prevalence of anaemia is reported according to the WHO [1] haemoglobin cut-off (<11 g/dL) throughout pregnancy. In cases where severe anaemia (haemoglobin <7 g/dL) was detected, the women were referred to the medical doctor on site and treated according to maternity care guidelines [11]. The treatment entailed higher doses of oral iron supplementation and these cases were therefore retained in analyses. In this study no women received parenteral iron therapy or blood transfusion. Serum was separated within 1h after blood draw and stored at -20°C for a maximum of 14 days until transportation for storage at -80°C until analysis. Ferritin and soluble transferrin receptor (sTfR) concentrations were determined using the Q-Plex Human Micronutrient Array (7-plex, Quansys Bioscience, Logan, UT, USA) [28]. This fully quantitative chemiluminescent multiplex assay also includes the acute phase proteins C-reactive protein (CRP) and α1-acid glycoprotein (AGP). Ferritin concentrations were adjusted for inflammation using the correction factors recommended by Thurnham et al. [29]. Iron depletion (ID) was defined as adjusted ferritin 8.3 mg/L [31]. Iron deficiency anaemia (IDA) was defined as ferritin <15 μg/L plus haemoglobin <11 g/dL [1]. Socio-economic and -demographic data, including maternal age and living standards measurements (reflective of socio-economic status) [32], were collected at the first visit early in pregnancy by means of an interviewer-administered questionnaire. Maternal anthropometric measurements (height and weight) were obtained using standardised methods from the International Society for the Advancement of Kinanthropometry [33] at each study visit. To determine body mass index (BMI) weight (kg) was divided by height (m) squared. An obstetrician conducted foetal ultrasonography examination to confirm gestational age and singleton pregnancy at the first visit [34,35]. A 2-hour 75 g oral glucose tolerance test was performed between 24 and 28 weeks of gestation using standard procedures [36]. Medical files were inspected to obtain data on maternal medical history, including parity, HIV status, mode of delivery, labour induction, as well as sex of the baby. During analyses, women were considered HIV positive irrespective of date of HIV contraction (prior to or during pregnancy). Sample size calculation was done using the G*Power 3.1.9.2 statistical programme [37]. The calculation was based on multiple linear regression analysis (fixed model, single regression coefficient); a small effect size F2 of 0.05; probability of error (alpha) of 5%; a power of 80% and 10 predictors with birth weight as outcome. The result indicated a required sample size of 196 pregnant women. Considering an attrition rate of 25%, a minimum of 245 women were required. The sample size for this study was 250. Data processing and statistical analysis of data were performed using SPSS version 25 (SPSS Inc, Chicago, IL, USA). Raw data were captured in Microsoft Access (Microsoft Corporation, Washington, USA) and 20% of all captured data were randomly checked for correctness. Dietary data were captured in Microsoft Excel (Microsoft Corporation, Washington, USA) and all electronic entries were double checked for the correct food code and a realistic amount captured. Data were tested for outliers and normality by means of Q-Q plots, histograms and Shapiro-Wilk test. Normally distributed data are expressed as means ± SD; non-normally distributed data are expressed as medians (25th percentile – 75th percentile), except in the second results table which displays medians with minimum–maximum ranges. Descriptive statistics were conducted to describe iron intake at early pregnancy. To examine the longitudinal trajectory of the iron status parameters with pregnancy progression, median concentrations were determined at each visit. Univariable analyses per outcome were performed using Mann-Whitney U-test for continuous variables and Chi-square test for categorical variables. To test for significance of change in haematological biomarkers (haemoglobin, ferritin, sTfR, CRP and AGP) over time we used the 2-tailed paired t test. For the significance of change in proportions for the conditions (anaemia, ID, IDE, IDA and inflammation) over time we used the McNemar test. Next we used logistic regression analyses to investigate the relationship between the exposure (haemoglobin, ferritin, sTfR) and outcome variables (low birth weight and preterm birth) as binary outcomes with odds ratios (OR) and 95% confidence intervals (CI). Multiple linear regression analyses were conducted for continuous outcome variables (birth weight in grams and gestational age at birth in days). The β coefficient was reported with 95% CIs. In both regression analyses, 3 models were applied and different sets of covariates for the two outcome variables. For birth weight, model 1 adjusted for maternal age, gestational age at birth and sex of the baby. Model 2 included the covariates of model 1 plus parity and socio-economic status. Model 3 included the covariates of models 1 and 2 plus HIV status, maternal BMI at enrolment and glucose tolerance. For gestational age at birth, model 1 adjusted for maternal age, baby sex and delivery intervention (induction or caesarean section). Model 2 adjusted additionally for parity and socio-economic status. Model 3 adjusted in addition to models 1 and 2 for HIV status, maternal BMI at enrolment and glucose tolerance. Lastly, univariate comparisons were done between quartiles of each iron biomarker adjusted for the same covariates as with the regression analyses. P values of <0.05 were considered significant. During recruitment, an informed consent form was supplied to potentially eligible women who were interested in being part of the study. Written informed consent was obtained at the first visit from all the women before data collection. This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by both the Human Research Ethics Committees of the North-West University, Potchefstroom (NWU-00186-15-A1 and NWU-00049-16-A1) and the University of the Witwatersrand, Johannesburg (M150968 and M161045). The Gauteng Health Department, City of Johannesburg District Research Committee and Clinical Manager of Rahima Moosa Mother and Child Hospital gave permission to conduct research at the indicated clinical setting.

Materials

https://efashare.b-cdn.net/materials/c3b8c0720bdecee929c1cc0695312a137ab684749efade4b2cd2ec7e2b6de976/pone.0221299.s001.docx

Innovations

Based on the provided information, 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 access to information and resources related to maternal health. These apps can provide educational content, appointment reminders, nutrition guidance, and access to healthcare professionals through telemedicine.

2. Telemedicine Services: Implement telemedicine services that allow pregnant women to consult with healthcare professionals remotely. This can help overcome geographical barriers and provide access to prenatal care, monitoring, and consultations, especially for women in rural or underserved areas.

3. Community Health Workers: Train and deploy community health workers who can provide maternal health education, prenatal care, and support to pregnant women in their communities. These workers can bridge the gap between healthcare facilities and pregnant women, ensuring that they receive the necessary care and support.

4. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to access maternal health services. These vouchers can cover the cost of prenatal care, delivery, postnatal care, and essential medications, making healthcare more affordable and accessible.

5. Maternal Health Clinics: Establish dedicated maternal health clinics that provide comprehensive prenatal care, including regular check-ups, screenings, and counseling services. These clinics can be equipped with necessary medical equipment and staffed by trained healthcare professionals specializing in maternal health.

6. Maternal Health Education Programs: Develop and implement educational programs that focus on maternal health, including nutrition, prenatal care, breastfeeding, and postnatal care. These programs can be conducted in community settings, schools, and healthcare facilities to reach a wider audience.

7. 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 supplement public healthcare services and expand their reach.

8. Maternal Health Hotlines: Establish toll-free hotlines staffed by trained healthcare professionals who can provide information, guidance, and support to pregnant women. These hotlines can be available 24/7 and serve as a resource for addressing concerns, answering questions, and providing immediate assistance.

9. Maternal Health Awareness Campaigns: Launch public awareness campaigns to promote the importance of maternal health and encourage pregnant women to seek early and regular prenatal care. These campaigns can utilize various media channels, including television, radio, social media, and community outreach programs.

10. Maternal Health Monitoring Systems: Implement digital health solutions that enable real-time monitoring of maternal health indicators, such as blood pressure, weight, and fetal movements. These systems can provide early detection of potential complications and enable timely interventions to ensure the well-being of both the mother and the baby.

It is important to note that the specific implementation of these innovations would require careful planning, collaboration with relevant stakeholders, and consideration of local context and resources.

AI Innovations Description

Based on the study mentioned, here is a recommendation that can be developed into an innovation to improve access to maternal health:

1. Implement targeted iron supplementation programs: The study found that despite routine iron supplementation, the prevalence of iron deficiency and anemia increased during pregnancy. To address this issue, healthcare providers can consider implementing targeted iron supplementation programs that take into account individual iron status. This can be done through regular monitoring of iron levels and adjusting supplementation accordingly. By providing personalized iron supplementation, pregnant women can receive the appropriate amount of iron needed to prevent iron deficiency and anemia, leading to improved birth outcomes.

Additionally, the innovation can include the following strategies:

2. Improve antenatal screening practices: The study highlighted the need to revisit current antenatal screening practices in South Africa. Healthcare providers can enhance screening methods to accurately identify iron deficiency and anemia in pregnant women. This can involve using more sensitive biomarkers and incorporating regular screening throughout pregnancy to detect changes in iron status.

3. Enhance education and awareness: To improve access to maternal health, it is crucial to educate pregnant women about the importance of iron supplementation and the potential risks associated with iron deficiency and anemia. Healthcare providers can develop educational materials and conduct counseling sessions to raise awareness among pregnant women about the benefits of iron supplementation and the impact it can have on birth outcomes.

4. Strengthen healthcare infrastructure: To ensure effective implementation of targeted iron supplementation programs, it is essential to strengthen healthcare infrastructure. This can involve training healthcare providers on the latest guidelines for iron supplementation, improving access to iron supplements, and ensuring adequate monitoring and follow-up of pregnant women. Additionally, healthcare facilities can establish partnerships with community organizations to reach pregnant women in remote or underserved areas.

By implementing these recommendations, it is possible to develop an innovation that improves access to maternal health by addressing iron deficiency and anemia during pregnancy. This can lead to better birth outcomes and overall maternal well-being.

AI Innovations Methodology

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

1. Increase awareness and education: Implement public health campaigns and educational programs to raise awareness about the importance of maternal health, including the role of iron supplementation and its impact on birth outcomes. This can be done through various channels such as community health centers, antenatal clinics, and social media platforms.

2. Improve antenatal screening: Enhance the screening process for iron deficiency and anemia during pregnancy. This can involve regular monitoring of iron status at different stages of pregnancy, including early, mid, and late pregnancy, to identify women at risk and provide appropriate interventions.

3. Tailored iron supplementation: Develop personalized iron supplementation plans based on individual iron status and needs. This can involve adjusting the dosage and duration of iron supplementation based on the specific requirements of each pregnant woman, taking into account factors such as iron depletion and iron deficiency erythropoiesis.

4. Strengthen healthcare infrastructure: Invest in improving healthcare infrastructure, particularly in underserved areas, to ensure access to quality antenatal care. This can include increasing the number of healthcare facilities, trained healthcare professionals, and availability of essential resources for maternal health.

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

1. Define the target population: Identify the specific population group for which the recommendations are intended, such as pregnant women in a particular region or country.

2. Collect baseline data: Gather relevant data on the current status of maternal health in the target population, including information on iron deficiency prevalence, birth outcomes, and access to antenatal care.

3. Develop a simulation model: Create a mathematical or statistical model that incorporates the key variables and relationships relevant to the recommendations. This model should consider factors such as iron supplementation rates, antenatal screening coverage, and healthcare infrastructure.

4. Input data and parameters: Input the collected baseline data into the simulation model, along with the parameters related to the recommendations. This can include data on the effectiveness of awareness campaigns, the impact of improved screening, and the expected outcomes of tailored iron supplementation.

5. Run simulations: Use the simulation model to run multiple scenarios that reflect the implementation of the recommendations. Vary the parameters and assumptions to assess the potential impact on access to maternal health, such as changes in iron deficiency prevalence, birth outcomes, and healthcare utilization.

6. Analyze results: Analyze the simulation results to evaluate the potential impact of the recommendations on improving access to maternal health. This can involve comparing the outcomes of different scenarios and identifying the most effective strategies.

7. 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.

8. Communicate findings: Present the findings of the simulation study in a clear and concise manner, highlighting the potential benefits of the recommendations for improving access to maternal health. This can be done through reports, presentations, or other communication channels to inform policymakers, healthcare providers, and other stakeholders.

Author

Dima Health

Statistics:

Citations: 14

Identifiers:

DOI: 10.1371/journal.pone.0221299

Research Areas:

Cancer, Community Interventions, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health

Study Countries:

South Africa

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Female