The effect of maternal and child factors on stunting, wasting and underweight among preschool children in Northern Ghana

BMC Nutrition, Volume 3, No. 1, Year 2017

Ukuhunyushwa

Background: Undernutrition among preschool children in Northern region is the highest in Ghana. However, there is scarcity of data on the factors that determine undernutrition in these children. This study investigated the effect of maternal and child factors on undernutrition among preschool children in Northern Ghana. Methods: This study was a community based analytical cross-sectional survey on a sample of 425 mother- child pairs drawn from 25 clusters. A semi- structured questionnaire was used to collect data on maternal and child socio-demographic characteristics, feeding practices and anthropometry. Anthropometric indices of Height-for-age Z-scores (HAZ), Weight-for-height Z-scores (WHZ) and Weight-for – age Z-scores (WAZ) were used to classify child stunting, wasting and underweight respectively. Bivariate and multivariate analyses were performed to determine associations between explanatory variables and undernutrition. Results: The prevalence of stunting, wasting and underweight were 28.2, 9.9 and 19.3% respectively. Multiple logistic regression analysis showed that, the odds of stunting was higher among male children [AOR = 1.99; 95% CI (1.26-3.13); p = 0.003], children of mothers less than 150 cm in height [AOR = 3.87; 95% CI (1.34-11.20); p = 0.01], mothers 155-159 cm tall [AOR = 2.21; 95% CI (1.34-3.66); p = 0.002], and older children aged 12-23 months [AOR 9.81; 95% CI (2.85-33.76); p < 0.001]. Wasting was significantly higher among male children [AOR = 2.40; 95% CI (1.189-4.844); p = 0.015], consumption of less than four food groups [AOR = 3.733; 95% CI (1.889-7.376); p < 0.001] and among children of underweight mothers [AOR = 3.897; 95% CI (1.404-10.820); p = 0.009]. Male children [AOR = 2.685; 95% CI (1.205-5.98); p = 0.016] and having low birth weight [AOR = 3.778; 95% CI (1.440-9.911); p < 0.001] were associated with higher odds of underweight in children. Conclusion: Maternal height associated negatively with stunting but not wasting. Factors that affect low height -for-age z-score (HAZ) may not necessarily be the same as stunting. Infant and child feeding practices as measured by dietary diversity score associated positively with weight-for-height Z-scores than length-for-age Z-scores of young children. Surprisingly, consumption of some specific food groups including, animal source foods, legumes, staples and eggs were associated with lower HAZ but with increased likelihood of higher WHZ among children 6-59 months. The study was conducted in the Central Gonja District which is located at the south-western part of the Northern Region of Ghana with its administrative capital at Buipe. The District covers approximately 7555 km2 land area which represent 11.0% of the total land area of the region and shares boundary with the southern parts of Ghana. The district is divided into five sub- districts for administrative purposes with a total population of 87,877 with 49.9% as males and 50.1% as females. The population of the district is largely youthful with children aged 0–4 years forming the largest part representing 17.6% of the total population. The main economic activity of the people is agriculture (74.2%) involving crop production, livestock and fish farming. Some of the crops cultivated are maize, sorghum, millet, groundnut, cowpea, soy beans, yam, rice, as well as cassava. Fishing and livestock are considered supplementary activities to crop farming [12]. A community-based analytical cross-sectional design was used in this study. The target population was children under five years and their mothers/primary caregivers. In households with more than one eligible child, one child was chosen at random to participate in the study. An eligible child and its mother were all present at the point of data collection for inclusion. A sample size of 425 was determined on the assumption that the prevalence of stunting in the study district was unknown and so 50% was assumed with 5% marginal error and 95% confidence interval (CI) and a none response rate of 10%. The study used a multi-stage cluster sampling procedure which involved selecting communities and households. To make valid conclusions in a community- based cluster study, a minimum of 25 clusters is usually required [13, 14]. This study used this minimum number of clusters by randomly choosing 25 communities from a list of communities from four sub-districts across the Central Gonja district using Excel generated random numbers. The primary sampling units in selected clusters was households. With a required sample size of 425 and 25 clusters, the minimum sample required for each cluster was 17 households. Systematic sampling technique was used to select the required number of households in each cluster. A list of all households with an eligible child (a child aged 6–59 months) in a selected cluster was compiled and numbered in ascending order. The sampling interval for the cluster was then determined by dividing the total number of households 17. A number within the sampling interval was randomly selected to get the first household to visit. Subsequently, households to visit were identified by adding the sampling interval to the number selected. This was done until the 17 household required from a cluster was obtained. In households with more than one eligible child mother pairs, one was selected for interview using simple random sampling technique. Face-to-face interviews were conducted in homes using pre-tested and validated structured questionnaires to collect representative data on socio-demographic characteristics, illness history, nutritional status, health and nutrition behaviors of mothers/caretakers. Information on the economic well-being of families (e.g. household wealth index) was also collected (see Additional fie 1 for study questionnaire). Both potential proximal and distal determinants of stunting were investigated. The proximal determinants relate to biological functions of both mother and child or to specific maternal practices that influence food in-take, health, and caregiving. These included child’s gender, age in months, birth weight, birth interval, breastfeeding status, dietary intake, diarrhoea and fever in the last two weeks, mother’s nutritional status, mother’s age, maternal health seeking behaviors such as prenatal and postnatal care for mothers, and caring practices for children. The distal determinants are the resources necessary for achieving adequate food security, care, and a healthy environment. The distal determinants assessed included household socioeconomic status, maternal education, access to safe water and access to sanitary toilet facilities. Briefly, a description of main independent variables is as follows: The exact age of the child was recorded in months, based on date of birth information contained in child health records booklets, birth certificates and baptismal cards. The weight of children was assessed with Seca Electronic UNISCALE (SECA 890). Weight was measured to the nearest gram. Weighing scale was calibrated to zero before taking every measurement. The children were weighed with minimal clothing, without shoes and with minimal movement on the scale. Tare weighing was performed for children who could not stand well on the scale to take a meaningful measure. The length measurements of children less than two years of age (i.e. up to and including 23 months) were taken following WHO standard procedures. A specialized wooden device (that is, an infantometer) was used to take the measurement in a supine position. This was done by placing the child on its back between the slanting sides with the head placed gently against the fixed top end. The knees were gently pushed down by a helper while the foot-piece is moved toward the child until it presses softly against the soles of the child’s feet and the feet are at right angles to the legs. The length was then read to the nearest 0.1 cm. For children who were more than two years, height was measured using the infantometer in standing position. Anthropometric data were then transformed into Z-scores for height-for-age Z-score (HAZ), weight-for-length Z-score (WLZ) and weight-for-age Z-score (WAZ). Categorical variables were stunting, wasting and underweight which reflect HAZ, WAZ and WLZ below −2 standard deviations below the population median. BMI is a simple index of weight-to-height commonly used to classify underweight, overweight and obesity in adults. It is defined as the weight in kilograms divided by the square of the height in metres (kg/m2). Weight of mothers was measured with a Seca electronic weighing scale to the nearest 0.1 kg. Height of mothers was measured with a Seca microtoise to the nearest 0.1 cm. This was diagnosed by placing both thumbs on the upper side of the feet and applying pressure for about three seconds. Edema was considered to be present if a skin depression remained on both feet after the pressure was released. Overall dietary quality was assessed using the dietary diversity score. The WHO validated 7-item food groups frequency questionnaire (FFQ) was used to quantify child dietary intake [15]. Food that was fed to the children was assessed using a structured 24-h food frequency questionnaire. Mothers were asked to recall the number of times, in the past 24 h, a child had received anything to eat, aside from breast-milk, including meals and snacks. The dietary diversity score therefore ranged from 0 to 7 with minimum of 0 if none of the food groups is consumed to 7 if all the food groups are consumed. The WHO defined minimum dietary diversity as the proportion of children aged 6–23 months who received foods from at least four out of seven food groups [15, 16]. The seven food groups used in defining children’s minimum dietary diversity indicator are: (i) grains, roots and tubers; (ii) legumes and nuts; (iii) dairy products; (iv) flesh foods (meats/fish/poultry) (v) eggs (vi) vitamin A rich fruits and vegetables; and (vii) other fruits and vegetables. WHO Infant and young child feeding (IYCF) indicators [minimum dietary diversity (MDD), minimum meal frequency (MMF), minimum acceptable diet (MAD)] were the main dietary intake indicators for children. These indicators were measured by recall of food and liquid consumption during the previous day or night preceding survey as per WHO/UNICEF food frequency questionnaire (FFQ) [17]. Minimum meal frequency is the proportion of children who received complementary foods at least the minimum recommended number of times in the last 24 h. A child is judged to have taken ‘adequate number of meals if he/she received at least the minimum frequency for appropriate complementary feeding (that is, 2 times for 6–8 months and 3 times for 9–11 months, 3 times for children aged 12–23 months) in last 24 h. For non-breastfed children, the minimum meal frequency is 4. A household wealth index based on household assets and housing quality was used as a proxy indicator for socio-economic status (SES) of households. Absolute household wealth index was calculated from information collected on housing quality (floor, walls, and roof material), source of drinking water, type of toilet facility, the presence of electricity, type of cooking fuel, and ownership of modern household durable goods and livestock (e.g. bicycle, television, radio, motorcycle, sewing machine, telephone, cars, refrigerator, mattress, bed, computer and mobile phone) [18–21]. These facilities or durable goods are often regarded as modern goods that have been shown to reflect household wealth. A household of zero index score for example means that household had not a single modern good. The scores were thus added up to give the proxy household wealth index. The median score of 7 was used as the cut-off point to define low and high household wealth index. A number of measures were used to ensure that accurate and reliable data were collected and analyzed. These included extensive practical training of data enumerators, pre-testing of data collection tools and extensive monitoring of the field teams. Field supervisors provided on-the-spot assistance to interviewers. Data were checked for completeness and consistency by field supervisors in the field and during data entry in order to ensure good quality. Data analyses was performed with PASW/SPSS, version 21.0 for Windows using procedures in SPSS complex samples module for Windows. Design weights were added to perform weighted analysis. This module of SPSS takes into account the complex nature of the cluster sample design. The Z- scores for weight-for-age (WAZ), weight-for- height (WHZ), height-for-age (HAZ) and prevalence underweight, wasting and stunting were calculated using the 2006 World Health Organization (WHO) growth standards. WHO Anthro software was used to convert height, weight and age measurements to Height- for- age Z- scores (HAZ), weight- for –height Z- scores (WHZ) and weight-for – age (WAZ) which were used to classify stunting, wasting and underweight respectively. Child undernutrition was defined as Z- scores below −2 standard deviations below the median of the reference population. Bivariate and multivariate analyses were both performed to identify the determinants of stunting, wasting and underweight. Chi-square (χ 2) tests were performed to identify the predictors of stunting, wasting and underweight significant at p < 0.05. ANOVA was used to compare mean anthropometric Z-scores and selected predictors. The association between undernutrition and the independent variables was determined using binary logistic regression modeling. This test statistic was used because stunting, wasting and underweight were coded into two categories (that is: stunted and normal, wasted and normal, and underweight and normal). All of the potential predictors of stunting, wasting and underweight that were significant at p < 0.05 in bivariate analysis using Chi-square (χ 2) tests were included in the regression modeling. The association between selected factors and HAZ was determined using multiple linear regression modeling. This test statistic was used because the dependent factor (HAZ) is a continuous variable. Before testing for associations between independent variables and the dependent outcomes, the data were cleaned and outliers removed. Z-scores which were outside the WHO flags: WHZ −5 to 5; HAZ −6 to 6; and WAZ −6 to 5 were excluded from the data set. Permission to carry out this study was sought from the district health directorate and the study protocol was approved by the School of Allied Health Sciences, University for Development Studies, Ghana. Informed consent was verbally obtained from participant mothers after needed information and procedures were explained. Participation was voluntary and participants signed or thumb printed a statement of an informed consent before being interviewed.

I-AI Digest

Study Justification:

The study aimed to investigate the factors that contribute to undernutrition among preschool children in Northern Ghana. This is important because undernutrition is a significant public health issue in the region, with high prevalence rates. However, there is a lack of data on the specific factors that contribute to undernutrition in this population. Understanding these factors is crucial for developing effective interventions and policies to address undernutrition and improve the health and well-being of preschool children in Northern Ghana.

Study Highlights:

– The study found that the prevalence of stunting, wasting, and underweight among preschool children in Northern Ghana was 28.2%, 9.9%, and 19.3% respectively.
– Maternal and child factors were found to be associated with undernutrition. For example, male children, children of shorter mothers, and older children were more likely to be stunted. Male children, children of underweight mothers, and those with poor dietary diversity were more likely to be wasted. Male children and those with low birth weight were more likely to be underweight.
– The study also highlighted the complex relationship between dietary diversity and different anthropometric indices. Some specific food groups were associated with lower height-for-age Z-scores (indicating stunting), but with increased likelihood of higher weight-for-height Z-scores (indicating wasting) among children aged 6-59 months.

Study Recommendations:

Based on the findings, the study makes the following recommendations:

1. Interventions should focus on improving maternal and child nutrition, with a particular emphasis on addressing the factors associated with stunting, wasting, and underweight.
2. Strategies to improve dietary diversity and quality should be implemented, ensuring that children receive foods from a variety of food groups.
3. Health education programs should be developed to raise awareness among mothers and caregivers about the importance of proper nutrition and feeding practices for their children.
4. Efforts should be made to improve access to healthcare services, including prenatal and postnatal care, to support maternal and child health.

Key Role Players:

To address the recommendations, the following key role players are needed:

1. Government health agencies and policymakers: They play a crucial role in developing and implementing policies and programs to address undernutrition among preschool children.
2. Healthcare providers: They are responsible for delivering healthcare services, including prenatal and postnatal care, and providing nutrition counseling and support to mothers and caregivers.
3. Community leaders and organizations: They can help raise awareness about the importance of proper nutrition and feeding practices, and facilitate community-based interventions.
4. Non-governmental organizations (NGOs): They can provide support and resources for implementing nutrition programs and interventions.

Cost Items for Planning Recommendations:

While the actual cost will vary depending on the specific interventions and programs implemented, some key cost items to consider in planning the recommendations include:

1. Training and capacity building for healthcare providers and community workers.
2. Development and dissemination of educational materials and resources.
3. Implementation of nutrition programs, including provision of nutritious foods and supplements.
4. Monitoring and evaluation of interventions to assess their effectiveness and make necessary adjustments.
5. Coordination and collaboration among different stakeholders and organizations involved in addressing undernutrition.
Please note that the above cost items are general and may vary based on the context and specific needs of the region.

Amandla Obufakazi

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it presents the results of a community-based analytical cross-sectional survey with a sample size of 425 mother-child pairs. The study used bivariate and multivariate analyses to determine associations between explanatory variables and undernutrition. However, to improve the evidence, the abstract could provide more details on the methodology, such as the sampling technique used, data collection tools, and statistical analysis methods.

Abstract

The study was conducted in the Central Gonja District which is located at the south-western part of the Northern Region of Ghana with its administrative capital at Buipe. The District covers approximately 7555 km2 land area which represent 11.0% of the total land area of the region and shares boundary with the southern parts of Ghana. The district is divided into five sub- districts for administrative purposes with a total population of 87,877 with 49.9% as males and 50.1% as females. The population of the district is largely youthful with children aged 0–4 years forming the largest part representing 17.6% of the total population. The main economic activity of the people is agriculture (74.2%) involving crop production, livestock and fish farming. Some of the crops cultivated are maize, sorghum, millet, groundnut, cowpea, soy beans, yam, rice, as well as cassava. Fishing and livestock are considered supplementary activities to crop farming [12]. A community-based analytical cross-sectional design was used in this study. The target population was children under five years and their mothers/primary caregivers. In households with more than one eligible child, one child was chosen at random to participate in the study. An eligible child and its mother were all present at the point of data collection for inclusion. A sample size of 425 was determined on the assumption that the prevalence of stunting in the study district was unknown and so 50% was assumed with 5% marginal error and 95% confidence interval (CI) and a none response rate of 10%. The study used a multi-stage cluster sampling procedure which involved selecting communities and households. To make valid conclusions in a community- based cluster study, a minimum of 25 clusters is usually required [13, 14]. This study used this minimum number of clusters by randomly choosing 25 communities from a list of communities from four sub-districts across the Central Gonja district using Excel generated random numbers. The primary sampling units in selected clusters was households. With a required sample size of 425 and 25 clusters, the minimum sample required for each cluster was 17 households. Systematic sampling technique was used to select the required number of households in each cluster. A list of all households with an eligible child (a child aged 6–59 months) in a selected cluster was compiled and numbered in ascending order. The sampling interval for the cluster was then determined by dividing the total number of households 17. A number within the sampling interval was randomly selected to get the first household to visit. Subsequently, households to visit were identified by adding the sampling interval to the number selected. This was done until the 17 household required from a cluster was obtained. In households with more than one eligible child mother pairs, one was selected for interview using simple random sampling technique. Face-to-face interviews were conducted in homes using pre-tested and validated structured questionnaires to collect representative data on socio-demographic characteristics, illness history, nutritional status, health and nutrition behaviors of mothers/caretakers. Information on the economic well-being of families (e.g. household wealth index) was also collected (see Additional fie 1 for study questionnaire). Both potential proximal and distal determinants of stunting were investigated. The proximal determinants relate to biological functions of both mother and child or to specific maternal practices that influence food in-take, health, and caregiving. These included child’s gender, age in months, birth weight, birth interval, breastfeeding status, dietary intake, diarrhoea and fever in the last two weeks, mother’s nutritional status, mother’s age, maternal health seeking behaviors such as prenatal and postnatal care for mothers, and caring practices for children. The distal determinants are the resources necessary for achieving adequate food security, care, and a healthy environment. The distal determinants assessed included household socioeconomic status, maternal education, access to safe water and access to sanitary toilet facilities. Briefly, a description of main independent variables is as follows: The exact age of the child was recorded in months, based on date of birth information contained in child health records booklets, birth certificates and baptismal cards. The weight of children was assessed with Seca Electronic UNISCALE (SECA 890). Weight was measured to the nearest gram. Weighing scale was calibrated to zero before taking every measurement. The children were weighed with minimal clothing, without shoes and with minimal movement on the scale. Tare weighing was performed for children who could not stand well on the scale to take a meaningful measure. The length measurements of children less than two years of age (i.e. up to and including 23 months) were taken following WHO standard procedures. A specialized wooden device (that is, an infantometer) was used to take the measurement in a supine position. This was done by placing the child on its back between the slanting sides with the head placed gently against the fixed top end. The knees were gently pushed down by a helper while the foot-piece is moved toward the child until it presses softly against the soles of the child’s feet and the feet are at right angles to the legs. The length was then read to the nearest 0.1 cm. For children who were more than two years, height was measured using the infantometer in standing position. Anthropometric data were then transformed into Z-scores for height-for-age Z-score (HAZ), weight-for-length Z-score (WLZ) and weight-for-age Z-score (WAZ). Categorical variables were stunting, wasting and underweight which reflect HAZ, WAZ and WLZ below −2 standard deviations below the population median. BMI is a simple index of weight-to-height commonly used to classify underweight, overweight and obesity in adults. It is defined as the weight in kilograms divided by the square of the height in metres (kg/m2). Weight of mothers was measured with a Seca electronic weighing scale to the nearest 0.1 kg. Height of mothers was measured with a Seca microtoise to the nearest 0.1 cm. This was diagnosed by placing both thumbs on the upper side of the feet and applying pressure for about three seconds. Edema was considered to be present if a skin depression remained on both feet after the pressure was released. Overall dietary quality was assessed using the dietary diversity score. The WHO validated 7-item food groups frequency questionnaire (FFQ) was used to quantify child dietary intake [15]. Food that was fed to the children was assessed using a structured 24-h food frequency questionnaire. Mothers were asked to recall the number of times, in the past 24 h, a child had received anything to eat, aside from breast-milk, including meals and snacks. The dietary diversity score therefore ranged from 0 to 7 with minimum of 0 if none of the food groups is consumed to 7 if all the food groups are consumed. The WHO defined minimum dietary diversity as the proportion of children aged 6–23 months who received foods from at least four out of seven food groups [15, 16]. The seven food groups used in defining children’s minimum dietary diversity indicator are: (i) grains, roots and tubers; (ii) legumes and nuts; (iii) dairy products; (iv) flesh foods (meats/fish/poultry) (v) eggs (vi) vitamin A rich fruits and vegetables; and (vii) other fruits and vegetables. WHO Infant and young child feeding (IYCF) indicators [minimum dietary diversity (MDD), minimum meal frequency (MMF), minimum acceptable diet (MAD)] were the main dietary intake indicators for children. These indicators were measured by recall of food and liquid consumption during the previous day or night preceding survey as per WHO/UNICEF food frequency questionnaire (FFQ) [17]. Minimum meal frequency is the proportion of children who received complementary foods at least the minimum recommended number of times in the last 24 h. A child is judged to have taken ‘adequate number of meals if he/she received at least the minimum frequency for appropriate complementary feeding (that is, 2 times for 6–8 months and 3 times for 9–11 months, 3 times for children aged 12–23 months) in last 24 h. For non-breastfed children, the minimum meal frequency is 4. A household wealth index based on household assets and housing quality was used as a proxy indicator for socio-economic status (SES) of households. Absolute household wealth index was calculated from information collected on housing quality (floor, walls, and roof material), source of drinking water, type of toilet facility, the presence of electricity, type of cooking fuel, and ownership of modern household durable goods and livestock (e.g. bicycle, television, radio, motorcycle, sewing machine, telephone, cars, refrigerator, mattress, bed, computer and mobile phone) [18–21]. These facilities or durable goods are often regarded as modern goods that have been shown to reflect household wealth. A household of zero index score for example means that household had not a single modern good. The scores were thus added up to give the proxy household wealth index. The median score of 7 was used as the cut-off point to define low and high household wealth index. A number of measures were used to ensure that accurate and reliable data were collected and analyzed. These included extensive practical training of data enumerators, pre-testing of data collection tools and extensive monitoring of the field teams. Field supervisors provided on-the-spot assistance to interviewers. Data were checked for completeness and consistency by field supervisors in the field and during data entry in order to ensure good quality. Data analyses was performed with PASW/SPSS, version 21.0 for Windows using procedures in SPSS complex samples module for Windows. Design weights were added to perform weighted analysis. This module of SPSS takes into account the complex nature of the cluster sample design. The Z- scores for weight-for-age (WAZ), weight-for- height (WHZ), height-for-age (HAZ) and prevalence underweight, wasting and stunting were calculated using the 2006 World Health Organization (WHO) growth standards. WHO Anthro software was used to convert height, weight and age measurements to Height- for- age Z- scores (HAZ), weight- for –height Z- scores (WHZ) and weight-for – age (WAZ) which were used to classify stunting, wasting and underweight respectively. Child undernutrition was defined as Z- scores below −2 standard deviations below the median of the reference population. Bivariate and multivariate analyses were both performed to identify the determinants of stunting, wasting and underweight. Chi-square (χ 2) tests were performed to identify the predictors of stunting, wasting and underweight significant at p < 0.05. ANOVA was used to compare mean anthropometric Z-scores and selected predictors. The association between undernutrition and the independent variables was determined using binary logistic regression modeling. This test statistic was used because stunting, wasting and underweight were coded into two categories (that is: stunted and normal, wasted and normal, and underweight and normal). All of the potential predictors of stunting, wasting and underweight that were significant at p < 0.05 in bivariate analysis using Chi-square (χ 2) tests were included in the regression modeling. The association between selected factors and HAZ was determined using multiple linear regression modeling. This test statistic was used because the dependent factor (HAZ) is a continuous variable. Before testing for associations between independent variables and the dependent outcomes, the data were cleaned and outliers removed. Z-scores which were outside the WHO flags: WHZ −5 to 5; HAZ −6 to 6; and WAZ −6 to 5 were excluded from the data set. Permission to carry out this study was sought from the district health directorate and the study protocol was approved by the School of Allied Health Sciences, University for Development Studies, Ghana. Informed consent was verbally obtained from participant mothers after needed information and procedures were explained. Participation was voluntary and participants signed or thumb printed a statement of an informed consent before being interviewed.

Izinto zokwakha

https://efashare.b-cdn.net/materials/a9cb962f349f2e8fbb78a6e0deeed1a86da16728fa9a08519935d45e1ea070d0/40795_2017_154_MOESM1_ESM.docx

Emisha

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

1. Mobile Health (mHealth) Solutions: Develop and implement mobile health applications or text messaging services to provide pregnant women and new mothers with important health information, reminders for prenatal and postnatal care appointments, and guidance on nutrition and breastfeeding.

2. Community Health Workers: Train and deploy community health workers to provide education and support to pregnant women and new mothers in rural areas. These workers can conduct home visits, provide health counseling, and assist with referrals to healthcare facilities.

3. Telemedicine: Establish telemedicine services to enable pregnant women in remote areas to access prenatal care consultations and receive medical advice from healthcare professionals through video conferencing or phone calls.

4. Maternal Health Clinics: Establish dedicated maternal health clinics in underserved areas to provide comprehensive prenatal and postnatal care services, including regular check-ups, vaccinations, and counseling on nutrition and family planning.

5. Transportation Support: Improve transportation infrastructure and provide transportation vouchers or subsidies to pregnant women in remote areas to ensure they can easily access healthcare facilities for prenatal and postnatal care.

6. Maternal Health Education Programs: Develop and implement community-based maternal health education programs to raise awareness about the importance of prenatal and postnatal care, proper nutrition, and breastfeeding. These programs can be conducted in collaboration with local community leaders and organizations.

7. Maternity Waiting Homes: Establish maternity waiting homes near healthcare facilities to provide a safe and comfortable place for pregnant women to stay during the final weeks of pregnancy, ensuring they have timely access to skilled birth attendants and emergency obstetric care.

8. Financial Support: Implement financial assistance programs to reduce the financial barriers to accessing maternal healthcare services, such as providing subsidies for prenatal and postnatal care, childbirth, and emergency obstetric care.

9. Partnerships and Collaboration: Foster partnerships and collaboration between government agencies, non-governmental organizations, healthcare providers, and community organizations to collectively address the challenges in improving access to maternal health services.

10. Data Collection and Monitoring: Implement robust data collection and monitoring systems to track maternal health indicators, identify gaps in service delivery, and inform evidence-based decision-making for targeted interventions and resource allocation.

AI Innovations Description

Based on the description provided, the recommendation to improve access to maternal health and address undernutrition among preschool children in Northern Ghana is to implement a comprehensive maternal and child health program. This program should focus on the following key areas:

1. Nutrition education and counseling: Provide mothers and caregivers with information on proper nutrition during pregnancy and early childhood. This should include promoting a diverse and balanced diet, emphasizing the importance of breastfeeding, and encouraging the consumption of nutrient-rich foods.

2. Antenatal and postnatal care: Strengthen the existing healthcare system to ensure that pregnant women have access to regular check-ups and necessary interventions during pregnancy. This includes monitoring the growth and development of the fetus, providing iron and folic acid supplements, and addressing any nutritional deficiencies.

3. Immunization: Ensure that all children receive the recommended vaccinations to protect them from preventable diseases. This can be achieved through the expansion of immunization services and raising awareness about the importance of vaccination.

4. Water, sanitation, and hygiene (WASH) interventions: Improve access to clean water and sanitation facilities in communities to reduce the risk of waterborne diseases and improve overall hygiene practices. This can be done through the construction of water sources, latrines, and handwashing stations.

5. Maternal and child healthcare infrastructure: Strengthen the healthcare infrastructure in the region by increasing the number of health facilities and trained healthcare providers. This will help ensure that pregnant women and children have access to quality healthcare services.

6. Community engagement and empowerment: Involve the community in the planning and implementation of maternal and child health programs. This can be done through community health workers, community-based organizations, and local leaders. Empower women and caregivers with knowledge and skills to make informed decisions about their health and the health of their children.

By implementing these recommendations, it is expected that access to maternal health services will improve, leading to a reduction in undernutrition among preschool children in Northern Ghana.

AI Innovations Methodology

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

1. Increase awareness and education: Implement community-based education programs to raise awareness about the importance of maternal health and nutrition. This can include educating women and their families about proper nutrition during pregnancy, the importance of antenatal care visits, and the benefits of breastfeeding.

2. Improve access to healthcare facilities: Increase the number of healthcare facilities in rural areas and ensure they are equipped with the necessary resources and trained healthcare professionals to provide quality maternal healthcare services. This can include providing transportation services for pregnant women to reach healthcare facilities.

3. Strengthen antenatal care services: Enhance the quality and availability of antenatal care services by providing comprehensive screenings, regular check-ups, and counseling on nutrition and healthy behaviors during pregnancy. This can also include promoting early and regular antenatal care visits.

4. Enhance nutrition support: Implement programs that provide nutritional support to pregnant women, such as the provision of nutrient-rich foods and supplements. This can help address maternal malnutrition and improve the health outcomes of both the mother and the child.

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 the focus of the simulation, such as pregnant women in a particular region or community.

2. Collect baseline data: Gather relevant data on the current state of access to maternal health services in the target population, including factors such as healthcare facility availability, utilization rates, and maternal health outcomes.

3. Develop a simulation model: Create a simulation model that incorporates the various factors and variables related to access to maternal health. This model should be based on the specific context and needs of the target population.

4. Input data and parameters: Input the baseline data and parameters into the simulation model, including information on the recommended interventions and their expected impact on access to maternal health.

5. Run the simulation: Run the simulation model to simulate the impact of the recommended interventions on access to maternal health. This can include analyzing changes in healthcare facility utilization rates, maternal health outcomes, and other relevant indicators.

6. Evaluate results: Analyze the results of the simulation to assess the potential impact of the recommended interventions on improving access to maternal health. This can include comparing the simulated outcomes with the baseline data to determine the effectiveness of the interventions.

7. Refine and iterate: Based on the evaluation of the simulation results, refine the model and interventions as necessary. Repeat the simulation process to further assess the potential impact and make any necessary adjustments.

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

Ababhali & Co-Ababhali

Statistics:

Citations: 38

Authors: 5

Identifiers:

DOI: 10.1186/s40795-017-0154-2

Research Areas:

Community Interventions, Disparities, Environmental, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Mental Health, Noncommunicable Diseases, Quality of Care, Social Determinants

Study Countries:

Ghana

Study Design:

Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Male & Female

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