Prevalence and factors associated with the triple burden of malnutrition among mother-child pairs in sub-saharan africa

Nutrients, Volume 13, No. 6, Year 2021

Interpretation

Despite concerns about the coexistence of overnutrition, undernutrition and micronutrient deficiencies, which is compositely referred to as the triple burden of malnutrition (TBM), little is known about the phenomenon in sub-Saharan Africa (SSA). We, therefore, aimed to examine the prevalence and investigate the factors associated with TBM in SSA. This study uses cross-sectional survey data collected through the Demographic and Health Surveys (DHS) Program from 2010 to 2019. Data from 32 countries in SSA were used for the analysis. The prevalence of TBM were presented in tables and maps using percentages. The predictors of TBM were examined by fitting a negative log-log regression to the data. The results were then presented using adjusted odds ratios (aORs) at 95% Confidence Intervals (CIs). Out of the 169,394 children, 734 (1%) suffered from TBM. The highest proportion of children with TBM in the four geographic regions in SSA was found in western Africa (0.75%) and the lowest in central Africa (0.21%). Children aged 1 [aOR = 1.283; 95% CI = 1.215–1.355] and those aged 2 [aOR = 1.133; 95% CI = 1.067–1.204] were more likely to experience TBM compared to those aged 0. TBM was less likely to occur among female children compared to males [aOR = 0.859; 95% CI = 0.824–0.896]. Children whose perceived size at birth was average [aOR = 1.133; 95% CI = 1.076–1.193] and smaller than average [aOR = 1.278; 95% CI = 1.204–1.356] were more likely to suffer from TBM compared to those who were larger than average at birth. Children born to mothers with primary [aOR = 0.922; 95% CI = 0.865–0.984] and secondary [aOR = 0.829; 95% CI = 0.777–0.885] education were less likely to suffer from TBM compared to those born to mothers with no formal education. Children born to mothers who attended antenatal care (ANC) had lower odds of experiencing TBM compared to those born to mothers who did not attend ANC [aOR = 0.969; 95% CI = 0.887–0.998]. Children born to mothers who use clean household cooking fuel were less likely to experience TBM compared to children born to mothers who use unclean household cooking fuel [aOR = 0.724; 95% CI = 0.612–0.857]. Essentially, higher maternal education, ANC attendance and use of clean cooking fuel were protective factors against TBM, whereas higher child age, low size at birth and being a male child increased the risk of TBM. Given the regional variations in the prevalence and risk of TBM, region-specific interventions must be initiated to ensure the likelihood of those interventions being successful at reducing the risk of TBM. Countries in Western Africa in particular would have to strengthen their current policies and programmes on malnutrition to enhance their attainment of the SDGs. This study uses cross-sectional survey data collected through the Demographic and Health Surveys (DHS) Program from 2010 to 2019. The data of 32 countries in SSA (see Figure 1) in the geographic regions, western, eastern, central and southern Africa (see Figure 2), were obtained for analysis. For each geographic region in SSA, countries were considered based on the availability of data on (i) key anthropometrics and background characteristics including sex, height-for-age z-scores, weight-for-height z-scores, weight-for-age z-scores and anaemia level of children under the age of 5 years and their respective mothers; (ii) household characteristics including the background characteristics of household head and household’s access to basic services such as water, toilet facility and cooking fuel, among others. Spatial distribution of the study countries in Sub-Saharan Africa. Source: constructed based on shapefiles from https://tapiquen-sig.jimdofree.com/descargas-gratuitas/mundo/ (1 December 2020) with permission from Carlos Efrain Porto Tapiquen, 2021. Spatial distribution of study countries by regions of Sub-Saharan Africa. Source: constructed based on shapefiles from https://tapiquen-sig.jimdofree.com/descargas-gratuitas/mundo/ (1 December 2020) with permission from Carlos Efrain Porto Tapiquen, 2021. The DHS Program since 1984 has gathered nationally representative data on important population, nutrition and other health indicators of women, men and children at the household level in over 90 low-to-middle-income countries around the world. The program employs standardised protocols and instruments in all its surveys to allow for inter-country comparisons. A two-stage stratified sampling technique involving the demarcation of enumeration areas (clusters) and household selection for interviews was done. Questionnaires are often translated into a country’s major local language, pre-tested and validated before implementation of the surveys. This study included 169,394 child-mother pairs who had complete data for all the variables of interest. We adhered to the strengthening the reporting of observational studies in epidemiology (STROBE) statement for developing this manuscript. The dataset can be accessed freely by download at: https://dhsprogram.com/data/available-datasets.cfm (22 March 2021). The outcome variable TBM was derived from four child malnutrition indicators (stunting, wasting, underweight and anaemia status) and the body mass index (BMI) of their respective mothers. For parsimony and relevance to this study, anaemia levels were measured using four response categories (severe, moderate, mild and not anaemic), which were dichotomized into “anaemic” and “normal”, where anaemic was “severe”, “moderate” and “mild” were combined and coded as “1”, and not anaemic was labelled “normal” and coded “0”. Additionally, following previous studies [13], stunting, wasting, underweight and BMI of the mother were dichotomized and coded as 0 for “normal” and 1 for “stunted”, “wasting”, “underweight” and “obese/overweight”, respectively. Four combinations of these variables—Obese/overweight Mother and Anaemic Child (OM/AC), Obese/overweight Mother and Stunted Child (OM/SC), obese/overweight mother and wasted child (OM/WC), and obese/overweight mother and underweight child (OM/UC)—were made. Following Kumar et al. [2], the binary response variable TBM was measured using response categories “normal” and “TBM”, where the latter included obese/overweight mother with an undernourished child, i.e., children with stunting/wasting/underweight who were also anaemic. The independent variables included in this study were considered based on literature and the availability of data. Previous studies [7,13,14,15,16] have documented several variables associated with child malnutrition spanning child, mother and household characteristics and contextual factors. The relevant variables on child characteristics considered include the age of the child in years (0, 1, 2, 3, 4); sex of child (male, female); birth order (1, 2, 3 and above); perceived birth size (larger than average, average, smaller than average, do not know). With regards to the mothers’ characteristics, the relevant variables include the age of mother in years (15–19, 20–24, 25–29, 30–34, 35–49, 40–44, 45–49); educational attainment (no formal, primary, secondary, higher); employment status (no, yes); antenatal care (ANC) visits (no, yes); postnatal care (PNC) visits (No, Yes). The household characteristics considered are the age of household head (“young adults” for those below 35 years, “middle-aged adults” for 35–55 years and “old-aged adults” for those aged 55 years and above [17]; sex of household head (male, female); household size (“small” for those with 1–5 members, “medium” for 6–10 members and “large” for more than 10 members (see [17,18]); wealth status (poor, middle, rich); access to electricity (no, yes); source of drinking water (improved, unimproved [17,18]); type of toilet facility (improved, unimproved [17,18]); and type of cooking fuel (unclean, clean [19,20]). Urbanicity (urban, rural) and geographic region (western Africa, eastern Africa, central Africa and southern Africa) were the contextual variables included in this study. All statistical analyses were performed using the Stata SE version 14.2 (StataCorp, College Station, TX, USA) software. Before analyses were conducted, the data were first declared as survey data using the Stata command “svyset” specifying the cluster, sample weighting and strata variables. This procedure was done to allow for robust estimation of effect sizes by preventing potential clustering and adjusting for oversampling and undersampling. Descriptive statistics (frequencies and percentages) were used to present the distribution of all variables of interest in tables. To enhance visualization and understanding of the study context, the data were then integrated into a geographic information system (GIS) environment and key variables presented in maps. The Chi-square test of independence was then used to assess the associations between the independent variables and the TBM. All independent variables associated with the TBM were tested for multicollinearity and there was no evidence of multicollinearity (see Table S1). The effects of these independent variables on the TBM were then examined by fitting a negative log-log regression to the data. A negative log-log generalized linear regression was deemed plausible considering the skewed distribution of the TBM to the non-affirmation (99%) [19,21,22]. The results were then presented using adjusted odds ratios (aORs) at 95% Confidence Intervals (CIs). Ethical clearance for DHS reports is taken from the Ethics Committee of ORC Macro Inc. as well as the ethics boards of partner institutions (e.g., ministries of health) of the studied countries. The DHS protocols guarantee that ethical standards for the protection of respondents’ anonymity, privacy and confidentiality are adhered to. Inner City Fund International also ensures that the survey meets the United States Department of Health and Human Services’ regulations for the respect of human subjects. The study used secondary datasets; hence, no further ethical approval was required. The datasets can be accessed freely via download. Further information about the DHS data usage and ethical standards is available at http://goo.gl/ny8T6X (22 March 2021).

AI Digest

Study Justification:

The study aimed to investigate the prevalence and factors associated with the triple burden of malnutrition (TBM) among mother-child pairs in sub-Saharan Africa (SSA). This is important because there is limited knowledge about TBM in SSA, despite concerns about the coexistence of overnutrition, undernutrition, and micronutrient deficiencies. Understanding the prevalence and factors associated with TBM can inform interventions and policies to address this issue in the region.

Study Highlights:

– The study used cross-sectional survey data from the Demographic and Health Surveys (DHS) Program collected between 2010 and 2019.
– Data from 32 countries in SSA were analyzed to examine the prevalence of TBM.
– The study found that 1% of the 169,394 children in the sample suffered from TBM.
– The highest proportion of children with TBM was found in western Africa (0.75%), while the lowest was in central Africa (0.21%).
– Factors associated with TBM included child age, sex, perceived size at birth, maternal education, attendance of antenatal care, and use of clean cooking fuel.
– Maternal education, antenatal care attendance, and use of clean cooking fuel were protective factors against TBM.
– The study highlighted the need for region-specific interventions to reduce the risk of TBM, with a particular focus on countries in western Africa.

Recommendations for Lay Reader and Policy Maker:

– The study recommends implementing interventions to address the triple burden of malnutrition among mother-child pairs in sub-Saharan Africa.
– These interventions should focus on improving maternal education, increasing antenatal care attendance, and promoting the use of clean cooking fuel.
– Region-specific interventions should be developed to address the variations in the prevalence and risk of TBM across sub-Saharan Africa.
– Policy makers should strengthen current policies and programs on malnutrition, particularly in countries in western Africa, to achieve the Sustainable Development Goals (SDGs).

Key Role Players:

– Researchers and academics in the field of nutrition and public health.
– Government officials and policymakers responsible for health and nutrition policies.
– Non-governmental organizations (NGOs) working on nutrition and maternal-child health.
– Health professionals, including doctors, nurses, and nutritionists.
– Community leaders and organizations involved in community health and development.

Cost Items for Planning Recommendations:

– Education and training programs for mothers and caregivers on nutrition and healthy cooking practices.
– Implementation of antenatal care programs to ensure pregnant women receive appropriate nutrition and healthcare.
– Promotion of clean cooking fuel technologies and access to clean cooking facilities.
– Development and implementation of region-specific interventions to address the triple burden of malnutrition.
– Monitoring and evaluation systems to assess the effectiveness of interventions and make necessary adjustments.
– Research and data collection to continuously monitor the prevalence and factors associated with TBM in sub-Saharan Africa.

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 large sample size (169,394 child-mother pairs) and uses data collected through the Demographic and Health Surveys (DHS) Program, which is known for its standardized protocols and instruments. The study also presents adjusted odds ratios (aORs) with 95% confidence intervals (CIs) to quantify the associations between variables. To improve the evidence, the study could have included more recent data, as the data collection period was from 2010 to 2019. Additionally, the abstract could have provided more information about the methodology used for data analysis and the limitations of the study.

Abstract

This study uses cross-sectional survey data collected through the Demographic and Health Surveys (DHS) Program from 2010 to 2019. The data of 32 countries in SSA (see Figure 1) in the geographic regions, western, eastern, central and southern Africa (see Figure 2), were obtained for analysis. For each geographic region in SSA, countries were considered based on the availability of data on (i) key anthropometrics and background characteristics including sex, height-for-age z-scores, weight-for-height z-scores, weight-for-age z-scores and anaemia level of children under the age of 5 years and their respective mothers; (ii) household characteristics including the background characteristics of household head and household’s access to basic services such as water, toilet facility and cooking fuel, among others. Spatial distribution of the study countries in Sub-Saharan Africa. Source: constructed based on shapefiles from https://tapiquen-sig.jimdofree.com/descargas-gratuitas/mundo/ (1 December 2020) with permission from Carlos Efrain Porto Tapiquen, 2021. Spatial distribution of study countries by regions of Sub-Saharan Africa. Source: constructed based on shapefiles from https://tapiquen-sig.jimdofree.com/descargas-gratuitas/mundo/ (1 December 2020) with permission from Carlos Efrain Porto Tapiquen, 2021. The DHS Program since 1984 has gathered nationally representative data on important population, nutrition and other health indicators of women, men and children at the household level in over 90 low-to-middle-income countries around the world. The program employs standardised protocols and instruments in all its surveys to allow for inter-country comparisons. A two-stage stratified sampling technique involving the demarcation of enumeration areas (clusters) and household selection for interviews was done. Questionnaires are often translated into a country’s major local language, pre-tested and validated before implementation of the surveys. This study included 169,394 child-mother pairs who had complete data for all the variables of interest. We adhered to the strengthening the reporting of observational studies in epidemiology (STROBE) statement for developing this manuscript. The dataset can be accessed freely by download at: https://dhsprogram.com/data/available-datasets.cfm (22 March 2021). The outcome variable TBM was derived from four child malnutrition indicators (stunting, wasting, underweight and anaemia status) and the body mass index (BMI) of their respective mothers. For parsimony and relevance to this study, anaemia levels were measured using four response categories (severe, moderate, mild and not anaemic), which were dichotomized into “anaemic” and “normal”, where anaemic was “severe”, “moderate” and “mild” were combined and coded as “1”, and not anaemic was labelled “normal” and coded “0”. Additionally, following previous studies [13], stunting, wasting, underweight and BMI of the mother were dichotomized and coded as 0 for “normal” and 1 for “stunted”, “wasting”, “underweight” and “obese/overweight”, respectively. Four combinations of these variables—Obese/overweight Mother and Anaemic Child (OM/AC), Obese/overweight Mother and Stunted Child (OM/SC), obese/overweight mother and wasted child (OM/WC), and obese/overweight mother and underweight child (OM/UC)—were made. Following Kumar et al. [2], the binary response variable TBM was measured using response categories “normal” and “TBM”, where the latter included obese/overweight mother with an undernourished child, i.e., children with stunting/wasting/underweight who were also anaemic. The independent variables included in this study were considered based on literature and the availability of data. Previous studies [7,13,14,15,16] have documented several variables associated with child malnutrition spanning child, mother and household characteristics and contextual factors. The relevant variables on child characteristics considered include the age of the child in years (0, 1, 2, 3, 4); sex of child (male, female); birth order (1, 2, 3 and above); perceived birth size (larger than average, average, smaller than average, do not know). With regards to the mothers’ characteristics, the relevant variables include the age of mother in years (15–19, 20–24, 25–29, 30–34, 35–49, 40–44, 45–49); educational attainment (no formal, primary, secondary, higher); employment status (no, yes); antenatal care (ANC) visits (no, yes); postnatal care (PNC) visits (No, Yes). The household characteristics considered are the age of household head (“young adults” for those below 35 years, “middle-aged adults” for 35–55 years and “old-aged adults” for those aged 55 years and above [17]; sex of household head (male, female); household size (“small” for those with 1–5 members, “medium” for 6–10 members and “large” for more than 10 members (see [17,18]); wealth status (poor, middle, rich); access to electricity (no, yes); source of drinking water (improved, unimproved [17,18]); type of toilet facility (improved, unimproved [17,18]); and type of cooking fuel (unclean, clean [19,20]). Urbanicity (urban, rural) and geographic region (western Africa, eastern Africa, central Africa and southern Africa) were the contextual variables included in this study. All statistical analyses were performed using the Stata SE version 14.2 (StataCorp, College Station, TX, USA) software. Before analyses were conducted, the data were first declared as survey data using the Stata command “svyset” specifying the cluster, sample weighting and strata variables. This procedure was done to allow for robust estimation of effect sizes by preventing potential clustering and adjusting for oversampling and undersampling. Descriptive statistics (frequencies and percentages) were used to present the distribution of all variables of interest in tables. To enhance visualization and understanding of the study context, the data were then integrated into a geographic information system (GIS) environment and key variables presented in maps. The Chi-square test of independence was then used to assess the associations between the independent variables and the TBM. All independent variables associated with the TBM were tested for multicollinearity and there was no evidence of multicollinearity (see Table S1). The effects of these independent variables on the TBM were then examined by fitting a negative log-log regression to the data. A negative log-log generalized linear regression was deemed plausible considering the skewed distribution of the TBM to the non-affirmation (99%) [19,21,22]. The results were then presented using adjusted odds ratios (aORs) at 95% Confidence Intervals (CIs). Ethical clearance for DHS reports is taken from the Ethics Committee of ORC Macro Inc. as well as the ethics boards of partner institutions (e.g., ministries of health) of the studied countries. The DHS protocols guarantee that ethical standards for the protection of respondents’ anonymity, privacy and confidentiality are adhered to. Inner City Fund International also ensures that the survey meets the United States Department of Health and Human Services’ regulations for the respect of human subjects. The study used secondary datasets; hence, no further ethical approval was required. The datasets can be accessed freely via download. Further information about the DHS data usage and ethical standards is available at http://goo.gl/ny8T6X (22 March 2021).

Materials

https://efashare.b-cdn.net/materials/9fa60d3c52136c92b54fcf75fa4fd09311a10569f7703c103d9e290199af1900/nutrients-13-02050-s001.zip

Innovations

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide pregnant women and new mothers with access to important health information, reminders for prenatal and postnatal care appointments, and educational resources on nutrition, breastfeeding, and infant care.

2. Telemedicine Services: Establish telemedicine services that allow pregnant women in remote or underserved areas to consult with healthcare professionals through video calls or phone consultations. This would enable them to receive medical advice, prenatal check-ups, and postnatal care without the need for travel.

3. Community Health Workers: Train and deploy community health workers who can provide essential maternal health services, including antenatal and postnatal care, in rural and marginalized communities. These workers can also conduct health education sessions and refer women to higher-level healthcare facilities when necessary.

4. Maternal Health Vouchers: Implement voucher programs that provide pregnant women with financial assistance to access quality maternal healthcare services, including prenatal check-ups, delivery, and postnatal care. These vouchers can be distributed through community health centers or local organizations.

5. Maternal Waiting Homes: Establish maternal waiting homes near healthcare facilities in remote areas to accommodate pregnant women who live far away and need to stay closer to the facility during the final weeks of pregnancy. These homes would provide a safe and comfortable environment for women to wait for labor and delivery.

6. Transportation Support: Develop transportation initiatives that address the challenges of accessing healthcare facilities by providing affordable or subsidized transportation options for pregnant women, especially in rural areas with limited public transportation.

7. Maternal Health Education Campaigns: Launch targeted education campaigns to raise awareness about the importance of maternal health and encourage women to seek timely prenatal and postnatal care. These campaigns can utilize various communication channels, including radio, television, social media, and community outreach programs.

8. Maternal Health Clinics in Workplaces: Collaborate with employers to establish on-site maternal health clinics in workplaces, providing convenient access to prenatal care and health education for pregnant employees.

9. Integration of Maternal Health Services: Integrate maternal health services with other existing healthcare programs, such as family planning, immunization, and HIV/AIDS prevention and treatment, to provide comprehensive care for women and their children.

10. Strengthening Health Systems: Invest in strengthening healthcare infrastructure, training healthcare professionals, and improving supply chains for essential maternal health commodities to ensure the availability and quality of services.

It is important to note that the specific context and needs of each region or country should be considered when implementing these innovations to improve access to maternal health.

AI Innovations Description

The study you provided focuses on the prevalence and factors associated with the triple burden of malnutrition (TBM) among mother-child pairs in sub-Saharan Africa. While the study provides valuable insights into the issue of malnutrition, it does not directly address access to maternal health. However, based on the findings and recommendations of the study, we can propose a recommendation that can be developed into an innovation to improve access to maternal health in the context of TBM:

Recommendation: Implement region-specific interventions to address the risk factors associated with TBM and improve access to maternal health services.

Explanation: The study highlights regional variations in the prevalence and risk of TBM in sub-Saharan Africa. To effectively address the issue, it is important to tailor interventions to the specific needs and challenges of each region. By implementing region-specific interventions, policymakers and healthcare providers can target the identified risk factors and improve access to maternal health services.

Specific actions that can be taken to develop this recommendation into an innovation include:

1. Strengthening existing policies and programs: Countries in Western Africa, where the highest proportion of children with TBM was found, should focus on strengthening their current policies and programs on malnutrition. This can include initiatives to improve nutrition education, promote breastfeeding, and provide access to nutritious food for pregnant women and young children.

2. Enhancing maternal education: The study found that higher maternal education was a protective factor against TBM. Therefore, efforts should be made to improve access to education for women, particularly in regions with higher prevalence of TBM. This can be achieved through targeted educational programs and scholarships for girls and women.

3. Promoting antenatal care attendance: The study also found that children born to mothers who attended antenatal care (ANC) had lower odds of experiencing TBM. To improve access to maternal health services, it is crucial to promote ANC attendance among pregnant women. This can be done through community outreach programs, mobile clinics, and awareness campaigns highlighting the importance of ANC.

4. Ensuring access to clean cooking fuel: The study identified that children born to mothers who use clean household cooking fuel were less likely to experience TBM. Therefore, efforts should be made to improve access to clean cooking fuel, particularly in regions where unclean fuel is commonly used. This can involve promoting clean cooking technologies, such as improved cookstoves and renewable energy sources.

By implementing these region-specific interventions, policymakers and healthcare providers can address the risk factors associated with TBM and improve access to maternal health services. This can contribute to reducing the prevalence of TBM and improving the overall health and well-being of mother-child pairs in sub-Saharan Africa.

AI Innovations Methodology

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

1. Strengthen maternal education programs: Given that higher maternal education was found to be a protective factor against the triple burden of malnutrition (TBM), it is important to invest in education programs for women. These programs can focus on providing information and resources related to nutrition, antenatal care, and overall maternal health.

2. Enhance antenatal care (ANC) services: The study found that children born to mothers who attended ANC had lower odds of experiencing TBM. Therefore, it is crucial to improve access to and quality of ANC services. This can be achieved by increasing the number of ANC facilities, ensuring availability of skilled healthcare providers, and promoting early and regular ANC visits.

3. Promote clean household cooking fuel: The study revealed that children born to mothers who use clean household cooking fuel were less likely to experience TBM. To improve access to maternal health, efforts should be made to promote the use of clean cooking fuel, such as LPG or improved cookstoves, which can reduce indoor air pollution and its negative impact on maternal and child health.

4. Implement region-specific interventions: The study highlighted regional variations in the prevalence and risk of TBM. Therefore, it is important to tailor interventions to the specific needs and challenges of each region. This can involve working closely with local communities, healthcare providers, and policymakers to develop and implement targeted strategies to address maternal health issues.

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

1. Define the indicators: Identify key indicators that reflect access to maternal health, such as ANC coverage, maternal education rates, clean cooking fuel usage, and prevalence of TBM.

2. Collect baseline data: Gather data on the selected indicators for the target population or region. This can be done through surveys, existing databases, or other sources of relevant information.

3. Set targets: Determine specific targets for each indicator based on desired improvements in access to maternal health. These targets should be realistic and measurable.

4. Implement interventions: Implement the recommended interventions, such as strengthening maternal education programs, enhancing ANC services, promoting clean cooking fuel, and implementing region-specific interventions.

5. Monitor and evaluate: Continuously monitor the progress of the interventions and collect data on the selected indicators. Evaluate the impact of the interventions by comparing the post-intervention data with the baseline data.

6. Analyze the data: Use statistical analysis techniques to assess the impact of the interventions on the selected indicators. This can involve comparing the pre- and post-intervention data, conducting regression analyses, or using other appropriate methods.

7. Interpret the results: Interpret the findings to understand the extent to which the interventions have improved access to maternal health. Identify any challenges or areas that require further attention.

8. Adjust and refine: Based on the results and lessons learned, make any necessary adjustments or refinements to the interventions. This can involve scaling up successful interventions, addressing implementation challenges, or identifying new strategies to further improve access to maternal health.

By following this methodology, policymakers and stakeholders can gain insights into the potential impact of the recommended interventions on improving access to maternal health and make informed decisions for future interventions.

Authors & Co-Authors

Statistics:

Citations: 3

Authors: 9

Identifiers:

DOI: 10.3390/nu13062050

Research Areas:

Community Interventions, Environmental, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female