Prevalence and multi-level factors associated with acute malnutrition among children aged 6–59 months from war affected communities of Tigray, Northern Ethiopia, 2021: a cross-sectional study

Conflict and Health, Volume 17, No. 1, Year 2023

Ukuhunyushwa

Background: Armed conflicts greatly affect the health, nutrition, and food security of conflict affected settings particularly children. However, no empirical data exist regarding context specific factors contributing towards acute malnutrition in the war-torn Tigray, Ethiopia. Thus, this study aimed to identify individual and community level factors associated with acute malnutrition among children aged 6–59 months from armed conflict affected settings of Tigray, Ethiopia. Methods: A community based cross-sectional study was conducted among 3,614 children aged 6–59 months in Tigray, from July 15 to Aug 15, 2021. Study participants were selected using a two-stage random sampling method. A structured questionnaire was used to collect data by interviewing mothers/caregivers. Mid upper arm circumference (MUAC) measurements were taken from upper left arm of the children using MUAC tapes. Multivariable multilevel logistic regression analysis was used to determine factors associated with acute malnutrition. Adjusted Odds ratio (AOR) with 95% CI were estimated to describe the strength of associations at p < 0.05. Results: More than half (52.5%) of the sampled children were males in sex. Immediately after the first nine months into the conflict, the prevalence of severe, moderate, and global acute malnutrition was very high (5.1%, 21.8%, and 26.9%, respectively) in Tigray. The lowest and highest burden of child acute malnutrition was reported from Mekelle zone (13.3%) and Southeastern zone (36.7%), respectively. Individual-level factors such as older child age (AOR = 0.13, 95% CI: 0.10, 0.18), female child sex (AOR = 1.24, 95% CI 1.05, 1.480.95), Vitamin-A supplementation (AOR = 1.3, 95% CI: 1.05, 1.65), and history of diarrhea (AOR = 1.22, 95%CI: 1.02, 1.53) and community-level factors like unimproved drinking water source (AOR = 1.31, 95%CI: 1.08, 1.58), unimproved toilet facility (AOR = 1.24, 95% CI: 1.01, 1.52), and severe food insecurity (AOR = 1.55, 95% CI: 1.16. 2.07) were significantly associated with childhood acute malnutrition. Conclusions: The burden of acute malnutrition is a severe public health problem in Tigray. To prevent the untimely suffering and death of children, regular nutrition screening, speedy, and appropriate referral of all malnourished children to nutritional services and large-scale humanitarian assistance including access to food; nutrition supplies; water, sanitation and hygiene supplies; and health care in a timely manner are required. In the prevailing armed conflict, these have been very difficult to achieve. Thus, immediate international intervention is needed. The study was conducted in Tigray, the northern most part of Ethiopia. Tigray is bordered with Eritrea from the north, Sudan from the west, Afar region from the east and Amhara region from the south. Administratively, Tigray is divided in to seven zones namely Central, Eastern, Mekelle, North western, Southern, South eastern and Western zones and 93 districts. In this study, 52 districts in all the zones except the western zone were included. The western zone was excluded for security concerns. The study was conducted between July 15 and Aug 15, 2021. A community based cross-sectional study was conducted in six zones and 52 randomly selected districts of Tigray. Only mothers or caretakers of children 6–59 months of age were included in the study. Inclusion criteria: all households with under one years of age children were included. Then, all children under five in the selected household were measured for nutritional status. Exclusion criteria: Serious illness in children under five. The minimum sample size was estimated using proportional allocation of the minimum sample size for a Tabia (smallest administrative unit) from each of the districts. The sample size was determined based on the burden of acute malnutrition. According to the EDHS 2016, the burden of acute malnutrition was 11% in Tigray [15]. For situations where power and prevalence are known, effective sample size can easily be estimated. For 11% prevalence, 20 subjects are sufficient to reach a power value greater than 80 [16]. Accordingly, 20 households were included from each Tabia and four Tabias were selected from each district. Thus, adding 5% of non-response, the total sample size was calculated to be 4368 [20 subjects per Tabia*4 Tabias per district*52 districts) + (5%*4160)]. However, 754 subjects were excluded from the final analysis for the following reasons; 1) 444 were under the age of 6 months; 2) 308 had no MUAC measurements, and 3) 2 were outliers. Therefore, the findings of this study were based on data from 3614 subjects. A multistage sampling technique was employed. All the zones except the western zone were included in the study. At the first stage, a total of 52 districts were randomly selected from the 93 districts. In the second stage, four Tabias were randomly selected from each of the 52 districts. Then, 20 households with under one year of age children were randomly selected from the selected tabias using the registration book of the Health Extension Workers (HEWs) as a sampling frame. When the registration book of the HEWs was not available, a new list of the households with under one year of age children was generated and used as a sampling frame to randomly select the study households. However, it must be noted that the districts from the Western zone of Tigray were not included in the sampling frame. Data were collected using a pretested and interviewer-administered structured questionnaire. The tool contained items regarding socio-demographic, health and obstetric, childhood illness and vaccination characteristics; water, hygiene and sanitation conditions; and household food insecurity, and anthropometric measurement (MUAC). Experienced HEWs were the data collectors and health and nutrition researchers/experts from Mekelle University and Tigray Health Bureau worked as supervisors of the data collection. MUAC measurements were taken from the upper left arm of the children using MUAC tapes; household food insecurity was measured using the Household Food Insecurity Access Scale (HFIAS), which was answered by the mothers/caregivers of the children. The questionnaire was initially prepared in English and then translated into the local language (Tigrigna) and was then translated back to English for consistency check. Three days training was given for the data collectors and supervisors. Moreover, fieldwork was accompanied by strict follow up and supervision. These included child age, child sex, Vitamin-A supplementation status, measles vaccine status, deworming, maternal education, paternal education, child had diarrhea in the last nine months, child had fever in the last nine months, child had cough in the last nine months, place of delivery, and antenatal care (ANC) visits. Residence, toilet facility, drinking water source, handwashing facility close to toilet, solid waste disposal, liquid waste disposal, family size, and household food insecurity were considered as community level factors. The dependent variable for this study was child acute malnutrition as measured by MUAC with two categories (“Yes” if MUAC < 12.5 cm and “No” if MUAC ≥ 12.5 cm). Data (Additional file 1) were cleaned and analyzed using Statistical package for Social Sciences (SPSS) software version 25. Categorical variables were summarized using percentages. Multilevel binary logistic regression analysis was employed to identify factors significantly associated with the outcome variable. Within the multilevel multivariable logistical regression analysis, Adjusted odds ratios (AOR) with their 95% confidence intervals were computed to measure the fixed effects of individual-level and community-levels factors on the prevalence of child acute malnutrition. During bi-variable logistic regression analysis, we used p-value of ≤ 0.2 to screen factors for multivariable logistic regression analysis. In the final multivariable logistic model, four models including an intercept-only model containing no explanatory variables, an individual-only model, a community-only model, and a combined model containing both individual and community-level variables were fitted. This helped to come up with a model where the effect of clustering is controlled and to determine the independent effect of each individual and community level factors on the dependent variable. Associations were declared statistically significant at a p-value of < 0.05. Prior to running the multivariable logistic analysis, multicollinearity among individual and community level variables was checked using Variance Inflation Factor (VIF) cutoff value of 10. From the four fitted models, the one with the lowest value of Akaike information criterion (AIC) and/or Bayesian information criterion (BIC) was selected as the best model to our data. Both AIC and BIC consist of a part that represents model fitness and a part that represent the size and dimensionality of the model as shown in the equation: IC= − 2logf(y│Ӫ)+λd, where IC stands for information criterion, f(y│Ӫ) is the likelihood of the data y evaluated using the model parameters, λ denotes the penalty weight that differs for AIC and BIC, and d represents the size or dimensionality of the model [17]. Variables that showed significant association at p ≤ 0.20 in the bivariate analysis were entered to multivariable logistic regression analysis. These included individual level variables like child age, child sex, Vitamin A supplementation, diarrhea in the last nine months, treatment sought for diarrhea, and fever. Community level variables that met this criteria were drinking water source, toilet facility, and HFIAS. Our analysis showed multicollinearity between presence of diarrhea and treatment sought for diarrhea. From the bivariate logistic regression, the p-value for treatment sought for diarrhea was higher than the p-value for presence of diarrhea, thus, we removed the variable treatment sought for diarrhea.

I-AI Digest

Study Justification:

This study aimed to investigate the prevalence and factors associated with acute malnutrition among children aged 6–59 months in war-affected communities of Tigray, Northern Ethiopia. The justification for this study is that armed conflicts have a significant impact on the health, nutrition, and food security of conflict-affected settings, particularly children. However, there is a lack of empirical data on the specific factors contributing to acute malnutrition in the war-torn region of Tigray. This study fills this gap by identifying individual and community-level factors associated with acute malnutrition in this context.

Study Highlights:

– The prevalence of severe, moderate, and global acute malnutrition was found to be very high in Tigray, particularly in the immediate aftermath of the conflict.
– The burden of acute malnutrition varied across different zones, with the highest burden reported in the Southeastern zone.
– Individual-level factors such as older child age, female child sex, Vitamin-A supplementation, and history of diarrhea were significantly associated with acute malnutrition.
– Community-level factors such as unimproved drinking water source, unimproved toilet facility, and severe food insecurity were also significantly associated with acute malnutrition.

Study Recommendations:

Based on the findings of this study, the following recommendations are made:

1. Regular nutrition screening should be conducted to identify malnourished children.
2. Malnourished children should be referred to nutritional services promptly and appropriately.
3. Large-scale humanitarian assistance is needed, including access to food, nutrition supplies, water, sanitation and hygiene supplies, and healthcare.
4. International intervention is urgently required to address the challenges faced in providing these essential services in the context of armed conflict.

Key Role Players:

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

1. Government agencies responsible for health and nutrition programs.
2. Non-governmental organizations (NGOs) involved in humanitarian assistance.
3. Health professionals, including doctors, nurses, and nutritionists.
4. Community health workers and volunteers.
5. International organizations and donors providing support for emergency response.

Cost Items for Planning Recommendations:

While the actual cost of implementing the recommendations cannot be provided, the following budget items should be considered in the planning process:

1. Procurement and distribution of food supplies.
2. Provision of nutrition supplements and therapeutic foods.
3. Construction and maintenance of water and sanitation facilities.
4. Training and capacity building for healthcare workers.
5. Monitoring and evaluation of nutrition programs.
6. Coordination and logistics for humanitarian assistance.
7. Communication and awareness campaigns for community engagement.
Please note that the above information is based on the provided study description and is intended for a lay reader and policy maker.

Amandla Obufakazi

N/A

Abstract

The study was conducted in Tigray, the northern most part of Ethiopia. Tigray is bordered with Eritrea from the north, Sudan from the west, Afar region from the east and Amhara region from the south. Administratively, Tigray is divided in to seven zones namely Central, Eastern, Mekelle, North western, Southern, South eastern and Western zones and 93 districts. In this study, 52 districts in all the zones except the western zone were included. The western zone was excluded for security concerns. The study was conducted between July 15 and Aug 15, 2021. A community based cross-sectional study was conducted in six zones and 52 randomly selected districts of Tigray. Only mothers or caretakers of children 6–59 months of age were included in the study. Inclusion criteria: all households with under one years of age children were included. Then, all children under five in the selected household were measured for nutritional status. Exclusion criteria: Serious illness in children under five. The minimum sample size was estimated using proportional allocation of the minimum sample size for a Tabia (smallest administrative unit) from each of the districts. The sample size was determined based on the burden of acute malnutrition. According to the EDHS 2016, the burden of acute malnutrition was 11% in Tigray [15]. For situations where power and prevalence are known, effective sample size can easily be estimated. For 11% prevalence, 20 subjects are sufficient to reach a power value greater than 80 [16]. Accordingly, 20 households were included from each Tabia and four Tabias were selected from each district. Thus, adding 5% of non-response, the total sample size was calculated to be 4368 [20 subjects per Tabia*4 Tabias per district*52 districts) + (5%*4160)]. However, 754 subjects were excluded from the final analysis for the following reasons; 1) 444 were under the age of 6 months; 2) 308 had no MUAC measurements, and 3) 2 were outliers. Therefore, the findings of this study were based on data from 3614 subjects. A multistage sampling technique was employed. All the zones except the western zone were included in the study. At the first stage, a total of 52 districts were randomly selected from the 93 districts. In the second stage, four Tabias were randomly selected from each of the 52 districts. Then, 20 households with under one year of age children were randomly selected from the selected tabias using the registration book of the Health Extension Workers (HEWs) as a sampling frame. When the registration book of the HEWs was not available, a new list of the households with under one year of age children was generated and used as a sampling frame to randomly select the study households. However, it must be noted that the districts from the Western zone of Tigray were not included in the sampling frame. Data were collected using a pretested and interviewer-administered structured questionnaire. The tool contained items regarding socio-demographic, health and obstetric, childhood illness and vaccination characteristics; water, hygiene and sanitation conditions; and household food insecurity, and anthropometric measurement (MUAC). Experienced HEWs were the data collectors and health and nutrition researchers/experts from Mekelle University and Tigray Health Bureau worked as supervisors of the data collection. MUAC measurements were taken from the upper left arm of the children using MUAC tapes; household food insecurity was measured using the Household Food Insecurity Access Scale (HFIAS), which was answered by the mothers/caregivers of the children. The questionnaire was initially prepared in English and then translated into the local language (Tigrigna) and was then translated back to English for consistency check. Three days training was given for the data collectors and supervisors. Moreover, fieldwork was accompanied by strict follow up and supervision. These included child age, child sex, Vitamin-A supplementation status, measles vaccine status, deworming, maternal education, paternal education, child had diarrhea in the last nine months, child had fever in the last nine months, child had cough in the last nine months, place of delivery, and antenatal care (ANC) visits. Residence, toilet facility, drinking water source, handwashing facility close to toilet, solid waste disposal, liquid waste disposal, family size, and household food insecurity were considered as community level factors. The dependent variable for this study was child acute malnutrition as measured by MUAC with two categories (“Yes” if MUAC < 12.5 cm and “No” if MUAC ≥ 12.5 cm). Data (Additional file 1) were cleaned and analyzed using Statistical package for Social Sciences (SPSS) software version 25. Categorical variables were summarized using percentages. Multilevel binary logistic regression analysis was employed to identify factors significantly associated with the outcome variable. Within the multilevel multivariable logistical regression analysis, Adjusted odds ratios (AOR) with their 95% confidence intervals were computed to measure the fixed effects of individual-level and community-levels factors on the prevalence of child acute malnutrition. During bi-variable logistic regression analysis, we used p-value of ≤ 0.2 to screen factors for multivariable logistic regression analysis. In the final multivariable logistic model, four models including an intercept-only model containing no explanatory variables, an individual-only model, a community-only model, and a combined model containing both individual and community-level variables were fitted. This helped to come up with a model where the effect of clustering is controlled and to determine the independent effect of each individual and community level factors on the dependent variable. Associations were declared statistically significant at a p-value of < 0.05. Prior to running the multivariable logistic analysis, multicollinearity among individual and community level variables was checked using Variance Inflation Factor (VIF) cutoff value of 10. From the four fitted models, the one with the lowest value of Akaike information criterion (AIC) and/or Bayesian information criterion (BIC) was selected as the best model to our data. Both AIC and BIC consist of a part that represents model fitness and a part that represent the size and dimensionality of the model as shown in the equation: IC= − 2logf(y│Ӫ)+λd, where IC stands for information criterion, f(y│Ӫ) is the likelihood of the data y evaluated using the model parameters, λ denotes the penalty weight that differs for AIC and BIC, and d represents the size or dimensionality of the model [17]. Variables that showed significant association at p ≤ 0.20 in the bivariate analysis were entered to multivariable logistic regression analysis. These included individual level variables like child age, child sex, Vitamin A supplementation, diarrhea in the last nine months, treatment sought for diarrhea, and fever. Community level variables that met this criteria were drinking water source, toilet facility, and HFIAS. Our analysis showed multicollinearity between presence of diarrhea and treatment sought for diarrhea. From the bivariate logistic regression, the p-value for treatment sought for diarrhea was higher than the p-value for presence of diarrhea, thus, we removed the variable treatment sought for diarrhea.

Izinto zokwakha

https://efashare.b-cdn.net/materials/1955afc0328b02ca5041d816ba28e09f64d41f577dcb71ec9d5f01c6f39a4fe3/13031_2023_508_MOESM1_ESM.sav

Emisha

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

1. Mobile Health (mHealth) Applications: Develop 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.

2. Telemedicine Services: Implement telemedicine services that allow pregnant women in remote or underserved areas to consult with healthcare professionals through video calls or phone calls, reducing the need for travel and improving access to medical advice.

3. Community Health Workers: Train and deploy community health workers who can provide basic prenatal and postnatal care, conduct health screenings, and educate women on maternal health practices in their own communities.

4. Transportation Solutions: Develop transportation solutions, such as ambulances or mobile clinics, to ensure that pregnant women have access to timely and safe transportation to healthcare facilities for prenatal care, delivery, and postnatal care.

5. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to cover the costs of maternal healthcare services, including prenatal care, delivery, and postnatal care.

6. Maternal Health Education Campaigns: Launch targeted education campaigns to raise awareness about the importance of maternal health and encourage women to seek prenatal and postnatal care, as well as promote healthy behaviors during pregnancy.

7. Maternal Health Clinics: Establish dedicated maternal health clinics that offer comprehensive prenatal, delivery, and postnatal care services in underserved areas, ensuring that women have access to specialized care throughout their pregnancy journey.

8. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services, leveraging the resources and expertise of both sectors to address the challenges faced in delivering quality care.

9. Maternal Health Hotlines: Set up toll-free hotlines staffed by trained healthcare professionals who can provide information, guidance, and support to pregnant women and new mothers, addressing their concerns and connecting them to appropriate healthcare services.

10. Maternal Health Monitoring Systems: Develop digital platforms or systems that enable the real-time monitoring of maternal health indicators, allowing healthcare providers to identify high-risk cases and provide timely interventions.

These innovations aim to address barriers to accessing maternal health services, improve the quality of care, and ultimately reduce maternal and infant mortality rates.

AI Innovations Description

The study conducted in Tigray, Ethiopia aimed to identify individual and community-level factors associated with acute malnutrition among children aged 6-59 months in conflict-affected settings. The study found that factors such as older child age, female child sex, Vitamin-A supplementation, history of diarrhea, unimproved drinking water source, unimproved toilet facility, and severe food insecurity were significantly associated with childhood acute malnutrition.

Based on the findings of the study, the following recommendations can be developed into an innovation to improve access to maternal health:

1. Regular nutrition screening: Implement regular nutrition screening programs for children aged 6-59 months in conflict-affected areas. This will help identify malnourished children and provide timely interventions.

2. Speedy and appropriate referral: Establish a system for speedy and appropriate referral of all malnourished children to nutritional services. This will ensure that children receive the necessary treatment and support to address their malnutrition.

3. Large-scale humanitarian assistance: Provide large-scale humanitarian assistance to conflict-affected areas, including access to food, nutrition supplies, water, sanitation, and hygiene supplies. This will help address the underlying causes of malnutrition and improve the overall health and well-being of children and their mothers.

4. Timely access to healthcare: Ensure timely access to healthcare services for mothers and children in conflict-affected areas. This includes access to antenatal care, delivery services, and postnatal care to promote safe pregnancies and childbirth.

5. International intervention: Advocate for immediate international intervention to address the severe public health problem of acute malnutrition in conflict-affected areas. This can involve mobilizing resources, expertise, and support from international organizations and governments to provide the necessary assistance and resources.

By implementing these recommendations, it is possible to improve access to maternal health and reduce the burden of acute malnutrition in conflict-affected areas, ultimately saving the lives of mothers and children.

AI Innovations Methodology

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

1. Strengthening healthcare infrastructure: Investing in the development and improvement of healthcare facilities, including hospitals, clinics, and maternity centers, can help increase access to maternal health services. This includes ensuring the availability of skilled healthcare professionals, essential medical equipment, and necessary supplies.

2. Enhancing transportation services: Improving transportation infrastructure and services can help pregnant women in remote or rural areas reach healthcare facilities more easily. This can involve initiatives such as providing ambulances or mobile clinics, improving road networks, and implementing transportation subsidies or vouchers for pregnant women.

3. Promoting community-based healthcare: Implementing community-based healthcare programs can improve access to maternal health services, especially in areas with limited healthcare facilities. This can involve training and empowering community health workers to provide basic prenatal care, education, and referrals to pregnant women in their communities.

4. Increasing awareness and education: Conducting awareness campaigns and educational programs can help pregnant women and their families understand the importance of maternal health and the available services. This can include providing information on prenatal care, nutrition, family planning, and the benefits of skilled birth attendance.

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

1. Define the indicators: Identify specific indicators that measure access to maternal health, such as the number of pregnant women receiving prenatal care, the percentage of births attended by skilled healthcare professionals, or the distance traveled by pregnant women to reach healthcare facilities.

2. Collect baseline data: Gather data on the current status of these indicators in the target population or region. This can involve conducting surveys, interviews, or reviewing existing data sources.

3. Develop a simulation model: Create a simulation model that incorporates the potential impact of the recommendations on the identified indicators. This can be done using statistical software or simulation tools.

4. Input intervention scenarios: Define different scenarios that represent the implementation of the recommendations. For example, scenario 1 could represent the strengthening of healthcare infrastructure, scenario 2 could represent the enhancement of transportation services, and so on.

5. Simulate the impact: Run the simulation model with each intervention scenario to estimate the potential impact on the selected indicators. This can involve adjusting the relevant variables and parameters in the model based on the expected effects of the recommendations.

6. Analyze the results: Compare the simulation results for each scenario to assess the potential improvements in access to maternal health. This can include analyzing changes in the selected indicators, identifying any trade-offs or synergies between the recommendations, and evaluating the cost-effectiveness of each scenario.

7. Refine and validate the model: Review the simulation model and its assumptions based on the results and feedback from experts or stakeholders. Make any necessary adjustments or refinements to improve the accuracy and reliability of the model.

8. Communicate the findings: Present the simulation results in a clear and concise manner, highlighting the potential benefits of the recommendations in improving access to maternal health. This can involve creating visualizations, reports, or presentations to effectively communicate the findings to policymakers, healthcare providers, and other relevant stakeholders.

By following this methodology, policymakers and stakeholders can gain insights into the potential impact of different recommendations on improving access to maternal health and make informed decisions on resource allocation and implementation strategies.

Umbhali

Dima Health

Identifiers:

DOI: 10.1186/s13031-023-00508-x

Research Areas:

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

Study Countries:

Eritrea, Ethiopia, Multi-Countries, Sudan

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Female

Yabelana ngalokhu: