Level and determinants of food insecurity in East and West Gojjam zones of Amhara Region, Ethiopia: A community based comparative cross-sectional study

BMC Public Health, Volume 16, No. 1, Year 2016

Interpretação

Background: Food insecurity remains highly prevalent in developing countries and over the past two decades it has increasingly been recognized as a serious public health problem, including in Ethiopia. An emerging body of literature links food insecurity to a range of negative health outcomes and causes of a decline in productivity. The objectives of the present study were to determine the level of food insecurity in East Gojjam zone where the productive safety net program is available, and in West Gojjam zone where there is no program, and to identify the determinants of food insecurity in both East and West Gojjam zones of Amhara Region, Ethiopia. Methods: Community based comparative cross-sectional study design was used from 24 May 2013- 20 July 2013. Multistage sampling technique was implemented. A total of 4110 randomly selected households in two distinct populations were approached to be included in the study. Availability and absence of the productive safety net program between the two study areas was used to categorize them as comparative groups; otherwise the two communities are comparable in many socio-cultural characteristics. The household food security access scale questionnaire, developed by the Food and Nutrition Technical Assistant Project, was used to measure food security level. Socio-demographic and other household level information were collected by using a structured questionnaire. The binary logistic regression model was used to assess factors associated with food insecurity. Results: From the total 4110 households, 3964 (96.45 %) gave complete responses. The total prevalence of food insecurity was 55.3 % (95 % CI: 53.8, 56.8). To compare food insecurity levels between the two zones, nearly sixty percent, 59.2 % (95 % CI: 57 %, 61.4 %) of the East Gojjam and 51.3 % (95 % CI: 49.1 %, 53.5) of West Gojjam households were food insecure. Family size (2-4) (AOR = 0.641, 95 % CI: 0.513, 0.801), non-merchant women (AOR = 1.638, 95 % CI: 1.015, 2.643), household monthly income quartiles, 1st (AOR = 2.756, 95 % CI: 1.902, 3.993), and 2nd (AOR =1.897, 95 % CI: 1.299, 2.775) were the significant socio-demographic determinants in east Gojjam zone. Illiterate mothers (AOR = 1.388, 95 % CI: 1.011, 1.905), household monthly income quartiles, 1st (AOR = 3.110232, 95 % CI: 2.366, 4.415), 2nd (AOR =2.618, 95 % CI: 1.892, 3.622) and 3rd (AOR = 2.177, 95 % CI: 1.6911, 2.803) were the significant socio-demographic predictors in west Gojjam zone. Rural residential area (AOR = 3.201, 95 % CI: 1.832, 5.594) and (AOR = 2.425, 95 % CI: 1.79, 3.272), highland agro-ecology (AOR = 2.193, 95 % CI: 1.348, 3.569 and AOR = 3.669, 95 % CI: 2.442, 5.513) and lack of livestock (AOR = 1.553, 95 % CI: 1.160, 2.078 and AOR = 1.568 95 % CI: 1.183, 2.080) were significant environmental predictors in east and west Gojjam zones respectively. Conclusion: Food insecurity is highly prevalent in both study areas; however, there are different predictor factors. Intervention strategies should give emphasis to women’s education, diversified income generating opportunities, and for each agro-ecological zone, mixed agriculture strategy. The study was conducted in Amhara Regional State which covers some 157,647 km2 across north western and eastern Ethiopia and has a total population of 20,018,999 (10,011,795 males and 10,007,204 females) from 24 May 2013- 20 July 2013 [24, 25]. The region is divided in to a number of highland areas separated by deep river valleys, and the eastern and western escarpments and their associated lowlands [25]. Specifically the study was conducted in the east and west Gojjam zones of the region. East Gojjam zone, which is located in the northwest 300 km distance from Addis Ababa, has 2,451,959 total population (1,199,952 males and 1,252,006 females). West Gojjam zone, which is located in the same direction at 385 km from Addis Ababa, has a total population of 2,474,254 (1,220,477 males and 1,253,777 females) [24]. The mean annual temperature of the region ranges from 22-27OC in the lowlands and between 10 and 22OC in the highlands up to 3,000 meter above sea level [25]. The long term mean annual rainfall of the region is 1165.2 mm [26]. However areas in the specific study sites received 1100 to 1360 mm of mean annual rainfall per year [26]. Within the region four major cereal systems have been recognized: sorghum-maize system in the lowland agro-ecological zone, wheat-teff system in the single rain season area of the mid-land agro-ecological zone, wheat-teff system in the double rain seasons of the mid-land agro-ecological zone and barley system in the high land agro-ecological zone [25]. Community based comparative cross-sectional study design was used to determine the level of food insecurity and its determinants. Households in the study area were used as a sampling unit and all the necessary data were drawn from the mother in the household. The two groups were classified based on the availability of the productive safety net program; Group 1 with the productive safety net program and Group 2 without the productive safety net program. The current study used a sample size determined for another larger study that aimed to see the association between food insecurity and malnutrition. Although the study concerns for stunting, wasting and underweight, the prevalence of stunting has been taken to determine the sample size as it is considered to be the best feature of nutritional status of the community and also since it is not affected by acute events. The 2011 EDHS national prevalence (44 %) has been taken as the malnutrition prevalence for food surplus area and 50 %, which is the worst, for food insecure area as there is no specific study for this area. The study is designed to show the difference at the significance level of 1 % and power of 90 %. Where, P1 is the prevalence of stunting in Ethiopian children underfive; Therefore, the sample size will be; n = 2055 households for each Therefore the sample size was 2050 for each (4110 total). This sample was compared with the sample that was determined for food insecurity objectives that was calculated using StatCal of Epi Info utility with P = 50 % (the possible maximum sample size) and a precision level of 0.02. The sample was found to be 2396 (1198 for each category) at 95 % confidence level. Multistage sampling technique was implemented to reach and select the final study units. In the first place the two zones (east and west Gojjam) were selected purposely by taking into account the availability and absence of the productive safety net program in the two zones. This is because areas covered by the productive safety net program are considered as food insecure (the three districts in east Gojjam zone in this case) and west Gojjam zone is considered as a food surplus area (based on highly productive nature of the zone) by the regional government. Six districts from the two zones (three from each zone) were selected. The three districts from east Gojjam zone (Enebsie Sar Midir, Goncha Siso Enesie, and Shebel Berenta) covered by the safety net program were purposely selected. Three equal numbers of districts (Mecha, North Achefer, and Jabi Tehinan) in the west Gojjam zone were selected randomly from the total 14 districts. The two zones are more comparable in many socio-cultural characteristics than the other zones of the region. Once the districts were identified, kebeles (the smallest administrative unit in the country) from those districts with the program were selected randomly and included in the study. The kebeles were selected based on agro-ecological zones and urban rural settings. Four town kebeles, three rural high land kebeles, eleven rural mid-land kebeles, and six rural lowland kebeles were selected randomly. Then, the total sample size was divided proportionally to the kebele households. The households from these kebeles were selected using a systematic random sampling technique using household registration as a sampling frame. For the case of east Gojjam zone, safety net program registration was used as a sampling frame. The total number of households in each kebele was divided by the allocated sample size to get the sampling interval. When there was more than one mother in the same household, one mother was selected by lottery method. Structured questionnaires, adopted from different standard questionnaires [27, 28] and developed by the authors, were used to collect the data. Some of the variables adopted from the EDHS questionnaire include age of the mother, marital status, educational level, family size, occupation, household monthly income and housing conditions. Variables like household (HH) head, female authority and agro-ecological zone were prepared and included in the questionnaire by the authors. Household food security (access) information was collected by using the questionnaire adopted from the Household Food Insecurity Access Scale (HFIAS) measurement tool which is developed by Food and Nutrition Technical Assistant Project (FANTA) [29]. The questionnaire was translated in to the local language (Amharic). Beside the translation of the questionnaire from English to Amharic and back to English, a pre-test was done on 120 subjects to check if they understood it easily or not. After the pre-test was done, a detailed demonstration was given to data collectors especially on ways how to explain the questionnaire to the respondent. Food secure – household experiences none of the food insecurity conditions, or just experiences worry, but rarely [29]. Mildly food insecure – household worries about not having enough food sometimes or often, and/or is unable to eat preferred foods, and/or eats a more monotonous diet than desired and/or some foods considered undesirable, but only rarely [29]. Moderately food insecure – household sacrifices quality more frequently, by eating a monotonous diet or undesirable foods sometimes or often, and/or has started to cut back on quantity by reducing the size of meals or number of meals, rarely or sometimes [29]. Severely food insecure – household has graduated to cutting back on meal size or number of meals often, and/or experiences any of the three most severe conditions (running out of food, going to bed hungry, or going a whole day and night without eating). In other words, any household that experiences one of these three conditions even once in the last four weeks [29]. To assure the quality of the data and to make sure that all assessment team members were able to administer the questionnaires properly, a total of five days rigorous training of enumerators and supervisors was given. Before the actual data collection work, data collectors and supervisors carried out role play practices and then had field pre-test activities. The data collectors and supervisors were university graduate BSc holders. At the end of every data collection day, each questionnaire was examined for completeness and consistency by the supervisors and the principal investigator, and pertinent feedback was given to the data collectors and supervisors. The data were coded, entered and cleaned by Epi-Info 2000 version 3.5.3 and transported to SPSS version 20. Descriptive summaries such as frequencies, proportions, percentages, mean, standard deviations and prevalence were determined. Excel was used to determine food insecurity prevalence and to identify the four categories (food secure, mildly food insecure, moderately food insecure and severely food insecure) by using IF OR/AND logical test function formula. For determinant variable identification, first bivariate logistic regression analyses were carried out to identify candidate variables for multivariate model at P-value < 0.25. Then, to identify the predictors of food insecurity variables that were significantly associated with food insecurity in the bivariate models were entered in the multivariate logistic regression model. At this step, model fitness and the presence of multicollinearity were assessed. The covariate also categorized into socio-demographic and environmental determinants. The model fitness was checked by observing the difference of the -2 log likelihood ratio between the model with only the constant and with the predictors. The significance of each predictor in the equation was also assessed by Wald statistics test at a significance level of P-value < 0.05. Few variables were excluded from the last model due to instability of the model with their presence and their high correlation (maternal occupation; hose wife versus farmer, r = 0.934).

AI Digest

Study Justification:

– Food insecurity is a prevalent issue in developing countries, including Ethiopia.
– The study aims to determine the level of food insecurity in two zones of Amhara Region, Ethiopia, and identify the factors contributing to food insecurity.
– The findings will contribute to the understanding of food insecurity and its determinants, which can inform policy and intervention strategies.

Study Highlights:

– The study found that the overall prevalence of food insecurity in the study areas was 55.3%.
– There was a higher prevalence of food insecurity in the East Gojjam zone (59.2%) compared to the West Gojjam zone (51.3%).
– Socio-demographic factors such as family size, women’s occupation, and household income were significant determinants of food insecurity in both zones.
– Environmental factors such as residential area, agro-ecology, and livestock ownership also played a role in food insecurity.

Study Recommendations:

– Intervention strategies should prioritize women’s education and provide diversified income-generating opportunities.
– Different agro-ecological zones require tailored approaches to address food insecurity.
– Emphasis should be placed on implementing mixed agriculture strategies.

Key Role Players:

– Government agencies responsible for food security and agriculture policies.
– Non-governmental organizations (NGOs) working on poverty alleviation and food security.
– Community leaders and local authorities.
– Researchers and academics specializing in food security and public health.

Cost Items for Planning Recommendations:

– Education and training programs for women.
– Income-generating projects and support for small-scale farmers.
– Infrastructure development for agriculture and livestock production.
– Monitoring and evaluation systems to assess the effectiveness of interventions.
– Research and data collection to inform evidence-based policies and strategies.

Força da prova

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study design is appropriate for determining the level of food insecurity and its determinants. The sample size calculation is based on a previous study and the statistical analysis is well-described. However, the abstract could be improved by providing more information on the methods used for data collection and analysis. Additionally, it would be helpful to include information on the limitations of the study and any potential biases that may have influenced the results.

Resumo

The study was conducted in Amhara Regional State which covers some 157,647 km2 across north western and eastern Ethiopia and has a total population of 20,018,999 (10,011,795 males and 10,007,204 females) from 24 May 2013- 20 July 2013 [24, 25]. The region is divided in to a number of highland areas separated by deep river valleys, and the eastern and western escarpments and their associated lowlands [25]. Specifically the study was conducted in the east and west Gojjam zones of the region. East Gojjam zone, which is located in the northwest 300 km distance from Addis Ababa, has 2,451,959 total population (1,199,952 males and 1,252,006 females). West Gojjam zone, which is located in the same direction at 385 km from Addis Ababa, has a total population of 2,474,254 (1,220,477 males and 1,253,777 females) [24]. The mean annual temperature of the region ranges from 22-27OC in the lowlands and between 10 and 22OC in the highlands up to 3,000 meter above sea level [25]. The long term mean annual rainfall of the region is 1165.2 mm [26]. However areas in the specific study sites received 1100 to 1360 mm of mean annual rainfall per year [26]. Within the region four major cereal systems have been recognized: sorghum-maize system in the lowland agro-ecological zone, wheat-teff system in the single rain season area of the mid-land agro-ecological zone, wheat-teff system in the double rain seasons of the mid-land agro-ecological zone and barley system in the high land agro-ecological zone [25]. Community based comparative cross-sectional study design was used to determine the level of food insecurity and its determinants. Households in the study area were used as a sampling unit and all the necessary data were drawn from the mother in the household. The two groups were classified based on the availability of the productive safety net program; Group 1 with the productive safety net program and Group 2 without the productive safety net program. The current study used a sample size determined for another larger study that aimed to see the association between food insecurity and malnutrition. Although the study concerns for stunting, wasting and underweight, the prevalence of stunting has been taken to determine the sample size as it is considered to be the best feature of nutritional status of the community and also since it is not affected by acute events. The 2011 EDHS national prevalence (44 %) has been taken as the malnutrition prevalence for food surplus area and 50 %, which is the worst, for food insecure area as there is no specific study for this area. The study is designed to show the difference at the significance level of 1 % and power of 90 %. Where, P1 is the prevalence of stunting in Ethiopian children underfive; Therefore, the sample size will be; n = 2055 households for each Therefore the sample size was 2050 for each (4110 total). This sample was compared with the sample that was determined for food insecurity objectives that was calculated using StatCal of Epi Info utility with P = 50 % (the possible maximum sample size) and a precision level of 0.02. The sample was found to be 2396 (1198 for each category) at 95 % confidence level. Multistage sampling technique was implemented to reach and select the final study units. In the first place the two zones (east and west Gojjam) were selected purposely by taking into account the availability and absence of the productive safety net program in the two zones. This is because areas covered by the productive safety net program are considered as food insecure (the three districts in east Gojjam zone in this case) and west Gojjam zone is considered as a food surplus area (based on highly productive nature of the zone) by the regional government. Six districts from the two zones (three from each zone) were selected. The three districts from east Gojjam zone (Enebsie Sar Midir, Goncha Siso Enesie, and Shebel Berenta) covered by the safety net program were purposely selected. Three equal numbers of districts (Mecha, North Achefer, and Jabi Tehinan) in the west Gojjam zone were selected randomly from the total 14 districts. The two zones are more comparable in many socio-cultural characteristics than the other zones of the region. Once the districts were identified, kebeles (the smallest administrative unit in the country) from those districts with the program were selected randomly and included in the study. The kebeles were selected based on agro-ecological zones and urban rural settings. Four town kebeles, three rural high land kebeles, eleven rural mid-land kebeles, and six rural lowland kebeles were selected randomly. Then, the total sample size was divided proportionally to the kebele households. The households from these kebeles were selected using a systematic random sampling technique using household registration as a sampling frame. For the case of east Gojjam zone, safety net program registration was used as a sampling frame. The total number of households in each kebele was divided by the allocated sample size to get the sampling interval. When there was more than one mother in the same household, one mother was selected by lottery method. Structured questionnaires, adopted from different standard questionnaires [27, 28] and developed by the authors, were used to collect the data. Some of the variables adopted from the EDHS questionnaire include age of the mother, marital status, educational level, family size, occupation, household monthly income and housing conditions. Variables like household (HH) head, female authority and agro-ecological zone were prepared and included in the questionnaire by the authors. Household food security (access) information was collected by using the questionnaire adopted from the Household Food Insecurity Access Scale (HFIAS) measurement tool which is developed by Food and Nutrition Technical Assistant Project (FANTA) [29]. The questionnaire was translated in to the local language (Amharic). Beside the translation of the questionnaire from English to Amharic and back to English, a pre-test was done on 120 subjects to check if they understood it easily or not. After the pre-test was done, a detailed demonstration was given to data collectors especially on ways how to explain the questionnaire to the respondent. Food secure – household experiences none of the food insecurity conditions, or just experiences worry, but rarely [29]. Mildly food insecure – household worries about not having enough food sometimes or often, and/or is unable to eat preferred foods, and/or eats a more monotonous diet than desired and/or some foods considered undesirable, but only rarely [29]. Moderately food insecure – household sacrifices quality more frequently, by eating a monotonous diet or undesirable foods sometimes or often, and/or has started to cut back on quantity by reducing the size of meals or number of meals, rarely or sometimes [29]. Severely food insecure – household has graduated to cutting back on meal size or number of meals often, and/or experiences any of the three most severe conditions (running out of food, going to bed hungry, or going a whole day and night without eating). In other words, any household that experiences one of these three conditions even once in the last four weeks [29]. To assure the quality of the data and to make sure that all assessment team members were able to administer the questionnaires properly, a total of five days rigorous training of enumerators and supervisors was given. Before the actual data collection work, data collectors and supervisors carried out role play practices and then had field pre-test activities. The data collectors and supervisors were university graduate BSc holders. At the end of every data collection day, each questionnaire was examined for completeness and consistency by the supervisors and the principal investigator, and pertinent feedback was given to the data collectors and supervisors. The data were coded, entered and cleaned by Epi-Info 2000 version 3.5.3 and transported to SPSS version 20. Descriptive summaries such as frequencies, proportions, percentages, mean, standard deviations and prevalence were determined. Excel was used to determine food insecurity prevalence and to identify the four categories (food secure, mildly food insecure, moderately food insecure and severely food insecure) by using IF OR/AND logical test function formula. For determinant variable identification, first bivariate logistic regression analyses were carried out to identify candidate variables for multivariate model at P-value < 0.25. Then, to identify the predictors of food insecurity variables that were significantly associated with food insecurity in the bivariate models were entered in the multivariate logistic regression model. At this step, model fitness and the presence of multicollinearity were assessed. The covariate also categorized into socio-demographic and environmental determinants. The model fitness was checked by observing the difference of the -2 log likelihood ratio between the model with only the constant and with the predictors. The significance of each predictor in the equation was also assessed by Wald statistics test at a significance level of P-value < 0.05. Few variables were excluded from the last model due to instability of the model with their presence and their high correlation (maternal occupation; hose wife versus farmer, r = 0.934).

Materiais

N/A

Inovações

Based on the description provided, the study focused on determining the level of food insecurity and its determinants in East and West Gojjam zones of Amhara Region, Ethiopia. The study used a community-based comparative cross-sectional study design and collected data from 4,110 randomly selected households. The study found that the total prevalence of food insecurity was 55.3%, with 59.2% of households in East Gojjam and 51.3% of households in West Gojjam being food insecure.

The study identified several socio-demographic and environmental determinants of food insecurity. In East Gojjam zone, family size (2-4), non-merchant women, and household monthly income quartiles (1st and 2nd) were significant determinants. In West Gojjam zone, illiterate mothers and household monthly income quartiles (1st, 2nd, and 3rd) were significant determinants. Additionally, rural residential area, highland agro-ecology, and lack of livestock were significant environmental predictors in East and West Gojjam zones, respectively.

Based on these findings, potential innovations to improve access to maternal health could include:

1. Income generation programs: Implementing income generation programs targeted towards women, such as vocational training or microfinance initiatives, can help improve household income and reduce food insecurity.

2. Women’s education programs: Investing in education programs for women, particularly in rural areas, can empower them with knowledge and skills to improve their livelihoods and make informed decisions regarding maternal health.

3. Agricultural interventions: Introducing agricultural interventions, such as improved farming techniques or crop diversification, can enhance food production and increase household food security.

4. Livestock support programs: Providing support for livestock ownership, such as training in animal husbandry or access to veterinary services, can contribute to household food security by ensuring a sustainable source of food and income.

5. Safety net programs: Expanding the coverage and effectiveness of safety net programs, like the productive safety net program mentioned in the study, can help mitigate the impact of food insecurity on maternal health by providing targeted assistance to vulnerable households.

These innovations can contribute to improving access to maternal health by addressing the underlying factors of food insecurity and creating sustainable solutions for vulnerable populations.

AI Innovations Description

The study mentioned focuses on determining the level of food insecurity and its determinants in the East and West Gojjam zones of Amhara Region, Ethiopia. The study found that food insecurity is highly prevalent in both study areas, with different predictor factors. The significant socio-demographic determinants in the East Gojjam zone were family size, non-merchant women, and household monthly income quartiles. In the West Gojjam zone, the significant socio-demographic predictors were illiterate mothers and household monthly income quartiles. Significant environmental predictors in the East and West Gojjam zones were rural residential area, highland agro-ecology, and lack of livestock.

Based on this study, a recommendation to improve access to maternal health could be to implement interventions that address the determinants of food insecurity identified in the study. These interventions could include:

1. Women’s education: Providing educational opportunities for women can empower them to make informed decisions about their health and nutrition, leading to improved maternal health outcomes.

2. Diversified income generating opportunities: Creating income-generating opportunities for households can help alleviate poverty and improve access to nutritious food, thus reducing food insecurity and its negative impact on maternal health.

3. Mixed agriculture strategy: Promoting a diversified and sustainable agricultural approach can enhance food production and ensure food security for households, contributing to improved maternal health.

By addressing these determinants of food insecurity, access to maternal health can be improved, leading to better health outcomes for mothers and their children.

AI Innovations Methodology

The study you provided focuses on determining the level of food insecurity in East and West Gojjam zones of Amhara Region, Ethiopia, and identifying the determinants of food insecurity in these areas. While the study does not directly address maternal health, improving access to maternal health can be achieved through various innovations. Here are some potential recommendations for improving access to maternal health:

1. Mobile clinics: Implementing mobile clinics that travel to remote areas can provide essential maternal health services to women who have limited access to healthcare facilities.

2. Telemedicine: Utilizing telemedicine technology can connect pregnant women in remote areas with healthcare professionals, allowing them to receive prenatal care and consultations without the need for travel.

3. Community health workers: Training and deploying community health workers who can provide basic maternal health services, education, and referrals in underserved areas can improve access to care.

4. Transportation support: Providing transportation support, such as ambulances or vouchers for transportation, can help pregnant women reach healthcare facilities in a timely manner, especially in emergency situations.

5. Maternal health education: Conducting community-based maternal health education programs can increase awareness and knowledge about prenatal care, safe delivery practices, and postnatal care, empowering women to make informed decisions about their health.

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 prenatal visits, percentage of deliveries attended by skilled birth attendants, or maternal mortality rates.

2. Baseline data collection: Gather data on the current status of the selected indicators in the study area before implementing the recommendations. This can be done through surveys, interviews, or existing data sources.

3. Intervention implementation: Implement the recommended innovations, such as mobile clinics, telemedicine services, or community health worker programs, in the selected areas.

4. Data collection after intervention: Collect data on the selected indicators after the implementation of the interventions. This can be done using the same methods as the baseline data collection.

5. Data analysis: Compare the baseline data with the post-intervention data to assess the impact of the recommendations on the selected indicators. Statistical analysis, such as t-tests or chi-square tests, can be used to determine if there are significant differences.

6. Interpretation and reporting: Analyze the results and interpret the findings to understand the impact of the recommendations on improving access to maternal health. Prepare a report summarizing the methodology, results, and conclusions.

By following this methodology, researchers and policymakers can gain insights into the effectiveness of the recommended innovations in improving access to maternal health and make informed decisions for future interventions.

Autor

Dima Health

Statistics:

Citations: 42

Identifiers:

DOI: 10.1186/s12889-016-3186-7

Research Areas:

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

Study Countries:

Ethiopia

Study Design:

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

Study Approach:

Quantitative

Participants Gender:

Female

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