Household food insecurity and its association with school absenteeism among primary school adolescents in Jimma zone, Ethiopia

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

Interpretation

Background: Household food insecurity and lack of education are two of the most remarkable deprivations which developing countries are currently experiencing. Evidences from different studies showed that health and nutrition problems are major barriers to educational access and achievement in low-income countries which poses a serious challenge on effort towards the achieving Sustainable Development Goals. Evidence on the link between food security and school attendance is very important to address this challenge. This study aimed to assess to what extent food insecurity affects school absenteeism among primary school adolescents. Methods: A school based cross-sectional study was conducted among primary school adolescents in Jimma zone from October-November, 2013. Structured questionnaire was used to collect data on the household food security and socio-demographic variables. Data were analyzed using SPSS for windows version 16.0 after checking for missing values and outliers. Multivariable logistic regression analyses were used to determine the association of school absenteeism and food insecurity with independent variables using odds ratio and 95 % of confidence intervals. Variables with p ≤ 0.25 in the bivariate analyses were entered into a multivariable regression analysis to control for associations among the independent variables. Results: The frequency of adolescent school absenteeism was significantly high (50.20 %) among food insecure households (P < 0.001) compared to their peers whose households were food secure (37.89 %). Findings of multivariable logistic regression analysis also showed that household food insecurity [AOR = 2.81 (1.70, 4.76)] was positively associated with poor school attendance while female-headed household [AOR = 0.23 (0.07, 0.72)], urban residence [AOR = 0.52 (0.36, 0.81)] and male-gender [AOR = 0.64 (0.54, 0.74)] were inversely associated with school absenteeism. Household food insecurity was positively associated with lack of maternal education [AOR = 2.26 (0.57, 8.93)] and poor household economic status [AOR = 1.39 (1.18, 2.83)]. However, livestock ownership [AOR = 0.17 (0.06, 0.51)] was negatively associated with household food insecurity. Conclusions: Findings of this study showed that household food insecurity has strong linkage with adolescent school absenteeism. Maternal education and household economic status were significantly associated with household food security status. Therefore, national policies and programs need to stress on how to improve family income earning capacity and socioeconomic status to handle household food insecurity which is a key contributor of adolescent school absenteeism. A school based cross-sectional study was conducted in the Jimma from October-November, 2013. Jimma Zone is one of the 20 administrative zones of Oromia Regional State with its capital Jimma town located at 350 km from Addis Ababa, Ethiopia, in the southwest direction. Jimma Zone has 18 administrative districts containing a total population of 2.5 million with the majority (94 %) living in the rural settings. The study area was stratified into urban schools and rural schools to represent a range of ecological and developmental settings. Four primary schools were selected from both urban and rural schools. Then, from each school, sections (classes) were identified and eligible study participants were randomly selected. A total of 1000 students who were permanent residents attending the school of the study area were randomly selected using their rosters as a frame. The sample was calculated using Gpower 3.0 with the following assumptions: 90.4 % an expected prevalence of poor dietary practices among food insecure school adolescents, 0.42 odds ratio of poor dietary practices among food insecure adolescents (29), margin of error of 5 % and power of 88 %. This gives a total sample size 434 and multiplied with a design effect of two. Finally, 15 % non-response was added to a final sample. Structured questionnaire was used to collect data on food security, socio-demographic and economic variables. Questionnaires were prepared in English and translated to Amharic and Afan Oromo by language experts. Finally, questionnaires were retranslated back to English by a person who can speak both languages. After the recruitment of data collectors, questionnaire was pre-tested for its clarity and time required. Based on a pretest, additional adjustment was made on terminologies and the formats of the questionnaire. Data were collected by trained data collectors who were selected depending on their abilities of speaking the local language. Supervisors kept track of the field procedures and daily checked the completed questionnaires to ensure accuracy of the collected data. To avoid the possibility of measurement bias, data collectors were blinded of the study objective. Household food insecurity was measured in the last three months using household food insecurity scales that were validated for use in developing countries [6, 7, 11] which included questions (I) the respondent worried about food (II) the household run out of food (III) reduced food variety and enforced to eat similar food (IV) reduced the amount of food intake and skipped meal (V), the respondent or another adult did not have enough to eat (VI) and felt hungry due to lack of food and stayed without food for 24 h. All “Yes” responses were coded as one and “No” responses were coded as zero. Finally, all responses were summed and dichotomized to food secure and food insecure to create an index of household food insecurity. School absenteeism was defined as any illegitimate absence from school for at least a day within the last semester, which do not include formal school closure days (national holidays or religious days). Data analyses were done using SPSS for windows version 16.0 (Chicago, Illinois) after checking for missing values and outliers. Descriptive statistics were presented using standard statistical parameters such as percentages, means and standard deviations. Multivariable logistic regression analyses were used to determine the association of school absenteeism and food insecurity with independent variables using odds ratio and 95 % of confidence intervals. Multicollinearity among independent variables was assessed using the standard errors. The standard errors for regression coefficients <2.0, as a familiar cutoff value, showed that there was no multicollinearity among independent variables. Variables that have p values ≤0.25 with dependent variables in the bivariate analyses were selected as a candidate variable for multivariable logistic regression analyses to control for associations among the independent variables.

AI Digest

Study Justification:

– Household food insecurity and lack of education are significant challenges in developing countries.
– Health and nutrition problems hinder educational access and achievement in low-income countries.
– Understanding the link between food security and school attendance is crucial for addressing these challenges.

Highlights:

– A school-based cross-sectional study was conducted in Jimma Zone, Ethiopia.
– The study found that school absenteeism was significantly higher among adolescents from food insecure households compared to food secure households.
– Female-headed households, urban residence, and male gender were inversely associated with school absenteeism.
– Maternal education and household economic status were significantly associated with household food security status.

Recommendations:

– National policies and programs should focus on improving family income earning capacity and socioeconomic status to address household food insecurity.
– Efforts should be made to empower female-headed households and promote education for girls.
– Strategies to improve food security and reduce school absenteeism should be integrated into existing educational and health programs.

Key Role Players:

– Government agencies responsible for education, health, and social welfare.
– Non-governmental organizations working on poverty alleviation and education.
– Community leaders and local authorities.
– School administrators, teachers, and parents.

Cost Items for Planning Recommendations:

– Capacity building and training programs for government officials, NGO staff, and community leaders.
– Awareness campaigns and educational materials.
– Income generation projects and vocational training programs.
– School feeding programs and nutrition interventions.
– Monitoring and evaluation activities to assess the impact of interventions.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it presents the findings of a school-based cross-sectional study conducted in Jimma, Ethiopia. The study used a structured questionnaire to collect data on household food security and socio-demographic variables. Data analysis was done using multivariable logistic regression analysis, which allows for controlling for associations among independent variables. The study found a significant association between household food insecurity and school absenteeism among primary school adolescents. The abstract provides specific details about the study design, data collection methods, and statistical analysis. However, to improve the evidence, it would be helpful to include information on the sample size calculation, response rate, and any limitations of the study.

Abstract

A school based cross-sectional study was conducted in the Jimma from October-November, 2013. Jimma Zone is one of the 20 administrative zones of Oromia Regional State with its capital Jimma town located at 350 km from Addis Ababa, Ethiopia, in the southwest direction. Jimma Zone has 18 administrative districts containing a total population of 2.5 million with the majority (94 %) living in the rural settings. The study area was stratified into urban schools and rural schools to represent a range of ecological and developmental settings. Four primary schools were selected from both urban and rural schools. Then, from each school, sections (classes) were identified and eligible study participants were randomly selected. A total of 1000 students who were permanent residents attending the school of the study area were randomly selected using their rosters as a frame. The sample was calculated using Gpower 3.0 with the following assumptions: 90.4 % an expected prevalence of poor dietary practices among food insecure school adolescents, 0.42 odds ratio of poor dietary practices among food insecure adolescents (29), margin of error of 5 % and power of 88 %. This gives a total sample size 434 and multiplied with a design effect of two. Finally, 15 % non-response was added to a final sample. Structured questionnaire was used to collect data on food security, socio-demographic and economic variables. Questionnaires were prepared in English and translated to Amharic and Afan Oromo by language experts. Finally, questionnaires were retranslated back to English by a person who can speak both languages. After the recruitment of data collectors, questionnaire was pre-tested for its clarity and time required. Based on a pretest, additional adjustment was made on terminologies and the formats of the questionnaire. Data were collected by trained data collectors who were selected depending on their abilities of speaking the local language. Supervisors kept track of the field procedures and daily checked the completed questionnaires to ensure accuracy of the collected data. To avoid the possibility of measurement bias, data collectors were blinded of the study objective. Household food insecurity was measured in the last three months using household food insecurity scales that were validated for use in developing countries [6, 7, 11] which included questions (I) the respondent worried about food (II) the household run out of food (III) reduced food variety and enforced to eat similar food (IV) reduced the amount of food intake and skipped meal (V), the respondent or another adult did not have enough to eat (VI) and felt hungry due to lack of food and stayed without food for 24 h. All “Yes” responses were coded as one and “No” responses were coded as zero. Finally, all responses were summed and dichotomized to food secure and food insecure to create an index of household food insecurity. School absenteeism was defined as any illegitimate absence from school for at least a day within the last semester, which do not include formal school closure days (national holidays or religious days). Data analyses were done using SPSS for windows version 16.0 (Chicago, Illinois) after checking for missing values and outliers. Descriptive statistics were presented using standard statistical parameters such as percentages, means and standard deviations. Multivariable logistic regression analyses were used to determine the association of school absenteeism and food insecurity with independent variables using odds ratio and 95 % of confidence intervals. Multicollinearity among independent variables was assessed using the standard errors. The standard errors for regression coefficients <2.0, as a familiar cutoff value, showed that there was no multicollinearity among independent variables. Variables that have p values ≤0.25 with dependent variables in the bivariate analyses were selected as a candidate variable for multivariable logistic regression analyses to control for associations among the independent variables.

Materials

N/A

Innovations

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

1. Mobile health (mHealth) interventions: Develop mobile applications or text messaging services that provide pregnant women and new mothers with information on prenatal care, nutrition, and postnatal care. These interventions can also send reminders for appointments and medication adherence.

2. Telemedicine: Implement telemedicine programs that allow pregnant women in remote or underserved areas to consult with healthcare providers through video calls. This can help overcome geographical barriers and provide access to specialized care.

3. Community health workers: Train and deploy community health workers to provide education and support to pregnant women and new mothers in their communities. These workers can conduct home visits, provide counseling, and facilitate access to healthcare services.

4. Maternal health clinics: Establish dedicated maternal health clinics that offer comprehensive services, including prenatal care, delivery, and postnatal care. These clinics can be equipped with skilled healthcare providers and necessary medical equipment.

5. Financial incentives: Introduce financial incentives, such as conditional cash transfers or vouchers, to encourage pregnant women to seek and utilize maternal health services. This can help alleviate financial barriers and increase access to care.

6. Public-private partnerships: Foster collaborations between the public sector, private sector, and non-profit organizations to improve access to maternal health services. This can involve leveraging private sector resources and expertise to expand healthcare infrastructure and service delivery.

7. Health education programs: Develop and implement health education programs that target pregnant women and their families. These programs can focus on raising awareness about the importance of prenatal care, nutrition, and safe delivery practices.

8. Maternity waiting homes: Establish maternity waiting homes near healthcare facilities to accommodate pregnant women who live far away. These homes provide a safe and supportive environment for women to stay during the final weeks of pregnancy, ensuring timely access to care.

9. Transportation support: Provide transportation support, such as subsidized or free transportation services, to pregnant women who face challenges in reaching healthcare facilities. This can help overcome transportation barriers and ensure timely access to maternal health services.

10. Quality improvement initiatives: Implement quality improvement initiatives in healthcare facilities to enhance the overall quality of maternal health services. This can involve training healthcare providers, improving infrastructure, and strengthening referral systems.

It is important to note that the specific context and needs of the target population should be considered when implementing these innovations.

AI Innovations Description

Based on the study conducted in Jimma, Ethiopia, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Implement targeted interventions: Based on the findings that household food insecurity is positively associated with poor school attendance among primary school adolescents, targeted interventions should be implemented to address this issue. These interventions can include providing nutritious meals or food assistance programs specifically for pregnant women and new mothers to ensure they have access to adequate nutrition.

2. Improve maternal education: The study also found that lack of maternal education is significantly associated with household food insecurity. To address this, innovative programs can be developed to improve maternal education and awareness about nutrition, health, and hygiene practices. This can be done through community-based education programs, mobile health clinics, or online platforms that provide accessible and culturally appropriate information.

3. Enhance household economic status: The study identified poor household economic status as a significant factor contributing to household food insecurity. To improve access to maternal health, innovative approaches should be developed to enhance household economic status. This can include providing microfinance opportunities, vocational training, or entrepreneurship programs specifically targeted towards women to empower them economically.

4. Strengthen livestock ownership: The study found that livestock ownership is negatively associated with household food insecurity. Therefore, innovative strategies should be implemented to strengthen livestock ownership among households. This can include providing training and support for livestock rearing, access to veterinary services, and market linkages to ensure sustainable income generation.

5. Collaborate with stakeholders: To effectively implement these recommendations, collaboration with various stakeholders is crucial. This can include government agencies, non-governmental organizations, community leaders, and healthcare providers. By working together, resources can be pooled, and expertise can be shared to develop and implement innovative solutions to improve access to maternal health.

AI Innovations Methodology

To improve access to maternal health, here are some potential recommendations:

1. Mobile health clinics: Implementing mobile health clinics that can travel to remote areas and provide essential maternal health services such as prenatal care, vaccinations, and postnatal care.

2. Telemedicine: Utilizing telemedicine technology to connect pregnant women in remote areas with healthcare professionals who can provide virtual consultations and guidance.

3. Community health workers: Training and deploying community health workers who can provide basic maternal health services, education, and support in underserved areas.

4. Maternal health vouchers: Introducing voucher programs that provide financial assistance to pregnant women, enabling them to access quality maternal healthcare services.

5. Maternal waiting homes: Establishing maternal waiting homes near healthcare facilities, where pregnant women from remote areas can stay closer to the facility before and after giving birth.

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

1. Define the target population: Identify the specific population group that will benefit from the recommendations, such as pregnant women in remote areas.

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

3. Develop a simulation model: Create a mathematical or computer-based model that simulates the impact of the recommendations on access to maternal health. This model should consider factors such as the number of mobile health clinics or community health workers deployed, the reach of telemedicine services, and the utilization rates of maternal waiting homes or voucher programs.

4. Input data and parameters: Input the baseline data and parameters into the simulation model, including the characteristics of the target population, the geographical distribution of healthcare facilities, and the capacity of the recommended interventions.

5. Run simulations: Run multiple simulations using different scenarios, such as varying the number of mobile health clinics or the coverage of telemedicine services. Each simulation should generate estimates of the impact on access to maternal health, such as the reduction in travel distance, increase in utilization rates, or improvement in health outcomes.

6. Analyze results: Analyze the results of the simulations to determine the potential impact of the recommendations on improving access to maternal health. Compare the different scenarios to identify the most effective interventions and their potential benefits.

7. Validate and refine the model: Validate the simulation model by comparing the predicted outcomes with real-world data or conducting field studies. Refine the model based on the validation results to improve its accuracy and reliability.

8. Communicate findings: Present the findings of the simulation study to policymakers, healthcare providers, and other stakeholders to inform decision-making and prioritize the implementation of the recommended interventions.

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

Author

Dima Health

Statistics:

Citations: 18

Identifiers:

DOI: 10.1186/s12889-016-3479-x

Research Areas:

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

Study Countries:

Ethiopia

Study Design:

Case-Control Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female