The effect of school feeding programme on class absenteeism and academic performance of schoolchildren in Southern Ethiopia: A prospective cohort study

Public Health Nutrition, Volume 24, No. 10, Year 2021

Interpretation

Abstract Objective: Ethiopia recently scaled up the implementation of a school feeding programme (SFP). Yet, evidence on the impact of such programmes on academic outcomes remains inconclusive. We evaluated the effect of the SFP on class absenteeism and academic performance of primary school students (grade 5-8) in Sidama zone, Southern Ethiopia. Design: This prospective cohort study enrolled SFP-beneficiary (n 240) and non-beneficiary (n 240) children 10-14 years of age from sixteen public schools and followed them for an academic year. School absenteeism was measured as the number of days children were absent from school in the year. Academic performance was defined based on the average academic score of the students for ten subjects they attended in the year. Data were analysed using multivariable mixed effects negative binomial and linear regression models. Setting: Food insecure districts in Sidama zone, Southern Ethiopia. Participants: SFP-beneficiary and non-beneficiary children 10-14 years of age. Results: The mean (sd) number of days children were absent from school was 4·0 (sd 1·5) and 9·3 (sd 6·0), among SFP beneficiaries and non-beneficiaries, respectively. Students not covered by the SFP were two times more likely to miss classes (adjusted rate ratio = 2·30; 95 % CI 2·03, 2·61). Pertaining to academic performance, a significant but small 2·40 (95 % CI 0·69, 4·12) percentage point mean difference was observed in favour of SFP beneficiaries. Likewise, the risk of school dropout was six times higher among non-beneficiaries (adjusted rate ratio = 6·04; 95 % CI 1·61, 22·68). Conclusions: SFP promotes multiple academic outcomes among socio-economically disadvantaged children. This prospective cohort study compared class absenteeism and academic performance of students enrolled in SFP beneficiary and non-beneficiary second-cycle primary schools in four rural districts of Sidama zone, Southern Ethiopia. The study was conducted in the 2017/2018 academic year. Baseline information was gathered at enrolment, and class attendance and academic performance were measured at the end of the first and second semesters of the year. The study was conducted in sixteen schools from four SFP-targeted rural districts (Borecha, Dara, Bona and Loko Abaya) of Sidama zone. Sidama is located approximately 300 km south of the national capital Addis Ababa. The zone covers nearly 10 000 km2 area and is administratively divided into ten districts. In 2017, the zone had approximately four million inhabitants of whom 95 % were rural dwellers(17). Sidama is characterised by three agroecological zones: lowlands (20 %), midlands (48 %) and highlands (32 %). The economy in the area is reliant on rain-fed subsistence agriculture(18). According to a recent study, 18 % of the households in the area were food insecure and the prevalence of severe food insecurity was as high as 48 %(18). In the Ethiopian education system, primary education is divided into first (grade 1–4) and second (grade 5–8) cycles. At the time of the survey, 111 s-cycle public schools (second-cycle primary schools) were functional in the four study districts, of which 27 were targeted by the SFP. Inclusion of schools into the programme is determined based on extent of food insecurity in the school catchment area as judged by administrative bodies and donors. Students enrolled in SFP-targeted schools daily receive a free cooked school meal prepared from cereals, legumes and vegetables. The nature of the SFP implemented in all the schools was more or less the same in terms of food served and frequency of feeding. Students enrolled in sixteen rural second-cycle primary schools (eight schools with SFP and eight schools without SFP) in the aforementioned four districts were eligible for the study. Students registered in SFP-targeted schools were considered as SFP beneficiaries, whereas those enrolled in non-targeted schools were assumed otherwise. The sample size was calculated using G*Power 3.1 programme(19), assuming that the two primary outcomes (class absenteeism and academic performance) would be compared between the two groups using one-tailed mean difference test. The sample size was computed with 95 % confidence level, 80 % power, one-to-one allocation ratio between the two groups, medium effect size (d = 0·4) and design effect of 2. Further, 20 % compensation for possible dropout was added. Ultimately, the sample size of 480 (240 SFP beneficiaries and 240 non-beneficiaries) was determined. From each of the four districts, two SFP-targeted and two non-targeted schools, in total sixteen schools, were included in the study. In each district, two schools with SFP were selected at random among the SFP-targeted schools and matching schools without SFP were identified using predefined matching criteria – being within the same district and having comparable agroecological features. In each school, thirty students were selected from the available sections using a proportional stratified sampling technique. Ultimately, based on class rosters and a table of random numbers, simple random sampling (SRS) was performed to select the students. With the intention of maximising the sample size of the study, at enrolment study, subjects who were not willing to take part in the study were replaced by randomly chosen eligible children from the same section (Fig. (Fig.11). Flow chart of the study The exposure of interest was SFP status (beneficiary v. non-beneficiary), while the two primary outcomes were total number of school-days missed in the year and average academic score of the students in the academic year. School dropout rate (yes/no) was also considered as a secondary outcome. Factors considered as potential confounders included: age and sex of the child, maternal and paternal educational status, household wealth index, monthly income and food insecurity, head of the household (male v. female) and enrolment of the household in the Productive Safety Net Program. Socio-demographic and economic characteristics of the study participants and their caregivers were assessed at enrolment using interviewer-administered questionnaires prepared in the local Sidamu Afoo language. The data were primarily collected from the parents of the index children through home interviews by trained and experienced enumerators and supervisors. Household food insecurity was assessed using the Household Food Insecurity Access Scale and categorised as food secure or mild, moderate or severe food insecurity(20). Class absenteeism rate and academic performance were measured based on school administrative records. Class attendance was measured as the total number of days children were absent from school in the year, while class performance was quantified based on the average score of the students (minimum 0 and maximum 100) for all ten courses they attended in that year. The ten courses were English language, Amharic language, Sidamu Afo language, Mathematics, Social Science, Sport Education, Civics, Integrated Basic Sciences (grades 5–6), Art (grades 5–6), Music (grades 5–6), Physics (grades 7–8), Chemistry (grades 7–8) and Biology (grades 7–8). Each course was rated with a scale ranging 0–100. We used STATA version 14 for data analysis. Principal Component Analysis was performed for computing household wealth index – a composite index of living standard – based on multiple variables including materials used for house construction, access to improved drinking water source and sanitation facilities, and ownership of livestock, private house and agricultural land. Principal Component Analysis was done using varimax rotation. The Kaiser–Meyer–Olkin measure of sampling adequacy was acceptable (0·64), and Bartlett’s test of sphericity was significant. Only variables that had communality scores above 50 % were retained in the analysis. Ultimately, the factor with the highest eigenvalue was taken and divided into three equal tertiles: poor, middle and rich. Pearson’s χ 2 or Fisher’s Exact tests were used to check the presence of systematic differences between students retained in the study (n 463) and lost to follow-up (n 17) depending on whether the assumptions of χ 2 test were fulfilled or not. χ 2 test was also performed for comparing SFP beneficiaries and non-beneficiaries based on socio-economic characteristics. Factors that were significantly different (P value < 0·05) between the two groups were statistically adjusted using multivariable regression models. Mixed effects negative binomial regression for count outcome was fitted to measure the effect of the SFP on school absenteeism. Poisson regression for count outcome was not used because the assumption of equidispersion, as evaluated by comparing the mean and variance, was violated. Mixed effects linear regression was performed to assess the effect of the SFP on average academic performance, and the assumptions of the model (normality and homoscedasticity of error terms and linearity of relationship) were assessed using partial plots and found to be satisfied. School dropout was modelled using mixed effects Poisson regression for binary outcome. In all multivariable models, absence of multicollinearity was evaluated using variance inflation factor and found to be within the acceptable range (variance inflation factor < 10). In all the mixed effects models, random intercept was set for schools. In these mixed effects models, we did not consider school-level variables as predictors because all schools had more or less similar profile – all were public schools, second-cycle primary schools and the teacher to student ratio was very comparable.

AI Digest

Study Justification:

– The study aimed to evaluate the impact of a school feeding program on class absenteeism and academic performance of primary school students in Southern Ethiopia.
– The study was conducted in an area characterized by high levels of food insecurity, where 18% of households were food insecure and 48% had severe food insecurity.
– The study addressed the need for evidence on the effectiveness of school feeding programs in improving academic outcomes for socio-economically disadvantaged children.

Study Highlights:

– The study compared students enrolled in the school feeding program (SFP) with non-beneficiary students in four rural districts of Sidama zone, Southern Ethiopia.
– The study found that SFP beneficiaries had significantly lower rates of class absenteeism compared to non-beneficiaries.
– SFP beneficiaries also had a small but significant improvement in academic performance compared to non-beneficiaries.
– The study also found that the risk of school dropout was significantly higher among non-beneficiaries.

Recommendations for Lay Reader:

– The study provides evidence that school feeding programs can have a positive impact on class attendance and academic performance of disadvantaged students.
– Implementing school feeding programs in food insecure areas can help reduce absenteeism and improve academic outcomes.
– Policymakers should consider scaling up the implementation of school feeding programs to benefit more students and address food insecurity in schools.

Recommendations for Policy Maker:

– Based on the study findings, it is recommended to expand the implementation of school feeding programs in food insecure districts.
– Policymakers should allocate resources to ensure the sustainability and effectiveness of school feeding programs.
– Collaboration between relevant stakeholders, including government bodies, donors, and educational institutions, is crucial for the successful implementation of school feeding programs.

Key Role Players:

– Government bodies responsible for education and food security policies.
– Donors and funding agencies.
– School administrators and teachers.
– Community leaders and parents.

Cost Items for Planning Recommendations:

– Procurement and distribution of food supplies for the school feeding program.
– Hiring and training of additional staff, such as cooks and program coordinators.
– Infrastructure improvements, such as kitchen facilities and storage areas.
– Monitoring and evaluation activities to assess the impact and effectiveness of the program.
– Awareness campaigns and community engagement initiatives.
– Research and data collection to inform program implementation and improvement.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is relatively strong, but there are some areas for improvement. The study design is a prospective cohort study, which is generally considered to be a strong design for assessing causal relationships. The study enrolled a sufficient sample size of 480 students and used appropriate statistical analyses. The results show a significant difference in class absenteeism and academic performance between SFP beneficiaries and non-beneficiaries. However, there are a few areas that could be improved. First, the abstract does not provide information on the specific methods used to measure class absenteeism and academic performance, which could affect the reliability of the results. Second, the abstract does not mention any potential limitations or confounding factors that may have influenced the results. It would be helpful to include this information to provide a more comprehensive assessment of the study’s findings. Finally, the abstract does not provide any information on the generalizability of the study’s findings beyond the specific population and setting. Including this information would help readers understand the broader implications of the study. To improve the evidence, the authors could provide more details on the methods used to measure class absenteeism and academic performance, discuss potential limitations and confounding factors, and provide information on the generalizability of the findings.

Abstract

This prospective cohort study compared class absenteeism and academic performance of students enrolled in SFP beneficiary and non-beneficiary second-cycle primary schools in four rural districts of Sidama zone, Southern Ethiopia. The study was conducted in the 2017/2018 academic year. Baseline information was gathered at enrolment, and class attendance and academic performance were measured at the end of the first and second semesters of the year. The study was conducted in sixteen schools from four SFP-targeted rural districts (Borecha, Dara, Bona and Loko Abaya) of Sidama zone. Sidama is located approximately 300 km south of the national capital Addis Ababa. The zone covers nearly 10 000 km2 area and is administratively divided into ten districts. In 2017, the zone had approximately four million inhabitants of whom 95 % were rural dwellers(17). Sidama is characterised by three agroecological zones: lowlands (20 %), midlands (48 %) and highlands (32 %). The economy in the area is reliant on rain-fed subsistence agriculture(18). According to a recent study, 18 % of the households in the area were food insecure and the prevalence of severe food insecurity was as high as 48 %(18). In the Ethiopian education system, primary education is divided into first (grade 1–4) and second (grade 5–8) cycles. At the time of the survey, 111 s-cycle public schools (second-cycle primary schools) were functional in the four study districts, of which 27 were targeted by the SFP. Inclusion of schools into the programme is determined based on extent of food insecurity in the school catchment area as judged by administrative bodies and donors. Students enrolled in SFP-targeted schools daily receive a free cooked school meal prepared from cereals, legumes and vegetables. The nature of the SFP implemented in all the schools was more or less the same in terms of food served and frequency of feeding. Students enrolled in sixteen rural second-cycle primary schools (eight schools with SFP and eight schools without SFP) in the aforementioned four districts were eligible for the study. Students registered in SFP-targeted schools were considered as SFP beneficiaries, whereas those enrolled in non-targeted schools were assumed otherwise. The sample size was calculated using G*Power 3.1 programme(19), assuming that the two primary outcomes (class absenteeism and academic performance) would be compared between the two groups using one-tailed mean difference test. The sample size was computed with 95 % confidence level, 80 % power, one-to-one allocation ratio between the two groups, medium effect size (d = 0·4) and design effect of 2. Further, 20 % compensation for possible dropout was added. Ultimately, the sample size of 480 (240 SFP beneficiaries and 240 non-beneficiaries) was determined. From each of the four districts, two SFP-targeted and two non-targeted schools, in total sixteen schools, were included in the study. In each district, two schools with SFP were selected at random among the SFP-targeted schools and matching schools without SFP were identified using predefined matching criteria – being within the same district and having comparable agroecological features. In each school, thirty students were selected from the available sections using a proportional stratified sampling technique. Ultimately, based on class rosters and a table of random numbers, simple random sampling (SRS) was performed to select the students. With the intention of maximising the sample size of the study, at enrolment study, subjects who were not willing to take part in the study were replaced by randomly chosen eligible children from the same section (Fig. (Fig.11). Flow chart of the study The exposure of interest was SFP status (beneficiary v. non-beneficiary), while the two primary outcomes were total number of school-days missed in the year and average academic score of the students in the academic year. School dropout rate (yes/no) was also considered as a secondary outcome. Factors considered as potential confounders included: age and sex of the child, maternal and paternal educational status, household wealth index, monthly income and food insecurity, head of the household (male v. female) and enrolment of the household in the Productive Safety Net Program. Socio-demographic and economic characteristics of the study participants and their caregivers were assessed at enrolment using interviewer-administered questionnaires prepared in the local Sidamu Afoo language. The data were primarily collected from the parents of the index children through home interviews by trained and experienced enumerators and supervisors. Household food insecurity was assessed using the Household Food Insecurity Access Scale and categorised as food secure or mild, moderate or severe food insecurity(20). Class absenteeism rate and academic performance were measured based on school administrative records. Class attendance was measured as the total number of days children were absent from school in the year, while class performance was quantified based on the average score of the students (minimum 0 and maximum 100) for all ten courses they attended in that year. The ten courses were English language, Amharic language, Sidamu Afo language, Mathematics, Social Science, Sport Education, Civics, Integrated Basic Sciences (grades 5–6), Art (grades 5–6), Music (grades 5–6), Physics (grades 7–8), Chemistry (grades 7–8) and Biology (grades 7–8). Each course was rated with a scale ranging 0–100. We used STATA version 14 for data analysis. Principal Component Analysis was performed for computing household wealth index – a composite index of living standard – based on multiple variables including materials used for house construction, access to improved drinking water source and sanitation facilities, and ownership of livestock, private house and agricultural land. Principal Component Analysis was done using varimax rotation. The Kaiser–Meyer–Olkin measure of sampling adequacy was acceptable (0·64), and Bartlett’s test of sphericity was significant. Only variables that had communality scores above 50 % were retained in the analysis. Ultimately, the factor with the highest eigenvalue was taken and divided into three equal tertiles: poor, middle and rich. Pearson’s χ 2 or Fisher’s Exact tests were used to check the presence of systematic differences between students retained in the study (n 463) and lost to follow-up (n 17) depending on whether the assumptions of χ 2 test were fulfilled or not. χ 2 test was also performed for comparing SFP beneficiaries and non-beneficiaries based on socio-economic characteristics. Factors that were significantly different (P value < 0·05) between the two groups were statistically adjusted using multivariable regression models. Mixed effects negative binomial regression for count outcome was fitted to measure the effect of the SFP on school absenteeism. Poisson regression for count outcome was not used because the assumption of equidispersion, as evaluated by comparing the mean and variance, was violated. Mixed effects linear regression was performed to assess the effect of the SFP on average academic performance, and the assumptions of the model (normality and homoscedasticity of error terms and linearity of relationship) were assessed using partial plots and found to be satisfied. School dropout was modelled using mixed effects Poisson regression for binary outcome. In all multivariable models, absence of multicollinearity was evaluated using variance inflation factor and found to be within the acceptable range (variance inflation factor < 10). In all the mixed effects models, random intercept was set for schools. In these mixed effects models, we did not consider school-level variables as predictors because all schools had more or less similar profile – all were public schools, second-cycle primary schools and the teacher to student ratio was very comparable.

Materials

N/A

Innovations

Based on the information provided, it seems that the study focused on evaluating the impact of a school feeding program on class absenteeism and academic performance of primary school students in Southern Ethiopia. The study found that students who were beneficiaries of the school feeding program had lower rates of class absenteeism and slightly better academic performance compared to non-beneficiaries.

To improve access to maternal health, some potential innovations could include:

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

2. Telemedicine: Using telecommunication technology to provide remote consultations and medical advice to pregnant women in areas with limited access to healthcare facilities.

3. Community health workers: Training and deploying community health workers who can provide basic maternal health services and education in rural areas, helping to bridge the gap between communities and healthcare facilities.

4. Maternal health vouchers: Introducing a voucher system that provides pregnant women with access to essential maternal health services, such as prenatal check-ups, delivery, and postnatal care, regardless of their financial status.

5. Maternal health education programs: Developing and implementing educational programs that focus on raising awareness about maternal health issues, promoting healthy behaviors during pregnancy, and providing information on available healthcare services.

These are just a few potential innovations that could be considered to improve access to maternal health. It’s important to assess the specific needs and challenges of the target population to determine the most effective and appropriate innovations to implement.

AI Innovations Description

The study mentioned in the description evaluated the effect of a school feeding program (SFP) on class absenteeism and academic performance of primary school students in Sidama zone, Southern Ethiopia. The study found that students who were beneficiaries of the SFP had lower rates of class absenteeism and slightly better academic performance compared to non-beneficiaries. The study also found that the risk of school dropout was higher among non-beneficiaries.

Based on this study, a recommendation to improve access to maternal health could be to implement a similar school feeding program specifically targeting pregnant women and new mothers. This program could provide nutritious meals to pregnant women and new mothers in schools, ensuring they receive adequate nutrition during this critical period. This could help improve maternal health outcomes by addressing nutritional deficiencies and promoting healthy pregnancies and postpartum recovery. Additionally, the program could include educational components on maternal health and nutrition to further support the well-being of pregnant women and new mothers.

AI Innovations Methodology

The study described in the provided text focuses on the impact of a school feeding program (SFP) on class absenteeism and academic performance of primary school students in Southern Ethiopia. The study used a prospective cohort design, enrolling SFP-beneficiary and non-beneficiary children from sixteen public schools and following them for an academic year.

To simulate the impact of recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Identify the recommendations: Start by identifying specific recommendations that could improve access to maternal health. These recommendations could include interventions such as increasing the number of healthcare facilities, improving transportation infrastructure, implementing telemedicine services, or providing training for healthcare providers.

2. Define the indicators: Determine the indicators that will be used to measure the impact of the recommendations on improving access to maternal health. These indicators could include the number of pregnant women receiving prenatal care, the number of deliveries attended by skilled birth attendants, or the maternal mortality rate.

3. Collect baseline data: Gather baseline data on the current state of maternal health in the target population. This data could include information on the number of healthcare facilities, the availability of transportation options, or the current utilization of maternal health services.

4. Develop a simulation model: Create a simulation model that incorporates the recommendations and their potential impact on the defined indicators. This model should take into account factors such as population size, geographic distribution, and existing healthcare infrastructure.

5. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations on improving access to maternal health. Vary the parameters of the recommendations to explore different scenarios and their potential outcomes.

6. Analyze results: Analyze the results of the simulations to determine the potential impact of the recommendations on improving access to maternal health. Look for trends, patterns, and significant changes in the defined indicators.

7. Validate the model: Validate the simulation model by comparing the simulated results with real-world data, if available. This step helps ensure the accuracy and reliability of the model’s predictions.

8. Refine and iterate: Based on the analysis and validation, refine the simulation model and iterate the process if necessary. Adjust the parameters of the recommendations or explore additional recommendations to further improve access to maternal health.

By following this methodology, policymakers and stakeholders can gain insights into the potential impact of different recommendations on improving access to maternal health. This information can inform decision-making and help prioritize interventions that are most likely to have a positive impact.

Author

Dima Health

Statistics:

Citations: 5

Identifiers:

DOI: 10.1017/S1368980021000501

ISSN: 13689800

Research Areas:

Disparities, Environmental, 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, Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female