School feeding program has resulted in improved dietary diversity, nutritional status and class attendance of school children

Italian Journal of Pediatrics, Volume 44, No. 1, Year 2018

Interpretação

Background: School Feeding Program (SFP) is a targeted safety net program designed to provide educational and health benefits to vulnerable children. However, limited evidence exists regarding the effect of the intervention on the nutritional status and school attendance of children. The study is aimed at examining the effects of SFP on dietary diversity, nutritional status and class attendance of school children in Boricha district, Southern Ethiopia. Methods: The study was conducted based on a representative data collected from 290 students drawn from the district. A school-based comparative cross-sectional study was conducted on school children aged 10-14 years. Data were collected using structured pretested questionnaire. The effects of SFP on dietary diversity score (DDS), class attendance rate, body-mass-index for age (BAZ) and height-for-age (HAZ) Z-scores were assessed using multivariable linear regression model. Results: The finding showed significantly higher mean (±SD) of DDS in SFP beneficiaries (5.8 ± 1.1) than the non-beneficiaries (3.5 ± 0.7) (P < 0.001). BAZ and HAZ of the beneficiaries were also higher than their counterparts, which were (0.07 ± 0.93), (- 0.50 ± 0.86) and (- 1.45 ± 1.38), (- 2.17 ± 1.15) respectively (P < 0.001). The mean (±SD) days of absence from school for non-beneficiaries (2.6 ± 1.6) was significantly higher than that of the beneficiaries (1.3 ± 1.7) (P < 0.05). Conclusion: Given the positive effects of the program in improving the DDS, nutritional status, and class attendance of school children, we strongly recommend scaling up the program to other food insecure areas. The study was conducted in Sidama Zone, Boricha district, Southern Ethiopia, which is located 311kms south of Addis Ababa – the capital city of Ethiopia. The total number of primary schools in the district is 45. There are about 80,857 primary school children in the district, of which 42,306 are boys and 38,551 are girls. In 2016, in the district three primary schools were implementing the SFP and all of them were included in this study. The number of children attending school with the SFP is 5065–2673 boys and 2392 girls. According to the program standard, each SFP beneficiary student gets a 150 g of meal prepared from wheat, corn or bean once a day from Monday to Friday. School-based comparative cross-sectional study with both quantitative and qualitative components was conducted from January to February 2016. The quantitative study was done using both primary and secondary data. SFP beneficiary and non-beneficiary schools were matched based on pre-defined criteria. All students aged from 10 to 14 years studying in schools with and without SFP in Boricha district were considered as the source population of the study; while, similar group of children enrolled in selected six schools of Boricha were taken as the study population. All students registered in schools with feeding programs were considered as beneficiaries of the program; whereas, students from school not undertaking feeding program were considered as non-beneficiary. Children who were absent during the survey date were excluded from the study. The sample size required for the study was calculated using Gpower software [version 3.1] considering; 95% confidence level, 90% power, 1:1 allocation ratio, 0.4 standardized mean difference effect size (equivalent to medium effect size) and 10% non response rate. The final sample size was 292 of which 146 were students from beneficiary schools, while another 146 were taken from non-beneficiary schools. All the three schools that were implementing the SFP (Gesarakue, Hanjachafa and Shelo Abore) were included in the study. Among schools which were not implementing the feeding program, three schools were purposively selected using pre-defined matching criteria. The criteria were: (1) being nearest to the SFP implementing school, and (2) having a comparable kebele level (the smallest administrative unit in Ethiopia comprising approximately 1000 households) socio-demographic indicators (literacy rate and agro-ecological character). Accordingly, three SFP non-beneficiary schools (Mankite, Alawarfe and Sheloelancho) were included. The sample size assigned to the two groups was distributed proportional to the size of the schools. Students were selected using a systematic random sampling technique by taking school rosters as a sampling frame. In the study dietary diversity score (DDS), nutritional status (height-for-age z-score (HAZ) and body mass index (BMI)-for-age) and class attendance were the dependent variables while the SFP enrollment status was the exposure variable. Socio-demographic and economic factors including child’s age, maternal educational status, mother’s occupation, father’s occupation, household wealth index, size of agricultural land and household food insecurity status were considered as potential confounders. Primary data were collected from the parents/primary caregivers of the index children by trained data collectors. Pre-tested structured questionnaire was used to assess social-demographic and economic characteristics, food security status, child characteristics and dietary diversity in both groups. The section of the questionnaire on socio-demographic characteristics was adopted from the standard Demographic and Health Survey (DHS) questionnaire. The part of the questionnaire on DDS was adapted from the Food and Nutrition Technical Assistant (FANTA) guideline. The eight food groups DDS scale was used to assess the quality of diet based on foods consumed in the preceding day of the survey [14]. FAO’s 1 week Food Frequency Questionnaire (FFQ) having 16 food groups was used to compare the frequency of consumption of each food group between SFP beneficiary and non-beneficiary children [10]. FANTA’s Household Food Insecurity Access Scale (HFIAS) was used to assess the household food insecurity status of the households [14]. The scale explores the occurrence and frequency of occurrence of nine food insecurity related events in the past 30 days of the survey. HFIAS classifies the household as food secure, mild food insecurity, moderate food insecurity or severe food insecurity [14]. Household wealth status was assessed based on ownership of household assets (radio, television, chair, table, mobile phone, bicycle and horse or donkey cart), materials used to construct the house, numbers of livestock owned, ownership of improved drinking water source and latrine. Each household asset was assigned a score of zero or one, where an increased value indicates a better status; whereas, the number of livestock owned was entered as discrete numeric variable. Ultimately household wealth index was constructed using Principal Components Analysis (PCA) and the study participants were ranked into three tertiles; low, medium and high. Anthropometric measurements were made by one of the investigators in order to avoid inter-observer variation. Measurements were made using calibrated equipments following standardized procedures. Body height and weight were measured with the child in light clothing and without shoes. The height of the children was measured to the nearest 0.1 cm using a measuring board, whereas weight was measured to the nearest 100 g using an electronic (Seca 770) scale. Ultimately, BMI-for-age and height-for-age (HFA) z-scores were computed based on the 2007 WHO growth reference data. The qualitative data were collected through conducting key informant interviews with the concerned regional officer and focal person of the WFP and the principals of the six schools included in the quantitative study. Data were collected by one of the investigators using a semi-structured guide. All the interviews were tape recorded, translated and transcribed for analysis. Secondary data were used to determine absenteeism and dropout rates of students. Absenteeism rate was determined as the number of days the child got absent from school in the preceding 2 weeks of the survey. School level dropout rate was determined for the academic year of 2015–16 for both beneficiary and non-beneficiary schools. The data were entered into EpiInfo [version 3.5.1] and then exported to SPSS [version 20] computer software program for statistical analysis. Descriptive statistics (frequency, percentage, measures of central tendency and dispersion) were used to summarize categorical and continuous variables. Linear regression model was used to assess the net effects of the SFP on the dependent variables while controlling for potential confounders. Maternal education, mother’s occupation, husband’s occupation, agricultural land size, household food insecurity status and child age were found to be unbalanced variables in the groups being compared and hence controlled in the model. Assumptions of the model (normality and homoscedasticity of error terms, the absence of multicollinearity and linearity of association between the dependent and independent variables) were checked to be satisfied. The normality of the error terms was assessed using P-P plot and Kolmogorov-Smirnov test. Anthropometric indices – HAZ and BAZ – were generated using WHO Anthro plus software [version2.0.2] based on the WHO 2007 growth reference data. Stunting and wasting were defined as Z-scores less than − 2 standard deviations. For class attendance, the mean (±SD) of absence rate was calculated from the number of days the child got absent from school in the preceding 2 weeks and compared for both the beneficiary and non-beneficiary schools. The dropout rate obtained from the secondary sources were also compared between the groups. The findings from the key informant interviews were translated, transcribed, and narrated according to coherent themes. The findings of the qualitative study were used to triangulate and complement the findings of the survey.

AI Digest

Study Justification:

This study aimed to examine the effects of the School Feeding Program (SFP) on dietary diversity, nutritional status, and class attendance of school children in Boricha district, Southern Ethiopia. The justification for this study is that limited evidence exists regarding the effect of the SFP on the nutritional status and school attendance of children. By conducting this study, the researchers aimed to provide evidence on the positive effects of the program and justify the need for scaling up the program to other food insecure areas.

Highlights:

1. The study found that school children who were beneficiaries of the SFP had significantly higher dietary diversity scores (DDS) compared to non-beneficiaries.
2. The nutritional status of the beneficiaries, as measured by body mass index for age (BAZ) and height-for-age (HAZ) Z-scores, was also higher than their non-beneficiary counterparts.
3. The mean days of absence from school for non-beneficiaries were significantly higher than that of the beneficiaries.
4. The study recommends scaling up the SFP to other food insecure areas based on the positive effects observed in improving dietary diversity, nutritional status, and class attendance of school children.

Recommendations:

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

1. Scale up the School Feeding Program to other food insecure areas to improve the dietary diversity, nutritional status, and class attendance of school children.
2. Increase the coverage of the program to reach more vulnerable children in need.
3. Ensure the sustainability of the program by securing adequate funding and resources.
4. Conduct regular monitoring and evaluation of the program to assess its effectiveness and make necessary adjustments.

Key Role Players:

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

1. Ministry of Education: Responsible for policy formulation and implementation of the School Feeding Program.
2. Ministry of Health: Provides technical support and guidance on nutrition and health aspects of the program.
3. World Food Programme (WFP): Collaborates with the government to provide food assistance and support for the program.
4. Local government authorities: Responsible for coordinating and implementing the program at the district and community levels.
5. School administrators and teachers: Play a crucial role in the implementation and monitoring of the program at the school level.
6. Parents and caregivers: Support the program by ensuring their children’s participation and providing feedback on its effectiveness.

Cost Items:

While the actual cost of implementing the recommendations may vary depending on the context and scale, the following cost items should be considered in planning:

1. Food procurement and distribution: Includes the cost of purchasing and transporting food items for the program.
2. Infrastructure and equipment: Includes the cost of constructing or renovating school kitchens, dining areas, and storage facilities.
3. Human resources: Includes the cost of hiring and training staff involved in program management, monitoring, and evaluation.
4. Monitoring and evaluation: Includes the cost of data collection, analysis, and reporting to assess the program’s impact and effectiveness.
5. Awareness and communication: Includes the cost of conducting awareness campaigns and communication materials to promote the program and engage stakeholders.
6. Sustainability measures: Includes the cost of developing strategies to ensure the long-term sustainability of the program, such as capacity building and resource mobilization.
Please note that the provided cost items are general categories and the actual cost estimates would require a detailed budget analysis based on the specific context and scale of the program implementation.

Força da prova

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on a representative data collected from 290 students and uses a multivariable linear regression model to assess the effects of the School Feeding Program (SFP) on dietary diversity, nutritional status, and class attendance. The study shows significantly higher mean dietary diversity score (DDS) in SFP beneficiaries compared to non-beneficiaries, as well as higher body mass index (BMI)-for-age and height-for-age (HAZ) Z-scores. The study also found a lower mean number of days absent from school for SFP beneficiaries. However, the abstract does not provide information on potential confounders that were controlled for in the analysis, and it does not mention any limitations or potential biases in the study design or data collection methods. To improve the strength of the evidence, future studies could consider including a larger sample size, conducting a randomized controlled trial, and addressing potential confounders more explicitly in the analysis.

Resumo

The study was conducted in Sidama Zone, Boricha district, Southern Ethiopia, which is located 311kms south of Addis Ababa – the capital city of Ethiopia. The total number of primary schools in the district is 45. There are about 80,857 primary school children in the district, of which 42,306 are boys and 38,551 are girls. In 2016, in the district three primary schools were implementing the SFP and all of them were included in this study. The number of children attending school with the SFP is 5065–2673 boys and 2392 girls. According to the program standard, each SFP beneficiary student gets a 150 g of meal prepared from wheat, corn or bean once a day from Monday to Friday. School-based comparative cross-sectional study with both quantitative and qualitative components was conducted from January to February 2016. The quantitative study was done using both primary and secondary data. SFP beneficiary and non-beneficiary schools were matched based on pre-defined criteria. All students aged from 10 to 14 years studying in schools with and without SFP in Boricha district were considered as the source population of the study; while, similar group of children enrolled in selected six schools of Boricha were taken as the study population. All students registered in schools with feeding programs were considered as beneficiaries of the program; whereas, students from school not undertaking feeding program were considered as non-beneficiary. Children who were absent during the survey date were excluded from the study. The sample size required for the study was calculated using Gpower software [version 3.1] considering; 95% confidence level, 90% power, 1:1 allocation ratio, 0.4 standardized mean difference effect size (equivalent to medium effect size) and 10% non response rate. The final sample size was 292 of which 146 were students from beneficiary schools, while another 146 were taken from non-beneficiary schools. All the three schools that were implementing the SFP (Gesarakue, Hanjachafa and Shelo Abore) were included in the study. Among schools which were not implementing the feeding program, three schools were purposively selected using pre-defined matching criteria. The criteria were: (1) being nearest to the SFP implementing school, and (2) having a comparable kebele level (the smallest administrative unit in Ethiopia comprising approximately 1000 households) socio-demographic indicators (literacy rate and agro-ecological character). Accordingly, three SFP non-beneficiary schools (Mankite, Alawarfe and Sheloelancho) were included. The sample size assigned to the two groups was distributed proportional to the size of the schools. Students were selected using a systematic random sampling technique by taking school rosters as a sampling frame. In the study dietary diversity score (DDS), nutritional status (height-for-age z-score (HAZ) and body mass index (BMI)-for-age) and class attendance were the dependent variables while the SFP enrollment status was the exposure variable. Socio-demographic and economic factors including child’s age, maternal educational status, mother’s occupation, father’s occupation, household wealth index, size of agricultural land and household food insecurity status were considered as potential confounders. Primary data were collected from the parents/primary caregivers of the index children by trained data collectors. Pre-tested structured questionnaire was used to assess social-demographic and economic characteristics, food security status, child characteristics and dietary diversity in both groups. The section of the questionnaire on socio-demographic characteristics was adopted from the standard Demographic and Health Survey (DHS) questionnaire. The part of the questionnaire on DDS was adapted from the Food and Nutrition Technical Assistant (FANTA) guideline. The eight food groups DDS scale was used to assess the quality of diet based on foods consumed in the preceding day of the survey [14]. FAO’s 1 week Food Frequency Questionnaire (FFQ) having 16 food groups was used to compare the frequency of consumption of each food group between SFP beneficiary and non-beneficiary children [10]. FANTA’s Household Food Insecurity Access Scale (HFIAS) was used to assess the household food insecurity status of the households [14]. The scale explores the occurrence and frequency of occurrence of nine food insecurity related events in the past 30 days of the survey. HFIAS classifies the household as food secure, mild food insecurity, moderate food insecurity or severe food insecurity [14]. Household wealth status was assessed based on ownership of household assets (radio, television, chair, table, mobile phone, bicycle and horse or donkey cart), materials used to construct the house, numbers of livestock owned, ownership of improved drinking water source and latrine. Each household asset was assigned a score of zero or one, where an increased value indicates a better status; whereas, the number of livestock owned was entered as discrete numeric variable. Ultimately household wealth index was constructed using Principal Components Analysis (PCA) and the study participants were ranked into three tertiles; low, medium and high. Anthropometric measurements were made by one of the investigators in order to avoid inter-observer variation. Measurements were made using calibrated equipments following standardized procedures. Body height and weight were measured with the child in light clothing and without shoes. The height of the children was measured to the nearest 0.1 cm using a measuring board, whereas weight was measured to the nearest 100 g using an electronic (Seca 770) scale. Ultimately, BMI-for-age and height-for-age (HFA) z-scores were computed based on the 2007 WHO growth reference data. The qualitative data were collected through conducting key informant interviews with the concerned regional officer and focal person of the WFP and the principals of the six schools included in the quantitative study. Data were collected by one of the investigators using a semi-structured guide. All the interviews were tape recorded, translated and transcribed for analysis. Secondary data were used to determine absenteeism and dropout rates of students. Absenteeism rate was determined as the number of days the child got absent from school in the preceding 2 weeks of the survey. School level dropout rate was determined for the academic year of 2015–16 for both beneficiary and non-beneficiary schools. The data were entered into EpiInfo [version 3.5.1] and then exported to SPSS [version 20] computer software program for statistical analysis. Descriptive statistics (frequency, percentage, measures of central tendency and dispersion) were used to summarize categorical and continuous variables. Linear regression model was used to assess the net effects of the SFP on the dependent variables while controlling for potential confounders. Maternal education, mother’s occupation, husband’s occupation, agricultural land size, household food insecurity status and child age were found to be unbalanced variables in the groups being compared and hence controlled in the model. Assumptions of the model (normality and homoscedasticity of error terms, the absence of multicollinearity and linearity of association between the dependent and independent variables) were checked to be satisfied. The normality of the error terms was assessed using P-P plot and Kolmogorov-Smirnov test. Anthropometric indices – HAZ and BAZ – were generated using WHO Anthro plus software [version2.0.2] based on the WHO 2007 growth reference data. Stunting and wasting were defined as Z-scores less than − 2 standard deviations. For class attendance, the mean (±SD) of absence rate was calculated from the number of days the child got absent from school in the preceding 2 weeks and compared for both the beneficiary and non-beneficiary schools. The dropout rate obtained from the secondary sources were also compared between the groups. The findings from the key informant interviews were translated, transcribed, and narrated according to coherent themes. The findings of the qualitative study were used to triangulate and complement the findings of the survey.

Materiais

N/A

Inovações

Based on the provided information, the following innovations could be considered to improve access to maternal health:

1. Mobile Health Clinics: Implementing mobile health clinics that travel to remote areas, including schools, to provide maternal health services such as prenatal care, vaccinations, and health education.

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

3. Community Health Workers: Training and deploying community health workers who can provide basic maternal health services, education, and support to pregnant women in their communities.

4. Maternal Health Vouchers: Introducing a voucher system that provides pregnant women with access to essential maternal health services, including antenatal care, skilled birth attendance, and postnatal care.

5. Maternal Health Education Programs: Developing and implementing educational programs that focus on maternal health, targeting schools and communities to raise awareness and promote healthy practices during pregnancy.

6. Transportation Support: Providing transportation support for pregnant women in remote areas to access healthcare facilities for prenatal care, delivery, and postnatal care.

7. Telemonitoring Devices: Introducing telemonitoring devices that allow pregnant women to remotely monitor their vital signs and share the data with healthcare providers, enabling early detection of complications and timely interventions.

8. Maternal Health Hotline: Establishing a toll-free hotline staffed by trained healthcare professionals who can provide information, guidance, and support to pregnant women, addressing their concerns and connecting them to appropriate services.

9. Maternal Health Incentives: Implementing incentive programs that encourage pregnant women to seek and receive regular maternal health services, such as providing financial incentives or rewards for attending prenatal care visits.

10. Maternal Health Partnerships: Collaborating with local organizations, NGOs, and government agencies to strengthen maternal health services, improve infrastructure, and increase access to care in remote areas.

AI Innovations Description

The recommendation from the study is to scale up the School Feeding Program (SFP) to other food insecure areas in order to improve access to maternal health. The study found that the SFP had positive effects on dietary diversity, nutritional status, and class attendance of school children in Boricha district, Southern Ethiopia. The SFP provided a 150g meal prepared from wheat, corn, or beans once a day from Monday to Friday to beneficiary students. The study showed that SFP beneficiaries had significantly higher mean dietary diversity scores (DDS) compared to non-beneficiaries. The beneficiaries also had higher body mass index (BMI)-for-age and height-for-age z-scores (BAZ and HAZ) indicating better nutritional status. Additionally, the mean days of absence from school were significantly lower for SFP beneficiaries compared to non-beneficiaries.

Based on these findings, it is recommended to expand the SFP to other food insecure areas to improve access to maternal health. By providing nutritious meals to school children, the program can contribute to improving their dietary diversity, nutritional status, and class attendance. This can have long-term benefits for their overall health and well-being.

AI Innovations Methodology

Based on the provided information, the study examined the effects of a School Feeding Program (SFP) on dietary diversity, nutritional status, and class attendance of school children in Boricha district, Southern Ethiopia. The study found that SFP beneficiaries had significantly higher dietary diversity scores, better nutritional status, and lower rates of absenteeism compared to non-beneficiaries.

To improve access to maternal health, the following innovations could be considered:

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

2. Telemedicine: Utilizing telemedicine technology to connect pregnant women in rural areas with healthcare providers, allowing them to receive virtual consultations and access to medical advice.

3. Community Health Workers: Training and deploying community health workers who can provide basic maternal health services, conduct health education sessions, and refer women to appropriate healthcare facilities.

4. Maternal Health Vouchers: Introducing a voucher system that provides pregnant women with access to essential maternal health services, such as antenatal care, skilled birth attendance, and postnatal care.

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 that will benefit from the innovations, such as pregnant women in rural areas.

2. Collect baseline data: Gather data on the current access to maternal health services, including the number of women receiving prenatal care, skilled birth attendance rates, and postnatal care utilization.

3. Implement the innovations: Introduce the recommended innovations, such as mobile clinics, telemedicine, community health workers, or maternal health vouchers, in the target population.

4. Monitor and evaluate: Track the implementation of the innovations and collect data on key indicators, such as the number of women reached, the utilization of maternal health services, and any changes in health outcomes.

5. Analyze the data: Use statistical analysis to compare the baseline data with the data collected after the implementation of the innovations. Assess the impact of the innovations on access to maternal health services, including changes in utilization rates, improvements in health outcomes, and any disparities in access.

6. Adjust and scale-up: Based on the findings, make any necessary adjustments to the innovations and develop strategies for scaling up successful interventions to reach a larger population.

By following this methodology, it will be possible to simulate the impact of the recommended innovations on improving access to maternal health and identify effective strategies for scaling up these interventions.

Autor

Dima Health

Statistics:

Citations: 31

Identifiers:

DOI: 10.1186/s13052-018-0449-1

ISSN: 17208424

Research Areas:

Community Interventions, 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, Cross Sectional Study, randomised-control-trial

Study Approach:

Mixed-methods, Qualitative, Quantitative

Participants Gender:

Male & Female

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