Nutrition conditions of pupils

of AVSI-supported schools

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ACKNOWLEDGMENTS:This report was made possible thanks to the kind contribution of UNICEF

In a bid to strengthen INTERSECTORIAL response, in particularthe education-food security-nutrition nexus, with UNICEFfunding, AVSI has piloted a cost-effective methodology togather nutrition data in schools

MethodologyOn March 2019, in the 62 schools supported by AVSI – 10 in LAKES (Cueibet) and 52 in EES (Torit,Magwi, Ikwoto, Kapoeta South and East) – it was conducted a nutrition screening of everystudent enrolled in primary two (P2) grade and in class the day of the data gathering. As schoolswere purposely selected (not randomly selected), the nutrition figures obtained providereference only for the children enrolled in AVSI-supported schools.

Anthropometric measures (eight, weight and age) were gathered for a total of 2,189 P2 pupils,1,278 of whom were estimated to be children between 5 and 10 years of age.

The Body Mass Index (BMI) was calculated only for the children between 5 and 10 years of age(as human growth is less accelerated for that age group, nutrition estimates for those childrenare expected to be more robust); still, enumerators gathered anthropometric data for all 2,189pupils in order to avoid biases or distortion in the reporting of age (blind test).

Similarly, the choice to base the analysis on the BMI, rater than GAM or SAM, is aimed atreducing even further the potential errors related to the incorrect estimation of pupils’ age, asthe BMI ratio varies less depending on age.

BMI cut-off points employed to classify pupils’ nutrition condition were those suggested byWHO for 9 years of age children: for females 12 for thinness and 13 for severe thinness; and formales 12.5 for thinness and 13.5 for severe thinness. (https://www.who.int/growthref/who2007_bmi_for_age/en/).

Cuiebet 399Abiriu esc p/s 65Abyei-Chok P/S 71Bar-el-Ghel P/S 31Cueibet Boys P/S 40Cueibet Girls p/s 64Gak-Mar-Piny P/S 26Langdit Boys P/S 31Langdit Girls p/s 17Malou-Pec P/S 23Mayath Girls p/s 31

Greater Magwi 484Abara p/s 32Blessed p/s 13Emmanuel P/S 15Green Valley p/s 53Hai Kanisa P/S 77Lobone p/s 47Magwi p/s 74Ngachigak p/s 32Ohitir P/S 5Owiny Kibul P/S 17Pajok P/S 8Paluonganyi P/S 27Rei P/S 57Rock of Agnes P/S 27

Torit Christ Bright Accademy 41Dumak p/s 33Grace Community P/s 24Hiyala P/S 28Imilai P/S 50Lohui P/S 48Torit East 91Torit West p/s 24

TORT

MAGWIIKWOTO

KAPOETA

Greater Ikwotos 750A.I.C Ikwoto P/S 57Bira P/S 56Chahari P/S 29Hafoliere P/S 44Hatire p/s 60Hiriafit P/S 53Huma P/S 33Ikwoto P/S 62Imotong P/S 22Iteuso P/S 32Iyak P/S 15Lobwaye p/s 20Loduruno/Chorokol P/S 26Lodwara 14Momoria P/S 26Ngaluma P/S 29St Mathew P/S 53St. Kizito 81Tserentenya P/S 34Woroworo P/S 4

School location EES and LAKES

CUEIBET

Kapoeta 217Bishop Mazzoldi p/s 56Brainstorm P/S 36Little Lamb academy 25St Bakhita Girls P/S 50St Daniel Comboni P/S 50

Data plausability check and main finding

Data are consistent with secondaryinformation:- at the county level: Cueibet is certainly themost food insecure county (schools screenedin Kapoeta are mostly prestigious town school)- at the subcounty level: schools’ averagenutrition conditions reflect either the level offood security in the community where theschool is located (see Ikwoto) or thestatus/school fee costs/offering of boardingservice of the school, and thus that of pupils’families (see Kapoeta and Cueibet).

The main finding is that in communitiesseverely food insecure (like Cueibet) there aremarked differences across schools; thenutrition screening of pupils could beparticularly useful in emergency context toprioritize food security interventions.

AVSI-supported Schools %ok ThinSeverely

thinAVSI Cueibet 55% 12% 32%

Abiriu esc p/s 18% 26% 56%Abyei-Chok P/S 8% 22% 69%Bar-el-Ghel P/S 85% 0% 15%Cueibet Boys P/S 100% 0% 0%Cueibet Girls p/s 100% 0% 0%Gak-Mar-Piny P/S 75% 25% 0%Langdit Boys P/S 100% 0% 0%Langdit Girls p/s 100% 0% 0%Malou-Pec P/S 38% 13% 50%Mayath Girls p/s 12% 18% 71%

AVSI Greater Ikwotos 89% 9% 3%A.I.C Ikwoto P/S 80% 13% 8%Bira P/S 97% 3% 0%Chahari P/S 88% 13% 0%Hafoliere P/S 89% 7% 4%Hatire p/s 100% 0% 0%Hiriafit P/S 86% 11% 3%Huma P/S 78% 7% 15%Ikwoto P/S 87% 11% 2%Imotong P/S 100% 0% 0%Iteuso P/S 83% 17% 0%Iyak P/S 100% 0% 0%Lobwaye p/s 100% 0% 0%Loduruno/Chorokol P/S 62% 24% 14%Lodwara 71% 29% 0%Momoria P/S 91% 9% 0%Ngaluma P/S 92% 8% 0%St Mathew P/S 88% 12% 0%St. Kizito 96% 4% 0%Tserentenya P/S 100% 0% 0%Woroworo P/S 100% 0% 0%

AVSI-supported Schools %ok ThinSeverely

thinAVSI Greater Magwi 83% 11% 6%

Abara p/s 83% 17% 0%Blessed p/s 91% 0% 9%Emmanuel P/S 100% 0% 0%Green Valley p/s 91% 9% 0%Hai Kanisa P/S 77% 16% 7%Lobone p/s 92% 0% 8%Magwi p/s 93% 7% 0%Ngachigak p/s 37% 22% 41%Ohitir P/S 100% 0% 0%Owiny Kibul P/S 75% 25% 0%Pajok P/S 83% 17% 0%Paluonganyi P/S 94% 6% 0%Rei P/S 81% 16% 3%Rock of Agnes P/S 85% 10% 5%

AVSI Kapoeta Town 76% 19% 5%Bishop Mazzoldi p/s 91% 9% 0%Brainstorm P/S 54% 33% 13%Little Lamb academy 77% 15% 8%St Bakhita Girls P/S 75% 21% 4%St Daniel Comboni P/S 79% 17% 4%

AVSI Torit 95% 4% 1%Christ Bright Accademy 100% 0% 0%Dumak p/s 90% 10% 0%Grace Community P/s 82% 18% 0%Hiyala P/S 88% 6% 6%Imilai P/S 95% 5% 0%Lohui P/S 100% 0% 0%Torit East 100% 0% 0%Torit West p/s 100% 0% 0%

Advantages of the nutrition screening of lower-primary pupils (1/3)AVSI intends to continue to fine-tune the methodology for the nutrition screening of lower-primary pupils because it regards it particularly apt to the local context :

1) Robust nutrition estimates even in a context where age is difficult to asses. In a countrywhere most children have unreliable birth certificates (if any), thus age is difficult to estimate,by focusing on the group age (6 to 10 years) in which human development is slowest, thenutrition screening of lower-primary pupils is set to provide fairly robust estimates.

2) Cost-effective. By focusing on above 5 years of age children, the measurement of children’sheight and weight can be done with affordable equipment and does not require intensetraining of enumerators or nutrition savvy staff (school social workers suffice); similarly, bytargeting schools, instead of households, the logistic and technical expertise required isgreatly diminished.

3) Rapid reference indication for all school-age children. Given the high turnover of pupils,especially among children of lower classes (P1 and P2), the screening of pupils may provide areference figure for out-of-school children too. While the methodology needs rigorousvalidation, in the current context it could work as a sort of early nutrition warning system fornutrition conditions of school age children.

Advantages of the nutrition screening of lower primary pupils (2/3)AVSI intends to continue to fine-tune the methodology for the nutrition screening of lower-primary pupils for it regards it apt to increase the impact of school feeding:

1) Permits the focusing of school nutrition interventions. With information on actual pupilsnutrition conditions, food quantity delivered to schools can be adjusted to reflect the concreteneeds of each school, thus increasing the effectiveness of school meals programs. It could beused, for instance, to identify schools who deserve extraordinary food assistance.

2) Prioritizing school for the piloting of innovative nutrition interventions. Data that permit toprioritize nutrition interventions in schools may turn particularly handy to help the targeting ofpilot programs, which by definition cannot cover many schools. The Home- Grown SchoolMeals piloting could indeed be used to select schools where delivering additional food.

Advantages of the nutrition screening of lower primary pupils (3/3)AVSI intends to continue to fine-tune the methodology for the nutrition screening of lower-primary pupils for it regards it appropriate to gain more robust education data:

1) Minimize distortions in education data. To the extent that the estimation of food delivered toschool is not based (only) on enrollment data, the incentives to report inflated enrollmentnumbers in a bid to receive greater support is reduced, contributing to obtain more reliableeducation data.

2) Gather fairly precise estimates of key education data. A by-product of the nutrition screeningis the estimation of pupils’ age, which is another key education figure to monitor in a countrywhere late entrance to school severely limits overall schooling (years of education) andeducation performance. Additionally, the listing of screened pupils (names) permits toestimate with great precision the level of dropout when the screening is repeated.

3) Focus on ultimate impact indicators, namely nutrition and cognitive capacity. Permits to addto the standard analysis of the nexus between food delivery and pupils’ presence at school thenexus between nutrition conditions and pupils’ cognitive results.

Way forward & limitations

AVSI intends to repeat the gathering of anthropometric measures during the second school-termin 2019, as a means to:- Validate results and verify the consistency of the methodology.- Provide updated school level nutrition data for Cueibet and Kapoeta given the dire food

security conditions in those two areas (AVSI supported schools through the monitoring ofWFP’s FFE in Cueibet and UNICEF-funding in Kapoeta; additional schools depending onfunding)

- Complement the comprehensive monitoring of food security / nutrition conditions in Ikwotoaimed at informing the design of a pilot project of Home- Grown School Feeding in that area.

Progressively, AVSI intends to refine this methodology in consultation with the NIWG, so as todevelop a companion tool to its broad Multi-Sector Household Survey conducted yearly in itsareas of operation (see list in the appendix for a list of reports).

Under no circumstances this school level information should be considered a substitute tostandardized information gathered throughout household surveys for it does not reflect theconditions of the communities and population groups “far from or not included in” establishedschools. Likewise, the data are indicative of the conditions of the students enrolled at AVSI-supported schools and not for other schools.

Appendix 1 – Anthropometric data

0

50

100

150

200

5 6 7 8 9 10 11 12 13 14 15 17

Age distribution (when exact age was

reported) (number of pupils in that height bracket on Y-axe)

0

200

400

600

800

1000

OLD YOUNG

Age distribution (when bracket range

was reported; young <10 old>=11)

(number of pupils in that height bracket on Y-axe

58%

42%

% of over-aged pupils

YOUNG OLD (4 or more years over-aged)

• The “inverted pyramid” visible in the enrollment of the 1st school-Term 2017, after the 2016 clashes, was an alarmingsign of the worsening conditions for young children/pupils in lower-primary grades. Ikwoto showed marked resilience.

-80 -60 -40 -20 0 20 40 60 80

P8

P6

P4

P2

TORIT

Male Females

-80 -60 -40 -20 0 20 40 60 80

P8

P6

P4

P2

MAGWI

Male

-80 -60 -40 -20 0 20 40 60 80

P8

P6

P4

P2

IKWOTO

Male

-80 -60 -40 -20 0 20 40 60 80

P1

P2

P3

P4

P5

P6

P7

P8TORIT

Females Males -80 -60 -40 -20 0 20 40 60 80

P1

P2

P3

P4

P5

P6

P7

P8MAGWI

Females Males-80 -60 -40 -20 0 20 40 60 80

P1

P2

P3

P4

P5

P6

P7

P8KAPOETAS

Females Males-80 -60 -40 -20 0 20 40 60 80

P1

P2

P3

P4

P5

P6

P7

P8IKWOTO

Females Males

• Two years later, on April 2019, in most counties fresh intake continues to be “too small” (not enough tocounterbalance the high dropout). The relatively privileged position of Ikwoto County in EES is still very visible

-80 -60 -40 -20 0 20 40 60 80

P1

P2

P3

P4

P5

P6

P7

P8CUEIBET

Females Males

Appendix 2 – Additional key information on the targeted schools (average enrollment and barriers to school entry)

• AVSI Foundation (2019), Atlas of 62 schools supported by AVSI in LAKES and EES and Overall Education Response in South Sudan https://www.humanitarianresponse.info/en/operations/south-sudan/assessment/atlas-62-schools-supported-avsi-lakes-and-ees-and-avsis-overall

• AVSI Foundation (2019), AVSI’s footprint in education in Ikwoto county, EES, South Sudan https://www.humanitarianresponse.info/en/operations/south-sudan/assessment/avsi-distance-support-program-beneficiaries-academic-results

• AVSI Foundation (2019), AVSI Lesson learned on EiE construction interventions https://www.humanitarianresponse.info/en/operations/south-sudan/assessment/avsi-lesson-learned-eie-construction-interventions

• AVSI Foundaiton (2018), Impact Evaluation of UNICEF-funded AVSI "PEACE" project. https://www.humanitarianresponse.info/en/operations/south-sudan/assessment/impact-evaluation-unicef-funded-avsi-peace-project

• AVSI Foundation (2017), The state of primary education in Eastern Equatoria State and a call for a context-specific, integrated intervention. https://www.humanitarianresponse.info/en/operations/south-sudan/assessment/state-education-eastern-equatoria-state-and-call-context-specific

Appendix 2 - List of researches and evaluation on Education conducted by AVSI Foundation

https://www.avsi.org/en/country/south-sudan/4/

• AVSI Foundation (2019), AVSI 2019 Torit Multi-Sector Household Survey https://www.humanitarianresponse.info/en/operations/south-sudan/assessment/avsi-2019-torit-multi-sector-household-survey

• AVSI Foundaiton (2019), AVSI 2018 Cueibet Multi-Sector Household Survey https://www.humanitarianresponse.info/en/operations/south-sudan/assessment/2018-cueibet-multi-sector-household-survey

• AVSI Foundation (2018), 2018 Greater Ikwoto Multi-Sector Household Survey https://www.humanitarianresponse.info/en/operations/south-sudan/assessment/2018-greater-ikwoto-multi-sector-survey

• AVSI Foundation (2018), Kapoeta region Multi-Sector Survey Presentation https://www.humanitarianresponse.info/en/operations/south-sudan/assessment/kapoeta-region-multi-sector-survey-results-%E2%80%93-nov-2017

• AVSI Foundation (2017) Ikwoto county Nutrition and Mortality SMART survey https://www.humanitarianresponse.info/en/operations/south-sudan/assessment/ikwoto-county-nutrition-and-mortality-smart-survey

Appendix 3 - List of researches and evaluation on Food Security and Nutrition conducted by AVSI Foundation

https://www.avsi.org/en/country/south-sudan/4/