Smart - Nutrition and Mortality Survey

SMART - NUTRITION AND MORTALITY SURVEY

FINAL REPORT

Kaga, Konduga, Magumeri, and Jere LGAs

Borno State, Nigeria

July 2019

Save the Children International

Acknowledgements Save the Children International (SCI) would like to acknowledge the important contribution of the following towards the success of the survey: SCI staff and management in the MEAL and nutrition sections for the leadership, guidance and oversight. SCI administration and security units who provided logistical support through vehicles and security arrangements. The survey teams who worked tirelessly during training and data collection. The community leaders who allowed the survey teams to work without hindrance. Mothers, caregivers, fathers and children who graciously took part in the survey.

Table of contents Acknowledgements……………………………………………………………………………2 Executive summary……………………………….………………………..…………...….…..7 1. Introduction……………..…………………………..…..…………...……….…….....14 1.1 Background………………………………………………….……………………………14 1.2 Survey Justification………………………………………………………………………..15 1.3 Survey Objectives…………………………………………………………………..…….16 2. Methodology…….……………………….……………………………….….…...... 17 2.1 Survey design…….…………………………………………….……….….….………...... 17 2.2 Sample size determination…….….………………………………….….…….…...... …....17 2.3 Survey target population…………………………………….…………………….…...... 13 2.4 Sampling procedure….………………………....…...... 14 2.4.1 Selecting clusters……………………………………………………………………….18 2.4.2 Selecting households and children…………………………………………………...…18 2.5 Survey implementation……….……………………………………………………...…....18 2.5.1 Questionnaire and training……………………………………………………………..18 2.5.2 Data collection and supervision……………………………………………………..….19 2.5.3 Data cleaning and analysis…………………………………………………………...….19 2.5.4 Data collection tools…………………………………………………………………....19 2.5.5 Case definitions, inclusion criteria and classification…………………………………...20 2.6 Limitations……………………………………………………………………………...... 21 2.7 Classification of malnutrition……………………………………………………………..21 3. Results…….…………………….………………………………………..……....…....23 3.1 Household characteristics and demographics………….…………………….……..…..24 3.1.1 Response rates……………………………………………………………………….....24 3.1.2 Data quality…………………………………………………………………………..…24 3.1.3 Age and sex ratio in children 6-59 months…………………………….………..……..24 3.2 Anthropometric results (based on WHO standards 2006)……...….. ……………...... 25 3.3 Mortality results………………………………………………………………………...31 3.4 Children’s morbidity …………………………………………………………………..31 3.5 Measles vaccination and Vitamin A supplementation ………..………………………..32 3.6 Infant and young child feeding (IYCF)……………………………………………….....33 3.7 Women of reproductive age……………………………………………….………...... 34 4. Discussion………………….…………………..…………………………...….….…..36 5. Conclusion ………………………………………………………………………...…37 6. Recommendations……………………………………………………………………37 Annex 1 List of individuals who participated in the survey………………………………….39 Annex 2 Assigned clusters………………………………………………………...……….....40 Annex 3 Survey questionnaire…………………………………………………….…………41 Annex 4 Standardisation test report………………………………………….….………… 45 Annex 5 Survey local calendar of events………………………………………...…………. 48 Annex 6 Plausibility report for anthropometry………………………………………….…..51

List of tables Table 1 Child Health, Nutrition and Morbidity…………………….…………………………9 Table 2 Infant and Young Child Feeding……………………………….……………………..10 Table 3 Women of reproductive age (15-49 years) ………………………..……………….10 Table 4 Mortality……………………………………………………...…………………...…10 Table 5 Sample size calculation………………………………………….………………...…17 Table 6 Classification of malnutrition using WHO 2006 Growth Standards………….……21 Table 7 Classification of public health significance for children under 5 years of age………22 Table 8 MUAC cut-off’s for women of reproductive age………………………..……….…23 Table 9 Minimum dietary diversity for women (MDD-W) ……………...……………….…23 Table 10 Survey response rates………………..……………………………………….……24 Table 11 Overall survey data quality…………………………………..…………….…….…24 Table 12 Distribution of age and sex of sample…………………………….………..………25 Table 13 Prevalence of acute malnutrition based on weight-for-height z-scores (and/or oedema) and by sex……………..………………………………………………………....…25 Table 14 Prevalence of acute malnutrition disaggregated by LGA……………..………...….26 Table 15 Weighted prevalence of global acute malnutrition………………………………...26 Table 16: Prevalence of acute malnutrition by age, based on weight-for-height z-scores and/or oedema……………………………………………………………………………….26 Table 17: Prevalence of acute malnutrition based on MUAC cut off's (and/or oedema) and by sex…………………………………………………………………………..…………….27 Table 18 Prevalence of acute malnutrition by age, based on MUAC cut off's and/or oedema…………………………………………………………………………………….…28 Table 19 Prevalence of underweight based on weight-for-age z-scores by sex………….….28 Table 20 Prevalence of underweight by age, based on weight-for-age z-scores…………….29 Table 21 Prevalence of stunting based on height-for-age z-scores and by sex………...……29 Table 22 Prevalence of stunting by age based on height-for-age z-scores……………….….30 Table 23 Mean z-scores, Design Effects and excluded subjects…………………………..…31 Table 24 Mortality rates, Borno SMART Survey, July 2019…………………………………31 Table 25 Prevalence of reported illness in children in the two weeks prior to interview (n=590)………………………………………………………………………………….……31 Table 26 Measles vaccination and Vitamin A supplementation vaccination coverage……….33 Table 27 Infant and Young Child Feeding results……………………………………….……33

List of figures Figure 1 Trend of SAM admissions in SCI operational areas of Borno state, June 2018-July 2019…………………………………………………………………………..15 Figure 2: Weight-for-Height z-scores………………………………..………………..……...27 Figure 3: Height-for-age z-scores……….……………………….………………….….……..30 Figure 4: Proportion of children who sought treatment for illness….…….…………….…..32 Figure 5: Health-seeking behaviour…………………………………………………………..32 Figure 6: Complementary feeding for children 6-23 months..…….……………………...….34 Figure 7: Minimum dietary diversity for women 15-49 years…………….………………….34 Figure 8: Dietary diversity for women 15-49 years……………………………….……....….35 Figure 9: Proportion of malnutrition by pregnancy and lactation status…………………….35

List of acronyms ARI A-cute Respiratory Infection CI- Confidence Interval ENA- Emergency Nutrition Assessment FAO- Food and Agricultural Organisation GAM- Global Acute Malnutrition HAZ- Height-for-age z-score IYCF- Infant and young child feeding IPC- Integrated phase classification LGA- Local government area MAM- Moderate acute malnutrition MDD-W Minimum dietary diversity for women MUAC Mid upper arm circumference NBS National Bureau of Statistics OTP Outpatient therapeutic care programme PPS Probability proportional to size SAM- Severe acute malnutrition SCI- Save the Children International SFP- Supplementary feeding programme SMART- Standardised Monitoring and Assessment for Relief and Transitions UNICEF- United Nations Children’s Fund WAZ- Weight-for-age z-score WHO- World Health Organisation WHZ- Weight-for-height z-score

Executive Summary Introduction Given the prevailing acute food security situation and high malnutrition prevalence observed in the previous survey, it is necessary to update the nutrition and mortality indicators by determining the prevalence of acute malnutrition and mortality as well as to investigate factors related to malnutrition. According to the 2018 SMART Nutrition and Mortality Survey conducted in Kaga, Jere, Konduga and Magumeri LGAs in Borno State, the prevalence of global acute malnutrition (GAM) was 15.7% (12.2-20.0, 95% C.I), with a severe acute malnutrition (SAM) prevalence of 4.2% (2.9-6.1, 95% C.I). The crude death rate was 0.79 deaths per 10,000 per day (0.52-1.20, 95% C.I), with an under 5 death rate of 1.60 deaths per 10,000 per day (0.83-3.07, 95% C.I). However, due to the persistent on-going conflicts in the areas, it is necessary to also continue monitoring the mortality trends, and as such, this survey will also measure the mortality rates.

The overall objective of the survey was to determine the magnitude and severity of malnutrition and retrospective mortality rates amongst the population in the accessible communities of the 4 LGAs (Kaga, Jere, Konduga and Magumeri) in Borno State within the areas in which Save the Children is operational.

Specific objectives

 To determine prevalence of Malnutrition (acute malnutrition, chronic malnutrition and underweight) among children 6-59 months in the target population in the accessible communities of the 4 LGAs.  To assess retrospective morbidity among children under 5 in the target population in the 4 LGAs.  To assess retrospective mortality (Crude Mortality and U5 Mortality rates) over 3 months’ recall period among target populations in the 4 LGAs.  To estimate measles vaccination coverage of children 9-59 months and Vitamin A supplementation coverage of children 6-59 months in the target population in the 4 LGAs.  To assess IYCF practices among the households with children under two years of age in the target population in the 4 LGAs.  To estimate the prevalence of malnutrition in women of reproductive age (15-49 years) in the target population in the 4 LGAs.  To establish recommendations on actions to address identified gaps, to support planning, advocacy, decision making and monitoring in the 4 LGAs.

Methodology The Standardized Methodology for Assessment in Relief and Transitions (SMART) which applies a two-stage cluster sampling was used. At the first stage, 48 clusters were selected from the list of accessible communities using sampling with probability proportional to size (PPS). At the second stage, 10 households in each cluster were selected using simple random sampling.

A total of 397 children aged 6-59 months from 480 households in 48 clusters were sampled for anthropometric measurements. The mortality assessment was conducted concurrently in the 480 households.

Additional information was collected in the 480 households visited to provide more insight into possible risk factors associated with the high acute malnutrition prevalence (morbidity, infant and young child feeding and women of reproductive age).

ENA-for-SMART calculated a required sample size of 477 households. It was estimated that a single team was able to complete 10 households within a day, therefore the survey design was 48 x 10 (48 clusters each with 10 households). A list of accessible communities and their population was entered in ENA-for-SMART, from which the 48 clusters were assigned baded on sampling with probability proportional to size (PPS). Households within the cluster were selected using simple random sampling. In all sampled households, all children below 5 years and all women aged 15-49 years were surveyed.

Summary of findings Table 1 Child Health, Nutrition and Morbidity INDICATOR n/N % (95% C.I)

Prevalence of acute malnutrition by WHZ WHZ (WHO) 6-59 months Global Acute Malnutrition 123/582 21.1 (17.6-25.2) (WHZ <-2) Moderate Acute Malnutrition 91/582 15.6 (12.6-19.3) (-2>WHZ>=-3) Severe Acute Malnutrition 32/582 5.5 (3.6-8.2) (WHZ<-3) Prevalence of global acute malnutrition by LGA1 Jere 28/189 14.8 (9.5-22.4) Konduga 22/91 24.2 (13.6-39.2) Kaga 31/144 21.5 (15.0-29.9) Magumeri 45/159 28.3 (21.0-36.9) Prevalence of acute malnutrition by MUAC MUAC 6-59 months Global Acute Malnutrition 75/593 12.6 (10.2-15.6) (MUAC <-125mm) Moderate Acute Malnutrition 55/593 9.3 (7.1-12.0) (125mm>MUAC>=115mm) Severe Acute Malnutrition 20/593 3.4 (2.1-5.4) (MUAC<115mm) Prevalence of stunting by HAZ HAZ (WHO) 6-59 months Stunting 269/550 48.9 (43.3-54.6) (HAZ <-2) Moderate Acute Malnutrition 149/550 27.1 (22.5-32.2) (-2>HAZ>=-3 Severe stunting 120/550 21.8 (18.1-26.0) (HAZ<-3) Prevalence of underweight by WAZ WAZ (WHO) 6-59 months Underweight 250/578 43.3 (37.9-48.8) (WAZ<-2) Moderate underweight 160/578 27.7 (23.7-32.1) (-2>WAZ>=-3) Severe underweight 90/578 15.6 (12.1-19.9) (WAZ<-2) Morbidity (6-59 months) Diarrhoea 271/590 45.9 (40.8-51.1) Fever 241/590 40.8 ((34.4-47.3) Acute Respiratory Infection 292/590 49.4 (43.2-55.8) Treatment for illness Sought treatment 315/426 73.9 (67.5-80.4) Did not seek treatment 111/426 26.1 (19.6-32.5) Source of treatment Government clinic/hospital 150/315 47.6 (36.4-58.9) Private clinic 48/315 15.2 (5.4-25.1) Pharmacy 83/315 26.3 (16.2-36.5) Friend/relative 1/315 0.3 (0.0-1.0) Religious leader 1/315 0.3 (0.0-1.0)

1 To be interpreted with caution.

Traditional healer 4/315 1.3 (0.0-2.8) Other 28/315 8.9 (2.1-15.6) Measles vaccination (9-59 months) By card 90/555 16.2 (10.6-21.8) By recall 182/593 30.7 (22.3-39.0) Vitamin A supplementation By recall 421/590 71.4 (60.8-81.9) Table 2 Infant and Young Child Feeding INDICATOR n/N % (95% C.I) Introduced to solid foods at 6 months (6-8 months) 7/37 18.9 (5.7-32.1) Continued breastfeeding at 1 year (12-15 months) 67/72 93.1 (86.9-99.2) Continued breastfeeding at 2 years (20-23 months) 5/20 25.0 (0.5-49.5) Consumption of iron-rich foods (6-23 months) 42/211 19.9 (13.7-26.1) Mean dietary diversity score (6-23 months) 2.9 (2.5-3.2) Minimum meal frequency (6-23 months) >= 4 times 21/211 10.0 (5.3-14.6) Minimum dietary diversity (6-23 months) >= 4 food groups 39/211 18.5 (11.6-25.3) Minimum acceptable diet (MAD) children 6-23 months 4/211 1.9 (0.0-4.2)

Table 3 Women of reproductive age (15-49 years) INDICATOR n/N % (95% C.I) Minimum dietary diversity for women (MDD-W) Good (>=5 groups) 200/445 44.9 (36.8-53.1) Poor (0-4 groups) 245/445 55.1 (46.9-63.2) Acute malnutrition based on MUAC All MUAC <210mm (all) 21/441 4.8 (2.3-7.2) MUAC 210-229mm 67/441 15.2 (12.0-18.4) Pregnant MUAC <210mm (all) 3/77 3.9 (0.0-8.2) MUAC 210-229mm 12/77 15.6 (7.6-23.6) Lactating MUAC <210mm (all) 10/223 4.5 (1.6-7.3) MUAC 210-229mm 33/223 14.8 (10.3-19.3)

Table 4 Mortality INDICATOR Deaths/10,000/day (95% C.I) Crude death rate (deaths per 10,000 per day) 0.83 (0.55-1.27) Under 5 death rate (deaths per 10,000 per day) 1.88 (0.92-3.82)

Discussion With respect to quantitative result, the comparison between 2018 and 2019 refers to the 2 SMART surveys conducted by SCI in accessible communities within the SCI area of operation.

Child Health, Nutrition and Morbidity Based on the WHO 2006 standards, the prevalence of global acute malnutrition (GAM) was above the WHO critical threshold which defines an emergency situation. This is of concern particularly given the fact that the last survey in the same area conducted by SCI in 2018 was also above emergency levels but was significantly lower. The prevalence of severe acute malnutrition (SAM) by weight-for-height z-score was also very high and higher than in 2018. Acute malnutrition was also higher by MUAC classification in 2019 compared to 2018. Acute malnutrition was much higher in Magumeri, Konduga and Kaka LGAs compared to Jere LGA. The LGAs with high acute malnutrition have been affected by displacements and the associated food insecurity. A World Food Programme assessment revealed that communities with IDPs were more food insecure than those without IDPs2. The analysis of the trend of admissions for SAM over the period June 2018 to July 2019 shows that the admissions were highest in the month of July in both 2018 and 2019, with the peak coinciding with the peak of the hunger season. This partly explains the prevalence being very high. It would be expected that, due to this seasonality aspect, the prevalence would be lower during the period after the harvest, as the SAM admissions show. Apart from the seasonality factor, it must be noted that, according to the Nigeria Nutrition in Emergency Sector Strategic Response Plan 2017-2018, a rapid SMART assessment conducted in April 2016 by Action Against Hunger in the LGAs of MMC and Jere revealed a GAM rate of 19.1% and SAM rate of 3.1% (April 2016). Nutrition assessments undertaken in April- June 2016 in some LGAs in Borno state also showed that there were pockets with extremely high acute malnutrition rates which included Konduga LGA (16.4% GAM and 5.0% SAM). The same report also indicated that the state of malnutrition in the North East of Nigeria is related to high food insecurity, sub optimal infant and young children feeding practices negative coping strategies, increasing spread of endemic diseases, low coverage of programs targeting children with moderate acute malnutrition, limited dietary diversity, loss of livelihoods, disruption of access to quality water and optimal sanitation, population displacement and destruction of housing, compromising the privacy necessary for breastfeeding; and the poor and deteriorating health care system. Some of these factors were investigated in the report and are discussed below.

Stunting was also of major concern as it was also above the critical category of WHO classification and was higher than in 2018. In Nigeria, 37 percent of children under 5 years are stunted. Nigeria has the highest number of children under 5 years with chronic malnutrition (stunting or low height-for-age) in sub-Saharan Africa at more than 11.7 million, according to the most recent Demographic and Health Survey. The prevalence of stunting increases with age, peaking at 46 percent among children 24–35 months. While stunting prevalence has improved since 2008 (41 percent), the extent of acute malnutrition (wasting or low weight- for-height) has worsened, from 14 percent in 2008 to 18 percent in 2013 among children under 5 years3.

2 https://reliefweb.int/sites/reliefweb.int/files/resources/WFP%20Nigeria-2019%20EFSA%20Report%20- %20Final%20Version%20to%20be%20shared.pdf

3 https://www.usaid.gov/sites/default/files/documents/1864/Nigeria-Nutrition-Profile-Mar2018-508.pdf

The prevalence of underweight also increased in 2019 compared to 2018. A significant proportion of children reported having experienced diarrhoea, fever or acute respiratory infection in the preceding 2 weeks. Most children sought treatment from government clinics/hospitals followed by pharmacies and private clinics. Measles vaccination coverage was quite low both by recall and confirmation with card, while Vitamin A supplementation coverage was quite high. Infant and Young Child Feeding Infant and young child feeding results were generally poor. A low proportion of children had been introduced to solid foods between 6 to 8 months, showing very late introduction to solid foods. Although continued breastfeeding was very high at 1 year, it was very low at 2 year. A low proportion of children 6-23 months had consumed iron-rich foods. Minimum dietary diversity was only met by less than a fifth of children, while a very low proportion met the minimum acceptable diet.

Women of reproductive age Less than half of women of reproductive age had an acceptable dietary diversity. This indicator was however not investigated in the last survey. In terms of acute malnutrition, the proportion of women with MUAC below 210mm and between 210 and 230mm was not very high and was comparable between 2018 and 2019.

Mortality Crude death rate and under 5 death rate were below the emergency threshold, despite acute malnutrition being very high. This was the same trend observed in 2018 and the results in both surveys are also very similar in terms of both indicators.

Conclusion The nutrition situation in the SCI operational areas of Borno state is at a critical level and has deteriorated as the results show. The concern is particularly with respect to acute and chronic malnutrition, as well as infant and young child feeding.

Recommendations Child Health, Nutrition and Morbidity  Given the critical prevalence of acute malnutrition, there is a need to scale up the integrated management of acute malnutrition, ensuring that the services are accessible to the whole population, and to incorporate a targeted supplementary feeding component given the large MAM caseload which the findings highlight.  Integration of existing nutrition services and programmes with the health programmes is essential to facilitate linkages which will guarantee that children have access to a comprehensive package of health and nutrition services, including vaccination, supplementation and treatment.  Given the gap which exists between classification of acute malnutrition by WHZ and MUAC, it is important to set up a system whereby there is a way to screen at-risk children at the second stage using WHZ. This will ultimately increase programme coverage.  A SQUEAC assessment to investigate the barriers to optimal CMAM coverage is recommended in targeted LGAs.

Infant and Young Child Feeding  The assessed IYCF indicators were generally poor in terms of continued breastfeeding, complementary feeding and dietary diversity, which calls for a scaling up of efforts to improve IYCF practices through the use of community-based approaches to spread appropriate messages through platforms such as religious gatherings and other community groupings.  Recent KAP studies should be reviewed in the context of this survey’s results, with a view to implementing the strategies recommended which are applicable.

Women of reproductive age (15-49 years)  Improve dietary diversity for women of reproductive age (given the importance of nutrition in the lifecycle) through community and health facility health education sessions using IEC material such as flyers, posters, and counselling cards.  Women of reproductive age should be included in the management of acute malnutrition programme.

1. Introduction 1.1 Background The ongoing conflict in the North East parts of Nigeria continues to increase population displacements, restrict income-generating opportunities, limit trade flows and escalate food prices. As a result of the reduced access and availability of food, local and internally displaced persons (IDPs) populations in worst-affected areas of Borno, Yobe and Adamawa states continue to experience food gaps. According to the Integrated Phase Classification (IPC) analysis, most households in southern Yobe, northern Adamawa and Borno states worst affected by the insurgency will continue facing Crisis (IPC Phase 3) and Emergency (IPC Phase 4) until September 20194. According to Food and Agriculture Organization (FAO), 6.4% of the population of Borno are Internally Displaced Persons (IDPs) and another 3.4% are refugee, which increases their vulnerability to food security, health and nutrition services. In addition, at least 35% of the resident households in Borno State have an IDP or returnee in household5. The nutrition situation in Borno State has been classified as serious according to the Nutrition in Emergency Sector Working Group which noted that the prevalence of Global Acute Malnutrition (GAM) increased from 6% in 2010 to 11.5% in 2015, with the peak being in 2012 when the prevalence of GAM was estimated as 13.8%6. In addition, the National Nutrition and Health Survey conducted in 2018 showed the prevalence of global acute malnutrition as 10.6%7 which is classified as serious. However, a SMART Survey conducted by Save the Children International (SCI) in its operational areas8 in Borno in August 2018 showed a critical nutrition situation with the prevalence of global acute malnutrition estimated as 15.7%9. The same survey also found poor infant and young child feeding indicators, particularly exclusive breastfeeding, complementary feeding and dietary diversity.

Figure 1 below is an analysis of the trend of admissions for severe acute malnutrition between June 2018 and July 2019 shows that the admissions peaked in July in both 2018 and 2019, which reflects the seasonal nature of acute malnutrition, given that this coincides with the beginning of the rainy season, where food stocks are expected to be at their lowest.

4http://fews.net/west-africa/nigeria 5Food Security, Livelihood and Vulnerability Assessment Report (2016) by FAO and NBS 6 http://fscluster.org/sites/default/files/documents/nutrition_in_emergency_sector_response_plan_nigeria_draft_1_.pdf 7https://www.unicef.org/nigeria/media/2181/file 8 Konduga, Jere, Magumeri and Kaga Local Government Authorities (LGAs) 9SMART Survey Report, Save the Children, Borno 2018

Trend in SAM admissions in SCI operational areas of Borno state, June 2018-July 2019 1200 1099 1000 993 966

800 761 792 622 600 567 460 495 400 347 375 388 295 336 200

0 Number of SAM admissions SAM of Number

Month

Figure 1 Trend of SAM admissions in SCI operational areas of Borno state, June 2018- July 2019

The nutrition situation in the North East has further been aggravated by high food insecurity, sub optimal infant and young children feeding practices such as untargeted/uncontrolled infant formula distribution, negative coping strategies, increasing spread of endemic diseases, low coverage of programs targeting children with moderate acute malnutrition, limited dietary diversity, loss of livelihoods, disruption of access to quality water and optimal sanitation, population displacement and destruction of housing, compromising the privacy necessary for breastfeeding; and the poor and deteriorating health care system10. An assessment of the drivers of malnutrition conducted by SPRING in 2017 revealed that some of the key contributing factors to high malnutrition included poor child feeding practices in addition to other household, agricultural and WASH behaviours. The review recommended targeted, multi-channel and high quality social behaviour communication interventions incuding information and communication at household level ad mother support groups dapated to the local context11. In order to continue monitoring the health and nutrition situation in the SCI operational areas in Borno State, another SMART survey was proposed by SCI. The results of the proposed SMART Survey will be critical for planning and making evidence-based decision, for the ultimate goal of reaching the most in need.

1.2 Survey Justification Given the prevailing acute food security situation, high malnutrition prevalence observed in the previous survey and ongoing programming to address the nutrition, health and food security needs of the population, it is necessary to update the nutrition and mortality indicators by determining the prevalence of acute malnutrition and mortality as well as to investigate factors related to malnutrition. According to the 2018 SMART Nutrition and Mortality Survey conducted in Kaga, Jere, Konduga and Magumeri LGAs in Borno State, t-he prevalence of global acute malnutrition (GAM) was 15.7% (12.2-20.0, 95% C.I), with a severe

10 https://www.humanitarianresponse.info/en/operations/nigeria/document/nutrition-and-food-security-surveillance-north-eastnigeria- %E2%80%93-0

11 https://www.spring-nutrition.org/publications/reports/assessing-drivers-malnutrition-nigeria

acute malnutrition (SAM) prevalence of 4.2% (2.9-6.1, 95% C.I). The crude death rate was 0.79 deaths per 10,000 per day (0.52-1.20, 95% C.I), with an under 5 death rate of 1.60 deaths per 10,000 per day (0.83-3.07, 95% C.I). However, due to the persistent on-going conflicts in the areas, it is necessary to also continue monitoring the mortality trends, and as such, this survey will also measure the mortality rates.

1.3 Survey Objectives The overall objective of the survey is to determine the magnitude and severity of malnutrition and retrospective mortality rates amongst the population in 4 LGAs (Kaga, Jere, Konduga and Magumeri) in Borno State within the areas in which Save the Children is operational9.

The specific objectives of the survey are as follows:

 To determine prevalence of Malnutrition (acute malnutrition, chronic malnutrition and underweight) among children 6-59 months in the target population in the 4LGAs  To assess retrospective morbidity among children under 5 in the target population in the 4 LGAs  To assess retrospective mortality (Crude Mortality and U5 Mortality rates) over 3 months’ recall period among target populations in the 4 LGAs  To estimate measles vaccination of children 9-59 months and Vitamin A supplementation coverage of children 6-59 months in the target population in the 4 LGAs  To assess IYCF practices among the households with children under two years of age in the target population in the 4 LGAs  To estimate the prevalence of malnutrition in women of reproductive age (15-49 years) in the target population in the 4 LGAs  To establish recommendations on actions to address identified gaps, to support planning, advocacy, decision making and monitoring in the 4 LGAs

1.4 Survey area and timing The SMART survey was conducted between 15 and 28 July 2019 in the accessible communities of 4 LGAs (Kaga, Jere, Konduga and Magumeri) of Borno state in which SCI is operational. It is important to note that the survey was not representative of the whole Borno state.

2 Methodology

2.1 Survey design The survey followed a cross-sectional survey design and used the two-stage cluster sampling method based on the SMART methodology.

2.2 Sample size determination The survey was based on the Standardized Monitoring and assessment of Relief and Transitions (SMART) methodology12 . The sample size was calculated using ENA-for-SMART, July 9, 2015 version, based on the assumptions shown in Table 5.

Table 5 Sample size calculation, Borno State SMART Survey, July 2019 Parameters for Value Assumptions Based on Context Anthropometry13 Estimated Prevalence of GAM 20.0 Upper limit of 2018 SMART Nutrition and (%) Mortality Survey Report ± Desired Precision (%) 4.5 Rule of thumb as recommended by SMART for a prevalence of GAM of 15-20% Design Effect (if applicable) 1.2 SMART Survey, August 2018 – Save the Children Children to be Included 397 As calculated from ENA Average HH Size 5.9 SMART Survey, August 2018 – Save the Children % Children Under 5 17.4 SMART Survey, August 2018 – Save the Children % Non-response HH’s 10 An increase from 5% in the 2018 SMART Nutrition and Mortality Survey Report given that there was a higher than expected non-response rate. Households to be Included 477 As calculated from ENA Parameters for Mortality Value Assumptions based on context (add reference) Estimated Death Rate 1.20 Upper limit of 2018 SMART Nutrition and /10000/day Mortality Survey Report SMART surveys, start with global norms ± Desired precision / 10 000 / 0.6 Precision based on SMART guidance day Design Effect (if applicable) 1.2 SMART Survey, August 2018 – Save the Children Recall Period in days 90 Based on 3 month recall period Population to be included 1859 As calculated from ENA Average HH Size 5.9 SMART Survey, August 2018 – Save the Children % Non-response of Households 10 An increase from 5% in the 2018 SMART Nutrition and Mortality Survey Report given that there was a higher than expected non-response rate. Households to be included 350 As calculated by ENA

Sample sizes were therefore calculated separated for anthropometry and mortality. Given that the anthropometry sample size was larger, this was taken as the final sample size for the survey in order to fulfil both objectives.

12 www.smartmethodology.org

13 Kaga, Mafa, Konduga, Magumeri and Jere LGAs

2.3 Survey Target Population Anthropometric indictors were assessed for children aged 6 to 59 months based on the WHO 2006 standards. In each sampled household, all children were measured. Age was obtained from the official age documentation, either the birth certificate or child health card. In this case, the date of birth was recorded. In the event that the official document was not available, the age in months was estimated using the local calendar of events. IYCF practices were assessed by interviewing the mothers or primary caregivers of children aged 6 to 23 months. Morbidity for the preceding 14 days (diarrhoea, acute respiratory infection and fever) was assessed for 6-59 months. Vitamin A supplementation and measles vaccination coverage were applied to children 6-59 and 9-59 months respectively. The mother/caregiver recall, and the child health card were used for measles vaccination. For Vitamin A supplementation, only the mother/caregiver recall was used. The main respondent was an adult woman responsible for preparing food for the household. For the women questionnaire, all women of reproductive age (15-49 years) were interviewed for the assessment of minimum dietary diversity for women (MDD-W) as well as measurement of mid upper arm circumference (MUAC).

2.4 Sampling procedure 2.4.1 Selecting clusters The 2-stage cluster sampling method was used to select 48 clusters from the accessible communities in the 4 LGAs. The primary sampling unit (PSU) was the settlement. At the first stage, a list of settlements and their populations was made before the required number of clusters were selected by ENA-for-SMART, July 9, 2015 version using sampling with probability proportional to size (PPS). Reserve clusters were also assigned in case at least 10% of the clusters could not be reached. However, they were not used as all 48 clusters were reached. Assigned clusters are presented in Annex 2.

2.4.2 Selecting households and children Based on the sampling design and the sample size calculation, 10 households were selected in each of the 48 clusters, giving a total of 480 households. The number of households per cluster was determined by considering the number of households which a single survey team was able to realistically complete within a single day, taking into account the travelling time, the time required to collect data and move between the different households. At the second stage of cluster sampling, the required number of households were selected using the simple random sampling method. The target sample size for children was 397 children. In the event that individuals or children were absent at the time the team arrived in the household, the team revisited the household before leaving the cluster. The supervisors assigned to each team ensured that absent households were revisited before the end of the day using a cluster control sheet. At the end of the survey, a total of 432 households had been interviewed, which was 90% of the target of 480. A total of 593 children had been measured, which was 149% of the target of 397. All clusters were reached (100%).

2.5 Survey implementation

2.5.1 Questionnaire and training The questionnaire was adapted from the standard SMART questionnaire with additional modules from the 2018 SMART survey. It was prepared in in English language and administered in the local language through translation. The questionnaire was validated in the Kobo Toolbox system then pre-tested during the pilot test conducted on the last day of survey training. See Annex 3 for the questionnaire. The survey team received a 5-day training

which included 3 days classroom training, 1-day standardization test and 1-day pilot test. The training curriculum included the following key topics: survey objectives, survey team organization, roles and responsibilities, sampling and selection, anthropometric measurements, interviewing techniques, questionnaire familiarization and mobile data collection. The standardisation test involved a total of 10 measurers who measured a total of 10 children twice. The objective was to measure accuracy, which was measured by the difference between individual measurements and the supervisor measurement or the median of the team’s measurements. Precision was measured by comparing the fisrt and second measurements for the same child and measurer. Results of the test are displayed in Annex 4. The scores for height and MUAC were satisfactory. However, the scores for weight were poor. This was corrected by an additional training session with the team. The pilot test was conducted by all the survey teams in a settlement which was within Maiduguri town and outside the survey sample. This enabled the teams to familiarize themselves with sampling and household selection as well as the questionnaire and measurement methods.

2.5.2 Data Collection and Supervision Based on the sample size of 480 households and the agreed number of households to be covered by each team, a total of 6 survey teams were formed, with each having the responsibility of completing a cluster per day. Each team was made up of 4 members, who were; 1 measurer responsible for anthropometric measurements for children (who was also the team leader), 1 assistant measurer to assist in anthropometric measurements, 1 interviewer and 1 supervisor. A community guide assisted the teams in each location in terms of identifying the settlements, obtaining access to respondents and translation where required.

Each team collected data for a total of 8 days with each team completing 10 households per day. The supervisors ensured that there was quality control in the field. The Survey Consultant provided overall supervision.

Android mobile phones were used for data entry using the Kobo collect application. Each of the survey teams had 2 mobile phones. The questionnaires were designed using excel form designer and uploaded on the SCI server.

2.5.3 Data Cleaning and Analysis In addition to this, after each day of data collection, the team leaders submitted completed mobile phones to supervisors, who checked the completeness and accuracy of data before uploading. Data was then exported in excel format before being exported to ENA for SMART for generation of the plausibility report for anthropometry, and EPI Info. A daily check was performed for plausibility of the anthropometric data in ENA-for-SMART with feedback being given to teams each day before the next day of data collection. SMART flags were used for exclusion of out or range values (-/+3 and +/- 3 SD of WHZ from the observed WHZ mean). Anthropometric and mortality data were analysed using ENA for SMART, while EPI INFO was used to analyse the remaining modules.

2.5.4 Data collection tools The modules of the questionnaire were as follows: Module 1: Children- Questions and measures for children aged 6-59 months. Anthropometry, measles vaccination, Vitamin A supplementation, morbidity, health-seeking behavior and infant and young child feeding (IYCF). Module 2: Women- Information relating to women’s pregnancy and lactation status, dietary diversity and MUAC measurement.

Module 3: Mortality- Household census for determination of crude and under 5 death rate.

2.5.5 Case definitions, inclusion criteria and classification

Methods of measurement and definitions:

Households (HH): A household was defined as a group of people who normally live together and eat from the same pot and resources.

Sex of children: sex was recorded as male or female.

Birth date or age in months for children 6-59 months: the exact date of birth (day, month, and year) was recorded from birth certificates or child health cards. A local calendar of events (Annex 5) was used in the absence of official documentation, and the age in months was recorded.

Age of women 15-49 years: The reported age was recorded in years.

Weight of children 6-59 months: measurements were taken to the nearest 0.1kg using an electronic scale (SECA scale).

Height/Length of children 6-59 months: children’s height or length was taken to the nearest 0.1cm using a wooden height board. Children less than 2 years were measured lying down, while those greater than or equal to 2 years were measured standing up.

Oedema in children 6-59 months: Bilateral oedema was assessed by applying gentle thumb pressure on to the tops of both feet of the child for a period of three seconds and thereafter observing for the presence or absence of an indent.

MUAC of children 6-59 months and women 15-49 years: MUAC was measured at the mid- point of the left upper arm between the elbow and the shoulder and taken to the nearest 1mm using a standard tape.

Measles vaccination in children 9-59 months: measles vaccination was assessed by checking for the measles vaccine on the child health card if available or by asking the caregiver to recall if no child health card was available or if it was not recorded.

Vitamin A supplementation in last 6 months in children 6-59 months: whether the child received a vitamin A capsule over the past six months was recorded using recall from the mother/caregiver.

Morbidity: Retrospective morbidity was assessed using recall for the past 2 weeks.

Diarrhoea: Diarrhoea was defined as three loose stools or more in 24 hours. Caregivers were asked if their child had suffered episodes of diarrhoea in the past two weeks.

Fever (without cough): Fever was assessed through a two-week recall, defined as fever in the absence of respiratory symptoms (cough) in children 6-59 months. This indicator is a proxy for suspected malaria.

Acute Respiratory Infection (ARI): Cough, breathing difficulties, chest in-drawing, rapid breathing.

Crude death rate: Number of deaths from all causes per 10,000 people per day

Under five death rates: Number of deaths among children under five from all causes per 10,000 people per day

2.6 Limitations

 The estimation of age was a challenge given that 87% of children did not have an official age document to confirm the date of birth. However, the local calendar of events was used to estimate the age in months. It must be noted that recall bias cannot be excluded under such circumstances.  In a few of the communities, particularly in Gajigana and Ngamdu, due to the longer distances, as well as security protocols, the target number of households could not be achieved. However, sufficient children were surveyed, and the number of households met the minimum number given that a contingency had been made based on the 10% additional households anticipated for non-response.  The sample size was calculated based on children 6-59 months. For indicators requiring sub-groups which are small, particularly the IYCF section, results must be interpreted with caution given the larger width of the confidence interval.  Children between 0-5 months were not included in the sample, and therefore early initiation of breastfeeding, exclusive breastfeeding and bottle feeding could not be calculated. This was an oversight during questionnaire design.  The survey results cannot represent the whole of Borno state, as the sampling frame only included accessible communities within the areas of operation of SCI in 4 LGAs of Borno state (Kaga, Konduga, Magumeri, and Jere LGAs).

2.7 Classification of malnutrition Table 6 shows the definition and classification of the nutritional indicators used. Main results are reported according the World Health Organisation (WHO) Growth Standards 2006.

Table 6 Classification of malnutrition using WHO 2006 Growth Standards Indicator Definitive criteria Acute Global Acute WHZ <-2SD and/or Malnutrition Malnutrition Presence of Bilateral oedema MUAC <125mm Moderate Acute WHZ <-2 and ≥-3 Malnutrition MUAC ≥115mm and <125mm Severe Acute WHZ <-3 and/or oedema Malnutrition MUAC <115mm Stunting Overall stunting HAZ <-2 Moderate Stunting HAZ <-2 and ≥-3 Severe stunting HAZ <-3 Underweight Overall WAZ <-2 Underweight Moderate WAZ <-2 and ≥-3 Underweight Severe WAZ <-3 Underweight

Table 7 Classification of public health significance for children under 5 years of age14 Prevalence % Very High Medium Low Very low High Wasting ≥15 10-<15 5-<10 2.5-<5 <2.5 Stunting ≥30 20-<30 10-<20 2.5-<10 <2.5

Infant and young child feeding practices in children 0-23 months Infant and young child feeding practices were assessed as follows based on standard WHO indicators15:

Continued breastfeeding at 1 year: Proportion of children 12–15 months of age who were fed breast milk during the previous day.

Introduction of solid, semi-solid or soft foods: Proportion of infants 6–8 months of age who receive solid, semi-solid or soft foods during the previous day.

Children ever breastfed: Proportion of children born in the last 24 months who were ever breastfed.

Continued breastfeeding at 2 years: Proportion of children 20–23 months of age who were fed breast milk during the previous day. Commented [MO1]: replace with ----a day preceding the survey

Consumption of iron-rich or iron-fortified foods: Proportion of children 6-23 months of age who received an Iron-rich food or Iron-fortified food that is specially designed for infant and young children or that is fortified in the home during the previous day. Minimum dietary diversity: Proportion of children 6-23 months of age who receive foods from 4 or more food groups during the previous day.

The 7 foods groups used for calculation of this indicator are: 1. Grains, roots and tubers 2. Legumes and nuts 3. Dairy products (milk, yogurt, cheese) 4. Flesh foods (meat, fish, poultry and liver/organ meats) 5. Eggs 6. Vitamin-A rich fruits and vegetables 7. Other fruits and vegetables

Minimum meal frequency: Proportion of non-breastfed children 6-23 months who received at least 4 full meals during the previous day.

Minimum acceptable diet: Proportion of children 6-23 months who received at least 4 full meals and at least 4 of the food groups during the previous day.

Diarrhoea: Three or more loose or watery stools in a 24-hour period.

14 (UNICEF/WHO/World Bank, 2018), Levels and trends in child malnutrition-Joint Malnutrition Estimates 15 (WHO, 2010), Indicators for assessing infant and young child feeding practices

Under nutrition in women of reproductive age:

Table 8 MUAC cut-off’s for women of reproductive age Classification MUAC Severe malnutrition <210mm Moderate malnutrition 210-229mm

Minimum dietary diversity for women (MDD-W) MDD-W is a dichotomous indicator of whether women 15–49 years of age have consumed at least five out of ten defined food groups the previous day or night. The 10 groups are: 1. Grains, white roots and tubers, and plantains; 2. Pulses (beans, peas and lentils); 3. Nuts and seeds; 4. Dairy; 5. Meat, poultry and fish; 6. Eggs; 7. Dark green leafy vegetables; 8. Other vitamin A-rich fruits and vegetables; 9. Other vegetables; 10. Other fruits.

Table 9 Minimum dietary diversity for women (MDD-W)16 MDD-W Threshold Good >=5 food groups Poor 0-4 food groups

16 (FAO/FANTA/USAID, 2019. Minimum Dietary Diversity for Women-A Guide to Measurement.

3. Results

3.1 Household characteristics and demographics 3.1.1 Response rates A total of 726 households were interviewed, which was 90% of the planned 480 households (Table 10). The total survey population was 2,787, giving an average household size of 6.5. The number of children aged 6-59 months was 593, which was 149% of the target of 397.

Table 10 Survey response rates No of households interviewed 432 Planned no. of households 480 % households achieved 90% Total population surveyed 2,787 Average household size 6.5 No of children 6-59 years surveyed 593 Planned no. of children 6-59 years 397 % children 6-59 months achieved 149%

3.1.2 Data quality The overall data quality of the survey was good, as shown by the summary scores in Table 11. Only 1.4% of measurements were flagged. There was no significant difference between different age categories. Digit preference for all indicators was also of an acceptable standard. Standard deviation froor WHZ was within the acceptable range of 0.8-1.2. The complete report is shown in Annex 6.

Table 11 Overall survey data quality Criteria Score Conclusion Flagged data 0 (1.4 %) Excellent Overall Sex ratio 0 (p=0.129) Excellent Age ratio(6-29 vs 30-59) 4 (p=0.015) Acceptable Digit preference score - weight 0 (3) Excellent Digit preference score - Height 2 (8) Good Digit preference score - MUAC 0 (7) Excellent Standard Deviation WHZ 5 (1.15) Good Skewness WHZ 1 (-0.27) Good Kurtosis WHZ 1 (-0.35) Good Poisson distribution WHZ-2 0 (p=0.153) Excellent Overall Data Quality Score 13% Good

3.1.3 Age and sex ratio in children 6-59 months The distribution of age and sex of children 6-59 months (Table 12) showed an acceptable sex ratio (1.1). The age ratio was also acceptable according to the plausibility report.

Table 12 Distribution of age and sex of sample Boys Girls Total Ratio AGE (mo) no. % no. % no. % Boy:girl

6-17 88 56.1 69 43.9 157 26.5 1.3 18-29 84 57.9 61 42.1 145 24.5 1.4 30-41 68 48.2 73 51.8 141 23.8 0.9 42-53 57 48.7 60 51.3 117 19.7 0.9 54-59 18 54.5 15 45.5 33 5.6 1.2

3.2 Anthropometric results (based on WHO standards 2006) Three forms of under-nutrition were assessed through this survey, and they included wasting, underweight and chronic malnutrition. Global acute malnutrition indicator was used to assess wasting, while underweight indicator was used to assess underweight and stunting indicator was used to assess chronic malnutrition. The target population for these three indicators were children aged 6 to 59 months, whose anthropometric measurements were taken from all the sampled households.

3.2.1 Prevalence of aute malnutrition The prevalence of global acute malnutrition (Table 13) was 21.1% (17.6-25.2, 95% C.I), with a severe acute malnutrition prevalence of 5.5% (3.6-8.2, 95% C.I). The GAM prevalence was well above the emergency threshold of 15% and increased significantly (p<0.05) compared to 15.7% (12.2-20.0, 95% C.I) in 2018.

Table 13 Prevalence of acute malnutrition based on weight-for-height z-scores (and/or oedema) and by sex All Boys Girls n = 582 n = 308 n = 274 Prevalence of global (123) 21.1 % (79) 25.6 % (44) 16.1 % malnutrition (17.6 - 25.2 (20.1 - 32.1 (11.8 - 21.5 (<-2 z-score and/or oedema) 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate (91) 15.6 % (58) 18.8 % (33) 12.0 % malnutrition (12.6 - 19.3 (14.5 - 24.1 (8.6 - 16.6 (<-2 z-score and >=-3 z-score, 95% C.I.) 95% C.I.) 95% C.I.) no oedema) Prevalence of severe (32) 5.5 % (21) 6.8 % (11) 4.0 % malnutrition (3.6 - 8.2 (4.2 - 10.8 (1.7 - 9.0 (<-3 z-score and/or oedema) 95% C.I.) 95% C.I.) 95% C.I.) The prevalence of oedema is 0.0 %

Table 14 contains a disaggregated analysis by LGA in order to get an idea of the LGAs in which acute malnutrition was highest, which may be classified as “hot spots”. The prevalence of acute malnutrition was highest in Magumeri, followed by Konduga, then Kaga, and was much lower in Jere. It is important to note that the survey was not representative with respect to LGAs as the sampling frame included accessible settlements within the operational areas of the 4 LGAs and was not a stratified survey. The figures represented in the table are therefore only indicative.

Table 14 Prevalence of acute malnutrition disaggregated by LGA LGA Global acute Moderate acute Severe acute malnutrition (GAM) malnutrition (MAM) malnutrition (SAM) n/N % (95% C.I) n/N % (95% C.I) n/N % (95% C.I) Jere 28/189 14.8 (9.5-22.4) 21/189 11.1 (6.7-17.9) 7/189 3.7 (1.6-8.3) Konduga 22/91 24.2 (13.6-39.2) 14/91 15.4 (8.1-27.1) 8/91 8.8 (1.8-33.7) Kaga 31/144 21.5 (15.0-29.9) 26/144 18.1 (11.4-27.5) 5/144 3.5 (1.7-7.0) Magumeri 45/159 28.3 (21.0-36.9) 30/159 18.9 (12.8-26.9) 15/159 9.4 (5.2-16.4)

Table 15 shows the weighted prevalence using the 4 LGAs as strata. The calculated weighted prevalence of GAM was 23.6%, which was slightly higher than the survey GAM prevalence of 21.1%.

Table 15 Weighted prevalence of global acute malnutrition LGA Population Proportion Sample Weights Prevalence size Jere 4,398 9.1% 189 23 14.8 Konduga 2,697 5.6% 91 30 24.2 Kaga 22,700 47.0% 144 158 21.5 Magumeri 18,528 38.3% 159 117 28.3 Weighted prevalence 23.6%

Table 16 clearly reveals that wasting was highest in the 6-17 age group, followed by the 18- 29 age group, followed by the 18-29 age group. This finding is consistent with the observation that acute malnutrition affects younger children more than older children.

Table 16: Prevalence of acute malnutrition by age, based on weight-for-height z- scores and/or oedema Severe Moderate Normal Oedema wasting wasting (> = -2 z (<-3 z-score) (>= -3 and <-2 score) z-score ) Age Tota No. % No. % No. % No. % (mo) l no. 6-17 153 19 12.4 42 27.5 92 60.1 0 0.0 18-29 143 8 5.6 25 17.5 110 76.9 0 0.0 30-41 138 3 2.2 15 10.9 120 87.0 0 0.0 42-53 115 2 1.7 7 6.1 106 92.2 0 0.0 54-59 33 0 0.0 2 6.1 31 93.9 0 0.0 Total 582 32 5.5 91 15.6 459 78.9 0 0.0

The survey and WHO WHZ curves are shown in Figure 2. The survey curve was to the left of the WHO curve, indicating a higher prevalence of malnutrition than the WHO standard population.

Figure 2 Weight-for-Height z-scores

Analysis of acute malnutrition by MUAC revealed a global malnutrition prevalence of 12.6% (10.2-15.6, 95% C.I) and SAM prevalence of 3.4% (2.1-5.4, 95% C.I) as shown in Table 17. The prevalence of global malnutrition using MUAC was higher than in 2018 (9.0%, 6.2-12.8, 95% C.I), although the increase was not statistically significant (p=0.088).

Table 17: Prevalence of acute malnutrition based on MUAC cut off's (and/or oedema) and by sex All Boys Girls n = 593 n = 315 n = 278 Prevalence of global (75) 12.6 % (37) 11.7 % (38) 13.7 % malnutrition (10.2 - 15.6 (8.6 - 15.8 (10.2 - 18.1 (< 125 mm and/or oedema) 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate (55) 9.3 % (29) 9.2 % (26) 9.4 % malnutrition (7.1 - 12.0 (6.5 - 12.9 (6.8 - 12.8 (< 125 mm and >= 115 mm, no 95% C.I.) 95% C.I.) 95% C.I.) oedema) Prevalence of severe (20) 3.4 % (8) 2.5 % (12) 4.3 % malnutrition (2.1 - 5.4 (1.2 - 5.3 (2.2 - 8.2 (< 115 mm and/or oedema) 95% C.I.) 95% C.I.) 95% C.I.)

Acute malnutrition for MUAC was also higher for younger children, and this is consistent with the observation that MUAC identifies a higher proportion of younger children, who are at a higher risk of mortality (Table 18).

Table 18 Prevalence of acute malnutrition by age, based on MUAC cut off's and/or oedema Severe Moderate Normal Oedema wasting wasting (> = 125 mm ) (< 115 mm) (>= 115 mm and < 125 mm) Age Tota No. % No. % No. % No. % (mo) l no. 6-17 157 15 9.6 33 21.0 109 69.4 0 0.0 18-29 145 5 3.4 13 9.0 127 87.6 0 0.0 30-41 141 0 0.0 9 6.4 132 93.6 0 0.0 42-53 117 0 0.0 0 0.0 117 100.0 0 0.0 54-59 33 0 0.0 0 0.0 33 100.0 0 0.0 Total 593 20 3.4 55 9.3 518 87.4 0 0.0

3.2.2 Prevalence of underweight The prevalence of underweight was 43.3% (37.9-48.8, 95% C.I), with 15.6% (12.1-19.9, 95% C.I) classified as severely underweight (Table 19).

Table 19 Prevalence of underweight based on weight-for-age z-scores by sex All Boys Girls n = 578 n = 307 n = 271 Prevalence of underweight (250) 43.3 % (144) 46.9 % (106) 39.1 % (<-2 z-score) (37.9 - 48.8 (40.0 - 54.0 (32.4 - 46.3 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate (160) 27.7 % (89) 29.0 % (71) 26.2 % underweight (23.7 - 32.1 (24.0 - 34.6 (20.4 - 33.0 (<-2 z-score and >=-3 z-score) 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of severe (90) 15.6 % (55) 17.9 % (35) 12.9 % underweight (12.1 - 19.9 (12.6 - 24.8 (8.5 - 19.1 (<-3 z-score) 95% C.I.) 95% C.I.) 95% C.I.)

The prevalence of underweight was much higher among younger children than older children (Table 20).

Table 20 Prevalence of underweight by age, based on weight-for-age z-scores Severe Moderate Normal Oedema underweight underweight (> = -2 z (<-3 z-score) (>= -3 and <-2 score) z-score ) Age Tota No. % No. % No. % No. % (mo) l no. 6-17 149 34 22.8 51 34.2 64 43.0 0 0.0 18-29 144 28 19.4 40 27.8 76 52.8 0 0.0 30-41 137 20 14.6 37 27.0 80 58.4 0 0.0 42-53 115 7 6.1 24 20.9 84 73.0 0 0.0 54-59 33 1 3.0 8 24.2 24 72.7 0 0.0 Total 578 90 15.6 160 27.7 328 56.7 0 0.0

3.2.3 Prevalence of stunting The prevalence of stunting was 48.9% (43.3-54.6, 95% C.I), which is well above the 30% “very high” category of WHO classification (Table 21). The proportion of children who were severely stunted was 21.8% (18.1-26.0, 95% C.I).

Table 21 Prevalence of stunting based on height-for-age z-scores and by sex All Boys Girls n = 550 n = 291 n = 259 Prevalence of stunting (269) 48.9 % (143) 49.1 % (126) 48.6 % (<-2 z-score) (43.3 - 54.6 (42.3 - 56.0 (41.0 - 56.4 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate (149) 27.1 % (80) 27.5 % (69) 26.6 % stunting (22.5 - 32.2 (22.1 - 33.6 (20.4 - 33.9 (<-2 z-score and >=-3 z-score) 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of severe stunting (120) 21.8 % (63) 21.6 % (57) 22.0 % (<-3 z-score) (18.1 - 26.0 (17.1 - 27.0 (16.9 - 28.1 95% C.I.) 95% C.I.) 95% C.I.) The height-for-age z-score (HAZ) curve is compared to the WHO curve in Figure 3. The survey curve was positioned well to the left of the WHO curve, showing a poor nutritional status. The curve was also much flatter than the WH curve.

Figure 3 Height-for-age z-scores

The prevalence of stunting by age group is shown in Table 22.

Table 22 Prevalence of stunting by age based on height-for-age z-scores Severe Moderate Normal stunting stunting (> = -2 z score) (<-3 z-score) (>= -3 and <-2 z-score ) Age Tota No. % No. % No. % (mo) l no. 6-17 149 17 11.4 42 28.2 90 60.4 18-29 135 36 26.7 42 31.1 57 42.2 30-41 124 36 29.0 36 29.0 52 41.9 42-53 111 24 21.6 23 20.7 64 57.7 54-59 31 7 22.6 6 19.4 18 58.1 Total 550 120 21.8 149 27.1 281 51.1

A summary of the mean z-scores, design effects and excluded subjects is shown in Table 23. For WHZ, the SD was 1.15, which is acceptable given that the expected range for good quality of data is below 1.20. The design effect was 1.24, with 3 z-scores unavailable and 8 out of range. The final survey design effect was close to the planned figure of 1.2, indicating that the homogeneity was similar to what was observed in the last survey.

Table 23 Mean z-scores, Design Effects and excluded subjects Indicator n Mean z- Design Effect z-scores z-scores scores ± (z-score < - not out of SD 2) available* range

Weight-for- 582 -1.01±1.15 1.24 3 8 Height Weight-for-Age 578 -1.78±1.17 1.73 1 14 Height-for-Age 550 -1.96±1.34 1.76 2 41 * contains for WHZ and WAZ the children with edema.

3.3 Mortality results Retrospective mortality was assessed for the 90 days preceeding the survey. In each household, the main respondent was requested to recall and list the current household members, those who joined or left the household, and those who died or were bor during the recall period. Cause of death was not included as it was not a specific survey objective. The crude death rate (CDR) was 0.83 deaths per 10,000 per day (0.55-1.27, 95% C.I), with an under 5 death rate (U5DR) of 1.88 (0.92-3.82, 95% C.I) as shown in Table 24. The mortality rates are well below the global emergency thresholds of 1 and 2, respectively.

Table 24 Mortality rates Total population 2,787 Total Joined 137 Total Left 183 Population below 5 years 671 Below 5 years Joined 22 Below 5 years Left 11 Births 45 Deaths total 21 Deaths below 5 years 11 CMR (total deaths/10,000 people / day) 0.83 (0.55-1.27) (95% CI) U5MR (deaths in children under five/10,000 children under five / 1.88 (0.92-3.82) (95% day) CI)

3.4 Children’s morbidity For children 6-59 months, retrospective morbidity was assessed for the preceding 2 weeks/14 days (Table 25). Acute respiratory infection, with 49.4% (43.2-55.8, 95% C.I) was the highest reported morbidity, followed by diarrhoea (45.9%, 40.8-51.1, 95% C.I) then fever (without cough) at 40.8% (34.4-47.3, 95% C.I). Overall, 71.8% (66.4-77.3, 95%C.I) of sampled children reported at least one of the three illnesses (diarrhoea, fever without cough or acute respiratory infection.

Table 25 Prevalence of reported illness in children in the two weeks prior to interview (n=590) Prevalence of reported n/N % (95% C.I.) illness Diarrhoea 271/590 45.9 (40.8-51.1) Fever without cough 241/590 40.8 ((34.4-47.3) Acute respiratory infection 292/590 49.4 (43.2-55.8) Of those who reported the morbidities, 73.9% sought treatment, while 26.1% did not seek treatment (Figure 4).

Health-seeking behaviour for children 6-59 months

Sought treatment Did not seek treatment

Figure 4 Proportion of children who sought treatment for illness

Of those who sought treatment, the highest proportion sought treatment from a government clinic/hospital (47.6%), with 26.3% seeking treatment from a pharmacy, and 15.2% from a private clinic (Figure 5).

Health-seeking behaviour

Other 8.9

Traditional healer 1.3

Religious leader 0.3

Friend/relative

Pharmacy

Treatment modalities Treatment Private clinic

Government…

Figure 5 Health-seeking behaviour

3.5 Measles vaccination and Vitamin A supplementation Measles vaccination coverage was assessed for both the card and recall, while Vitamin A supplementation coverage was assessed by recall (Table 26). Only 16.2% (10.6-21.8, 95% C.I) had received measles vaccination confirmed with the card, with 30.7% (22.3-39.0, 95% C.I) by recall. This is considerably low. The coverage of Vitamin A supplementation was 71.4% (60.8- 81.9%, 95% C.I).

Table 26 Measles vaccination and Vitamin A supplementation vaccination coverage Measles Measles Vitamin A (with confirmation (with card or (with confirmation from card) confirmation from from N=555 mother/caregiver) mother/caregiver) N=593 N=590 (90) 16.2% (182) 30.7% (421) 71.4% (10.6-21.8, 95% C.I.) (22.3-39.0, 95% C.I.) (60.8-81.9, 95% C.I)

3.6 Infant and young child feeding (IYCF) Infant and Young Child Feeding (IYCF) practices are analysed in Table 27 for children 6-59 months. Late introduction of solid foods is evident given that only 18.9% (5.7-32.1, 95% C.I) had been introduced to solid foods between the ages of 6 to 8 months. Continued breastfeeding at 1 years was very high at 93.1% (86.9-99.2, 95% C.I). However, continued breastfeeding at 2 years was very low at 25.0% (0.5-49.5, 95% C.I). It must be noted that the width of the confidence interval shows that the precision was low due to the low sample size for sub-groups of the population. Interpretation must therefore be done with caution.

About a fifth (19.9%, 13.7-26.1, 95% C.I) of children between 6 to 23 months had consumed iron-rich foods. The mean dietary diversity score for the 6-23 months age group was 2.9 (2.5- 3.2, 95% C.I). The proportion of children of the same age group meeting the minimum dietary diversity was only 18.5% (11.6-25.3, 95% C.I). The proportion who met the minimum acceptable diet (MAD) was only 1.9% (0.0-4.2, 95% C.I). The results show poor infant and young child feeding practices, although they must be interpreted with caution given the lower precision as a result of the lower sample sizes for smaller age categories.

Table 27 Infant and Young Child Feeding results INDICATOR n/N % (95% C.I) Introduction of solid foods at 6 months (6-8 months) 7/37 18.9 (5.7-32.1) Continued breastfeeding at 1 year (12-15 months) 67/72 93.1 (86.9-99.2) Continued breastfeeding at 2 years (20-23 months) 5/20 25.0 (0.5-49.5) Consumption of iron-rich foods (6-23 months) 42/211 19.9 (13.7-26.1) Mean dietary diversity score (6-23 months) 2.9 (2.5-3.2) Minimum dietary diversity (6-23 months) >= 4 food 39/211 18.5 (11.6-25.3) groups Minimum acceptable diet (MAD) children 6-23 months 4/211 1.9 (0.0-4.2)

There was a high consumption of grains, roots and tubers (90.9%), other fruits/vegetables (75.5%), and legumes/nuts (55.6%). Consumption of vitamin A-rich fruits and vegetables (18.2%), flesh foods (21.7%), dairy products (16.1%) and eggs (6.3%) was very low (Figure 6).

Complementary feeding, 6-23 months

100 90.9

80 75.5

56.6

60 % 40 21.7 18.2 20 16.1 6.3 0 Grains, Legumes or Vitamin A Other fruitsFlesh foods Eggs Dairy roots and nuts rich fruits and products tubers and vegetables vegetables CATEGORY

Figure 6 Complementary feeding (dietary diversity) for children 6-23 months

3.7 Women of reproductive age Dietary diversity was assessed for women of reproductive age (15-49 years). Less than half (44.9%) had good diversity (consumed at least 5 of the 10 food groups), while 55.1% showed poor diversity (consumed less than 5 of the 10 food groups) as shown in Figure 7.

Minimum dietary diversity for women (MDD-W)

Good diversity (>=5 groups) Poor diversity (0-4 groups)

Figure 7 Minimum dietary diversity for women 15-49 years

Consumption of cereals, roots and tubers (99.85) and other vegetables (84.5%) was very high as well as the consumption of dark green leafy vegetables (78.7%) and pulses (72.8%). Consumption of other Vitamin-A rich vegetables and fruits (16.2%), dairy products (19.6%) and flesh foods (33.3%) were particularly low (Figure 8).

Dietary diversity for women 15-49 years 120 99.8 100 78.7 84.5 80 72.8

% 60 33.3 40 26.8 19.6 16.2 17.3 20 5.6 0

Food category

Figure 8 Dietary diversity for women 15-49 years

Nutritional status was analysed using the less than 210mm (severe malnutrition) and 210- 229mm (moderate malnutrition) cut-off value for MUAC (Figure 9). There was no significant difference between the different categories in terms of malnutrition.

Prevalence of malnutrition by pregnancy and lactation status

All Pregnant Lactating

<210mm (severe) 210 to 229mm (moderate)

Figure 9 Proportion of malnutrition by pregnancy and lactation status

4. Discussion With respect to quantitative result, the comparison between 2018 and 2019 refers to the 2 SMART surveys conducted by SCI in accessible communities within the SCI area of operation.

The analysis of the trend of admissions for SAM over the period June 2018 to July 2019 shows that the admissions were highest in the month of July in both 2018 and 2019, with the peak coinciding with the peak of the hunger season. This partly explains the prevalence being very high. It would be expected that, due to this seasonality aspect, the prevalence would be lower during the period after the harvest, as the SAM admissions show. Apart from the seasonality factor, it must be noted that, according to the Nigeria Nutrition in Emergency Sector Strategic Response Plan 2017-2018, a rapid SMART assessment conducted in April 2016 by Action Against Hunger in the LGAs of MMC and Jere revealed a GAM rate of 19.1% and SAM rate of 3.1% (April 2016). Nutrition assessments undertaken in April- June 2016 in some LGAs in Borno state also showed that there were pockets with extremely high acute malnutrition rates which included Konduga LGA (16.4% GAM and 5.0% SAM). The same report also indicated that the state of malnutrition in the North East of Nigeria is related to high food insecurity, sub optimal infant and young children feeding practices negative coping strategies, increasing spread of endemic diseases, low coverage of programs targeting children with moderate acute malnutrition, limited dietary diversity, loss of livelihoods, disruption of access to quality water and optimal sanitation, population displacement and destruction of housing, compromising the privacy necessary for breastfeeding; and the poor and deteriorating health care system. Some of these factors were investigated in the report and are discussed below.

quite low both by recall and confirmation with card, while Vitamin A supplementation coverage was quite high.

Infant and Young Child Feeding Infant and young child feeding results were generally poor. A low proportion of children had been introduced to solid foods between 6 to 8 months, showing very late introduction to solid foods. Although continued breastfeeding was very high at 1 year, it was very low at 2 year. A low proportion of children 6-23 months had consumed iron-rich foods. Minimum dietary diversity was only met by less than a fifth of children, while a very low proportion met the minimum acceptable diet.

5. Conclusion The nutrition situation in the SCI operational areas of Borno state is at a critical level and has deteriorated as the results show. The concern is particularly with respect to acute and chronic malnutrition, as well as infant and young child feeding.

6. Recommendations Child Health, Nutrition and Morbidity  Given the critical prevalence of acute malnutrition, there is a need to scale up the integrated management of acute malnutrition, ensuring that the services are accessible to the whole population, and to incorporate a targeted supplementary feeding component given the large MAM caseload which the findings highlight.  Integration of existing nutrition services and programmes with the health programmes is essential to facilitate linkages which will guarantee that children have access to a comprehensive package of health and nutrition services, including vaccination, supplementation and treatment.  Given the gap which exists between classification of acute malnutrition by WHZ and MUAC, it is important to set up a system whereby there is a way to screen at-risk children at the second stage using WHZ. This will ultimately increase programme coverage.  A SQUEAC assessment to investigate the barriers to optimal CMAM coverage is recommended in targeted LGAs.

appropriate messages through platforms such as religious gatherings and other community groupings.  Recent KAP studies should be reviewed in the context of this survey’s results, with a view to implementing the strategies recommended which are applicable.

Annex 1 List of individuals who participated in the survey

SMART SURVEY TEAM COMPOSITION (20-28th July, 2019) Team # Name Sex Role Supervisor Consultant: Blessing Mureverwi Naomi Bello F Interviewer 1 Iko Faith Eneoche F Measurer Waziri Andrew Yakubu M Measurer Bukar Usman Hauwar F Interviewer 2 Saidu Aishatu Kudaba F Measurer Iliya Achara Bazhigila M Measurer Ishaya Agnes F Interviewer 3 Chuwang Martin M Measurer Comfort Yamta Alhaji Kowmi M Measurer Bassi Godiya F Interviewer 4 Aboyeji Bukunmi M Measurer Drenkat Nathan David M Measurer Yakubu Christy F Interviewer 5 Paul M Measurer Magdalene John Andrew M Measurer Pam Patricia F Interviewer 6 Ogu Genevieve F Measurer Benson Bana Abdulsalam Gana M Measurer

Annex 2 Assigned clusters

Lga Ward Settlement Name Population Cluster Jere Galtimari Molai Juddumri 297 1 Jere Galtimari Molai Quarters 870 2,3,4 Jere Galtimari Molai Shuwari 2,279 5,6,7,8,RC,9,10,11,12 Kaga Ngamdu Barkamri 728 13,14,15

Kaga Ngamdu Bonuri Balama Modu Fandi 118 16 Bura Modu Sumi Balam Kaga Ngamdu Ghirgiri 293 17 Kaga Ngamdu Gss Ngamdu 381 18 Kaga Ngamdu Kinuya Borti Guwori 324 19,RC Kaga Ngamdu Laminuri 635 20,21 Kaga Ngamdu Mattari 273 22 Kaga Ngamdu Tamsukawu Dispensary 184 23 Magumeri Gajiganna Ambudu 393 24 Magumeri Gajiganna Bare Kadaure 214 25 Magumeri Gajiganna Bare Modu Mala 307 RC Magumeri Gajiganna Dawale 715 26,27,28 Magumeri Gajiganna Fuguri 247 29 Magumeri Gajiganna Jilori (Njolluri) 224 30 Magumeri Gajiganna Kenjimiram 2 553 31,32 (Ambutu) Mainari Magumeri Gajiganna Abatchari 299 33 Malayi WH Bulama Ali Magumeri Gajiganna Kontoma 307 34 Magumeri Gajiganna Walliri Zundir 186 35 Magumeri Gajiganna Yuramti 523 36,37 Konduga Auno Zarmari 1,908 38,39,40,RC,41,42,RC Konduga Auno Atomri 310 43

Kaga Ngamdu Karawa Chira 344 44,45 Magumeri Gajiganna Kanguri 626 46,47

Magumeri Hoyo Chingua Goni Bamari Bagoni Buma 168 48

Annex 3 Survey questionnaire

SCI Borno State SMART Survey Questionnaire-July 2019 DATE: TEAM: LGA: WARD: SETTLEMENT: CLUSTER: HOUSEHOLD:

INTRODUCTION AND CONSENT Hello, My name is______and my colleagues’ are______. We are working for Save the Children International. We are here to gather information related to mortality, nutrition, and health of people living in Borno state. If there are any women (aged 15-49 years) or children under five years old in the household we would like to take some measurements to assess their nutritional status. All personal information will be kept confidential. Please note that it is not currently known what actions if any will be taken after the results of the survey are finalized. This survey will provide important information to guide programmes which seek to improve the general living conditions of people in Borno state. The questions will take about 30 minutes. Do you have any questions? May I begin? 1. Mortality

a) List all members currently living in the household

Joined on Left on Born on Died on or after Sex Age or after or after or after (start of recall No. Name (M/F) (years) (start of (start of (start of period) recall recall recall

period) period) period)

WRITE ‘Y’ for YES. Leave BLANK if NO.

1 2 3 4 5 6 7 8 9 10 11 12 List all members who have left the households since the start of the recall period 1 Y 2 Y 3 Y 4 Y List all members who died since the start of the recall period 1 Y 2 Y 3 Y 4 Y Was anyone pregnant at the start of the recall period? YES ( ), NO ( ). If yes, how many? ( )

(Mobile data entry) Count the numbers from the print-based mortality module Number Number Number Number Number Number Number Number Number of current of current of current of current of past of past of of of household household household household household household births deaths deaths members members members members members members during during (under (total) (under 5 who who who left who left recall recall 5 years) years) arrived arrived during during during during during recall recall recall recall recall (exclude (exclude (under 5 deaths) deaths) years) (under 5 years)

2. Anthropometry (6-59 months)

Child Sex Age Date of birth Age in Weight Height/ MUAC Oedema N0 (M/F) documentation (if age months (if age (kg) Length (mm) (Y/N) available documentation documentation (cm) (Yes=1/ available) unavailable) No=2)

1 2 3

3. Immunization, Morbidity and Health Practices (6-59 Months) In the past 2 weeks (14 days) has your child had the following illnesses? (Yes=1, No=2) Diarrhoea Acute Respiratory Infections (Cough, breathing difficulties Yes=1,No=2 Chest in-drawing, Rapid breathing) Yes=1, No=2 Fever (without cough) Yes=1, No=2 Has your child received treatment for illnesses? YES=1, No=2 (If yes to above), where was treatment sought?

[1= Government clinic/hospital, 2= private clinic, 3= Pharmacy 4= Friend/relative, 5=Religious leader, 6=Traditional healer, 7=Other Has the child received measles immunization? (1=Yes confirmed by card; 2=Yes by recall; 3=No, 4=Don’t Know) Did the child receive Vitamin A in last six months? (Yes=1, No=2)

4. Infant and Young Child Feeding-IYCF Practices (6 – 23 Months)

Has (NAME) ever been breastfed? (1=Yes, 2=No, 4=Don’t Know) (If yes) How long after delivery was [NAME] put to the breast/nipple? (1=Less than 1 hour, 2=1-24 hours, 3=More than 24 hours, 4=Don’t Know) 5.3 Was [NAME] breastfed yesterday during the day or at night? (1=Yes, 2=No) 5.4 Was [NAME] bottle fed with nipple yesterday during the day or at night? (1=Yes, 2=No, 4=Don’t Know) 5.5 Did [NAME] drink any of the following liquids yesterday during the day or at night? (1=Yes,

2=No, 4=Don’t Know) Water Sugar water Fruit Juice/ juice drinks/Coconut water Container milk, milk powder Curd Infant/ Baby Formula Did [NAME] receive any soft/ semi-solid/ solid food yesterday during the day or at night? (1=Yes, 2=No, 4=Don’t Know) (If yes) How many times did (NAME) eat solid, semi-solid, or soft foods other than liquids yesterday during the day or at night?

Did your child eat any of the following food groups in the PAST 24-HOURS (1=Yes, 2=No) 1. Grains, roots, tubers (bread, rice, 1.A. Porridge, bread, noodles or other foods made from potato) rice, corn, maize, sorghum, millet, white potatoes, yam,

cassava

2. Legumes or nuts (lentils) Beans, peas, other lentils, nuts (peanuts) or seeds (pumpkin seed, spinach seed, jackfruit seed) or any foods made from these ( 3. Dairy products (milk, yoghurt, Milk (tinned, powdered or fresh animal milk) yogurt, cheese) cheese or other milk products 4. Flesh foods (meat, fish, poultry, A. Liver, kidney, heart or other organ meats or blood- liver/organ meat) based foods B. Meat such as beef, pork, lamb, mutton, rabbit, game, chicken, duck, pigeon other birds C.. Fresh or dried fish, shellfish or seafood like shrimp ( 5. Vitamin A rich fruits and vegetables Pumpkin, carrots, squash, sweet potatoes, sweet peppers; (carrot, pumpkin, orange sweet potato, any dark green leafy vegetables such as spinach, pumpkin mango, papaya, dark green leafy leaf, ripe mangoes, cantaloupe, ripe papaya (dried peach, vegetables, long beans) and 100% fruit juice made from these items 6. Egg Eggs from chickens, duck, guinea fowl or any other egg 7. Other fruit and vegetables (banana, Cabbage, tomato, onion, eggplant, cucumber , long bean, apples, pineapple, watermelon, eggplant, garlic onion, cucumbers, tomatoes) 8. Any oil, fats, butter, ghee or foods made with any of these

9. Any sugary foods such as chocolates, sweets, candies, pastries, cakes, biscuits or just sugar 10. Any lipid based nutrient supplement (LNS) like Plumpy nut, Plumpy sup; any other specialized nutritious foods like fortified blended foods (FBFs) or high energy biscuits (HEBs) like WSB+/++ or WFP biscuits

5. Women of reproductive age (15-49 years)

(How old are you? (in years) MUAC in mm Are you pregnant? (1=Yes, 2=No, 4=Don’t Know) Are you lactating? Yes, 1, No=2, Don’t know=4

(Minimum Dietary Diversity for Women (MDD-W)) Now I would like to ask you about your consumption of different foods and drinks. Would you please tell me whether you consumed any food item from a number of food groups yesterday during the day and night? NOW PUT ‘1’ FOR THE FOOD GROUPS (OR ANY FOOD ITEM WITHIN A FOOD GROUP) REPORTED BY THE RESPONDENT TO BE CONSUMED YESTERDAY DURING THE DAY AND NIGHT. OTHERWISE, CIRCLE THE FOOD GROUP(S) ‘0’. (INCLUDE FOODS PURCHASED AND EATEN OUTSIDE THE HOME)

Consumed = 1 Not consumed = 0

Cereals (rice, wheat, maize puffed rice, bread etc.)

White roots and tubers (potato, white/red sweet potato, yam, radish)

Vitamin A rich vegetables, roots and tubers (orange/yellow sweet potato, sweet pumpkin, carrot etc.)

Vitamin A rich fruits (mango (ripe), papaya (ripe), water melon, black berry, cantaloupe etc.) Other fruits (banana, guava, jackfruit, lychee etc. Dark green leafy vegetables (red amaranth, spinach, Indian spinach, water spinach, sweet potato leaves etc.)

Other vegetables (tomato, eggplant, ladies finger, cauliflower, bitter gourd, bottle gourd etc.)

Organ meat (liver, brain etc.)

Flesh meats (beef, goat, poultry etc.)

Fish and seafood (river fishes, sweet water fishes, marine fishes, fish eggs, prawn, lobster, crab etc.)

Eggs (duck’s egg, hen’s egg etc.) Pulses (beans and lentil, dried beans, dried peas, Nuts and seeds (peanut, pumpkin seed, jackfruit seed etc.) Milk and milk products (Milk, cheese, yogurt and other dairy products)

Annex 4 Standardisation test results STANDARDISATION TEST RESULTS Weight subjects mean SD Precision # kg kg (TEM) Accuracy (Bias) Supervisor 11 9.2 2.1 TEM poor Bias reject Enumerator 1 11 9.2 2.1 TEM poor Bias reject Enumerator 2 11 9.2 2.1 TEM poor Bias reject Enumerator 3 11 9.3 2.2 TEM poor Bias reject Enumerator 4 11 9.2 2.2 TEM reject Bias reject

TEM Enumerator 5 11 9.3 2.2 acceptable Bias reject Enumerator 6 11 9 2.2 TEM reject Bias reject Enumerator 7 11 9.3 2.2 TEM poor Bias reject Enumerator 8 11 9.2 2.1 TEM poor Bias reject Enumerator 9 11 9.3 2.2 TEM poor Bias reject enum inter 1st 9x11 9.2 2.1 TEM reject

TEM enum inter 2nd 9x11 9.3 2.1 acceptable inter enum + sup 10x11 9.2 2.1 TEM reject TOTAL intra+inter 9x11 - - TEM reject Bias reject TOTAL+ sup 10x11 - - TEM reject

Height subjects mean SD Precision # cm cm (TEM) Accuracy (Bias) Supervisor 11 72.9 7.1 TEM poor Bias good Enumerator 1 11 73.6 7.2 TEM poor Bias good Enumerator 2 11 73.1 6.9 TEM reject Bias good Enumerator 3 11 73.5 6.8 TEM poor Bias good Enumerator 4 11 73.6 7 TEM poor Bias good Enumerator 5 11 73.4 7.1 TEM reject Bias good

Enumerator 6 11 74 6.6 TEM reject Bias acceptable Enumerator 7 11 73.1 7.2 TEM poor Bias good Enumerator 8 11 73.5 6.9 TEM reject Bias good Enumerator 9 11 74.6 7.4 TEM reject Bias poor enum inter 1st 9x11 73.6 7.2 TEM reject

TEM enum inter 2nd 9x11 73.6 6.7 acceptable inter enum + sup 10x11 73.5 6.9 TEM reject TOTAL intra+inter 9x11 - - TEM reject Bias good TOTAL+ sup 10x11 - - TEM reject

MUAC subjects mean SD Precision # mm mm (TEM) Accuracy (Bias) Supervisor 11 147.8 12.8 TEM reject Bias good Enumerator 1 11 150.3 12.1 TEM poor Bias good Enumerator 2 11 144 12.9 TEM reject Bias good Enumerator 3 11 149.4 13.4 TEM reject Bias good

TEM Enumerator 4 11 153.2 14 acceptable Bias poor Enumerator 5 11 152 13.4 TEM reject Bias good Enumerator 6 11 146.2 12.3 TEM reject Bias good Enumerator 7 11 155.6 13.9 TEM reject Bias reject Enumerator 8 11 154 11.9 TEM poor Bias poor Enumerator 9 11 147.4 13.5 TEM reject Bias good enum inter 1st 9x11 150.3 13.1 TEM reject enum inter 2nd 9x11 150.2 13.6 TEM reject inter enum + sup 10x11 150 13.2 TEM reject TOTAL intra+inter 9x11 - - TEM reject Bias good TOTAL+ sup 10x11 - - TEM reject

Suggested cut-off points for acceptability of measurements MUAC Weight Height Parameter mm Kg cm individual good <2.0 <0.04 <0.4 TEM acceptable <2.7 <0.10 <0.6 (intra)

Annex 5 Survey local calendar of events

SCI Borno State SMART Survey 2019

Calendar of Local Events constructed in July, 2019 Month 2014 2015 2016 2017 2018 2019 January BH raze New Year NAF Jet New Year Presidential Baga town Bomb IDPs Campaign rally 54 42 camp in 30 18 6 Borno February General Budget Day Aapchi Depchi School Elections Elections and School Girls Girls Kidnapped Postponed Padding Kidnapping 53 Scandal 41 29 17 5

General Fuel crisis Governoship March Release of Election. elections, Fire Depchi School Gen. Buhari disaster in Girls. Fulani Elected 52 40 28 16 Gajigana 4 Herds attack in President Benue April Maulud Budget President visit Celebrations passed to Borno for Commision of Projects, Good Friday, Easter, Relocation of 51 39 27 15 Jakana 3 Residents to Maiduguri, Attack in Molai

Eid El Fitr. Ramadan Police fasting, Fire Demostrations Disasters in 82 Chibok Kaga, Gajigana, Gen. Buhari Hike in Girls VP visits becomes May 50 Fuel Prices 38 Released. 26 14 Maiduguri 2 President Ramadan Boko Haram attack in Magumeri

June Ramadan Ramadan Eid El Fitr. Eid El Fitr. Police Bomb Blast in Chorela Demostrations Konduga (30 Outbreak Dead) Claiming Female Corps Many Lives Member Released by 49 37 25 13 BH 1

July Eid El Fitr Eid El Fitr. President Visit Death of to 48 Shettima 36 24 Borno. Army 12 0 Ali Day Mongumo August Ebola Death of Cholera Eid El Kabir Virus Deputy Outbreak in Disease. Governor Maiduguri, BH 59 47 35 Mafa 23 11 Declares Caliphate in Borno

September BH Eid El Adha. Eid El Eid El Adha. Harvest Capture Death of Adha. Gwoza Bama. Borno Attacks by Attack of 58 Deputy 46 34 BH 22 10 Gwoza Governor by BH October Eid El Death of Eid El Kabr 13 Chibok Harvest Adha. Eid Former Celebration Girls Kabir Gov. of Kogi Released. State. President 57 Ministrial 45 33 Buhari visit 21 9 Release by to Maiduguri President Buhari

November BH Series of Suicide Attack of Melete BH Capture Bombings Bombing Damasak by Attack in Damasak took Place kills 2 at BH. Death of Guzamala on in Maiduguri Motor Park First Army Battallion, 56 44 Maiduguri 32 Republic 20 Maulud 8 Vice President Dr. Alex Ekuende December BH Zaria Christmas Christmas Christmas attack Kaduna Yobe and Attack. Borno Arrest of 55 Shittle 43 31 19 7 Leader Elzakzaky

Annex 6 Plausibility report for anthropometry

Plausibility check for: Borno State_Nigeria.as

Standard/Reference used for z-score calculation: WHO standards 2006 (If it is not mentioned, flagged data is included in the evaluation. Some parts of this plausibility report are more for advanced users and can be skipped for a standard evaluation)

Overall data quality

Criteria Flags* Unit Excel. Good Accept Problematic Score

Flagged data Incl % 0-2.5 >2.5-5.0 >5.0-7.5 >7.5 (% of out of range subjects) 0 5 10 20 0 (1.4 %)

Overall Sex ratio Incl p >0.1 >0.05 >0.001 <=0.001 (Significant chi square) 0 2 4 10 0 (p=0.129)

Age ratio(6-29 vs 30-59) Incl p >0.1 >0.05 >0.001 <=0.001 (Significant chi square) 0 2 4 10 4 (p=0.015)

Dig pref score - weight Incl # 0-7 8-12 13-20 > 20 0 2 4 10 0 (3)

Dig pref score - height Incl # 0-7 8-12 13-20 > 20 0 2 4 10 2 (8)

Dig pref score - MUAC Incl # 0-7 8-12 13-20 > 20 0 2 4 10 0 (7)

Standard Dev WHZ Excl SD <1.1 <1.15 <1.20 >=1.20 . and and and or . Excl SD >0.9 >0.85 >0.80 <=0.80 0 5 10 20 5 (1.15)

Skewness WHZ Excl # <±0.2 <±0.4 <±0.6 >=±0.6 0 1 3 5 1 (-0.27)

Kurtosis WHZ Excl # <±0.2 <±0.4 <±0.6 >=±0.6 0 1 3 5 1 (-0.35)

Poisson dist WHZ-2 Excl p >0.05 >0.01 >0.001 <=0.001 0 1 3 5 0 (p=0.153)

OVERALL SCORE WHZ = 0-9 10-14 15-24 >25 13 %

The overall score of this survey is 13 %, this is good.