SMART SURVEY REPORT
CONDUCTED IN JERE, KONDUGA, MAFA AND MMC LOCAL GOVERNMENT AREAS (LGA’s),
BORNO STATE, NIGERIA.
SMART survey consultants: Kevin Mutegi & Joseph Njuguna
Co-investigators: Matthew Gabriel & Olive Muthamia
Survey timeline: 31ST January-11TH February 2021
Funded by OFDA/FFP
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Table of Contents LIST OF ABBREVIATIONS/ACRONYMS ...... 6 1.0 BACKGROUND INFORMATION ...... 7 Introduction ...... 7 Survey location ...... 8 Purpose ...... 8 Objectives ...... 8 General objective ...... 8 Specific objectives ...... 8 Target population ...... 9 2.0 METHODOLOGY ...... 9 Survey design...... 9 Sampling procedure: selection of clusters ...... 9 Sampling Procedure: Selection of Households ...... 9 Sample size calculation ...... 10 Number of clusters ...... 11 Survey teams, training, data collection and data management ...... 12 Survey Teams: ...... 12 Training and standardization test: ...... 12 Field Test ...... 13 Data collection: ...... 14 Supervision:...... 14 Data management...... 14 Data Quality ...... 14 Questionnaire ...... 14 Data to be collected ...... 14 Data Analysis, results and output ...... 15 Ethical consideration ...... 16 Covid-19 measures ...... 16 Survey methodology approval ...... 16 3.0 SURVEY RESULTS ...... 17 3.1Sample size (achieved and planned) ...... 17 3.2 Data quality ...... 18 3.3 Demographic and household characteristics ...... 18 3.3.1 Residence status of the respondent ...... 18 3.3.2 Marital status of the respondent ...... 19
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3.3.3 Main source of income at household level ...... 19 3.3.4 Education level ...... 19 3.4 Results of nutritional status among children aged 6-59 months ...... 20 3.4.1 Prevalence of acute malnutrition (wasting) by WHZ-scores, MUAC and combined (WHZ & MUAC) and/or oedema ...... 20 3.4.2(a) Distribution of age and sex of the children aged (6-59) months based on weight-for- height z-scores and/or oedema in Jere LGA ...... 22 3.4.2(b) Distribution of age and sex of the children aged (6-59) months based on weight-for- height z-scores and/or oedema in Konduga LGA ...... 22 3.4.2(c) Distribution of age and sex of the children aged (6-59) months based on weight-for- height z-scores and/or oedema in Mafa LGA ...... 24 3.4.2(d) Distribution of age and sex of the children aged (6-59) months based on weight-for- height z-scores and/or oedema in MMC LGA ...... 25 3.4.3 Prevalence of stunting and underweight...... 26 3.5 Child Health ...... 27 3.5.1 Morbidity patterns ...... 27 3.5.2 Measles vaccination coverage among children 9-59 months ...... 29 3.6 Maternal health ...... 30 3.6.1 Physiological status of WRA (15-49 years) ...... 30 3.6.2 Maternal nutritional status by MUAC ...... 30 3.7 Food Security ...... 31 3.7.1(a) Main source of food at household level ...... 31 3.7.2(a): Household Dietary Diversity Score (HDDS) ...... 32 3.7.2(b) Foods frequency of food consumed at household level ...... 32 3.7.3 Coping strategy index ...... 32 3.8 Infant Young Child Feeding (IYCF) Proxy Indicators ...... 33 3.8.1 Early initiation of Breastfeeding <1 hour ...... 33 3.8.1 Minimum IYCF practices (EBF <6 months, MMF, MDD and MAD among children 6- 23.9months) for respective surveyed LGA ...... 33 3.8.2 Dietary Diversity of food consumed by children aged (6-<24) months ...... 34 3.9 Water, Sanitation and Hygiene ...... 34 3.10 Mortality ...... 37 3.10(a) Crude Mortality Rate (CMR) and Under-five Mortality Rate (U5MR) ...... 37 3.11 Limitations of the survey...... 37 4Mortality rates ...... 40 5.0 Recommendations ...... 40 References ...... 42
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Annex ...... 43 Annex 1: Survey timeline ...... 43 Annex 2: Mean z-scores, design effect and excluded subjects ...... 43 Annex 3: Map of Borno State illustrating surveyed LGAs (blue outline) ...... 44 Annex 4: List of survey teams per LGA ...... 45 Annex 5: Standardization test results ...... 46 Annex 6: SMART data collection tools ...... 46 Annex 7: Anthropometric data plausibility reports ...... 46 Annex 8: Event calendar ...... 46
LIST OF TABLES Table 1: Anthropometric and Mortality sample size and justification...... 10 Table 2: Calculation of households to be assessed per team per day ...... 11 Table 3: Number of clusters and teams per survey location ...... 12 Table 4: Number of clusters and households (achieved and planned) ...... 17 Table 5: Number of child and persons (achieved vs planned) ...... 17 Table 6: Summary of data plausibility score for Anthropometric indicators for each LGA ...... 18 Table 7: Marital status ...... 19 Table 8: Main source of income at household level ...... 19 Table 9: Education level of the household head per each LGA ...... 19 Table 10: Prevalence of acute malnutrition among children (6-59 months) for each LGA ...... 20 Table 11: Concordance in cGAM prevalence of by both WHZ<-2SD, MUAC<125mm and/or bilateral oedema ...... 21 Table 12: Concordance in cSAM prevalence by both WHZ<-2SD, MUAC<125mm and/or bilateral oedema ...... 21 Table 13: Distribution of age and sex of the children aged (6-59) months based on weight-for-height z-scores and/or oedema in Jere LGA ...... 22 Table 14: Distribution of age and sex of the children aged (6-59) months based on weight-for-height z-scores and/or oedema in Konduga LGA ...... 22 Table 15: Distribution of age and sex of the children aged (6-59) months based on weight-for-height z-scores and/or oedema in Mafa LGA ...... 24 Table 16: Distribution of age and sex of the children aged (6-59) months based on weight-for-height z-scores and/or oedema in MMC LGA ...... 25 Table 17: Prevalence of stunting and underweight ...... 26 Table 18 Reported child illness in the past two weeks prior to survey data collection: ...... 27 Table 19: Health seeking option sought by caregivers of reported ill children ...... 28 Table 20: Measles vaccination coverage among children (9-59 months) ...... 29 Table 21: Vitamin A supplementation coverage among children aged (6-59) months ...... 29 Table 22: Deworming coverage among children aged (12-59 months) ...... 30 Table 23: Physiological status of WRA (15-49 years) ...... 30 Table 24: Nutritional status of WRA (15-49 years) by MUAC ...... 31 Table 25: Nutritional status of PLW by MUAC ...... 31 Table 26: Main source of food at household level ...... 31
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Table 27: HDDS...... 32 Table 28: Dietary diversity at household level based on 24hour recall ...... 32 Table 29: Coping strategies embraced at household level ...... 33 Table 30: Coping strategies embraced at household level ...... 33 Table 31: Timely initiation to breastfeeding ...... 33 Table 32: Minimum IYCF practices for respective surveyed LGA ...... 34 Table 33: Dietary diversity among children aged (6-23.9 months) ...... 34 Table 34: Main source of water for drinking at household level ...... 34 Table 35: Water treatment methods at household level ...... 36 Table 36: Hand washing practices (multi-response) ...... 36 Table 37: Hand washing at four (4) critical times ...... 36 Table 38: Sanitation coverage ...... 37 Table 39: CDR and U5DR in each LGA ...... 37
LIST OF FIGURES
Figure 1: Residence status of the respondent per each LGA ...... 18 Figure 2: Morbidity patterns among children aged (6-59 months) based on two-week recall ...... 27 Figure 3: other main source of water at household level ...... 35 Figure 4: Treatment of water for drinking at household level ...... 35
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ACKNOWLEDGEMENT
Save the Children International would like to thank the following stakeholders for their tremendous support during the implementation of this survey. They include; Borno State Primary Health Care Development Agency Board (BSPHCDA) for availing four (4) staff who participated as supervisors during the implementation of this survey. NiEWG for technical guidance throughout the survey implementation phase. SCI Maiduguri team for administrative support and the role they took as team leaders. EYN for availing their staff who participated as team leaders. UNICEF for the support of anthropometric equipment’s. IOM for sharing population data for IDP camps. Community guides and mobilizers for seeking consent from gate keepers and informing them earlier about the assessment. Survey enumerators for taking part in the survey implementation.
Report drafted by Kevin Mutegi
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LIST OF ABBREVIATIONS/ACRONYMS BSPHCDA Borno State Primary Health Care Development Agency CDR Crude Death Rate CH Cadre Harmonize DEFF Design Effect ENA Emergency Nutrition Assessment EYN Ekklesiyar Yan’uwa a Nigeria GAM Global Acute Malnutrition HH HouseHold IOM International Organization for Migration IYCF Infant, Young and Child Feeding MAM Moderate Acute Malnutrition LGA Local Government Area MUAC Mid-Upper Arm Circumference NFSS Nutrition and Food Security Surveillance NiEWG Nutrition in Emergency Working Group PPS Probability Proportional to Size (of its population). SCI Save the Children International SD Standard Deviation SMART Standardized Monitoring and Assessment of Relief and Transition SAM Severe Acute Malnutrition U5DR Under-Five Death Rate WHO World Health Organization WHZ Weight-for-Height-Z-score
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1.0 BACKGROUND INFORMATION
Introduction Save the Children International (SCI) has been working in Nigeria since 2001. The early focus was on getting children actively involved in shaping the decisions that affect their lives. Today, SCI is working in 20 states focusing on child survival, education and protecting children in both development and humanitarian contexts. The humanitarian response started in 2014 with Save the Children among one of the first responders to the conflict bringing relief to children and families affected by the humanitarian crisis. The ongoing conflict in the North East continues to increase population displacements, restrict income generating opportunities, limit trade flows and escalate food prices. As a result of the reduced food availability and access, local and IDP (Internally Displaced Persons) populations in worst-affected areas of Borno, Yobe and Adamawa States continue to experience food gaps, in line with crisis (IPC Phase 4) acute food insecurity, with an estimated 4.6 Million people in Phase 3-5 (Cadre Harmonizé (CH) Analysis). Borno State remains at the center of the ongoing crises in Nigeria; 5.2 million vulnerable men, women, girls, and boys remain in need of assistance. Women, girls, and boys account for 79% of the IDP population – with 28% of the population being children aged below five years1.
In line with the three (3) global breakthroughs of the organization – Survive, Learn and Protect, SCI provides interventions in the areas of Child Protection, Nutrition, Education, WASH, Food Security and Livelihoods in Borno State. The ongoing conflict in the North East continues to increase population displacements, poor sanitation and hygiene, poor access to safe water supplies, increase protection concerns as well as the need for health and nutrition services.
The conflict in Northeast Nigeria has resulted in widespread displacement, abuse and violation of human rights, destruction of livelihoods and disruption of basic services. The recent update of Cadre Harmonisé analysis conducted in March 2020 and the FEWSNET Nigeria Food Security Outlook (June 2020 – January 2021) indicated deterioration of the nutrition and food security situation2 with more people facing crisis mainly attributed to sustained conflict associated with the insurgents coupled with the COVID-19 pandemic and associated restrictions3.
The conflict has also resulted in high levels of malnutrition specifically Global Acute Malnutrition (GAM) and Severe Acute Malnutrition (SAM). The Nutrition and Food Security Surveillance round 1- 9 surveys were conducted at domain level, where findings of various LGAs were aggregated into domains. The recent (NFSS) round 9 survey conducted in October 2020 found the GAM (WHZ<- 2SD) prevalence for Central Borno domain (under which Mafa and Konduga LGAs are located) was at 10.7% (8.4-13.6 95% C.I.) While for (Jere and MMC) domain the GAM (WHZ<-2SD) prevalence was 9.9% (7.5-12.9 95% C.I.)4. The trend in GAM (WHZ<-2SD) prevalence in Mafa and Konduga LGAs has been on increasing trend based on comparison with October 2020 and November 2019 where
1 Global report on Food Crisis, 2018 (http://www.fsincop.net/fileadmin/user_upload/fsin/docs/global_report/2018/GRFC_2018_Full_report_EN_Low_resolution .pdf) 2 Cadre Harmonisé Result for Identification of Risk Areas and Vulnerable Populations in Sixteen (16)Northern States and the Federal Capital Territory (FCT) of Nigeria available @https://fscluster.org/sites/default/files/documents/march_2020_fiche-nigeria_results.pdf 3 NIGERIA Food Security Outlook June 2020 to January 2021 available @ https://reliefweb.int/report/nigeria/nigeria-food- security-outlook-update-june-2020-january-2021 4 National Bureau of Statistics (NBS), Nigeria Nutrition in Emergency Working Group (NiEWG), Federal Ministry of Health (October 2020), Preliminary report of Nutrition and Food Security Surveillance-North East Nigeria Emergency Survey.
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GAM (WHZ<-2SD) was at 9.8%5. A similar increase in GAM prevalence (WHZ<-2SD) was observed in MMC and Jere LGAs where in 2019 the GAM was at 6.9%5.
The preliminary NFSS survey conducted in October 2020 unveiled Crude Death Rate (CDR) for Central Borno domain in which Mafa and Konduga LGA were classified at 0.20 (0.1-0.38) while for MMC and Jere the CDR was at 0.34 (0.17-0.7) respectively. The trends in CDR in the mentioned LGAs remained stable when compared to survey findings conducted in similar period in 2019.
Survey location The SMART Survey was implemented in four (4) Local Government Authorities (LGAs) of Borno State located in North East Nigeria. The four LGAs include: Jere, Konduga, Mafa and MMC. The survey was implemented during the end of harmattan season (October to February).
Purpose The main purpose for conducting the SMART survey in the Jere, Konduga, Mafa, and MMC LGAs was to assess baseline information on nutritional status of children aged 6-59 months for purposes of decision making towards better programming for SCI and other stakeholders. Previous surveys were conducted at domain level (representing more than one LGA) and thus it was not possible to have findings reflect the actual situation at LGA level.
Objectives General objective The general objective of this survey was to assess the nutritional status of children aged (6-59 months), crude and under-five mortality rates in Jere, Konduga, Mafa, and MMC LGAs in Borno State. The survey was implemented at LGA level as per recommendation of the Nutrition in Emergency Working Group (NiEWG). Specific objectives a. To determine the prevalence of acute malnutrition, chronic malnutrition, underweight and overweight among children aged 6-59 months. b. To assess the following: a. Two-week recall on main child illnesses among children aged 6-59 months. b. Crude Death Rate (CDR) and under-five Death rate (U5DR). c. Selected IYCF practices: including Exclusive Breastfeeding (EBF) <6 months, early initiation of breastfeeding <1 hour of birth and the Minimum Dietary Diversity (MDD) among children aged (6-<24 months), Minimum Meal Frequency among children aged 6-<24 months, Minimum Acceptable Diet among children aged 6-<24 months. d. Vitamin A supplementation coverage among children aged 6-59 months. e. Deworming coverage among children aged (12-59) months. f. Measles vaccination coverage among children aged (9-59) months. g. Prevalence of maternal malnutrition by MUAC among women aged 15-49 years. h. Water, sanitation and hygiene practices at household level. i. Household Dietary Diversity Score and coping strategy situation at the household level. c. To establish recommended actions to address identified gaps to support planning, advocacy, decision making and monitoring.
5 NBS, FMOH & NiEWG, Nutrition & Food Security Surveillance report, Round 8 conducted in November 2019.
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Target population The main target population for this survey were children aged (6 – 59) months to be assessed for anthropometric indicators, while general population in the sampled households in selected clusters were targeted for the mortality indicators. Caregivers of children aged (0 – 59) months were targeted during interviews as respondents to provide information on child health and nutrition as well as other cross-cutting areas as indicated in the specific objectives.
2.0 METHODOLOGY Survey design The survey applied a two-stage cluster sampling using the SMART survey methodology with Stage 1(one) involving selection of clusters through Probability Proportional to Size (PPS) of their population using Emergency Nutrition Assessment (ENA) for SMART software version 11th January 2020. In Stage 2 (two) it involved selection of households from the sampled clusters using systematic random sampling. Sampling procedure: selection of clusters The smallest administrative unit that is communities/settlements for each LGA was considered as a cluster. The estimated list of communities and their respective populations for each cluster was updated through consultations with local authorities, key informants and referenced from secondary data. The population for each LGA was projected from the 2006 census conducted by Nigeria Population Commission (NPOpC). Internally Displaced Persons (IDPs) camp population data was obtained from IOM latest Displacement Tracking Matrix (DTM) report round 34. Selection of clusters involved transferring the updated list of communities and respective population of each LGA from Microsoft Excel into ENA for SMART software version 11th January 2020 planning tab for selection using PPS and assigning the number of clusters required for each LGA. It is important to note that inaccessible communities/settlements were not included in the sampling frame. Sampling Procedure: Selection of Households Definition of household for the survey: A household was defined as a group of people living together, cook and eat from the same cooking pot. Polygamous families were defined based on the same concept, if a husband has several wives who have their own separate cooking pot in which they prepare meals for their families, even if living in the same compound, then, it was treated as different households. On arrival in the selected clusters, the team leader did meet with the community leader (Bulama/Lawan and IDP camp chairman). The team did introduce themselves, explaining the survey objectives as well as relaying information on support required of having a guide for the survey team within the period of survey data collection. Household selection techniques: The standard definition of a household was shared to aide in developing the HH listing within the cluster. The total of 14 households per sampled cluster recalculated prior to data collection were the target for each team per cluster per day. In the case child, caregiver or entire household was absent the team did visit the household on the same day after completion of the other households and in cases where the teams could not assess the household on the same day, the team indicated in the cluster control form and a revisit was made within the period of data collection. Households were systematically randomly selected from the total number of HH using the random number generator app installed in android Smart phones used in data collection. The community guide with support of Bulama/IDP chairman confirmed present and abandoned households. Abandoned households were never included in the list of households to be sampled. The total list of households in each community was confirmed from secondary data as well as key informants such as Bulama/Lawan and IDP chairman. A Sampling Interval (S.I.) was calculated by team leaders and later followed by
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selection of random number between 1 and S.I which was confirmed as first household. The team leader with support of the team then proceeded to calculate the remaining households by add the sampling interval from the first selected household using round-down, up or alternate procedures based on the S.I units. For clusters with more than 250HHs, segmentation was applied followed by random selection of one portion or segment. Segmentation entailed that teams had to seek information as well as observe on geographic and physical features that exist within the community such as roads, boreholes, mosque, schools, borehole/water point to aid in segmentation of clusters that had more than 250HHs. In all selected households, children aged (6-59) months (as confirmed from health records, birth certificate, immunization card or calendar of events) were included in the anthropometric survey. In HH without children 6-59 months, other variables (mortality, maternal nutrition among others) were also collected. All children in the age range of (6-59) months were included in the anthropometric, immunization and health components. Sample size calculation The sample size for the assessment was calculated on the basis of key parameters for the primary outcomes that is (Global Acute Malnutrition (GAM WHZ<-2SD) and Crude Mortality Rate (CDR)). The table 1 illustrate the sample size calculations for both the anthropometric and mortality which was done using the ENA for SMART software version 11th January 2020. Anthropometric and Mortality sample size Table 1: Anthropometric and Mortality sample size and justification
Description Jere Konduga Mafa MMC Assumptions Based on Context
Parameters for Anthropometry (children aged 6-59 months) Value Assumptions based on context Estimated Prevalence 12.9 13.6 13.6 12.9 Obtained from NFSS, 9th Round at of GAM (%) domain level; Upper confidence limit was used since no previous survey conducted at LGA level was used and that it considerable be adequate to inform scenario where one LGA might have different nutrition situation as compared to the other. GAM (WHZ<- 2SD) prevalence for Central Borno domain under which Mafa, Konduga, Kaga and Magumeri LGAs are classified was 10.7% (8.4-13.6 95% C.I.) While for Jere and MMC the GAM (WHZ<-2SD) prevalence was 9.9% (7.5-12.9 95% C.I.). ± Desired Precision 4.0 4.0 4.0 4.0 A low precision will be applied to keep the sample size to a necessary minimum in line to SMART methodology guideline. The justification of the low precision was based on estimated GAM prevalence used.. Design Effect (if 1.5 1.5 1.5 1.5 Rule of thumb since previous DEFF at applicable) LGA level was unavailable. Children to be 440 461 461 440 As calculated from ENA for SMART included software
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Average HH Size 5.4 4.7 4.7 5.4 Obtained from NFSS round 9 % Children under- 5 17.0 19.9 19.9 17.0 Obtained from NFSS round 9 % Non-response 3% 5% 5% 3% To cater for any unforeseen challenges Households but also reported experiences on non- responses from recent NFSS round 9 at domain level. Households 550 576 576 550 As calculated from ENA Parameters for Mortality Value Assumptions based on context Estimated Death Rate/ 0.7 0.38 0.38 0.7 Obtained from recent NFSS round 9 at 10,000/day domain level where CDR was 0.20(0.1- 0.38) for Central Borno (Mafa, Konduga, Kaga and Magumeri) while MMC & Jere the CDR was 0.34(0.17-0.70); Upper confidence limit was applied since no data on the same existed at LGA level. ± Desired Precision 0.4 0.31 0.31 0.4 The precision used is based on estimated /10,000/day death rate applied in sample size calculation as guided by SMART methodology. Design Effect (if 1.5 1.5 1.5 1.5 Previous DEFF at LGA level was applicable) unavailable. Recall Period in Days 95 95 95 95 The recall event memorable to respondents will be Mawlid celebrations cited on 29th October 2020 while the total recall days will be from 29th October 2020 to 4th February 2021(mid interval of survey data collection period). Population 2889 2611 2611 2899 As calculated from ENA Average HH size 5.4 4.7 4.7 5.4 Obtained from NFSS round 9 % Non-response 3% 5% 5% 3% To cater for any unforeseen challenges Households but also reported experiences on non- responses from recent NFSS round 9 at domain level. Households to be 552 585 585 552 As calculated from ENA for SMART included software
Number of clusters To determine the number of cluster and households required per surveyed LGA the team can comfortably assess in a day, the calculation is indicated in the table 2 and 3. This will be adjusted into context based on SMART survey interim guidelines on conducting household level data collection in humanitarian situations during COVID-19 pandemic6. Table 2: Calculation of households to be assessed per team per day
Activity Estimated Time
6 SMART interim guidance on conducting household level data collection in humanitarian situations during COVID-19 Pandemic, October 2020. 11 | P a g e
Departure from Office to the Field 8:00 AM Daily morning Briefings 10 minutes Travel to clusters 30 minutes Introduction and household list development 30 minutes Lunch break/prayers 40 minutes Average time taken from one household to next 7 minutes Travel back to base 30 minutes Total time for household listing, travelling and breaks (a + b + 140 minutes c + d + f) Arrival back to Base 5:00 PM Total Available time in a day 9hrs (540 minutes) Available time for work 540 –140minutes= 400 minutes Average time taken to complete one questionnaire 20 minutes
Given the above, the number of households that a team can comfortably visit in a day is calculated as follows: Available time for work = 400 minutes. Average time taken from one household to next =7 minutes Average time taken to complete one questionnaire=20minutes 400 (min) / 27(min) = 14.8(rundown) =14 households per day
Given the above, the number of clusters to be assessed is presented in the table 3 below: Table 3: Number of clusters and teams per survey location
Consideration Jere Konduga Mafa MMC
Total number of households based on sample 552 585 585 552 size calculation Total number of households to be assessed 14 14 14 14 per day per team Number of Clusters required 40 42 42 42 Number of days required 8 9 9 11 Survey teams required 5 5 5 4
Survey teams, training, data collection and data management Survey Teams: The survey team was composed of four (4) members (1 Team Leader, 2 Measurers and 1 Local Guide). The survey enumerators were recruited by Save the Children with the support of the State Primary Health Care Management Board. The enumerators derived from the local communities. The team leaders were derived from State Primary Health Care, Save the Children International and EYN. The total of five (5) survey teams for each LGA that is Jere, Konduga and Mafa were engaged while in MMC the total of four(4) survey teams were involved. Training and standardization test: Training took five (5) days from 26th January to 30th January 2021. The training began with a session of team leader debriefing, followed survey team theoretical training, standardization test and field test. The training covered various components including anthropometric measurements, sampling of households, data collection tools, data collection using Kobo, data quality checks, standardization test
12 | P a g e exercise, field procedures among other standard training package for enumerators. The training of the enumerators was facilitated by the SMART survey consultants. During the training adherence to WHO, Federal and Borno State Primary Health Care Development Agency Board COVID-19 guidelines on social-distancing, wearing masks, hand washing/sanitizing and monitoring of training participants was observed7. Standardization test was conducted to test accuracy and precision of measurers/enumerators on anthropometric measurement. The total of 40 enumerators took part in standardization test while two supervisors derived from SCI and consultants took part in taking supervisor measurements. During the day of standardization test, SCI with support of State Primary Health Care Management Board were involved in mobilizing mothers and their children aged (6-59 months) to voluntary be part of exercise. Mothers and their children were provided transport, meals, masks and sanitizers during the exercise. Standardization test was conducted in two zones each with 10 stations. The main purpose of having two zones was to avail more space for survey teams to undertake the anthropometric measurements at the same time ensure that all survey enumerators and team leaders took part in the exercise. In each station, a functional electronic weight SECA scale, height board, MUAC tape were availed. The enumerators were grouped into two (2) with specified role of measurer and assistant and each measurer issued with a standardization test form in which to record the measurements. The measurers and assistants were to rotate from station 1 to 10 taking measurements of children across the stations. In the first sets of measurements the measurer took first (1st) and second (2nd) round measurements followed by a break. After the break, second sets of measurements began and the measurer switched roles with assistant where the assistant became measurer and vice versa. During the exercise a team of observers including a Medical Doctor, SCI and State Primary Health Care staff were mandated to ensure continuous support to mothers and their children as well as ensuring the exercise was smooth. After completion of standardization test, the consultants collected standardization forms and entered data into ENA for SMART training tab. ENA for SMART software was used to generate standardization test results and other observations from supervisors were shared with survey enumerators for improvements. The standardization test results were also used in survey team composition with enumerators who were strong in measurements grouped with the weaker enumerators prior to participating in the field test exercise.
Field Test The survey teams were engaged in pre-testing data collection tools and methods, sampling of households, interviews and recording of responses etc day after standardization test exercise. The field test exercise took place in Maiduguri on 30th January 2021. The participants were divided into 10 teams composed of enumerators, team leaders and supervisors and assigned a certain community/cluster and number. The following communities/settlements were selected from MMC LGA as clusters for field test exercise; Federal low cost Chezcoan camp, SabonGari buzu camp, Bolori 2 HA_1, Bolori 2 HA_2, Allami Dagashe camp, Gidan Mandora, Railway Terminus camp, NYSC camp, Almin Garage and Garba Buzu IDP. The communities mentioned above were not among the sampled clusters for actual data collection and their selection was only to allow the team to take part in the field test and return back to the training hall before 1:00pm in order to share experiences observed, feedback and way forward prior to actual data collection. It is important to note, prior to field test, SCI mobilization team had earlier sought approval and consent of SEMA chairman and IDP camp chairman and other relevant authorities. During the field test exercise adherence to WHO, Federal MOH and Borno State Primary Health Care Development Agency (BSPHCDA) COVID-19 guidelines
7 SMART interim guidance on conducting household level data collection in humanitarian situations during COVID-19 Pandemic, October 2020. 13 | P a g e on social-distancing, wearing masks, hand washing/sanitizing and monitoring of participants were observed. Data collection: Supervision: The overall management of the surveys was done by the consultant with the support of Save the Children, EYN and the BSPHCDA staff. Maximum supervision of the survey teams was ensured. During data collection adherence to WHO, Federal and Borno State Primary Health Care Development Agency Board COVID-19 guidelines on social-distancing, wearing masks, hand washing/sanitizing and monitoring of participants were observed. Data management Data was collected through Kobo Collect and Save the Children Server was used to transmit the data. The data collection tools were programmed and uploaded into the tablets which was used by the survey teams. The teams uploaded the collected data to a central server on daily basis to allow the Survey Manager to review the data collected NB: Backup manual forms were carried and used by each team as a contingency plan in any eventuality that teams face challenges with the SMART phones and have to make reference. Data Quality In order to ensure optimal and high data quality, a number of measures were put in place, they include: a) Qualified and competent consultants (Lead and Co-Lead) were engaged to oversee the survey implementation. b) The survey was conducted in accordance to validated protocol by SCI and NiEWG and with below key aspects streamlined: . Training of survey teams was conducted using standardised material as recommended by SMART Methodology . Survey team took part in standardisation test exercise as part of the training; taking appropriate steps thereafter based on performance of the survey teams . Appropriate calibration of survey equipment during the training and every morning before proceeding to the field for data collection was followed. . Plausibility checks were conducted on daily basis and debriefing sessions were shared with the teams on daily basis. c) Data was collected through digital platform (Kobo collect), and control checks and skip patterns were programmed to improve the data quality d) Anthropometry data was auto analysed using ENA for SMART software anthropometry section. The same software was used to analyse the mortality data.
Questionnaire The survey did adopt data collection tools in accordance to SMART Survey objectives and similarly to SCI and NiEWG recommendation. Data to be collected 1. Anthropometrics a) Age of target children was determined using birth/health cards/ records and local events calendar. An updated calendar of events was used in estimating the ages of children when health card or document on proof of actual age of child was lacking. b) Sex: Male or female c) Weight: Children’s weights was taken with minimal clothing or while naked using Electronic weight (SECA scales with precision of 100gm).
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d) Height/length: Children were measured using the wooden UNICEF measuring boards (precision of 0.1cm). Children aged (6- 23months) were measured lying down, while those greater than or equal to 24 months were measured standing up. e) Mid-Upper Arm Circumference (MUAC): MUAC measurements was taken at the mid-point of the left upper arm. Both the child and maternal MUAC tapes (precision of 0.1cm) were used to screen children aged (6-59) months and women of reproductive age (15-49 years) as Pregnant and Lactating Women. f) Bilateral pitting oedema was assessed by the application of thumb pressure on both feet of all children (6-59 months) for three (3) seconds. g) Referral: All children with acute malnutrition confirmation by either of bilateral Oedema and MUAC <125mm or sick during the data collection period were referred using referral forms to existing nutrition and health facility within the respective LGA. 2. Health Interventions data: Data on Vitamin A supplementation coverage among children (6-59) months, deworming coverage among children aged (12-59) months and Measles vaccination (9- 59) months were collected through verification from health cards or recall. 3. Morbidity: Two-week retrospective morbidity data was collected from mothers/caregivers of all children (6-59 months) included in the anthropometric survey. 4. Infant and Young Child Feeding: key IYCF indicators were collected and analysis followed the IYCF indicator guideline8. They include: a. Exclusive Breastfeeding (EBF) for under 6 months- Proportion of infants aged (0–5.9 months) who are fed exclusively with breast milk. b. Minimum Dietary Diversity (MDD)-Proportion of children aged (6–23.9 months) who received foods from 4 or more food groups. c. Early initiation to breast milk in less than 1 hour after birth d. Minimum Meal Frequency (MMF) – proportion of children aged (6-23.9 months) who received foods the minimum number of times (2 times for breastfed infants aged (6–8 months). 3 times for breastfed children aged (9–23 months) or more. For non-breastfed children, minimum is defined as 4 times for children 6–23.9 months. e. Minimum Acceptable Diet (MAD): Proportion of children 6–23.9 months of age who receive a minimum acceptable diet. Combination of MMF and MDD. 5. Maternal nutrition: The MUAC tape was used to assess maternal nutrition status. WRA aged (15- 49 years) and PLW detected with MUAC cut-off (<210mm) were regarded as acute malnourished while those above (210mm to <230mm) were categorised as at risk of malnutrition. 6. Water, sanitation, and hygiene indicators a) Main source of drinking water at household level. b) Treatment of water for drinking at household level. b) Caregiver critical hand washing practices c) Access to safe disposal of human faecal matter at household level/sanitation coverage. 7. Food Security and Livelihoods a) Household Dietary Diversity Score b) Coping strategy index Data Analysis, results and output The anthropometric and mortality data was analyzed using ENA for SMART software version 11th January 2020. Data on immunization, maternal nutrition, morbidity and other indicators were analyzed using SPSS version 26. The preliminary findings were shared with SCI and survey supervisors on 24th
8 World Health Organization (2010) Indicators for assessing Infant and Young Child Feeding (IYCF) practices, part III Country profiles.
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February 2021, thereafter results will be shared with NiEWG for the validation. The final reports will be available within 14 days after the validation of the survey results.
Ethical consideration Informed consent was read out to all respondents within the sampled households. Only respondents who voluntarily accepted to participate in the survey were engaged in interview sessions. The survey teams also ensured compliance to SCI child protection and safeguarding measures at all times.
Covid-19 measures The measures did include social distancing, wearing of masks at all times and cleaning hands using hand sanitizer as well using soap and water. In addition to this SCI provided the following items to all survey teams during the training and data collection phase; Functional digital temperature thermometer provided to each survey teams. Adequate surgical masks provided to all survey teams and community guides on daily basis. All children and their caregivers who participated in standardization test also issued with mask and sanitizers. Sanitizers issued to each survey teams. Cotton wool and methylated spirit provided to each survey team to clean anthropometric equipment after measuring each child or PLW. SCI also hired a spacious and well ventilated hall to support training and standardization test activities which enabled social distancing and adherence to other safety COVID-19 protocols. SCI provided water and soaps for hand washing to all participants during training and standardization test. In additional to provisional of necessary items to support adherence of COVID-19 measures, the survey teams were taken through COVID-19 measures during training and constantly reminded to observe the measures during the data collection period as defined by WHO and federal MOH.
SCI medical officer with support of other SCI staffs involved in continuously monitoring the participants health information during training, standardization test and data collection. This involved closely monitoring health information of participants and contact helpline and online whatsup group created by SCI staff for participants to update on security aspect as well as reported illnesses or symptoms that corresponded to COVID-19.
Minimal number of teams included in the survey as well as minimum number travelled to the surveyed locations and at all times mass gatherings were prohibited.
Survey methodology approval The survey protocol was drafted by SMART survey consultant and shared with SCI on 5th January 2021. The consultant received internal reviews from SCI staff and upon incorporation of inputs, SCI team based in Borno State shared the protocol with NiEWG for validation on 17th January 2021. The NiEWG later scheduled a meeting where the SMART survey consultant presented the revised protocol at NiEWG on 22nd January 2021. The protocol was validated with inputs for consideration which the consultants revised and shared back with NiEWG as the final version.
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3.0 SURVEY RESULTS 3.1Sample size (achieved and planned) The survey data collection implemented on 31st January 2020 to 11th February 2021. The survey was implemented in Jere, Konduga, MMC and Mafa LGAs with the previously plan to include Magumeri and Kaga LGA was aborted due to inaccessibility as result of security challenges. The survey was implemented during the harmattan season characterised by cold/windy and dry season. The planned and achieved sample size based on clusters, households and children (6-59 months) and persons is illustrated in table 4 and 5. The survey teams were able to successfully achieve minimal sample size (>80%) across the surveyed LGAs with the exception of Mafa LGA where 13 clusters located in central Mafa were never assessed due to insecurity at the time of data collection. In the same note, seven (7) clusters located in Central Konduga were affected by deteriorating security situation. The road network became inaccessible especially for sampled clusters located past Konduga town. Despite efforts to use reserve clusters since they met 10% rule, the teams were only able to assess two (2) reserve clusters located in accessible zones of Konduga LGA with the remaining three (3) reserve clusters also located in the insecure locations of Central Konduga. The average household size was 5.7, 5.1, 6.0 and 5.8 for Jere, Konduga, Mafa and MMC LGAs respectively. All the LGAs achieved above 90% of children aged (6-59 months) as well population assessed in mortality section. Table 4: Number of clusters and households (achieved and planned) Planned Achieved(%) Planned Achieved(%) (Actual) Surveyed LGA No# of Clusters No# of Clusters No# of HHs No# of HHs
Jere 40 39 (97.5%) 552 540 (95.0%) Konduga 42 37 (88.1%) 585 506 (86.7%) MMC 40 39 (97.5%) 552 537 (95.3%) Mafa 42 29 (*69%) 585 402 (*68.7%)
Table 5: Number of child and persons (achieved vs planned)
Planned Achieved Planned Achieved Surveyed No# of children No# of children No# of persons No# of persons (% LGA (% achieved) achieved) Jere 441 626 (141.9%) 2889 3114(107.7%) Konduga 461 596 (129.3%) 2611 2569(98.4%) MMC 441 669(151.7%) 2889 3117.5(107.9%) Mafa 461 528(114.5%) 2611 2395(91.7%)
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3.2 Data quality The Data Plausibility Scores (DPS) was used to interpret quality of anthropometric data on daily basis for each survey team using ENA for SMART software version 11th January 2020. The summary of DPS is illustrated in table 6. The overall DPS across the surveyed LGAs was excellent. The DPS interprets z-score flags based on SMART flags (+/-3). Both boys and girls were equally represented across the LGAs. The Age ratio of children aged (6-29) versus (30-59) months were as expected, however more than 50% of children across all the LGAs had their age confirmed through recall using event calendar. Anthropometric measurements (weight, height and MUAC) had excellent to good scores across the surveyed LGAs. Skewness was symmetrical across all the surveyed LGAs. The Poisson distribution indicates the wasted cases were uniformly distributed across the clusters in all survey LGAs. The standard deviation for WHZ-scores within the range of (0.8-1.2) across the surveyed LGAs. The comprehensive DPS reports for respective surveyed LGAs is available in annex 7 of this report. Table 6: Summary of data plausibility score for Anthropometric indicators for each LGA
Digit preference Overal Flagged SD Skewnes Kurtosis Poisson LGA Sex ratio Age ratio Wei Heig MU DPS data WHZ s WHZ WHZ WHZ ght ht AC WHZ 0(p=1.00 0(p=0.22 Jere 0(1.3%) 0(p=0.217) 0(2) 2(9) 2(11) 0(1.01) 0(-0.18) 1(-0.33) 5% 0) 4) 0(p=0.25 0(p=0.06 Konduga 0(0.8%) 0(p=0.317) 0(6) 2(10) 2(8) 0(0.99) 0(-0.01) 1(0.23) 5% 1) 5) 0(p=0.72 0(p=0.51 MMC 0(0.4%) 0(p=0.724) 0(5) 2(10) 2(11) 0(0.93) 1(-0.20) 1(0.16) 6% 7) 2) 0(p=0.86 0(p=0.10 Mafa 0(0.8%) 0(p=0.637) 0(4) 2(9) 0(6) 0(1.05) 0(0.03) 0(0.05) 2% 2) 9)
3.3 Demographic and household characteristics The analysis on residence status of the respondent unveiled over 50% were IDPs in Jere and Konduga LGAs. Similarly, 46% and 38.2% of the respondents were IDPs in Mafa and MMC LGAs respectively as indicated in figure 1. The host/residents were above 50% only in MMC LGA with marginal returnees observed across the surveyed LGAs. 3.3.1 Residence status of the respondent Residence status of the households
80.0% 74.7% 70.0% 58.9% 60.9% 60.0% 52.2% 50.0% 46.0% 38.2% 40.0% 35.6% 30.0% Percentage 23.9% 20.0% 10.0% 5.6% 1.4% 1.7% 0.9% 0.0% Jere Konduga Mafa MMC
IDPs Residents Returnees
Figure 1: Residence status of the respondent per each LGA
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3.3.2 Marital status of the respondent The marital status of the respondents was married representing 90% across the surveyed LGAs. There were households where respondents reported they were widowed, separated, divorced and single as indicated in table 7. Table 7: Marital status
LGA Married Divorced Separated Single Widowed Jere 94.1% 0.6% 0.4% 0.0% 5.0% Konduga 94.5% 1.4% 0.2% 0.2% 3.8% Mafa 97.5% 0.2% 0.0% 0.0% 2.2% MMC 89.0% 1.9% 0.9% 1.7% 6.5%
3.3.3 Main source of income at household level The main source of income in Konduga and MMC was petty trade, while in Jere LGA the main source of income was casual labour as indicated in table 8. In Mafa most household representing 50.5% reported no income with 26.9% reporting casual as major source of income. Table 8: Main source of income at household level Sale Sale of Casual Sale of Petty Permanent No LGA of Livestock Others Labour Livestock Trading Jobs Income Crops Products n 208 10 48 148 4 23 90 9 Jere % (38.5%) (1.9%) (8.9%) (27.4%) (0.7%) (4.3%) (16.7%) (1.7%) n 92 3 48 167 12 32 148 4 Konduga % (18.2%) (0.6%) (9.5%) (33.0%) (2.4%) (6.3%) (29.2%) (0.8%) n 108 12 14 45 2 9 203 9 Mafa % (26.9%) (3.0%) (3.5%) (11.2%) (0.5%) (2.2%) (50.5%) (2.2%) n 122 4 12 223 7 51 95 23 MMC % (22.7%) (0.7%) (2.2%) (41.5%) (1.3%) (9.5%) (17.7%) (4.3%) 3.3.4 Education level The highest education level attained was Islamia (religious education) representing 40% and above of all the respondents across the surveyed LGAs. The respondents who confirmed attaining formal education up to primary level were less than 20% across the LGA with exception of MMC. The findings also unveiled over 20% of respondents across the LGAs except MMC reported to have not received any form of education as illustrated in table 9. Table 9: Education level of the household head per each LGA LGA Islamia None Primary Secondary Tertiary Total Count 295 144 49 31 21 540 Jere (26.7%) % (54.6%) (9.1%) (5.7%) (3.9%) (100%) Count 240 179 37 16 34 506 Konduga (35.4%) % (47.4%) (7.3%) (3.2%) (6.7%) (100%) Count 228 138 16 14 6 402 Mafa (34.3%) % (56.7%) (4.0%) (3.5%) (1.5%) (100%) MMC Count 301 45 108 41 42 537 (8.4%) % (56.1%) (20.1%) (7.6%) (7.8%) (100%)
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3.4 Results of nutritional status among children aged 6-59 months The Nutritional status of children aged 6-59 months were assessed by taking their anthropometric measurements that is weight, height/length, MUAC and bilateral oedema. In addition to this was verification of important variables such as age and sex. In emergency contexts children below five years are most vulnerable to changes that might distort stability of their well-being and are likely to be affected most as compared to other members of the household in the case the changes are immediate. In this report, findings illustrating nutritional status of children aged 6-59 months in the surveyed LGAs are interpreted based on WHO growth standards with SMART flags (+/-3) applied for WHZ, HAZ and WAZ-scores. The mean-z-scores, DEFF and excluded subjects for the four (4) surveyed LGAs is available in annex 2 of this report. Malnutrition of children aged 6-59 months is presented in this report as acute (wasting), chronic (stunting) or combination of acute and chronic malnutrition (underweight). The prevalence of acute, chronic and underweight across the surveyed LGAs was analyzed using ENA for SMART software and it’s indicated in table (10 – 16) of this report. 3.4.1 Prevalence of acute malnutrition (wasting) by WHZ-scores, MUAC and combined (WHZ & MUAC) and/or oedema The prevalence of global acute malnutrition by WHZ-scores and/or oedema was at 7.3%, 6.4%, 7.1% and 6.9% in Jere, Konduga, Mafa and MMC LGAs respectively as indicated in table 10. The GAM by WHZ-scores across the LGAs was interpreted at poor (medium) level based on new WHO classification of acute malnutrition thresholds (>5.0-9.9). Both boys and girls were equally acute malnourished as indicated in the tables 10-11. Generally, Severe Acute malnutrition (SAM) based on WHZ-scores was high among younger children aged 6-29 months as compared to 30-59 months age group. However Moderate Acute Malnutrition (MAM) was evenly distributed among other age groups across the LGAs as indicated in table 12-16 of this report. The GAM prevalence by MUAC was 3.4%, 3.4%, 2.7% and 2.5% in Jere, Konduga, Mafa and MMC LGAs respectively, confirming presence of both moderate and severe forms of acute malnutrition in the surveyed LGA’s. Table 10: Prevalence of acute malnutrition among children (6-59 months) for each LGA
Acute Malnutrition by WHZ-scores Acute Malnutrition by MUAC and/or oedema (SMART flags) and/or oedema LGA GAM MAM SAM GAM MAM SAM (WHZ<- (WHZ>-3- (WHZ<- (<125mm) (>115- (115mm) 2SD) <-2SD) 3SD) <125mm) N 95% C.I N 95% C.I 618 ( 45) ( 41) 6.6% ( 4) 0.6% ( 626 ( 21) ( 19) 3.0% ( ( 2) 0.3% ( 7.3% ( ( 4.7- 9.3 0.2- 1.7 3.4% ( 1.8- 5.1 95% 0.1- 1.3 95% Jere 5.2- 95% CI) 95% CI) 2.0- 5.5 CI) CI) 10.2 95% CI) 95% CI) N 95% C.1 N 95% C.I 591 (38) ( 35) 5.9% ( 3) 0.5% ( 596 ( 20) (19)3.2% ( ( 1) 0.2% 6.4% ( 4.0- 8.8 0.2- 1.6 3.4% ( 2.0- 5.1 95% (0.0- 1.2 Konduga ( 4.4- 95% CI) 95% CI) 2.1- 5.4 CI) 95% CI) 9.3 95% CI) 95% CI) N 95% C.I N 95% C.I 524 ( 37) ( 30) 5.7% ( 7) 1.3% ( 528 ( 14) (12)2.3% ( (2)0.4% ( Mafa 7.1% ( ( 3.8- 8.6 0.7- 2.6 2.7% ( 1.2- 4.2 95% 0.1- 1.6 95% 4.8- 95% CI) 95% CI) 1.6- 4.5 CI) CI) 10.2 95% CI)
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95% CI) N 95% C.I N 95% C.I 666 ( 46) ( 39) 5.9% ( 7) 1.1% ( 667 ( 18) ( 14) 2.1% ( ( 4) 0.6% ( MMC 6.9% ( ( 4.4- 7.8 0.5- 2.1 2.7% ( 1.4- 3.2 95% 0.2- 1.5 95% 5.2- 9.2 95% CI) 95% CI) 1.9- 3.7 CI) CI) 95% 95% CI) CI)
MUAC and WHZ diagnose different children and generate prevalence estimates that are not similar. The report by WHO 2009 found that the proportion of acutely malnourished children identified by both WHZ and MUAC was higher than the proportion of children identified by WHZ or MUAC. The study also unveiled about 40% of children are detected as acute malnourished by both WHZ and MUAC9 (Leidman, E., Couture, A., Hulland, E. & Bilukha, O., 2019). And thus by using one indicator and not combination of other might have missed opportunity especially if a single indicator is applied in programming. The combined GAM(cGAM) prevalence by both WHZ<-2SD, MUAC <125mm and/0r bilateral oedema was 10.2%, 8.1%, 8.0% and 8.4% in Jere, Konduga, Mafa and MMC respectively as illustrated in table 11. The combined SAM prevalence was 1.9%, 1.2%, 1.7% and 1.6% in Jere, Konduga, Mafa and MMC respectively as illustrated in table 12. Table 11: Concordance in cGAM prevalence of by both WHZ<-2SD, MUAC<125mm and/or bilateral oedema Count Combined GAM by MUAC<125mm and WHZ<-2SD) Both(WHZ<- (WHZ<- 2SD) & 95%C.1. LGA 2SD) MUAC<125mm MUAC<125mm Jere 51 21 8 (64)10.2%(7.9-12.6) Konduga 41 20 13 (48)8.1%(5.9-10.2) Mafa 37 14 9 (42)8.0%(5.6-10.3) MMC 46 18 8 (56)8.4%(6.3-10.5)
Table 12: Concordance in cSAM prevalence by both WHZ<-2SD, MUAC<125mm and/or bilateral oedema
Count Combined GAM by (WHZ<- Both(WHZ<-2SD) & MUAC<115mm and WHZ<- LGA 3SD) MUAC<115mm MUAC<125mm 3SD) 95%C.1. Jere 10 2 0 (12)1.9%(0.8-3.0) Konduga 6 1 0 (7)1.2%(0.3-2.0) Mafa 7 2 0 (9)1.7%(0.6-2.8) MMC 7 4 0 1.6%(0.7-2.6)
9 (Leidman, E., Couture, A., Hulland, E. & Bilukha, O., 2019) 21 | P a g e
3.4.2(a) Distribution of age and sex of the children aged (6-59) months based on weight-for-height z-scores and/or oedema in Jere LGA Table 13: Distribution of age and sex of the children aged (6-59) months based on weight-for-height z-scores and/or oedema in Jere LGA
SMART Severe wasting Moderate wasting Normal flags (<-3 z-score) (>= -3 and <-2 z-score (> = -2 z score) ) Age (mo) No. % No. % No. % SMART flags All Boys Girls (n =618) (n =309) (n =309)
6-17 0 0.0 16 11.0 130 89.0 GAM prev. (45) 7.3 % (23) 7.4 % (22) 7.1 % (<-2 z-score and/or (5.2 - 10.2 95% (5.2 - 10.5 (4.5 - 11.0 95% oedema) C.I.) 95% C.I.) C.I.)
18-29 2 1.3 6 4.0 142 94.7 MAM prev. (41) 6.6 % (20) 6.5 % (21) 6.8 % (<-2 z-score and (4.7 - 9.3 95% C.I.) (4.4 - 9.3 (4.3 - 10.5 95% >=-3 z-score, no 95% C.I.) C.I.) oedema)
30-41 0 0.0 7 4.5 150 95.5 SAM prev. (4) 0.6 % (3) 1.0 % (1) 0.3 % (<-3 z-score (0.2 - 1.7 95% C.I.) (0.3 - 3.1 (0.0 - 2.5 95% and/or oedema) 95% C.I.) C.I.)
42-53 2 1.7 6 5.0 111 93.3
54-59 0 0.0 6 13.0 40 87.0
The prevalence of oedema is 0.0 % 3.4.2(b) Distribution of age and sex of the children aged (6-59) months based on weight-for-height z-scores and/or oedema in Konduga LGA Table 14: Distribution of age and sex of the children aged (6-59) months based on weight-for-height z-scores and/or oedema in Konduga LGA
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SMART Severe wasting Moderate wasting Normal flags (<-3 z-score) (>= -3 and <-2 z-score (> = -2 z score) ) Age (mo) No. % No. % No. % SMART flags All Boys Girls (n =591) (n =208) (n =308)
6-17 2 1.6 12 9.4 114 89.1 GAM prev. (38) 6.4 % (19) 6.7 % (19) 6.2 % (<-2 z-score and/or (4.4 - 9.3 95% (4.0 - 11.0 (4.0 - 9.3 95% oedema) C.I.) 95% C.I.) C.I.)
18-29 1 0.6 13 8.4 140 90.9 MAM prev. (35) 5.9 % (17) 6.0 % (18) 5.8 % (<-2 z-score and >=-3 (4.0 - 8.8 95% (3.5 - 10.2 (3.7 - 9.0 95% z-score, no oedema) C.I.) 95% C.I.) C.I.)
30-41 0 0.0 5 3.2 149 96.8 SAM prev. (3) 0.5 % (2) 0.7 % (1) 0.3 % (<-3 z-score and/or (0.2 - 1.6 95% (0.2 - 2.9 (0.0 - 2.4 95% oedema) C.I.) 95% C.I.) C.I.)
42-53 0 0.0 4 3.4 112 96.6
54-59 0 0.0 1 2.6 38 97.4
The prevalence of oedema is 0.0 %
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3.4.2(c) Distribution of age and sex of the children aged (6-59) months based on weight-for-height z-scores and/or oedema in Mafa LGA Table 15: Distribution of age and sex of the children aged (6-59) months based on weight-for-height z-scores and/or oedema in Mafa LGA
SMART Severe wasting Moderate wasting Normal flags (<-3 z-score) (>= -3 and <-2 z-score (> = -2 z score) ) Age (mo) No. % No. % No. % SMART flags All Boys Girls (n =524) (n =264) (n =260)
6-17 2 1.7 9 7.7 106 90.6 GAM prev. (37) 7.1 % (17) 6.4 % (20) 7.7 % (<-2 z-score and/or (4.8 - 10.2 (3.7 - 11.0 (4.9 - 11.9 95% oedema) 95% C.I.) 95% C.I.) C.I.)
18-29 3 2.3 5 3.9 121 93.8 MAM prev. (30) 5.7 % (12) 4.5 % (18) 6.9 % (<-2 z-score and >=-3 (3.8 - 8.6 95% (2.4 - 8.5 (4.5 - 10.6 95% z-score, no oedema) C.I.) 95% C.I.) C.I.)
30-41 1 0.7 3 2.2 133 97.1 SAM prev. (7) 1.3 % (5) 1.9 % (2) 0.8 % (<-3 z-score and/or (0.7 - 2.6 95% (0.8 - 4.5 (0.2 - 3.1 95% oedema) C.I.) 95% C.I.) C.I.)
42-53 1 1.0 10 9.6 93 89.4
54-59 0 0.0 3 8.1 34 91.9
The prevalence of oedema is 0.0 %
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3.4.2(d) Distribution of age and sex of the children aged (6-59) months based on weight-for-height z-scores and/or oedema in MMC LGA Table 16: Distribution of age and sex of the children aged (6-59) months based on weight-for-height z-scores and/or oedema in MMC LGA SMART Severe wasting Moderate wasting Normal flags (<-3 z-score) (>= -3 and <-2 z-score (> = -2 z score) ) Age (mo) No. % No. % No. % SMART flags All Boys Girls (n = 666) (n = 337) (n = 329)
6-17 5 3.2 17 11.0 133 85.8 GAM prev. (46) 6.9 % (26) 7.7 % (20) 6.1 % (<-2 z-score and/or (5.1 - 9.2 95% (5.0 - 11.7 (3.9 - 9.3 95% oedema) C.I.) 95% C.I.) C.I.)
18-29 2 1.3 5 3.2 147 95.5 MAM prev. (39) 5.9 % (20) 5.9 % (19) 5.8 % (<-2 z-score and >=-3 (4.3 - 7.9 95% (3.7 - 9.5 (3.7 - 8.9 95% z-score, no oedema) C.I.) 95% C.I.) C.I.)
30-41 0 0.0 2 1.2 169 98.8 SAM prev. (7) 1.1 % (6) 1.8 % (1) 0.3 % (<-3 z-score and/or (0.5 - 2.1 95% (0.8 - 3.7 (0.0 - 2.2 95% oedema) C.I.) 95% C.I.) C.I.)
42-53 0 0.0 10 7.0 132 93.0
54-59 0 0.0 5 11.9 37 88.1 The prevalence of oedema is 0.0 %
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3.4.3 Prevalence of stunting and underweight The prevalence of stunting in Jere, Konduga, Mafa and MMC LGA’s was above 30% across the surveyed LGAs as indicated in table 17. The prevalence of stunting was interpreted as high based on WHO and UNICEF chronic malnutrition thresholds (>30.0-39.9). The recalculated stunting prevalence in Konduga and Mafa LGA was 32.1% and 27.7% respectively based on (SD =1.00) with exclusion from observed mean (SMART flags). The prevalence of underweight was interpreted as high (>20.0-29.9) across the surveyed LGAs with exception of Mafa LGA was at 19.0%. . Table 17: Prevalence of stunting and underweight Stunting Underweight Prevalence Prevalence of severe Prevalence Prevalence of severe Surveyed LGA of stunting (HAZ<-2SD) stunting (HAZ<-3SD) of underweight(HAZ<-2SD) underweight(HAZ<-3SD)
N 95% C.I N 95% C.I Jere 610 (229) 37.5% (33.1-42.2 95% ( 85) 13.9% (11.3-17.1 95% 624 (141) 22.6% (19.4-26.2 95% ( 22) 3.5% ( 2.2- 5.7 95% CI) CI) CI) CI) N 95% C.I N 95% C.I Konduga 568 (199) 35.0% (30.2-40.2 95% ( 85) 15.0% (12.1-18.3 95% 594 (130) 21.9% (17.4-27.1 95% ( 25) 4.2% ( 2.7- 6.5 95% CI) CI) CI) CI) N 95% C.I N 95% C.I Mafa 502 (165) 32.9% (28.3-37.8 95% ( 54) 10.8% ( 8.6-13.4 95% 522 ( 99) 19.0% (15.0-23.7 95% ( 19) 3.6% ( 2.2- 6.0 95% CI) CI) CI) CI) N 95% C.I N 95% C.I MMC 656 (230) 35.1% (29.9-40.5 95% (75) 11.4% ( 8.9-14.6 95% 666 (136) 20.4% (17.3-23.9 95% (19)2.9% ( 1.7- 4.7 95% CI) CI) CI) CI)
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3.5 Child Health 3.5.1 Morbidity patterns The UNICEF conceptual framework provides causal factors to child malnutrition and death with disease and inadequate food intake cited as immediate causal factors. The assessment on morbidity was derived from inquiring from caregivers of children aged (6-59 months whether the child was ill two (2) weeks prior to the survey data collection. In Jere, Konduga, Mafa and MMC eligible children confirmed as ill were 17.9%, 12.8%, 15.2% and 27.2% respectively as indicated in figure 2.
3.5.1 (a) prevalence of main child illnesses
Morbidity patterns among children aged (6-59 months) based on two-weeks recall prior to data collection
MMC 72.8% 27.2%
Mafa 84.8% 15.2%
Konduga 87.2% 12.8%
Jere 82.1% 17.9%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
No Yes
Figure 2: Morbidity patterns among children aged (6-59 months) based on two-week recall The main child illnesses reported across the surveyed LGAs was fever with chills-like malaria except for MMC where the main illness reported was cough/acute respiratory infections. Other illnesses reported among the ill children per the surveyed LGAs are illustrated in table 18.
Table 18 Reported child illness in the past two weeks prior to survey data collection: Morbidity LGA Fever with chills like ARI/Cough Diarrhea Malaria Jere 12.6% [10.1-15.4] (n=79) 4.3% [2.8-6.2] (n=27) 3.2% [1.9-4.8] (n=20)
Konduga 8.2% [6.1-10.7] (n=49) 4.2% [2.7-6.1] (n=25) 2.2% [1.1-3.7] (n=13)
Mafa 10.0% [7.6-12.9] (n=53) 8.3% [6.1-11.0] (n=44) 2.3% [1.1-3.9] (n=12)
MMC 14.1% [11.1-16.4] (n=94) 15.5% [12.5-17.9] (n=104) 6.0% [4.1-7.8] (n=40)
3.5.1 (b) Health seeking option sought by caregivers of reported ill children The main treatment sought by caregivers of children reported ill was through medicine store/shop except in Mafa LGA where 41.3% of cases reported ill sought treatment at public hospital/clinic. The caregivers in Mafa, MMC, Konduga and Jere LGAs representing 16.3%, 7.1%, 6.6% and 2.7% did not seek any treatment for their ill children as illustrated in table 19. At the time of the data collection, there were several children referred by the survey team screened as sick or malnourished children. The caregiver of the child was issued with a referral form with need to take the sick child to the nearest hospital/clinic. Children reported sick and left at home for long are at higher risk of death and may suffer severe forms of acute malnutrition. It was also observed from the survey findings that some caregivers opted for treatment options from the traditional healer, relatives etc which are termed not appropriate and might illustrate poor health seeking practices among the caregivers. It’s important to
27 | P a g e note that presence of NGO supported hospitals/clinics was among the avenues of treatment sought by caregivers representing over 20% across the surveyed LGAs except in Mafa LGA.
Table 19: Health seeking option sought by caregivers of reported ill children NGO/Faith Based Public Medicine Commun Relativ No Organizatio Traditiona Others(Sp LGA hospital/ shop/Pharm ity Health Shop e/Frie Treatmen Total ns l Healer ecify) Clinic acy Volunteer nd t Sought Clinics/hospi tal n (16) (28) (53) (2) (3) (0) (5) (2) (3) (112) Jere % 14.3% 25.0% 47.3% 1.8% 2.7% 0.0% 4.5% 1.8% 2.7% 100.00% n (15) (19) (26) (5) (6) (0) (0) (0) (5) (76) Konduga % 19.7% 25.0% 34.2% 6.6% 7.9% 0.0% 0.0% 0.0% 6.6% 100.00% n (33) (6) (5) (3) (12) (0) (8) (0) (13) (80) Mafa % 41.3% 7.5% 6.3% 3.8% 15.0% 0.0% 10.0% 0.0% 16.3% 100.00% n (27) (50) (63) (2) (18) (4) (2) (3) (13) (182) MMC % 14.8% 27.5% 34.6% 1.1% 9.9% 2.2% 1.1% 1.6% 7.1% 100.00%
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3.5.2 Measles vaccination coverage among children 9-59 months The measles vaccination coverage among children aged 9-59 months verified by both card and recall was below national targets of 90% across the surveyed LGAs as indicated in table 20. The highest coverage was reported in MMC at 78.1% with the lowest reported in both Jere and Mafa LGAs at 59.0% and 62.0% respectively. It’s important to note that the majority of data for children vaccinated with measles at 9 months was confirmed by recall. There were children aged 9-59 months who reported to have not received measles vaccination in Jere, Konduga, Mafa and MMC representing 36.4%, 23.8%, 27.9% and 21.0% respectively.
Table 20: Measles vaccination coverage among children (9-59 months) Total Yes (Card and Don't LGA Yes, Card Yes, Recall No Recall) Know
Count (357) (77) (280) (220) (28) (605) Jere % 59.0% [54.9-62.9] 12.7% 46.3% 36.4% 4.6% 100% Count (415) (120) (295) (134) (15) (564) Konduga % 73.6% [69.7-77.1] 21.3% 52.3% 23.8% 2.7% 100% Count (309) (137) (172) (139) (50) (498) Mafa % 62.0% [57.6-66.3] 27.5% 34.5% 27.9% 10.0% 100% Count (499) (197) (302) (134) (6) (639) MMC % 78.1% [74.6-81.2] 30.8% 47.3% 21.0% 0.9% 100%
3.5.3 (b)Vitamin A supplementation among children aged 6-59 months in the past six(6) months Vitamin A supplementation coverage as verified by both card and recall was above 50% across the surveyed LGAs except in Konduga LGA. Vitamin A supplementation recall period of six (6) months. Vitamin A supplementation coverage was below WHO target of 80% and above across the surveyed LGAs. It’s also important to note over 30% of children aged (6-59 months) had not received vitamin A supplementation as indicated in table 21.
Table 21: Vitamin A supplementation coverage among children aged (6-59) months Yes (Card and Don't Total LGA Yes, Card Yes, Recall No Recall) Know Count (314) (55) (259) (284) (28) (626) Jere % 50.2% [46.1-54.1] 8.8% 41.4% 45.4% 4.4% 100% Count (277) (94) (183) (257) (62) (596) Konduga % 46.5% [42.4-50.5] 15.8% 30.7% 43.1% 10.4% 100% Count (309) (134) (175) (159) (60) (528) Mafa % 58.5% [54.1-62.7] 25.4% 33.1% 30.1% 11.4% 100% Count (412) (157) (255) (247) (8) (667) MMC % 61.7% [57.9-65.4] 23.6% 38.2% 37.0% 1.2% 100%
3.5.4 (c ) Deworming coverage among children aged 12-59 months in the past six(6) months Deworming coverage among children aged (12-59 months) in the past six (6) months was below 50% across the surveyed LGAs with exception of Mafa at 58.3%. Deworming coverage was below the WHO target of 80% and above across the surveyed LGAs. The main verification source for confirmation whether child had received deworming was through recall as shown in table 22.
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Table 22: Deworming coverage among children aged (12-59 months) Yes (Card and Don't Total LGA Recall) Yes, Card Yes, Recall No Know Jere Count (224) (37) (187) (315) (25) (564) % 39.8% [35.6-43.8] 6.6% 33.2% 55.9% 4.3% 100% Konduga Count (250) (80) (170) (246) (45) (541) % 46.2% [41.9-50.5] 14.8% 31.4% 45.5% 8.3% 100% Mafa Count (274) (111) (163) (149) (47) (470) % 58.3% [53.6-62.8] 23.6% 34.7% 31.7% 10.0% 100% MMC Count (296) (106) (190) (295) (7) (598) % 49.5% [45.4-53.5] 17.7% 31.8% 49.3% 1.2% 100%
3.6 Maternal health 3.6.1 Physiological status of WRA (15-49 years) The physiological status of Women of Reproductive Age (WRA) (15-49 years) across the surveyed LGAs is indicated in table 23. Majority of WRA were lactating while others were neither pregnant nor lactating.
Table 23: Physiological status of WRA (15-49 years) Not Pregnant & Pregnant & Total Pregnant Lactating LGA Not Lactating Lactating Count (64) (196) (194) (3) (457) Jere % 14.0% 42.8% 42.5% 0.7% 100% Count (40) (135) (133) (2) (310) Konduga % 12.9% 43.5% 42.9% 0.6% 99.9%* Count (40) (143) (132) (0) (315) Mafa % 12.7% 45.4% 41.9% 0.0% 100% Count (59) (207) (173) (5) (444) MMC % 13.3% 46.6% 39.0% 1.1% 100% 3.6.2 Maternal nutritional status by MUAC 3.6.2(a) Nutritional status of WRA (15-49 years) by MUAC Maternal malnutrition increases the risk of poor pregnancy outcomes including obstructed labour, premature or low-birth-weight babies and postpartum haemorrhage and increased mortality at labour10 (World Health Organization, 2008). The nutritional status of WRA by MUAC cut-off (<190mm) termed as acute malnourished was adopted from NiEWG and WHO recommendation. The findings unveiled percentage of acute malnutrition based on MUAC (<190mm) among WRA was 0.4%, 0.3%, 1.3% and 2.0% in Jere, Konduga, Mafa and MMC LGAs respectively. The percentage of WRA at risk of acute malnutrition based on MUAC cut-off (>190-<230mm) was 10.9%, 9.4%, 19.4% and 14.2% in Jere, Konduga, Mafa and MMC LGAs respectively. The percentage of WRA classified by MUAC cut-off l (>230mm) are indicated in table 24.
10 (World Health Organization, 2008)
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Table 24: Nutritional status of WRA (15-49 years) by MUAC Acutely At Risk Normal Malnourished (>190-<230mm) (>230mm) LGA (<190mm) Count 2 50 405 Jere % 0.4%(0.0-1.0) 10.9%(8.1-13.8) 88.6% [85.7-91.5] Count 1 29 280 Konduga % 0.3%(0.0-1.0) 9.4%(6.1-12.6) 90.3% [87.0-93.6] Count 4 61 250 Mafa % 1.3%(0.0-2.5) 19.4%(15.0-23.7) 79.4% [74.9-83.8] Count 9 63 379 MMC % 2.0%(0.7-3.3) 14.2%(10.9-17.4) 85.4% [82.1-88.6] 3.6.2(b) Nutritional status of Pregnant and Lactating Women(PLW) by MUAC Pregnant and lactating women are among the most vulnerable population to malnutrition. The analysis of nutritional status of PLW based on MUAC cut-off ((<190mm) and termed as acute malnourished was 0.8%, 0.5%and 0.4% in Jere, Mafa and MMC respectively. The percentage of PLW termed at risk of acute malnutrition (>190-<230mm) in Jere, Konduga, Mafa and MMC LGAs was 10.3%, 7.9%, 16.9% and 14.4% respectively. The percentage of PLW categorised as normal (>230mm) is illustrated in (table 25). Table 25: Nutritional status of PLW by MUAC Malnourished At Risk Normal LGA (<190mm) (>190-<230mm) (>230mm) Count 2 27 234 Jere % 0.8%(0.3-1.5) 10.3%(6.6-13.9) 89.0% [85.2-92.8] Count 0 14 163 Konduga % 0 92.1% [88.1-96.1] 7.9%(3.9-11.9) Count 1 31 151 Mafa % 0.5%(0.1-1.6) 16.9%(11.5-22.4) 82.5% [77.0-88.0] Count 1 39 231 MMC % 0.4%(0.0-1.1) 14.4%(10.2-18.6) 85.2% [81.0-89.5]
3.7 Food Security 3.7.1(a) Main source of food at household level The main source of food at household level across the surveyed LGA was purchase except for Konduga where the main source was derived from food aid (table 26).
Table 26: Main source of food at household level
LGA Jere Konduga Mafa MMC Purchase 51.7% 40.1% 53.5% 73.2% Food for work 2.0% 1.0% 3.0% 0.4% Casual labour 16.1% 0.2% 10.4% 4.7% Borrowed food 0.9% 0.6% 2.2% 0.4% Food aid 21.7% 47.3% 1.9% 15.8% Own production 4.1% 10.7% 28.9% 5.4% Others 3.5% 0.2% 0.0% 0.2%
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Total 100% 100% 100% 100% 3.7.2(a): Household Dietary Diversity Score (HDDS) The HDDS was assessed based on 24hour recall of foods consumed at household level. HDDS is a better indicator for measurement on food accessibility at household level. The household dietary diversity score (HDDS) is meant to reflect, in a snapshot form, the economic ability of a household to access a variety of foods11 (Food and Agricultural Organization, 2010). The findings revealed that 24.1%, 44.1%, 45.0% and 25.1% of households in Jere, Konduga, Mafa and MMC LGAs were categorized at low HDDS (having consumed <4 food groups). Households categorized at high and medium HDDS are indicated in table 27. The overall HDDS was at 6.1, 4.8, 4.9 and 6.9 in Jere, Konduga, Mafa and MMC LGAs respectively.
Table 27: HDDS Low HDDS High HDDS Total Medium HDDS LGA (<4 food (>7 food groups) 5.0-6.9 food groups) groups) n (130) (174) (236) (540) Jere % 24.1% 32.2% 43.7% 100% n (223) (142) (141) (506) Konduga % 44.1% 28.0% 27.9% 100% n (181) (66) (155) (402) Mafa % 45.0% 16.4% 38.6% 100% n (135) (135) (267) (537) MMC % 25.1% 25.1% 49.7% 100%
3.7.2(b) Foods frequency of food consumed at household level The common foods consumed across the surveyed LGAs in the past 24hours at household level did include cereals, vegetables, oils& fats, condiments and pulses/legumes. The least foods consumed include; Eggs, Milk & milk products, meat, poultry & offal and fruits as indicated in table 28. Table 28: Dietary diversity at household level based on 24hour recall Food Groups Jere Konduga Mafa MMC Cereals 99.5% (n=558) 95.5% (n=485) 90.6% (n=367) 97.3% (n=511) Roots and Tubers 34.2% (n=192) 19.9% (n=101) 37.0% (n=150) 41.0% (n=215) Vegetables 96.6% (n=542) 87.6% (n=445) 82.0% (n=332) 94.9% (n=498) Fruits 46.2% (n=259) 29.7% (n=151) 49.4% (n=200) 38.7% (n=203) Meat, Poultry and Offal 21.7% (n=122) 18.3% (n=93) 40.5% (n=164) 32.0% (n=168) Eggs 20.3% (n=114) 11.0% (n=56) 10.1% (n=41) 21.7% (n=114) Fish and Sea Food 50.1% (n=281) 36.8% (n=187) 44.0% (n=178) 56.4% (n=296) Pulses/Legumes/Nuts 67.7% (n=380) 54.7% (n=278) 50.1% (n=203) 51.4% (n=270) Milk and Milk Products 29.8% (n=167) 23.2% (n=118) 43.0% (n=174) 16.6% (n=87) Oils and Fats 79.9% (n=448) 66.5% (n=338) 40.5% (n=164) 79.0% (n=415) Sugar/Honey 62.4% (n=350) 30.9% (n=157) 41.0% (n=166) 54.1% (n=284) Condiments 61.9% (n=347) 47.8% (n=243) 46.9% (n=190) 72.6% (n=381)
3.7.3 Coping strategy index Households were embracing coping strategies in order to meet food needs at household level for the past seven (7) days is indicated in table 29. In Jere and MMC more than 50% of the household were embracing coping strategies. The most common coping strategies embraced by household level
11 (Food and Agricultural Organization, 2010)
32 | P a g e across the surveyed LGAs was that of reducing meals eaten in a day and limiting portion sizes at meal times were above 50% in Jere and MMC. The most embraced coping strategy in Konduga and Mafa was reducing number of meals eaten in a day representing 21.1% and 33.8% of households as indicated in table 30. The reduced coping strategy index calculated based on severity weight and average number of days each coping strategy in each LGA was 26.3, 25.9, 20.8 and 25.8 in Jere, Konduga, Mafa and MMC respectively. Table 29: Coping strategies embraced at household level
Households that embraced coping strategies in order to meet household food needs LGA Yes No Total n (307) (233) (540) Jere % 56.9% 43.1% 100% n (122) (384) (506) Konduga % 24.1% 75.9% 100% n (170) (232) (402) Mafa % 42.3% 57.7% 100% n (291) (246) (537) MMC % 54.2% 45.8% 100% Table 30: Coping strategies embraced at household level LGA Coping Strategies Rely on less Borrow food, Limit portion Restrict Reduce number preferred and or rely on help size at consumption by of meals eaten less expensive from a friend mealtimes? adults in order in a day? foods? or relative? for small children to eat? Jere 53.9% (n=291) 31.9% (n=172) 54.3% (n=293) 50.0% (n=270) 54.3% (n=293) Konduga 18.6% (n=94) 13.0% (n=66) 17.6% (n=89) 19.0% (n=96) 21.1% (n=107) Mafa 31.3 (n=126) 28.9% (n=116) 36.3% (n=146) 33.8% (n=136) 33.8% (n=136) MMC 45.1% (n=242) 41.0% (n=220) 50.7% (n=272) 49.2% (n=264) 49.0% (n=263)
3.8 Infant Young Child Feeding (IYCF) Proxy Indicators 3.8.1 Early initiation of Breastfeeding <1 hour Early initiation to breast milk in less than one hour after birth was above 80% across the surveyed LGAs as indicated in table 31. Table 31: Timely initiation to breastfeeding LGA <1 Hour 1 to 24 Hours >24 Hours n (163) (26) (7) Jere % 83.2% [77.2-88.1] 13.3% [8.9-18.8] 3.6% [1.4-7.2] n (172) (20) (3) Konduga % 88.2% [82.3-92.3] 10.3% [6.4-15.4] 1.5% [0.0-4.4] n (169) (18) (1) Mafa % 89.9% [84.7-93.8] 9.6% [5.8-14.7] 0.5% [0.0-2.9] n (208) (45) (6) MMC % 80.3% [74.9-84.9] 17.4% [12.9-22.6] 2.3% [0.1-4.9]
3.8.1 Minimum IYCF practices (EBF <6 months, MMF, MDD and MAD among children 6- 23.9months) for respective surveyed LGA Exclusive breastfeeding rate from birth to six (6) months was above 50% in Jere and MMC LGAs respectively, however in Konduga and Mafa EBF rate was at 37.9% and 19.0% respectively as
33 | P a g e illustrated in (table 32). The MMF met was below 70% across the surveyed LGAs except in Jere LGA (72.2%). The MDD met was below 50% across all LGAs while MAD met was at 29.7%, 18.6%, 23.2% and 10.6% in Jere, Konduga, Mafa and MMC LGAs respectively. Table 32: Minimum IYCF practices for respective surveyed LGA
LGA EBF MMF MDD MAD Yes Yes Yes Yes Jere 24 109 46 47 53.3% [37.9-68.3] 72.2% [64.3-79.2] 30.5% [23.2-38.5] 29.7% [22.7-37.5] Konduga 22 82 26 26 37.9% [25.5-51.6] 59.9% [51.1-68.1] 19.0% [12.8-26.6] 18.6% [12.5-26.0] Mafa 12 77 48 29 19.0% [10.3-30.9] 61.6% [52.5-70.2] 38.4% [29.8-47.5] 23.2% [16.1-31.6] MMC 40 94 21 20 63.5% [50.4-75.3] 48.0% [40.7-55.1] 10.7% [6.7-15.9] 10.6% [6.6-15.9]
3.8.2 Dietary Diversity of food consumed by children aged (6-<24) months The main diet consumed by children aged (6-<24 months) was breast milk, grains, roots and tubers. Table 33: Dietary diversity among children aged (6-23.9 months) Food Groups Jere Konduga Mafa MMC Grains, Roots and Tubers 63.3% 73.6% 67.2% (n=84) 72.5% (n=137) (n=100) (n=103) Legumes and Nuts 58.2% (n=92) 42.9% (n=60) 59.2% (n=74) 33.9% (n=64) Milk and Milk Products 25.3% (n=40) 8.6% (n=12) 30.4% (n=38) 9.5% (n=18) Meat and Meat Products 9.5% (n=15) 12.1% (n=17) 40.8% (n=51) 3.2% (n=6) Eggs 22.2% (n=35) 7.1% (n=10) 6.4% (n=8) 18.0% (n=34) Vitamin A Rich Fruits and 33.5% (n=53) 20.0% (n=28) 38.4% (n=48) 13.2% (n=25) Vegetables Other Fruits and Vegetable 48.1% (n=76) 35.0% (n=49) 40.0% (n=50) 19.0% (n=36) Breast milk 90.6% 85.4% 84.4% (n=103) 78.1% (n=153) (n=135) (n=117)
3.9 Water, Sanitation and Hygiene 3.9.1: Main source of water for drinking at household level The main source of water at household across the surveyed LGA was derived from a pipe into the household or central location in proximity to the households representing 60.4%, 49.8%, 41.8% and 46.2% respectively (table 34). In most cases the piped water was derived from another source through mechanized or solar powered means to channel water to the households. Other main sources of water include borehole, dams/ponds, water vendor, protected and open well. Other main sources of water at household level did include water trucking, Mai Moya, sachet water etc as illustrated in figure 3. Table 34: Main source of water for drinking at household level Piped Protected Open Water Total LGA Dams/Ponds Borehole Others Water Well Well Vendor n (326) (24) (7) (70) (3) (89) (21) (540) Jere % 60.4% 4.4% 1.3% 13.0% 0.6% 16.5% 3.8% 100%
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n (252) (11) (3) (34) (14) (186) (6) (506) Konduga % 49.8% 2.2% 0.6% 6.7% 2.8% 36.8% 1.1% 100% n (168) (5) (6) (128) (11) (72) (12) (402) Mafa % 41.8% 1.2% 1.5% 31.8% 2.7% 17.9% 3.0% 100% MMC n (248) (10) (1) (119) (92) (54) (13) (537) % 46.2% 1.9% 0.2% 22.2% 17.1% 10.1% 2.3% 100%
Other main sources of water at household level
0.1%
0.4%
0.2% 1.0%
1.0%
Hand Pump Mai moya Sachet Water Water Trucking Rain Water
Figure 3: other main source of water at household level
3.9.2: Treatment of water for drinking at household level The findings as indicated in figure 4 revealed more than 60% of households in the survey LGAs do not treat their water before drinking. This signifies the treatment of water at household as below 50% minimum SPHERE thresholds set for rural settings.
Treatment of water for drinking at household level
Mafa 63.5% 36.5%
MMC 71.0% 29.0%
Konduga 80.7% 19.3%
Jere 65.8% 34.2%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
No Yes
Figure 4: Treatment of water for drinking at household level
3.9.3: Water treatment methods at household level The main methods embraced by households reported treating water before drinking was use of chemical tabs provided by aid agencies representing 72.9% and 56.7% for Jere and MMC respectively (table 35). In Mafa and Konduga, household’s preferred boiling and filtration representing 80.1% and 32.7% respectively.
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Table 35: Water treatment methods at household level Aqua Others(treatment Total Let it tabs by water Stand LGA (given by Straining/Filtration Boiling management and Aid board at the Settle agencies) source) n (140) (25) (15) (9) (3) (192) Jere % 72.9% 13.0% 7.8% 4.7% 1.6% 100% n (31) (32) (8) (27) (0) (98) Konduga % 31.6% 32.7% 8.2% 27.5% 0.0% 100% n (20) (4) (5) (117) (0) (146) Mafa % 13.7% 2.7% 3.4% 80.1% 0.0% 100% n (85 (34) (0) (19) (12) (150) MMC % 56.7% 22.7% 0.0% 12.7% 8.0% 100% 3.9.4(a): Instances of Hand washing practices in the past 24hours at household level The hand washing practices most practiced was after visiting toilet and before eating. Other instances of hand washing such as before feeding child, after cleaning child’s bottom and before cooking as illustrated in (table 36) were less practiced across the surveyed LGAs. Table 36: Hand washing practices (multi-response) After Cleaning After Visiting Before Before Before Child’s Toilet Cooking Feeding Child Eating LGA Bottom Jere (n=532)98.5% (n=406)75.2% (n=279)51.7% (n=246)45.6% (n=461)85.4%
Konduga (n=479)94.7% (n=291)57.5% (n=236)46.6% (n=207)40.9% (n=324)64.0%
Mafa (n=341)84.8% (n=189)47.0% (n=207)51.5% (n=192)47.8% (n=259)64.4%
MMC (n=492)91.6% (n=330)61.5% (n=265)49.3% (n=285)53.1% (n=470)87.5%
3.9.4(b): Hand washing at four(4) critical times The hand washing at least at four critical times as indicated in (table 37) across the surveyed LGAs was below 50% minimum SPHERE thresholds. Table 37: Hand washing at four (4) critical times Total LGA Less than 4 Times 4 or above times
n (289) (251) (540) Jere % 53.5% 46.5% 100% n (329) (177) (506) Konduga % 65.0% 35.0% 100% n (237) (165) (402) Mafa % 59.0% 41.0% 100% n (285) (252) (537) MMC % 53.1% 46.9% 100%
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3.9.4(b): Sanitation coverage The main avenue household’s safe disposal of human excreta was pit latrine representing 76.5%, 60.3%, 77.4% and 69.8% in Jere, Konduga, Mafa and MMC respectively. Other improved sources include; VIP latrine, flush toilet and shared pit latrine as indicated in (table 38). Unimproved sources such as bush/open was less than 5% reported in Jere, Konduga and MMC LGAs. Sanitation coverage was above 50% SPHERE minimum thresholds across the surveyed LGAs.
Table 38: Sanitation coverage LGA Shared Total Pit Bush/Open VIP Flush Pit Latrine Field Latrine Toilet Latrine Jere n (413) (5) (4) (43) (75) (540) % 76.5% 0.9% 0.7% 8.0% 13.9% 100% Konduga n (305) (9) (30) (57) (105) (506) % 60.3% 1.8% 5.9% 11.3% 20.8% 100% Mafa n (311) (0) (73) (16) (2) (402) % 77.4% 0.0% 18.2% 4.0% 0.5% 100% n (375) (2) (53) (107) (0) (537) MMC % 69.8% 0.4% 9.9% 19.9% 0.0% 100%
3.10 Mortality 3.10(a) Crude Mortality Rate (CMR) and Under-five Mortality Rate (U5MR) The birth rate in Jere, Konduga, Mafa and MMC was at 0.85, 0.98, 1.19 and 0.74 respectively as indicated in table 39. Those who left (out-migration rate) was at 0.98, 1.64, 0.97 and 0.68 in Jere, Konduga, Mafa and MMC LGA respectively. The CDR and U5DR across the surveyed LGAs was below emergency thresholds of 1 death/10,000 persons per day and 2/deaths per 10,000 persons per day. The DEFF for U5DR and CDR in respective LGAs illustrates distribution of deaths across the sampled clusters in the respective LGAs was homogenous. The main cause of reported death among under- fives and general population was illnesses representing 84.6%, 66.7%, 50% and 94.1% in Jere, Konduga, Mafa and MMC LGAs respectively. The reported death occurred mainly at current location representing 84.6%, 66.7%, 87.5% and 100% in Jere, Konduga, Mafa and MMC respectively. Table 39: CDR and U5DR in each LGA LGA CDR U5DR Birth In-migration Out- (deaths/10,000p DEFF (deaths/10,000p DEFF rate Rate migration ersons/day) ersons/day) (joined) Rate (left)
Jere 0.44 (0.25-0.78) 1.04 0.61 (0.23-1.61) 1.00 0.85 0.64 0.98 Konduga 0.37(0.20-0.67) 1.00 0.61(0.23-1.64) 1.00 0.98 0.97 1.64 Mafa 0.35(0.16-0.78) 1.00 0.53(0.12-2.33) 1.94 1.19 0.48 0.97 MMC 0.57(0.31-1.05) 1.54 0.84(0.0-1.79) 1.00 0.74 0.68 0.68
3.11 Limitations of the survey Deteriorating security situation in Mafa at the time of data collection hindered ability of survey teams to assess 13 clusters located in central Mafa. In the same note, seven (7) clusters located in Central Konduga (past Konduga town) were affected by deteriorating security situation and teams had to use reserve clusters where only two of the reserve clusters were assessed while the rest were not. The aspect of travelling from field office posed a security challenge to survey teams with too many road blocks along the way and AOG reports of attacks in surrounding communities, road users and occurrence of the same were eminent based on SCI security reports.
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Majority of caregivers within the surveyed LGAs had no verifiable documents to confirm age of their children and other key child health and nutrition records such as measles, vitamin A and deworming. And thus, verification of the above was through recall. For age, the team relied on events calendar. Concurrent activities such as food distribution in certain locations hindered completion of assignment and thus the survey teams had to revisit the clusters before the completion of the assessment. Delayed departure time at SCI office attributed to logistical and security constraints thus delaying survey teams from arriving to the clusters early. A cluster located in 202 quarters in Jere was also not accessed since the overall authority of the estate did not permit survey team despite mobilization team informing the relevant stakeholders about the exercise and revisiting the clusters once more. There were a lot of challenges especially conducting SMART survey in the context of COVID-19 pandemic. Survey team’s compliance to COVID-19 precaution measures was mandatory and continuous monitoring of teams was tedious though paramount to the success of the survey.
4.0 Discussions and Conclusions The survey was implemented at LGA level and since there is no previous survey conducted at LGA level for comparability the survey findings will be applied as a baseline. The survey was implemented during the harmattan season in the month of February 2021. Acute malnutrition Malnutrition among children aged (6-59 months) has two immediate causal links that is disease and inadequate dietary intake. The GAM prevalence based on WHZ-scores across the surveyed LGAs was interpreted at poor (medium) levels based on new WHO classification of acute malnutrition thresholds (>5.0-9.9). The prevalence of Global Acute Malnutrition (GAM) by WHZ-scores and/or oedema was at 7.3%, 6.4%, 7.1% and 6.9% in Jere, Konduga, Mafa and MMC LGAs. The assessment was conducted during the harmattan season and thus as we approach hot and dry season there is likelihood of worsening of the situation if response mitigations are not activated on timely manner. GAM prevalence based on WHZ revealed both boys and girls were equally acute malnourished. Severe Acute malnutrition (SAM) based on WHZ-scores was higher in younger children aged (6-29) months as compared to older group aged (30-59) months age group. However Moderate Acute Malnutrition (MAM) was evenly distributed among other age groups. The GAM prevalence by MUAC confirmed presence of both moderate and severe forms of acute malnutrition in the surveyed LGA’s. Lastly, GAM prevalence is more pronounced when both WHZ-scores, MUAC, both and/or bilateral oedema are applied. The concordance of GAM prevalence by both WHZ<-2SD, MUAC <125mm and/0r bilateral oedema was 10.2%, 8.1%, 8.0% and 8.4% in Jere, Konduga, Mafa and MMC respectively. The concordance of SAM prevalence was 1.9%, 1.2%, 1.7% and 1.6% in Jere, Konduga, Mafa and MMC respectively. It will be ideal in the case of programming to apply combined GAM in estimation of caseloads and possibly use both indicators (WHZ and MUAC) in management of acute malnutrition so that no case is missed out. Chronic malnutrition and underweight The findings revealed that one (1) in every three (3) children in Jere, Konduga, Mafa and MMC was stunted. The prevalence stunting in the surveyed LGAs was interpreted as high based on new UNICEF/WHO classification of malnutrition. The prevalence of underweight across the surveyed LGA was interpreted as high except for Mafa. Chronic malnutrition is a resultant effect of prolonged deprivation of vital nutrients in the body of the leading to children too short for their age and poor cognitive and growth development. Morbidity and health seeking patterns According to UNICEF conceptual framework, disease has an immediate link to child undernutrition and death. In Jere, Konduga, Mafa and MMC eligible children confirmed as ill based on two week recall period represented 17.9%, 12.8%, 15.2% and 27.2% respectively. The main illnesses reported among
38 | P a g e children aged (6-59 months) were fever with chills like malaria, diarrhoea and ARI/cough. Further analysis through revealed 6.3%, 8.2% and 7.4% of acute malnourished cases in Jere, Konduga and MMC LGA were ill two weeks prior to survey data collection. There was no statistical significance between fever and acute malnutrition with p-value of 0.837, 0.766 and 0.826 in Jere, Konduga and MMC LGA respectively. There was statistical significance difference between children reported with acute malnutrition and fever in Mafa LGA with (p-value of 0.023). There was no statistical significance difference between children reported with acute malnourished and diarrhoea representing 16.7% and 15.3% in Mafa and Konduga LGA with (p-value of 0.203 and 0.223) respectively. There was statistical significant difference between acute malnourished children (6-59 months) reported with diarrhoea in MMC representing 17.5% with (p-value of 0.015). In Jere, Konduga, Mafa and MMC LGAs, 11.1%, 4.0%, 9.1% and 5.8% of acute malnourished children were reported ill from ARI/cough though not statistically significant (p-values of 0.421, 0.475, 0.536 and 0.833 respectively). Findings also revealed some caregivers in Mafa, MMC, Konduga and Jere LGAs do not seek any treatment for their ill children while other seek treatment from inappropriate providers such as traditional healers and relatives. The caregivers who seek treatment from public hospital represented 14.3%, 19.7%, 41.3% and 14.8% in Jere, Konduga, Mafa and MMC respectively. The caregivers who sought treatment in NGO recommended facilities represented 25.0%, 25.0%, 7.5% and 27.5% in Jere, Konduga, Mafa and MMC respectively. This implies less than 50% of caregivers sought treatment for their ill children at appropriate facilities such as public or NGO- based health facilites. Measles vaccination, vitamin A supplementation and deworming Generally measles coverage among children aged (9-59 months) across the surveyed LGA’s was below 90% national target with coverage of measles at 59.0%, 73.6%, 62.0% and 78.1% in Jere, Konduga, Mafa and MMC LGAs . Vitamin A supplementation among children (6-59 months) in the past six (6) months was below WHO targets of 80% with vitamin A coverage at 50.2%, 46.5%, 58,5% and 61.7% in Jere, Konduga, Mafa and MMC LGAs. . Deworming coverage among children aged (12-59 months) was below WHO target of 80% and above across the surveyed LGAs with exception of Mafa at 58.3%. Maternal nutritional status It’s important to note maternal nutritional status was assessed by MUAC among all women of reproductive age (15-49 years) as well as PLWin the sampled households. PLW have a higher risk of malnutrition than others due to additional nutrients required for growing foetus and nutrients and/or breast milk production. The findings revealed presence of acute malnutrition based on MUAC among WRA and PLW as well. The prevalence of acute malnutrition among PLW categorised based on MUAC cut-off <190mm) in Jere Mafa and MMC was 0.8% 0.5% and 0.4% respectively. The prevalence of acute malnutrition by MUAC cut-off (<190mm) among WRA was 0.4%, 0.3%, 1.3% and 2.0% in Jere, Konduga, Mafa and LGA respectively.. Household Dietary Diversity Score and coping strategies The findings revealed one (1) in four (4) households in Konduga and Mafa were categorised within the low HDDS while in MMC and Jere the analysis revealed one (1) in five(5) households were categorised within the low HDDS. The common foods consumed across the surveyed LGAs in the past 24hours at household level include cereals, vegetables, oils& fats, condiments and pulses/legumes while the least foods consumed include; eggs, milk & milk products, meat, poultry & offal and fruits. In Jere and MMC more than 50% of the household were embracing coping strategies. The most common coping strategies embraced by household level across the surveyed LGAs was that of reducing meals eaten in a day while limiting portion sizes at meal times. IYCF practices Early initiation to breast milk was above 80% across all LGAs. The EBF rate was above 50% in Jere and MMC except Konduga and Mafa which rates below 50%. The MDD was below 50% across all the LGA’s. Diets mostly consumed by children aged (6-23.9 months) include; grains, root tubers while the least consumed did include; meat & meat products, eggs and milk/milk products. In conclusion IYCF
39 | P a g e practices across the surveyed LGAs were sub-optimal and this detrimental to child ability to obtain vital nutrients at an early age. Water, sanitation and hygiene practices Improved sources of water available at household level with the main one being piped water, borehole, protected well among others. Despite this, treatment of water at household level remained below 50% minimum SPHERE thresholds for rural settings. The few households that treat their water before drinking use chemical tabs provided by aid agencies in Jere and MMC. However in Konduga and Mafa the few that treat their water before drinking filtrate and boil their water. Instances of hand washing at critical times is a key measure to prevent cross contamination of pathogens through fecal-oral means. The findings revealed that caregivers reportedly observe hand washing after visiting toilet and before eating however, other instances such as cleaning baby bottom, feeding the baby amongst others were marginally practiced. The sanitation coverage across the LGAs was above 50% SPHERE minimum standards. 4Mortality rates The CDR and U5DR across the surveyed LGAs was below emergency thresholds of 1/10,000/day and 2/10,000/day. Distribution of deaths across the surveyed clusters revealed homogeneity in distribution based on design effect. 5.0 Recommendations Sector Recommendations Stakeholder Timeline GAM prevalence Continuum of nutrition activities such as All stakeholders Ongoing interpreted at community management and treatment of (BSPHCDA and poor (medium) acute malnutrition in fixed health facilities partners) in Jere, levels (>5.0-9.9). and mobile sites across the surveyed LGAs. Konduga, Mafa Continuous monitoring of the nutrition and MMC. situation of under-fives. Scale up of SAM and MAM treatment SCI and other services across all SCI nutrition partners implementation LGAs in line with the 2021 humanitarian response.
GAM prevalence The nutrition sector should recommend and Nutrition cluster March 2021 is more adopt the use of combined GAM and SAM in Partners onwards pronounced when estimating the burden of acute malnutrition both WHZ- in Jere, Konduga, Mafa and MMC. Active case scores, MUAC, finding of acute malnourished children by both and/or using both WHZ and MUAC. This will bridge bilateral oedema gap of not missing any acute malnourished are applied. child that might require lifesaving interventions. Vitamin A Vitamin A supplement distribution ongoing All stakeholders Ongoing supplementation, in 19 health facilities and 8 mobile (BSPHCDA and deworming and outreaches and 10,173 children have partners) in Jere, measles covered across the surveyed LGAs. Konduga, Mafa vaccination below Continuum mop-up campaigns on measles and MMC. national targets. vaccination integrated with vitamin A supplementation and deworming. Caregivers March 2021 should be encouraged to always use and onwards carry along maternal and child clinic card while visiting health centre to ease in updating and monitoring of the child progress since the last visit.
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High stunting Need for further studies that might offer All stakeholders Medium- levels (>30%) possible causal pathways to high stunting (BSPHCDA and term to levels in surveyed LGAs. Once this is done, partners) in Jere, long –term. aligning pragmatic nutrition sensitive and Konduga, Mafa specific interventions might be key in long- and MMC. term to medium-term management of stunting and underweight among children aged (6-59 months) within the surveyed LGAs. (<50%) of Ongoing solutions to improve health seeking BSPHCDA, SCI Ongoing caregivers seek practices, optimal infant and young child and other treatment for feeding practices for better health and partners their ill children at nutrition outcomes of children. Ongoing bi- public or NGO weekly Radio programmes (jingles and health facilities phone- in) in sensitization campaigns as well as engagement of elders and religious leaders (gate keepers) in mobilization and sensitization efforts. Existence of Expand nutrition specific interventions to All stakeholders Short-to- maternal target pregnant and lactating women in (BSPHCDA and medium malnutrition effort to minimize maternal malnutrition partners) in Jere, term among PLW. High across all locations of surveyed LGAs. Konduga, Mafa percentage of and MMC. PLW at risk of acute malnutrition. The findings Continuum food security programmes for SCI and other Ongoing revealed one (1) vulnerable households such as food basket partners in four (4) distribution, food for vouchers and cash households in transfer since they offer improvement in Konduga and Mafa dietary diversity and access to nutritious categorised food at household level. within the low Provision of voucher card, activation and HDDS while in distribution to newly registered beneficiaries SCI and other March 2021 MMC and Jere (1) in vulnerable households. partners in five(5) Monitoring of food accessibility through households market/food price and diversity in availability categorised of local foods at market in Konduga and within the low Mafa. Completed in Jere and MMC. HDDS. Households still embracing more than one coping strategies. Sub-optimal IYCF Formation of new support groups and SCI and other Ongoing practices continuum of support to existing strategies partners that promote behaviour change communication with regards to IYCF practices. Proposal to conduct an investigation f on possible reasons for poor/sub-optimal IYCF practices.
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Poor water Continuum provision of safe/improved All stakeholders Short-to- treatment at source of water at household level while at (BSPHCDA and medium household level. the same time offer households with water partners) in Jere, term treatment kits such as chemical tabs for Konduga, Mafa them to treat their water for drinking. and MMC. Poor hygienic Foster messages that are familiar to practices such as caregivers as composite for increasing adherence to all awareness on hand washing practices four critical hand especially before feeding the child, changing washing times. diaper or cleaning child bottom.
References IPC forum, (2020). Cadre Harmonisé Result for Identification of Risk Areas and Vulnerable Populations in Sixteen (16)Northern States and the Federal Capital Territory (FCT) of Nigeria available https://fscluster.org/sites/default/files/documents/march_2020_fiche-nigeria_results.pdf Food and Agricultural Organization. (2010). Guidelines for measuring household and individual dietary diversity. Rome: FAO. Global report on Food Crisis, 2018 http://www.fsincop.net/fileadmin/user_upload/fsin/docs/global_report/2018/GRFC_2018_Full_report _EN_Low_resolution.pdf
Leidman, E., Couture, A., Hulland, E. & Bilukha, O. (2019). Concordance between estimates of acute malnutrition measured by weight-forheight and by mid-upper arm after age adjustment: population-representative surveys from humanitarian settings. BMC Nutrition, 5-39. National Bureau of Statistics (NBS), Nigeria Nutrition in Emergency Working Group (NiEWG), Federal Ministry of Health (October 2020), Preliminary report of Nutrition and Food Security Surveillance-North East Nigeria Emergency Survey. NBS, FMOH & NiEWG, (November 2019) Round 8 Nutrition & Food Security Surveillance report. IPC forum, (2021) NIGERIA Food Security Outlook (June 2020 – 2021) available https://reliefweb.int/report/nigeria/nigeria-food-security-outlook-update-june-2020-january-2021
SMART forum (October 2020) interim guidance on conducting household level data collection in humanitarian situations during COVID-19 Pandemic. World Health Organization. (2008). Maternal and Child Undernutrition: global and regional exposures and health consequences. Maternal and child undernutrition series 1, 5-22. World Health Organization (2010) Indicators for assessing Infant and Young Child Feeding (IYCF) practices, part III Country profiles.
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Annex Annex 1: Survey timeline 12TH Dec 26TH - 31ST Jan- 13TH - 18TH & 28TH Feb 2020- 24TH 30TH Jan 11TH 17TH Feb 24th Feb 2021 Jan 2021 2021 Feb 2021 2021 2021
Planning of the survey Training, standardization & field test Data collection
Analysis/data collation
Preliminary findings/
Dissemination/validation Draft report
Annex 2: Mean z-scores, design effect and excluded subjects Annex 2(a) Mean z-scores, design effect and excluded subjects for Jere LGA Indicator n Mean z- Design z-scores z-scores scores ± Effect (z- not out of SD score < -2) available* range Weight-for- 618 -0.43±1.01 1.32 0 8 Height Weight-for-Age 624 -1.19±0.99 1.00 0 2 Height-for-Age 610 -1.59±1.22 1.30 0 16 * contains for WHZ and WAZ the children with Oedema.
Annex 2(b) Mean z-scores, design effect and excluded subjects for Konduga LGA Indicator n Mean z- Design z-scores z-scores scores ± Effect (z- not out of SD score < -2) available* range Weight-for- 591 -0.46±0.99 1.36 0 5 Height Weight-for-Age 594 -1.16±1.02 1.97 0 2 Height-for-Age 568 -1.53±1.34 1.51 0 28 * contains for WHZ and WAZ the children with Oedema. Annex 2(c) Mean z-scores, design effect and excluded subjects for Mafa LGA Indicator n Mean z- Design z-scores z-scores scores ± Effect (z- not out of SD score < -2) available* range Weight-for- 524 -0.51±1.05 1.32 0 4 Height Weight-for-Age 522 -1.10±1.04 1.51 0 6 Height-for-Age 502 -1.41±1.25 1.22 0 26 * contains for WHZ and WAZ the children with Oedema.
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Annex 2(a) Mean z-scores, design effect and excluded subjects for MMC LGA Indicator n Mean z- Design z-scores z-scores scores ± Effect (z- not out of SD score < -2) available* range Weight-for- 666 -0.53±0.93 1.00 0 3 Height Weight-for-Age 666 -1.20±0.95 1.10 0 3 Height-for-Age 656 -1.52±1.16 1.99 0 13 * contains for WHZ and WAZ the children with Oedema
Annex 3: Map of Borno State illustrating surveyed LGAs (blue outline)
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Annex 4: List of survey teams per LGA
SURVEY TEAM ROLE ORGANIZATION LGA Kevin Mutegi Lead SMART survey consultant, Consultant All supervisor, training facilitator & survey coordinator Joseph Njuguna SMART survey consultant, facilitator & Consultant All supervisor Matthew Gabriel Humanitarian MEAL manager & overall SCI All support Olive Muthamia Nutrition program manager SCI All Bob-Uchenna MEAL coordinator& administrative All support Abdulkarim Ahmed Supervisor & administrative support SCI All Abba Laminu Community mobilization focal person SCI All Habiba Bukar Kwaya Supervisor BSPHCDA Jere Maimuna J. Garba Supervisor BSPHCDA Konduga Buji Mustapha Lawam Supervisor BSPHCDA Mafa Fanna Jibril Ngala Supervisor BSPHCDA MMC Divine Agbor Team leader SCI Mafa Hauwa Ali Zarma Team leader SCI Jere Umar Idris Team leader SCI Konduga Anna Yakubu Mshelia Team leader SCI MMC Jael B. Iliya Team leader EYN Jere Freedom Moses Team leader EYN Jere Luka Yasika Team leader EYN Jere Joshua Saidu Team leader EYN Jere Ishaya Musa N. Team leader EYN Konduga Musa Ishaku Buba Team leader EYN Konduga Falmata Baba Shehu Team leader EYN Konduga Fatuma Musa Bwala Team leader EYN Konduga Maryann Amos Team leader EYN Mafa Abdullahi Muktar Team leader EYN Mafa Ayuba Weina Team leader EYN Mafa Suleiman Bata Team leader EYN Mafa Paul Kwaja Team leader EYN Mafa Jemimah John Dawha Team leader EYN Mafa Mariam Abba Team leader EYN Mafa Shettima Waziri Enumerator - Jere Bullus Naomi Enumerator - Jere Yakubu Sandra Enumerator - Jere Mohammed Kassim Enumerator - Jere Zara Hyelahenda Enumerator - Jere Shettima Abubakar Enumerator - Jere Victoria vosea Enumerator - Jere Fariku Miklawda Enumerator - Jere Thawus Nungmabal Enumerator - Jere Nawada Mercy Enumerator - Jere Mohammed Salisu Enumerator - Konduga Idakwo Harrison Enumerator - Konduga James Sarah Enumerator - Konduga
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Bappa Yusuf Enumerator - Konduga Sadiq Lukman Enumerator - Konduga Abdullahi Fati Enumerator - Konduga Nuriddin Mohamed Enumerator - Konduga Aisha Bullama Talba Enumerator - Konduga Mohammad Ahmad Enumerator - Konduga Bukar Gadi Balu Enumerator - Konduga Samson Hyulafrya Enumerator - MMC Burabe Aishatu Enumerator - MMC Gidado Tarimin Enumerator - MMC Umar Abbas Enumerator - MMC Aliyu Mustapha Enumerator - MMC Ijoma John Andrew Enumerator - MMC Asiya precious Enumerator - MMC Evansely Basil Enumerator - MMC Tijjani Mustapha Enumerator - Mafa Zuwaira Ibrahim Enumerator - Mafa Gumba Abubakar Enumerator - Mafa Pogu Jamila Enumerator - Mafa Nathan David Enumerator - Mafa Musa Sandra Enumerator - Mafa Ibrahim Muhamed Enumerator - Mafa Ijoma John Andrew Enumerator - Mafa Yerima Ali Enumerator - Mafa Nwokike Juliet Enumerator - Mafa Community guides Sampled clusters in each LGAs
Annex 5: Standardization test results
Standardization Standardization test results_Zone A.xlstest results_Zone B.xls
Annex 6: SMART data collection tools
Cluster control Standardization Mortality SMART survey Anthropometric form.docx Test Form.docx Questionnaire.doc questionnaire.docx back-up questionnaire.docx
Annex 7: Anthropometric data plausibility reports
Jere Anthro DPS Konduga Anthro Mafa Anthro DPS MMC Anthro DPS report.doc DPS report.doc report.doc report.doc
Annex 8: Event calendar
Events calendar Events calendar V2.docx V1.docx
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