Health, Nutrition and Food Security Assessment in Ibanda, Kabale, Kanungu, Nebbi and Pader Districts

Report

UNICEF – Uganda

January 2013

Dr Wamani Henry Makerere University School of Public Health P.O. Box 7272 Kampala Tel: 077665500 or 0755443300 Email: [email protected]; or [email protected]

Table of Contents

Abbreviations ...... iv List of tables ...... v List of figure ...... vii Acknowledgements ...... viii Summary of findings ...... ix Nutrition ...... ix Morbidity and immunization ...... ix Water and sanitation ...... x Socioeconomic status, hunger and food security ...... x Gender profiles ...... x Chapter 1 ...... 1 INTRODUCTION ...... 1 1.1 Objectives ...... 2 1.1.1 Broad objective ...... 2 1.1.2 Specific objectives for the assessment ...... 2 1.2 Conceptual framework for the causes of malnutrition and food insecurity ...... 3 Chapter 2 ...... 5 METHODOLOGY ...... 5 2.1 Target population ...... 5 2.2 Sample size and sampling procedure ...... 5 4.3 Variable measurements and data collection instruments ...... 6 4.4 Data collection ...... 8 4.5 Quality assurance procedures during data collection ...... 9 4.6 Data Management ...... 9 4.7 Data analysis and interpretation of findings ...... 9 4.7.1 Analysis of anthropometric data ...... 9 4.7.2 Analysis of morbidity and other health and sanitation data ...... 10 4.7.3 Analysis of food security data ...... 10 4.8 Ethical considerations ...... 11 Chapter 3 ...... 12 FINDINGS AND DISCUSSION ...... 12 3.1 Socio-demographic characteristics ...... 12 3.1.1 Age and sex distribution of the sampled children ...... 12 3.1.2 Caregiver characteristics ...... 12 3.1.3 Education status of mothers and/or caregivers ...... 13 3.1.4 Mother pregnancy and/or breastfeeding status ...... 14 3.2 Nutritional status of children and mothers ...... 15 3.2.1 Prevalence of wasting, stunting and underweight ...... 15 3.2.2 Prevalence of child malnutrition by sex ...... 16 3.2.3 Prevalence of malnutrition by age ...... 17 3.2.4 Distribution of malnutrition in the five SUN districts ...... 17 ii

3.2.6 Wasting assessed by Mid Upper Arm Circumference (MUAC) in children ...... 19 3.2.7 Wasting status of mothers assessed using MUAC and BMI ...... 19 3.4 Infant and young child feeding practices ...... 20 3.4.1 Breastfeeding practices and knowledge ...... 20 3.4.2 Complementary feeding practices ...... 21 3.3 Status of health, water and sanitation ...... 23 3.3.1 Morbidity due to common childhood illness among children under five ...... 23 3.3.2 Use of mosquito nets ...... 24 3.3.3 Immunization, vitamin A supplementation and de-worming coverage ...... 25 Anemia prevalence among children and mothers ...... 27 3.3.5 Water and sanitation ...... 28 3.5. Status of household socioeconomic status, hunger and food security ...... 31 3.5.1 Wealth profile of households ...... 31 3.5.2 Household hunger scores (HHS) ...... 32 3.5.3 Household food consumption scores (FCS-Low) ...... 33 3.6 Gender dynamics at household level ...... 39 3.6.1 Time allocation among husbands and wives on key household work and leisure ...... 39 3.6.2 Ownership and control profiles for selected items between husbands and wives ...... 40 Chapter 4 ...... 42 CONCLUSIONS AND RECOMMENDATIONS ...... 42 Appendix 1: Supervisors ...... 44 Appendix 2: Results based on NCHS reference 1977 ...... 45 Appendix 3: Plausibility checks for data using ENA software ...... 62 Appendix 5: Referral form ...... 86 Appendix 6: Questionnaire ...... 87

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Abbreviations

ARI Acute respiratory Infections BMI Body Mass Index CCP Community Connector Project DHO District Health Officer ENA Emergency Nutrition Assessment GAM Global Acute Malnutrition IDDS Individual Dietary Diversity Scores Mak-SPH School of Public Health, Makerere University College of Health Sciences MAM Moderate Acute Malnutrition MOH Ministry of Health MUAC Mid Upper Arm Circumference NCHS National Centre for Health Statisitics SAM Severe Acute Malnutrition SMART Standardized Monitoring and Assessment of Relief and Transition SPSS Statistical Package for Social Sciences SUN Scaling Up Nutrition UNICEF United Nations Children’s Fund WHO World Health Organization UDHS Uganda Demographic and Health Survey

UGX Uganda Shillings

List of tables

Table 1.1: GAM, SAM, Stunting and Underweight prevalence according to district

Table 3.1: Number of children assessed for anthropometry by sex, age group, and by district

Table 3.2: Respondents category and their mean age by district

Table 3.3: Parity of the biological mothers

Table 3.4: Mothers education status by district

Table 3.5: Pregnancy and breastfeeding status of the biological mothers of sampled children

Table 3.6: Prevalence of GAM, SAM, stunting and underweight

Table 3.7: A diagrammatic view of malnutrition expressed according to the WHO classification of prevalence of malnutrition, by district

Table 3.8: Sex differences in GAM and stunting by district

Table 3.9: Wasting status of children 6-59 months assessed with MUAC by district

Table 3.10: Wasting status of mothers and caregivers 15-49 years assessed using MUAC

Table 3.11: Malnutrition status of mothers/caregivers 15-49 years of age

Table 3.12: Breastfeeding status among children 6-23 month by district

Table 3.13: Meal frequency in children of different age categories

Table 3.14: Prevalence of common illnesses amongst children 6-59 months old by district

Table 3.15: Household ownership of an insecticide treated net by district

Table 3.16: Mosquito bed net usage amongst children 6-59 months by district

Table 3.17: Measles immunization coverage among children 9-23 months by district

Table 3.18: Vitamin A coverage among children 6-59 months by district

Table 3.19: DPT3 coverage among children 9 – 23 months by district

Table 3.20: De-worming coverage among children 12-59 months by district

Table 3.21: Anemia prevalence among children 6-59 months

Table 3.22: Anemia prevalence among children 6-59 months

Table 3.23: Source of drinking water in households by district

Table 3.24: Treatment of drinking water by district

Table 3.25: Household ownership of latrine by district

Table 3.26: Household latrine ownership by type of facility and by district

Table 3.27: Household ownership of hand washing facilities at the toilet facility and by district

Table 3.28: Proportion of households with garbage pit, sun and kitchen racks Table 3.29: Distribution of households by socioeconomic status according to districts

Table 3.30: Household asset ownership according to district

Table 3.31: Households involvement in cultivation farming in the first and/or second agricultural season of 2012

Table 3.32: Household agricultural production by district

Table 3.33: Mean household expenditure (UGX) on food items in past 30 days

Table 3.34: Average time spent on households’ tasks by men and women according to districts

Table 3.35: Proportion of men and women who own and control key household items

List of figure

Figure 3.1: Prevalence of GAM, stunting and underweight by age categories

Figure 3.2: Distribution of Weight-for-Height Z-scores for both sexes

Figure 3.3: Distribution of height-for-age z-scores for both sexes

Figure 3.4: Distribution of weight-for-age z-scores for both sexes

Figure 3.5: Survival function of breastfeeding in children 6 – 23 months of age

Figure 3.6: Proportion of children aged 6-8 months who received complementary food in the 24 hours preceding assessment by district

Figure 3.7: Individual dietary diversity score for children 6-23 months

Figure 3.8: Distribution of households with moderate or severe hunger according to district

Figure 3.9: Food consumption status at household level by district

Figure 3.10: Diversity of food consumed in the seven days of the recall period per food consumption grouping in Ibanda district

Figure 3.11: Diversity of food consumed in the seven days of the recall period per food consumption grouping in Kabale

Figure 3.12: Diversity of food consumed in the seven days of the recall period per food consumption grouping in Kanungu district

Figure 3.13: Diversity of food consumed in the seven days of the recall period per food consumption grouping in Nebbi district

Figure 3.14: Diversity of food consumed in the seven days of the recall period per food consumption grouping in Pader district

Figure 3.15: Proportion of household confirming the different income sources

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Acknowledgements

The School of Public Health, Makerere University College of Health Sciences (Mak-SPH) team led by Dr Henry Wamani – the Principal Investigator - and assisted by Dr Arthur Bagonza acknowledges the enormous support from UNICEF, Ministry of Health and District Health Offices of Ibanda, Kabale, Kanungu, Nebbi and Pader for supporting the surveillance surveys in different ways. We sincerely thank the 15 Central Supervisors, 60 Enumerators from the five districts and 5 Data Entrants who supported the Surveillance surveys. Our special thanks go to Dr Lilia Turcan and Ms Nelly Birungi of UNICEF, Dr Robert Mwadime the Chief of Party of the Community connector Project, and Dr Neil Fisher for the invaluable input to the assessment tools.

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Summary of findings

Nutrition • The greatest challenge of malnutrition in children 6-59 months in the assessed SUN districts was that of stunting. Stunting prevalence was critical in Kabale and Kanungu, and serious in Ibanda and Nebbi districts

Table 1.1: GAM, SAM, Stunting and Underweight prevalence according to district

District GAM SAM Stunting Underweight % (95%CI) % (95%CI) % (95%CI) % (95%CI) Ibanda (N=292) 1.7 (0.7 - 4.0) 0.7 (0.2 - 2.5) 34.9 (29.7 - 40.6) 8.3 (5.6 - 12.0) Kabale (N=295) 3.6 (2.0 - 6.3) 1.9 (0.9 - 4.2) 42.0 (36.6 - 47.6) 9.2 (6.4 - 13.0) Kanungu (N=329) 3.1 (1.7 - 5.6) 0.9 (0.3 - 2.7) 43.0 (37.7 - 48.4) 13.6 (10.3 - 17.7) Nebbi (N=357) 4.5 (2.8 - 7.2) 1.1 (0.4 - 2.9) 33.6 (28.9 - 38.7) 13.4 (10.3 - 17.4) Pader (N=342) 5.3 (3.4 - 8.2) 1.8 (0.8 - 3.8) 21.8 (17.7 - 26.5) 10.9 (8.0 - 14.6) Combined (N=1626) 3.5 (2.7 - 4.4) 1.1 (0.6 - 1.6) 35.0 (32.9 - 37.2) 11.6 (10.0 - 13.2)

• Exclusive breastfeeding among children less than six months was 76.9% in Ibanda, 61.4% in Kabale, 80.0% in Kanungu, 80.6% in Nebbi and 60.5% in Pader. • Initiation of complementary feeding was late in some districts like Kabale and Nebbi where 40.0% and 26.7%, respectively, of children 6-8 months were exclusively breastfed the previous day of the assessment when they would have been provided with complementary food • Over two third of the children 6-23 months in all districts had low or moderate Individual Dietary Diversity Score (IDDS) with the worst district being Kabale where 64.3% of the children had low IDDS • Up to 75.8% of the children had any form of anemia (9.7% severe, 49.6% moderate and 16.5% mild anemia). Anemia was most prevalent in children of Nebbi district (91.9%) where 17.4% had hemoglobin of less than 7 g/dl. Anemia in children was least prevalent in Ibanda (62.1%) • Up to 50.0% of mothers had anemia with 75.4% of mothers in Nebbi anemic and only 31.7% and 42.1% of mothers in Ibanda and Pader, respectively, having any form of anemia. The high anemia prevalence in Nebbi deserves to be investigated further to establish the causes. • Using BMI, 5.2% of the mothers were wasted, 1.2% severely. Kanungu (29.8%) and Kabale 27.3%) had the highest proportion of overweight and/or obesed mothers (BMI > 25) while Nebbi had the least overweight and/or obese mothers (6.8%)

Morbidity and immunization • The two-week prevalence of ARI (63.1%), fever (41.0%) and diarrhea (29.7%) were high and with almost similar prevalence in all districts except Ibanda which had a lower prevalence of diarrhea (20.6%). The prevalence of diarrhea was higher than what has been recently reported in other studies in Uganda like UDHS 2011 (23%). • Up to 72.6% of the households possessed mosquito bed nets. Ibanda district (84.0%) reported the highest availability of bed nets among households while Kabale district (63.9%) reported the least. • Up to 80.1% of the children were reported to have slept under a bed net the night of the assessment. The highest proportion was reported in Nebbi district (96.7%) while the lowest proportion was reported in Kabale district (74.1%). • All districts had measles immunization coverage above 80% among children 9-23 months when mothers’ reports (those without cards) were considered. Otherwise 62.6% of the children had received measles vaccination identified by marked health card. Over 10% of the eligible

children were found with cards but not having been immunized Kanungu, Nebbi and Pader districts. • Vitamin A supplementation had been received by 87.2% of the children 6-59 months, verified either by a health card or caretaker’s recall. All the assessed districts had coverage of 80% and above.

Water and sanitation • Ibanda (53.4%), Kabale (70.1%), Kanungu (74.6%), Nebbi (59.7%) and Pader (71.7%) had safe sources for drinking water. In addition, over 50% of the households reported to be treating water largely by boiling and chlorination although Nebbi (20.1%) and Pader (26.5%) had a fewer proportion of households treating water. • A total of 39 (2.7%) households tested positive for presence of E.coli in drinking water. Most of the households with contaminated water were in Kanungu16 (5.9%), Ibanda 16 (5.2%) and Kabale 6 (2.3%). Nebbi had only one household with contaminated drinking water while Pader had none. There is still more effort to sensitize households about domestic water contamination and water treatment. • Up to 12.5% of the households in the assessed SUN districts lacked latrines. A few of the households (7.7%) shared toilets with their neighbours. The district with the lowest latrine coverage was Pader (37.1%), while Kanungu (98.6%) had most latrine coverage. • The majority of the households (82.9%) had no hand washing facilities after toilet. Only 8.0% had water without soap. There were no variations between districts (results breakdown not presented in main report). Likewise, only 37.6% of the households had had garbage pits (details in body of report). These are basic domestic hygiene practices that are still relevant in rural settings and should be promoted.

Socioeconomic status, hunger and food security • Using a socioeconomic index derived from valuable household assets and ownership of shoes and clothes, Pader (3.1%) and Nebbi (6.3%) had the least proportion of households in the better off quintile while Ibanda (34.5%) had the highest proportion of socioeconomically better off households. Nebbi (44.1%) and Pader (36.7%) had the highest concentration of households in the poorest quintile. • Using the Household Hunger Scores (HHS), 13.9% of all the households assessed were experiencing moderate or severe hunger. The majority of the households with moderate to severe hunger were in Nebbi (58.7%), Kabale (18.4%) and Pader (15.5%). • Using Food Consumption Scores (FCS Low), the proportion of highly food insecure and borderline households was most prevalent in Nebbi district (33.9%). Most of the households in Ibanda district (88.0%), Kanungu (85.6%), Pader (83.3%) and Kabale (70.9%) were food secure.

Gender profiles • In all districts there were statistically significant differences in how time was used by men and women concerning different household tasks and leisure. On the day preceding the assessment, women spent significantly more time on agricultural work, household work and childcare while men significantly spent more time on non-agricultural work and leisure • Ownership of household items varied between men and women but the majority of items are jointly owned. Generally men tended to own and control cash generating items, radio and telephones. Surprisingly a larger number of wives (30.5%) reported to own savings than their husbands (16.5%) although much of the savings were jointly owned. Descriptively, a higher proportion of men in Nebbi tended to control items/assets than their wives.

Chapter 1

INTRODUCTION

Ibanda, Kabale, Kanungu, Nebbi and Pader are some of the Scale Up Nutrition (SUN) districts where UNICEF will be partnering with USAID through the Community Connector Project (CCP) to implement community-based nutrition interventions. The SUN Movement reflects the collective efforts of Governments, organizations and individuals – working together towards the vision of ending hunger and malnutrition in all its forms so that within our lifetimes, every mother, child and family can realize their full potential and right to adequate nutritious food. The Movement’s mission is to ensure high quality and tailored support for efforts to scale up nutrition within participating countries – in line with both national and global targets. Within the Movement, different stakeholders strive to harmonize existing strategies and programs in ways that reflect best practice, increase investments in the most successful outcomes and ensure accountability to those they seek to serve as well as to each other.

To this end, they accelerate action for a durable end to malnutrition with a focus on the 1,000 days between a mother’s pregnancy and her child’s second birthday. Under the leadership of National Government Focal Points, all stakeholders commit to plan together, align their actions and resources and take joint responsibility for scaling up both direct nutritional interventions nutrition-sensitive strategies.

Within the framework of the Uganda Nutrition Action Plan (UNAP), a number of partners in Uganda have joined hands to Scale Up Nutrition interventions in various districts. To ensure effective monitoring of the interventions and eventual impact, monitoring of programs and surveillance of critical indicators at community level forms an important aspect of service delivery. The purpose of the current assessment was therefore to generate data for use in early warning of likely crises, programmatic monitoring, and for policy development and/or improvement. Carrying out large-scale regular assessments is however costly. Hence in 2009 UNICEF initiated the development of small sample surveys simple enough to be adopted by districts for nutrition surveillance purposes. The small sample surveillance assessments have

been implemented regularly in Karamoja sub-region and findings from such surveys are comparable to those from large sample surveys carried in the same region.

Using experience from Karamoja sub-region, UNICEF is supporting the scale up of nutrition surveillance to five more districts of Kabale, Kanungu, Ibanda, Nebbi and Pader. The surveillance indicators address nutrition status of children 0-59 months, mothers/caregiver 15- 49 years, household food security, morbidity due to common childhood illness, access/coverage of health services, water and household sanitation, and gender. The School of Public Health, Makerere University College of Health Sciences carried out the current survey in November/December 2012. By the time the survey was carried out some of the partners such as the CCP had already been operational in the districts for over three months with a potential of affecting some of the outcome variables.

1.1 Objectives

1.1.1 Broad objective The broad objective of the assessment was to obtain data on indicators of health, nutrition, food security, gender and socioeconomic status to monitor and/or improve programming and policy interventions in five districts of Ibanda, Kabale, Kanungu, Nebbi and Pader.

1.1.2 Specific objectives for the assessment

Nutrition objectives • Assess the prevalence of malnutrition (wasting, stunting and underweight) among children aged 6-59 months; • Estimate the coverage of vitamin A supplementation and deworming among children 6-59 months of age; • Estimate the prevalence of malnutrition using BMI among women of reproductive age • Assess breastfeeding and complementary feeding knowledge among mothers/caregivers and the feeding practices among children 0-23 months of age; • Estimate the individual dietary diversity (IDDS) among children 6-23 months • Determine the prevalence of anemia among children 0-23 months and women 15-49 years

Health and sanitation objectives • Assess the prevalence of common diseases (diarrhoea, fever, and ARI) among children 6 – 59 months, two weeks prior to the assessment • Assess the coverage of routine immunizations coverage (DPT and measles) • Estimate the proportion of households with access to improved water sources and sanitation

Food security objectives • Assess the crop cultivation patterns at household level • Assess current household hunger and food security status • Estimate the proportion of households at short term risks of food insecurity; • Estimate livestock ownership of households • Assess the household socioeconomic status

Gender based objectives • Profile ownership and control of key household items/assets among wives and husbands • Estimate the time spent on household chores among wife and husband in the 24 hours preceding the survey

1.2 Conceptual framework for the causes of malnutrition and food insecurity The surveys was based on the conceptual framework of the causes of malnutrition adapted from the 1990 UNICEF model, which suggests that fundamental influences to nutrition and food security outcomes remain within the environment where people live (Figure 2).

Food and Nutrition Security Conceptual Framework

Nutrition Status/ Mortality

Individual level Individual Food Health Status/ Intake Disease

Context/ HH level Food Availability/ Access to Health Household Access Social and Care Livelihood Markets Care & Health to Food Environment Outcomes Environment Political, Economical, Institutional, Security, Social, Cultural, Gender HH Food Production, Income Environment Generating Activities, Exchange, Livelihood Strategies Loans, Savings, Transfers Agro-ecological Conditions/ Climate (Change) Community/ E X P O R S U E T O H S O C KD S A HN A Z A R D S E X P O R S U E T O H S O C KD S A HN A Z A R D S HH level Natural Physical Human Economic Social Capital/Assets Livelihood Assets

Figure 1.1: Conceptual framework to analyze food security and nutrition in society (adapted from UNICEF 1990)

Information was collected on factors at most of the framework levels with the exception of the total potential resources.

Chapter 2

METHODOLOGY

These were small sample surveys carried out for surveillance purposes. The surveys were population based and cross-sectional targeting five districts of Kabale, Kanungu, Ibanada, Nebbi and Pader. The districts are beneficiaries of the community-based nutrition programs being undertaken in partnership with district local governments, UNICEF with European Union (EU) funding, and USAID through the Community Connector Project.

2.1 Target population The targets were representative households in the five districts regardless of who occupies them. Children between the ages of 0 and 59 months and their mothers if they existed in the sampled households were assessed. Where children and/or mothers never existed in a household the head of household was interviewed to collect information only on food security. Age of children was confirmed by use of child health cards. Children with physical disabilities were assessed but findings on anthropometry were excluded.

2.2 Sample size and sampling procedure The target was to detect a minimum variation of 5% of Global Acute Malnutrition (GAM) with 85% precision. Empirically it was established that a minimum of 25 clusters was required for a survey to be representative and valid in sub-Saharan setups. We therefore aimed to sample a total of 300 representative households using a two-stage, 25x12 cluster randomization design. At the first stage a probability sample of 25 clusters was selected using an updated list of villages that constitute a district (with their corresponding populations). The updated lists were obtained from the District Population Offices. At the second stage households were systematically sampled. Systematic sampling was done by ensuring a random start and using a calculated sampling interval using a list of village households obtained from the village head. A total of 1500 households were therefore targeted for sampling in the five districts. However in special circumstances over-sampling was carried out in areas where the Community Connector

Project was being implemented. All children 0-59 months leaving in the sampled households were assessed.

4.3 Variable measurements and data collection instruments Data was collected on the following variables: age; sex; weight; height; bilateral pedal oedema; morbidity for common diseases and conditions; infant feeding practices; ownership of household assets, livestock and land; income sources and expenditures; food consumption diversity; hunger and food security; education status of mother and household head; water and sanitation; immunization/supplementation and deworming; ownership and control of key household items/assets between husbands and wives; and time allocation to household chores between husband and wives.

Age and sex: Exact age of the child was reported in months using information on child health cards. Where these did not exist, age (month and year of birth) was determined using a local calendar of events. An age chart (Annex…..) was used to read off age in months if date of birth (month and year) was known. Sex was assessed using mothers reports and/or observation as appropriate.

Weight Any child falling within the age bracket of 0 to 59 months found in the household sampled was weighed. The weight was recorded to the nearest 0.1kg accuracy on the conventional scales. Even those with oedema were weighed and the Emergency Nutrition Assessment (ENA) for SMART software was used for data analysis and accounted for such.

Height Children above the age of two years were measured standing upright whilst those below 2 years were measured lying down to nearest 0.1cm. Where age was difficult to determine, those measuring less than 85cm were generally measured lying down and those taller than 85cm measured standing upright. Note: Only data of children measuring between 65cm and 110cm were used for analysis where age was not known.

Bilateral oedema Oedema was assessed by exerting medium thumb pressure on the upper side of each foot for three seconds. Oedema was recorded as present if a skin depression remained on both feet after pressure was released.

BMI and MUAC Mothers/caregivers 15-49 years of age were assessed for weight and height to calculate their Body Mass Index (BMI). Children 6-59 months and mothers were also assessed for Mid-Upper Arm Circumference (MUAC) using tapes to nearing 0.1 cm.

Morbidity and care seeking Morbidity from common childhood illness like acute respiratory infections (ARI), fever and diarrhea were assessed over a two-week recall period. In addition, coverage of the essential primary care services such as immunization, vitamin supplementation and deworming among infants and young children, and environmental and domestic sanitation factors such as latrine and safe water coverage were assessed. WHO definitions for diseases and conditions were used.

Infant feeding practices Breastfeeding and complementary feeding practices were assessed for each child. Assessment covered exclusive breast-feeding rates (using 24-hour recall), quality and quantity of complementary feeding and active feeding practices. Individual dietary diversity scores (IDDS) were assessed to establish adequacy of complementary feeding among children 6-23 months.

Household hunger and food security: Standard and valid questions from Feed The Future (FTF) indicators and UNICEF/UNWFP were used to assess household hunger and food security. Data was collected on household agricultural food production for common crops such as maize, millet, sorghum, potato, cassava and banana. The types of food and the number of times they are eaten in the past 7 days, any foods bought by the household and the income sources will be assessed. In addition hunger/ starvation was assessed using standard questions1. Household socioeconomic status was assessed by collecting information on household assets (bicycle, radio, hoe/axe, mobile phone, motorcycle/car, shoes, clothes, television, etc); animals (cow, goat, sheep, chicken, and pig); and education status of mothers and/or household head.

1 FANTA. Household Food Insecurity Access Scale (HFIAS) for Measurement of Food Access: Indicator Guide. 2007

Water and sanitation Household source of water and rapid tests for E. coli in household drinking were assessed. Faecal, garbage and other domestic hygiene practices such as ownership of garbage pit, utensil racks were assessed as well.

Immunization/Supplementation and de-worming Vitamin A supplementation and de-worming in the last 6 months, and DPT3 and Measles vaccination coverage was ascertained from Child health cards and/or mothers recall.

Assessment of anemia status Blood samples were collected through a finger prick from children and mothers/caregivers to determine the hemoglobin level. Hemocue analyzer machines 301 were used and assessments were done by qualified/trained health workers. Anemia was assessed in only three districts due to insufficient numbers of hemocue analyzer machines.

Gender profiling Questions were asked on who between husband and wife owns, controls and makes major decisions on household key items/assets such as land, gardens, cash crops, animals, radio, telephone, bicycles, savings and incomes. Time allocation on daily household chores was also assessed. Questions on gender were skipped in case of households for singles.

4.4 Data collection Data was collected using a single questionnaire (Annex…), administered face-to-face to mothers and/or household heads in their home settings. The data collection tool was in English but a translated tool was used to administer the questionnaire. Data was collected simultaneously in all the five districts by trained research assistants. Field data collection lasted a total of 8 days in each district while training of research assistants last for 3 days. For successful data collection in Uganda, the use of local and civic leaders is imperative. In this regard, local officials were identified and used as guides to identify households for interviews and to support anthropometric measurements. Data was collected in November 2012.

4.5 Quality assurance procedures during data collection To ensure that good and accurate information was collected by research assistants, the following quality assurance measures were put in place: • Research assistants were required to edit research tools or data at the point of data collection. This enabled effective correction and verification of data collected; • The supervisors edited questionnaires and ensured that they are correct and complete while in the field; • A record of daily activities showing the number of tools completed, by whom and the location where they were undertaken was kept; and • Daily debriefing of the research team was ensured at the end of every day’s activities.

4.6 Data Management Data were entered in Epidata 3.1 software by clerks based at the School of Public Health. Entered data was copied, saved and exported to ENA software for generation of z-scores and eventual analysis of the nutrition data. Data was backed-up daily including saving it on distant servers through the email system. Other data were analysed in SPSS Version 21.

4.7 Data analysis and interpretation of findings Data were analyzed by the Principal Investigator assisted by the co-Investigators. Findings were interpreted based on national indicators and/or according to plan in some aspects especially for gender variables. District specific and pooled data were concurrently presented. As much as possible data were disaggregated by sex and age. Current findings were compared to previous surveys to establish any positive or negative changes.

4.7.1 Analysis of anthropometric data Anthropometric indices were presented based on the WHO standard. However, results with NCHS references have been provided in Annex … for comparison with previous surveys. Acute malnutrition or wasting was estimated from the weight for height (WFH) index values combined with the presence of oedema. WFH indices were expressed in Z-scores.

Global acute malnutrition (GAM) Was estimated using Weight-for-Height index and oedema. Children presenting with a weight for height index less than –2 z scores with/without oedema were considered to fall in this category.

Moderate Acute Malnutrition (MAM) Was estimated using Weight-for-Height index. Children presenting less than –2 z-scores but greater than –3 z-scores were regarded as moderately malnourished.

Severe Acute Malnutrition (SAM): Was estimated using Weight-for-Height index and oedema. Children presenting with a weight for height index less than –3 z-scores and/or presence of bilateral oedema were regarded as severely malnourished. Likewise, underweight (weight-for-age) and stunting (height-for-age) were analysed.

MUAC and BMI Were interpreted based on WHO criteria.

Anemia Was interpreted based on the WHO classification.

4.7.2 Analysis of morbidity and other health and sanitation data Prevalence of diseases and conditions occurring two weeks prior the survey, latrine and coverage of health indicators were reported using descriptive statistics.

4.7.3 Analysis of food security data Food security data was systematically analyzed. First, a household wealth index was generated from ownership of household property using principal components analysis. The wealth index was derived from the first principal component, which was then ranked and categorized into quintiles. Second, household food consumption scores were generated based on 8 food groups derived from the 16 food columns in the questionnaire using the UNWFP/UNICEF – weighted scores of certain food groups. These pre-assigned weights for starch, meat, pulses, sugar, oil and milk are 2, 4, 3, 0.5, 0.5 and 4, respectively, were used. Third, other facet of food security such as food sources, expenditures on food and coping mechanisms were accordingly analysed. Forth, household hunger scores were generated based on FTF guidelines.

4.8 Ethical considerations Permission to collect data was sought from local authorities with the DHO’s involvement. The purpose of the survey was clearly explained. Protocol was observed while entering any community. A written consent was sought from survey participant before any interview and confidentiality ensured.

Chapter 3

FINDINGS AND DISCUSSION

3.1 Socio-demographic characteristics

3.1.1 Age and sex distribution of the sampled children Although total of 1830 children were assessed including those below six months, only 1632 children 6 – 59 months were included in the analysis for anthropometry. That is, 292 for Ibanda, 308 for Kabale, 331 for Kanungu, 359 for Nebbi and 342 for Pader (Table 3.1). WHO flagged cases were excluded even though there were minimal flagged cases. Overall, there was an equal representation of male and female children in each district depicting effective sampling procedures.

Table 3.1: Number of children assessed for anthropometry by sex, age group, and by district

Sex ratio of sampled District children Distribution of sampled children by age group Total Boy:Girl Boys Girls ratio 6-17 18-28 29-39 40-50 5-59 Ibanda 152 140 71 67 71 42 41 292 1.09 Kabale 176 132 87 73 52 65 31 308 1.33 Kanungu 172 159 83 76 63 69 40 331 1.08 Nebbi 178 181 97 85 61 56 60 359 0.98 Pader 157 185 99 85 74 64 40 342 0.85 Combined 835 797 1.05 437 366 321 296 212 1632

3.1.2 Caregiver characteristics Overall, primary care giving for children assessed was by their biological mothers, 1096 (85.4%). Kanungu and Nebbi districts recorded the highest presence of biological mothers 262 (90.1%) and 223 (90.1%) respectively while Ibanda district reported the highest 60 (23.9%) of the other caregivers (Table 3.2). The mean (SD) age of the biological mothers was 30.5 (11.3) years.

Table 3.2: Respondents category and their mean age by district

Respondents category Respondents mean age Mothers Caregivers All respondents Biological Mothers District N(%) N(%) Years (SD) Years (SD) Ibanda (N=251) 191 (76.1) 60 (23.9) 36.1 (15.2) 35.0 (14.5) Kabale (N= 264) 237 (89.8) 27 (10.2) 29.7 (8.5) 28.3 (6.7) Kanungu (N=262) 236 (90.1) 26 (9.9) 30.9 (10.7) 29.5 (9.3) Nebbi (N=223) 201 (90.1) 22 (9.9) 31.1 (10.7) 30.2 (9.9) Pader (N= 284) 231 (81.3) 53 (18.7) 29.9 (15.8) 29.2 (12.8)

Combined (N=1284) 1096 (85.4) 188 (14.6) 31.7 (12.8) 30.5 (11.3)

At a mean age of 31 years, the biological mothers had on average given birth to four live children. Mothers in Kanungu district were having a lower average of 3.6 children compared to other districts (Table 3.3)

Table 3.3: Parity of the biological mothers

Mean number Standard District of live births Deviation Ibanda (N=170) 3.9 2.4 Kabale (N=200) 3.7 2.4 Kanungu (N=225) 3.6 2.1 Nebbi (N=239) 4.5 2.9 Pader (N=194) 4.5 3.4 Combined (N=1028) 4.0 2.7

3.1.3 Education status of mothers and/or caregivers More than a tenth of the mothers in the selected districts had no formal education (Table 3.4). Ibanda district had the highest proportion of the mothers without formal education (17.8%). Since the level of mother’s education correlates positively with nutrition status, it is important that focus on child education should even be strengthened further.

Table 3.4: Mothers education status by district

Mothers Education Status Zero Primary Ordinary Above ordinary

N (%) N (%) N (%) N (%) Ibanda 34 (17.8) 133 (69.6) 19 (9.9) 5 (2.6) Kabale 18 (7.8) 166 (72.2) 32 (13.9) 14 (6.1) Kanungu 18 (7.7) 163 (70.0) 38 (16.3) 14 (6.0) Nebbi 32 (16.0) 152 (76.0) 13 (6.5) 3 (1.5) Pader 26 (13.3) 160 (81.6) 9 (4.6) 1 (0.5) Combined 128 (12.2) 774 (73.7) 111 (10.6) 37 (3.5)

3.1.4 Mother pregnancy and/or breastfeeding status The majority, 655 (45.6%) of the mothers were found breastfeed, while 13 (0.9%) were pregnant and breastfeeding. A higher proportion (43.8%) of mothers than previously observed in studies done in Uganda were neither pregnant nor breastfeeding (Table 3.5). This situation still calls for a concerted effort to improve reproductive health services.

Table 3.5: Pregnancy and breastfeeding status of the biological mothers of sampled children

Breastfeeding Pregnant and Neither pregnant District Pregnant (Lactating) breastfeeding nor breastfeeding N (%) N (%) N (%) N (%) Ibanda (N= 314) 26 (8.3) 109 (34.7) 2 (0.6) 177 (56.4) Kabale (N=262) 20 (7.6) 157 (59.9) 1 (0.4) 82 (31.3) Kanungu (N=284) 21 (8.5) 125 (44.0) 0 (0) 135 (47.5) Nebbi (N=298) 26 (8.7) 132 (44.3) 2 (0.7) 138 (46.3) Pader (N=277) 27 (9.7) 132 (47.7) 8 (2.9) 96 (34.7) Combined (N=1435) 123 (8.6) 655 (45.6) 13 (0.9) 628 (43.8)

3.2 Nutritional status of children and mothers

3.2.1 Prevalence of wasting, stunting and underweight The prevalence of Global Acute Malnutrition (GAM) was 3.5% (95% CI 2.6 - 4.4) and the prevalence of Severe Acute Malnutrition (SAM) was 1.1% (95% CI 0.6 - 1.6) in pooled analyses. All results are based on weight-for-height Z-scores and/or oedema (Table 3.6). The prevalence of (GAM) was below 5% (acceptable level) in all the districts except Pader (5.3%) where it was poor. The point prevalence of GAM in the assessed in the five SUN districts could be considered better than the national average of about 5%. The point prevalence of stunting (35.0%) is close to the national average (34%) while that for underweight (11.7%) was better than the national average (14%). The south-west districts had stunting rates in comparable ranges and mostly in critical state.

Table 3.6: Prevalence of GAM, SAM, stunting and underweight according to district District GAM SAM Stunting Underweight % (95%CI) % (95%CI) % (95%CI) % (95%CI) Ibanda (N=292 ) 1.7 (0.7 - 4.0) 0.7 (0.2 - 2.5) 34.9 (29.7 - 40.6) 8.3 (5.6 - 12.0) Kabale (N=295) 3.6 (2.0 - 6.3) 1.9 (0.9 - 4.2) 42.0 (36.6 - 47.6) 9.2 (6.4 - 13.0) Kanungu (N=329) 3.1 (1.7 - 5.6) 0.9 (0.3 - 2.7) 43.0 (37.7 - 48.4) 13.6 (10.3 - 17.7) Nebbi (N=357) 4.5 (2.8 - 7.2) 1.1 (0.4 - 2.9) 33.6 (28.9 - 38.7) 13.4 (10.3 - 17.4) Pader (N=342) 5.3 (3.4 - 8.2) 1.8 (0.8 - 3.8) 21.8 (17.7 - 26.5) 10.9 (8.0 - 14.6) Combined (N=1626) 3.5 (2.7 – 4.4) 1.1 (0.6 – 1.6) 35.0 (32.9 – 37.2) 11.6 (10.0 – 13.2)

WHO classifies severity of malnutrition prevalence based on the following criteria:

Wasting: acceptable (0-5%) / poor (5%-10%) / serious (10%-15%) / critical (greater than 15%);

Stunting: acceptable (less than 20%) / poor (20%-30%) / serious (30%-40%) / critical (greater than 40%);

Underweight: acceptable (less than 10%) / poor (10%-20%) / serious (20%-30%) / critical (greater than 30%),

GAM in all districts was within acceptable ranges except in Pader where it was poor, although at borderline (Table 3.7). The greatest challenge the five SUN district is that of stunting which was

critical in Kablae and Kanungu districts. Integrated interventions aimed at improving the standard the living ought to be intensified in these districts.

Table 3.7: A diagrammatic view of malnutrition expressed according to the WHO classification of prevalence of malnutrition, by district

District Wasting Stunting Underweight Ibanda Acceptable Serious Acceptable Kabale Acceptable Critical Acceptable Kanungu Acceptable Critical Poor Nebbi Acceptable Serious Poor Pader Poor Poor Poor Combined Acceptable Serious Poor

3.2.2 Prevalence of child malnutrition by sex The differences in malnutrition between sex was statistically significant with stunting: 38.9% (35.6- 42.2) for boys compared to 30.6% (27.4 – 33.8) for girls (Table 3.8); and with underweight, that is, 13.5% (11.2 – 15.9) for boys compared to 9.5% (7.4 – 11.5) for girls in pooled analyses (results not presented in table). The differences in under nutrition between male and female children are common findings in studies done in sub-Saharan Africa. Unfortunately there are no programmatic actions, which have been instituted to address the sex differences and even to examine the causes of such differences in the Ugandan setup.

Table 3.8: Sex differences in GAM and stunting by district

District GAM Stunting Male Female Male Female % (95% CI) % (95%CI) %(95% CI) %(95% CI) Ibanda 2.0 (0.2-4.3) 0.7 (-0.7-2.2) 36.2 (28.5-43.8) 35.3 (27.3-43.2) Kabale 4.0 (1.1-6.9) 2.3 (-0.3-4.9) 46.3 (38.9-53.7) 36.6 (28.4-44.9) Kanungu 3.0 (0.4-5.5) 2.7 (0.1-5.3) 49.1 (41.6-56.6) 35.8 (28.1-43.4) Nebbi 5.6 (2.2-9.1) 3.4 (0.7-6.1) 36.0 (28.9-43.0) 31.1 (24.3-37.9) Pader 4.5 (2.2-9.0) 5.9 (3.3-10.3) 25.5 (18.7-32.3) 18.4 (12.8-24.0) Combined 4.2 (2.9-5.6) 3.1 (1.9-4.3) 38.9 (35.6-42.2) 30.7 (27.4-33.9)

3.2.3 Prevalence of malnutrition by age The prevalence of GAM peaked at 6–17 months while that of stunting at 18-29 and underweight at 54 – 59 months (Figure 3.1). Similar trends have been observed before in Uganda.

45 41.6 41.2 40.7 40 33.6 35 30 22.9 25 20 13 15 11.2 12.1 10.7 11.7 10 4.9 2.9 5 1.7 0.9 0 0 6-17 months 18-29 30-41 42-53 54-59 months months months months

Wasting Stunting Underweight

Figure 3.1: Prevalence of GAM, stunting and underweight by age categories

3.2.4 Distribution of malnutrition in the five SUN districts The pooled mean weight-for-height z-score was 0.16 (SD=1.2). There were nine cases of oedema (0.6%) in the entire sampled children of which, four were from Kabale and three from Kanungu. Apparently the distribution of GAM depicts an almost normal pattern (Figure 3.2).

Figure 3.2: Distribution of Weight-for-Height Z-scores for both sexes

The pooled mean height-for-age z-score was -1.46 (SD=1.44). The distribution shifted to left depicting a high problem of stunting but the curve also depicts problems associated with taking height measurements or age measurements by the enumerators (Figure 3.3).

Figure 3.3: Distribution of height-for-age z-scores for both sexes

Figure 3.4: Distribution of weight-for-age z-scores for both sexes

The mean Weight-for-Age z-score was -0.67 (SD=1.24). The distribution shift to the left calls for improved intervention to address underweight (Figure 3.4).

3.2.6 Wasting assessed by Mid Upper Arm Circumference (MUAC) in children The Mid Upper Arm Circumference (MUAC) assessments in children 6-59 months depicted 12.4% risk (< 13.5 cm) of being undernourished in pooled analysis (Table 3.9). The proportion of children at risk was highest in Kanungu and Kabale district with 15.9% and 14.9%, respectively.

Table 3.9: Wasting status of children 6-59 months assessed with MUAC by district

District MUAC CATEGORISED <11.5 11.5-12.5 12.6-13.5 >13.5 N (%) N (%) N (%) N (%) Ibanda (283) 1 (0.4) 5 (1.8) 22 (7.8) 255 (90.1) Kabale (303) 0 (0.0) 7 (2.3) 35 (11.6) 261 (86.1) Kanungu (327) 4 (1.2) 11 (3.4) 37 (11.3) 275 (84.1) Nebbi (355) 1 (0.3) 7 (2.0) 30 (8.5) 317 (89.3) Pader (340) 0 (0.0) 6 (1.8) 34 (10.0) 300 (88.2) Combined (1608) 6 (0.4) 36 (2.2) 158 (9.8) 1408 (87.6)

3.2.7 Wasting status of mothers assessed using MUAC and BMI Mid-upper arm circumference (MUAC) was assessed for mothers and female caregivers in reproductive age (15-49 years of age). Using a cut-off of less than 22.5 cm, 2.2% of the women were classified as malnourished (Table 3.10). Ibanda district with 3.7% had the highest proportion of women classified as malnourished while Kabale district with 1.1% recorded the least malnourished women of the districts.

Table 3.10: Wasting status of mothers and caregivers 15-49 years assessed using MUAC

District Mothers MUAC <22.5 cm >22.5 cm N (%) N (%) Ibanda (245) 9 (3.7) 236 (96.3) Kabale (183) 2 (1.1) 181 (98.9) Kanungu (265) 8 (3.0) 257 (97.0) Nebbi (276) 4 (1.4) 272 (98.6) Pader (214) 3 (1.4) 211 (98.6) Combined (1183) 26 (2.2) 1157 (97.8)

Additionally mothers were weighed and their height taken. The BMI indicate that 5.2% of the mothers were wasted (thin), while 1.2% were severely wasted. Kanungu (29.8%) and Kabale 27.3%) had the highest proportion of overweight and/or obesed mothers (Table 3.11). The relatively high level of obesity in Kanungu should be investigated further.

Table 3.11: Malnutrition status of mothers/caregivers 15-49 years of age

Severely wasted Wasted Normal Overweight Obese District (BMI <16.5) (BMI <18.5) (BMI 18.5 – 25) (BMI 25.1 – 30) (BMI >30) % % % % % Ibanda N=212) 1.4 4.7 67.5 19.8 6.6 Kabale (N=234) 0.4 1.7 70.5 22.2 5.1 Kanungu (N=248) 0.0 2.8 67.3 16.9 12.9 Nebbi (N=248) 2.0 8.5 82.7 5.2 1.6 Pader (N=220) 2.3 8.6 74.1 7.3 7.7 Combined (N=1162) 1.2 5.2 72.5 14.2 6.8

3.4 Infant and young child feeding practices

3.4.1 Breastfeeding practices and knowledge Exclusive breastfeeding assessed using 24-hour recall among children less than six months was 76.9% in Ibanda, 61.4% in Kabale, 80.0% in Kanungu, 80.6% in Nebbi and 60.5% in Pader. Additionally, 326 (76.3%) of children aged 6-23 months whose mothers were interviewed were still breastfeeding (Table 3.12). The highest proportion of non-breastfeeding children was in Ibanda district 20 (32.3%) and the least proportion was in Kanungu district 18 (19.1%). The high breast-feeding practice is commendable and should be promoted further.

Table 3.12: Breastfeeding status among children 6-23 month by district

District Still breastfeeding Stopped breastfeeding N (%) N (%) Ibanda (62) 42 (67.7) 20 (32.3) Kabale (98) 73 (74.5) 25 (25.5) Kanungu (94) 76 (80.9) 18 (19.1) Nebbi (92) 70 (76.1) 22 (23.9) Pader (93) 65 (69.9) 28 (30.1) Combined (439) 326 (74.3) 113 (25.7)

Up to 92.6% of the mothers were knowledgeable that children 6-12 months should be breastfed on demand.

Using Kaplan Meier survival curves below (Figure 3.5), the median duration of breastfeeding was 22 months. The practice of breastfeeding beyond two years is in line with national recommendations and should be promoted.

Survival function of breastfeeding among children 6 – 23 months 1.00

0.75

0.5

0.25

0.00

0 5 10 15 20 25 Age of child in months

Figure 3.5: Survival function of breastfeeding in children 6 – 23 months of age

3.4.2 Complementary feeding practices

Initiation of complementary feeding Introduction of food to the baby who is still breastfeeding normally referred to as complementary feeding was inappropriately timed in the majority of the districts except Kanungu. Among children aged 6 – 8 months, up to 18% of the children had not received any complementary food in the 24 hours preceding the survey. The district with the lowest proportion of timely initiation of complementary feeding was Kabale 40.0% followed by Nebbi 26.7% (Figure 3.6). This implies that a good number of children aged 6-8 months in Kabale and Nebbi were still breastfed exclusively, which was not an appropriate practice. Breast milk alone is not sufficient for children in this age group, thus the need for specially prepared (transitional) complementary food.

120 100 100 90 77.8 80 73.3 60 60 40 40 26.7 22.2 20 10 0 0 Ibanda Kabale Kanungu Nebbi Pader

Yes No

Figure 3.6: Proportion of children aged 6-8 months who received complementary food in the 24 hours preceding assessment by district

Frequency of meals for children 6 –23 months However, the majority of the children 6-8 months who received complementary food had above the recommended number of two meals a day. In some districts like Kanungu, the average meal frequency was 3.5, which was not necessarily better (Table 3.13), since too frequently meals risk displacing the much-desired breastfeeding practice. For children 9 - 23 months who were breastfeeding, the average meal frequency was 3.0.

Table 3.13: Meal frequency in children of different age categories

District Children 6-8 months Children 9-23 months N Mean SD N Mean SD Ibanda 10 2.6 1.4 32 2.7 1.0 Kabale 7 3.1 1.5 61 3.2 0.8 Kanungu 16 3.5 1.2 58 3.0 0.9 Nebbi 14 2.6 1.8 58 2.8 0.9 Pader 7 2.4 1.0 61 2.7 1.1 Combined 54 2.9 1.4 270 2.9 1.0

Diversity of complementary foods eaten by children 6-23 months Using 24-hour recall, individual dietary diversity score (IDDS) was assessed based on seven food groups. The assessment was done only in children 6-23 months. Minimum dietary diversity has been defined as the proportion of children who received foods from at least 4 food groups the previous day2. The majority of the children in all districts had low or moderate IDDS. Low IDDS was highest in Kabale (64.3%) (Figure 3.7).

100%

90% 20 24.8 80% 39 36 37.3 70% 15 60% 28.1 14.9 50% 26 31.6 40% 30% 64.3 47.8 47.1 20% 34.4 32.5 10% 0% Ibanda Kabale Kanungu Nebbi Pader

Low Moderate High

Figure 3.7: Individual dietary diversity score for children 6-23 months

In summary, the quality of complementary feeding as depicted by timing, frequency and diversity of foods provided to children 6-23 months was poor. There is need to intensify programming targeted at improving complementary feeding in the five SUN districts.

3.3 Status of health, water and sanitation

3.3.1 Morbidity due to common childhood illness among children under five Caretakers were asked if the child had been ill during the two weeks prior to the survey. The survey specifically asked about diarrhoea (watery or bloody), ARI and fever. In pooled analysis,

2 Low ≤ 3; medium > 3 but ≤ 5; high >5

ARI and fever were the most commonly reported problems with 63.1% and 41.0%, respectively, of all the children having experienced the two conditions (Table 3.14).

Table 3.14: Prevalence of common illnesses amongst children 6-59 months old by district

District Diarrhoea Fever ARI N (%) N (%) N (%) Ibanda 60 (20.6) 117 (40.2) 201 (69.3) Kabale 92 (30.0) 70 (22.7) 171 (55.3) Kanungu 100 (30.5) 100 (30.5) 229 (69.8) Nebbi 117 (32.8) 188 (52.7) 224 (62.6) Pader 113 (33.0) 179 (57.9) 199 (58.7) Combined 482 (29.7) 654 (41.0) 1024 (63.1)

Morbidity patterns varied slightly among districts with Pader (33.0%) and Nebbi (32.8%) reporting the highest incidence of diarrhea. These two districts also reported the highest prevalence of fever as well. The prevalence of diarrhea was higher than what has been recently reported in other surveys in Uganda like UDHS 2011 where the national prevalence of diarrhea was reported as 23%.

3.3.2 Use of mosquito nets Almost three quarters of the households 1032 (72.6%) possessed Insecticide Treated Net (ITN). Ibanda district 265 (84.0%) reported the highest availability of ITN among households while Kabale district 170 (63.9%) reported the least (Table 3.15).

Table 3.15: Household ownership of an insecticide treated net by district

District Have ITN Don't Have ITN N (%) N (%) Ibanda (318 ) 267 (84.0) 51 (16.0) Kabale (360) 170 (63.9) 96 (36.1) Kanungu (377) 199 (68.9) 90 (31.1) Nebbi (391) 219 (76.8) 66 (23.2) Pader (335) 177 (67.3) 86 (32.7) Combined (1421) 1032 (72.6) 389 (27.4)

Concerning the actual use of ITNs by children, over three quarters (80.1%) of the children were reported to have slept under a bed net the night to the assessment. The highest proportion was

recorded in Nebbi district (96.7%) while the lowest proportion was reported in Kabale district (74.1%) (Table 3.16).

Table 3.16: Mosquito bed net usage amongst children 6-59 months by district

Slept under Didn't sleep District ITN under ITN N (%) N (%) Ibanda (296) 224 (75.7) 72 (24.3) Kabale (228) 169 (74.1) 59 (25.9) Kanungu (259) 197 (76.1) 62 (23.9) Nebbi (301) 291 (96.7) 10 (3.3) Pader (324) 247 (76.2) 77 (23.8) Combined (1408) 1128 (80.1) 280 (19.9)

3.3.3 Immunization, vitamin A supplementation and de-worming coverage

Measles coverage Two thirds (62.6%) of children aged 9-23 months had received a measles vaccination as identified with a marked health card (Table 3.17). A noteworthy percentage of children (10.7%) were found not having been immunized as evidenced by a card with Kanungu, Nebbi and Pader missing more than 10% of the children with cards. However, all districts had immunization coverage above 80% when mothers’ reports (those without cards) were considered.

Table 3.17: Measles immunization coverage among children 9-23 months by district

Yes with Yes without No with No without Don't District card card card card know N (%) N (%) N (%) N (%) N (%) Ibanda (98) 63 (64.3) 23 (23.5) 9 (9.2) 2 (2.0) 1 (1.0) Kabale (110) 79 (71.8) 27 (24.5) 3 (2.7) 1 (0.9) 0 (0.0) Kanungu (102) 58 (56.9) 24 (23.5) 16 (15.7) 4 (3.9) 0 (0.0) Nebbi (113) 69 (61.1) 19 (16.8) 14 (12.4) 11 (9.7) 0 (0.0) Pader (101) 59 (58.4) 27 (26.7) 14 (13.9) 0 (0) 0 (0.0) Combined (524) 328 (62.6) 120 (22.9) 56 (10.7) 18 (3.4) 1 (0.2)

Vitamin A supplementation coverage Vitamin A supplementation in the previous six months had been received by 1420 children (87.2%) aged 6-59 months; verified either by a health card or caretaker’s recall. Coverage levels

in the districts were highest in Pader, followed by Kabale and were lowest in Kanungu (Table 3.18). All the assessed SUN districts have met the national target of 80% and above for vitamin A in children less than 5 years of age when mothers reports are considered.

Table 3.18: Vitamin A coverage among children 6-59 months by district

Yes with Yes without No with No without Don't District card card card card know N (%) N (%) N (%) N (%) N (%) Ibanda (291) 144 (49.5) 118 (40.5) 8 (2.7) 19 (6.5) 2 (0.7) Kabale (308) 155 (50.3) 137 (44.5) 12 (3.9) 4 (1.3) 0 (0.0) Kanungu (330) 160 (48.5) 125 (37.9) 38 (11.5) 6 (1.8) 1 (0.3) Nebbi (358) 176 (49.2) 100 (27.9) 36 (10.1) 40 (11.2) 6 (1.7) Pader (340) 184 (54.1) 121 (35.6) 25 (7.4) 8 (2.4) 2 (0.6) Combined (1627) 819 (50.3) 601 (36.9) 119 (7.3) 77 (4.7) 11 (0.7)

DPT3 coverage Overall, DPT3 immunisation had been received by 94.2% of children aged 9-23 months, verified either by a health card or the caretaker’s recall (Table 3.19). The proportion of mothers without health cards was high. There is need to strengthen the health systems to provide the necessary utilities to health units.

Table 3.19: DPT3 coverage among children 9 – 23 months by district

Yes with Yes without No with No without Don't District card card card card know N (%) N (%) N (%) N (%) N (%) Ibanda (98) 69 (70.4) 24 (24.5) 3 (3.1) 1 (1.0) 1 (1.0) Kabale (110) 81 (73.6) 26 (23.6) 1 (0.9) 2 (1.8) 0 (0.0) Kanungu (102) 70 (68.6) 25 (24.5) 6 (5.9) 0 (0.0) 1 (1.0) Nebbi (113) 81 (71.7) 21 (18.6) 6 (5.3) 5 (4.4) 0 (0.0) Pader (117) 76 (65.0) 36 (30.8) 5 (4.3) 0 (0.0) 0 (0.0) Combined (540) 377 (69.8) 132 (24.4) 21 (3.9) 8 (1.5) 2 (0.4)

De-worming coverage De-worming of children above 0ne year in the past six months in all the five assessed SUN districts was 83.9% among children 12 – 59 months, verified either by a health card or the caretaker’s recall (Table 3.20).

Table 3.20: De-worming coverage among children 12-59 months by district

Yes with Yes without No with No without Don't District card card card card know N (%) N (%) N (%) N (%) N (%) Ibanda (256) 109 (42.6) 126 (49.6) 2 (0.8) 18 (7.0) 1 (0.4) Kabale (270) 130 (48.1) 126 (46.7) 8 (3.0) 4 (1.5) 2 (0.7) Kanungu (294) 132 (44.9) 103 (35.0) 39 (13.3) 17 (5.8) 3 (1.0) Nebbi (311) 119 (38.3) 81 (26.0) 45 (14.5) 50 (16.1) 16 (5.1) Pader (305) 167 (54.8) 111 (36.4) 19 (6.2) 6 (2.0) 2 (0.7) Combined (1436) 657 (45.8) 547 (38.1) 113 (7.9) 95 (6.6) 24 (1.7)

Anemia prevalence among children and mothers Anemia was measured by hemoglobin concentration in the blood with Hemocue machines 301, collected among children 6-59 months and mothers 15 – 49 years. A sub-sample of households in only three out of five districts was assessed due to logistical reasons. The cut-offs for mild, moderate and severe anemia among children were 10.0-10.9g/dl, 7-9.9 g/dl, and <7g/dl, respectively. Children with a hemoglobin concentration less than 11g/dl were therefore classified as anemic. The cut-offs for mild, moderate and severe anemia used for mothers was 10-11.9 g/dl, 7-9.9 g/dl and <7 g/dl, respectively (i.e. assumed all mothers were not pregnant). The results are not adjusted for altitude implying that the real status of anemia might be worse than reported.

Anemia was most prevalent in children of Nebbi district (91.9%) where 17.4% had hemoglobin of less than 7 g/dl (Table 3.21) and was least prevalent in Ibanda.

Table 3.21: Anemia prevalence among children 6-59 months

District Proportion (%) of anemia by level of severity Severe Moderate Mild No anemia Ibanda (N=66) 6.1 42.4 13.6 37.9 Nebbi (N=86) 17.4 62.8 11.6 8.1 Pader (N=84) 4.8 41.7 23.8 29.8 Combined (236) 9.7 49.6 16.5 24.2

Anemia was less prevalent in mothers (both pregnant and non-pregnant) with only 31.7% and 42.1% of mothers in Ibanda and Pader, respectively, having any form of anemia. However, 75.4% of the mothers in Nebbi had anemia (Table 3.22). Although the anemia data was not district representative, the high anemia prevalence in Nebbi deserves to be investigated further to rule out random error due to sampling.

Table 3.22: Anemia prevalence among women 15-49 years by district

District Proportion (%) of anemia by level of severity Severe Moderate Mild No anemia Ibanda (N=60) 1.7 8.3 21.7 68.3 Nebbi (N=61) 3.3 39.3 32.8 24.6 Pader (N=57) 0 10.5 31.6 57.9 Combined (178) 1.7 19.7 28.7 50

3.3.5 Water and sanitation

Access to safe water Over 65% of the household of the SUN districts reported to have access to safe drinking water (Table 3.23). Pader district recorded the highest (58.0%) access to safe water using boreholes while Kabale district had the least number of boreholes (0.4%) but the highest number of piped water (32.5%). Although coverage of safe water sources is moderate in the assessed SUN districts, the target should be to ensure 100% coverage since access to safe water is a fundamental human right.

Table 3.23: Source of drinking water in households by district

Piped Protected Open well/ Surface Rain Tank District (N) water well/spring Bore hole Spring/Well water water truck N (%) N (%) N (%) N (%) N (%) N (%) N (%) Ibanda (322) 50 (15.5) 66 (20.5) 56 (17.4) 65 (20.2) 82 (25.5) 2 (0.6) 1 (0.3) Kabale (265) 86 (32.5) 97 (36.6) 1 (0.4) 51 (19.2) 23 (8.7) 3 (1.1) 4 (1.5) Kanungu (292) 82 (28.1) 121 (41.4) 15 (5.1) 22 (7.5) 50 (17.1) 2 (0.7) 0 (0.0) Nebbi (305) 13 (4.3) 17 (5.6) 152 (49.8) 7 (2.3) 116 (38.0) 0 (0.0) 0 (0.0) Pader (300) 8 (2.7) 33 (11.0) 174 (58.0) 47 (15.7) 38 (12.7) 0 (0.0) 0 (0.0) Combined (1484) 239 (16.1) 334 (22.5) 398 (26.8) 192 (12.9) 309 (20.8) 7 (0.5) 5 (0.3)

Treatment of drinking water at household level Water treatment was average (51.7%) in the pooled analysis (Table 3.24). However, this could have been due to the high prevalence of safe water sources coupled with the fact that some districts like Kabale were experiencing severe rainfall. However, we cannot fully confirm whether that was the real reason when using the data collected in this assessment.

Table 3.24: Treatment of drinking water by district

District (N) Treatment Status Method of treatment Did not treat Treated water water Boil Chlorination Other N (%) N (%) N (%) N (%) N (%) Ibanda (314) 83 (26.4) 231 (73.6) 221 (92.5) 5 (2.1) 13 (5.4) Kabale (262) 112 (42.7) 150 (57.3) 147 (96.7) 1 (0.7) 4 (2.6) Kanungu (291) 65 (22.3) 226 (77.7) 210 (96.8) 2 (0.9) 5 (2.3) Nebbi (303) 242 (79.9) 61 (20.1) 14 (22.2) 6 (9.5) 43 (68.3) Pader (253) 186 (73.5) 67 (26.5) 12 (10.0) 54 (45.0) 54 (45.0) Combined (1423) 688 (48.3) 735 (51.7) 604 (76.4) 68 (8.6) 42 (2.9)

Contamination of drinking water Water kept in separate containers for drinking was tested for presence of E. coli using nitrite and nitrate rapid test kits. A total of 39 (2.7%) households had contaminated drinking water in all the districts. Most of the households with contaminated water were in Kanungu16 (5.9%), Ibanda 16 (5.2%) and Kabale 6 (2.3%). Nebbi had only one household with contaminated drinking water while Pader had none. There is still more effort to sensitize households about domestic water contamination and water treatment.

Sanitation Up to 179 (12.5%) of the households in the assessed five SUN districts combined lacked latrines. A few of the households 111(7.7%) shared toilets with their neighbors. The district with the lowest latrine coverage was Pader (32.8%), while districts in the southwest (over 99%) had highest coverage (Table 3.25).

Table 3.25: Household ownership of latrine by district

District Yes Shared None N (%) N (%) N (%) Ibanda (322) 301 (93.5) 19 (5.9) 2 (0.6) Kabale (256) 238 (93.0) 18 (7.0) 0 (0.0) Kanungu (290) 286 (98.6) 3 (1.0) 1 (0.3) Nebbi (305) 179 (58.7) 48 (15.7) 78 (25.6) Pader (299) 178 (59.5) 23 (7.7) 98 (32.8) Combined (1437) 1147 (79.8) 111 (7.7) 179 (12.5)

The majority of the households utilized pit latrines (Table 2.26).

Table 3.26: Household latrine ownership by type of facility and by district

Flush Latrine with no District toilet Pit latrine super structure N (%) N (%) N (%) Ibanda (314) 0 (0.0) 242 (77.1) 72 (22.9) Kabale (254) 4 (1.6) 250 (98.4) 0 (0.0) Kanungu (285) 4 (1.4) 238 (83.5) 43 (15.1) Nebbi (227) 0 (0.0) 212 (93.4) 15 (6.6) Pader (198) 0 (0.0) 144 (72.7) 54 (27.3) Combined (1279) 8 (0.6) 1086 (84.9) 184 (14.4)

Observations were made to determine presence of hand washing facilities in the household premises. In pooled analysis 85.6% of the households had no hand washing facilities after toilet, while 8.8% had water without soap (Table 3.27).

Table 3.27: Household ownership of hand washing facilities at the toilet facility and by district

Water but Soap but Water and District (N) No water no soap no water soap N (%) N (%) N (%) N (%) Ibanda (319) 246 (77.1) 35 (11.0) 4 (1.3) 34 (10.7) Kabale (253) 224 (88.5) 18 (7.1) 0 (0.0) 11 (4.3) Kanungu (286) 247 (86.4) 16 (5.6) 0 (0.0) 23 (8.0) Nebbi (225) 189 (84.0) 21 (9.3) 0 (0.0) 15 (6.7) Pader (195) 167 (85.6) 22 (11.3) 0 (0.0) 6 (3.1) Combined (1278) 1073 (85.6) 112 (8.8) 4 (0.3) 89 (7.0)

Household dwelling structures, kitchens and compounds were also observed for the presence of garbage pits, sun rack for drying washed household utensils (plates, cups, spoons, etc) and a rack in the kitchen for storing utensils. Only 37.6% of the households in pooled analysis had garbage pits (Table 3.28). These are basic domestic hygiene practices that are still relevant in rural settings and should be promoted. It should also be noted that the assessment was carried out after three months if implementation of the CCP which interventions to promote the three outcomes. Table 3.28: Proportion of households with garbage pit, sun and kitchen racks Gabage pit Sun rack Rack in Kitchen District % % % Ibanda 24.2 63.8 49.5 Kabale 43.2 55.9 25.4 Kanungu 41.4 63.1 28.2 Nebbi 45 46.8 17.9 Pader 36.1 21.8 16.4 Combined 37.6 50.6 27.9

3.5. Status of household socioeconomic status, hunger and food security Household hunger and food security status was assessed for all selected households irrespective of whether a household had or did not have a child in the target age group.

3.5.1 Wealth profile of households Household socioeconomic status is one of the factors, which aggravate hunger and food insecurity among households. A wealth index was generated from ownership of household assets and was categorized into quintiles as described in the methods section above. Ibanda district had the highest proportion of socioeconomically better off households 109 (34.5%) while Nebbi and Pader had the highest proportion of poor households (Table 3.29).

Table 3.29: Distribution of households by socioeconomic status according to districts

District (N) Richest (%) Rich (%) Middle (%) Poor (%) Poorest (%) Ibanda (316) 109 (34.5) 127 (40.2) 44 (13.9) 16 (5.1) 20 (6.3) Kabale (262) 70 (26.7) 88 (33.6) 72 (27.5) 13 (5.0) 19 (7.3) Kanungu (285) 79 (27.7) 82 (28.8) 99 (34.7) 6 (2.1) 19 (6.7) Nebbi (304) 19 (6.3) 2 (0.7) 12 (3.9) 137 (45.1) 134 (44.1) Pader (259) 8 (3.1) 0 (0.0) 36 (13.9) 120 (46.3) 95 (36.7) Combined (1426) 285 (20.0) 299 (21.0) 263 (18.4) 292 (20.5) 287 (20.1)

3.5.4 Household asset ownership The majority of the households 931 (63.1%) and 851(58.2%) of the assessed SUN districts own radios and telephones respectively. Overall, most households did not have cars and motocycles. Apparent the majority of the households reported to have some form of savings and income, which was largely from agricultural products (Table 3.30).

Table 3.30: Household asset ownership according to district

District Radio Telephone T.V Bicycle M/cycle Car Savings Income N (%) N (%) N (%) N (%) N (%) N (%) N (%) N (%) Ibanda 253 (77.8) 226 (69.5) 4 (1.2) 158 (48.6) 26 (8.0) 2 (0.6) 182 (56.0) 307 (94.5) Kabale 188 (70.9) 163 (61.5) 13 (4.9) 70 (26.4) 15 (5.7) 3 (1.1) 158 (59.6) 191 (72.1) Kanungu 233 (79.5) 205 (70.0) 11 (3.8) 54 (18.4) 32 (10.9) 3 (1.0) 241 (82.3) 284 (97.3) Nebbi 129 (42.3) 149 (48.9) 6 (2.0) 104 (34.1) 13 (4.3) 0 (0.0) 109 (35.7) 170 (55.7) Pader 128 (44.6) 108 (39.4) 4 (1.4) 153 (57.5) 9 (3.2) 0 (0.0) 166 (62.6) 241 (86.4) Combined 931 (63.1) 851 (58.2) 38 (2.6) 539 (37.1) 95 (6.5) 8 (0.6) 856 (58.9) 1193 (81.4)

3.5.2 Household hunger scores (HHS) Respondents were asked about the frequency with which three events of hunger were experienced by household members in the last four weeks, that is, i) no food at all in the house; ii) went to bed hungry, iii) went all day and night without eating. For each question, the following responses were possible: never (value=0), rarely or sometimes (value=1), often (value=2). Values for the three questions were summed for each household, producing a HHS score ranging from 0 to 6. A total of 206 (13.9%) of all the households assessed were experiencing moderate or severe hunger. The majority of the households 121 (58.7%) with moderate to severe hunger in pooled analysis were in Nebbi district (Figure 3.8)

Ibanda, 2.40%

Pader, 15.50% Kabale, 18.40%

Kanungu, 4.90%

Nebbi, 58.70%

Figure 3.8: Distribution of households with moderate or severe hunger according to district

3.5.3 Household food consumption scores (FCS-Low) The prevalence of households with poor food consumption scores was 5.3% in all the assessed five SUN districts combined. The proportion of highly food insecure households was most prevalent in Nebbi district 34 (11.2%) (Figure 3.9), while Ibanda district 285 (88.0%) had the highest proportions of food secure households.

Pader 0.9 15.8 83.3

Nebbi 11.2 22.7 66.1

Kanungu 1.7 12.7 85.6

Kabale 8.7 20.4 70.9

Ibanda 3.4 8.6 88

0 20 40 60 80 100

Poor Borderline Acceptable

Figure 3.9: Food consumption status at household level by district

Graphically Ibanda, Kanungu and Pader had a better distribution of food consumption while Kabale and Nebbi appeared to have a lower diversity of foods (Figure 3.10)

40 35 30 Condiment 25 Fruits 20 Oils 15 Vegetables 10 Meats 5 Pulses 0 Starches 0 42 45 51 54 57 60 70 73 79 82 48 76 22.5 31.5 35.5 38.5 63.5 66.5 94.5 Poor Borderline Acceptable

Figure 3.10: Diversity of food consumed in the seven days of the recall period per food consumption grouping in Ibanda district

25 Condiment Fruits 20 Oils 15 Vegetables

10 Meats Pulses 5 Starches 0 13 22 29 33 41 49 57 69 95 18 36 46 66 25.5 38.5 43.5 51.5 54.5 62.5 72.5 78.5 Poor Borderline Acceptable

Figure 3.11: Diversity of food consumed in the seven days of the recall period per food consumption grouping in Kabale

35 Condiment 30 Fruits 25 Oils 20 Vegetables 15 Meats 10 Pulses 5 Starches 0 14 35 41 44 47 50 53 75 79 28 38 56 68 31.5 59.5 62.5 71.5 86.5 93.5 105.5 Poor Borderline Acceptable

Figure 3.12: Diversity of food consumed in the seven days of the recall period per food consumption grouping in Kanungu district

50 45 40 35 Condiment 30 Fruits 25 Oils 20 Vegetables 15 Meats 10 Pulses 5 Starches 0 4 11 17 33 37 44 52 59 63 72 77 85 68 40.5 47.5 21.5 25.5 29.5 55.5 Poor Borderline Acceptable

Figure 3.13: Diversity of food consumed in the seven days of the recall period per food consumption grouping in Nebbi district

45 40 35 Condiment 30 Fruits 25 Oils 20 Vegetables 15 Meats 10 Pulses 5 Starches 0 8 29 32 37 42 47 50 61 58 26.5 34.5 39.5 44.5 52.5 55.5 65.5 70.5 77.5 Borderline Acceptable

Figure 3.14: Diversity of food consumed in the seven days of the recall period per food consumption grouping in Pader district

3.5.3 Household food production and other sources of foods Of the 1449 households, which, responded to the question on food production, a total of 50 (3.5%) reported to have never cultivated or planted any food crop in the first and/or second agricultural season of 2012 (Table 3.31).

Table 3.31: Households involvement in cultivation farming in the first and/or second agricultural season of 2012

District Didn’t cultivate Cultivated N (%) N (%) Ibanda (324) 22(4.8) 302(95.1) Kabale (265) 13(4.9) 252(95.1) Kanungu (293) 3(1.0) 290(99.0) Nebbi (305) 9(3.0) 296(97.0) Pader (262) 3(1.1) 259(98.9) Combined (1449) 50(3.5) 1399(96.5)

Apparently the main challenges to food production mentioned by the majority of the respondent who did not grow any food included no access to land. Generally, most households 1336 (90%) had land for cultivation or raring animals and most of them were engaged in growing cereals 1198 (81.7%) and root tubers 1249 (85.8%). About 50% of the households were engaged in Banana growing, cash crop production, sheep and poultry rearing. Only a fifth of the households 278 (19.1%) were involved in cattle keeping (Table 3.32).

Table 3.32: Household agricultural production by district

Banana Cash Sheep / District Land Cereals Garden Root tubers crops Cattle Goat Poultry N(%) N(%) N(%) N(%) N(%) N(%) N(%) N(%) Ibanda 305(93.8) 286(88.0) 286(88.0) 279(85.8) 210(64.6) 73(22.5) 165(50.9) 174(53.7) Kabale 246(92.8) 232(87.9) 104(39.2) 230(86.8) 60(22.6) 45(17.0) 102(38.5) 102(38.5) Kanungu 264(90.1) 263(89.8) 196(66.9) 271(92.80 183(62.5) 30(10.2) 127(43.3) 163(55.6) Nebbi 270(88.5) 167(55.3) 57(18.7) 264(86.6) 127(41.6) 31(10.2) 118(38.7) 142(46.6) Pader 251(84.5) 250(88.3) 86(30.6) 205(76.5) 115(40.6) 99(36.7) 168(60.6) 223(78.5) Combined 1336(90.0) 1198(81.7) 729(49.6) 1249(85.8) 695(47.2) 278(19.1) 680(46.4) 804(54.70)

3.5.6 Household expenditures and main sources of income The expenditure on food in the past 30 days recall was modest. The mean expenditures on cereals (UGX 37,318), cooking oil (UGX 5,191), and meats (UGX 13,146) were low. The mean expenditure on milk, sugar, fruits, and cooked/processed foods was relatively low for the majority of the districts (Table 3.33). Nebbi district reported the highest expenditures on food, which might depict a relatively higher food insecurity in the district.

Table 3.33: Mean household expenditure (UGX) on food items in past 30 days

Meat/ Pnuts/ Cooked/ Cooking eggs/ beans/ Milk/ Fruits and processed Drinking Other Cereals oil fish pulses Sugar yoghurt vegetables food water foods Ibanda N 220 179 265 130 230 201 147 110 115 288 Mean 24233 2546 12880 3350 4279 7407 969 53 516 6637 Kabale N 264 246 246 252 252 242 243 239 241 249 Mean 39036 1407 6183 14303 3487 1551 1198 223 707 2566 Kanungu N 274 197 240 232 213 170 164 104 119 236 Mean 39943 3617 17678 13347 6825 7467 2591 346 824 8259 Nebbi N 303 303 302 303 303 302 303 303 303 302

Meat/ Pnuts/ Cooked/ Cooking eggs/ beans/ Milk/ Fruits and processed Drinking Other Cereals oil fish pulses Sugar yoghurt vegetables food water foods Mean 45330 7629 18042 16626 5937 503 2502 256 520 2241 Pader N 156 237 237 172 174 105 146 114 133 177 Mean 32689 9309 9841 13655 7392 2806 4347 1293 2021 2045 Combined 1217 1162 1290 1089 1172 1020 1003 870 911 1252 Mean 37318 5191 13146 13336 5462 3509 2245 438 828 4423

3.5.7 Household income sources In general, results in Figure 3:14 show that food crop production was the main household income source in all districts.

40 Percenatage 30

Figure 3.15: Proportion of household confirming the different income sources

Cash crop production, unskilled wage labour and agriculture labour were also important household source of income. Agriculture remains the main source of income and thus the need to diversify income generation.

3.6 Gender dynamics at household level

3.6.1 Time allocation among husbands and wives on key household work and leisure In all districts there were statistically significant differences in how time was used by men and women concerning different household tasks and leisure. The day preceding the assessment, women spent significantly more time on agricultural work, household work and childcare while men significantly spent more time on non-agricultural work and leisure (Table 3.34).

Table 3.34: Average time spent on households’ tasks by men and women according to districts Women Men Mean Hours (95%CI) Mean Hours (95%CI) Ibanda (311) 4.7 (4.4 – 5.1) 3.8 (3.3 – 4.2)* * Agricultural work Kabale (262) 4.8 (4.4 – 5.3) 2.5 (2.0 – 3.1) Kanungu (250) 4.7 (4.4 – 5.1) 3.6 (3.1 – 4.1)* Nebbi (292) 3.2 (2.9 – 3.5) 3.5 (3.1 – 3.9) Pader (291) 4.8 (4.5 – 5.0) 4.0 (3.7 – 4.3)* Combined (1406) 4.4 (4.3 – 4.6) 3.6 (3.4 – 3.8)* Ibanda (311) 2.4 (2.1 – 2.7) 4.6 (4.0 – 5.1)* * Non-agricultural Kabale (262) 1.4 (1.0 – 1.7) 5.0 (4.2 – 5.8) * work Kanungu (250) 2.3 (1.9 – 2.7) 4.4 (3.8 – 5.0) Nebbi (292) 2.3 (2.1 – 2.6) 4.1 (3.8 – 4.5)* Pader (291) 3.2 (3.0 – 3.4) 3.1 (2.8 – 3.4) Combined (1406) 2.3 (2.2 – 2.5) 4.2 (3.9 – 4.4)* Ibanda (311) 4.9 (4.6 – 5.3) 1.2 (0.9 – 1.4)* Household work Kabale (262) 5.8 (5.4 – 6.2) 0.9 (0.5 – 1.2) * * and care of Kanungu (250) 6.1 (5.7 – 6.4) 1.0 (0.7 – 1.4) * children and sick Nebbi (292) 5.2 (5.0 – 5.5) 1.5 (1.3 – 1.8) Pader (291) 3.7 (3.5 – 3.8) 1.6 (1.4 – 1.9) * Combined (1406) 5.1 (5.0 – 5.2) 1.3 (1.2 – 1.4) * Leisure Ibanda (311) 3.1 (2.8 – 3.4) 4.3 (3.8 – 4.7) * Kabale (262) 2.4 (2.1 – 2.8) 4.7 (4.1 – 5.3) * Kanungu (250) 3.2 (2.8 – 3.5) 5.0 (4.5 – 5.5) * Nebbi (292) 4.1 (3.8 – 4.4) 6.3 (5.9 – 6.7) * Pader (291) 3.4 (3.2 – 3.7) 3.9 (3.6 – 4.2) Combined (1406) 3.3 (3.1 – 3.4) 4.8 (4.6 – 5.0) *

Women Men Mean Hours (95%CI) Mean Hours (95%CI) Sleeping Ibanda (311) 8.5 (8.2 – 8.7) 7.6 (7.2 – 8.0) Kabale (262) 9.5 (9.3 – 9.7) 8.9 (8.4 – 9.4) Kanungu (250) 8.8 (8.6 – 9.1) 7.6 (7.1 – 8.1) * Nebbi (292) 9.1 (8.9 – 9.3) 8.6 (8.4 – 8.7) * Pader (291) 8.2 (7.9 – 8.4) 6.9 (6.5 – 7.3) * Combined (1406) 8.8 (8.7 – 8.9) 7.8 (7.6 – 8.0) * * Statistically significant differences

It is important to note the relatively high sleeping duration of 8.8 hours, which is in line with current research recommendations. Insufficient sleep is associated with a number of chronic diseases and conditions such as diabetes, cardiovascular diseases, obesity and depression.

3.6.2 Ownership and control profiles for selected items between husbands and wives Ownership of household items varied between men and women although the majority of items were jointly owned. Generally men tended to own and control cash generating items (productive), radio and telephones. Paradoxically a larger number of wives (30.5%) reported to own savings than their husbands (16.5%) although much of the savings were jointly owned (Table 3.35). A higher proportion of men in Nebbi tended to control items in descriptive terms.

Table 3.35: Proportion of men and women who own and control key household items

Item Ownership Control Women Men Joint Women Men Joint % % % % % % Radio Ibanda 15.4 52.2 30.4 13.4 26.5 57.7 Kabale 12.1 45.8 40.5 12.6 37.9 47.7 Kanungu 12 42.9 43.8 12.0 10.3 76.4 Nebbi 5.6 66.7 27.8 5.6 57.6 36.8 Pader 26 46.8 26.6 14.4 29.5 55.4 Total 14.3 49.7 34.7 12.0 29.4 57.1 Telephone Ibanda 19 50.4 25.7 17.7 37.2 41.6 Kabale 22.4 49.4 25.6 21.3 47.6 28.0 Kanungu 21.4 35.8 42.3 17.9 24.4 57.2 Nebbi 10.2 68 21.1 9.5 59.9 29.9 Pader 25.4 64.1 9.2 11.6 59.7 26.4 Total 19.8 52 26.0 16.1 43.4 38.4

Item Ownership Control Women Men Joint Women Men Joint % % % % % % Savings Ibanda 22 22 54.9 21.4 20.3 57.7 Kabale 21.3 16.3 61.9 21.0 15.0 62.5 Kanungu 23.4 13.8 62.3 18.4 12.6 68.6 Nebbi 47.7 18.7 33.6 40.2 23.4 35.5 Pader 44.8 13.9 41.3 22.1 15.4 62.1 Total 30.5 16.5 52.5 23.1 16.5 59.8 Cereals Ibanda 34.6 2.8 63.2 32.5 5.9 61.2 Kabale 20.9 6.8 71.5 20.4 3.4 75.7 Kanungu 45.6 2.3 51.7 39.9 4.6 55.1 Nebbi 15.7 36.7 46.4 12.1 37.6 49.7 Pader 46.6 11.1 41.9 30.0 8.4 60.8 Total 34.5 9.9 55.0 28.5 10.0 61.0 Banana Ibanda 17.8 21.7 59.8 19.2 12.6 67.5 Kabale 14.4 13.5 69.2 12.5 7.7 77.9 Kanungu 18.4 16.3 64.3 19.4 12.2 66.3 Nebbi 14.3 26.8 58.9 12.5 39.3 48.2 Pader 13.1 48.6 35.5 20.3 19.0 59.5 Total 16.6 23.4 58.7 17.9 14.6 66.3 Goat Ibanda 19.9 22.9 57.2 18.7 20.5 60.8 Kabale 13.5 29.8 53.8 12.5 24.0 60.6 Kanungu 15.2 20.8 62.4 14.4 18.4 67.2 Nebbi 9.6 53 36.5 9.6 53.9 35.7 Pader 23.6 49.3 26.6 12.3 32.8 54.4 Total 17.5 35.9 45.6 13.8 29.5 56.0 Cash crops Ibanda 13.8 38.1 48.1 13.8 28.6 57.6 Kabale 13.3 6.7 80.0 16.7 8.3 75.0 Kanungu 14.3 25.3 59.3 15.4 22.5 62.1 Nebbi 7.9 44.9 47.2 7.9 51.2 40.9 Pader 15.6 56.3 27.4 13.9 36.1 50.0 Total 13.2 36.8 49.6 13.4 30.6 56.0

Chapter 4

CONCLUSIONS AND RECOMMENDATIONS

The greatest challenge of malnutrition in children 6-59 months in the assessed five SUN districts was that of stunting. Stunting prevalence was critical and above national UDHS 2011 average in Kabale (42.0%) and Kanungu (43.0%); was serious and similar to national average in Ibanda (34.9%) and Nebbi (33.6%) districts. Stunting is a manifestation of multiple nutritional and socioeconomic insults to child. Interventions in the five SUN districts should be multi- dimensional including empowering households economically, and improving social dimensions.

Infant and young child feeding in the assessed districts were inadequate. For instance 40% of the children 6-8 months were still exclusively breastfeeding in Kabale. Over two third of the children 6-23 months in all districts had low or moderate Individual Dietary Diversity Score (IDDS) with the worst district being Kabale where 64.3% of the children had low IDDS. Anemia prevalence was also unacceptably high in the districts more so in Nebbi. There is need to continue with nutrition and health promotional interventions to address anemia. Effort to be made to rule out random sampling error due to small sample sizes in this survey by investigating what could be the underlying causes of high anemia in districts like Nebbi.

Although the two-week prevalence of ARI (63.1%), fever (41.0%) and diarrhea (29.7%) were high, immunization, vitamin supplementation and deworming were close to the national target of 85% coverage. The high prevalence of diarrhea was particular concern since recent studies albeit from different districts have reported lower prevalence. Service delivery should target to meet national targets.

Safe water coverage was above the national average of 70% except for Ibanda (53.4%) and Nebbi (59.7%). Although contamination of drinking water at household level was low, 2.7% one in ten households lacked a latrine. The district with the lowest latrine coverage was Pader (32.8%), while Kanungu (98.6%) had most latrine coverage. In addition, the majority of the households (85.6%) had no hand washing facilities after toilet. These are basic domestic

hygiene practices that are still relevant in rural settings and should be promoted. Households should also be sensitized about domestic water contamination and treating of drinking water.

Nebbi (44.1%) and Pader (36.7%) had the highest concentration of households in the poorest socioeconomic quintile and the least proportion of households in the better off quintile. Conversely Ibanda (34.5%) had the highest proportion of socioeconomically better off households. To some extent household Hunger Scores (HHS), correlated with socioeconomic status where the majority of the households with moderate to severe hunger were in Nebbi (58.7%), Kabale (18.4%) and Pader (15.5%). Additionally food insecurity (poor and borderline) based on Food Consumption Scores (FCS Low), was most prevalent in Nebbi district (33.9%) while Ibanda district (88.0%), Kanungu (85.6%), Pader (83.3%) and Kabale (70.9%) were food secure. Nebbi district should have more additional support to improve food security.

In all districts there were statistically significant differences in how time was used by men and women concerning different household tasks and leisure. On the day preceding the assessment, women spent significantly more time on agricultural work, household work and childcare while men significantly spent more time on non-agricultural work and leisure Ownership of household items varied between men and women but the majority of items were jointly owned. Generally men tended to own and control cash generating items, radio and telephones. Surprisingly a larger number of wives (30.5%) reported to own savings than their husbands (16.5%) although much of the savings were jointly owned. Descriptively, a higher proportion of men in Nebbi tended to control items/assets than their wives. Communities should be sensitized on gender and the effects such imbalances might have on the standard of living and general welfare of children and mothers.

Appendix 1: Supervisors

No Name Phone Email

1. Baguma K Susan 0772663812 [email protected]

2. Bagonza Moses 0759129790 [email protected]

3. Katuramu Patrick 0775291307 [email protected]

4. Okot Patrick 0772349550 [email protected]

5. Kanyike Joseph 075172229 [email protected]

6. Kanyangabo Edward 0755028633 [email protected]

7. Orikiriza Grace 0788908731 [email protected]

8. Bagonza Arthur 0772408080 [email protected]

9. Rumoma Dickens 0776947183 [email protected]

10. Joab Tusaasire 0772581199 [email protected]

11. Karungi Clara 0776959087 [email protected]

12. Kansiime Rachel 07528800072 [email protected]

13. Albert Mugabi 0712962582 [email protected]

14. Mayengo Philomera 0702690043 [email protected]

15. Ngobi Johnathan 0772496630 [email protected]

16. Wamani Henry 0755443300 [email protected]

Appendix 2: Results based on NCHS reference 1977

Ibanda District

Table 3.2: Prevalence of acute malnutrition based on weight-for-height z-scores (and/or oedema) and by sex

All Boys Girls n = 290 n = 152 n = 138 Prevalence of global malnutrition (5) 1.7 % (4) 2.6 % (1) 0.7 % (<-2 z-score and/or oedema) (0.7 - 4.0 95% (1.0 - 6.6 95% (0.1 - 4.0 95% C.I.) C.I.) C.I.) Prevalence of moderate malnutrition (3) 1.0 % (2) 1.3 % (1) 0.7 % (<-2 z-score and >=-3 z-score, no (0.4 - 3.0 95% (0.4 - 4.7 95% (0.1 - 4.0 95% oedema) C.I.) C.I.) C.I.) Prevalence of severe malnutrition (2) 0.7 % (2) 1.3 % (0) 0.0 % (<-3 z-score and/or oedema) (0.2 - 2.5 95% (0.4 - 4.7 95% (0.0 - 2.7 95% C.I.) C.I.) C.I.) The prevalence of oedema is 0.3 %

Table 3.3: Prevalence of acute malnutrition by age, based on weight-for-height z-scores and/or oedema

Severe wasting Moderate Normal Oedema (<-3 z-score) wasting (> = -2 z score) (>= -3 and <-2 z- score ) Age Total No. % No. % No. % No. % (mo) no. 6-17 72 1 1.4 1 1.4 70 97.2 0 0.0 18-29 67 0 0.0 1 1.5 66 98.5 0 0.0 30-41 78 0 0.0 0 0.0 77 98.7 1 1.3 42-53 51 0 0.0 1 2.0 50 98.0 0 0.0 54-59 22 0 0.0 0 0.0 22 100.0 0 0.0 Total 290 1 0.3 3 1.0 285 98.3 1 0.3

Table 3.4: Distribution of acute malnutrition and oedema based on weight-for-height z-scores

<-3 z-score >=-3 z-score Oedema present Marasmic kwashiorkor Kwashiorkor No. 0 (0.0 %) No. 1 (0.3 %) Oedema absent Marasmic Not severely malnourished No. 1 (0.3 %) No. 288 (99.3 %)

Table 3.5: Prevalence of acute malnutrition based on MUAC cut off's (and/or oedema) and by sex

All Boys Girls n = 282 n = 149 n = 133 Prevalence of global malnutrition (282) 100.0 % (149) 100.0 % (133) 100.0 % (< 125 mm and/or oedema) (98.7 - 100.0 95% (97.5 - 100.0 (97.2 - 100.0 C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate malnutrition (0) 0.0 % (0) 0.0 % (0) 0.0 %

(< 125 mm and >= 115 mm, no (0.0 - 1.3 95% (0.0 - 2.5 95% (0.0 - 2.8 95% oedema) C.I.) C.I.) C.I.) Prevalence of severe malnutrition (282) 100.0 % (149) 100.0 % (133) 100.0 % (< 115 mm and/or oedema) (98.7 - 100.0 95% (97.5 - 100.0 (97.2 - 100.0 C.I.) 95% C.I.) 95% C.I.)

Table 3.6: Prevalence of acute malnutrition by age, based on MUAC cut off's and/or oedema

Severe wasting Moderate Normal Oedema (< 115 mm) wasting (> = 125 mm ) (>= 115 mm and < 125 mm) Age Total No. % No. % No. % No. % (mo) no. 6-17 70 70 100.0 0 0.0 0 0.0 0 0.0 18-29 65 65 100.0 0 0.0 0 0.0 0 0.0 30-41 77 77 100.0 0 0.0 0 0.0 1 1.3 42-53 49 49 100.0 0 0.0 0 0.0 0 0.0 54-59 21 21 100.0 0 0.0 0 0.0 0 0.0 Total 282 282 100.0 0 0.0 0 0.0 1 0.4

Table 3.7: Prevalence of underweight based on weight-for-age z-scores by sex

All Boys Girls n = 289 n = 151 n = 138 Prevalence of underweight (24) 8.3 % (15) 9.9 % (9) 6.5 % (<-2 z-score) (5.6 - 12.1 (6.1 - 15.7 (3.5 - 11.9 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate (18) 6.2 % (12) 7.9 % (6) 4.3 % underweight (4.0 - 9.6 95% (4.6 - 13.4 (2.0 - 9.2 95% (<-2 z-score and >=-3 z-score) C.I.) 95% C.I.) C.I.) Prevalence of severe underweight (6) 2.1 % (3) 2.0 % (3) 2.2 % (<-3 z-score) (1.0 - 4.5 95% (0.7 - 5.7 95% (0.7 - 6.2 95% C.I.) C.I.) C.I.)

Table 3.8: Prevalence of underweight by age, based on weight-for-age z-scores

Severe Moderate Normal Oedema underweight underweight (> = -2 z score) (<-3 z-score) (>= -3 and <-2 z- score ) Age Total No. % No. % No. % No. % (mo) no. 6-17 72 2 2.8 6 8.3 64 88.9 0 0.0 18-29 67 1 1.5 4 6.0 62 92.5 0 0.0 30-41 77 2 2.6 3 3.9 72 93.5 1 1.3 42-53 51 1 2.0 4 7.8 46 90.2 0 0.0 54-59 22 0 0.0 1 4.5 21 95.5 0 0.0 Total 289 6 2.1 18 6.2 265 91.7 1 0.3

Table 3.9: Prevalence of stunting based on height-for-age z-scores and by sex

All Boys Girls n = 290 n = 152 n = 138 Prevalence of stunting (103) 35.5 % (54) 35.5 % (49) 35.5 % (<-2 z-score) (30.2 - 41.2 95% (28.4 - 43.4 (28.0 - 43.8 95% C.I.) 95% C.I.) C.I.) Prevalence of moderate stunting (66) 22.8 % (33) 21.7 % (33) 23.9 % (<-2 z-score and >=-3 z-score) (18.3 - 27.9 95% (15.9 - 28.9 (17.6 - 31.7 95% C.I.) 95% C.I.) C.I.) Prevalence of severe stunting (37) 12.8 % (21) 13.8 % (16) 11.6 % (<-3 z-score) (9.4 - 17.1 95% C.I.) (9.2 - 20.2 (7.3 - 18.0 95% 95% C.I.) C.I.)

Table 3.10: Prevalence of stunting by age based on height-for-age z-scores

Severe stunting Moderate Normal (<-3 z-score) stunting (> = -2 z score) (>= -3 and <-2 z- score ) Age Total No. % No. % No. % (mo) no. 6-17 72 6 8.3 15 20.8 51 70.8 18-29 67 9 13.4 18 26.9 40 59.7 30-41 78 13 16.7 17 21.8 48 61.5 42-53 51 7 13.7 11 21.6 33 64.7 54-59 22 2 9.1 5 22.7 15 68.2 Total 290 37 12.8 66 22.8 187 64.5

Table 3.11: Mean z-scores, Design Effects and excluded subjects

Indicator n Mean z- Design Effect z-scores not z-scores out scores ± SD (z-score < -2) available* of range Weight-for-Height 289 0.49±1.15 1.00 1 0 Weight-for-Age 289 -0.44±1.16 1.00 1 0 Height-for-Age 290 -1.40±1.54 1.00 0 0 * contains for WHZ and WAZ the children with edema.

Kabale District

Table 3.2: Prevalence of acute malnutrition based on weight-for-height z-scores (and/or oedema) and by sex

All Boys Girls n = 308 n = 177 n = 131 Prevalence of global (11) 3.6 % (7) 4.0 % (4) 3.1 % malnutrition (2.0 - 6.3 95% (1.9 - 7.9 95% (1.2 - 7.6 95% (<-2 z-score and/or oedema) C.I.) C.I.) C.I.) Prevalence of moderate (5) 1.6 % (4) 2.3 % (1) 0.8 % malnutrition (0.7 - 3.7 95% (0.9 - 5.7 95% (0.1 - 4.2 95% (<-2 z-score and >=-3 z-score, no C.I.) C.I.) C.I.) oedema) Prevalence of severe (6) 1.9 % (3) 1.7 % (3) 2.3 % malnutrition (0.9 - 4.2 95% (0.6 - 4.9 95% (0.8 - 6.5 95% (<-3 z-score and/or oedema) C.I.) C.I.) C.I.) The prevalence of oedema is 1.3 %

Table 3.3: Prevalence of acute malnutrition by age, based on weight-for-height z-scores and/or oedema

Severe wasting Moderate Normal Oedema (<-3 z-score) wasting (> = -2 z score) (>= -3 and <-2 z-score ) Age Total No. % No. % No. % No. % (mo) no. 6-17 86 1 1.2 2 2.3 79 91.9 4 4.7 18-29 76 0 0.0 2 2.6 74 97.4 0 0.0 30-41 57 0 0.0 1 1.8 56 98.2 0 0.0 42-53 69 1 1.4 0 0.0 68 98.6 0 0.0 54-59 18 0 0.0 0 0.0 18 100.0 0 0.0 Total 306 2 0.7 5 1.6 295 96.4 4 1.3

Table 3.4: Distribution of acute malnutrition and oedema based on weight-for-height z-scores

<-3 z-score >=-3 z-score Oedema present Marasmic kwashiorkor Kwashiorkor No. 0 (0.0 %) No. 4 (1.3 %) Oedema absent Marasmic Not severely malnourished No. 2 (0.6 %) No. 302 (98.1 %)

Table 3.5: Prevalence of acute malnutrition based on MUAC cut off's (and/or oedema) and by sex

All Boys Girls n = 307 n = 175 n = 132 Prevalence of global (307) 100.0 % (175) 100.0 % (132) 100.0 % malnutrition (98.8 - 100.0 95% (97.9 - 100.0 (97.2 - 100.0 (< 125 mm and/or oedema) C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate (0) 0.0 % (0) 0.0 % (0) 0.0 % malnutrition (0.0 - 1.2 95% C.I.) (0.0 - 2.1 95% (0.0 - 2.8 95% (< 125 mm and >= 115 mm, no C.I.) C.I.) oedema) Prevalence of severe (307) 100.0 % (175) 100.0 % (132) 100.0 % malnutrition (98.8 - 100.0 95% (97.9 - 100.0 (97.2 - 100.0 (< 115 mm and/or oedema) C.I.) 95% C.I.) 95% C.I.)

Table 3.6: Prevalence of acute malnutrition by age, based on MUAC cut off's and/or oedema

Severe wasting Moderate Normal Oedema (< 115 mm) wasting (> = 125 mm ) (>= 115 mm and < 125 mm) Age Total No. % No. % No. % No. % (mo) no. 6-17 85 84 98.8 0 0.0 0 0.0 4 4.7 18-29 76 76 100.0 0 0.0 0 0.0 0 0.0 30-41 56 56 100.0 0 0.0 0 0.0 0 0.0 42-53 70 70 100.0 0 0.0 0 0.0 0 0.0 54-59 18 18 100.0 0 0.0 0 0.0 0 0.0 Total 305 304 99.7 0 0.0 0 0.0 4 1.3

Table 3.7: Prevalence of underweight based on weight-for-age z-scores by sex

All Boys Girls n = 304 n = 174 n = 130 Prevalence of underweight (28) 9.2 % (18) 10.3 % (10) 7.7 % (<-2 z-score) (6.4 - 13.0 95% (6.6 - 15.8 95% (4.2 - 13.6 95% C.I.) C.I.) C.I.) Prevalence of moderate (21) 6.9 % (15) 8.6 % (6) 4.6 % underweight (4.6 - 10.3 95% (5.3 - 13.7 95% (2.1 - 9.7 95% (<-2 z-score and >=-3 z-score) C.I.) C.I.) C.I.) Prevalence of severe (7) 2.3 % (3) 1.7 % (4) 3.1 % underweight (1.1 - 4.7 95% C.I.) (0.6 - 4.9 95% (1.2 - 7.6 95% (<-3 z-score) C.I.) C.I.)

Table 3.8: Prevalence of underweight by age, based on weight-for-age z-scores

Severe Moderate Normal Oedema underweight underweight (> = -2 z score) (<-3 z-score) (>= -3 and <-2 z-score ) Age Total No. % No. % No. % No. % (mo) no. 6-17 83 0 0.0 6 7.2 77 92.8 4 4.8 18-29 77 2 2.6 8 10.4 67 87.0 0 0.0 30-41 57 2 3.5 2 3.5 53 93.0 0 0.0 42-53 69 3 4.3 5 7.2 61 88.4 0 0.0 54-59 18 0 0.0 0 0.0 18 100.0 0 0.0 Total 304 7 2.3 21 6.9 276 90.8 4 1.3

Table 3.9: Prevalence of stunting based on height-for-age z-scores and by sex

All Boys Girls n = 307 n = 175 n = 132 Prevalence of stunting (129) 42.0 % (81) 46.3 % (48) 36.4 % (<-2 z-score) (36.6 - 47.6 95% (39.1 - 53.7 95% (28.7 - 44.8 95% C.I.) C.I.) C.I.) Prevalence of moderate stunting (89) 29.0 % (54) 30.9 % (35) 26.5 % (<-2 z-score and >=-3 z-score) (24.2 - 34.3 95% (24.5 - 38.1 95% (19.7 - 34.6 95% C.I.) C.I.) C.I.) Prevalence of severe stunting (40) 13.0 % (27) 15.4 % (13) 9.8 % (<-3 z-score) (9.7 - 17.3 95% C.I.) (10.8 - 21.5 95% (5.8 - 16.1 95% C.I.) C.I.)

Table 3.10: Prevalence of stunting by age based on height-for-age z-scores

Severe Moderate Normal stunting stunting (> = -2 z score) (<-3 z-score) (>= -3 and <-2 z-score ) Age Total No. % No. % No. % (mo) no. 6-17 86 5 5.8 20 23.3 61 70.9 18-29 76 14 18.4 26 34.2 36 47.4 30-41 57 7 12.3 18 31.6 32 56.1 42-53 70 11 15.7 19 27.1 40 57.1 54-59 18 3 16.7 6 33.3 9 50.0 Total 307 40 13.0 89 29.0 178 58.0

Table 3.11: Mean z-scores, Design Effects and excluded subjects

Indicator n Mean z- Design z-scores z-scores scores ± Effect (z- not out of SD score < -2) available* range Weight-for- 304 0.41±1.05 1.00 7 0 Height Weight-for-Age 304 -0.67±1.10 1.00 7 0 Height-for-Age 307 -1.72±1.53 1.00 4 0 * contains for WHZ and WAZ the children with edema.

Kanungu District

Table 3.2: Prevalence of acute malnutrition based on weight-for-height z-scores (and/or oedema) and by sex

All Boys Girls n = 328 n = 171 n = 157 Prevalence of global (12) 3.7 % (6) 3.5 % (6) 3.8 % malnutrition (2.1 - 6.3 95% C.I.) (1.6 - 7.4 95% C.I.) (1.8 - 8.1 95% C.I.) (<-2 z-score and/or oedema) Prevalence of moderate (7) 2.1 % (3) 1.8 % (4) 2.5 % malnutrition (1.0 - 4.3 95% C.I.) (0.6 - 5.0 95% C.I.) (1.0 - 6.4 95% C.I.) (<-2 z-score and >=-3 z-score, no oedema) Prevalence of severe (5) 1.5 % (3) 1.8 % (2) 1.3 % malnutrition (0.7 - 3.5 95% C.I.) (0.6 - 5.0 95% C.I.) (0.4 - 4.5 95% C.I.) (<-3 z-score and/or oedema) The prevalence of oedema is 0.9 %

Table 3.3: Prevalence of acute malnutrition by age, based on weight-for-height z-scores and/or oedema

Severe wasting Moderate Normal Oedema (<-3 z-score) wasting (> = -2 z score) (>= -3 and <-2 z-score ) Age Total No. % No. % No. % No. % (mo) no. 6-17 82 1 1.2 2 2.4 77 93.9 2 2.4 18-29 78 0 0.0 2 2.6 76 97.4 0 0.0 30-41 74 0 0.0 2 2.7 72 97.3 0 0.0 42-53 70 1 1.4 1 1.4 67 95.7 1 1.4 54-59 23 0 0.0 0 0.0 23 100.0 0 0.0 Total 327 2 0.6 7 2.1 315 96.3 3 0.9

Table 3.4: Distribution of acute malnutrition and oedema based on weight-for-height z-scores

<-3 z-score >=-3 z-score Oedema present Marasmic kwashiorkor Kwashiorkor No. 0 (0.0 %) No. 3 (0.9 %) Oedema absent Marasmic Not severely malnourished No. 2 (0.6 %) No. 323 (98.5 %)

Table 3.5: Prevalence of acute malnutrition based on MUAC cut off's (and/or oedema) and by sex

All Boys Girls n = 328 n = 170 n = 158 Prevalence of global (328) 100.0 % (170) 100.0 % (158) 100.0 % malnutrition (98.8 - 100.0 95% (97.8 - 100.0 95% (97.6 - 100.0 95% (< 125 mm and/or oedema) C.I.) C.I.) C.I.) Prevalence of moderate (0) 0.0 % (0) 0.0 % (0) 0.0 % malnutrition (0.0 - 1.2 95% C.I.) (0.0 - 2.2 95% C.I.) (0.0 - 2.4 95% (< 125 mm and >= 115 mm, C.I.) no oedema) Prevalence of severe (328) 100.0 % (170) 100.0 % (158) 100.0 % malnutrition (98.8 - 100.0 95% (97.8 - 100.0 95% (97.6 - 100.0 95% (< 115 mm and/or oedema) C.I.) C.I.) C.I.)

Table 3.6: Prevalence of acute malnutrition by age, based on MUAC cut off's and/or oedema

Severe wasting Moderate Normal Oedema (< 115 mm) wasting (> = 125 mm ) (>= 115 mm and < 125 mm) Age Total No. % No. % No. % No. % (mo) no. 6-17 81 81 100.0 0 0.0 0 0.0 2 2.5 18-29 79 79 100.0 0 0.0 0 0.0 0 0.0 30-41 74 74 100.0 0 0.0 0 0.0 0 0.0 42-53 70 69 98.6 0 0.0 0 0.0 1 1.4 54-59 23 23 100.0 0 0.0 0 0.0 0 0.0 Total 327 326 99.7 0 0.0 0 0.0 3 0.9

Table 3.7: Prevalence of underweight based on weight-for-age z-scores by sex

All Boys Girls n = 325 n = 170 n = 155 Prevalence of underweight (45) 13.8 % (30) 17.6 % (15) 9.7 % (<-2 z-score) (10.5 - 18.0 95% C.I.) (12.7 - 24.1 95% C.I.) (6.0 - 15.4 95% C.I.) Prevalence of moderate (35) 10.8 % (23) 13.5 % (12) 7.7 % underweight (7.8 - 14.6 95% C.I.) (9.2 - 19.5 95% C.I.) (4.5 - 13.0 95% C.I.) (<-2 z-score and >=-3 z-score) Prevalence of severe (10) 3.1 % (7) 4.1 % (3) 1.9 % underweight (1.7 - 5.6 95% C.I.) (2.0 - 8.3 95% C.I.) (0.7 - 5.5 95% C.I.) (<-3 z-score)

Table 3.8: Prevalence of underweight by age, based on weight-for-age z-scores

Severe Moderate Normal Oedema underweight underweight (> = -2 z score) (<-3 z-score) (>= -3 and <-2 z-score ) Age Total No. % No. % No. % No. % (mo) no. 6-17 80 3 3.8 6 7.5 71 88.8 2 2.5 18-29 79 2 2.5 10 12.7 67 84.8 0 0.0 30-41 74 1 1.4 11 14.9 62 83.8 0 0.0 42-53 69 3 4.3 4 5.8 62 89.9 1 1.4 54-59 23 1 4.3 4 17.4 18 78.3 0 0.0 Total 325 10 3.1 35 10.8 280 86.2 3 0.9

Table 3.9: Prevalence of stunting based on height-for-age z-scores and by sex

All Boys Girls n = 328 n = 171 n = 157 Prevalence of stunting (141) 43.0 % (83) 48.5 % (58) 36.9 % (<-2 z-score) (37.7 - 48.4 95% (41.2 - 56.0 (29.8 - 44.7 C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate stunting (81) 24.7 % (49) 28.7 % (32) 20.4 % (<-2 z-score and >=-3 z-score) (20.3 - 29.6 95% (22.4 - 35.8 (14.8 - 27.4 C.I.) 95% C.I.) 95% C.I.) Prevalence of severe stunting (60) 18.3 % (34) 19.9 % (26) 16.6 % (<-3 z-score) (14.5 - 22.8 95% (14.6 - 26.5 (11.6 - 23.2 C.I.) 95% C.I.) 95% C.I.)

Table 3.10: Prevalence of stunting by age based on height-for-age z-scores

Severe Moderate Normal stunting stunting (> = -2 z score) (<-3 z-score) (>= -3 and <-2 z-score ) Age Total No. % No. % No. % (mo) no. 6-17 82 5 6.1 17 20.7 60 73.2 18-29 78 17 21.8 22 28.2 39 50.0 30-41 75 20 26.7 19 25.3 36 48.0 42-53 70 12 17.1 16 22.9 42 60.0 54-59 23 6 26.1 7 30.4 10 43.5 Total 328 60 18.3 81 24.7 187 57.0

Table 3.11: Mean z-scores, Design Effects and excluded subjects

Indicator n Mean z- Design z-scores z-scores scores ± Effect (z- not out of SD score < -2) available* range Weight-for- 325 0.38±1.31 1.00 5 0 Height Weight-for-Age 325 -0.69±1.20 1.00 5 0 Height-for-Age 328 -1.72±1.52 1.00 2 0 * contains for WHZ and WAZ the children with edema.

Nebbi District

Table 3.2: Prevalence of acute malnutrition based on weight-for-height z-scores (and/or oedema) and by sex

All Boys Girls n = 357 n = 178 n = 179 Prevalence of global (15) 4.2 % (9) 5.1 % (6) 3.4 % malnutrition (2.6 - 6.8 (2.7 - 9.3 (1.5 - 7.1 (<-2 z-score and/or oedema) 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate (10) 2.8 % (7) 3.9 % (3) 1.7 % malnutrition (1.5 - 5.1 (1.9 - 7.9 (0.6 - 4.8 (<-2 z-score and >=-3 z-score, no 95% C.I.) 95% C.I.) 95% C.I.) oedema) Prevalence of severe (5) 1.4 % (2) 1.1 % (3) 1.7 % malnutrition (0.6 - 3.2 (0.3 - 4.0 (0.6 - 4.8 (<-3 z-score and/or oedema) 95% C.I.) 95% C.I.) 95% C.I.) The prevalence of oedema is 0.0 %

Table 3.3: Prevalence of acute malnutrition by age, based on weight-for-height z-scores and/or oedema

Severe wasting Moderate Normal Oedema (<-3 z-score) wasting (> = -2 z score) (>= -3 and <-2 z-score ) Age Total No. % No. % No. % No. % (mo) no. 6-17 93 2 2.2 7 7.5 84 90.3 0 0.0 18-29 86 1 1.2 1 1.2 84 97.7 0 0.0 30-41 74 1 1.4 1 1.4 72 97.3 0 0.0 42-53 64 1 1.6 1 1.6 62 96.9 0 0.0 54-59 40 0 0.0 0 0.0 40 100.0 0 0.0 Total 357 5 1.4 10 2.8 342 95.8 0 0.0

Table 3.4: Distribution of acute malnutrition and oedema based on weight-for-height z-scores

<-3 z-score >=-3 z-score Oedema present Marasmic kwashiorkor Kwashiorkor No. 0 No. 0 (0.0 %) (0.0 %) Oedema absent Marasmic Not severely malnourished No. 5 No. 352 (1.4 %) (98.6 %)

Table 3.5: Prevalence of acute malnutrition based on MUAC cut off's (and/or oedema) and by sex

All Boys Girls n = 357 n = 177 n = 180 Prevalence of global (357) 100.0 (177) 100.0 (180) 100.0 malnutrition % % % (< 125 mm and/or oedema) (98.9 - 100.0 (97.9 - 100.0 (97.9 - 100.0 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate (0) 0.0 % (0) 0.0 % (0) 0.0 % malnutrition (0.0 - 1.1 (0.0 - 2.1 (0.0 - 2.1 (< 125 mm and >= 115 mm, no 95% C.I.) 95% C.I.) 95% C.I.) oedema) Prevalence of severe (357) 100.0 (177) 100.0 (180) 100.0 malnutrition % % % (< 115 mm and/or oedema) (98.9 - 100.0 (97.9 - 100.0 (97.9 - 100.0 95% C.I.) 95% C.I.) 95% C.I.)

Table 3.6: Prevalence of acute malnutrition by age, based on MUAC cut off's and/or oedema

Severe wasting Moderate Normal Oedema (< 115 mm) wasting (> = 125 mm ) (>= 115 mm and < 125 mm) Age Total No. % No. % No. % No. % (mo) no. 6-17 93 93 100.0 0 0.0 0 0.0 0 0.0 18-29 86 86 100.0 0 0.0 0 0.0 0 0.0 30-41 73 73 100.0 0 0.0 0 0.0 0 0.0 42-53 64 64 100.0 0 0.0 0 0.0 0 0.0 54-59 41 41 100.0 0 0.0 0 0.0 0 0.0 Total 357 357 100.0 0 0.0 0 0.0 0 0.0

Table 3.7: Prevalence of underweight based on weight-for-age z-scores by sex

All Boys Girls n = 361 n = 179 n = 182 Prevalence of underweight (52) 14.4 % (30) 16.8 % (22) 12.1 % (<-2 z-score) (11.2 - 18.4 (12.0 - 22.9 (8.1 - 17.6 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate (36) 10.0 % (23) 12.8 % (13) 7.1 % underweight (7.3 - 13.5 (8.7 - 18.5 (4.2 - 11.8 (<-2 z-score and >=-3 z-score) 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of severe (16) 4.4 % (7) 3.9 % (9) 4.9 % underweight (2.7 - 7.1 (1.9 - 7.9 (2.6 - 9.1 (<-3 z-score) 95% C.I.) 95% C.I.) 95% C.I.)

Table 3.8: Prevalence of underweight by age, based on weight-for-age z-scores

Severe Moderate Normal Oedema underweight underweight (> = -2 z score) (<-3 z-score) (>= -3 and <-2 z-score ) Age Total No. % No. % No. % No. % (mo) no. 6-17 95 4 4.2 12 12.6 79 83.2 0 0.0 18-29 86 3 3.5 8 9.3 75 87.2 0 0.0 30-41 74 5 6.8 5 6.8 64 86.5 0 0.0 42-53 65 4 6.2 4 6.2 57 87.7 0 0.0 54-59 41 0 0.0 7 17.1 34 82.9 0 0.0 Total 361 16 4.4 36 10.0 309 85.6 0 0.0

Table 3.9: Prevalence of stunting based on height-for-age z-scores and by sex

All Boys Girls n = 358 n = 179 n = 179 Prevalence of stunting (130) 36.3 % (68) 38.0 % (62) 34.6 % (<-2 z-score) (31.5 - 41.4 (31.2 - 45.3 (28.1 - 41.9 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate stunting (82) 22.9 % (40) 22.3 % (42) 23.5 % (<-2 z-score and >=-3 z-score) (18.9 - 27.5 (16.9 - 29.0 (17.9 - 30.2 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of severe stunting (48) 13.4 % (28) 15.6 % (20) 11.2 % (<-3 z-score) (10.3 - 17.3 (11.0 - 21.7 (7.4 - 16.6 95% C.I.) 95% C.I.) 95% C.I.)

Table 3.10: Prevalence of stunting by age based on height-for-age z-scores

Severe Moderate Normal stunting stunting (> = -2 z score) (<-3 z-score) (>= -3 and <-2 z-score ) Age Total No. % No. % No. % (mo) no. 6-17 93 7 7.5 15 16.1 71 76.3 18-29 86 11 12.8 25 29.1 50 58.1 30-41 74 14 18.9 19 25.7 41 55.4 42-53 64 8 12.5 12 18.8 44 68.8 54-59 41 8 19.5 11 26.8 22 53.7 Total 358 48 13.4 82 22.9 228 63.7

Table 3.11: Mean z-scores, Design Effects and excluded subjects

Indicator n Mean z- Design z-scores z-scores scores ± Effect (z- not out of SD score < -2) available* range Weight-for- 357 -0.04±1.22 1.00 4 0 Height Weight-for-Age 361 -0.81±1.23 1.00 0 0 Height-for-Age 358 -1.38±1.83 1.00 3 0 * contains for WHZ and WAZ the children with edema.

Pader District

Table 3.2: Prevalence of acute malnutrition based on weight-for-height z-scores (and/or oedema) and by sex

All Boys Girls n = 256 n = 116 n = 140 Prevalence of global (16) 6.3 % (6) 5.2 % (10) 7.1 % malnutrition (3.9 - 9.9 (2.4 - 10.8 (3.9 - 12.6 (<-2 z-score and/or oedema) 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate (10) 3.9 % (4) 3.4 % (6) 4.3 % malnutrition (2.1 - 7.0 (1.3 - 8.5 (2.0 - 9.0 (<-2 z-score and >=-3 z-score, no 95% C.I.) 95% C.I.) 95% C.I.) oedema) Prevalence of severe (6) 2.3 % (2) 1.7 % (4) 2.9 % malnutrition (1.1 - 5.0 (0.5 - 6.1 (1.1 - 7.1 (<-3 z-score and/or oedema) 95% C.I.) 95% C.I.) 95% C.I.) The prevalence of oedema is 0.0 %

Table 3.3: Prevalence of acute malnutrition by age, based on weight-for-height z-scores and/or oedema

Severe wasting Moderate Normal Oedema (<-3 z-score) wasting (> = -2 z score) (>= -3 and <-2 z-score ) Age Total No. % No. % No. % No. % (mo) no. 6-17 70 3 4.3 5 7.1 62 88.6 0 0.0 18-29 57 1 1.8 3 5.3 53 93.0 0 0.0 30-41 57 1 1.8 1 1.8 55 96.5 0 0.0 42-53 55 0 0.0 1 1.8 54 98.2 0 0.0 54-59 17 1 5.9 0 0.0 16 94.1 0 0.0 Total 256 6 2.3 10 3.9 240 93.8 0 0.0

Table 3.4: Distribution of acute malnutrition and oedema based on weight-for-height z-scores

<-3 z-score >=-3 z-score Oedema present Marasmic kwashiorkor Kwashiorkor No. 0 No. 0 (0.0 %) (0.0 %) Oedema absent Marasmic Not severely malnourished No. 6 No. 250 (2.3 %) (97.7 %)

Table 3.5: Prevalence of acute malnutrition based on MUAC cut off's (and/or oedema) and by sex

All Boys Girls n = 254 n = 114 n = 140 Prevalence of global (254) 100.0 (114) 100.0 (140) 100.0 malnutrition % % % (< 125 mm and/or oedema) (98.5 - 100.0 (96.7 - 100.0 (97.3 - 100.0 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate (0) 0.0 % (0) 0.0 % (0) 0.0 % malnutrition (0.0 - 1.5 (0.0 - 3.3 (0.0 - 2.7 (< 125 mm and >= 115 mm, no 95% C.I.) 95% C.I.) 95% C.I.) oedema) Prevalence of severe (254) 100.0 (114) 100.0 (140) 100.0 malnutrition % % % (< 115 mm and/or oedema) (98.5 - 100.0 (96.7 - 100.0 (97.3 - 100.0 95% C.I.) 95% C.I.) 95% C.I.)

Table 3.6: Prevalence of acute malnutrition by age, based on MUAC cut off's and/or oedema

Severe wasting Moderate Normal Oedema (< 115 mm) wasting (> = 125 mm ) (>= 115 mm and < 125 mm) Age Total No. % No. % No. % No. % (mo) no. 6-17 70 70 100.0 0 0.0 0 0.0 0 0.0 18-29 56 56 100.0 0 0.0 0 0.0 0 0.0 30-41 57 57 100.0 0 0.0 0 0.0 0 0.0 42-53 54 54 100.0 0 0.0 0 0.0 0 0.0 54-59 17 17 100.0 0 0.0 0 0.0 0 0.0 Total 254 254 100.0 0 0.0 0 0.0 0 0.0

Table 3.7: Prevalence of underweight based on weight-for-age z-scores by sex

All Boys Girls n = 256 n = 116 n = 140 Prevalence of underweight (32) 12.5 % (11) 9.5 % (21) 15.0 % (<-2 z-score) (9.0 - 17.1 (5.4 - 16.2 (10.0 - 21.8 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate (16) 6.3 % (4) 3.4 % (12) 8.6 % underweight (3.9 - 9.9 (1.3 - 8.5 (5.0 - 14.4 (<-2 z-score and >=-3 z-score) 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of severe (16) 6.3 % (7) 6.0 % (9) 6.4 % underweight (3.9 - 9.9 (3.0 - 11.9 (3.4 - 11.8 (<-3 z-score) 95% C.I.) 95% C.I.) 95% C.I.)

Table 3.8: Prevalence of underweight by age, based on weight-for-age z-scores

Severe Moderate Normal Oedema underweight underweight (> = -2 z score) (<-3 z-score) (>= -3 and <-2 z-score ) Age Total No. % No. % No. % No. % (mo) no. 6-17 70 3 4.3 2 2.9 65 92.9 0 0.0 18-29 57 5 8.8 5 8.8 47 82.5 0 0.0 30-41 57 2 3.5 4 7.0 51 89.5 0 0.0 42-53 55 5 9.1 3 5.5 47 85.5 0 0.0 54-59 17 1 5.9 2 11.8 14 82.4 0 0.0 Total 256 16 6.3 16 6.3 224 87.5 0 0.0

Table 3.9: Prevalence of stunting based on height-for-age z-scores and by sex

All Boys Girls n = 256 n = 116 n = 140 Prevalence of stunting (64) 25.0 % (32) 27.6 % (32) 22.9 % (<-2 z-score) (20.1 - 30.6 (20.3 - 36.3 (16.7 - 30.5 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate stunting (39) 15.2 % (18) 15.5 % (21) 15.0 % (<-2 z-score and >=-3 z-score) (11.3 - 20.1 (10.0 - 23.2 (10.0 - 21.8 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of severe stunting (25) 9.8 % (14) 12.1 % (11) 7.9 % (<-3 z-score) (6.7 - 14.0 (7.3 - 19.2 (4.4 - 13.5 95% C.I.) 95% C.I.) 95% C.I.)

Table 3.10: Prevalence of stunting by age based on height-for-age z-scores

Severe Moderate Normal stunting stunting (> = -2 z score) (<-3 z-score) (>= -3 and <-2 z-score ) Age Total No. % No. % No. % (mo) no. 6-17 70 1 1.4 9 12.9 60 85.7 18-29 57 10 17.5 10 17.5 37 64.9 30-41 57 7 12.3 11 19.3 39 68.4 42-53 55 7 12.7 6 10.9 42 76.4 54-59 17 0 0.0 3 17.6 14 82.4 Total 256 25 9.8 39 15.2 192 75.0

Table 3.11: Mean z-scores, Design Effects and excluded subjects

Indicator n Mean z- Design z-scores z-scores scores ± Effect (z- not out of SD score < -2) available* range Weight-for- 256 -0.30±1.22 1.00 0 0 Height Weight-for-Age 256 -0.84±1.24 1.00 0 0 Height-for-Age 256 -1.09±1.84 1.00 0 0 * contains for WHZ and WAZ the children with edema.

Appendix 3: Plausibility checks for data using ENA software

Ibanda District

Overall data quality

Criteria Flags* Unit Excel. Good Accept Problematic Score

Missing/Flagged data Incl % 0-2.5 >2.5-5.0 >5.0-10 >10 (% of in-range subjects) 0 5 10 20 5 (3.1 %) Overall Sex ratio Incl p >0.1 >0.05 >0.001 <0.000 (Significant chi square) 0 2 4 10 0 (p=0.483) Overall Age distrib Incl p >0.1 >0.05 >0.001 <0.000 (Significant chi square) 0 2 4 10 2 (p=0.060) Dig pref score - weight Incl # 0-5 5-10 10-20 > 20 0 2 4 10 2 (8) Dig pref score - height Incl # 0-5 5-10 10-20 > 20 0 2 4 10 2 (7) Standard Dev WHZ Excl SD <1.1 <1.15 <1.20 >1.20 0 2 6 20 0 (0.99) Skewness WHZ Excl # <±1.0 <±2.0 <±3.0 >±3.0 0 1 3 5 0 (0.02) Kurtosis WHZ Excl # <±1.0 <±2.0 <±3.0 >±3.0 0 1 3 5 0 (-0.02) Poisson dist WHZ-2 Excl p >0.05 >0.01 >0.001 <0.000 0 1 3 5 0 (p=) Timing Excl Not determined yet 0 1 3 5 OVERALL SCORE WHZ = 0-5 5-10 10-15 >15 11 %

At the moment the overall score of this survey is 11 %, this is acceptable.

Age distribution:

Month 6 : ### Month 7 : #### Month 8 : ###### Month 9 : ########## Month 10 : ######## Month 11 : ##### Month 12 : ####### Month 13 : ##### Month 14 : #### Month 15 : ###### Month 16 : ### Month 17 : ########## Month 18 : #### Month 19 : ######## Month 20 : ######## Month 21 : #### Month 22 : ######## Month 23 : ######## Month 24 : ##

Month 25 : ######### Month 26 : ##### Month 27 : ####### Month 28 : #### Month 29 : ## Month 30 : ######## Month 31 : ######## Month 32 : ########### Month 33 : ######## Month 34 : ####### Month 35 : ############# Month 36 : #### Month 37 : ### Month 38 : ##### Month 39 : ## Month 40 : ##### Month 41 : ##### Month 42 : ##### Month 43 : ## Month 44 : ## Month 45 : ### Month 46 : #### Month 47 : ##### Month 48 : #### Month 49 : ##### Month 50 : ## Month 51 : ######### Month 52 : ###### Month 53 : ##### Month 54 : ### Month 55 : #### Month 56 : #### Month 57 : ##### Month 58 : ### Month 59 : ##

Age ratio of 6-29 months to 30-59 months: 0.92 (The value should be around 1.0).

Statistical evaluation of sex and age ratios (using Chi squared statistic):

Age cat. mo. boys girls total ratio boys/girls ------6 to 17 12 43/35.3 (1.2) 28/32.5 (0.9) 71/67.7 (1.0) 1.54 18 to 29 12 33/34.4 (1.0) 36/31.7 (1.1) 69/66.1 (1.0) 0.92 30 to 41 12 36/33.3 (1.1) 43/30.7 (1.4) 79/64.0 (1.2) 0.84 42 to 53 12 31/32.8 (0.9) 21/30.2 (0.7) 52/63.0 (0.8) 1.48 54 to 59 6 9/16.2 (0.6) 12/14.9 (0.8) 21/31.2 (0.7) 0.75 ------6 to 59 54 152/146.0 (1.0) 140/146.0 (1.0) 1.09

The data are expressed as observed number/expected number (ratio of obs/expect)

Overall sex ratio: p-value = 0.483 (boys and girls equally represented) Overall age distribution: p-value = 0.060 (as expected) Overall age distribution for boys: p-value = 0.260 (as expected) Overall age distribution for girls: p-value = 0.049 (significant difference) Overall sex/age distribution: p-value = 0.004 (significant difference)

Digit preference Weight:

Digit .0 : ################################### Digit .1 : ################## Digit .2 : ############################## Digit .3 : ###################### Digit .4 : ########################### Digit .5 : ########################################## Digit .6 : ########################### Digit .7 : ########################## Digit .8 : ######################### Digit .9 : #######################################

Digit Preference Score: 8 (0-5 good, 6-10 acceptable, 11-20 poor and > 20 unacceptable)

Digit preference Height:

Digit .0 : ############################# Digit .1 : ################################ Digit .2 : #################################### Digit .3 : ##################### Digit .4 : ##################################### Digit .5 : ######################## Digit .6 : ############################# Digit .7 : ############################## Digit .8 : ################### Digit .9 : ##################################

Digit Preference Score: 7 (0-5 good, 6-10 acceptable, 11-20 poor and > 20 unacceptable)

Digit preference MUAC:

Digit .0 : # Digit .1 :

Digit .2 : ## Digit .3 : ########## Digit .4 : ############################## Digit .5 : ############################################ Digit .6 : ################################ Digit .7 : ################ Digit .8 : ###### Digit .9 : #

Digit Preference Score: 35 (0-5 good, 6-10 acceptable, 11-20 poor and > 20 unacceptable)

Evaluation of Standard deviation, Normal distribution, Skewness and Kurtosis using the 3 exclusion (Flag) procedures

. no exclusion exclusion from exclusion from . reference mean observed mean . (WHO flags) (SMART flags) WHZ Standard Deviation SD: 1.55 1.14 0.99 (The SD should be between 0.8 and 1.2) Prevalence (< -2) observed: 1.4% 1.4% calculated with current SD: 5.0% 1.5% calculated with a SD of 1: 0.5% 0.7%

HAZ Standard Deviation SD: 2.36 1.47 1.31 (The SD should be between 0.8 and 1.2) Prevalence (< -2) observed: 35.7% 34.4% 35.6% calculated with current SD: 43.7% 33.8% 35.5% calculated with a SD of 1: 35.6% 27.0% 31.3%

WAZ Standard Deviation SD: 1.17 1.17 1.14 (The SD should be between 0.8 and 1.2) Prevalence (< -2) observed: 8.2% 8.2% 8.3% calculated with current SD: 8.9% 8.9% 8.7% calculated with a SD of 1: 5.8% 5.8% 6.1%

Results for Shapiro-Wilk test for normally (Gaussian) distributed data: WHZ p= 0.000 p= 0.000 p= 0.683 HAZ p= 0.000 p= 0.138 p= 0.068 WAZ p= 0.209 p= 0.209 p= 0.237 (If p < 0.05 then the data are not normally distributed. If p > 0.05 you can consider the data normally distributed)

Skewness WHZ 4.22 0.49 0.02 HAZ -2.98 0.26 0.00 WAZ 0.17 0.17 0.10 If the value is: -below minus 2 there is a relative excess of wasted/stunted/underweight subjects in the sample -between minus 2 and minus 1, there may be a relative excess of wasted/stunted/underweight subjects in the sample. -between minus 1 and plus 1, the distribution can be considered as symmetrical. -between 1 and 2, there may be an excess of obese/tall/overweight subjects in the sample. -above 2, there is an excess of obese/tall/overweight subjects in the sample

Kurtosis WHZ 37.14 1.57 -0.02

HAZ 16.64 -0.17 -0.57 WAZ -0.05 -0.05 -0.14 (Kurtosis characterizes the relative peakedness or flatness compared with the normal distribution, positive kurtosis indicates a relatively peaked distribution, negative kurtosis indicates a relatively flat distribution) If the value is: -above 2 it indicates a problem. There might have been a problem with data collection or sampling. -between 1 and 2, the data may be affected with a problem. -less than an absolute value of 1 the distribution can be considered as normal.

Are the data of the same quality at the beginning and the end of the clusters? Evaluation of the SD for WHZ depending upon the order the cases are measured within each cluster (if one cluster per day is measured then this will be related to the time of the day the measurement is made).

Time SD for WHZ point 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3

(when n is much less than the average number of subjects per cluster different symbols are used: 0 for n < 80% and ~ for n < 40%; The numbers marked "f" are the numbers of SMART flags found in the different time points)

Kabale District

Standard/Reference used for z-score calculation: WHO standards 2006

Overall data quality

Criteria Flags* Unit Excel. Good Accept Problematic Score

Missing/Flagged data Incl % 0-2.5 >2.5-5.0 >5.0-10 >10 (% of in-range subjects) 0 5 10 20 0 (1.0 %) Overall Sex ratio Incl p >0.1 >0.05 >0.001 <0.000 (Significant chi square) 0 2 4 10 4 (p=0.012) Overall Age distrib Incl p >0.1 >0.05 >0.001 <0.000 (Significant chi square) 0 2 4 10 4 (p=0.009) Dig pref score - weight Incl # 0-5 5-10 10-20 > 20 0 2 4 10 2 (6) Dig pref score - height Incl # 0-5 5-10 10-20 > 20 0 2 4 10 4 (17) Standard Dev WHZ Excl SD <1.1 <1.15 <1.20 >1.20 0 2 6 20 0 (1.03) Skewness WHZ Excl # <±1.0 <±2.0 <±3.0 >±3.0 0 1 3 5 0 (-0.26) Kurtosis WHZ Excl # <±1.0 <±2.0 <±3.0 >±3.0 0 1 3 5 0 (0.03) Poisson dist WHZ-2 Excl p >0.05 >0.01 >0.001 <0.000 0 1 3 5 0 (p=) Timing Excl Not determined yet 0 1 3 5 OVERALL SCORE WHZ = 0-5 5-10 10-15 >15 14 %

At the moment the overall score of this survey is 14 %, this is acceptable.

Age distribution:

Month 6 : ####### Month 7 : ##### Month 8 : ####### Month 9 : ##### Month 10 : #### Month 11 : ######### Month 12 : ########## Month 13 : ##### Month 14 : ####### Month 15 : ############ Month 16 : ####### Month 17 : ######### Month 18 : ####### Month 19 : ######### Month 20 : ########## Month 21 : ### Month 22 : ####### Month 23 : ###### Month 24 : ####### Month 25 : ######### Month 26 : ###### Month 27 : ### Month 28 : ###### Month 29 : #### Month 30 : ######## Month 31 : #### Month 32 : ## Month 33 : ### Month 34 : ##### Month 35 : ## Month 36 : ######## Month 37 : ##### Month 38 : ### Month 39 : ######## Month 40 : ### Month 41 : ###### Month 42 : ######## Month 43 : ####### Month 44 : ####### Month 45 : #### Month 46 : ####### Month 47 : ####

Month 48 : ###### Month 49 : ### Month 50 : ########## Month 51 : #### Month 52 : ### Month 53 : ####### Month 54 : # Month 55 : #### Month 56 : ### Month 57 : ## Month 58 : ###### Month 59 : #

Age ratio of 6-29 months to 30-59 months: 1.14 (The value should be around 1.0).

Statistical evaluation of sex and age ratios (using Chi squared statistic):

Age cat. mo. boys girls total ratio boys/girls ------6 to 17 12 45/40.8 (1.1) 42/30.6 (1.4) 87/71.5 (1.2) 1.07 18 to 29 12 47/39.8 (1.2) 30/29.9 (1.0) 77/69.7 (1.1) 1.57 30 to 41 12 33/38.6 (0.9) 24/28.9 (0.8) 57/67.5 (0.8) 1.38 42 to 53 12 42/38.0 (1.1) 28/28.5 (1.0) 70/66.5 (1.1) 1.50 54 to 59 6 9/18.8 (0.5) 8/14.1 (0.6) 17/32.9 (0.5) 1.13 ------6 to 59 54 176/154.0 (1.1) 132/154.0 (0.9) 1.33

The data are expressed as observed number/expected number (ratio of obs/expect)

Overall sex ratio: p-value = 0.012 (significant excess of boys) Overall age distribution: p-value = 0.009 (significant difference) Overall age distribution for boys: p-value = 0.090 (as expected) Overall age distribution for girls: p-value = 0.103 (as expected) Overall sex/age distribution: p-value = 0.000 (significant difference)

Digit preference Weight:

Digit .0 : ###################################### Digit .1 : ######################### Digit .2 : ########################################### Digit .3 : ############################# Digit .4 : ########################## Digit .5 : ################################# Digit .6 : ############################### Digit .7 : ############################ Digit .8 : ######################## Digit .9 : ##############################

Digit Preference Score: 6 (0-5 good, 6-10 acceptable, 11-20 poor and > 20 unacceptable)

Digit preference Height:

Digit .0 : ################################### Digit .1 : ############### Digit .2 : ################## Digit .3 : ############## Digit .4 : ########## Digit .5 : ###################### Digit .6 : ######## Digit .7 : ########### Digit .8 : ########### Digit .9 : ########

Digit Preference Score: 17 (0-5 good, 6-10 acceptable, 11-20 poor and > 20 unacceptable)

Digit preference MUAC:

Digit .0 : Digit .1 : Digit .2 : ## Digit .3 : ############## Digit .4 : ############################ Digit .5 : ################################################### Digit .6 : ###################################### Digit .7 : ############## Digit .8 : ## Digit .9 : #

Digit Preference Score: 38 (0-5 good, 6-10 acceptable, 11-20 poor and > 20 unacceptable)

Evaluation of Standard deviation, Normal distribution, Skewness and Kurtosis using the 3 exclusion (Flag) procedures

. no exclusion exclusion from exclusion from . reference mean observed mean . (WHO flags) (SMART flags) WHZ Standard Deviation SD: 1.10 1.07 1.03 (The SD should be between 0.8 and 1.2) Prevalence (< -2) observed: 3.3% 3.0% 2.3% calculated with current SD: 1.6% 1.3% 0.9% calculated with a SD of 1: 0.9% 0.8% 0.8%

HAZ Standard Deviation SD: 1.54 1.26 1.09

(The SD should be between 0.8 and 1.2) Prevalence (< -2) observed: 42.2% 41.4% 42.0% calculated with current SD: 42.9% 41.1% 42.3% calculated with a SD of 1: 39.1% 38.8% 41.6%

WAZ Standard Deviation SD: 1.14 1.14 1.02 (The SD should be between 0.8 and 1.2) Prevalence (< -2) observed: 10.1% 10.1% 9.0% calculated with current SD: 12.8% 12.8% 10.0% calculated with a SD of 1: 9.7% 9.7% 9.5%

Results for Shapiro-Wilk test for normally (Gaussian) distributed data: WHZ p= 0.000 p= 0.003 p= 0.051 HAZ p= 0.000 p= 0.000 p= 0.262 WAZ p= 0.009 p= 0.009 p= 0.704 (If p < 0.05 then the data are not normally distributed. If p > 0.05 you can consider the data normally distributed)

Skewness WHZ -0.68 -0.48 -0.26 HAZ 1.25 0.59 0.12 WAZ -0.08 -0.08 0.01 If the value is: -below minus 2 there is a relative excess of wasted/stunted/underweight subjects in the sample -between minus 2 and minus 1, there may be a relative excess of wasted/stunted/underweight subjects in the sample. -between minus 1 and plus 1, the distribution can be considered as symmetrical. -between 1 and 2, there may be an excess of obese/tall/overweight subjects in the sample. -above 2, there is an excess of obese/tall/overweight subjects in the sample

Kurtosis WHZ 1.49 0.67 0.03 HAZ 7.45 1.46 0.24 WAZ 1.10 1.10 0.16 (Kurtosis characterizes the relative peakedness or flatness compared with the normal distribution, positive kurtosis indicates a relatively peaked distribution, negative kurtosis indicates a relatively flat distribution) If the value is: -above 2 it indicates a problem. There might have been a problem with data collection or sampling. -between 1 and 2, the data may be affected with a problem. -less than an absolute value of 1 the distribution can be considered as normal.

Time SD for WHZ point 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3

Kanungu District

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

Overall data quality

Criteria Flags* Unit Excel. Good Accept Problematic Score

Missing/Flagged data Incl % 0-2.5 >2.5-5.0 >5.0-10 >10 (% of in-range subjects) 0 5 10 20 0 (1.9 %) Overall Sex ratio Incl p >0.1 >0.05 >0.001 <0.000 (Significant chi square) 0 2 4 10 0 (p=0.441) Overall Age distrib Incl p >0.1 >0.05 >0.001 <0.000 (Significant chi square) 0 2 4 10 0 (p=0.290) Dig pref score - weight Incl # 0-5 5-10 10-20 > 20 0 2 4 10 0 (4) Dig pref score - height Incl # 0-5 5-10 10-20 > 20 0 2 4 10 4 (16) Standard Dev WHZ Excl SD <1.1 <1.15 <1.20 >1.20 0 2 6 20 0 (1.08) Skewness WHZ Excl # <±1.0 <±2.0 <±3.0 >±3.0 0 1 3 5 0 (-0.03) Kurtosis WHZ Excl # <±1.0 <±2.0 <±3.0 >±3.0 0 1 3 5 0 (-0.17) Poisson dist WHZ-2 Excl p >0.05 >0.01 >0.001 <0.000 0 1 3 5 0 (p=) Timing Excl Not determined yet 0 1 3 5 OVERALL SCORE WHZ = 0-5 5-10 10-15 >15 4 %

At the moment the overall score of this survey is 4 %, this is excellent.

Age distribution:

Month 6 : #### Month 7 : ######### Month 8 : ###### Month 9 : ##### Month 10 : ######## Month 11 : #### Month 12 : ##### Month 13 : ######### Month 14 : ######## Month 15 : ############## Month 16 : ### Month 17 : ####### Month 18 : ######### Month 19 : ####### Month 20 : ####### Month 21 : ##### Month 22 : ########

Month 23 : ### Month 24 : ###### Month 25 : ######## Month 26 : #### Month 27 : ########### Month 28 : ######## Month 29 : ### Month 30 : ### Month 31 : ####### Month 32 : ### Month 33 : #### Month 34 : ## Month 35 : ###### Month 36 : ############ Month 37 : ###### Month 38 : ###### Month 39 : ########### Month 40 : ######## Month 41 : ####### Month 42 : ###### Month 43 : ##### Month 44 : #### Month 45 : ###### Month 46 : ####### Month 47 : ###### Month 48 : #### Month 49 : ########## Month 50 : ###### Month 51 : ########## Month 52 : ###### Month 53 : # Month 54 : ######### Month 55 : #### Month 56 : ### Month 57 : ##### Month 58 : ##

Age ratio of 6-29 months to 30-59 months: 0.95 (The value should be around 1.0).

Statistical evaluation of sex and age ratios (using Chi squared statistic):

Age cat. mo. boys girls total ratio boys/girls ------6 to 17 12 43/39.9 (1.1) 39/36.7 (1.1) 82/76.6 (1.1) 1.10 18 to 29 12 42/38.9 (1.1) 37/35.7 (1.0) 79/74.7 (1.1) 1.14 30 to 41 12 41/37.7 (1.1) 34/34.6 (1.0) 75/72.4 (1.0) 1.21 42 to 53 12 32/37.1 (0.9) 39/34.1 (1.1) 71/71.2 (1.0) 0.82 54 to 59 6 14/18.4 (0.8) 9/16.9 (0.5) 23/35.2 (0.7) 1.56 ------6 to 59 54 172/165.0 (1.0) 158/165.0 (1.0) 1.09

The data are expressed as observed number/expected number (ratio of obs/expect)

Overall sex ratio: p-value = 0.441 (boys and girls equally represented) Overall age distribution: p-value = 0.290 (as expected) Overall age distribution for boys: p-value = 0.643 (as expected) Overall age distribution for girls: p-value = 0.333 (as expected) Overall sex/age distribution: p-value = 0.108 (as expected)

Digit preference Weight:

Digit .0 : ################################### Digit .1 : #################################### Digit .2 : ############################### Digit .3 : ###################################### Digit .4 : ################################## Digit .5 : ############################# Digit .6 : ############################### Digit .7 : ################################## Digit .8 : ####################### Digit .9 : ######################################

Digit Preference Score: 4 (0-5 good, 6-10 acceptable, 11-20 poor and > 20 unacceptable)

Digit preference Height:

Digit .0 : ################################## Digit .1 : ############## Digit .2 : ################# Digit .3 : ############## Digit .4 : ################ Digit .5 : ############################# Digit .6 : ############# Digit .7 : ######## Digit .8 : ######## Digit .9 : ##########

Digit Preference Score: 16 (0-5 good, 6-10 acceptable, 11-20 poor and > 20 unacceptable)

Digit preference MUAC:

Digit .0 : Digit .1 : ## Digit .2 : #####

Digit .3 : ############## Digit .4 : ###################################### Digit .5 : ##################################################### Digit .6 : ################################### Digit .7 : ########## Digit .8 : ###### Digit .9 :

Digit Preference Score: 36 (0-5 good, 6-10 acceptable, 11-20 poor and > 20 unacceptable)

Evaluation of Standard deviation, Normal distribution, Skewness and Kurtosis using the 3 exclusion (Flag) procedures

. no exclusion exclusion from exclusion from . reference mean observed mean . (WHO flags) (SMART flags) WHZ Standard Deviation SD: 1.23 1.13 1.08 (The SD should be between 0.8 and 1.2) Prevalence (< -2) observed: 2.8% 2.2% 1.6% calculated with current SD: 2.9% 1.8% 1.4% calculated with a SD of 1: 1.0% 0.9% 0.9%

HAZ Standard Deviation SD: 1.57 1.37 1.25 (The SD should be between 0.8 and 1.2) Prevalence (< -2) observed: 42.8% 41.5% 42.2% calculated with current SD: 43.3% 39.8% 41.2% calculated with a SD of 1: 39.5% 36.2% 39.0%

WAZ Standard Deviation SD: 1.51 1.17 1.11 (The SD should be between 0.8 and 1.2) Prevalence (< -2) observed: 14.1% 13.8% 13.1% calculated with current SD: 18.7% 13.1% 11.5% calculated with a SD of 1: 9.0% 9.5% 9.1%

Results for Shapiro-Wilk test for normally (Gaussian) distributed data: WHZ p= 0.000 p= 0.313 p= 0.210 HAZ p= 0.000 p= 0.063 p= 0.183 WAZ p= 0.000 p= 0.535 p= 0.436 (If p < 0.05 then the data are not normally distributed. If p > 0.05 you can consider the data normally distributed)

Skewness WHZ -0.81 -0.02 -0.03 HAZ -0.22 0.35 0.07 WAZ 3.55 -0.12 0.02 If the value is: -below minus 2 there is a relative excess of wasted/stunted/underweight subjects in the sample -between minus 2 and minus 1, there may be a relative excess of wasted/stunted/underweight subjects in the sample. -between minus 1 and plus 1, the distribution can be considered as symmetrical. -between 1 and 2, there may be an excess of obese/tall/overweight subjects in the sample. -above 2, there is an excess of obese/tall/overweight subjects in the sample

Kurtosis WHZ 3.87 0.08 -0.17 HAZ 3.37 0.12 -0.52 WAZ 40.76 0.10 -0.30

(Kurtosis characterizes the relative peakedness or flatness compared with the normal distribution, positive kurtosis indicates a relatively peaked distribution, negative kurtosis indicates a relatively flat distribution) If the value is: -above 2 it indicates a problem. There might have been a problem with data collection or sampling. -between 1 and 2, the data may be affected with a problem. -less than an absolute value of 1 the distribution can be considered as normal.

Time SD for WHZ point 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3

Nebbi District

Overall data quality

Criteria Flags* Unit Excel. Good Accept Problematic Score

Missing/Flagged data Incl % 0-2.5 >2.5-5.0 >5.0-10 >10 (% of in-range subjects) 0 5 10 20 0 (2.5 %) Overall Sex ratio Incl p >0.1 >0.05 >0.001 <0.000 (Significant chi square) 0 2 4 10 0 (p=0.833) Overall Age distrib Incl p >0.1 >0.05 >0.001 <0.000 (Significant chi square) 0 2 4 10 0 (p=0.117) Dig pref score - weight Incl # 0-5 5-10 10-20 > 20 0 2 4 10 2 (6) Dig pref score - height Incl # 0-5 5-10 10-20 > 20 0 2 4 10 10 (24) Standard Dev WHZ Excl SD <1.1 <1.15 <1.20 >1.20 0 2 6 20 0 (1.03) Skewness WHZ Excl # <±1.0 <±2.0 <±3.0 >±3.0 0 1 3 5 0 (0.12) Kurtosis WHZ Excl # <±1.0 <±2.0 <±3.0 >±3.0 0 1 3 5 0 (0.15) Poisson dist WHZ-2 Excl p >0.05 >0.01 >0.001 <0.000 0 1 3 5 0 (p=) Timing Excl Not determined yet 0 1 3 5 OVERALL SCORE WHZ = 0-5 5-10 10-15 >15 12 %

At the moment the overall score of this survey is 12 %, this is acceptable.

Age distribution:

Month 6 : ############ Month 7 : ## Month 8 : ######## Month 9 : ##### Month 10 : ########## Month 11 : ########## Month 12 : ##### Month 13 : ##### Month 14 : ################# Month 15 : ###### Month 16 : ######### Month 17 : ######## Month 18 : ###### Month 19 : ###### Month 20 : ######## Month 21 : #### Month 22 : ###### Month 23 : ######## Month 24 : ######## Month 25 : ############ Month 26 : ########### Month 27 : ####### Month 28 : ######### Month 29 : ##### Month 30 : ######### Month 31 : #### Month 32 : ##### Month 33 : ##### Month 34 : ## Month 35 : ## Month 36 : ############# Month 37 : ##### Month 38 : ###### Month 39 : ##### Month 40 : ##### Month 41 : #### Month 42 : ##### Month 43 : ## Month 44 : ####### Month 45 : ## Month 46 : Month 47 : ######### Month 48 : ############## Month 49 : #####

Month 50 : ### Month 51 : ####### Month 52 : ##### Month 53 : ####### Month 54 : #### Month 55 : #### Month 56 : ####### Month 57 : ## Month 58 : ###### Month 59 : ################# Month 60 : #

Age ratio of 6-29 months to 30-59 months: 1.09 (The value should be around 1.0).

Statistical evaluation of sex and age ratios (using Chi squared statistic):

Age cat. mo. boys girls total ratio boys/girls ------6 to 17 12 44/41.1 (1.1) 53/42.0 (1.3) 97/83.1 (1.2) 0.83 18 to 29 12 40/40.0 (1.0) 50/40.9 (1.2) 90/81.0 (1.1) 0.80 30 to 41 12 36/38.8 (0.9) 29/39.7 (0.7) 65/78.5 (0.8) 1.24 42 to 53 12 35/38.2 (0.9) 31/39.1 (0.8) 66/77.2 (0.9) 1.13 54 to 59 6 22/18.9 (1.2) 18/19.3 (0.9) 40/38.2 (1.0) 1.22 ------6 to 59 54 177/179.0 (1.0) 181/179.0 (1.0) 0.98

The data are expressed as observed number/expected number (ratio of obs/expect)

Overall sex ratio: p-value = 0.833 (boys and girls equally represented) Overall age distribution: p-value = 0.117 (as expected) Overall age distribution for boys: p-value = 0.880 (as expected) Overall age distribution for girls: p-value = 0.049 (significant difference) Overall sex/age distribution: p-value = 0.028 (significant difference)

Digit preference Weight:

Digit .0 : ########################################## Digit .1 : ############################################### Digit .2 : ############################# Digit .3 : ############################################ Digit .4 : ################################## Digit .5 : ############################## Digit .6 : ######################################### Digit .7 : ############################# Digit .8 : ##################################### Digit .9 : ##########################

Digit Preference Score: 6 (0-5 good, 6-10 acceptable, 11-20 poor and > 20 unacceptable)

Digit preference Height:

Digit .0 : ############################################### Digit .1 : ############## Digit .2 : ################## Digit .3 : ########## Digit .4 : ############## Digit .5 : #################################### Digit .6 : ############## Digit .7 : ########## Digit .8 : ########## Digit .9 : ######

Digit Preference Score: 24 (0-5 good, 6-10 acceptable, 11-20 poor and > 20 unacceptable)

Digit preference MUAC:

Digit .0 : Digit .1 : Digit .2 : ## Digit .3 : ########### Digit .4 : ########################### Digit .5 : ############################################################## Digit .6 : #################################################### Digit .7 : ############## Digit .8 : ###### Digit .9 : ##

Digit Preference Score: 40 (0-5 good, 6-10 acceptable, 11-20 poor and > 20 unacceptable)

Evaluation of Standard deviation, Normal distribution, Skewness and Kurtosis using the 3 exclusion (Flag) procedures

. no exclusion exclusion from exclusion from . reference mean observed mean . (WHO flags) (SMART flags) WHZ Standard Deviation SD: 1.16 1.14 1.03 (The SD should be between 0.8 and 1.2) Prevalence (< -2) observed: 4.5% 4.2% 3.5% calculated with current SD: 4.5% 4.1% 2.9% calculated with a SD of 1: 2.5% 2.4% 2.5%

HAZ Standard Deviation SD: 1.81 1.55 1.25 (The SD should be between 0.8 and 1.2) Prevalence (< -2) observed: 33.5% 33.4% 32.9%

calculated with current SD: 35.4% 34.0% 32.5% calculated with a SD of 1: 25.0% 26.1% 28.5%

WAZ Standard Deviation SD: 1.20 1.20 1.10 (The SD should be between 0.8 and 1.2) Prevalence (< -2) observed: 13.6% 13.6% 12.7% calculated with current SD: 15.4% 15.4% 12.9% calculated with a SD of 1: 11.1% 11.1% 10.7%

Results for Shapiro-Wilk test for normally (Gaussian) distributed data: WHZ p= 0.002 p= 0.008 p= 0.592 HAZ p= 0.000 p= 0.017 p= 0.041 WAZ p= 0.000 p= 0.000 p= 0.154 (If p < 0.05 then the data are not normally distributed. If p > 0.05 you can consider the data normally distributed)

Skewness WHZ 0.06 0.20 0.12 HAZ 1.14 0.22 0.06 WAZ -0.08 -0.08 -0.10 If the value is: -below minus 2 there is a relative excess of wasted/stunted/underweight subjects in the sample -between minus 2 and minus 1, there may be a relative excess of wasted/stunted/underweight subjects in the sample. -between minus 1 and plus 1, the distribution can be considered as symmetrical. -between 1 and 2, there may be an excess of obese/tall/overweight subjects in the sample. -above 2, there is an excess of obese/tall/overweight subjects in the sample

Kurtosis WHZ 1.15 0.84 0.15 HAZ 8.06 0.05 -0.50 WAZ 1.57 1.57 0.14 (Kurtosis characterizes the relative peakedness or flatness compared with the normal distribution, positive kurtosis indicates a relatively peaked distribution, negative kurtosis indicates a relatively flat distribution) If the value is: -above 2 it indicates a problem. There might have been a problem with data collection or sampling. -between 1 and 2, the data may be affected with a problem. -less than an absolute value of 1 the distribution can be considered as normal.

Time SD for WHZ point 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3

Pader District

Overall data quality

Criteria Flags* Unit Excel. Good Accept Problematic Score

Missing/Flagged data Incl % 0-2.5 >2.5-5.0 >5.0-10 >10 (% of in-range subjects) 0 5 10 20 0 (2.1 %) Overall Sex ratio Incl p >0.1 >0.05 >0.001 <0.000 (Significant chi square) 0 2 4 10 0 (p=0.130) Overall Age distrib Incl p >0.1 >0.05 >0.001 <0.000 (Significant chi square) 0 2 4 10 4 (p=0.013) Dig pref score - weight Incl # 0-5 5-10 10-20 > 20 0 2 4 10 0 (5) Dig pref score - height Incl # 0-5 5-10 10-20 > 20 0 2 4 10 4 (16) Standard Dev WHZ Excl SD <1.1 <1.15 <1.20 >1.20 0 2 6 20 0 (0.99) Skewness WHZ Excl # <±1.0 <±2.0 <±3.0 >±3.0 0 1 3 5 0 (-0.18) Kurtosis WHZ Excl # <±1.0 <±2.0 <±3.0 >±3.0 0 1 3 5 0 (0.34) Poisson dist WHZ-2 Excl p >0.05 >0.01 >0.001 <0.000 0 1 3 5 0 (p=) Timing Excl Not determined yet 0 1 3 5 OVERALL SCORE WHZ = 0-5 5-10 10-15 >15 8 %

At the moment the overall score of this survey is 8 %, this is good.

Age distribution:

Month 6 : ########## Month 7 : ### Month 8 : # Month 9 : ####### Month 10 : ### Month 11 : ############# Month 12 : ########### Month 13 : ########### Month 14 : ########### Month 15 : ####### Month 16 : ########### Month 17 : ########### Month 18 : #### Month 19 : ##### Month 20 : ###### Month 21 : ##### Month 22 : ###### Month 23 : ######

Month 24 : #### Month 25 : ######### Month 26 : #### Month 27 : ######## Month 28 : ######## Month 29 : ####### Month 30 : ######## Month 31 : ########## Month 32 : ######### Month 33 : ##### Month 34 : # Month 35 : ### Month 36 : ######### Month 37 : ### Month 38 : ############## Month 39 : ##### Month 40 : ########## Month 41 : #### Month 42 : ## Month 43 : ### Month 44 : #### Month 45 : #### Month 46 : ###### Month 47 : #### Month 48 : ####### Month 49 : ######### Month 50 : ########### Month 51 : ########## Month 52 : #### Month 53 : ##### Month 54 : #### Month 55 : ### Month 56 : ### Month 57 : #### Month 58 : #### Month 59 : ###

Age ratio of 6-29 months to 30-59 months: 1.00 (The value should be around 1.0).

Statistical evaluation of sex and age ratios (using Chi squared statistic):

Age cat. mo. boys girls total ratio boys/girls ------6 to 17 12 45/36.4 (1.2) 54/42.9 (1.3) 99/79.4 (1.2) 0.83 18 to 29 12 31/35.5 (0.9) 41/41.9 (1.0) 72/77.4 (0.9) 0.76 30 to 41 12 32/34.4 (0.9) 49/40.6 (1.2) 81/75.0 (1.1) 0.65 42 to 53 12 37/33.9 (1.1) 32/39.9 (0.8) 69/73.8 (0.9) 1.16 54 to 59 6 12/16.8 (0.7) 9/19.7 (0.5) 21/36.5 (0.6) 1.33 ------6 to 59 54 157/171.0 (0.9) 185/171.0 (1.1) 0.85

The data are expressed as observed number/expected number (ratio of obs/expect)

Overall sex ratio: p-value = 0.130 (boys and girls equally represented) Overall age distribution: p-value = 0.013 (significant difference) Overall age distribution for boys: p-value = 0.354 (as expected) Overall age distribution for girls: p-value = 0.017 (significant difference) Overall sex/age distribution: p-value = 0.001 (significant difference)

Digit preference Weight:

Digit .0 : ##################################### Digit .1 : ################################# Digit .2 : ################################# Digit .3 : ############################### Digit .4 : ##################################### Digit .5 : ############################### Digit .6 : ################################################ Digit .7 : ################################ Digit .8 : ########################### Digit .9 : #################################

Digit Preference Score: 5 (0-5 good, 6-10 acceptable, 11-20 poor and > 20 unacceptable)

Digit preference Height:

Digit .0 : ###################################### Digit .1 : ############## Digit .2 : ################## Digit .3 : ############ Digit .4 : ####################### Digit .5 : ###################### Digit .6 : ################ Digit .7 : ######### Digit .8 : ########## Digit .9 : ##########

Digit Preference Score: 16 (0-5 good, 6-10 acceptable, 11-20 poor and > 20 unacceptable)

Digit preference MUAC:

Digit .0 : Digit .1 :

Digit .2 : ## Digit .3 : ############ Digit .4 : ######################################## Digit .5 : ########################################################### Digit .6 : ######################################## Digit .7 : ############ Digit .8 : ## Digit .9 : #

Digit Preference Score: 40 (0-5 good, 6-10 acceptable, 11-20 poor and > 20 unacceptable)

Evaluation of Standard deviation, Normal distribution, Skewness and Kurtosis using the 3 exclusion (Flag) procedures

. no exclusion exclusion from exclusion from . reference mean observed mean . (WHO flags) (SMART flags) WHZ Standard Deviation SD: 1.38 1.04 0.99 (The SD should be between 0.8 and 1.2) Prevalence (< -2) observed: 5.9% 4.7% calculated with current SD: 11.3% 4.5% calculated with a SD of 1: 4.7% 3.9%

HAZ Standard Deviation SD: 1.76 1.28 1.11 (The SD should be between 0.8 and 1.2) Prevalence (< -2) observed: 21.6% 20.4% 19.6% calculated with current SD: 28.2% 23.0% 20.2% calculated with a SD of 1: 15.4% 17.3% 17.7%

WAZ Standard Deviation SD: 1.57 1.15 1.04 (The SD should be between 0.8 and 1.2) Prevalence (< -2) observed: 11.1% 10.6% 10.0% calculated with current SD: 21.1% 13.8% 11.8% calculated with a SD of 1: 10.4% 10.5% 10.9%

Results for Shapiro-Wilk test for normally (Gaussian) distributed data: WHZ p= 0.000 p= 0.003 p= 0.050 HAZ p= 0.000 p= 0.033 p= 0.805 WAZ p= 0.000 p= 0.001 p= 0.050 (If p < 0.05 then the data are not normally distributed. If p > 0.05 you can consider the data normally distributed)

Skewness WHZ -3.37 0.02 -0.18 HAZ 0.78 0.04 -0.05 WAZ 2.71 0.02 -0.16 If the value is: -below minus 2 there is a relative excess of wasted/stunted/underweight subjects in the sample -between minus 2 and minus 1, there may be a relative excess of wasted/stunted/underweight subjects in the sample. -between minus 1 and plus 1, the distribution can be considered as symmetrical. -between 1 and 2, there may be an excess of obese/tall/overweight subjects in the sample. -above 2, there is an excess of obese/tall/overweight subjects in the sample

Kurtosis WHZ 29.58 1.03 0.34 HAZ 3.98 0.80 -0.12 WAZ 40.00 1.03 0.32 (Kurtosis characterizes the relative peakedness or flatness compared with the normal distribution, positive kurtosis indicates a relatively peaked distribution, negative kurtosis indicates a relatively flat distribution) If the value is: -above 2 it indicates a problem. There might have been a problem with data collection or sampling. -between 1 and 2, the data may be affected with a problem. -less than an absolute value of 1 the distribution can be considered as normal.

Time SD for WHZ point 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3

Appendix 4: Chart for calculating age

(ACCURATE AT DECEMBER 2012)

Date of Birth Age (in months) Date Of Birth Age (in Months)

Jan-08 59 July–10 29 Feb-08 58 Aug -10 28 March-08 57 Sept-10 27 April-08 56 Oct-10 26 May-08 55 Nov-10 25 June-08 54 Dec-10 24 July-08 53 Jan-11 23 Aug –08 52 Feb-11 22 Sept-08 51 March-11 21 Oct–08 50 April-11 20 Nov -08 49 May –11 19 Dec-08 48 June-11 18 Jan-09 47 July–11 17 Feb-09 46 Aug -11 16 March-09 45 Sept-11 15 April-09 44 Oct-11 14 May-09 43 Nov-11 13 June-09 42 Dec-11 12 July-09 41 Jan-12 11 Aug –09 40 Feb-12 10 Sept-09 39 Mar-12 9 Oct–09 38 April-12 8 Nov- 09 37 May-12 7 Dec-09 36 June-12 6 Jan-10 35 July-12 5 Feb-10 34 Aug-12 4 March-10 33 Sept -12 3 Apr -10 32 Oct-12 2 May –10 31 Nov-12 1 June-10 30 Dec-12 0

Appendix 5: Referral form

MINISTRY OF HEALTH/ UNICEF/ MUSPH COLLABORATION HEALTH AND NUTRITION ASSESSMENT IN KABALE, KANUNGU, IBANDA, NEBBI AND PADER Under-5 Referral Card for Malnourished Children

Parent’s Name: …………………………………………………………… Household No…………………………..

Child’s Name: ……………………………………………………………

Age: …………………………………………………………… Sex: ……………………………………….

Village: ……………………………………………………………

Date: Screened: ……………………………………………………………

MUAC (cm): ………………………………

Oedema (y/n): ………………………………

TFP/SFP ……………………………………………………………. (indicate nearest centers) referred to:

Name of nearest Health Facility referred to: …………………………………………………………………………

SFC Referral MUAC below 12.5 cm criteria:

TFC Referral MUAC below 11.5 cm (height>=75 cm) and or oedema criteria:

Note: This form should be filled out in duplicate: one for the mother or caretaker of child and one for the team that has referred the child.

SIGNATURE – Referring Officer…………………………………………………..