Food Security and Nutrition Assessment for

Transcription

1 Food Security and Nutrition Assessment for Karamoja Pppppppprism Sub-region Report Submitted to UNWFP Uganda December 2010 Makerere University School of Public Health P.O. Box 7072 Kampala Tel: or

2 Table of Contents ABBREVIATIONS... iv LIST OF TABLES... v LIST OF FIGURES... vii ACKNOWLEDGEMENTS... ix EXECUTIVE SUMMARY... x Chapter BACKGROUND INTRODUCTION CONTEXT OF KARAMOJA JUSTIFICATION FOR CONDUCTING REGULAR FOOD SECURITY AND NUTRITION ASSESSMENTS IN KARAMOJA CONCEPTUAL FRAMEWORK FOR THE CAUSES OF MALNUTRITION AND FOOD INSECURITY OBJECTIVES FOR THE ASSESSMENT General objectives for the assessment Specific objectives for the assessment PARTNERSHIPS...27 Chapter METHODS AND MATERIALS TARGET POPULATION SAMPLING Sample size and sampling process for the households SAMPLING PROCEDURE FOR THE HOUSEHOLD ASSESSMENT Sampling universe Selection of primary sampling units (clusters) Selection of the basic sampling unit (households) INFORMATION COLLECTED VARIABLES ASSESSED Anthropometric measurements Family care practices Morbidity and mortality Assessment of food security and vulnerability TRAINING OF DATA COLLECTORS AND FIELD SUPERVISION DATA ENTRY AND ANALYSIS Data entry Data analysis ETHICAL CONSIDERATIONS CHALLENGES AND LIMITATIONS...39 Chapter ii P age

3 ASSESSMENT FINDINGS AND DISCUSSION SOCIO DEMOGRAPHIC CHARATERISTICS Age and sex distribution of the sampled children Age and education status of mothers and/or care givers NUTRITIONAL STATUS OF CHILDREN AND MOTHERS Global acute malnutrition Chronic malnutrition (stunting) Underweight status Mid Upper Arm Circumference (MUAC) Maternal nutrition HEALTH, WATER, SANITATION AND MORTALITY Health status of children under five Use of mosquito nets Immunization, vitamin A supplementation and de worming coverage Access to health facilities Water and sanitation Mortality assessment INFANT AND YOUNG CHILD FEEDING PRACTICES Caregiver characteristics Breastfeeding practices Complementary feeding FINDINGS AND DISCUSSION ON FOOD SECURITY IN KARAMOJA Wealth profile of households Household food consumption scores Household food production and other sources of foods Household animal ownership Household expenditures and main sources of income Food support Non Food support from government or humanitarian Agencies FACTORS ASSOCIATED WITH MALNUTRITION AND FOOD SECURITY EDUCATION OF CHILDREN AND HOUSEHOLD PRIORITIES Education of children Immediate and long term household priorities...80 Chapter CONCLUSIONS AND RECOMMENDATIONS CONCLUSIONS AND RECOMMENDATIONS...83 Appendices Appendix 1: National level supervisors...87 Appendix 2: District teams/enumerators...88 Appendix 3: Results based on NCHS reference Appendix 4: Plausibility checks for WHO standards with SMART flags Appendix 4: Referral form Appendix 4: Food security and nutrition assessment questionnaire iii P age

4 ABBREVIATIONS DHO ENA FCG FSNA GAM Mak SPH MAM MCHN MOH MUAC NCHS NGO PPS SAM SES SMART SFP TFP TLU UBOS UNICEF UNWFP WHO District Health Office(r) Emergency Nutrition Assessment Food Consumption Groups Food Security and Nutrition Assessment Global Acute Malnutrition Makerere University College of Health Sciences, School of Public Health Moderate Acute Malnutrition Maternal and Child Health Nutrition Ministry of Health Mid Upper Arm Circumference National Centre for Health Statistics Non Governmental Organisation Probability proportional to population size Severe Acute Malnutrition Socioeconomic Status Standardized Monitoring and Assessment of Relief and Transition Supplementary Feeding Programme Therapeutic Feeding Programme Tropical Livestock Unit Uganda Bureau of Statistics United Nations Children s Fund United Nations World Food Programme World Health Organisation iv P age

5 LIST OF TABLES Table 1.1: 2010 Karamoja Food Security and Nutrition Assessment: Comparison with 2009 Table 1.2: Table 2.1: Table 2.2: Table 2.1: Table 3.2: Table 3.3: Table 3.4: Table 3.5: Table 3.6: Table 3.7: Table 3.8: Table 3.9: Table 3.10: Table 3.11: Table 3.12: Table 3.13: Table 3.14: Table 3.15: Table 3.16: Table 3.17: Master table for the descriptive results: Food security, nutrition and mortality assessment in Karamoja region, November Sampled children and households per district Definition of malnutrition Number of children included for anthropometric measurements by age and district Mothers/care givers reported age in years by district Acute malnutrition rates by district, age and sex, Karamoja region November 2010 Proportion of GAM by sub county and by district Prevalence of stunting amongst children 6 59 months old by sex and by district Prevalence of underweight among children 6 59 months by district and sex MUAC of Children 6 59 months of age in Karamoja region MUAC of and caregivers and women of reproductive age (15 49 years) Prevalence of common illnesses amongst children 6 59 months old by district Bed net coverage amongst children 6 59 months by district Measles immunization coverage among children by district Vitamin A coverage by district DPT3 coverage by district De worming coverage by district Facilities where respondents reported to have sought health care services by district Access to safe water by district Latrine coverage by district v P age

6 Table 3.18: Table 3.19: Table 3.20: Table 3.21: Table 3.22: Table 3.23: Table 3.24: Table 3.25: Table 3.26: Table 3.27: Table 3.28: Table 3.29: Table 3.30: Table 3.31: Table 3.32 Duration of introduction of breast feeding after delivery Distribution of households by socioeconomic status and by district Food security status at household level by district Households food production in the first major agricultural season Projected population suffering from food insecurity in mid 2011 by district Household number of animal ownership by district Average tropical livestock units (TLU) per household by district Mean and median household expenditure on food items by district (UGX) Proportion of total expenditures for health by district Proportion of households that received non food assistance by district Factors associated with nutrition outcomes on crude analysis of the pooled dataset Factors independently associated with nutrition outcomes in a multivariate logistic regression model of the pooled dataset Reasons why children aged 6 12 years were out of School Immediate priority for household by district Long term priority for household by district vi P age

7 LIST OF FIGURES Figure 1.1: Figure 1.2: Figure 3.1: Figure 3.2: Figure 3.3: Figure 3.4: Figure 3.5: Figure 3.6: Figure 3.7 Prevalence of acute malnutrition amongst children 6 59 months old by district Conceptual framework to analyze food security and nutrition in society (adapted from UNICEF 1990) Prevalence of acute malnutrition amongst children 6 59 months old (Karamoja region ) Prevalence of stunting by age category Prevalence of underweight by age category Prevalence of common illnesses amongst children aged 6 59 months in Karamoja region, October, 2010 Treatment of drinking water by district Faecal disposal pooled for Karamoja region Rubbish disposal by district Figure 3.8: Figure 3.9: Figure 3.10: Figure 3.11: Figure 3.12: Figure 3.13: Figure 3.14: Figure 3.15: Foods reported to have been introduced before children were 6 months of age Variety of foods provided to children aged 6 24 months a day before the assessment Diversity of food consumed in the seven days of the recall period per food consumption grouping Average household food crop production during the 2010 main season Sources of food consumed in the seven days of the recall period Proportion of household expenditure on food, health, education and others Household income sources Proportion of households that received food from all programs between January and September 2010 by district vii P age

8 Figure 3.16: Figure 3.17: Figure 3.18: Figure 3.19: Number of households which had a member who participated in the food for education program by month and by district Number of children who participated in supplementary feeding program by month and by district Number of children who participated in therapeutic feeding program by month and by district Number of households which had a member who participated in the food for work program by month and by district viii P age

9 ACKNOWLEDGEMENTS The entire team of Investigators led by Dr Henry Wamani the Principal Investigator and Dr Monica Karuhanga, Mr Simon Peter Sebina Kibira, Dr David Lubogo, Ms Sheila Katureebe and Mr Geofrey Nyakuni Arijole the co Investigators would like to convey sincere thanks all partners and individuals who participated and supported the 2010 food security and nutrition assessment for the Karamoja region. The field assessment was carried out in November Special appreciation goes to the five District Health Officers (DHOs) of Abim, Kaabong, Kotido, Moroto and Nakapiripirit where the assessment was carried out, for the cooperation and support. To the 15 Central Supervisors, the 6 Data Managers, the 5 District Supervisors and the 60 Enumerators and Data Entrants recruited in all the five districts we say thank you for standing up to the task. We thank you all for the good team spirit, working meticulously and ensuring that all procedures were followed during training and data collection. We greatly acknowledge the support and technical insights received from Daniel Molla, Kenneth Anyanzo, and Dorothy Nabiwemba of UNWFP, Kampala and Nelly Birungi of UNICEF, Kampala. Additionally we acknowledge the support received from all the staff of UNWFP and UNICEF field offices specifically for providing us with computers for data entry and for supporting various field activities. We appreciate the trust and confidence the Ministry of Health, UNWFP and UNICEF has continued to place in the School of Public Health, Makerere University College of Health Sciences (Mak SPH) to carry out the food security and nutrition assessments in Karamoja. The financial support for the assessment from UNWFP and UNICEF is highly appreciated. We also thank Dr William Bazeyo the Dean of Mak SPH for supporting the assessment by ensuring that all necessary logistics were provided on time. All the support staff, drivers and field guides are very much appreciated. ix P age

10 EXECUTIVE SUMMARY Introduction In September 2010, UNWFP contracted the School of Public Health, Makerere University College of Health Sciences (Mak SPH) to conduct the Food Security and Nutrition Assessment in Karamoja sub region. Data collection was conducted during the period of 18 th October to 2 nd November, 2010, in the 5 districts of Abim, Kaabong, Kotido, Nakapiripirit and Moroto. However, sampled clusters falling in the new districts of Napak and Amudat were also covered. This survey was part of the routine assessments done in the sub region to generate information to monitor and improve programme and policy interventions. Objectives Similar to previous food security and nutrition assessments, the 2010 assessment was specifically designed to provide statistically representative results on the food security and nutritional situation of children aged 6 59 months and households in each of the district of Karamoja region. Specifically children aged 6 59 months were assessed for: malnutrition; coverage of services such as vitamin A supplementation, supplementary feeding programs (SFPs) and therapeutic feeding programs (TFPs), immunisation, water and sanitation; common diseases (diarrhoea, measles, ARI and fever); and maternal malnutrition. In addition the assessment covered household food security; crop cultivation and production, and livestock ownership; economic access to food at household level; retrospective underfive and crude mortality rates; and identification of the factors associated with nutrition and household food insecurity. Methods The survey was population based and cross sectional with district representative samples. The newly created districts of Amudat and Napak which became functional only in July 2010, were not assessed independently, however, clusters falling in these districts were included in the assessment. A modified two stage sampling criteria was followed to sample 3283 households, that is 682 in Abim, 667 in Kaabong, 620 in Nakapiripirit, 626 in Kotido and x P age

11 688 in Moroto. All the households were assessed for food security. The WHO standards were used to assess for malnutrition, however, the NCHS references were provided in appendices for purposes of comparability with past assessments. Analysis was by descriptive statistics and multivariate modelling using logistic regression models for predictors of key outcomes such as GAM and food insecurity. Findings Karamoja region had serious rates of malnutrition with a GAM prevalence of 11.7% in pooled data for the region. The prevalence of GAM was above 10% (alert level) in Kaabong (15.9%), Kotido (11.5%) and Moroto (13.8%) and lower in Abim (7.2%) and marginal in Nakapiripirit (9.8%) districts. Compared to the findings in the 2009 assessment there was no improvement in the GAM rates in 2010 (Table 1.1). In Kaabong district, the GAM rate worsened significantly from 9.6% in 2009 to a critical level of 15.9% in However, a smaller survey done by ACF in the same period reported a GAM prevalence of 7.2% in Kaabong. Table 1.1: 2010 Karamoja Food Security and Nutrition Assessment: Comparison with 2009 Indicator Year Abim Kaabong Kotido Nakapiripirit Moroto All Districts Global Acute Malnutrition (GAM), % Severe Acute Malnutrition (SAM), % Percentage Stunting Percentage underweight Percentage of Food Insecure Households Stunting prevalence was at 34.3% in pooled analysis which was lower than 40.2% reported in Underweight prevalence was 26.6% similar to the rate reported in Unlike GAM which had its peak in the age group 6 17 months (18.5%), stunting and underweight had peaks in the age group months. In multivariate analyses, the factors that were universally independently associated with all the three indicators of under nutrition in xi P age

12 children were age of the child and household socioeconomic status. Compared to older children in age group months and wealthier households in upper 25% quintile, children in younger age group and household in lower socioeconomic quintiles (bottom 25%), were significantly more likely to be malnourished. However being male (OR= % CI = ), and history of not having suffered from fever in the 2 weeks prior to the assessment (OR = % CI = ) were also associated with stunting. The overall 90 day recall crude mortality rates was 0.67 (95% CI = ) for the region and was 0.20 (95% CI = ) for Abim; 1.70 (95% CI = ) for Kaabong; 0.57 (95% CI = ) for Kotido; 0.60 (95% CI = ) for Nakapiripirit; and 0.24 (95% CI = ) for Moroto. Likewise under five mortality rates were 0.33 (95% CI = ), 2.93 (95% CI = ), 0.95 (95% CI = ), 1.24 (95% CI = ), and 0.97 (95% CI = ) for Abim, Kaabong, Kotido, Nakapiripirit and Moroto, respectively. Crude mortality in Kaabong district, just like it was in 2009 assessment was serious while the situation of under five mortality in the same district was describable as serious trouble. The findings also revealed that 10.6% of the households were food insecure while about half (50.6%) of the households were food secure according to the Food Consumption Group (FCG Low) index. The largest number of food insecure households was in Kotido (19.6%). The prevalence of households which never cultivated any food crops reduced to 19.6% compared to 21.5% in Abim district had the majority (88.9%) of households that cultivated food crops. Of the 80% households which did any cultivation, sorghum was the crop that was planted most by 2466 (96.2%) households in all districts. In the 7 days prior to the survey, the majority of the households in all districts depended on purchased food, with Moroto having over 60% of the households reported to have purchased food. Unlike in 2009, the proportion of households that consumed own produced food rose from 13% to 20% in The mean (median) expenditure on food was about Uganda shillings 39,833/= (15,500/=) during the month prior to the survey. In addition, the median expenditure was Uganda shillings zero for sugar, milk, fruits and other items such as bread and tea. The majority of xii P age

13 the households in Karamoja (54.2%) did not own large animals like cows, goats or sheep. Only 7.7% of the households in Karamoja owned over 15 heads of cow or sheep or goat. The overall average Tropical Livestock Units for the region were 1.3 SD = 3.3 and was 0.4 SD = 1.0 for Abim, 1.2 SD = 2.4 for Kaabong, 1.7 SD = 3.4 for Kotido, 2.6 SD = 4.6 for Nakapiripirit, and 0.7 SD = 3.5 for Moroto. Findings on care and service provision were better than national averages. The coverage of vitamin A supplementation and de worming was 96.0% and 92.2%, respectively, which were above the national target of 85% in all the districts. There was an increase in measles vaccination coverage as identified with a marked health card (58.2%) compared to the 2009 figure of 50%. DPT3 immunization had been received by 96% of children aged months, verified either by a health card or the caretaker s recall. Additionally, all districts had immunization coverage above 96% when mothers reports (those without cards) were considered. Bed net usage by children aged 6 59 months was reported at 86.1%. At the time of this assessment the majority (62.5%, 95% CI: ) of the mothers were breastfeeding their children. However, exclusive breastfeeding was not commonly practiced. In the pooled analysis, only 54.6% of mothers initiated breastfeeding within one hour after delivery. As was reported in the 2009 survey, family care practices still remained poor. For instance, active feeding practices for toddlers were only reported by 23.6% of the households (a drop from 25% in 2009 assessment). Although access to safe water was 85.4%, latrine coverage in Karamoja region remained poor with the majority of the households utilizing the bushes around the homesteads. Moroto district, similar to 2009 findings had the worst latrine coverage (2.1%) while Abim had the best at 54.4%. The fortnight prevalence for fever/malaria (61.1%), diarrhoea (32.7%), and ARI/cough (37.6%), was high. The ratio of boys 6 12 years to girls of the same age attending school was 1.04 while for every 78 boys out school an equivalent of 100 girls were out of school (ratio = 0.78). The formal education status of the assessed women was alarmingly low with for example 575 (95.2%) of the mothers assessed in Kaabong having never received any formal education. xiii P age

14 Conclusions and Recommendations 1. Malnutrition in Karamoja continues to be at serious levels with overall GAM prevalence of 11.7%. Kaabong, Kotido and Moroto districts had GAM prevalence above 10% (alert level) and Nakapiripirit was marginal (9.8%). The youngest age group of 6 17 months was the most vulnerable with the highest levels of GAM. Apparently only age of the child and household socioeconomic status were found to be the independently associated with GAM. Likewise child s sex (being male), and history of having suffered from fever were found to be independently and positively associated with stunting. It was also observed that exclusive breast feeding and complementary feeding practices were poor. These findings call for more effort on the part of programming to focus on promoting nutrition for the infants and toddlers especially on awareness of the MOH recommendations. As emphasised in previous assessment, focus should be on promoting complementary feeding practices. The issue of empowering household socioeconomically can not be overemphasised. 2. Coverage for vitamin A supplementation and DPT3 coverage for children months was above 96.0% while de worming coverage for children above 12 months was 92.2% exceeding the national targets of 85%.Measles immunisation coverage, verified by card for children month was at 58.2% improving to 96% when caregiver/mothers report were considered. Although the coverage sounds commendable, the lack of child health cards is an issue which should be addressed more seriously. It is very difficult to believe that antigens were present and given to children when easier health system issues such as provision of child health cards are being defaulted. Additionally, there were some prevalent measles cases that were reported in all districts. The measles cases need further investigation to establish the need for supplementary immunisation services. The reported high mortality in Kaabong should also be urgently investigated. 3. The majority of households (85.4%) accessed safe drinking water from bore holes. The amount of water used (average 60 litres) per day was low when compared to family size (average 7.0 people per household). The responsibility of collecting water at household level was primarily carried out by adult women (51.7%) and young girls (26.3%). Whereas reported access to safe water was appreciable, the amount of water usage per person is still xiv P age

15 miserable and far from the recommendations. Awareness on water use is needed and possibly the involvement of adult males in fetching the water. 4. Latrine coverage in Karamoja region remains poor with the majority of the households utilizing the bushes around the homesteads. Hand washing practice at critical times after easing oneself, before and after serving and eating meals, including feeding children was practiced by only 30.1% of the population. Basic hygiene at household level in Karamoja should be emphasised further. Else benefits of use of safe water from bore holes and efforts to address the larger picture such as the provision of medicines and food will be rendered useless due to the continued high prevalence of preventable diseases and conditions caused poor sanitation. 5. Although bed net use was very high (88.0) amongst children 5 59 months, it did not tally with the observed high prevalence of fever (61.1%), which was unacceptably high. This raises questions on whether the bed nets are used well in the Manyattas and thee need for continued sensitisation how to use the bed nets. Cough (37.6%) and diarrhoea (32.7%) were also high as similarly observed in assessments before. 6. The proportion of severely food insecure households assessed by food consumption scores was highest in Kotido district (19.6%), while Moroto (63.0%) had the highest proportions of food secure households. Compared to the 2009 assessment there is a remarkable improvement in food security in all districts but most especially in Kotido where food insecurity dropped from 47% to 19%. Despite the clearly evident differences in food consumption between food secure and food insecure households, there was no independent association of food security and malnutrition in children. Since this might imply that malnutrition in Karamoja is not only an issue of food consumption but could have a multitude of other factors not covered in the scope of this assessment, there is need to work with other sectors to address developmental issues in Karamoja. 7. The ratio of boys 6 12 years to girls of the same age attending school was 1.04 while for every 78 boys out school an equivalent of 100 girls were out of school (ratio = 0.78). The formal education status of the assessed women was also alarmingly low. Partners should ensure that 100% of the eligible children (boys and girls alike) are and remain in School. xv P age

16 Table 1.2: Master table for the descriptive results: Food security, nutrition and mortality assessment in Karamoja region, November 2010 (SMART flags excluded) Measure Indicator Abim Kaabong Kotido Nakapiripirit Moroto Combined % (95%CI) % (95%CI) % (95%CI) % (95%CI) % (95%CI) % (95%CI) Anthropometry GAM Male 6.5 ( ) 18.9 ( ) 11.5 ( ) 11.5 ( ) 15.9 ( ) 12.9 ( ) Females 7.9 ( ) 13.2 ( ) 11.8 ( ) 8.7 ( ) 11.5 ( ) 10.7 ( ) Total 7.2 ( ) 15.9 ( ) 11.5 ( ) 9.8 ( ) 13.8 ( ) 11.7 ( ) SAM Male 0.8 ( ) 4.6 ( ) 3.9 ( ) 2.9 ( ) 3.8 ( ) 3.3 ( ) Female 0.8 ( ) 4.3 ( ) 2.6 ( ) 2.0 ( ) 2.4 ( ) 2.2 ( ) Total 0.8 ( ) 4.4 ( ) 3.2 ( ) 2.4 ( ) 3.1 ( ) 2.7( ) Children with oedema 0.3% (N=2) 0.6% (N=5) 0.6% (N=4) 1.6% (N=9) 1.0% (N=6) 0.8% (N=16) GAM by age 6 17 months 15.3 ( ) 22.1 ( ) 20.1 ( ) 14.4 ( ) 19.9 ( ) 18.5 ( ) months ( ) 16.2 ( ) 10.6 ( ) 11.9 ( ) 14.4 ( ) 12.2 ( ) months 4.7 ( ) 13.3 ( ) 9.8 ( ) 5.1 ( ) 9.8 ( ) 8.8 ( ) months 3.4 ( ) 10.7 ( ) 5.0 ( ) 5.0 ( ) 10.6 ( ) 6.6 ( ) months ( ) 8.0 ( ) 7.1 ( ) 12.9 ( ) 7.6 ( ) Stunting Male 37.0 ( ) 37.9 ( ) 27.1 ( ) 29.0 ( ) 43.5 ( ) 36.6 ( ) Female 31.3 ( ) 32.4 ( ) 29.4 ( ) 31.7 ( ) 37.5 ( ) 32.2 ( ) Total 34.1 ( ) 35.0 ( ) 28.2 ( ) 30.5 ( ) 40.5 ( ) 34.4 ( ) Underweight Male 19.1 ( ) 35.3 ( ) 20.8 ( ) 26.0 ( ) 33.9 ( ) 27 ( ) Female 18.9 ( ) 29.8 ( ) 25.5 ( ) 20.9 ( ) 30.9 ( ) 25.5 ( ) Total 19.0 ( ) 32.5 ( ) 23.1 ( ) 23.2 ( ) 32.4 ( ) 26.5 ( ) MUAC for children <11.5cm 1.4 ( ) 1.7 ( ) 2.4 ( ) 2.7 ( ) 4.1 ( ) 2.4 ( ) xvi P age

17 Measure Indicator Abim Kaabong Kotido Nakapiripirit Moroto Combined >11.5 cm and < 12.5 cm 4.5 ( ) 12.2 ( ) 10.6 ( ) 5.5 ( ) 12.4 ( ) 9.1 (8.2 10) > 12.5 cm < 13.5 cm 15.6 ( ) 26.1 ( ) 28 ( ) 21.3 ( ) 32.6 ( ) 24.5 ( ) >13.5 cm 78.4( ) 60.0 ( ) 59 ( ) 70.6 ( ) 50.8 ( ) 64.0 ( ) Mean age of mothers 30 SD= SD= SD= SD= SD= SD=6.4 Family care practices Hygiene and sanitation Measles coverage (12 23 months with Health Card) N (%) N (%) N (%) N (%) N (%) N (%) 142 (64.3) 153 (61.5) 152 (72.0) 99 (59.6) 113 (68.5) 657 (65.3) DPT 3 coverage (12 23 months) 142 (72.8) 149 (59.8) 158 (78.6) 89 (62.7) 121 (72.9) 659 (69.2) with Health Card Vitamin A supplementation 753 (97.8) 784 (93.0) 749 (96.2) 591 (97.2) 587 (92.6) 3464 (95.3) Deworming (>12 months) 724 (93.8) 762 (93.0) 663 (94.0) 557 (87.6) 577 (94/7) 3266 (92.2) Child sleeps under a bed net 741 (97.5) 747 (91.0) 732 (92.5) 514 (86.8) 306 (56.8) 3038 (88.0) Seek care (hospital or h/centre) 666 (97.7) 652 (98.2) 605 (97.0) 592 (96.4) 674 (99.3) 3189 (97.8) Active feeding (12 23 months) 19 (11.1) 76 (45.2) 19 (11.4) 28 (27.1) 49 (37.3) 190 (22.1) N (%) N (%) N (%) N (%) N (%) N (%) Private latrine coverage 370 (54.4) 228 (34.5) 110 (17.7) 33 (6.1) 14 (2.1) 755 (23.7) Source of water is borehole 648 (95.0) 582 (87.5) 478 (76.4) 332 (60.1) 625 (91.8) 2665 (83.1) Household treats drinking water 150 (22.2) 128 (19.3) 44 (7.2) 42 (6.2) 127 (20.3) 491 (15.1) Average amount of water used by household (in 20L jerricans) 3.71(SD=1.82) 3.27(SD=1.98) 2.7(SD=1.77) 2.92(SD=2.26) 3.18(SD=2.68) 3.17(SD=2.15) Morbidity N (%) N (%) N (%) N (%) N (%) N (%) Malaria/Fever 505 (62.2) 589 (65.9) 427 (52.5) 428 (69.9) 357 (55.7) 2306 (61.1) Measles 6 (0.7) 4 (0.4) 2 (0.2) 7 (1.1) 5 (0.8) 24 (0.6) xvii P age

18 Measure Indicator Abim Kaabong Kotido Nakapiripirit Moroto Combined Diarrhea 240 (29.6) 339 (37.9) 253 (31.1) 198 (32.4) 203 (31.7) 1233 (32.7) ARI/Cough 273 (33.6) 435 (48.7) 270 (33.2) 236 (38.6) 204 (31.8) 1418 (37.6) Skin disease 33 (4.1) 120 (13.4) 34 (4.2) 29 (4.7) 46 (7.2) 262 (6.9) Eye disease 11 (1.4) 155 (17.3) 40 (4.9) 59 (9.6) 11 (1.7) 276 (7.3) Others 37 (4.6) 3 (0.3) 80 (9.8) 12 (2.0) 15 (2.3) 147 (3.9) No illness 111 (13.7) 85 (9.5) 147 (18.1) 100 (16.3) 141 (22.0) 584 (15.5) Mortality Crude mortality rate /10000 / day Under five mortality /10000/ day 0.20 ( ) 1.70 ( ) 0.57 ( ) 0.60 ( ) 0.24 ( ) 0.67 ( ) 0.33 ( ) 2.93 ( ) 0.95 ( ) 1.24 ( ) 0.97 ( ) 1.30( ) Food Security N (%) N (%) N (%) N (%) N (%) N (%) Average household size 6.9 Households with 2 children 252 (36.9) 223 (33.4) 233 (37.3) 175 (28.2) 150 (21.8) 1033 (31.5) Households with 3 children 40 (5.8) 31 (4.6) 32 (5.1) 10 (1.6) 7 (1.0) 120 (3.6) Households with 4 or more children 34 (5.0) 50 (7.4) 28 (4.4) 11 (1.7) 14 (2.0) 137 (4.2) Socioeconomic status (SES) N (%) N (%) N (%) N (%) N (%) N (%) Poorest 61 (9.0) 298(45.4) 195(33.9) 203(30.2) 198(32.2) 955(29.9) Poor 123(18.2) 194(29.5) 253(43.9) 337(50.1) 263(42.8) 1170(36.6) Least poor 493(72.8) 165(25.1) 128(22.2) 132(19.6) 153(24.9) 1071(33.5) Ownership of cow/ goat/sheep N (%) N (%) N (%) N (%) N (%) N (%) Zero 389 (57.2) 345 (52.0) 276 (44.3) 259 (42.7) 493 (72.7) 1762 (54.2) 1 5 animals 248 (36.5) 151 (22.8) 159 (25.5) 130 (21.5) 119 (17.6) 807 (24.8) 6 15 animals 35 (5.1) 118 (17.8) 114 (18.3) 117 (19.3) 47 (6.9) 431 (13.3) Over 15 animals 8 (1.2) 49 (7.4) 74 (11.9) 100 (16.5) 19 (2.8) 250 (7.7) xviii P age

19 Measure Indicator Abim Kaabong Kotido Nakapiripirit Moroto Combined Household never cultivated any 75 (11.1) 115 (17.7) 82 (13.5) 149 (25.0) 205 (31.0) 626 (19.6) food crop in 1 st season of 2010 Mean expenditure on food the month prior to survey (USD equivalent) 47,202 (20.5) 36,051 (15.7) 23,839 (10.4) 36,529 (15.9) 37,345 (16.3) 36,411 (15.7) Percentage household expenditure on: Food Health Education Others Child Education Household food diversity in N (%) N (%) N (%) N (%) N (%) N (%) past 7 days (FCG low) Poor consumption 54 (8.2) 34 (5.4) 121 (19.6) 36 (10.7) 56 (9.2) 301 (10.6) Borderline consumption 322 (49.1) 259 (41.3) 248 (40.1) 107 (31.8) 170 (27.9) 1106 (38.8) Acceptable consumption 280 (42.7) 334 (53.3) 249 (40.3) 193 (57.4) 384 (63.0) 1440 (50.6) Number of boys aged 6 12 yrs in school Number of girls aged 6 12 yrs in school Boy/Girl ratio Number of boys 6 12 yrs out of school Number of girls 6 12 yrs out of school Boy/Girl ratio xix P age

20 Chapter 1 BACKGROUND 1.1 INTRODUCTION In September 2010, UNWFP contracted Makerere University College of Health Sciences, School of Public Health (Mak SPH) to conduct the Food Security and Nutrition Assessment in Karamoja Sub region. Data collection was conducted between 18 th October to 2 nd November, 2010 in the 5 districts of Abim, Kaabong, Kotido, Nakapiripirit and Moroto. However, sampled clusters falling in the new districts of Napak and Amudat were also covered. This survey was part of the routine assessments done in the sub region to generate information to monitor and improve programme and policy interventions. In this assessment, information on health, nutrition, food security situation and mortality indicators among children aged 6 59 months and households in the sub region were collected. A team from UNWFP and MakSPH supervised the data collection and entry process. The report is divided into four main parts. The background outlines the context of Karamoja sub region, justification, the partners involved and objectives of this assessment. The methodology provides a description of the survey design and sampling procedures, the data collection process, variables assessed and how the data was analysed. The findings and discussion section comprehensively provides the results of the assessment. The final part is the conclusions and recommendations that are based on the key findings in line with the assessment objectives. 20 P age

21 1.2 CONTEXT OF KARAMOJA Karamoja region is a semi arid area located in northeastern Uganda covering approximately 27,900 square kilometres with a population of 1,102,300 1 and population density of 48 people per square kilometer comprising of eleven different social groupings with largely similar dialects. The majority of the Karamojong are pastoral people and the complex livelihood zones in Karamoja make it difficult to clearly delineate them at parish and village level. Karamoja sub region is in a perennial development Fig: Karamoja region livelihood zones crisis, to the point that it exhibits the worst humanitarian indicators in the country. A fourth successive year of drought has heightened food, nutritional and livelihood insecurity, further aggravating the vulnerability of the human population and livestock in this largely pastoralist region. Karamoja remains saddled with the humanitarian consequences of chronic under development. It exists against a backdrop of limited livelihood options; negligible basic service infrastructure; weak local governance and rule of law structures; and continuing disarmament operations by the Uganda People s Defence Forces (UPDF). 2 Unlike the rest of Uganda, the Karamoja region has only one annual harvest and relies on timely rainfall to enable planting. The region s semi arid climate, the subsistence based livelihoods and relative isolation of its inhabitants, and its volatile civil security status heavily influence its food security. While the region s inhabitants are often characterized as highly resilient, the direct and indirect effects of consecutive years of poorly distributed rainfall, crop and livestock pests and diseases, and continual changes in the civil security environment have contributed to an overall decline in their food security status and coping capacity 3. 1 According to the projections released by UBOS, Kotido district has a population of 188,100 (99,000 males and 89,100 females). Moroto District has a population of 276, 000 (137,100 males and 138,900 females). Abim district has a population of 97,000. Kaabong District has a population of 316,600 (158,700 males and 158,900 females). Nakapiripirit has a population of 226,700 (117,000 males and 109,700 females). 2 Uganda Consolidated Appeal (CAP) FEWS NET Uganda; January and March P age

22 Human welfare, living conditions and quality of life of the people in Karamoja have declined considerably due to various factors such as environmental issues, insecurity, marginalization, illiteracy, poor health and poor infrastructure with Human development Index (HDI) of less than 0.2 compared to an average of for Uganda 4. This region also has the highest Human Poverty Indices (HPI) of above 50%, compared to the national average of 37.5% attributed to persistent poor harvest as a result of dry spells and droughts, cattle rustling and insecurity, animal death, lack of water, poor farming practices, ill health and disability, high bride price for marriage, lack of skills and unemployment, limited sources of income, poor governance and landlessness. 5 More importantly though are the health implications of low availability of food leading to under nutrition. Health and nutrition surveys have been conducted biannually since 2006 in the Karamoja region with support from UNICEF and UNWFP to ensure adequate representation of the pre and post harvest season situations. Over the past 2 years, annual assessments have been conducted just before the harvest period between September and December which represent the peak of the hunger season, before the main staple crop harvest allowing comparison with the previous FSNAs. Trend data depict some improvements in the prevalence of Global Acute Malnutrition (GAM) in all the districts from prevalence well above 20% to about 10%. However over the last two years there seems to be no significant improvements in the rates of malnutrition. Karamoja is also ranked as a region with one of the largest burden of preventable communicable diseases in Uganda. The most prevalent conditions are malaria, diarrhoea and acute respiratory tract infections. 4 Karamoja Microfinance Committee (KMFC): Karamoja Microfinance Strategy, AMFIU Working Paper No. 4, Kampala, (UPPAP 2002) 22 P age

23 1.3 JUSTIFICATION FOR CONDUCTING REGULAR FOOD SECURITY AND NUTRITION ASSESSMENTS IN KARAMOJA Karamoja is a food insecure region due largely due to the unfavourable physical conditions. Health, nutrition and food security indicators in the Karamoja region have been unacceptably high for a long period. In addition the government and partners have continued to implement interventions to mitigate the problems of hunger and disease and to improve the livelihoods of the people in the region. These factors necessitate continuous monitoring to establish gains and any challenges that might be arising. In such a way programmers can take stock of changes at household level and obtain a glimpse of the effectiveness of the interventions being implemented. 1.4 CONCEPTUAL FRAMEWORK FOR THE CAUSES OF MALNUTRITION AND FOOD INSECURITY The survey was based on the conceptual framework on 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. 23 P age

24 Food and Nutrition Security Conceptual Framework Nutrition Status/ Mortality E X P O S U R E T O S H O C K S A N D H A Z A R D S Context/ Food Availability/ Markets Political, Economical, Institutional, Security, Social, Cultural, Gender Environment Agro-ecological Conditions/ Climate (Change) Household Access to Food Individual Food Intake Social and Care Environment Natural Physical Human Economic Social Capital/Assets Health Status/ Disease HH Food Production, Income Generating Activities, Exchange, Loans, Savings, Transfers Access to Health Care & Health Environment Individual level HH level Livelihood Outcomes Livelihood Strategies Community/ HH level Livelihood Assets Figure 1.2: Conceptual framework to analyze food security and nutrition in society (adapted from UNICEF 1990) In addition to the basic factors, policies and total potential resources are important influences (Figure 1.2). We collected information on factors at most of the framework levels with the exception of the total potential resources. 1.5 OBJECTIVES FOR THE ASSESSMENT Food security and nutrition assessments in Karamoja region is a routine annual activity carried out in similar periods of the year (around November/December), the pre harvest period. The assessment was jointly supported by UNWFP and UNCEF. The core objectives are similar in the surveys but often with some modifications. 24 P age

25 1.5.1 General objectives for the assessment The main objective of the assessment was to obtain data on indicators of health, nutrition, food security and retrospective mortality amongst populations in the Karamoja region, which will be used to monitor and improve programming and policy interventions Specific objectives for the assessment Similar to the previous Food security and Nutrition Assessments in 2009, the 2010 assessment was specifically designed to provide statistically representative results on the nutritional status of children aged 6 59 months and food security among households in each of the 5 districts of Karamoja region. The following were therefore included in the specific objectives. Nutrition objectives To determine the prevalence of malnutrition (wasting, stunting and underweight) among children aged 6 59 months (and/or measuring cm in length or height); To estimate the coverage of vitamin A supplementation among children 6 59 months of age; To estimate the coverage of supplementary feeding programs (SFPs) and therapeutic feeding programs (TFPs) for malnourished children in communities; To estimate the prevalence of maternal malnutrition using the mid upper arm circumference (MUAC) among women of reproductive age; To assess factors associated with malnutrition and food security; and To recommend interventions to improve the nutritional situation in the region Health and public health environment objectives To determine the incidence of common diseases (diarrhoea, measles, ARI and fever) among the target population, two weeks prior to the assessment and access to/ uptake of health services for treatment among children 6 59 months of age; 25 P age

26 To determine the coverage of routine immunizations coverage (DPT, measles, polio and de worming) among children 6 59 months of age; To estimate the proportion of households with access to improved water sources and sanitation; To identify possible constraints to water and firewood collection; and To determine the levels of retrospective crude mortality rates (CMR) and age specific mortality rates for under 5s (U5MR) in a specific period and the main causes of death in a specified recall period Food security objectives To assess the current food security status of households, including food consumption and dietary diversity (using 7 day dietary recall methods); To assess the crop cultivation patterns at household level among residents and main difficulties encountered in farming; To estimate livestock ownership by residents and main difficulties encountered in animal production; To asses economic access to food at household level, and in conjunction with changes in market prices and market performance; To describe the current food consumption patterns and estimate the proportion of households at short term risks to lives and livelihoods; To identify the main factors associated with household food and economic insecurity in the short and longer term; To estimate coverage of food and non food assistance programs; To determine the needs for immediate food and non food assistance and suggested modalities of delivery and targeting criteria; and To recommend medium term interventions to improve the food security and livelihoods of the conflict affected populations. 26 P age

27 1.6 PARTNERSHIPS The 2010 FSNA was conducted by UNWFP in collaboration with MoH and Makerere University School of Public Health. 27 P age

28 Chapter 2 METHODS AND MATERIALS Guidelines outlined in the Emergency Nutrition Assessment (ENA) for Standardized Monitoring and Assessment of Relief and Transition (SMART) methodology for assessing nutrition and mortality in crisis situations were used. The assessment was population based and cross sectional. Although the number of districts in Karamoja region increased from five to seven, this assessment was planned before the new district of Amudat and Napak had become functional. The assessment therefore covered the old five districts of Karamoja namely Abim, Kaabong, Kotido, Nakapiripirit and Moroto. This implies that the clusters falling in the current new districts were considered part of the old districts sample size. The field data collection was carried out during the last two weeks of October and the first week of November (18 th October 2 nd November 2010). 2.1 TARGET POPULATION The target population were children between the ages of 6 and 59 months and their mothers or caretakers. Age was ascertained using local calendars and probe discussions with adult household members and/or mothers. Children with physical disabilities were excluded from the survey. Mothers were also assessed for MUAC. 2.2 SAMPLING Sample size and sampling process for the households The ENA for SMART software was used to estimate the sample size for individual district representative samples for both nutrition and mortality indicators. Sample size estimates were made to ensure that the key indicators would be statistically representative at the individual district level and/ or overall population level for Karamoja region. Sample size was calculated 28 P age

29 with 0.05, statistical significance (95% confidence interval) (Table 2.1). Data from the December 2009 Karamoja Health and Nutrition Assessment was used for sample size calculation assumptions, that is, to obtain current prevalence on indicators of nutrition and mortality. The projected 2002 population census data was also used to estimate the total target population. Since it is difficult to perform random sampling techniques in many African set ups, the cluster sample size calculator was used. A two stage cluster sampling technique taking into consideration the design effect (2%), anticipated non response (3%) and desired precision (ranging between 3 4%) to ensure adequate sample size was used. The size of household sample required for the statistical comparisons of the food security situation between districts could not be calculated in the same way because there is no single food security indicator that can represent the multiple dimensions of food security and be used as a basis. However considering experiences from the 2010 HNA, the food security questionnaire was administered in all households that were sampled. This was deemed fit to allow for food security comparisons between districts and even at lower levels of disaggregation (between the population groups). Table 2.1: Sampled children and households per district Abim Kaabong Kotido Nakapiripirit Moroto Number of households Number of kids sampled Number of clusters SAMPLING PROCEDURE FOR THE HOUSEHOLD ASSESSMENT Sampling universe The sampling universe for this survey consisted of approximately 1.1 million people residing in 5 former districts of Karamoja region as evidenced by the UBOS data. The two new districts of Amudat and Napak were considered as part of their mother districts, Nakapiripirit and Moroto, respectively. 29 P age

30 2.3.2 Selection of primary sampling units (clusters) A modified two stage cluster randomization was used to select the households. Randomization was modified because of lack of population data on many of the clusters/manyattas/villages in the Karamoja region. This was attributed to the fact that the manyattas are not static. Luckily, there was population data on all parishes. Therefore at first stage, a total of 30 clusters were sampled per district. Clusters were sampled using probability proportional to population size (PPS) technique. Two main methods were used to achieve PPS sampling, depending on the situation, that is, geographical segmentation and population density. The region was segmented using parishes. A cumulative population list by parish was compiled and a random number table was used to select the cluster location. The probability sample technique resulted in some highly populated parishes providing more than one cluster. The two stage randomization was modified due to the fact that at the parish level clusters were sampled using simple random sampling technique. The names of all the manyattas/villages in the parish were obtained and written on pieces of paper. The papers with lists of names of the manyattas/villages per parish were then folded and one manyatta/village was randomly selected for the study representing a cluster (as described above sometimes more than one cluster were sampled per parish as specified by the probability sample) Selection of the basic sampling unit (households) To ensure that the required number of children to estimate the various indicators was met, a decision was made to select at least 21 households in each of the 30 clusters and systematic random sampling was used to select the households. On reaching the manyattas and/or village the study team would make a transect walk through the manyatta or village to establish the total number of households with a community leader(s). The sampling interval was determined by dividing the total number of households in the cluster by 21 and the interval used by the 30 P age

31 team leader to identify the households to be visited. All chosen households were selected, whether or not they had a child 6 59 months of age. If household members were not present, the community leadership was asked to help inform them to wait for the interviewing team at specified time. Such households were visited at least three times in an effort to identify household members unless time constraints could not allow for this. If selected village/cluster had more than one manyatta, households in all manyattas were included in the sampling frame. For clusters that fell within township settings, where many households stay within the same building or where households are arranged in blocks and/or lines, the whole cluster was surveyed as in case of a village or manyatta. Another methodological modification made was on the number of children sampled per household. All children within the selected household aged between 6 59 months were recruited into the study. This implies that the number of households which were sampled totalling (3,284) were much less than the total number of children (4,574) that were sampled. 2.4 INFORMATION COLLECTED A detailed questionnaire is provided in appendix 4. The household questionnaire comprised of four main sections: i) Household demographics and sanitation - Demographic data on the households head and membership, and current status (IDP, resident, pastoralists); - Deaths over the previous 90 days and causes of death (to estimate crude mortality and the under 5 mortality rates); - Movement of household members - Sources of water and fuel, responsibilities and constraints for their collection - Sanitation facilities 31 P age

32 ii) Maternal health - Pregnancy and breastfeeding status - Feeding practices of infants below 6 months of age - Literacy level of mothers or caregivers - Nutritional status (MUAC) of mothers/caregivers iii) Child health - Child feeding practices - Health status - Enrolment in therapeutic or supplementary feeding programmes - Recent illness - Measles, DPT3, vitamin A supplementation and deworming coverage - Anthropometric measurements (weight, height, oedema and MUAC) iv) Food security and livelihoods - Income sources - Constraints faced in livestock production, land cultivation and income generation activities - Ownership of physical assets and of animals - Land cultivation - Indebtedness - Food expenditures over the previous week, and share of monthly food, health and other expenditures - Dietary frequency and diversity over the previous week, and the main sources of food consumed - Coping strategies in case of food shortages - Receipt of food and non food assistance - Child education - Household priorities 2.5 VARIABLES ASSESSED Anthropometric measurements Field enumerators measured children s weight and height/length, and assessed the presence of oedema. Weight was measured to the nearest 0.1kg using digital UNICEF Seca scales. Children 32 P age

33 less than 2 years or less than 85 centimetres (cm) length were measured to the nearest millimetre in the recumbent position using a standard height board and those taller than 85cm were measured standing upright. Oedema was assessed by applying thumb pressure on the back of both feet for approximately 3 seconds and then examining for the presence of a shallow print or pit. MUAC was measured for all mothers in the reproductive age (15 49 months) in the survey using a MUAC measuring tape to the nearest millimetre. Children who were identified as severely malnourished were referred to therapeutic feeding centres or supplementary feeding centres if identified as moderately malnourished. Child s age was obtained through birth certificates, health cards, or recall using a calendar of local events while taking precautions to minimize field errors. No anthropometry was assessed for oedematous children Family care practices Mothers of children aged 6 59 months were asked questions regarding breast feeding practices, pregnancy and illnesses in the last two weeks prior to the survey. For mothers with children 0 24 months of age, questions were asked regarding breastfeeding initiation and duration and infant and young child feeding practices. For children 6 59 months of age, feeding practices that were assessed included the breastfeeding (current practices, period of exclusive breast feeding and the duration of breastfeeding period); and complementary feeding practices (active feeding, types of foods served, and frequency). Measles and DPT3 vaccination coverage (children >9 months) were ascertained from child health cards or mothers recall. Vitamin A supplementation (children >5 months) and de worming (children >11 months) in the last six months was assessed through any documented record or mothers recall. Household source of water, ease of accessing the water and daily water usage was ascertained in 20 litre Jerrycan units (commonly used containers in Uganda). Access to latrines was probed and observed from individual households whilst extent of sharing (people/stance ratio) was 33 P age

34 established. Information was also collected on bed net availability, persons using them in household, and where care is sought in case of illness Morbidity and mortality Morbidity patterns were assessed by obtaining history of any episodes of fever/malaria, measles, diarrhoea, ARI/cough, skin disease, eye disease or any other illness in the 2 weeks prior to the interview. The WHO definitions for these disease conditions were used. Mortality was assessed in all household that were visited using the retrospective household census method. Respondents were asked to list down all members living in the household in a 90 days recall period from this survey. First, all the household members living in the household at that time were listed by age and sex, with the household head listed first. The respondent was then asked where each person is at the time of interview. Possible choices were: alive and living in the household, alive and living elsewhere, missing and dead. Births and deaths occurring in each household between this time and the date of the survey were recorded along with month of occurrence. Events calendar for Karamoja region was used to accurately estimate age. Cause of death was collected from the respondent Assessment of food security and vulnerability As opposed to using a sub sample to assess for food security, the food security questionnaire was administered to all selected households irrespective of whether household was having a child in the target age group, 6 59 months. Data was gathered on household agricultural food production of common crops such as maize, millet, sorghum, potato, cassava and banana. The types of food and the number of times they were eaten in the past 7 days were assessed. Any foods bought by the household and the income sources were gathered. Coping strategies in case of starvation and any assistance (food and non food) obtained by households were assessed. Household socioeconomic status was established by collecting information on household assets (donkey cart, bicycle, radio, hoe/axe, mobile phone, motorcycle/car, tables, chairs, beds and television); and animals (cow, donkey, camel, goat, sheep, chicken, horse and pig). 34 P age

35 2.6 TRAINING OF DATA COLLECTORS AND FIELD SUPERVISION Each district had four teams. Each team comprised a national level (central) supervisor and three enumerators. A total of 25 national level supervisors were recruited five of them supervising data entry. Sixty five enumerators and 15 data entrants were hired at district level. Two training activities were conducted. First, national level training was done to train supervisors and was facilitated by the Principal and co Investigators and UNWFP technical staff. Secondly, three day district level training of enumerators and data entrants was carried out in each of the 5 districts. Enumerators were trained by central level supervisors. In addition technical officers from UNWFP were also invited to facilitate sessions especially on food security assessment, but they also facilitated health and nutrition aspects. During the training of the supervisors, emphasis was placed on sampling procedures as they were responsible for this activity. Additionally the training included piloting of the draft household questionnaire in the field and finalization of the versions that were used for the subsequent enumerators training at district level. Training of the enumerators included a general presentation on food security and nutrition and their linkages. Each question was reviewed individually through the questionnaire, in plenary discussions to ensure that enumerators understand the rationale and possible answers. The method of discussion was participatory. Similarly anthropometry was reviewed and class demonstrations made on individual participants. Field testing was done on the final day of the training followed by a debriefing. To ensure that the survey report was produced on time, data entry was undertaken on a daily basis during the data collection period by the trained data entrants at the district level. Data collection was supervised by the Principal Investigator, co Investigators, staff from WFP Kampala, namely Kenneth Anyanzo and Dorothy Nabiwemba, WFP field office and the supervisors. 35 P age

36 2.7 DATA ENTRY AND ANALYSIS Data entry Data entry clerks and supervisors participated in the enumerators training in order to familiarize themselves with the questionnaire. Fifteen (three data clerks per district) underwent an additional 1 day entry using specially designed Microsoft Access based data entry software. Data entry took place simultaneously in the 5 districts using Microsoft Access. Data entry was completed 1 day after the data collection except for data in Kaabong district which was done after the data collection exercise. This was due to lack of enough computers to enter data in Kaabong. All data files and questionnaires were transferred to Kampala and merged for analysis Data analysis Under nutrition was analyzed and reported based on the new WHO standards. However, NCHS references were also used and the results are provided in annexes. The Epi ENA for Smart exclusion criteria for outliers was followed. The categorization criteria of malnutrition as highlighted in the Uganda national nutrition survey guidelines was adhered to i.e. for cut off of Global Acute Malnutrition (GAM), Severe Acute Malnutrition (SAM) and other indicators 6. The cut off used for purposes of interpretation of the severity of all forms of malnutrition are presented in Table 2.2. Table 2.2: Definition of malnutrition Type of Malnutrition Anthropometric Degree of malnutrition Definition using z scores index Acute None 2.0 Moderate 3.0 but < 2.0 Weight for Height Severe < 3.0 and/or oedema Global Acute (GAM) Moderate + Severe < 2.0 and/or oedema Severe Acute (SAM) Severe < 3.0 and/or oedema None 2.0 Moderate 3.0 but < Guidelines on nutrition and health survey methodology in Uganda 36 P age

37 Type of Malnutrition Anthropometric Degree of malnutrition Definition using z scores index Stunting Height for Age Severe < 3.0 and/or oedema Moderate + Severe < 2.0 and/or oedema Severe < 3.0 and/or oedema None 2.0 Moderate 3.0 but < 2.0 Underweight Weight for Age Severe < 3.0 and/or oedema Moderate + Severe < 2.0 and/or oedema Severe < 3.0 and/or oedema For MUAC of mothers, severe wasting categorized as <22.5 cm, while that for children was <11.5 cm. Analysis of all other variables from maternal and child sections as well as food security of the household survey was carried out in SPSS (version 15). Indicators of the precision of prevalence estimates, such as confidence intervals, for major health outcomes accounted for the cluster sampling used in selecting the sample for this survey. Information on births, deaths, in migration and out migration of all household members present for the recall period of 90 days were used to calculate Crude Mortality Rate. The data presented in deaths/10,000/day was calculated using the following formula: Crude Mortality Rate = 10,000/a*f/(b+f/2 e/2+d/2 c/2) Where: a = Number of recall days b = Number of current household members c = Number of people who joined the household d = Number of people who left the household e = Number of births during recall f = Number of deaths during recall period 37 P age

38 The same formula was used to estimate the under five mortality rate. However, household members were limited to children under 5 years old (including the 0 6 months old). Unfortunately the people who left during the recall period were not captured in the questionnaire. Therefore reduced rates of mortality could have been reported. Food security data was systematically analyzed. First, a household wealth index was generated from all the variables on ownership of household assets (productive and non productive) using principal components analysis. The wealth index used was derived from the first principal component which was ranked and then categorized into tertiles and quintiles. Second, household food consumption scores were generated based on 8 food groups derived from the 16 food columns in the questionnaire. Two food consumption score variables were generated i.e. one based on the UNWFP/UNICEF weighted scores of certain food groups and another based on the USAID/FANTA, un weighted scores. In the former case starch, meat, pulses, sugar, oil and milk were weighted 2, 4, 3, 0.5, 0.5 and 4, respectively. The food consumption scores were cross tabulated with socio economic tertiles, household crowding levels and coping mechanism variables. To assess for factors independently associated with GAM and the household food consumption diversity (food consumption groups), we used binary logistic regression in case of the former since it was a dichotomous variable (1=wasted, 0=not wasted) and multinomial logistic regression for the latter. Multinomial logistic regression could be viewed as running several binary logistic regression analyses in the same model building process, rendering it possible to determine various potential risk factors. We used the variable FCG Low with 3 categories (1) poor consumption, (2) borderline consumption, and (3) acceptable consumption. The category for acceptable food consumption was used as reference category. The covariates modelled included (a) household socioeconomic status, (b) family care practices and (c) morbidity factors; and wasting status in case of the food security models. 38 P age

39 2.8 ETHICAL CONSIDERATIONS Informed consent to participate in the survey was sought from all respondents. Prior to obtaining consent, the respondents were informed of the purpose of the study, potential benefits and the fact that the study posed no direct or indirect harm to them. Protocol was observed in respect to the set up in which data was collected. Introductory letters from the Makerere University School of Public Health were sent to all the relevant district authorities before the study teams began their work. 2.9 CHALLENGES AND LIMITATIONS 1. Age estimation for children without child health cards and for adults/mothers remains a challenge with potential methodological implications. Emphasis during training was the use of a calendar of events. Nonetheless, analysis of anthropometric findings of children still suggested eminent errors with age estimation. 2. Two new districts of Napac and Amudat were created and are now functional in Karamoja region. However, the assessment was planned before the districts had become functional. The current assessment covered the old five districts. However, clusters that fell into the new districts were also assessed although the analysis did not take stratify information by the new districts. 3. Although data entrants were trained to enter data in the field, not enough computers were raised in all districts. The problem was most pronounced in Kaabong where data entry was completed in Kampala after the field exercise. This translated into a delay in having all the data ready for analysis. This problem was addressed by Mak SPH requesting for a no cost extension of the contract from UNWFP. 4. A few codes were added to the questionnaire during training in agreement with the WFP team. However, the edit could not be included in the data capture screen because it was not easily editable. Exporting data to other statistical packages had some challenges since it was only possible to export a few variables at a time. There was a lot of manual edits in the excel sheets such as headings where data could then be exported. 39 P age

40 Chapter 3 ASSESSMENT FINDINGS AND DISCUSSION 3.1 SOCIO DEMOGRAPHIC CHARATERISTICS Age and sex distribution of the sampled children Of the 3284 households that were sampled, a total of 4574 children were assessed. However, of the assessed children only 3818 were included in the analysis for anthropometry. That is, 807 for Abim, 893 in Kaabong, 810 for Kotido, 632 for Nakapiripirit, and 676 for Moroto (Table 3.1). ENA flagged cases were excluded. Sampling was carried out effectively. Overall, there was an equal representation of male and female children in each district. Table 3.1: Number of children included for anthropometric measurements by age and by district District Boys Girls Boy:Girl Total ratio months months months months months Abim : Kaabong : Kotido : Nakapiripirit : Moroto : Total : Age and education status of mothers and/or care givers Using reported ages the average age of mothers was 32 (SD=11.1) years while the majority of the mothers had no formal education (Table 3.2). The education status of mothers was very low with almost all mothers in Kaabong (95.2%) having never undergone through formal training. 40 P age

41 Since level of mother s education correlates positively with nutrition status 7, it is important that partners continue or put more focus on child education. Table 3.2: Mothers/care givers reported age in years by district District Mothers age Mothers education status N Mean Age (SD) No education N (%) Primary N (%) Above primary N (%) Abim (10.5) 255 (51.9) 179 (36.5) 57 (11.6) Kaabong (7.2) 575 (95.2) 17 (2.8) 12 (2.0) Kotido (10.6) 481 (89.7) 37 (6.9) 18 (3.4) Nakapiripirit (16.4) 428 (85.8) 60 (12.0) 11 (2.2) Moroto (8.8) 441 (85.6) 51 (9.9) 23 (4.5) Combined (11.1) 2180 (82.4) 344 (13.0) 121 (4.6) 3.2 NUTRITIONAL STATUS OF CHILDREN AND MOTHERS Global acute malnutrition The prevalence rate of Global Acute Malnutrition (GAM) was found to be 11.7% (95% CI ) and the prevalence of Severe Acute Malnutrition (SAM) was 2.7 % (95% CI ). All results are based on weight for height Z scores and/or oedema (Table 3.3). The prevalence of (GAM) was above 10% (alert level) in Kaabong, Kotido and Moroto and lower in Abim and Nakapiripirit districts. The mean weight for height Z score was 0.68 (SD=1.13). There were 26 cases of oedema, or 0.8% of the sample. Oedema constituted 6.4% of the identified severe malnutrition cases. 7 Wamani H, Tylleskar T, Astrom AN, et al. Mothers' education but not fathers' education, household assets or land ownership is the best predictor of child health inequalities in rural Uganda. Int J Equity Health. 2004;3:9. 41 P age

42 Table 3.3: Acute malnutrition rates by district, age and sex, Karamoja region November 2010 Abim Kaabong Kotido Nakapiripirit Moroto Combined Indicator %(95% CI) % (95% CI) %(95% CI) %(95% CI) %(95% CI) %(95% CI) GAM Males 6.5 ( ) 18.9 ( ) 11.5 ( ) 11.5 ( ) 15.9 ( ) 12.9 ( ) Females 7.9 ( ) 13.2 ( ) 11.8 ( ) 8.7( ) 11.5 ( ) 10.7 ( ) Total 7.2 ( ) 15.9 ( ) 11.5 ( ) 9.8 ( ) 13.8 ( ) 11.7 ( ) SAM Males 0.8 ( ) 4.6 ( ) 2.9 ( ) 2.9 ( ) 3.8 ( ) 3.3 ( ) Females 0.8 ( ) 4.3 ( ) 2.0 ( ) 2.0 ( ) 2.4 ( ) 2.2 ( ) Total 0.8 ( ) 4.4 ( ) 2.7 ( ) 2.4 ( ) 3.1 ( ) 2.7( ) Oedema [%(N)] 0.3 (2) 0.6 (5) 0.6 (4) 1.6 (9) 1.0 (6) 0.8 (26) GAM by age 6 17 months 15.3 ( ) 22.1 ( ) 20.1 ( ) 14.4 ( ) 19.9 ( ) 18.5 ( ) months ( ) 16.2 ( ) 10.6 ( ) 11.9 ( ) 14.4 ( ) 12.2 ( ) months 4.7 ( ) 13.3 ( ) 9.8 ( ) 5.1 ( ) 9.8 ( ) 8.8 ( ) months 3.4 ( ) 10.7 ( ) 5.0 ( ) 5.0 ( ) 10.6 ( ) 6.6 ( ) months ( ) 8.0 ( ) 7.1 ( ) 12.9 ( ) 7.6 ( ) Mean WFH Z 0.31 SD= SD= SD= SD= SD= SD=+1.08 score By district, there were significant differences (χ 2 =for test of trend p value <0.001) in the GAM rates across districts. By sex, as previously observed in other similar assessments in Uganda and other sub Saharan countries, boys tended to show higher rates of acute malnutrition. However in adjusted analyses (see table 3.26), the differences were not statistically significant except for stunting. By age, at regional level and at district level, a higher proportion of children aged 6 29 months are malnourished (highest among the 6 17 months, followed by month) in comparison to children months. These findings are similar to those in other surveys carried out in Uganda 8 and the Karamoja 2009 nutrition assessment. The findings depict a large problem with complementary feeding practices. 8 UBOS. Demographic and Health Survey, P age

43 GAM rates over the past one year Percentage Abim Kaabong Kotido Nakapiripirit Moroto All Districts Figure 3.1: Prevalence of acute malnutrition amongst children 6 59 months old (Karamoja region ) Overall, there was no improvement in the GAM rates from 2009 to 2010 (Figure 3.1). Kaabong district exhibited the highest increase with GAM from 9.6% in 2009 to 15.9% in The high rate of GAM in Kaabong could be explained by the fact that the district had the largest proportion of mothers (95%) who never underwent through any formal education. GAM rates by sub county The prevalence of acute malnutrition in Kaabong district was homogeneous through subcounties (Table 3.4). The situation was almost similar in Moroto while in Nakapiripirit there appeared to be clustering. Table 3.4: Proportion of GAM by sub county and by district Number not Wasted Number Wasted %age wasted Abim district Abim Town council Alerek Lotukei P age

44 Number not Wasted Number Wasted %age wasted Morulem Nyakwae Kaabong district Kaabong Kapalata Kapedo Karenga Kathile Lolelia Loyoro Sidok Kotido district Kacheri Kotido Kotido Nakapelimoru Panyangara Rangen Nakapiripirit district Amudat Kakamangole Karita Lolachat Lorengedwat Loroo Moruita Nabilatuk Namalu Moroto Iriri Katikekile Lokopo Lopeei Lorengechorwa Lotome Matany Nadunget Ngoleriet Rupa Moroto Municipality Tapac P age

45 3.2.2 Chronic malnutrition (stunting) The survey also estimated prevalence of stunting (low height for age), which reflects chronic malnutrition, and underweight (low weight for age) which reflect both acute and chronic malnutrition (Table 3.5). All efforts were made to record the age of the children as accurately as possible, as described in the methodology section. Even so it is difficult to collect precise age data due to the inadequate universal system for birth certification and registration in Karamoja region. Stunting, at 34.3% overall, is marginally lower in comparison to figures reported in 2009 (40.2%). The difference is statistically significant at district level (p=0.001). At this level the stunting rate compares favourably with the national rate of 34.4% observed in 2006 Uganda Demographic Health Survey (UDHS). Table 3.5: Prevalence of stunting amongst children 6 59 months old by sex and by district Sex Abim N=776 Kaabong N=833 Kotido N=739 Nakapiripirit N=633 Moroto N=580 Combined N=3561 % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) Males 37.0 ( ) 37.9 ( ) 27.1 ( ) 29.0 ( ) 43.5 ( ) 36.6 ( ) Females 31.3 ( ) 32.4 ( ) 29.4 ( ) 31.7 ( ) 37.5 ( ) 32.2 ( ) Total 34.1 ( ) 35.0 ( ) 28.2 ( ) 30.5 ( ) 40.5 ( ) 34.4 ( ) Moroto district continues to stand out as the most affected as indicated by the high stunting 40.5% (95% CI: ). Unlike wasting which peaked earlier, stunting peaked in the age group months (38.3%) although it remained high except in the age group months (Figure 3.2). Nearly one child in two was stunted in the age group months. The high level of stunting reflects, generally, the low level of the standard of living in the entire Karamoja district. 45 P age

46 Not stunted Stunted 50 Percent months months months months months Age category Figure 3.2: Prevalence of stunting by age category Underweight status The prevalence of underweight, just like that of other anthropometric indicators presented above was high 26.5% (95% CI: ) (Table 3.6), in comparison to (26.6%), from 2009 data. There were statistically significant differences between districts in underweight status (p value for test of trend <0.001). Underweight figures are also above those reported in the 2006 UDHS (31%). Male children were more likely to be underweight than females. Similarly the age group below 30 months had a higher prevalence of underweight than older age groups. Table 3.6: Prevalence of underweight among children 6 59 months by district and sex Sex Abim N=776 Kaabong N=833 Kotido N=739 Nakapiripirit Moroto Combined N=633 N=580 N=710 % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) Males 19.1 ( ) 35.3 ( ) 20.8 ( ) 26.0 ( ) 33.9 ( ) 27 ( ) Females 18.9 ( ) 29.8 ( ) 25.5 ( ) 20.9 ( ) 30.9 ( ) 25.5 ( ) Total 19.0 ( ) 32.5 ( ) 23.1 ( ) 23.2 ( ) 32.4 ( ) 26.5 ( ) Underweight varied significantly across the age categories (χ 2 for test of trend p value <0.001) (Fig 3.3). 46 P age

47 Percent Not underweig Underweight months months months months months Age category Figure 3.3: Prevalence of underweight by age category Mid Upper Arm Circumference (MUAC) The Mid Upper Arm Circumference (MUAC) assessed in children 6 59 months depicted a very high proportion (24.5% (95% CI: )) of children at risk (> 12.5 cm < 13.5 cm) of being under nourished (Table 3.7). The proportion of children at risk was highest in Moroto district 32.6% (95% CI: ). A total of 2.4% (95% CI: ) up from 1.5% (without 95% CI Values) in December 2009 of the children in the entire region were severely wasted while 9.1 % (95% CI: ) were moderately wasted. The most vulnerable age group was 6 17 months with over 30% either already severely and moderately wasted or at risk. There was a statistically significant difference in the wasting status as measured with MUAC across the age groups (χ 2 =52.5; p<0.001). 47 P age

48 Table 3.7: MUAC of Children 6 59 months of age in Karamoja region Abim (N=774) Kaabong (N=836) Kotido (N=754) Nakapiripirit (N=602) Moroto (N=604) Combined (N=3,570) MUAC categories % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) <11.5cm 1.4 ( ) 1.7 ( ) 2.4 ( ) 2.7 ( ) 4.1 ( ) 2.4 ( ) >11.5 to < 12.5 cm 4.5 ( ) 12.2 ( ) 10.6 ( ) 5.5 ( ) 12.4 ( ) 9.1 (8.2 10) > 12.5 to < 13.5 cm 15.6 ( ) 26.1 ( ) 28 ( ) 21.3 ( ) 32.6 ( ) 24.5 ( ) >13.5 cm 78.4 ( ) 60 ( ) 59 ( ) 70.6 ( ) 50.8 ( ) 64.0 ( ) Maternal nutrition Mid upper arm circumference (MUAC) was collected from 3,864 mothers and caregivers in reproductive age (15 45 years of age). This included pregnant and lactating women if they were also mothers of children less than five years of age. Using a cut off of less than 22.5 cm, about 10% of the women were classified as malnourished (Table 3.8). Table 3.8: MUAC of caregivers and women of reproductive age (15 49 years) by district Abim Kaabong Kotido Nakapiripirit Moroto Combined MUAC categories N (%) N (%) N (%) N (%) N (%) N (%) Malnourished (<22.5cm) 43 (5.2) 100 (10.8) 60 (7.5) 72 (11.0) 99 (15.0) 374 (9.7) Not Malnourished ( 22.5cm) 778 (94.8) 826 (89.2) 742 (92.5) 581 (89.0) 563 (85.0) 3490 (90.3) The results vary by district, with Moroto, Nakapiripirit and Kaabong having more women classified as malnourished while Abim and Kotido had a lower that 10% classified as malnourished. These results seem to show some level of deterioration in the nutritional status of women from Karamoja region in comparison to December 2009 assessment results. 3.3 HEALTH, WATER, SANITATION AND MORTALITY Health status of 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), cough, fever and measles. Fever was the most commonly reported problem, with more than half (61.1%) of all children having suffered from it in all five districts (Figure 3.4). Cough affected 37.6% of children, and 32.7% had suffered from diarrhoea in the fortnight before the survey. 48 P age

49 Percent Malaria/Fever Measles Diarrhea ARI/Cough Skin Diseases Eye Disease Others No illness Figure 3.4: Prevalence of common illnesses amongst children aged 6 59 months in Karamoja region, October, 2010 Rates of morbidity did not vary greatly between districts, though Kotido district reported the highest prevalence of cough/difficulty breathing and diarrhea (11.5%, 95% CI: and 11.2% 95% CI: respectively), while 14.4% reported not having had any illness in the previous two weeks prior to this assessment (Table 3.9). Table 3.9: Prevalence of common illnesses amongst children 6 59 months old by district Indicator Abim Kaabong Kotido Nakapiripirit Moroto N (%) N (%) N (%) N (%) N (%) Malaria/Fever 505 (62.2) 589 (65.9) 427 (52.5) 428 (69.9) 357 (55.7) Measles 6 (0.7) 4 (0.4) 2 (0.2) 7 (1.1) 5 (0.8) Diarrhea 240 (29.6) 339 (37.9) 253 (31.1) 198 (32.4) 203 (31.7) ARI/Cough 273 (33.6) 435 (48.7) 270 (33.2) 236 (38.6) 204 (31.8) Skin Diseases 33 (4.1) 120 (13.4) 34 (4.2) 29 (4.7) 46 (7.2) Eye Disease 11 (1.4) 155 (17.3) 40 (4.9) 59 (9.6) 11 (1.7) Others 37 (4.6) 3 (0.3) 80 (9.8) 12 (2.0) 15 (2.3) 49 P age

50 Skin and eye diseases were higher in Kaabong than in other districts, while fever prevalence was consistent across residential categories. It is important to note that there were measles cases that were reported in all districts. This requires further investigation to confirm that the reported cases were indeed for measles for further action. The high prevalence of illnesses and conditions could be attributed to lack of diversified food for both children and mothers, or insufficient health care and poor hygiene Use of mosquito nets The assessment results indicated that 86.1% (95% CI: ) of children reportedly sleep under a bed net. The lowest proportion was in Moroto district (51.6%). The high availability and use of bed nets (Table 3.10) does not seem to correspond to the high prevalence of fever/malaria as there was no significant relationship between net usage and fever, that is, children who were reported to have slept under mosquito nets were just as likely to have fever as those who did not sleep under mosquito nets. This might suggest a misuse of bednets or rather call for more awareness creation on the use or treatment of bednets. Table 3.10: Bed net coverage amongst children 6 59 months by district Abim N=776 Kaabong N=833 Kotido N=739 NakapiripiritN=633 Moroto N=580 Combined N= ( ) 91.3 ( ) 97.8 ( ) 88.6 ( ) 51.6 ( ) 86.1 ( ) Immunization, vitamin A supplementation and de worming coverage i) Measles coverage Overall, 58.2% of children aged months had received a measles vaccination as identified with a marked health card (Table 3.11). This reflects an improvement when compared to 50% coverage reported in A small percentage (1.7%) of caretakers reported the child not having been immunized as evidenced by a card with Abim, Kotido and Moroto performing much better than other districts. Additionally, all districts had immunization coverage above 50 P age

51 80% when mothers reports (those without cards) were considered. There was a significant difference in measles vaccination rates between the districts (χ 2 =8.6, p=0.003). Table 3.11: Measles immunization coverage among children by district Abim (n=201) Kaabong (n=243) Kotido (n=201) Nakapiripirit (n=159) Moroto (n=167) Combined (n=984) % (95% CI) Yes with card 65.2( ) 62.1( ) 72.1( ) 61( ) 67.1( ) 65.3( ) Yes without card 31.3( ) 32.1( ) 24.9( ) 38.4( ) 25.1( ) 30.5( ) No with card 2( ) 1.2( ) 3( ) 0.6( ) 6.6( ) 2.5( ) No without card 1.5( ) 4.5( ) ( ) 1.6( ) While there is an overall improvement in coverage in relation to previous years, the coverage remains too low to ensure community level protection. ii) Vitamin A supplementation coverage Overall, vitamin A supplementation had been received by 96.0% [95% CI: ] of children aged 6 59 months, verified either by a health card or the caretaker s recall. Coverage levels were highest in Abim, followed by Kotido and lowest in Kabong (Table 3.12). Table 3.12: Vitamin A coverage by district Abim (n=753) Kaabong (n=784) Kotido (n=749) Nakapiripirit (n=591) Moroto (n=587) Combined (n=3464) % 95% CI Yes with card 56.2( ( ( ( ) 55.7) 67.1) 51.1( ) 60( ) 67.9) Yes without card 42.1( ) 41.6( ) 33.5(30 37) 46.8( ) 33.1( ) 37.9( ) No with card 1.7( ) 1.8( ) 2.6( ) 0.7( ) 5.2( ) 6.0( ) No without card 0 4.5( ) 0.3(0 0.7) 1.4( ) 1.7( ) 2.4( ) Total 753 (97.8) 784 (93.0) 749 (96.2) 591 (97.2) 587 (92.6) 3464 (95.3) 51 P age

52 Karamoja region has met the national target (80% and above) for vitamin A in children less than 5 years of age. There were some differences in vitamin A coverage rates in relation to districts; however the differences are not significant. iii) DPT3 coverage Overall, DPT3 immunisation had been received by 96% of children aged months, verified either by a health card or the caretaker s recall (Table 3.13). Table 3.13: DPT3 coverage by district Abim (n=201) Kaabong (n=247) Kotido (n=204) Nakapiripirit (n=159) Moroto (n=167) Combined (n=978) %(95% CI) Yes with card 65.2( ) 59.7( ) 74.1( ) 61.6( ) 71.3( ) 66.4( ) Yes without card 32.8( ) 35.0( ) 24.9( ) 38.4( ) 27.5( ) 31.5( ) No with card 1.0( ) 1.2( ) 1.0( ) 0 1.2( ) 0.9( ) No without card 1.0( ) 4.1( ) ( ) iv) De worming coverage Overall, de worming had been received by 92.2% of children aged >12 months, verified either by a health card or the caretaker s recall (Table 3.14). Table 3.14: De worming coverage by district Abim Kaabong Kotido Nakapiripirit Moroto Combined Deworming % (95% CI) 54.9( ) 50.0( ) 59.1( ) 50.5( ) 57.5( ) 54.4( ) Yes with card 41.2( ) 43.2( ) 34.8( ) 45.5( ) 32.0( ) 39.4( ) Yes without card 2.9( ) 2.6( ) 5.0( ) 1.5( ) 8.6( ) 4( ) No with card 1.0( ) 4.2( ) 1.1( ) 2.5( ) 1.9( ) 2.1( ) Total 724 (93.8) 762 (93.0) 663 (94.0) 557 (87.6) 577 (94/7) 3266 (92.2) 52 P age

53 3.3.4 Access to health facilities Health centres were reported as the most commonly available health facilities in communities (80.4% CI: ), followed by hospitals (19.0%, CI: ). Kotido reported zero care seeking from Hospitals (Table 3. 15). In some areas in Uganda the private sector provides care to over 60% of children presenting with fever 9. Failure to seek care from, and/or the lack of private health care providers in the Karamoja region implies that partners should ensure that public facilities are adequately supported. Table 3.15: Facilities where respondents reported to have sought health care services by district Abim (n=682) Kaabong (n=664) Kotido (n=679) Nakapiripirit (n=614) Moroto (n=624) % 95% CI % 95% CI % 95% CI % 95% CI % 95% CI Hospital Health center Private clinic Traditional healer Drug shop Water and sanitation i) Access to safe water Access to safe drinking water (water from a borehole) was high 85.4% (95% CI: ) in the pooled analysis, with minimal district variations (Table 3.16). These findings are similar to the previous assessment in 2009 where borehole accounted for 85%. However, the amount of water used (average 60 litres) per day was low when compared to family size (average 7.0 people per household). The recommended daily water use per capita is 20 litres. The 9 Rutebemberwa E, Pariyo G, Peterson S, et al. (2009) Utilization of public or private health care providers by febrile children after user fee removal in Uganda. Malar J, 8, P age

54 responsibility for collection of water at household level lied majorly in the hands of adult women (51.7%) as well as young girls (26.3%). Table 3.16: Access to safe water by district Abim Kaabong Kotido Nakapiripirit Moroto Combined N (%) N (%) N (%) N (%) N (%) N (%) Source of water is borehole Average amount of water used by household (in 20L jerrycans) 648 (95.0) 582 (87.5) 478 (76.4) 332 (60.1) 625 (91.8) 2665 (83.1) 3.7(SD=1.8) 3.3(SD=2.0) 2.7(SD=1.8) 2.9(SD=2.3) 3.2(SD=2.7) 3.2(SD=2.15) ii) Treatment of drinking water at household level Overall, 15.1% of the surveyed population reported that water was treated, and in all cases community treatment of water was reported to be by chlorination (Figure 3.5). A higher proportion of households in Abim reported treatment of water (30.5%) with fewer communities reporting water treatment in Nakapiripirit (9.0%) and Moroto (8.6%). There was a significant difference in water treatment between districts (χ 2 =16.7, p<0.001). It was interesting to note that the households which reported to have treated water were less likely to have suffered common illnesses as compared to those who did not treat water. For instance households which did not treat water reported 84.9% of all the diarrhoea cases as opposed to 15.1% for those who treated water. The proportions were similar for other illnesses. 54 P age

55 Percent Chlorination Boiling Other Abim Kaabong Kotido Nakaprirpirit Moroto Combined Figure 3.5: Treatment of drinking water by district iii) Sanitation Latrine coverage in Karamoja region remains poor with the majority of the households utilizing the bushes around the homesteads. The worst district in terms of latrine coverage was Moroto (2.1% ) and the best Abim (54.4%) (Table 3.17). Table 3.17: Latrine coverage by district Private latrine Community latrine Bush (open air) Neighbors latrine Others N (%) N (%) N (%) N (%) N (%) Abim (n=680) 370(54.4) 30(4.4) 207(30.4) 74(10.9) 2(0.3) Kaabongo (n=660) 228(34.5) 29(4.4) 334(50.6) 68(10.3) 1(0.2) Kotido (n=621) 110(17.7) 48(7.7) 435(70.0) 9(1.4) 71(11.4) Nakapiripirit (n=604) 40(6.6) 14(2.3) 534(88.4) 10(1.7) 6(1.0) Moroto (n=679) 14(2.1) 53(7.8) 600(88.4) 8(1.2) 9(1.3) Combined (n=3244) (23.5) (5.4) (65.0) (5.2) (2.7) Illustrated more graphically (Figure 3.6), such a poor level of latrine coverage for Karamoja will impact negatively on reported better coverage of the health care services. Likewise, hand washing practice at critical times after easing oneself, before and after serving and eating 55 P age

56 meals, including feeding children was practiced by only 30.1% of the population. This proportion was a relative improvement since previous assessment reported poorer practices, often not exceeding 20% private latrine community latrine bush (open air) 63.9 neighbor s latrine others Figure 3.6: Faecal disposal pooled for Karamoja region For rubbish disposal, the majority of the households still deposit it in the bush, 56.2%, or gardens, and a few 27.7% in compost pits (Figure 3.7). This implies that poor management of sanitation within the households is rampant in the Karamoja region and this was evidenced by the visible rubbish on the compounds and droppings of animals as well as food wastes. 56 P age

57 Compost pit Garden Bush Others 60 Percentage Abim Kaabong Kotido Nakaprirpirit Moroto Combined Figure 3.7 Rubbish disposal by district Mortality assessment In interpreting mortality the following guidelines are usually employed. CMR = deaths/10,000/day: emergency phase <1 = Under control >1 = Serious condition >2 = Out of control >4 = Major catastrophe 57 P age

58 Mortality rate for <5 age group 1 = Normal in a developing country <2 = Emergency phase: under control >2 = Emergency phase: in serious trouble >3 = Emergency phase: out of control The overall 90 day recall crude mortality rates was 0.67 (95% CI = ) for the region and was 0.20 (95% CI = ) for Abim; 1.70 (95% CI = ) for Kaabong; 0.57 (95% CI = ) for Kotido; 0.60 (95% CI = ) for Nakapiripirit; and 0.24 (95% CI = ) for Moroto. The mortality level in Kaabong district, just like it was in 2009 assessment was serious. Likewise under five mortality rates were 0.33 (95% CI = ), 2.93 (95% CI = ), 0.95 (95% CI = ), 1.24 (95% CI = ), and 0.97 (95% CI = ) for Abim, Kaabong, Kotido, Nakapiripirit and Moroto, respectively. The situation in Kaabong is describable as serious trouble. The high rate of under five mortality in Kaabong should be urgently investigated. 3.4 INFANT AND YOUNG CHILD FEEDING PRACTICES Caregiver characteristics At the time of this assessment the majority (62.5%, 95% CI: ) of the mothers were breastfeeding their children, while 1.9% (95% CI: ) were pregnant and breastfeeding. Overall, the primary caregiver 87.7% (95% CI: ) of children under two years were their own mothers. There were significant differences between districts as regards the care provided by either mother or caregiver (χ 2 =23.1, p<0.001). For instance in Kaabong 97.4% of caregivers, were the real mothers while in Moroto this was 85.2%. Leaving a child under the age of two years alone during the day increases the risk for negative health outcomes such as malnutrition and illness. Even when infants and young children are cared for by siblings, the inherent risks 58 P age

59 are similar as the sibling responsible for the child (often an older sister) is only 6 or 7 years old and therefore does not properly understand what the child s needs or their own needs are. Grandmothers serve an important role, and are most frequently those who were classified as caregivers caring for the child. There may be traditional practices which are potentially detrimental to the health and development of the child and education campaigns may benefit this particular group Breastfeeding practices Assessment of breastfeeding was based upon maternal recall. To minimize on the recall bias only mothers with children less than 2 years were assessed for breast feeding. In the pooled analysis, 54.6% of mothers introduced breast milk within one hour after delivery, while 45.1% initiated breastfeeding after 1 hour and 0.4% did not breast feed at all. Nakapiripirit and Moroto performed better in the initiation of breastfeeding after birth, while Abim (32.1%) had the lowest proportion of mothers/caregivers initiating breastfeeding within 1 hour after birth (Table 3.18). There was a significant difference in the period of initiation of breastfeeding between the districts (χ 2 =42.2, p<0.001). Table 3.18: Duration of introduction of breast feeding after delivery 95% Confidence Interval Districts Duration N % Lower Upper Abim Initiated within first 1 hr After 1 hr Did not breast feed Kaabong Initiated within first 1 hr After 1 hr Nakapriripirit Initiated within first 1 hr After 1 hr Moroto Initiated within first 1 hr After 1 hr Did not breast feed Kotido Initiated within first 1 hr After 1 hr Did not breast feed P age

60 In addition, mothers of children less than 2 years of age introduced liquids or semi solid foods to the current children at a very early age. For instance fluids/semi solid foods were introduced to children 0 1 months by 1.2% of the mothers; 2 3 months by 5.9% of the mothers; 4 5 months by 51.1% of the mothers; at 6 months by 22.5% of the mothers; and 7 8 months by 17.1%. Up to 7% of the mothers reported to have given children 0 3 month old, liquids or semi solid foods. It is recommended to start liquids or semi solid food only when the child is six months of age Complementary feeding Considering breastfeeding children aged 6 8 months, 129 (54.4%) received meals/snack twice a day while 12.5% never or received only one meal. Likewise, among children 9 23 months 316 (29.8%) received 3 meals, 548 (51.8%) received 2 meals and 148 (13.6%) received one or no meal at all. It is important to note that balancing meal frequency in breastfeeding children is very important. Much is not necessarily better as it might displace breastfeeding and too little is not good at all. The meal frequency observed in Karamoja was particularly low and could explain the peak of malnutrition observed in the region in the second year of life. Mother reported that foods introduced before 6 months included, water, milk, juices and cereal porridge and of all the foods given to children cereal porridge was the main food followed by milk/milk tea and porridge (Figure 3.8). 60 P age

61 water milk/milk tea juice cereal porridge tea without milk others unknown 1.3 Figure 3.8: Foods reported to have been introduced before children were 6 months of age Cereal meal and porridge had also been consumed by 48.4% of the children the previous day before the survey, legumes by 21.9% and fruits and vegetables by 14.1% (Figure 3.9). This trend was consistent across the districts, implying that the foods served to children in Karamoja region is mainly composed of cereals and legumes. The level of active feeding was not good. Amongst children aged months 64% ate on their own plates while only 23.6% were assisted by a care giver and about 14% shared food on the same plate with adults and siblings. The poor care practices and quality of cereal meals could be a contributing factor to the recurrence of malnutrition in the region as cereal based foods tend to be bulky and thus supplying less nutrients for growth and development. 61 P age