Food Security and Nutrition Assessment in Ibanda, , , Nebbi and Pader districts

November 2014

Re po rt

By Dr. He nry Wamani

Makerere University School of Public Health

ii Table of content Acknowledgements ...... vii Executive summary ...... viii Introduction and Background ...... 1 Methods ...... 1 Findings and discussion ...... 2 Socio-demographic characteristics ...... 2 Age and sex distribution of sampled children ...... 2 Sex, age and marital status of household head ...... 2 Age and parity status of respondents ...... 3 Education status of mothers ...... 4 Nutritional status of children 6-59 months ...... 4 Trends of stunting rates for 2012 to 2014 ...... 5 Prevalence of stunting stratified by sex among children 6-59 months ...... 6 Prevalence of severe stunting according to age among children 6-59 months ...... 7 Wasting prevalence stratified by sex among children 6-59 months ...... 7 Mean z-score of nutrition indicators ...... 8 Nutrition status of mothers 15-49 years ...... 8 BMI status of mothers ...... 8 Infant and young child feeding practices ...... 9 Exclusive breastfeeding among children 0-5 months ...... 9 Timing of introduction of complementary feeding ...... 9 Minimum meal frequency ...... 10 Minimum dietary diversity ...... 10 Minimum acceptable diet ...... 11 Factors significantly associated with minimum acceptable diet (MAD) ...... 12 Use of micronutrient powders (MNP) ...... 12 Mothers knowledge of infant and young child feeding practices ...... 12 Morbidity and health care seeking practices ...... 13 Immunization deworming and vitamin A supplementation coverage ...... 13 Prevalence of common childhood illness among children 0-59 months ...... 14 Safe water and sanitation coverage ...... 15 Safe water coverage ...... 15 Latrine coverage ...... 16 Household socioeconomic, food security and livelihood status ...... 16 Household socioeconomic status ...... 16 Household food security status ...... 17 Factors associated with household food security or moderate to severe hunger ...... 19 Diagrammatic description of 7-day food consumption frequencies ...... 22 Household sources of livelihood ...... 24 Household ownership of livestock ...... 25 Household expenditure ...... 26 Gender dynamics at household level ...... 28 Time allocation between husbands and wives in households ...... 28 Decision making, ownership and control profiles between men and women ...... 28 Factors associated with malnutrition ...... 30 Conclusions ...... 33 Recommendations ...... 36 Appendix 1– Questionnaire ...... 37 Appendix 2– ENA reports and plausibilty checks ...... 49

iii

List of figures

FIGURE 1: PREGNANCY AND BREASTFEEDING STATUS OF AMONG MOTHERS 15-49 YEARS ...... 4 FIGURE 2: TREND OF STUNTING 2012 TO 2014 ACCORDING TO DISTRICT ...... 6 FIGURE 3: STUNTING AND SEVERE STUNTING PREVALENCE ACCORDING TO SEX ...... 6

FIGURE 4: PREVALENCE OF SEVERE STUNTING ACCORDING TO AGE ...... 7 FIGURE 5: PREVALENCE OF WASTING ACCORDING TO SEX ...... 7 FIGURE 6: EXCLUSIVE BREASTFEEDING RATES ACCORDING TO DISTRICT ...... 9

FIGURE 7: PROPORTIONS OF CHILDREN 6-8 MONTHS WHO DID NOT RECEIVE COMPLEMENTARY FOOD 24 HOURS

BEFORE THE ASSESSMENT, ACCORDING TO DISTRICT ...... 10 FIGURE 8: MEAL FREQUENCY AMONG CHILDREN 6-23 ACCORDING TO DISTRICT ...... 10 FIGURE 9: MINIMUM ACCEPTABLE DIETARY DIVERSITY FOR CHILDREN 6-23 MONTHS ACCORDING TO DISTRICT . 11

FIGURE 10: PROPORTION OF CHILDREN 6-23 MONTHS WHO HAD MINIMUM ACCEPTABLE DIET, ACCORDING TO

DISTRICT ...... 12

FIGURE 11: ASSOCIATION BETWEEN CHILDREN WHO HAD MINIMUM ACCEPTABLE DIET, WITH MOTHERS’

EDUCATION ...... 12

FIGURE 12: CORRECT RESPONSE ON QUESTIONS ASSESSING MOTHERS KNOWLEDGE ON INFANT AND YOUNG CHILD

FEEDING PRACTICES AND BIRTH SPACING ...... 13

FIGURE 13: IMMUNIZATION, DEWORMING AND VITAMIN A SUPPLEMENTATION COVERAGE AMONG CHILDREN 12-

23 MONTHS ACCORDING TO DISTRICT ...... 14

FIGURE 14: TWO-WEEK PREVALENCE OF FEVER, ARI AND DIARRHEA ACCORDING TO DISTRICT ...... 15 FIGURE 15: LATRINE COVERAGE ACCORDING TO DISTRICT ...... 16 FIGURE 16: ASSOCIATION OF HOUSEHOLD SOCIOECONOMIC STATUS AND CHILDHOOD MALNUTRITION ...... 17

FIGURE 17: FOOD SECURITY STATUS OF HOUSEHOLDS BASED ON FOOD CONSUMPTION SCORES ACCORDING TO

DISTRICT ...... 18

FIGURE 18: PREVALENCE OF HOUSEHOLDS EXPERIENCING MODERATE TO SEVERE HUNGER A MONTH PRIOR TO THE

SURVEY ...... 19

FIGURE 19: ASSOCIATION BETWEEN HOUSEHOLD SOCIOECONOMIC STATUS AND HOUSEHOLD FOOD CONSUMPTION

SCORES (FCS) ...... 19 FIGURE 20: ASSOCIATION BETWEEN HOUSEHOLD SOCIOECONOMIC STATUS AND HOUSEHOLD HUNGER SCORES ... 20 FIGURE 21: ASSOCIATION OF SEX OF HOUSEHOLD HEAD WITH HOUSEHOLD FOOD SECURITY STATUS ...... 20

FIGURE 22: ASSOCIATION OF SEX OF HOUSEHOLD HEAD WITH HOUSEHOLD HUNGER SCORES ...... 21 FIGURE 23: ASSOCIATION OF MOTHERS’ EDUCATION WITH HOUSEHOLD FOOD SECURITY STATUS ...... 21 FIGURE 24: ASSOCIATION OF MOTHERS’ EDUCATION WITH HOUSEHOLDS WITH MODERATE TO SEVERE HUNGER 21

FIGURE 25: FREQUENCY OF FOODS CONSUMED ACCORDING TO HOUSEHOLD FOOD SECURITY STATUS FOR IBANDA

DISTRICT ...... 22

FIGURE 26: FREQUENCY OF FOODS CONSUMED ACCORDING TO HOUSEHOLD FOOD SECURITY STATUS FOR KABALE

DISTRICT ...... 22

iv FIGURE 27: FREQUENCY OF FOODS CONSUMED ACCORDING TO HOUSEHOLD FOOD SECURITY STATUS FOR KANUNGU

DISTRICT ...... 23

FIGURE 28: FREQUENCY OF FOODS CONSUMED ACCORDING TO HOUSEHOLD FOOD SECURITY STATUS FOR NEBBI

DISTRICT ...... 23

FIGURE 29: FREQUENCY OF FOODS CONSUMED ACCORDING TO HOUSEHOLD FOOD SECURITY STATUS FOR PADER

DISTRICT ...... 23

FIGURE 30: PROPORTION OF HOUSEHOLD THAT REPORTED NO INCOME SOURCE ...... 24 FIGURE 31: FIRST MOST IMPORTANT SOURCE OF LIVELIHOOD ACCORDING TO DISTRICT ...... 24 FIGURE 32: PROPORTION OF HOUSEHOLDS OWNING LIVESTOCK ACCORDING TO DISTRICT ...... 25

FIGURE 33: RELATIVE PROPORTION OF HOUSEHOLD EXPENDITURES IN PAST 30 DAYS ON FOOD, HEALTH,

EDUCATION AND OTHERS ...... 27 FIGURE 34: ASSOCIATION BETWEEN MOTHER’S EDUCATION AND MALNUTRITION IN CHILDREN 6-59 MONTHS .. 30 FIGURE 35: ASSOCIATION BETWEEN MOTHER’S EDUCATION AND HOUSEHOLD FOOD SECURITY STATUS ...... 30

FIGURE 36: ASSOCIATION OF HOUSEHOLD SOCIOECONOMIC STATUS AND CHILDHOOD MALNUTRITION ...... 31 FIGURE 37: ASSOCIATION OF HOUSEHOLD FOOD SECURITY AND STUNTING ...... 31 FIGURE 38: ASSOCIATION OF HOUSEHOLD HUNGER SCORES AND UNDERWEIGHT ...... 32 FIGURE 39: ASSOCIATION OF MATERNAL BMI AND CHILDHOOD MALNUTRITION ...... 32

FIGURE 40: ASSOCIATION OF TYPE OF WATER SOURCE WITH MALNUTRITION ...... 33

v List of tables

TABLE 1: SEX RATIO AND DISTRIBUTION OF SAMPLED CHILDREN 6-59 MONTHS ...... 2 TABLE 2: SEX OF HOUSEHOLD HEADS BY DISTRICT ...... 2 TABLE 3: AGE OF HOUSEHOLD HEADS BY DISTRICT ...... 2 TABLE 4: MARITAL STATUS OF HOUSEHOLD HEADS BY DISTRICT ...... 3

TABLE 5: RESPONDENTS AGE AND PARITY ...... 3 TABLE 6: EDUCATION STATUS OF MOTHERS ...... 4 TABLE 7: PREVALENCE OF GAM, SAM, STUNTING AND UNDERWEIGHT ACCORDING TO DISTRICT (WHO FLAGS) . 5

TABLE 8: A DIAGRAMMATIC VIEW OF MALNUTRITION EXPRESSED ACCORDING TO THE WHO CLASSIFICATION OF

PREVALENCE OF MALNUTRITION, BY DISTRICT ...... 5 TABLE 9: MEAN Z-SCORES FOR WFH, WFA AND HFA ...... 8 TABLE 10: BMI STATUS OF MOTHERS ACCORDING TO DISTRICT ...... 8

TABLE 11: SOURCES OF DRINKING WATER ...... 15 TABLE 12: PREPARATION OF DRINKING WATER AT HOME ...... 16 TABLE 13: HOUSEHOLD SOCIOECONOMIC STATUS ACCORDING TO DISTRICT ...... 17 TABLE 14: TOTAL LIVESTOCK OWNED AND ITS DISTRIBUTION IN HOUSEHOLDS ...... 26

TABLE 15: AVERAGE HOUSEHOLD EXPENDITURE IN UGX ON FOOD ITEMS IN PAST 30 DAYS ACCORDING TO

DISTRICT ...... 27 TABLE 16: TWENTY-FOUR HOUR ACTIVITY CALENDAR FOR WOMEN AND MEN ACCORDING TO DISTRICT ...... 28 TABLE 17: PROPORTION OF MEN AND WOMEN WHO OWN AND CONTROL KEY HOUSEHOLD ASSETS ..... 29

vi Acknowledgements

The team at the School of Public Health, Makerere University College of Health Sciences (MakSPH) acknowledges the support received from the different institutions and individuals who participated in this population based nutrition surveillance exercise. Special thanks go to UNICEF for funding the activity; to the Ministry of Health and District Health Offices of Ibanda, Kabale, Kanungu, Nebbi and Pader; and to the regional staff of the USAID Community Connector Project in the southwest and the north for supporting especially the community data collection exercise. Our special thanks go to Ms Nelly Birungi of UNICEF, Dr Robert Mwadime the Chief of Party of the Community connector Project for ensuring that this assessment was carried out successfully.

vii Executive summary

Nutrition • There was improvement in stunting prevalence from critical levels (above 40%) observed in previous years in Kabale, Kanungu and Ibanda to the current serious level (above 30%) in all districts except Pader (21.8%). GAM continued to be within acceptable limits (less than 5%) for most districts except Kanungu (5.5%) and Pader (5.3%).

District GAM SAM Stunting Underweight % (95%CI) % (95%CI) % (95%CI) % (95%CI) Ibanda (N=335) 3.3 (1.9-5.9) 0.9 (0.3-2.6) 30.9 (26.2-36.1) 11.7 (8.7-15.6) Kabale (N= 451) 3.8 (2.4-6.0) 2.9 (1.7-4.9) 33.5 (29.3-38.0) 7.9 (5.8-10.8) Kanungu (N=446) 5.5 (3.7-8.0) 3.4 (2.1-5.6) 37.5 (33.1-42.2) 12.8 (10.0-16.3) Nebbi (N=429) 4.2 (2.7-6.6) 1.2 (0.5-2.7) 36.6 (32.1-41.2) 17.1 (13.8-20.9) Pader (N=293) 5.3 (3.2-8.6) 2.8 (1.4-5.5) 21.8 (17.4-27.0) 8.3 (5.6-12.1)

• Exclusive breastfeeding assessed through a 24-hour feeding recall improved in the majority of the districts except Nebbi when the 2012 rate was considered. Among children less than six months exclusive breastfeeding was 91.1% Vs 76.9% in Ibanda, 89.8% Vs 61.4% in Kabale, 92.9% Vs 80.0% in Kanungu, 79.2% Vs 80.6% in Nebbi and 88.2% Vs 60.5% in Pader. • Initiation of complementary feeding was late in some districts like Pader and Nebbi where 36.4% and 26.9%, respectively, of the children 6-8 months were exclusively breastfed the previous day of the assessment when they should have been provided with complementary food • Minimum acceptable diet (MAD), the combination of children 6-23 months who had minimum dietary diversity and those who had minimum meal frequency was generally low. MAD was 24.9%, 29.7%, 31.5%, 34.1% and 37.6% in Kabale, Pader, Ibanda. Nebbi and Kanungu districts, respectively. MAD was significantly associated with household socioeconomic status, mothers education and food security status. • Using BMI, underweight mothers (BMI <18.5) were most prevalent in Nebbi (11.1%) and Pader (10.3%) and was lowest in Kabale (2.4%). However, Kabale (35.4%), Ibanda (31.5%) and Kanungu (28.7%) had high proportions of overweight or obese mothers (BMI > 25) while Nebbi (10.2%) and Pader (10.7%) had the least overweight or obese mothers.

Morbidity and immunization • had the best performing program for immunization, deworming and vitamin A supplementation with measles immunization coverage of 79.8%, deworming, 69.7% and vitamin A supplementation, 82.9%. The least performing district was Nebbi with measles coverage of

viii 60.0%, deworming, 52.5% and vitamin A supplementation, 67.6%. Most of the districts did not meet the immunization coverage target of 85% coverage. Absence of immunization cards was also high with almost one third of the children without cards in all districts except Pader. • Up to 66.3%, 66.2%, 59.6% 37.7% and 32.7% of the children had suffered Acute Respiratory Infections (ARI) in the two weeks preceding the assessment in Kabale, Pader, Nebbi, Kanungu and Ibanda, respectively. Diarrhoea prevalence was 29.9% in Pader, and 23.4% in Nebbi, which was above the national average of 23%. There was however no significant correlation between the burden of common childhood illnesses and GAM or any other indicators of malnutrition.

Water and sanitation • Safe water coverage was highest in (92.4%) followed by Pader district (87.8%) and Nebbi (63.5%). Safe water coverage was below 60% in the districts of Ibanda and Kanungu. Large proportions of households in Nebbi (30.5%) and Kanungu (26.9%) obtained drinking water from ponds and rivers, which is unacceptable. • The main preparation for drinking water was boiling, which is acceptable. However, the majority of the households in Nebbi (79.8%) and Pader (57.5%) did not do any form of treatment to the drinking water. In some districts like Nebbi where coverage for safe water was still low, households should be educated and encouraged to treat their drinking water. • Kabale (99.3%) and Ibanda (98.6%) districts had almost universal coverage of latrine while Nebbi (92.6%) and Kanungu (84.7%) had good coverage. However, one in three households in Pader district lacked a latrine and coverage was only 66.5%. There is therefore an obvious need to promote latrine coverage in Pader and Kanungu districts.

Socioeconomic, food security and livelihood status • Using a socioeconomic index derived from valuable household assets and ownership of shoes and clothes, Pader (36.6%) and Nebbi (33.5%) had the highest proportion of households in the poorest quintile while Ibanda (29.4%) had the highest proportion of socioeconomically better off households. • Household socioeconomic status was significantly associated with all indicators of malnutrition. Children in the socioeconomically poor households were more likely to be stunted, underweight or wasted than their wealthier counterparts. • Using Food Consumption Scores (FCS Low), the proportion of highly food insecure households was most prevalent in Kabale (14.3%) followed by

ix Pader district (10.2%), Ibanda (8.6%), Kanungu (5.7%) and Nebbi (4.7%). • However, when using Household Hunger Scores (HHS), (37.7%) followed by Kabale (12.2%) had the highest experience of moderate to severe hunger. Kanungu (7.4%), Ibanda (7.2%) and Pader (6.7%) had low experience with moderate to severe hunger. There was remarkable improvement in the prevalence of moderate to severe hunger compared to the findings in assessments of the previous two years. For instance in 2012 moderate to severe hunger was 58.7% in Nebbi, and 18.4% in Kabale districts. • Food insecurity based on FCS was significantly associated with stunting prevalence. Hunger had a negative association with underweight status that was statistically significant while stunting and wasting had a clear trend with hunger although not significant. Other factors such as sex of head of household, education of mother, and household socioeconomic status, were also associated with food security and hunger.

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 were jointly owned. Generally men tended to own and control cash generating items, radio and telephones. A larger number of wives than husbands reported to own savings.

Factors associated with malnutrition • A number of factors that were statistically significantly associated with malnutrition and these included: mothers education which was negatively associated with all indicators of malnutrition; household socioeconomic status also negatively associated with all indicators of malnutrition; Food Consumption Scores were associated with stunting but not with underweight and wasting; Household Hunger Scores were associated with underweight; Mothers BMI status was significantly associated with childhood malnutrition. Overweight or obese mothers were more likely to have less stunted, underweight or wasted children; and unsafe water sources – such as ponds and rivers and unprotected wells were associated with higher prevalence of malnutrition compared to safe water sources such as boreholes, protected springs and piped water.

x Recommendations 1. Recommended practices for both breastfeeding and complementary feeding should be promoted further. Messages on timing of introduction of complementary foods, quality and frequency of complementary foods should be emphasized in all the five SUN districts. 2. Health care programs such as immunization, vitamin A supplementation and deworming should be continuously evaluated and/or supervised by district health teams to sustain coverage within or above national targets. 3. The MDG target for safe water coverage is 95%, which has not yet been met in the five SUN districts. More effort should be made to ensure universal coverage for safe water especially in the three districts of Ibanda, Kanungu and Nebbi whose coverage for safe water was still low. 4. One in three households in Pader district lacked a latrine and coverage was only 66.5%. Latrine coverage should be universal and therefore the need for continued promotion of increased latrine coverage especially in Pader district. 5. Kabale and Nebbi districts had the highest proportion of households with food insecurity and hunger. Efforts to improve household food security should be intensified in these two districts. 6. Mothers’ education, household socioeconomic and food security status were some of the factors negatively associated with the indicators of malnutrition. Efforts to educate all children, improve household socioeconomic and food security status should be instituted and sustained in all the districts

xi Introduction and Background

In 2011, the government of joined the Scaling Up of Nutrition (SUN) program championed by USAID and other development partners such as UNICEF, UNWFP, DFID, Irish AID and others. The sole aim was to have an operational framework to guide all nutritional players achieve a common goal. The SUN movement has built operational frameworks where policies have been put in place; implementing and aligning programs; and mobilizing resources. Since inception, the SUN movement has been very pivotal in supporting processes and policies targeted at scaling up nutrition. Governments have been lobbied to stimulate political interest in nutrition with the aim of improving maternal and child nutrition.

A number of international organizations have set up ambitious targets to reduce malnutrition especially in children younger than five years and women of reproductive age. For three years running, UNICEF has been supporting implementation of nutrition programs in the districts of Kabale, Kanungu, Ibanda, Pader and Nebbi in Uganda. Programs implemented have been monitored annually at community level through food and nutrition security assessments.

This was the third survey in the five SUN districts since 2012. The main aim was to generate data for programmatic monitoring, planning and eventually informing policy. The indicators assessed covered aspects of nutrition status for children 0-59 months; mothers 15-49 years; household food security; morbidity; program implementation coverage, and coverage of primary health care services and WASH; and gender related analyses.

Methods

A two-stage cluster sampling methodology was used to obtain probability district representative samples. All sampled households were therefore interviewed using a semi-structured electronic questionnaire, regardless of who the occupants were. Where children existed their nutrition status was assessed and where they did not exist food security and other household indicators were assessed. Analysis was based on national definitions of indicators except where not applicable like in some of the gender related indicators. Independent district reporting was done but in a few instances combined results were presented to provide a broader perspective.

1 Findings and discussion Socio-demographic characteristics

Age and sex distribution of sampled children A total of 1,954 children aged 6-59 months were considered for anthropometric analysis although children below 6 months were also sampled. There was equal representation of males to females (Table 1).

Table 1: Sex ratio and distribution of sampled children 6-59 months

District Sex ratio of sampled children Distribution of sampled children Boys Girls Boy:Girl ratio 6-17 18-29 30-41 42-53 54-60 Total Ibanda 163 172 0.9 84 89 84 59 19 335 Kabale 214 237 0.9 134 96 97 95 29 451 Kanungu 226 220 1.0 99 99 131 79 38 446 Nebbi 200 229 0.9 127 10 99 69 24 429 Pader 140 153 0.9 79 94 64 42 14 293 Combined 943 1011 0.9 523 488 475 344 124 1954

Sex, age and marital status of household head The majority of the household heads in all districts were men (Table 2). Nebbi district had the highest number of female-headed households (35.1%) while a quarter of the households in Ibanda, Kabale and Pader were also female-headed.

Table 2: Sex of household heads by district

District Male Female N (%) N (%) Ibanda 419 (74.6) 143 (25.4) Kabale 437 (75.9) 139 (24.1) Kanungu 448 (73.0) 166 (27.0) Nebbi 360 (64.9) 195 (35.1) Pader 192 (75.6) 62 (24.4)

The oldest household heads were in Kanungu (35.6 years) while the youngest were from Pader district (31.5 years) (Table 3). Table 3: Age of household heads by district

District Mean age Std. deviation Ibanda (N=562) 33.0 12.2 Kabale (N=576) 34.6 13.4 Kanungu (N=614) 35.6 13.7 Nebbi (N=555) 35.1 15.0 Pader (N=254) 31.5 10.0

2 The majority (77.3%) of the household heads were married (Table 4). Pader had the highest number of marrieds (84.6%), Kanungu highest number of widows (12.9%), while Nebbi district had the highest number of divorcees (11.5%).

Table 4: Marital status of Household heads by district

District Divorced/ Married Single Separated Widowed % % % % Ibanda 74.7 6.8 9.3 9.3 Kabale 80.6 4.9 4.9 9.7 Kanungu 79.3 3.9 3.9 12.9 Nebbi 71.0 5.9 11.5 11.5 Pader 84.6 4.3 4.7 6.3

Age and parity status of respondents Up to 92 percent of the respondents were female and had an average age above 30 years and parity of about 4 children (Table 5). Mothers in Pader were younger but had high parity.

Table 5: Respondents age and parity

Mean number District Mean age SD of live birth SD Ibanda (N=552) 33.5 11.6 3.9 2.6 Kabale (N=575) 34.6 13.4 3.8 2.7 Kanungu (N=609) 35.9 13.4 4.2 3.0 Nebbi (N=555) 35.1 15.0 4.2 2.8 Pader (N=254) 31.5 10.0 4.2 2.4

The highest number of mothers pregnant or breastfeeding or doing both was also in Pader (Figure 1). It can be noted that the proportion of mothers who were pregnant or breastfeeding has reduced from about 60% observed previously to about 50% in the current assessment. If rate of pregnancy is kept low, it might lead to reduced fertility and thus more economically sustainable families. Reproductive health services should be intensified in all the five SUN districts.

3 Pader (N=228) 15.8% 49.6% 1.8% 32.9%

Nebbi (N=445) 8.3% 46.5% 0.4% 44.7%

Kanungu (N=459) 8.7% 30.5% 0.7% 60.1%

Kabale (N=463) 9.3% 40.6% 0.2% 49.9%

Ibanda (N=456) 11.6% 32.9% 0.4% 55.0%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Pregnant Breaseeding Pregnant and Breaseeding Not pregnant and not breaseeding

Figure 1: Pregnancy and breastfeeding status of among mothers 15-49 years

Education status of mothers The majority of the mothers had stopped in primary school while a large proportion had zero years of formal schooling (Table 6). Pader district had the highest proportion of mothers with zero years of formal education (28.0%). The importance of education of both male and female children cannot be overemphasized since dividends have been well and widely documented.

Table 6: Education status of mothers

District Zero Primary Secondary Higher Ibanda 14.2% 66.8% 11.8% 7.2% Kabale 15.8% 68.1% 12.4% 3.7% Kanungu 14.7% 67.1% 14.4% 3.8% Nebbi 24.6% 63.8% 8.3% 3.3% Pader 28.0% 61.0% 7.1% 3.9%

Nutritional status of children 6-59 months

Two districts of Pader and Kanungu had Global Acute Malnutrition (GAM) levels above 5%, classified as poor according to the WHO categorization while the rest of districts were within acceptable/normal levels (Table 7).

4 Table 7: Prevalence of GAM, SAM, Stunting and Underweight according to district (WHO flags)

District GAM SAM Stunting Underweight % (95% CI) % (95% CI) % (95% CI) % (95% CI) Ibanda (N=335) 3.3 (1.9-5.9) 0.9 (0.3-2.6) 30.9 (26.2-36.1) 11.7 (8.7-15.6) Kabale (N= 451) 3.8 (2.4-6.0) 2.9 (1.7-4.9) 33.5 (29.3-38.0) 7.9 (5.8-10.8) Kanungu (N=446) 5.5 (3.7-8.0) 3.4 (2.1-5.6) 37.5 (33.1-42.2) 12.8 (10.0-16.3) Nebbi (N=429) 4.2 (2.7-6.6) 1.2 (0.5-2.7) 36.6 (32.1-41.2) 17.1 (13.8-20.9) Pader (N=293) 5.3 (3.2-8.6) 2.8 (1.4-5.5) 21.8 (17.4-27.0) 8.3 (5.6-12.1)

Score table A score table developed based on WHO classification on severity of malnutrition prevalence feature stunting as the outstanding challenge (Table 8). WHO classification is 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%),

Table 8: A diagrammatic view of malnutrition expressed according to the WHO classification of prevalence of malnutrition, by district

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

Trends of stunting rates for 2012 to 2014 Overall, the stunting rates reduced in Kabale, Kanungu and Ibanda over the past three years (Figure 2). There are appears to be no improvement in Nebbi and Pader districts, where stunting prevalence was worse or similar in 2014 compared to 2012 for Nebbi and Pader, respectively. There should be concerted effort to curb this trend especially in Nebbi district.

5 60

50 42 43 40 34.9 33.6

30 21.8

Percentage 48.1 43.8 20 38.3 30.3 25.2 10 33.5 37.5 30.9 36.6 21.8 0 Kabale Kanungu Ibanda Nebbi Pader

2012 2013 2014

Figure 2: Trend of stunting 2012 to 2014 according to district

Prevalence of stunting stratified by sex among children 6-59 months As commonly observed, stunting was more prevalent amongst the boys than the girls in most districts except Pader (Figure 3). Severe stunting was as well more prevalent amongst boys than girls except in Pader and Kanungu districts.

10.1 8.8 Pader 25 18.4 11.6 16.6 Nebbi 32.9 40.7 13.7 12.9 Kanungu 33.5 41.3 6.4 13.7 Kabale 28.8 38.7 12.4 13.7 Ibanda 26.6 35.4

0 5 10 15 20 25 30 35 40 45

Severe stunng Girls Severe stunng Boys Stunng Girls Stunng Boys

Figure 3: Stunting and severe stunting prevalence according to sex

Kabale district had the least severely stunted girls (6.4%) while Nebbi had the most severely stunted boys (16.6%) followed by Kabale and Ibanda districts

6 both having 13.7% of the boys severely stunted. Pader district had the least severely stunted boys (8.8%).

Prevalence of severe stunting according to age among children 6-59 months In Nebbi and Kanungu districts stunting increased with age of the child peaking at 54-59 months (Figure 4). This might suggest cumulative insults to the child due to both nutrition and general poor standard of living of children in this district. While in Ibanda, Kabale and Pader, severe stunting peaked earlier, which could suggest more of nutritional challenges.

Ibanda Kabale Kanungu Nebbi Pader 31.6 20.8 19.4 19.2 17.1 16.9 15.8 15.2 14.8 14.7 14.5 13.9 13.8 13.8 13.3 13 11.9 11.3 11.1 10.1 9.9 9.5 9.1 8.3 8.3 7.7 5.6 5.4 5.2 3.7

6-17 months 18-29 months 30-41 months 42-53 months 54-59 months Overall

Figure 4: Prevalence of severe stunting according to age

Wasting prevalence stratified by sex among children 6-59 months Boys were more wasted than girls in most districts except Kanungu where more girls were wasted than boyss (7.4% Vs 3.6%), and Kabale where the prevalence of wasting between boys and girls were similar (Figure 5).

4.8 Pader 5.8

1.8 Nebbi 7 Girls 7.4 Kanungu 3.6 Boys

3.8 Kabale 3.8

3 Ibanda 3.7

Figure 5: Prevalence of wasting according to sex

7 Mean z-score of nutrition indicators The mean z-scores for weight-for-height (WFH) was better than the WHO reference standard (Table 9). However, the finding might be due to the fact that infants and young children in the five SUN districts were getting stunted early as depicted by the mean height-age-age (HFA) z-scores. This therefore provides a situation where there many short children otherwise appearing well nourished. The mean weight-for-age (WFA) z-scores were shifted to the left confirming the fact that the mean WFH were false positives and were due to high prevalence of stunting in the five SUN districts.

Table 9: Mean z-scores for WFH, WFA and HFA

Mean z-scores ± SD District WFH WFA HFA Ibanda 0.33±1.13 -0.57±1.20 -1.39±1.51 Kabale 0.46±1.14 -0.41±1.20 -1.33±1.52 Kanungu 0.34±1.23 -0.59±1.26 -1.51±1.53 Nebbi 0.01±1.12 -0.81±1.26 -1.44±1.59 Pader 0.02±1.20 -0.38±1.27 -0.84±1.80

Nutrition status of mothers 15-49 years

BMI status of mothers The highest prevalence of underweight women was in Nebbi and was similar and in equal proportion to what was observed in 2013 (Table 10). Kabale district had the highest number of overweight (27.3%) or obese (8.1%) women, also similar to what was observed in 2013 but much higher than in 2012. Prevalence of overweight or obesity was generally higher in the south-west and measures to address the challenge should be instituted in the region while measures to address underweight should be strengthened in the north.

Table 10: BMI status of mothers according to district

District Underweight Normal Overweight Obese N (%) N (%) N (%) N (%) Ibanda 18 (4.3) 269 (64.2) 101 (24.1) 31 (7.4) Kabale 10 (2.4) 263 (62.3) 115 (27.3) 34 (8.1) Kanungu 23 (4.9) 315 (66.5) 104 (21.9) 32 (6.8) Nebbi 45 (11.1) 318 (78.7) 33 (8.2) 8 (2.0) Pader 23 (10.3) 177 (79.0) 16 (7.1) 8 (3.6) Total 119 (6.1) 1342 (69.1) 369 (19.0) 113 (5.8)

8 Infant and young child feeding practices

Exclusive breastfeeding among children 0-5 months Exclusive breastfeeding rate among infants 0-5 months in all districts was high (Figure 6). Even in the district with lowest prevalence, Nebbi, close 80% of the infants 0-5 months were exclusively breastfed. Exclusive breastfeeding is a good practice and should be promoted further.

95.0% 92.9% 91.1% 89.8% 90.0% 88.2%

85.0%

79.2% 80.0%

75.0%

70.0% Ibanda Kabale Kanungu Nebbi Pader

Figure 6: Exclusive breastfeeding rates according to district

Timing of introduction of complementary feeding Although the numbers of children 6-8 months were few, the trend suggested that Pader and Nebbi districts were not practicing timely introduction of complementary foods. Up to 36.4% of the children in Pader were exclusively breastfed the day prior the assessment, when they should have received complementary food (Figure 7).

40.0%

35.0% 36.4% 30.0%

25.0% 26.9% 20.0%

15.0% 16.7% 17.6% 10.0%

5.0% 0.0% 0.0% Kabale (N=18) Ibanda (N=26) Kanungu (N=17) Nebbi (N=26) Pader (N=11)

9 Figure 7: Proportions of children 6-8 months who did not receive complementary food 24 hours before the assessment, according to district

Minimum meal frequency Among children 6-23 months there were many who had received zero meals or had only been exclusively breastfed in the 24-hours preceding the assessment especially in Ibanda and Pader districts (Figure 8). This implies that there were some children especially in Ibanda who had been exclusively breastfed even when they were above 8 months.

Pader (N=103) 15.5% 11.7% 28.2% 44.8%

Nebbi (N=174) 8.6% 16.7% 29.3% 45.4%

Kanungu (N=129) 2.3% 1.6% 26.4% 69.8%

Kabale (N=162) 4.9% 29.0% 65.4%

Ibanda (N=111) 21.6% 7.2% 20.7% 50.4%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Zero One Two Three or above

Figure 8: Meal frequency among children 6-23 according to district

On average up to 44.4% of the children received less than three meals (minimum meal frequency). Meal frequency and quality should therefore be improved in all the five SUN districts.

Minimum dietary diversity Individual dietary diversity scores (IDDS), which is a measure of the diversity of food groups contained in the diet consumed by children 6-23 months was low (not acceptable) in Kabale districts in over 60% of the children (Figure 9). IDDS were assessed using a modified assessment criterion based on eight food groups namely: cereals, pulses, oils, meats, eggs, milk, fruits and vegetables.1 Minimum dietary diversity (MDD) has been defined as the proportion of children who received foods from at least four food groups the previous day2. In the current assessment 43.7% of the children were having low or unacceptable dietary diversity. Nutrient diversity of food consumed by children at household level is

1 WHO Indicators for assessing infant and young child feeding practices part 2: measurements. 2 Low ≤ 3; acceptable > 4

10 key for sustainability of good nutrition status of children. It is therefore important to address food security issues discussed before (above), while continuing to promote adequate complementary feeding practices.

Pader (N=129) 29.5% 70.5%

Nebbi (N=185) 32.4% 67.6%

Kanungu (N=142) 46.5% 53.5%

Kabale (N=182) 61.0% 39.0%

Ibanda (N=128) 46.9% 53.1%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Low Acceptable

Figure 9: Minimum acceptable dietary diversity for children 6-23 months according to district

Minimum acceptable diet Minimum acceptable diet (MAD), the combination of children 6-23 months who had minimum dietary diversity and those who had minimum meal frequency was lowest in Kabale district (Figure 10), and apparently was highest in Kanungu district (37.6%). The proportion of children receiving the minimum acceptable diet, although low, was much better than what was observed among children in the Karamoja region (2.2%) in December 2014 and among children in refugee settlements in Uganda (1.2%) in November 2014.

40.0%

35.0% 37.6% 34.1% 30.0% 31.5% 29.7% 25.0% 24.9% 20.0%

15.0%

10.0%

5.0%

0.0% Kabale (N=177) Pader (N=128) Ibanda (N=124) Nebbi (N=182) Kanungu (N=141)

11 Figure 10: Proportion of children 6-23 months who had minimum acceptable diet, according to district

Factors significantly associated with minimum acceptable diet (MAD) Minimum acceptable diet was statistically significantly associated with mothers’ education, household socioeconomic and food security status. All the factors had clear dose-effect relation as observed in Figure 11 – below.

18.0%

16.0% 16.3% 14.0% 14.3% 12.0%

10.0% 10.0% 8.0%

6.0% 6.6% 4.0%

2.0%

0.0% Zero formal Primary Secondary Terary educaon

Figure 11: Association between children who had minimum acceptable diet, with mothers’ education

Use of micronutrient powders (MNP) Mothers and or caregivers of children were showed the different micro nutrient powders (MNP) then asked whether they had fed their children aged between 6- 23 months on these micro nutrient powders. There were zero mothers who had seen or used the micronutrient powders.

Mothers knowledge of infant and young child feeding practices

The majority of the mothers were knowledgeable about a few infant and young child feeding indicators such as correct timing for breastfeeding initiation at birth and timing for complementary feeding, breastfeeding on demand and best child spacing. Relatively fewer mothers in Nebbi and Kabale were knowledgeable about timely initiation of breastfeeding (Figure 12). Health education on infant and young child feeding should be continued to ensure that all mothers are knowledgeable.

12 100 94.1

90 86.1 84.5 85.8 81.8 83.3 80.9 79.7 81.1 76.4 80 74.2 75 75.5 76 73 72 66.7 68.1 66.5 Child 6-12 months should be 70 breased as oen as child 57.1 wants 60 Breaseeding should be introduced within one hour 50 of birth Complementary feeding Percentage 40 should start at six months Best birth spacing is two 30 years or beyond 20

10

0 Ibanda Kabale Kanungu Nebbi Pader

Figure 12: Correct response on questions assessing mothers knowledge on infant and young child feeding practices and birth spacing

Morbidity and health care seeking practices

Immunization deworming and vitamin A supplementation coverage The status of immunization, deworming and vitamin A supplementation was measured using presence of an immunization card and mothers’ recall among children aged 12-23 months. Pader district had the best performing program for immunization, deworming and vitamin A supplementation with measles immunization coverage of 79.8% (Figure 13). Most of the districts did not meet the immunization coverage target of 85% coverage. Absence of immunization cards was also high with almost one third of the children without cards in all districts except Pader. There might be need to educate mothers on importance of protecting or safely keeping child health cards or to investigate the cause of the lack of the cards.

13 Vit. A suppl. 74.4 8.5 16.3 0.8 Deworming 61.2 8.5 27.9 2.4

Pader DPT3 85.3 8.5 5.4 0.8 Measles 71.3 8.5 17.1 3.1 Vit. A suppl. 41.1 26.5 15.1 17.3 Deworming 30.3 22.2 25.9 21.7

Nebbi DPT3 53 29.7 2.2 15.1 Measles 38.4 21.6 16.8 23.3 Vit. A suppl. 62 31 3.5 3.5 Deworming 50 31 14.8 4.2 DPT3 60.6 28.9 7 3.5 Kanungu Measles 47.9 23.2 17.6 10.6 Vit. A suppl. 47.8 47.8 1.6 2.7 Deworming 47.3 38.5 2.7 11.5

Kabale DPT3 48.4 47.3 1.6 2.7 Measles 44.5 39.6 3.3 12.6 Vit. A suppl. 56.3 23.4 11.7 8.6 Deworming 48.4 19.5 18 14.1

Ibanda DPT3 63.3 24.2 3.9 8.6 Measles 55.5 22.7 10.9 10.9 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Yes with card Yes without card No with card No without card

Figure 13: Immunization, deworming and vitamin A supplementation coverage among children 12-23 months according to district

Prevalence of common childhood illness among children 0-59 months Acute Respiratory Infections (ARI) were the most prevalence illnesses reported in all districts (Figure 14). In some districts like Kabale (66.3%), Pader (66.2%) and Neebi (59.6%), two out of three children had suffered an ARI episode two weeks prior the assessment. Fever was high in Pader (42.9%) and Nebbi (39.5%) districts. There was however no significant correlation between the burden of common childhood illnesses and GAM, but it could be observed that Pader with a high prevalence of illness including diarrhoea had GAM above the normal threshold.

14 100 90 80 66.3 70 66.2 59.6 60

50 42.9 37.7 39.5 40 32.7 28.3 29.9 30 21.3 23.1 23.4 18.1 18.6 16.6 20 10 0 Ibanda Kabale Kanungu Nebbi Pader

Diarrhoea Fever Ari

Figure 14: Two-week prevalence of fever, ARI and diarrhea according to district

Safe water and sanitation coverage

Safe water coverage Safe water coverage was below 60% in the districts of Ibanda and Kanungu (Table 11). Large proportions of households in Nebbi (30.5%) and Kanungu (26.9%) obtained drinking water from ponds and rivers, which is unacceptable.

Table 11: Sources of drinking water

Piped Protected Open Pond/ Rain Water District water spring Borehole well river water vendor Ibanda 25.3% 10.7% 20.3% 34.0% 8.6% 0.5% 0.5% Kabale 22.9% 68.9% 2.6% 2.6% 2.1% 0.2% 0.7% Kanungu 17.8% 27.9% 5.7% 20.2% 26.9% 0.5% 1.1% Nebbi 9.9% 1.3% 52.3% 5.4% 30.5% 0.5% 0.2% Pader 0.0% 3.9% 83.9% 5.5% 6.7% 0.0% 0.0%

The main preparation to drinking water was boiling which is acceptable. However, the majority of the households in Nebbi (79.8%) and Pader (57.5%) do not do any form of treatment to the drinking water (Table 12). Since Nebbi’s coverage for safe water was still low, households should be educated and encouraged to treat their drinking water.

15 Table 12: Preparation of drinking water at home

Leave it to District Boling Chlorination settle Do nothing Ibanda 74.2% 0.2% 3.0% 22.6% Kabale 74.7% 0.3% 0.9% 24.1% Kanungu 78.3% 0.5% 6.5% 14.7% Nebbi 8.1% 1.8% 10.3% 79.8% Pader 0.4% 5.9% 36.2% 57.5%

Latrine coverage Kabale and Ibanda districts had almost universal coverage of latrine while one in three households lacked a latrine in Pader district (Figure 15). There is therefore an obvious need to promote latrine coverage in Pader and Kanungu districts.

120.0%

100.0% 99.3% 98.6% 92.6% 80.0% 84.7%

60.0% 66.5%

40.0%

20.0%

0.0% Kabale Ibanda Nebbi Kanungu Pader

Figure 15: Latrine coverage according to district

Household socioeconomic, food security and livelihood status

Household socioeconomic status The socioeconomic status index was created using principal components analysis from a list of valuable assets that included: ownership of radio, phone, bicycle, motorcycle, car, construction material of the roof the dwelling structure, and ownership of at least two sets of clothes and one pair of shoes for every member of the household. Regression scores from the first principal component were ranked and then categorized into quintiles with the lowest quintile constituted of the poorest households and the highest with wealthiest households.

16 had the least number of households in the poorest quintile and had the highest number of households in the wealthiest quintile (Table 13). On the contrary Pader and Nebbi districts had the lowest proportions of households in the richest quintile.

Table 13: Household socioeconomic status according to district

Socioeconomic status quintile District Poorest Poor Middle Rich Richest Ibanda 8.4% 18.9% 18.2% 25.1% 29.4% Kabale 16.8% 19.6% 24.3% 27.3% 12.0% Kanungu 12.4% 18.6% 23.3% 33.1% 12.7% Nebbi 33.5% 22.2% 22.9% 15.5% 5.9% Pader 36.6% 28.0% 18.9% 11.0% 5.5%

Household socioeconomic status was significantly associated with all indicators of malnutrition (Figure 16). Children in the socioeconomically poor households were more likely to be stunted, underweight or wasted than their wealthier counterparts.

40.0% 35.6% 34.0% 35.0% 28.9% 29.8% 30.0% 26.0% 25.0% Stunng 20.0% Underweight 14.8% 12.8% 15.0% 11.6% GAM 10.3% 10.0% 6.9% 5.3% 5.6% 5.0% 3.4% 2.7% 3.5%

0.0% Poorest Poor Middle Rich Richest

Figure 16: Association of household socioeconomic status and childhood malnutrition

Household food security status Household food security was assessed on various dimensions namely food consumption scores, hunger scale, household incomes and expenditures, and household socioeconomic status.

17 Food consumption scores Food security assessed using household food consumption (FCS) scores were generated based on 8 food groups using the UNWFP– weighted scores of certain food groups. The pre-assigned weights for starches, meats, pulses, sugar, oil and milk are 2, 4, 3, 0.5, 0.5 and 4, respectively, were used.

Based on FCS the highest proportion of households experiencing severe food insecurity were in Kabale (14.3%) and Kanungu (10.2%) (Figure 17).

Pader 10.2% 24.0% 65.7%

Nebbi 4.7% 21.1% 74.2%

Kanungu 5.7% 21.7% 72.6%

Kabale 14.3% 11.7% 74.1%

Ibanda 8.6% 18.9% 72.5%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Food insecure Borderline Food secure

Figure 17: Food security status of households based on food consumption scores according to district

Household hunger scores This indicator measures the percent of households experiencing moderate or severe hunger, as indicated by a score of 2 or more on the household hunger scale (HHS). To collect data for this indicator, respondents are asked about the frequency with which three events were experienced by household members in the last four weeks: 1. no food at all in the house; 2. went to bed hungry, 3. went all day and night without eating. For each question, the following responses are possible: never (value=0), rarely or sometimes (value=1), often (value=2). Values for the three questions are summed for each household, producing a HHS score ranging from 0 to 6. The numerator for this indicator is the total number of households with a score of 2 or more on the HHS. The denominator is the total number of households in the sample with HHS data.

Nebbi district followed by Kabale had the highest experience of moderate to severe hunger (Figure 18). There was remarkable improvement in the prevalence of moderate to severe hunger compared to findings of the previous two years. For instance in 2012 moderate to severe hunger was 58.7% in Nebbi, and 18.4% in Kabale districts.

18 40.0%

35.0% 37.7%

30.0%

25.0%

20.0%

15.0%

10.0% 12.2%

5.0% 7.4% 7.2% 6.7% 0.0% Nebbi Kabale Kanungu Ibanda Pader

Figure 18: Prevalence of households experiencing moderate to severe hunger a month prior to the survey

Factors associated with household food security or moderate to severe hunger Food insecurity and moderate to severe hunger were significantly associated with some indicators malnutrition (see figure 36 and 37 below). Food insecurity based on FCS was significantly associated with stunting prevalence. Hunger had a negative association with underweight status that was statistically significant while stunting and wasting had a clear trend although not significant. Other factors such as sex of head of household, education of mother, and household socioeconomic status, were also associated with food security and hunger.

Up to 16.8% and 28.7% of the households belonging to the poorest quintile had poor or borderline food security status, respectively (Figure 19) while significantly less households in the wealthiest quintile suffered food insecurity.

88.9% Richest 7.5% 3.6%

82.6% Rich 12.4% 5.0% Food secure 74.8% Middle 17.9% 7.3% Borderline

64.6% Food insecure Poor 26.2% 9.1%

54.5% Poorest 28.7% 16.8%

0.0% 20.0% 40.0% 60.0% 80.0% 100.0%

Figure 19: Association between household socioeconomic status and household food consumption scores (FCS)

19

Likewise 31.9% of the households in the poorest socioeconomic quintile experienced moderate to severe hunger while compared to only 3.6% in the wealthiest quintile (Figure 20).

120.0%

96.4% 100.0% 90.9%

78.6% 80.4% 80.0% 68.1%

60.0% No Hunger Moderate to severe hunger 40.0% 31.9%

21.4% 19.6% 20.0% 9.1% 3.6% 0.0% Poorest Poor Middle Rich Richest

Figure 20: Association between household socioeconomic status and household hunger scores

Female-headed households suffered more food insecurity and hunger compared to male-headed households (Figures 21-22). The differences were statistically significant. Female-headed households should therefore be specially targeted in food security and livelihood programs.

80.0%

70.0% 76.1%

60.0% 63.4%

50.0%

40.0% Male-headed Female-headed 30.0%

20.0% 22.2% 17.7% 10.0% 14.4% 6.3% 0.0% Food insecure Borderline Food secure

Figure 21: Association of sex of household head with household food security status

20 Female headed 79.3% 20.7%

Male headed 83.5% 16.5%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

No hunger Moderate to severe hunger

Figure 22: Association of sex of household head with household hunger scores

Education status of mothers was also significantly associated with both food consumption and hunger scores (Figures 23-24). The importance of education especially for females is self explanatory according to the findings.

Higher 15.0% 81.4%

Secondary 8.3% 84.8%

Primary 18.8% 72.4%

Zero formal educaon 26.7% 63.7%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Food insecure Borderline Food secure

Figure 23: Association of mothers’ education with household food security status

30.0% 27.9%

25.0%

20.0% 17.5%

15.0%

10.0% 6.9% 5.3% 5.0%

0.0% Zero formal Primary Secondary Higher educaon

Figure 24: Association of mothers’ education with households with moderate to severe hunger

21 Diagrammatic description of 7-day food consumption frequencies The distribution of food consumption in a seven day recall (Figures 25-29) indicated that consumption of oils was more common in the northern districts of Nebbi and Pader than in Kabale and Kanungu. Meat consumption was highest in Nebbi than any other district while food insecure households in Kabale could not even afford to eat pulses.

60 Condiments 50 Sugar 40 Milk 30 Fruits

20 Oils

10 Vegetables

0 Meats 0 32 41 50 55 70 76 91 99 Number of days food was eaten in a week 15.5 22.5 27.5 36.5 45.5 60.5 65.5 80.5 85.5 Pulses Poor Borderline Acceptable Food consumpon scores (food security status) Staples

Figure 25: Frequency of foods consumed according to household food security status for Ibanda district

60 Condiments 50 Sugar 40 Milk 30 Fruits 20 Oils 10 Vegetables 0 2

14 19 26 36 40 47 54 61 75 86 Meats 31.5 43.5 50.5 57.5 65.5 70.5 104.5

Number of days food was eaten in a week Pulses Poor Borderline Acceptable Food consumpon scores (food security status) Staples

Figure 26: Frequency of foods consumed according to household food security status for Kabale district

22 60 Condiments 50 Sugar 40 Milk 30 Fruits 20 Oils 10 Vegetables 0 0

11 21 27 63 68 79 Meats 32 74 37.5 42.5 47.5 52.5 57.5 87.5 98.5

Number of days food was eaten in a week Pulses Poor Borderline Acceptable Food consumpon scores (food security status) Staples

Figure 27: Frequency of foods consumed according to household food security status for Kanungu district

60 Condiments 50 Sugar 40 Milk 30 Fruits 20 week Oils 10 0 Vegetables 13 26 35 53 58 77 86 44 72 Meats 20.5 30.5 39.5 48.5 62.5 67.5 100.5

Number of days food was eaten in a Pulses Poor Borderline Acceptable Food consumpon scores (food security status) Staples

Figure 28: Frequency of foods consumed according to household food security status for Nebbi district

60 Condiments 50 Sugar 40 Milk 30 Fruits 20 Oils 10 Vegetables 0 17 31 35 49 53 57

10 24 42 Meats 20.5 27.5 38.5 45.5 63.5 68.5 77.5 90.5

Number of days food was eaten in a week Pulses Poor Borderline Acceptable Food consumpon scores (food security status) Staples

Figure 29: Frequency of foods consumed according to household food security status for Pader district

23 Household sources of livelihood Pader district (23.2%) followed by Nebbi (17.4%) had the highest proportion of households that reported to have no income source (Figure 30).

25.0%

23.2% 20.0%

15.0% 17.4% 14.4% 14.4% 10.0% 10.5%

5.0%

0.0% Ibanda (N=561) Kabale (N=575) Kanungu Nebbi (N=555) Pader (N=254) (N=614)

Figure 30: Proportion of household that reported no income source

Otherwise, the most important source of livelihood in all districts was the sale of food crops, mentioned by 59.2%, 49.9%, 49.8%, 49.7%, and 72.4% of the households in Ibanda, Kabale, Kanungu, Nebbi and Pader, respectively (Figure 31).

80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0%

Ibanda Kabale Kanungu Nebbi Pader

Figure 31: First most important source of livelihood according to district

24 Sale of non-food crops was also an important source of livelihood in 17.1% of the households in Kanungu and 7.6% of the households in Nebbi. Other sources of livelihood mentioned as the most important were petty trading (12.7% in Ibanda), unskilled wage labour (22.3% in Kabale), agricultural labour (7.3% in Kanungu), salaries (7.7% and 7.3% in Nebbi and Kabale, respectively), and fishing for the case of Nebbi district

Household ownership of livestock Pader district had the largest number of households owning most livestock except pigs and sheep (Figure 32). Livestock ownership is low especially for poultry, which provides an easy source of proteins to households. Kabale had the lowest ownership of livestock. Livestock ownership should be promoted for both economic and nutritional reasons.

78.7% 58.3% Poultry 39.4% 36.0% 24.3% 9.8% 21.8% Pig 16.9% 2.9% 13.6% Pader 4.3% Kanungu 3.7% Sheep 2.5% 3.1% Ibanda 9.2% Nebbi 50.0% 40.4% Goat 32.2% Kabale 29.9% 22.4% 31.5% 9.9% Cale 16.0% 7.7% 9.9%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0%

Figure 32: Proportion of households owning livestock according to district

Among the households that owned livestock, the median number of animals or birds owned was low. For instance the median cattle ownership was highest in Ibanda and was 3 animals (Table 14). It was improvement in livestock in Pader compared to previous years.

25 Table 14: Total livestock owned and its distribution in households

Type of live stock District Cattle Goats Sheep Pig Poultry Ibanda Sum of livestock 381 613 50 165 1015 Mean 4.2 3.4 3.6 1.7 4.6 Std. Deviation 6.04 1.99 5.26 0.95 3.63 Median 3 3 2 1 3 N (Households) 90 180 14 95 221 Kabale Sum of livestock 112 355 135 109 480 Mean 2.0 2.8 2.6 1.4 3.4 Std. Deviation 1.69 2.25 1.71 0.81 2.68 Median 1 2 2 1 3 N (Households) 57 129 53 78 140 Kanungu Sum of livestock 146 746 33 192 1564 Mean 2.4 3.0 1.4 1.4 4.4 Std. Deviation 2.16 2.47 1.12 0.76 4.31 Median 2 2 1 1 3 N (Households) 61 248 23 134 358 Nebbi Sum of livestock 206 622 90 89 1135 Mean 4.8 3.8 5.3 5.6 5.7 Std. Deviation 7.11 2.98 11.63 9.01 8.35 Median 2 3 2 3 3.5 N (Households) 43 166 17 16 200 Pader Sum of livestock 264 482 57 42 1722 Mean 3.3 3.8 5.18 1.68 8.61 Std. Deviation 3.65 2.76 3.60 0.69 7.34 Median 2 3 4 2 6 N (Households) 80 127 11 25 200

Household expenditure The relative household expenditure on food was highest in Kabale and Nebbi (Figure 33). The expenditure on food correlated well with the food insecurity in both Kabale and Nebbi districts (Figure 12 and 13 above). In addition the health expenditures in Pader and Kanungu districts were relatively high depicting problems with public health care service delivery in the two districts.

26 Pader (N=234) 35.3 27.4 23.7 12.7

Nebbi (N=555) 41.5 23.1 13.9 21.6

Kanungu (N=614) 31.6 26.0 22.4 20.0

Kabale (N=575) 55.5 17.2 11.5 16.0

Ibanda (N=557) 39.0 23.4 21.9 15.7

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Food expenditures Health expenditures

Educaon expenditures All the rest of expenditures

Figure 33: Relative proportion of household expenditures in past 30 days on food, health, education and others

Although households in Kabale district reported food purchases constituting the highest proportion of their expenditures, the average amount of money that was spent on food in the district was low compared to other districts (Table 15). Ibanda and Kanungu spent relatively higher amounts of money on food than Kabale. The median expenditure on milk, fruits and vegetable, cooked or processed foods, and drinking water was zero Ugx in all the districts. Likewise median expenditure on meat and cooking oil was zero Ugx for Kabale district.

Table 15: Average household expenditure in UGX on food items in past 30 days according to district

Cereal/ Cooked/ Other foods other Cooking Meat/eg G/nuts/bea Milk/yoghu Fruit and processe Drinking (bread, coffee, District Staples oil gs/fish ns/pulses Sugar rt/cheese vegetables d food water tea, pasta etc.) Ibanda Mean 23,323 2,277 11,830 3,077 5,762 7,273 662 351 336 7,106 Median 15,000 0 7,000 0 2,000 0 0 0 0 4,000 Kabale Mean 12,601 854 4,000 9,918 3,424 1,552 794 1,121 41 5,774 Median 6,000 0 0 5,000 0 0 0 0 0 2,000 Kanungu Mean 26,368 1,939 10,678 5,947 4,530 3,750 1,037 391 608 7,632 Median 20,000 0 7,000 1,500 2,000 0 0 0 0 2,000 Nebbi Mean 14,550 5,135 17,951 11,725 4,729 625 1,928 1,260 931 2,636 Median 8,000 3,000 12,000 9,000 1,600 0 0 0 0 1,200 Pader Mean 9,991 6,987 3,740 4,920 4,469 228 403 99 148 4,069 Median 2,000 3,500 0 0 1,200 0 0 0 0 100

Generally expenditures of food in terms of money were still low in the five SUN districts. Kanungu followed by Ibanda districts spent the highest amount of money on staples compared to other districts, which in this case might be due to differences in socio-economic status as opposed to food insecurity. Both

27 Kanungu and Ibanda had high proportions of their households in the richest socioeconomic quintile (see Table 13 above).

Gender dynamics at household level

Time allocation between husbands and wives in households As observed in previous assessments, women in all districts spent significantly more time in agricultural work and household work while men spent more time on leisure and non-agricultural work (Table 16). As in previous assessment, about 8 hours are spent sleeping, which is a healthy practice.

Table 16: Twenty-four hour activity calendar for women and men according to district

Ibanda Kabale Kanungu Nebbi Pader Women Men Women Men Women Men Women Men Women Men Agricultural work 4.6 3.4 5.1 2.7 6.1 4.6 3.6 2.9 5.9 5.4

Nonagricultural work 4.1 4.5 3.1 5.0 3.5 4.4 4.4 5.0 4.3 4.4

Household and work with children /elderly 3.8 1.6 4.2 1.6 3.9 2.6 4.5 2.3 3.1 2.3

Personal time (Rest, leisure, social, etc.) 2.3 3.2 2.7 4.1 2.8 3.9 3.4 5.4 2.4 3.5

Sleeping 8.8 8.1 8.9 8.4 7.7 8.0 8.0 7.5 8.0 7.9

Decision making, ownership and control profiles between men and women Generally the majority of the assets in households were owned and controlled jointly between husbands and wives. However there was wide variations between assets and districts. For instance land and radio are mainly owned by men in Pader. Generally men tended to own and control cash generating items (productive), radio, bicycle, telephones and cash crops (Table 17). As previously observed a larger number of wives reported to own savings especially in Nebbi and Pader districts than their husbands, although much of the savings were jointly owned.

28 Table 17: Proportion of men and women who own and control key household assets

Item Ownership Control Women Men Joint Women Men Joint % % % % % % Land Ibanda (N=451) 17.7 27.1 55.0 18.0 27.9 53.4 Kabale (N=389) 14.4 20.1 64.0 15.7 14.4 69.2 Kanungu (N=582) 21.8 24.7 52.4 21.8 15.6 62.2 Nebbi (N=489) 25.2 26.6 44.0 25.4 24.5 46.4 Pader (N=223) 10.3 52.9 27.4 12.1 31.8 44.8 Radio Ibanda (N=364) 20.6 20.6 58.8 19.5 17.0 63.2 Kabale (N=346) 10.7 44.5 43.1 11.3 25.1 63 Kanungu (N=401) 15.0 21.2 63.8 15.2 11.2 73.3 Nebbi (N=192) 21.9 27.1 50.5 21.4 22.9 55.2 Pader (N=75) 13.3 49.3 37.3 12.0 34.7 53.3 Bicycle Ibanda (N=191) 6.3 63.4 30.4 7.9 63.9 28.3 Kabale (N=82) 4.9 80.5 12.2 4.9 79.3 13.4 Kanungu (N=49) 0.0 71.4 28.6 0.0 67.3 32.7 Nebbi (N=116) 14.7 65.5 19.8 12.9 61.2 25.9 Pader (N=125) 8.8 60.0 31.2 7.2 53.6 39.2 Telephone Ibanda (N=226) 27.0 26.1 45.7 28.2 25.9 46 Kabale (N=164) 17.8 36.5 43.6 18.1 38.3 42.4 Kanungu (N=201) 22.2 27.4 49.9 21.9 23.3 54.6 Nebbi (N=147) 22.7 47.2 30.1 22.2 44 33.8 Pader (N=142) 10.2 68.4 21.4 9.2 66.3 24.5 Savings Ibanda (N=237) 21.1 23.2 54.9 21.5 24.5 54.0 Kabale (N=263) 18.6 5.7 74.5 18.3 5.7 75.7 Kanungu (N=422) 27.0 8.3 64.5 23.7 7.1 68.7 Nebbi (N=199) 60.3 11.1 28.6 58.3 10.1 31.7 Pader (N=109) 38.5 14.7 46.8 32.1 16.5 51.4 Cereals Ibanda N=420) 41.1 6.4 51.7 38.6 7.1 52.6 Kabale (N=357) 26.9 4.8 68.3 26.3 5.9 67.5 Kanungu (N=531) 61.2 1.7 36.5 52.5 1.1 46.0 Nebbi (N=227) 35.2 3.1 59.5 32.6 5.3 60.8 Pader (N=171) 29.8 17 53.2 23.4 16.4 59.6 Goat Ibanda (N=176) 26.7 23.9 48.9 23.3 25.0 48.9 Kabale (N=155) 16.1 21.9 60.6 14.8 20.0 64.5 Kanungu (N=151) 26.5 19.2 53 23.8 12.6 62.3 Nebbi (N=164) 25.6 24.4 49.4 24.4 22.6 51.2 Pader (N=119) 14.3 43.7 41.2 14.3 37.0 47.9 Cash crops Ibanda (N=272) 14.0 36.0 50.0 13.2 37.5 48.5 Kabale (N=25) 8.0 16.0 72.0 12.0 16.0 72.0 Kanungu (N=379) 21.1 24.8 53.0 20.6 18.5 60.7 Nebbi (N=184) 21.2 26.1 52.2 21.2 25 52.2 Pader (N=22) 18.2 59.1 22.7 13.6 45 40.9

29 Factors associated with malnutrition

Many factors were statistically significantly associated with malnutrition. 1. Mothers’ education – any level of formal education was better than nothing (Figure 34). The more education as has been observed previously, the better the childhood nutrition status.

40.0% 38.0%

35.0% 32.2%

30.0%

25.0% 20.8% 18.5% 19.1% Stunng 20.0% Underweight 15.0% 11.3% Wasng 10.0% 6.0% 4.9% 4.5% 4.5% 5.0% 1.4% 0.0% 0.0% Zero formal Primary Secondary Higher educaon

Figure 34: Association between mother’s education and malnutrition in children 6-59 months

Maternal education was also associated with household food security status. A larger proportion of households of mothers with tertiary education (16.3%) were food secure compared to those with zero formal education (6.6%), (Figure 35).

18.0% 16.0% 16.3% 14.0% 14.3% 12.0% 10.0% 10.0% 8.0% 6.0% 6.6% 4.0% 2.0% 0.0% Zero formal Primary Secondary Terary educaon

Figure 35: Association between mother’s education and household food security status

30 2. Household socioeconomic status - all indicators of malnutrition were significantly associated with household socioeconomic status (Figure 36). Children in the socioeconomically poor households were more likely to be stunted, underweight or wasted than their wealthier counterparts.

40.0% 35.6% 34.0% 35.0% 28.9% 29.8% 30.0% 26.0% 25.0% Stunng 20.0% Underweight 14.8% 12.8% 15.0% 11.6% GAM 10.3% 10.0% 6.9% 5.3% 5.6% 5.0% 3.4% 2.7% 3.5%

0.0% Poorest Poor Middle Rich Richest

Figure 36: Association of household socioeconomic status and childhood malnutrition

3. Food security - assessed through Food Consumption Scores was associated with stunting but not with underweight and wasting (Figure 37), while household hunger scores were significantly associated with underweight (Figure 38).

Stunng

40.0%

35.0% 36.4% 34.6% 30.0% 29.7% 25.0%

20.0%

15.0%

10.0%

5.0%

0.0% Food insecure Borderline Food Secure

Figure 37: Association of household food security and stunting

31 Underweight

18.0% 16.0% 14.0% 15.8% 12.0% 10.0% 10.4% 8.0% 6.0% 4.0% 2.0% 0.0% Moderate to severe No hunger hunger

Figure 38: Association of household hunger scores and underweight

4. Maternal nutrition status – assessed by BMI was also significantly associated with childhood malnutrition. Overweight or obese mothers were more likely to have less stunted, underweight or wasted children (Figure 39). This could be due to the fact that overweight and obesity in Uganda are held in high esteem and are common in people who are socioeconomically better.

40.0% 35.2% 35.0% 31.2%

30.0% 26.8%

25.0% Underweight 20.0% 16.9% Normal 13.3% 15.0% Overweight or Obese

10.0% 5.8% 4.5% 5.0% 5.3% 2.1% 0.0% Stunng Underweight Wasng

Figure 39: Association of maternal BMI and childhood malnutrition

5. Unsafe water sources – such as ponds and rivers and unprotected wells were associated with higher prevalence of malnutrition compared to safe water sources such as boreholes, protected springs and piped water (Figure 40).

32 40.0%

35.0% 34.1% 30.0% 29.9% 25.0%

20.0% Unsafe water source Safe water source 15.0% 14.5% 10.0% 10.2% 5.0% 5.3% 4.0% 0.0% Stunted Underweight Wasted

Figure 40: Association of type of water source with malnutrition

7. Other factors – morbidity, immunization and deworming, infant and young child feeding indicators, and bed net use were not significantly associated with indicators of malnutrition.

Conclusions

GAM was within acceptable limits (<5%) in Ibanda (3.3%), Kabale (3.8%) and Nebbi (4.2%), and slightly above in Pader (5.3%) and Kanungu (5.5%). There was improvement in stunting prevalence from critical levels (above 40%) observed in Kabale, Kanungu and Ibanda in previous years to the current serious level (above 30%) in all districts except Pader (21.8%).

Exclusive breastfeeding assessed through a 24-hour feeding recall improved in the majority of the districts except Nebbi when compared to the 2012 rates. Among children less than six months exclusive breastfeeding was 91.1% Vs 76.9% in Ibanda, 89.8% Vs 61.4% in Kabale, 92.9% Vs 80.0% in Kanungu, 79.2% Vs 80.6% in Nebbi and 88.2% Vs 60.5% in Pader.

Initiation of complementary feeding was late in some districts like Pader and Nebbi where 36.4% and 26.9%, respectively, of children 6-8 months were exclusively breastfed the previous day of the assessment when they should have been provided with complementary food

Minimum acceptable diet (MAD), the combination of children 6-23 months who had minimum dietary diversity and those who had minimum meal frequency was generally low. MAD was 24.9%, 29.7%, 31.5%, 34.1% and 37.6% in Kabale, Pader, Ibanda. Nebbi and Kanungu districts, respectively. MAD was significantly associated with household socioeconomic status, mothers education and food security status.

33

Using BMI, underweight mothers (BMI <18.5) were most prevalent in Nebbi (11.1%) and Pader (10.3%) and was lowest in Kabale (2.4%). However, Kabale (35.4%), Ibanda (31.5%) and Kanungu (28.7%) had high proportions of overweight or obese mothers (BMI > 25) while Nebbi (10.2%) and Pader (10.7%) had the least overweight or obese mothers.

Pader district had the best performing program for immunization, deworming and vitamin A supplementation with measles immunization coverage of 79.8%, deworming, 69.7% and vitamin A supplementation, 82.9%. The least performing district was Nebbi with measles coverage of 60.0%, deworming, 52.5% and vitamin A supplementation, 67.6%. Most of the districts did not meet the immunization coverage target of 85% coverage. Absence of immunization cards was also high with almost one third of the children without cards in all districts except Pader.

Up to 66.3%, 66.2%, 59.6% 37.7% and 32.7% of the children had suffered Acute Respiratory Infections (ARI) in the two weeks preceding the assessment in Kabale, Pader, Nebbi, Kanungu and Ibanda, respectively. Diarrhoea prevalence was 29.9% in Pader, and 23.4% in Nebbi, which was above the national average of 23%. There was however no significant correlation between the burden of common childhood illnesses and GAM or any other indicator of malnutrition.

Safe water coverage was highest in Kabale district (92.4%) followed by Pader district (87.8%) and Nebbi (63.5%). Safe water coverage was below 60% in the districts of Ibanda and Kanungu. Large proportions of households in Nebbi (30.5%) and Kanungu (26.9%) obtained drinking water from ponds and rivers, which is unacceptable.

The main preparation to drinking water was boiling which is acceptable. However, the majority of the households in Nebbi (79.8%) and Pader (57.5%) did not do any form of treatment to the drinking water. In some districts like Nebbi where coverage for safe water was still low, households should be educated and encouraged to treat their drinking water.

Kabale (99.3%) and Ibanda (98.6%) districts had almost universal coverage of latrine while Nebbi (92.6%) and Kanungu (84.7%) had good coverage. However, one in three households in Pader district lacked a latrine and coverage was only 66.5%. There is therefore an obvious need to promote latrine coverage in Pader and Kanungu districts.

Using a socioeconomic index derived from valuable household assets and ownership of shoes and clothes, Pader (36.6%) and Nebbi (33.5%) had the

34 highest proportion of households in the poorest quintile while Ibanda (29.4%) had the highest proportion of socioeconomically better off households.

Household socioeconomic status was significantly associated with all indicators of malnutrition. Children in the socioeconomically poor households were more likely to be stunted, underweight or wasted than their wealthier counterparts.

Using Food Consumption Scores (FCS Low), the proportion of highly food insecure households was most prevalent in Kabale (14.3%) followed by Pader district (10.2%), Ibanda (8.6%), Kanungu (5.7%) and Nebbi (4.7%). However, when using Household Hunger Scores (HHS), Nebbi district (37.7%) followed by Kabale (12.2%) had the highest experience of moderate to severe hunger. Kanungu (7.4%), Ibanda (7.2%) and Pader (6.7%) had low experience with moderate to severe hunger. There was remarkable improvement in the prevalence of moderate to severe hunger compared to findings of the previous two years. For instance in 2012 moderate to severe hunger was 58.7% in Nebbi, and 18.4% in Kabale districts.

Food insecurity based on FCS was significantly associated with stunting prevalence. Hunger had a negative association with underweight status that was statistically significant while stunting and wasting had a clear trend although not significant. Other factors such as sex of head of household, education of mother, and household socioeconomic status, were also associated with food security and hunger.

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. A larger number of wives than husbands reported to own savings.

A number of factors that were statistically significantly associated with malnutrition and these included: mothers education which was negatively associated with all indicators of malnutrition; household socioeconomic status also negatively associated with all indicators of malnutrition; Food Consumption Scores were associated with stunting but not with underweight and wasting; Household Hunger Scores were associated with underweight; Mothers BMI status was significantly associated with childhood malnutrition. Overweight or obese mothers were more likely to have less stunted, underweight or wasted

35 children; and unsafe water sources – such as ponds and rivers and unprotected wells were associated with higher prevalence of malnutrition compared to safe water sources such as boreholes, protected springs and piped water.

Recommendations

1. Recommended practices for both breastfeeding and complementary feeding should be promoted further. Messages on timing of introduction of complementary foods, quality and frequency of complementary foods should be emphasized in all the five SUN districts.

2. Health care programs such as immunization, vitamin A supplementation and deworming should be continuously evaluated and/or supervised by district health teams to sustain coverage within or above national targets.

3. The MDG target for safe water coverage is 95%, which has not yet been met in the five SUN districts. More effort should be made to ensure universal coverage for safe water especially in the three districts of Ibanda, Kanungu and Nebbi whose coverage for safe water was still low.

4. One in three households in Pader district lacked a latrine and coverage was only 66.5%. Latrine coverage should be universal and therefore the need for continued promotion of increased latrine coverage especially in Pader district.

5. Kabale and Nebbi districts had the highest proportion of households with food insecurity and hunger. Efforts to improve household food security should be intensified in these two districts.

6. Mothers’ education, household socioeconomic and food security status were some of the factors negatively associated with the indicators of malnutrition. Efforts to educate all children, improve household socioeconomic and food security status should be instituted and sustained in all the districts.

36