NUTRITIONAL ANTHROPOMETRIC SURVEY

FINAL REPORT

LIRA DISTRICT, NORTHERN UGANDA

APRIL-MAY 2008

Funded by:

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ACKNOWLEDGMENTS

We would like to extend our appreciation to the Lira District Director of Health Services for fully supporting the nutrition survey and involving their staff in the survey. We would like to thank all of the ACF management team for their assistance in preparing and conducting the survey. Without the support of logistics and administration at both capital and base levels, the survey would not have been possible. We appreciate all of the team members from the office and field that spent two long weeks conducting the survey. Their dedication and hard work helped ensure the accuracy and reliability of the data. Further thanks to all of the drivers that ensured the teams’ safe delivery to and from the field. Special thanks are extended to all of the village leaders that welcomed and willingly assisted the teams in their home communities. Without their support and sensitization to the community, finding the houses and finding families at home would have been impossible. Finally, we offer much thanks to the individual families that patiently allowed us to weigh and measure their children and provided vital information for the survey.

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Table of content

I EXECUTIVE SUMMARY .................................................................................................................................. 4 II INTRODUCTION .............................................................................................................................................. 8 III OBJECTIVES ............................................................................................................................................. 10 IV METHODOLOGY....................................................................................................................................... 10

IV.1 Type of Survey and Sample Size ............................................................................................................ 10 IV.2 Data Collection ........................................................................................................................................ 11 IV.3 Indicators, Guidelines, and Formula’s Used ........................................................................................... 11

IV.3.1 Acute Malnutrition ............................................................................................................................ 11 IV.3.2 IV.3.2 Mortality ................................................................................................................................. 12

IV.4 Field Work ............................................................................................................................................... 12 IV.5 IV.5 Data Analysis ................................................................................................................................... 12

V RESULTS OF THE ANTHROPOMETRIC SURVEY ..................................................................................... 13 V.1 Anthropometric results in Lira district ...................................................................................................... 13

V.1.1 Distribution by Age and Sex ............................................................................................................. 13 V.1.2 Anthropometrics Analysis ................................................................................................................ 13

V.2 Measles Vaccination Coverage ............................................................................................................... 15 VI RESULTS OF RETROSTECTIVE MORTALITY ....................................................................................... 16 VII ADDITIONAL HOUSEHOLD INFORMATION ........................................................................................... 16

VII.1 Household characteristics ................................................................................................................... 16 VII.2 Food security ....................................................................................................................................... 17 VII.3 Water and Sanitation ........................................................................................................................... 18

VIII DISCUSSION ............................................................................................................................................. 18 IX RECOMMENDATIONS ............................................................................................................................. 20 X ANNEXES ...................................................................................................................................................... 21

Annex I. Assignment of Clusters, Lira district .................................................................................................... 21 Annex II:. Selected villages, Lira district ............................................................................................................ 23 Annex III: Household Selection .......................................................................................................................... 24 Annex IV: Anthropometric survey questionnaire ................................................................................................ 25 Annex V: Calendar of Events in Lira District ...................................................................................................... 26 Annex VI: Household enumeration data collection form for a death rate calculation survey (one sheet/household) ................................................................................................................................................ 27

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I EXECUTIVE SUMMARY

Lira District is one of three districts in the Langi sub-region of northern Uganda and is bordered by ten districts. The native inhabitants are Langi and remain the main ethnic group in the region. Luo and Lango are the main languages in Lira district. According to the 2002 census, the population of Lira District was 757,763, placing its national population share at 3.11%. The total area of the district is 7,251 km2 and the average altitude is 1170 meters. The terrain is characterized by rolling savannah that receives 1200 to 2000mm of annual rainfall, peaking in the months of April, May, August, and October. Agriculture is among the main activities of the region. Crops such as millet, cassava, cow peas, potatoes, beans, simsim and sunflower are among the main crops cultivated. Cash crops include cotton, coffee and sugarcane. Lira is one of the largest producers of seeds including simsim, sunflower and shea butter, the latter is an important crop and is a fast growing as a major income earner. Livestock include cattle, goats, sheep, pigs and chicken. The district has 42 health units including 1 government hospital, health centers (III, IV) and dispensaries. There are in additional private and NGO hospitals and dispensaries. Lira district had become progressively affected by the LRA conflict in Northern Uganda since 2002. The increased movement of rebels along the northern border made the district a clear target of attacks, abductions, and looting. This first led to a massive displacement of people into Lira municipality in May 2003. By the end of the same year, many of the displaced persons moved into rural camps which were formed as the need for protection increased throughout the district. The populations in the internally displaced camps (IDPs) have had and continue to face food shortages due to lack of access to farmland. As in most IDPs, access to water, and hygienic facilities was low and increased the risk of infection, a known exacerbation of malnutrition. In mid-2006, the conflict between the LRA and the Uganda People’s Defense Force (UPDF) subsided and a relative calm has come to the region as the LRA and the UPDF engaged in peace talks leading to massive movements of the populations to return areas. During the period of this survey, it was found that there are no more IDPs camps in the district3; the remaining IDPs camps have been closed. The agricultural and commercial activities had gradually resumed in the region since the beginning of the peace process. ACF is present in the area since 2004 and works in the following sectors:

- Nutrition: 5 SFC and 5 OTP in Adwari, Omoro, Orum, Olilim and Okwang. Moreover, a community based approach was initiated in 2007 to strengthen the capacity of the communities on detection and referral of acutely malnourished children less than 5

- Food security: seed distribution, seed security training, seed multiplication, animal traction training, oxen distribution, and income generating activities

- Water and sanitation: hygiene promotion, borehole construction, and rehabilitation. The last nutritional survey conducted in Lira district by ACF in May 2007 revealed rates of Global Acute Malnutrition and Severe Acute Malnutrition rates of 7.1% (CI: 4.5-9.7%) and 0.8% (IC: 0.1-1.4%) respectively (results presented in Z-scores with a confidence interval 95%). The current survey conducted in April-May 2008 is part of the of nutritional surveillance program of ACF.

OBJECTIVES

- To evaluate the nutritional status of children aged 6 to 59 months. - To estimate measles immunization coverage of children aged 9 to 59 months. - To estimate crude mortality rates through a retrospective mortality survey

1Uganda Communication Commission (UCC), 2003 http://www.lira.go.ug/ 2 Uganda Communication Commission (UCC), 2003 http://www.lira.go.ug/ 3 IASC Working Group. Population Movement_IDP, June 2008

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METHODOLOGY

A two-stage cluster sampling was used, according to the SMART methodology with probability of being selected proportional to the population size in each cluster. In each cluster, households were randomly selected and surveyed using the EPI method. A household was defined as all the inhabitants using the same cooking pot. All the children in the randomly selected household between the ages of 6 and 59 months were included in the survey.

A retrospective mortality survey over the period from January 1st up to the date of the survey (three and a half months) was undertaken alongside the anthropometric survey.

Anthropometric and mortality data were analyzed using Nutrisurvey version October 2007 software.

FIELD WORK

This anthropometric nutritional survey was conducted from April 17th to May 3rd 2008. Four teams of four people each (one team leader, two measurers and one surveyors for food security and water and sanitation component) completed the data collection. The nutritional survey team was composed of ACF field staff (working in nutrition feeding centers) and DDHS staff. All surveyors have participated in four days of training including theoretical and practical training on the standardization of anthropometric measurements and pilot survey in the field. The supervision of the survey was conducted by the 2 Nutrition Program Managers and the Medical and Nutrition Coordinator of ACF.

RESULTS

860 children were measured during the assessment. The data of 28 of them were not used for the analysis, as they did not meet eligibility criteria or were incorrect or incomplete. The following analysis is therefore done on the data of 832 children.

INDEX INDICATOR RESULTS4 (n=848)

NCHS

Z- scores

Global Acute Malnutrition W/H< -2 z and/or oedema 5.9% [4.1% - 7.7%]

Severe Acute Malnutrition W/H < -3 z and/or oedema 0.4% [0.0% - 0.7%]

% Median

Global Acute Malnutrition W/H < 80% and/or oedema 3.5% [2.2% - 4.8%]

Severe Acute Malnutrition W/H < 70% and/or oedema 0.0% [0.0% - 0.0%]

WHO Z-scores

Global Acute Malnutrition W/H< -2 z and/or oedema 4.6% [3.1 – 6.1%]

Severe Acute Malnutrition W/H < -3 z and/or oedema 0.0% [-0.0% - 0.0%]

MUAC Height> 65 cm

Global Acute Malnutrition MUAC (<120mm) 4.92%

Severe Acute Malnutrition MUAC (<110mm) 0.36%

Total crude retrospective mortality (last 3 months) /10,000/day Under five crude retrospective mortality /10,000/day

0.44(0.21-0.66) 0.85 (0.31-1.39)

Measles immunization coverage (N=782 children ≥ 9months old)

By card According to caretaker5 Not immunized

41.90% 40.90% 15.20%

Table 1: Results summary, Lira District, May 2008

1 Uganda Communication Commission (UCC), 2003 http://www.lira.go.ug/ 2 Uganda Communication Commission (UCC), 2003 http://www.lira.go.ug/

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The results (expressed in Z-score) of previous nutritional surveys allows making an analysis of the evolution of the nutritional situation in Lira district. Feb-05

(n=965) May-06(n=921)

Apr/May-07(n=651)

Apr/May-08 (n=848)

Global Acute Malnutrition (W/H <-2 Z-scores and/or oedema)

1.9% (0.9%-3.7%)

5.9% (3.9%-8.6%)

7.1% (4.5%-9.7%)

5.9% (4.1% - 7.7%)

Severe Acute Malnutrition (W/H <-3 Z-scores and/or oedema)

0.6% (0.1%-2.0%)

0.7% (0.1%-2.1%)

0.8% (0.1-1.4%)

0.4% (0.0-0.7%)

Retrospective mortality rate 10,000 person/day 0.7 0.4 1.15 0.44

Measles vaccine coverage Confirm by card Not confirmed by card

37.3% 62.7%

54.3% 19.6%

43.1% 39.8%

41.9% 40.9%

Table 2: Summary of nutrition surveys carried out in Lira since 2005

The first two nutritional surveys were conducted in a particular context where population lived in IDPs camps and depended almost entirely on humanitarian aid, which would explain the low rate of global acute malnutrition during this period. For the past three years, the highest prevalence of global acute malnutrition was observed during the April 2007 survey. Indeed this period matched with the transitional period characterized by the returning process of people to their villages of origin. During this time, the population did not benefit any more from the food rations from general distributions unlike in IDPs camps. Moreover at the time of return, the life conditions including basic amenities in return areas were reported poor. During the current nutritional survey, there were no IDPs camps in Lira district and all camps were officially closed. Most people had returned to their villages. The survey was conducted in a period when people are finishing their food stocks from the last harvests while planting and waiting for the upcoming harvest. The prevalence of Global Acute Malnutrition, as indicated by this survey, is lower than in 2007 and reflects an improvement in the nutritional status of the population. Similarly, the Crude Mortality Rate is low. These low rates of acute malnutrition can be explained by the following assumptions:

- The security situation has improved, allowing people to return to their villages and to settle down and to resume their regular activities

- The interventions of various NGOs active in the field of water and sanitation and food security have produced positive effects on the stabilization of health and nutritional status of the population.

- Primary health care is likely operational in the return areas, thus reducing morbidity and mortality. However, it should be noted the quality of health facilities and the health care coverage have not been assessed in the present survey.

Regarding the immunization coverage, 40.9% of children have received measles vaccine according to their mother and 41.9% of children were vaccinated based on the vaccination card. However one should be cautious when interpreting the vaccination reported by caretakers without cards to prove. WHO recommends coverage of 80% to avoid epidemic. Hence immunization coverage needs to be further improved to prevent childhood susceptibility to measles and other diseases. However based on the higher number of admissions in the supplementary feeding centers, there is still a need in terms of treatment of malnourished children. We can therefore say that the nutritional need has low intensity, but a large magnitude, since it affects a large number of people despite the relatively low rates. Although the rates of malnutrition are low, there are a number of factors that need to be taken into consideration:

Health Access: Lira is affected by high rates of malaria, water borne diseases and HIV/AIDS. The district often has a shortage of drugs, further intensifying infections and prolonging the recovery period and also discouraging the population from seeking health services.

Water and Sanitation: Half of the survey respondents reported collecting water from unprotected

water points. Access to clean and safe water can contribute to the reduction of morbidity due to water-

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borne diseases, which can reduce susceptibility to malnutrition in a vulnerable population. Hand washing practices need to be strengthened, particularly after latrine use.

Food Security: People reported low food stocks. Their low income and the current high cost of food

could have affected the purchasing ability. Some reported getting food on credit, which indicates that some households could be more vulnerable than others. Moreover, when food is inadequate, people tend to buy less expensive food, reduce the number of meals per day or tend to reduce portion size, which is likely to affect dietary quantity and quality.

Nutrition and Health Education: Children were also not fed frequently. They were most likely to be

left under the care of slightly older siblings. They were also more likely to be fed only at the same time as the adults (1-3 times a day). These indicators suggest that the children feeding practices combined with poor child care and hygiene practices increase the risk of infection among children which can lead to or aggravate malnutrition.

RECOMMENDATIONS

The global acute malnutrition rate is low and at acceptable level. A more global approach to managing malnutrition is necessary: Health and Nutrition:

- To continue the treatment of severely and moderately malnourished children under 5 years old in therapeutic and outpatient centers

- To reinforce the capacity of MOH staff (hospital and health center staff) in the management of acute malnutrition. The health staff at all levels including district hospitals and health centers (including HCII, HCIII and HC IV) need to be trained on detection and management of acute malnutrition.

- To strengthen the capacity of communities by training VHTs/community volunteers on how to detect and refer acutely malnourished children less than 5 years to enable early detection and referral

- To monitor the nutritional status of the population on an annual basis. - To conduct nutrition surveillance and to train MoH staff in nutrition surveillance to better monitor the

nutritional status and understand possible factors contributing to malnutrition - Strengthen the links between the various levels from VHTs to health centre staff to the district hospital

and district focal person so that there is effective collaboration and efficiency between the various levels in managing acute malnutrition

- Continue conducting EPI campaigns whenever necessary with systematic routine immunization activities to ensure all children are vaccinated against childhood diseases

- Promote nutrition education sessions in the communities and health centres, with an emphasis on breastfeeding, weaning, complementary foods, and balanced diets

Food Security:

- To go on with programs devoted to the generation of incomes at household level Water and Sanitation:

- Emphasize on the promotion of adequate hygiene practices and household latrines.

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II INTRODUCTION

Lira District is one of the three districts in Lango sub-region of northern Uganda and is bordered by ten districts. The native inhabitants are Langi and remain the main ethnic group in the region. According to the 2002 census, the population of Lira District was 757,763, placing its national population share at 3.1%. The total area of the district is 7,251 km² and the average altitude is 1170 meters (UCC, 2003). Located at around 350 km from Kampala, the area is accessible from the national road linking Kampala to southern Sudan and North-East to Democratic Republic of Congo.

The terrain is characterized by rolling savannah that receives 1200 to 2000mm of annual rainfall, peaking in the months of April, May, August, and October.

Lira district has two seasons: the dry season and wet season. The rainy season extends from April to November, with peaks of higher precipitation in April and August. The dry season extends from December to March. The average monthly maximum temperature is 290 C and the monthly average minimum temperature is 170 C (UCC, 2003).

Agriculture is among the main activities of the region. Crops such as millet, cassava, cow peas, potatoes, beans, simsim and sunflower are among the main crops cultivated. Cash crops include cotton, coffee and sugarcane. Lira is one of the largest producers of seeds including simsim, sunflower and shea butter, the latter is an important crop and is a fast growing as a major income earner. Livestock include cattle, goats, sheep, pigs and chicken.

The northern region of Uganda has witnessed a tragic armed conflict for more than two decades. The increased movement of rebels along the northern border made the district a clear target of attacks, abductions, and lootings. This situation led to a massive displacement of population in Lira district in May 2003. Living conditions in the IDP camps were very difficult and precarious. The camps had generally been established in close proximity to the Uganda People’s Defense Force (national army, UPDF) detaches. The population in the internally displaced camps (IDPs) faced food shortages due to lack of access to farmland. The peace talk negotiations started in Juba (southern Sudan) in July 2006 between the Ugandan government and the LRA had made progress with a formal cessation of hostilities agreement signed on August 26, 2006 which led to the return of peace in the region. Since that period, the improvement of security in northern Uganda in general and particularly in Lira District has increasingly encouraged the returning processes of the population to their original villages. During the period of this survey, it was found out that there are no more IDPs camps in the district; the remaining IDPs camps have been closed. The agricultural and commercial activities had gradually resumed in the region since the beginning of the peace process.

Like in any other rural community, the most important resource of the population is the land. One of the decisive factors for the return of displaced people to their villages of origin is, therefore, access to land. The people of the district have traditionally lived in their villages with their livestock. The soil of Lira district is very fertile. Before the war, the main activity was the cultivation of crops like millet and sorghum. Farmers also produced large quantities of corn, sweet potatoes, cassava, peas, beans, rice and various vegetables. The farming calendar of the area is characterized by two farming seasons:

Season 1: planting in April and harvesting in July

Season 2: planting in July and harvesting in November.

The hunger gap period extends from April to June/July. During this period the rates of malnutrition also increase as can be seen by the increasing number of admissions in the nutritional centers.

The district of Lira consists of 41 health centers6, among them, 39 are functional, and 2 are still under construction. Among the 39 functional health centers, 5 are health centers IV, 20 are health centers III and 14 are centers health II. Support for primary health care (drug supply, rehabilitation and training of local staff) is provided mostly by government and some private organizations. Efforts have been made by some humanitarian organizations involved in the district in water and sanitation sector but the needs are still huge.

6 Uganda Communication Commission (UCC), 2003 http://www.lira.go.ug/

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Malaria is by far the biggest cause of illness and death, followed by acute respiratory diseases and diarrhea diseases due to lack of safe drinking water and poor sanitation system. According to UNICEF 2006 annually report, malnutrition contributes to 60% of all deaths in children in Uganda. The HIV/AIDS is the additional problem which exacerbates the vulnerability of children. Indeed, HIV/AIDS is the major concern among children in ACF feeding centers; 20-40% of children test HIV positive.

The ratio of patient to health personnel is low. It is reported that patient to doctor ratios is 65,000:1 while patient to nurse ratio is 4,700 to 1 indicating the low staff to patient availability which can affect patient care.

ACF-USA has been present in the area since 2004 and works in the sectors of:

- Nutrition: 5 SFC and 5 OTP in Adwari, Omoro, Orum, Olilim and Okwang. A community based approach was initiated in 2007 to train communities on how to detect and refer acutely malnourished children less than 5.

- Food security: seed distribution, seed security training, seed multiplication, animal traction training, oxen distribution and income generating activities.

- Water and sanitation: hygiene promotion, borehole construction and rehabilitation.

The last nutritional survey conducted in Lira district by ACF-USA in April/May 2007 revealed rates of Global Acute Malnutrition and Severe Acute Malnutrition of 7.1% (CI: 4.5-9.7%) and 0.8% (IC: 0.1-1.4%) respectively (results presented in Z-scores with a confidence interval 95%).

The following table presents the admission in the 5 SFC operating in Lira district in 2007 and 2008

Admissions in ACF SFCs in 2007-2008, Lira

262 282

368

500

441

416

570

566

557

517

377 42

6

359

563

411

568

509

347

257

483

295

492

359 39

2

0

100

200

300

400

500

600

April May June July August September October November December January February March

2007

2008

Figure 1: Admission in ACF SFCs, Lira, 2007-2008 Other Organizations working in Lira There are many organizations that work in Lira in different sectors. Lira Hospital continues to operate the Therapeutic Feeding centre for the treatment of severe acute malnourished children. Activities NGOs working in LIRA

Agriculture and food security WFP, ACF, COOPI, FAO, CPAR, HA, PU, GAA, ASB

Health and nutrition UNICEF, CPAR, CARITAS, PAG, MARIE STOPPES, Light Force, WFP, WHO, ACF

Water and sanitation IRC, UNICEF, ACF, CCF, COOPI, CESVI, ASB, CCF Protection UNHCR, Save the children, CPA, SP, CCF, ASB

Table 3: List of NGOs working in Lira district and their field of activity

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III OBJECTIVES

ACF conducts a nutrition survey every year to monitor and follow the trends in nutrition status of children 6-59 months and to determine program strategies. The objectives of the nutrition survey conducted in Lira in April/May 2008 are:

- To evaluate the nutritional status of children aged 6 to 59 months. - To estimate the measles immunization coverage of children aged 9 to 59 months. - To estimate the crude mortality rate through a retrospective survey.

IV METHODOLOGY

IV.1 Type of Survey and Sample Size

Two-stage cluster sampling using SMART methodology was applied to randomly identify clusters with the probability of being selected proportional to the population size in each cluster. At the cluster level, households were randomly selected and surveyed using the EPI method. All children aged between 6 and 59 months living in the household, defined as those who ate from the same cooking pot, were included in the survey for anthropometric measurements. A retrospective mortality survey over the past three months and a half was undertaken alongside the anthropometric survey, using the SMART methodology. Anthropometric and mortality data were analyzed using ENA software, October 2007version. The sampling frame covered all villages of northern Lira District. The villages were sampled from population data that incorporated the areas covered by ACF activities and included Ogur, Apala, Adwari, Orum, Olilim, Omoro, Barr, Abako, Aromo, Amugu, Aloi and Okwang. This sampling frame did not include locations in the south of Lira District and Lira town. Population data was a compilation of various data sources. The majority of data was collected from DDHS, sub-county, and from other organizations. The geographical units and their respective population were then input into the Nutrisurvey software for SMART survey for planning the survey.

1. Cluster selection: At the first stage, the sample size was determined by inputting necessary information into the ENA software for both anthropometric and mortality surveys. This information included estimated population sizes, estimated prevalence rates of mortality and malnutrition, the desired precision and design effect. The prevalence was estimated at 10%, desired precision was 3% and the design effect was 2%. Based on these parameters, Nutrisurvey calculated the appropriate sample size: 765 children. The number of clusters was calculated based on the capacity of the teams per day in terms of number of children and people to survey (Appendix 1). All anthropometric measurements and measles coverage were based on this sample size.

A retrospective mortality survey over the period from January 1st up to the date of the survey (three and a half months, 112 days) was undertaken alongside the anthropometric survey.

2. Children selection: In each cluster, households were randomly selected and surveyed using the EPI

method. The survey team identified the centre of the village with the assistance of the leader. A pen was spun, and the team walked in the direction of the pen, using reference points to maintain a straight line, to the border of the camp or village. At the border, the team spun the pen again and followed the direction of the pen to the opposite border. As they walked, each house that was on the line was numbered. In villages, as the distance between houses was vast, houses that were visible from the line were counted. The team drew a map of the houses and numbered them on the paper. Upon reaching the opposite border of the location, the team used a random number table to blindly select a number in the range of houses counted. The number selected indicated the first house to be sampled. From this

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house, the team continued moving to the next closest house on the right. If the team hit the border, they spun the pin again and started the numbering and random number selection afresh (Appendix II).

All the children in the selected household between the ages of 6 and 59 months were included in the survey. A household was defined as all inhabitants using the same cooking pot. If there was more than one wife and they used the same cooking pot, then that wife and child were also included.

The selected households were interviewed for the retrospective mortality questionnaire, whether or not they had eligible children for the anthropometric survey.

IV.2 Data Collection

For each selected child, information was collected during the anthropometric survey using an anthropometric questionnaire (Appendix III). The information included:

• Age: recorded with the help of a local calendar of events (Appendix IV). • Gender: male or female • Weight: children were weighed without clothes, with a SALTER balance of 25kg (precision of 100g). • Height: children were measured on a measuring board (precision of 0.1cm). Children less than 85cm

were measured lying down, while those greater than or equal to 85cm were measured standing up. • Mid-Upper Arm Circumference: MUAC was measured at mid-point of left upper arm for measured

children (precision of 0.1cm). • Bilateral Oedema: assessed by the application of normal thumb pressure for at least 3 seconds to both

feet. • Measles vaccination: assessed by checking for measles vaccination on EPI cards and asking

caretakers. In addition, retrospective mortality survey (Appendix V) and household questionnaire (Appendix VI) assessments were conducted.

IV.3 Indicators, Guidelines, and Formula’s Used

IV.3.1 Acute Malnutrition

Weight for Height Index Acute malnutrition rates are estimated from the weight for height (WFH) index values combined with the presence of oedema. The WFH indices are expressed in both Z-scores and percentage of the median, according to both NCHS7 and WHO references8. The expression in Z-scores has mainly statistical meaning, and allows inter-study comparison. The percentage of the median, on the other hand, is used for the identification criteria of acute malnutrition in nutrition programs. Guidelines for the results expressed in Z-score: • Severe malnutrition is defined by WFH < -3 SD and/or existing bilateral oedema on the lower limbs of the

child. • Moderate malnutrition is defined by WFH < -2 SD and ≥ -3 SD and no oedema. • Global acute malnutrition is defined by WFH < -2 SD and/or existing bilateral oedema. Guidelines for the results expressed in percentage of median:

1. Severe malnutrition is defined by WFH < 70 % and/or existing bilateral oedema on the lower limbs 2. Moderate malnutrition is defined by WFH < 80 % and ≥ 70 % and no oedema.

• Global acute malnutrition is defined by WFH <80% and/or existing bilateral oedema

7 NCHS: National Center for Health Statistics (1977) NCHS growth curves for children birth-18 years. United States. Vital Health Statistics. 165, 11-74. 8 WHO reference, 2005

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Children’s Mid-Upper Arm Circumference (MUAC) The weight for height index is the most appropriate index to quantify wasting in a population in emergency situations where acute forms of malnutrition are the predominant pattern. However, the mid-upper arm circumference (MUAC) is a useful tool for rapid screening of children at higher risk of mortality. MUAC measurements are significant for children with a height of 65 cm or one year and above. The guidelines are as follows: MUAC < 110 mm severe malnutrition and high risk of mortality MUAC ≥ 110 mm and <120 mm moderate malnutrition and moderate risk of mortality MUAC ≥ 120 mm and <125 mm high risk of malnutrition MUAC ≥ 125 mm and <135 mm moderate risk of malnutrition MUAC ≥ 135 mm adequate’ nutritional status

IV.3.2 IV.3.2 Mortality

Mortality data was collected using Standardized Monitoring and Assessment of Relief. The crude mortality rate (CMR) is determined for the entire population surveyed for a given period. The CMR is calculated using Nutrisurvey for SMART software for Emergency Nutrition Assessment. The formula below is applied: Crude Mortality Rate (CMR) = 10,000/a*f/ (b+f/2-e/2+d/2-c/2), Where: a = Number of recall days (90) b = Number of current household residents c = Number of people who joined household d = Number of people who left household e = Number of births during recall f = Number of deaths during recall period The result is expressed per 10,000-people / day. The thresholds are defined as follows9: Total CMR: Alert level: 1/10,000 people/day

Emergency level: 2/10,000 people/day Under five CMR: Alert level: 2/10,000 people/day

Emergency level: 4/10,000 people/day

IV.4 Field Work

The anthropometric nutritional survey was conducted from April 23rd to May 3rd 2008. Four teams of four people each (one team leader, two measurers and one surveyor for food security and water and sanitation component) completed the data collection. The nutritional survey team included ACF field staff (working in nutrition feeding centers) and DDHS staff. Earlier all surveyors participated in four days of training that included theoretical and practical training, standardization of anthropometric measurements and pilot survey. The supervision of the survey was conducted by the 2 Nutrition Program Managers and the Medical and Nutrition Coordinator of ACF.

IV.5 IV.5 Data Analysis

Data processing and analysis for both anthropometric and mortality were carried out using ENA software for SMART, October 2007 version using both NCHS and WHO references. Excel was used to carry out analyses on MUAC, measles immunization coverage, and household questionnaire.

9 Health and nutrition information systems among refugees and displaced persons, Workshop report on refugee’s nutrition, ACC / SCN, Nov 95.

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V RESULTS OF THE ANTHROPOMETRIC SURVEY

V.1 Anthropometric results in Lira district

860 children between 6 and 59 months were measured during the survey. The data of 28 of them were excluded from the analysis, due to incoherence. The analysis is done on a sample of 832 children.

V.1.1 Distribution by Age and Sex

Age groups (months)

Boys Girls Total Sex ratio

n % n % n % 06 - 17 131 56.5 101 43.5 232 27.4 1.3 18 - 29 96 49.0 100 51.0 196 23.1 1.0 30 - 41 90 50.6 88 49.4 178 21.0 1.0 42 - 53 70 46.4 81 53.6 151 17.8 0.9 54 - 59 50 54.9 41 45.1 91 10.7 1.2 Total 437 51.5 411 48.5 848 100.0 1.1

Table 4: Distribution by age and Gender, Lira District

The above Table 4 shows the proportion of boys to girls gives a sex ratio (B/G) of 1.0, which is acceptable.

Distribution of age by sex, Lira district

0 10 20 30 40 50 60

06-17

18-29

30-41

42-53

54-59

Age

`gro

up (i

n m

onth

)

Percentage

GirlsBoys

Figure 2: Distribution by Age and Gender

V.1.2 Anthropometrics Analysis

Distribution of Acute Malnutrition in Z-Scores

Age groups

(months) Total

Severe wasting

(<-3 z-scores)

Moderate wasting

(>= -3 and <-2 z-scores)

Normal (> = -2 z scores)

Oedema

n % n % n % n % 06 - 17 218 0 0.0 20 9.2 198 90.8 0 0.0

18 - 29 195 0 0.0 8 4.1 187 95.9 0 0.0

30 - 41 178 0 0.0 1 0.6 177 99.4 0 0.0

42 - 53 152 0 0.0 2 1.3 150 98.7 0 0.0

54 - 59 89 0 0.0 6 6.7 83 93.3 0 0.0

Total 832 0 0.0 37 4.4 795 95.6 0 0.0

Table 5: Weight for Height distribution by age in Z-scores and/or oedema, Lira District (NCHS Reference)

14

All

n = 832Boys

n = 424 Girls

n = 408 Prevalence of global malnutrition (<-2 z-score and/or oedema)

5.9% (4.1% - 7.7%)

8.2% (5.6% - 10.8%)

3.4 % (0.6% - 6.3%)

Prevalence of moderate malnutrition (<-2 z-score and >=-3 z-score, no oedema)

5.5% (3.8% - 7.3%)

7.6 % (5.3% - 9.8%)

3.4 % (0.6% - 6.3%)

Prevalence of severe malnutrition (<-3 z-score and/or oedema)

0.4% (0.0% - 0.7%)

0.7% (0.0 %- 1.5%)

0.0% (0.0% - 0.0%)

Table 6: Prevalence of acute malnutrition based on weight-for-height z-scores (and/or oedema) and by sex

<-3 z-scores >=-3 z-scores

Oedema present Marasmic kwashiorkor

0 (0.0 %) Kwashiorkor

0 (0.0 %)

Oedema absent Marasmus 3 (0.4 %)

Normal 845 (99.6 %)

Table 7: Distribution of Weight for Height and oedema in Lira District in Z-scores (NCHS Reference)

Marasmus is the only type of severe malnutrition observed.

Figure 3: Z-scores distribution Weight-for-Height, Lira District May 2008

The Standard Deviation is 0.98 and is within the expected range of 0.80 -1.20. The skewness of WHZ is -0.176 and Kurtosis is -0.126 and both are within acceptable limits.

NCHS Reference WHO Reference

Global acute malnutrition

5.9% [4.1% - 7.7%]

4.6% [3.1 – 6.1%]

Severe acute malnutrition

0.4% [0.0% - 0.7%]

0.0 % [0.0% - 0.0%]

Table 8: Global and Severe Acute Malnutrition in Z-scores

15

Distribution of Malnutrition in Percentage of the Median

Severe wasting (<70% median)

Moderate wasting(>=70% and <80%

median) Normal

(> =80% median) Oedema

Age (mths)

Total no. No. % No. % No. % No. %

6-17 232 0 0.0 15 6.5 217 93.5 0 0.0 18-29 196 0 0.0 7 3.6 189 96.4 0 0.0 30-41 178 0 0.0 4 2.2 174 97.8 0 0.0 42-53 151 0 0.0 2 1.3 149 98.7 0 0.0 54-59 91 0 0.0 2 2.2 89 97.8 0 0.0 Total 848 0 0.0 30 3.5 818 96.5 0 0.0

Table 9: Distribution of Weight/Height by age in percentage of the median in Lira District (NCHS Reference)

There were no severely malnourished children. Children were most likely to be moderately malnourished. Children 6-29 months are more likely to be malnourished compared to older children.

n = 848

Prevalence of global acute malnutrition (<80% and/or oedema)

3.5% (2.2% - 4.8%)

Prevalence of moderate acute malnutrition (<80% and >= 70%, no oedema)

3.5% (2.2% - 4.8%)

Prevalence of severe acute malnutrition (<70% and/or oedema)

0.0% (0.0% - 0.0%)

Table 10: Weight for height and/or Oedema in Lira District in percentage of the median (NCHS Reference)

Risk of Mortality: Children’s MUAC

The data of all children whose height> 65cm are analyzed in the table below:

MUAC (mm) >65 – < 75 cm >=75 – < 90 cm >=90 cm Total N % N % N % N %

< 110 3 1.7 0 0 0 0 3 0.4

110<= & <120 25 14.3 12 3.4 1 0.3 38 4.6

120<= & <125 19 10.9 27 7.7 3 1.0 49 5.9

125<=& <135 53 30.3 67 19.1 19 6.2 139 16.7

>= 135 75 42.9 244 69.7 282 92.5 601 72.4

TOTAL 175 100.0 350 100 305 100 830 100.0

Table 11: MUAC Distribution in Lira District

MUAC analysis reveals that according to the MUAC criteria, 0.4% of children taller that 65cm are severely malnourished, and 4.6% of them are moderately malnourished.

V.2 Measles Vaccination Coverage

The source of information on immunization was either the child’s health card or the mother’s recall. A child was considered fully vaccinated if he had received the last dose from the EPI (from 9 months of age, according to the national protocol). It is important to mention however, that these results should be interpreted with caution since they are based on the caretaker’s recall, when no health card is available.

16

Nb children >= 9 months n=782 Immunized with card 41.9% Immunized without card 40.9% Not immunized 15.2%

Table 12: Measles Vaccination Coverage in all the divisions surveyed

VI RESULTS OF RETROSTECTIVE MORTALITY

The crude mortality rate was calculated from the figures collected from families selected, with or without children less than 5 years, over the past 3.5 months.

Demographic data Current resident HH 4356 Current resident < 5 years old 948 People who joined HH 75 < 5 years old who joined HH 12 People who left HH 394 < 5 years old who left HH 31 Birth 36 Death 22 Death < 5 years old 9 CMR (deaths /10,000 people/day) 0.44 [0.21 – 0.66] U5MR (deaths in children<5/ 10000 / day ) 0.85 [0.31 – 1.39]

Table 14: Demographic information

The crude mortality rate was below the alert levels of 1/10,000 and the under-5 mortality rate was below the alert level of 2/10,000 per day.

VII ADDITIONAL HOUSEHOLD INFORMATION

VII.1 Household characteristics

The majority of the households were headed by males (80.9%). Women (17.3%) and grandparents (1.7%) were also reported as being the head of households. Most heads of households had primary school education (63.1%) followed by secondary education (13.2%) and no education (3.0%). Type of resident The majority of residents were returnees (67.8%) or residents of villages (31.1%). Although there are no anymore IDPs camps in Lira district, a few families, however, remained in the former sites of displaced persons camps.

17

VII.2 Food security

Sources of income

Although a household may have several sources of income, it was noted that casual labor (33.1%) was among their main source of income followed by crop sales (31.3%) and sale of animal products (8.3%). It is likely that casual labor might be high during this hunger gap period when households do not have stock of foods. The data suggests that, although not investigated, “market purchase” is likely to be the primary or secondary household food source.

Figure 4: Source of income

Livestock

Nearly 71.8% households owned animals. Several households reported having more than one type of animal. The majority owned chicken (79%) followed by cattle (44.6%) and goats (43.6%).

Food stocks

Around 73.8% reported having no stock (68.2%) or less than 1 month stock (5.7%). This was based on self-report and the surveyors did not verify the declared stocks. The low food stock levels corroborate the assumed importance of market purchase of food and more generally of casual labor.

Coping strategies

When food is inadequate, the top three responses for coping include increasing casual labor (65.7%), buying less expensive food (58.0%) and reducing number of meals per day (44.6%). People also tend to reduce portion size (28.2%). Reducing number of meals and portion size and purchasing less expensive food (likely to be less nutritious) could affect dietary quantity and quality. Also, people tend to harvest immature crops (16.0%) affecting source of income and dietary quality. Furthermore, there is also a relatively high incidence of buying food on credit (27%). Although not assessed for the survey, it would be interesting to know the terms of credit, and whether interest is paid, and how this ultimately affects the household economy. Credit/debt could prove to be an erosive coping strategy indicating greater food insecurity for some households. The majority reported not receiving (99%) food aid.

Dietary diversity

Cereals (42.3%), vegetables (45.5%) and legumes (38.8%) are among the most commonly consumed food sources reported. Carbohydrates are the predominant source of nutrients. Although legumes are consumed,

Main Sources of Income

31.30

8.33

7.664.40

33.10

4.86

1.27

2.13

6.93

Crop sales Animal/Products Petty tradeHandicraft Casual labour Boda Boda SalaryBrewing Charcoal/wood sales

18

protein sources are less predominant. There is less food diversity with majority (91.7%) reporting consuming 1 to 3 food group sources. Although those who own animals reported having poultry and cattle, only a small percentage reported consuming meat/chicken/eggs. Similarly, milk consumption is not reported. It is possible that the products are being sold. Alternatively, due to the recall method, there could be errors in recall on the part of the respondent and in probing by the interviewers. It is also possible that people did not have these foods the previous day (especially eggs and chicken). Furthermore, children are likely to consume 1 to 3 meals per day (98.9%) while adults are more likely to consume 1 to 2 times (98.8%) per day. Although not assessed, it is likely that the dietary quantity is also not adequate. Thus the quantity and quality of food consumed by the children are less likely to be adequate given the fewer number of meals and less food diversity.

VII.3 Water and Sanitation

Water source

More than half of the households surveyed (51.4%) indicated that they do not collect water from a protected water point (protected spring, hand pump, or tap). The remaining respondents reported collecting water from un-protected springs, rivers, and streams. The statement of the household respondent was taken into consideration.

Latrine type

Close to half the households (49.5%) reported having a household latrine. Nearly 39.6% used the bushes while others use group latrine (1.2%) or neighbor’s (10.3%) latrines. Among those who do not have household latrines, the main reason for not having latrines include lack of money (39.9%), lack of materials (24.9%) and other reasons including having latrines under construction or being old or invalid (32.1%).

Hand washing Practices

People report washing hands before eating (88.6%), before eating (73.8%), after use of latrine (35.2%), after working in the garden (21.1%), before food preparation (13.4%), after handling animals (16.2%), and after changing the child (5%). Hand washing practices, although present, need to be reinforced, particularly hand washing after latrine use.

VIII DISCUSSION

The results (expressed in Z-score) of previous nutritional surveys allows making an analysis of the evolution of the nutritional situation in Lira district. Feb-05

(n=965) May-06(n=921)

Apr/May-07(n=651)

Apr/May-08 (n=848)

Global Acute Malnutrition (W/H <-2 Z-scores and/or oedema)

1.9% (0.9%-3.7%)

5.9% (3.9%-8.6%)

7.1% (4.5%-9.7%)

5.9% (4.1% - 7.7%)

Severe Acute Malnutrition (W/H <-3 Z-scores and/or oedema)

0.6% (0.1%-2.0%)

0.7% (0.1%-2.1%)

0.8% (0.1-1.4%)

0.4% (0.0-0.7%)

Retrospective mortality rate 10,000 person/day 0.7 0.4 1.15 0.44

Measles vaccine coverage Confirm by card Not confirmed by card

37.3% 62.7%

54.3% 19.6%

43.1% 39.8%

41.9% 40.9%

Table 15: Summary of nutrition surveys carried out in Lira since 2005

19

The first two nutritional surveys were conducted in a particular context where population lived in IDPs camps and depended almost entirely on humanitarian aid, which would explain the low rate of global acute malnutrition during this period. For the past three years, the highest prevalence of global acute malnutrition was observed during the April 2007 survey. Indeed this period matched with the transitional period characterized by the returning process of people to their villages of origin. During this time, the population did not benefit any more from the food rations from general distributions unlike in IDPs camps. Moreover at the time of return, the life conditions including basic amenities in return areas were reported poor. During the current nutritional survey, there were no IDPs camps in Lira district and all camps were officially closed. Most people had returned to their villages. The survey was conducted in a period when people are finishing their food stocks from the last harvests while planting and waiting for the upcoming harvest. The prevalence of Global Acute Malnutrition, as indicated by this survey, is lower than in 2007 and reflects an improvement in the nutritional status of the population. Similarly, the Crude Mortality Rate is low. These low rates of acute malnutrition can be explained by the following assumptions:

- The security situation has improved, allowing people to return to their villages and to settle down and to resume their regular activities

- The interventions of various NGOs active in the field of water and sanitation and food security have produced positive effects on the stabilization of health and nutritional status of the population.

- Primary health care is likely operational in the return areas, thus reducing morbidity and mortality. However, it should be noted the quality of health facilities and the health care coverage have not been assessed in the present survey.

Regarding the immunization coverage, 40.9% of children have received measles vaccine according to their mother and 41.9% of children were vaccinated based on the vaccination card. However one should be cautious when interpreting the vaccination reported by caretakers without cards to prove. WHO recommends coverage of 80% to avoid epidemic. Hence immunization coverage needs to be further improved to prevent childhood susceptibility to measles and other diseases. However based on the higher number of admissions in the supplementary feeding centers, there is still a need in terms of treatment of malnourished children. We can therefore say that the nutritional need has low intensity, but a large magnitude, since it affects a large number of people despite the relatively low rates. Although the rates of malnutrition are low, there are a number of factors that need to be taken into consideration:

Health Access: Lira is affected by high rates of malaria, water borne diseases and HIV/AIDS. The district often has a shortage of drugs, further intensifying infections and prolonging the recovery period and also discouraging the population from seeking health services.

Water and Sanitation: Half of the survey respondents reported collecting water from unprotected

water points. Access to clean and safe water can contribute to the reduction of morbidity due to water-borne diseases, which can reduce susceptibility to malnutrition in a vulnerable population. Hand washing practices need to be strengthened, particularly after latrine use.

Food Security: People reported low food stocks. Their low income and the current high cost of food

could have affected the purchasing ability. Some reported getting food on credit, which indicates that some households could be more vulnerable than others. Moreover, when food is inadequate, people tend to buy less expensive food, reduce the number of meals per day or tend to reduce portion size, which is likely to affect dietary quantity and quality.

Nutrition and Health Education: Children were also not fed frequently. They were most likely to be

left under the care of slightly older siblings. They were also more likely to be fed only at the same time as the adults (1-3 times a day). These indicators suggest that the children feeding practices combined with poor child care and hygiene practices increase the risk of infection among children which can lead to or aggravate malnutrition.

20

IX RECOMMENDATIONS

The global acute malnutrition rate is low and at acceptable level. A more global approach to managing malnutrition is necessary: Health and Nutrition:

- To continue the treatment of severely and moderately malnourished children under 5 years old in therapeutic and outpatient centers

- To reinforce the capacity of MOH staff (hospital and health center staff) in the management of acute malnutrition. The health staff at all levels including district hospitals and health centers (including HCII, HCIII and HC IV) need to be trained on detection and management of acute malnutrition.

- To strengthen the capacity of communities by training VHTs/community volunteers on how to detect and refer acutely malnourished children less than 5 years to enable early detection and referral

- To monitor the nutritional status of the population on an annual basis. - To conduct nutrition surveillance and to train MoH staff in nutrition surveillance to better monitor the

nutritional status and understand possible factors contributing to malnutrition - Strengthen the links between the various levels from VHTs to health centre staff to the district hospital

and district focal person so that there is effective collaboration and efficiency between the various levels in managing acute malnutrition

- Continue conducting EPI campaigns whenever necessary with systematic routine immunization activities to ensure all children are vaccinated against childhood diseases

- Promote nutrition education sessions in the communities and health centers, with an emphasis on breastfeeding, weaning, complementary foods, and balanced diets

Food Security:

- To go on with programs devoted to the generation of incomes at household level Water and Sanitation:

- Emphasize on the promotion of adequate hygiene practices and household latrines.

21

X ANNEXES

Annex I. Assignment of Clusters, Lira district

Geographical unit Population size Assigned cluster Aromo Acut Kumo 3116 1 Apua 6635 Apuce 3629 2 Arwot Omito 5847 Otara 2915 3 Walela 5413 Odoro 273 Bar Pii 4917 4 Ogur Abala 5614 Adwoa 4160 5 Akangi 6442 Akano 5437 6 Angolocom 7377 7 Apoka 6761 Ogur 8179 8 Orit 6152 9 Barr Abunga 7120 10 Alebere 5097 Ayira 7548 11 Ober 5715 Olilo 6568 12 Onywako 7455 13 Abako Acede 6891 14 Alanyi 9294 15 Amononeno 8719 Awapiny 7332 16 Ojul 5079 17 Owalo 5532 Olyet 4128 18 Aloi Akura 9192 19 Akwangkel 7328 Alal 8231 20 Alebtong 8827 21 Anara 4305 22 Anyanga 7721 Otweotoke 5635 23 Awiepek 3916 Amugu Abongoatin 5864 24 Abunga 6667 25 Ajonyi 4666 Omee 7372 26 Apala

22

Abia 5525 Abiting 5984 27 Atinkok 4315 Obim 5123 28 Okwangole 3020 Oteno 3766 29 Aberidwogo-Omera 773 Abangoimany 2309 Amonomito 3976 Tekulu 2684 30 Omoro Abukamola 7524 Alololo 4363 31 Angetta 10238 32 Ocokober 3830 Omarari 5868 33 Oculokori 3646 Adwari Alango 6716 34 Okee 2735 Olarokwon 3077 Omito 3234 35 Agweng 4068 Okere 2661 Okwang Amoyai 2869 36 Arwotongo 3828 Olworngu 3325 37 Opejal 3733 Barocok 1973 Olilim Amunga 1944 Anepkide 1766 Angetta 4318 38 Atira 2509 Ogwete 1543 Gotojwang 2053 39 Orum Alangi 3238 Anepmoroto 4257 Atang-gwata 2518 40 Ating 3201 Oluro 4112 Anyalima 1870

23

Annex II:. Selected villages, Lira district

Aromo Villages Cluster number___ Acut Kumo parish; Acan Mak kweri (Cluster 1) Apuce parish; Gulwoo (Cluster 2) Otara parish; Arwotolaro (Cluster 3) Bar Pii parish Opok Midila (Cluster 4) Ogur _____________________________________ Adwoa parish Bed Amwol (Cluster 5) Akano parish Iamto Ikwoto (Cluster 6) Angolocom Angolocom (Cluster 7) Ogur parish Abako (Cluster 8) Orit parish; Barlonyo (Cluster 9) Barr ______________________________________ Abunga parish Adaganwata (Cluster 10) Ayira parish Angucami (Cluster 11) Olilo parish Aumi (Cluster 12) Onywako parish Tegweng (Cluster 13) Abako _______________________________________ Acede parish Alam B (Cluster 14) Alanyi parish; Akango Rwai (Cluster 15) Awapiny parish Olano Amua (Cluster 16) Ojul parish Omia (Cluster 17) Olyet parish Oyeng Olwedo (Cluster 18) Aloi ______________________________________ Akura parish Agoro (Cluster 19) Alal parish Anino (Cluster 20) Alebtong parish Okwongo (Cluster 21) Anara parish Elupe (Cluster 22) Otweotoke parish Tebung Adwong (Cluster 23) Amugu __________________________________ Abongoatin parish; Atali (Cluster 24) Abunga parish; Acode (Cluster 25) Omee parish; Odolokwon (Cluster 26) Apala ________________________________________ Abiting parish; Ober (Cluster 27) Obi parish; Orupu (Cluster 28) Oteno parish; Okanycan (Cluster 29)

Te-kulu (Cluster 30) Omoro __________________________________ Alololo parish; Agwok (Cluster 31) Angetta aprish; Abong Nyeke (Cluster 32) Omarari parish; Obile (Cluster 33) Adwari __________________________________ Alango parish; Amon Maka (Cluster 34) Omito parish; Corner-Adwari (Cluster 35) Okwang __________________________________ Amoyai parish; Awinyoru (Cluster 36) Olworngu parish; Aluga (Cluster 37) Olilim _________________________________________ Angetta parish; Agwee (Cluster 38) Gotojwang parish; Aluga (Cluster 39) Orum _________________________________________ Atang-gwata parish; Amarolel (Cluster 40)

STEPS • Spin the Pen in the Center. • Walk to the border. • Spin the pen again and walk in that direction to the border,

numbering the houses. • At the second border, choose a random number and start the

survey at that house. Choose the next closest house to your right.

• If you run out of houses, return to

the nearest border point, spin the pen, and start the numbering again

• If turning right, you come to a house that has already been surveyed or is vacated, choose the next closet one to the right

• In villages, draw a map and write the number of each house on the map. It is not necessary to walk to the house and number it. (As it may be a long distance). Once the random number is selected, you can find the house based off your map.

Annex III: Household Selection

Center

Border

Annex IV: Anthropometric survey questionnaire

District/Village: _________________________ Date: _________________ Cluster number: _______ Team number: _______ Child no.

HH. no.

Name (optional) Sex (F/M)

Birthday Age in months

Weight (kg) ±100g

Height (cm) ±0.1cm

Oedema (Y/N)

% W/H Muac (mm)

Measles (0,1,2)

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

Annex V: Calendar of Events in Lira District

MONTH ANNUAL EVENTS EVENTS PER MONTH AND PER YEAR

2003 2004 2005 2006 2007 2008

JANUARY 1ST new year, 26th victory day

63

51

39

27

15

3

FEBUARY Cleaning the field, opening school

62

50

38

26

23rd presidential election 14

2

MARCH

Women’s day, Sea-nut season

Beginning of rainy season 61

Beginning of rainy season, Barlonyo attack7 march 2004,

49

Beginning of rainy season, 37

Beginning of rainy season 25

Beginning of rainy season 13

Beginning of rainy season 1

APRIL Easter, White-aunt season, Fools day 1stDay

60

48

36

24

12

0

MAY 1st labor day, Mango season

59

47

35

23

11

JUNE

3RD martyrs day, 9th heroes day

58

Attack on Aboke girls 28 people killed and 7 abducted

46

34

22

10

JULY Harvest of millet Harvest 1st

session 57 Harvest 1st session 45

Harvest 1st session 33

Harvest 1st sessions 21

9

AUGUST Ascension day

56

44

32

20

8

SEPTEMBER Weeding of 2nd session

55

43

31

19

7

OCTOBER 9th independence day

54

42

Death of former president Milton Obote 10/10/05

30

18 Flood

6

NOVEMBER Harvest of maize and millet

Harvest 2nd session 53

Harvest 2nd session 41

Harvest 2nd session 29

Harvest 2nd session 17

Harvest 2nd session 5

DECEMBER

25th Christmas, 26th boxing day

cessation of fire and peace talk between LRA and government

52

peace talks failed and Museveni declare war

40

28

16

4

Annex VI: Household enumeration data collection form for a death rate calculation survey (one sheet/household)

Survey district: Village: Cluster number: HH number: Date: Team number:

1 2 3 4 5 6 7

ID HH member

Present now

Present at beginning of recall (include those not present now and indicate

which members were not present at the start of the recall period )

Sex Date of

birth/or age in years

Born during recall

period?

Died during the recall

period

1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20

Tally (these data are entered into Nutrisurvey for each household):

Current HH members – total Current HH members - < 5 Current HH members who arrived during recall (exclude births) Current HH members who arrived during recall - <5 Past HH members who left during recall (exclude deaths) Past HH members who left during recall - < 5 Births during recall Total deaths Deaths < 5

Annex VII: Household Questionnaire Section A - Socio-Demographic

HH #

1. Who is the head of the household? 1 = Father 2 = Mother 3 = grandparents 4 = other (specify)

2. Is your family 1= resident in the village10 2 = returnee from camp 3 = IDP camp resident 4 = migrant from countryside 5=Other (specify)

3. Highest level of education of the head of household 1= Primary school 2=Secondary school 3=Higher education 4=University 5=None 6=Other (specify)

1 2 3 4 1 2 3 4 5 1 2 3 4 5 6 1

2

3

4

5

6

7

8

9

10 Regular resident and not displaced at any time in the past

Section B. Food Security

HH #

4. What are the top three sources of income (Enter zero < three) 1=Crop sales 2=Animals/products 3=Petty trade 4=Handicraft 5=Casual labour 6=Boda boda 7=Salary 8=Brewing 9=Charcoal/ wood sales

5. Does the HH own animals 1=Yes 2=No If no, go to Q.7

6. If yes, what types of animals are owned by the HH (Enter number; enter 0 if do not have type) 1=Poultry (chicken, ducks) 2=Pigs 3=Goats 4=Cattle 5=Other

7. What is the estimated duration of current HH food stocks in months

8. When food is not adequate in your HH what do you do? (List 3 only) 1= Less expensive less preferred food 2= Increase casual labour for food 3= Reduce number of meals per day 4= Limit meal portion size 5= Borrow or receive food assistance from relatives/next of kin 6= Begging food from non-relatives 7= Harvest immature crop 8= Buy food on credit 9= Ask children to work for cash 10= Sell household assets

9. Does the HH get food aid? 1= Yes 2= No

10. What foods have you eaten in the past 24 hours? List all 1= Cereals (Millet, sorg, ) 2= Legumes (bean, peas) 3=Tubers/ roots (Cassava, potato) 4= Milk & milk products 5= Meat/ chicken 6= Fish 7= Vegetables 8= Fruits 9= Oil 10= Eggs 11= Sugar / honey (Do not read) (Enter only the numbers)

11. Number of meals per day currently for children (C) & adults (A)

1 2 3 1 2 1 2 3 4 5 Months 1 2 3 1 2 A C

1 2 3 4 5 6 7 8 9 10 11

Section C. Water And Sanitation

HH #

12. Where do you collect water for drinking and household purposes? 1=River, stream 2=Handpump 3=Tap 4=Protected Spring 5=Unprotected Spring/unprotected hand dug well 6=Other (specify

13. Do you pay fees for your water? 1=Yes 2=No

14. What does the HH use for latrine? 1=Household latrine 2=Group latrine 3=Neighbour’s latrine 4=Bush 5=Other (specify) If 1 go to Q.16 If 2,3,4,5 go to Q.15

15. What prevents you from having a latrine in your house? 1=Lack of money 2=Lack of materials 3=Do not know how to construct latrine 4=no space 5=others (specify)

16. When do you wash your hands?1=Before eating 2=After eating 3=After defactation/use of latrine 4=After working in gardens 5=After touching animals 6=Before breastfeeding 7=Before food preparation 8=After changing the child 9=Other (specify) (Do not read)

1 2 3 4 5 6 1 2 1 2 3 4 5 1 2 3 4 5 6 1 2 3 4 5 6 7 8 91 2 3 4 5 6 7 8 9 10 11 12 13