NUTRITIONAL ANTHROPOMETRIC AND MORTALITY SURVEY CHILDREN UNDER FIVE YEARS OF AGE FINAL REPORT

Transcription

1 NUTRITIONAL ANTHROPOMETRIC AND MORTALITY SURVEY CHILDREN UNDER FIVE YEARS OF AGE FINAL REPORT BANGALE, MADOGO, BURA, GALOLE AND WENJE DIVISIONS TANA RIVER DISTRICT 17th 25th November 2008 Ministry of Health Action against Hunger (ACF-USA) Kenya Funded by

2 2 ACKNOWLEDGMENTS On behalf of Action Against Hunger USA (ACF-USA) the authors would like to express their deep gratitude to the following without which this survey would not have been successful. We would like to thank ACF-USA staff, particularly the management team at Garissa base for the preparation of the survey, logistics, personnel and administrative issues and fieldwork without which this survey would not have been possible. Furthermore, we thank all team members (measures, data recorders and team leaders) who were involved in ensuring the survey obtained good quality data. Thanks also to all the drivers who ensured timely movement of field staff. Special thanks to the chiefs of all the divisions and the elders of Baghale Division, Madogo Division, Bura Division, Galole Division and Wenje Division for their support during the survey, and to the elders and some home visitors for their support during the survey. We finally like to say many thanks to the mothers and the fathers who pleasantly allowed the team to measure their children and patiently sat through the interviews and shared with the team valuable information. ACF would like to thanks UNICEF for funding this capacity building project.

3 3 TABLE OF CONTENTS ACKNOWLEDGMENTS... 2 TABLE OF CONTENTS... 3 LIST OF ABBREVIATIONS EXECUTIVE SUMMARY METHODOLOGY FIELD WORK RESULTS INTRODUCTION METHODOLOGY TYPE OF SURVEY AND SAMPLE SIZE DATA COLLECTION Anthropometrical survey Mortality survey Qualitative data INDICATORS, GUIDELINES, AND FORMULA S USED Acute Malnutrition Mortality FIELD WORK DATA ANALYSIS RESULTS OF THE ANTHROPOMETRIC SURVEY ANTHROPOMETRIC RESULTS Distribution by Age and Sex Anthropometric Analysis HOUSEHOLD STATUS COMPOSITION OF THE HOUSEHOLDS RESULTS OF THE RETROSPECTIVE MORTALITY SURVEY DISCUSSION AND CONCLUSION LACK OF ACCESS TO ADEQUATE HEALTH CARE LACK OF SAFE DRINKING WATER AND SANITATION FACILITIES PERSISTENT FOOD INSECURITY LACK OF KNOWLEDGE ON NUTRITION/MALNUTRITION AND HEALTH ISSUES RECOMMENDATIONS CONCLUSION APPENDIX...18

4 LIST OF ABBREVIATIONS 4 ACF CHW CMR EPI GAM MUAC NCHS SAM SD SFP SMART U5MR UNICEF WFH WFP WHO ALRMP DC MoH Action Contre la Faim, Action Against Hunger Community Health Worker Crude Mortality Rate, Crude death rate Expanded Programme on Immunisation Global Acute Malnutrition Mid-upper arm circumference National Centre for Health Statistic Severe Acute Malnutrition Standard Deviation Supplementary Feeding Programme Standardized Monitoring and Assessment of Relief and Transition Under-five Mortality Rate, Under-five death rate United Nations Children and Educational Fund Weight-For-Height World Food Programme of the United Nations World Health Organisation Arid Lands Resource Management Programme District Commissioner Ministry of Health

5 5 1 Executive summary Tana River is one of the seven districts in Coast province with a total area of 38,782 km² and a projected population of persons 1. The district has been facing perennial food insecurity as a result of extreme climates, characterized by a succession of drought and floods over the past years additionally erratic and inadequate long rains in the district this year led to crop failure in the farming zones. The poor season contributed to household food insecurity. The Long Rains Food Security Assessment commissioned by the Kenya Food Security Meeting (KFSM) in June 2005 recommended the urgent need to monitor the nutritional status of vulnerable populations residing in Garissa, Wajir, Mandera and Tana River districts. Located within the Arid and Semi-Arid (ASAL) region, the district has historically been prone to repeated droughts that make the communities increasingly vulnerable to disasters and food insecurity. The last nutrition survey done in Tana River was in October 2005 and revealed GAM and SAM rates of 18.5% (CI: 16.1%-21.0%) and of 3.3% (2.6%-4.8%) respectively. UNICEF and MOH identified Tana river mixed farming livelihood for practical experience for nutrition assessment capacity building project. The area is also of concern since a high number of children at ACF OTP program in Garissa originate from the region. The SMART survey methodology was used in the planning, training, field data collection and analysis of the anthropometric and mortality surveys. The objectives of the surveys include: To assess the prevalence of acute malnutrition in children aged 6-59 months To estimate the crude and under five mortality rate To identify the underlying causes and factors of malnutrition Map of Tana River district livelihood zones 1 Tana River District Development Plan ( ).

6 6 1.1 METHODOLOGY 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. The sampling frame covered all accessible villages in the selected divisions. At cluster level, households were randomly selected and surveyed using the EPI method. All children aged between 6 and 59 months of the same family, defined as a woman and her own and adopted children living the same household, were included in the survey for anthropometric measurements. A retrospective mortality survey over the recall period of 99 days was undertaken alongside the anthropometric survey, using SMART methodology. Anthropometric and mortality data were analyzed using ENA software. 1.2 FIELD WORK The survey was carried out by five teams, each team comprising four members: 1 team leader, 1 data collector and 2 measurers. The teams had one or two MOH/KNBS participants who were trained previously for one week (10 th 16 th Nov 2008) on SMART methodology from different parts of Kenya and two local data collectors who were also trained intensively for 3 days (14 th 16 th Nov 2008) prior the start of the survey. This training included theoretical and practical sessions (standardization test and a pilot test) on conducting nutritional surveys. Local Data collectors and measurers were recruited for the survey from Tana River district with language balancing ensuring that all different dialects in the district were equally represented in the survey teams. All teams were closely supervised during their field work throughout the whole survey time by an ACF Nutritionist. The field data collection was conducted from November 17 th to 25 th, 2008, covering 45 clusters, 16 households per cluster and at least 16 children (6-59 months) per cluster. The actual number of children (6-59 months) measured for the anthropometric survey was 813 children and for the mortality, 720 households were interviewed. 1.3 RESULTS The final analysis included 793 children after exclusion of 20 children due to incoherency according NCHS 1977 reference table. Analysis in reference to WHO 2005 reference included 791 children (22 exclusions). Table 1: The following table provides a summary of the findings of the survey INDEX INDICATOR RESULTS Global Acute Malnutrition Z- scores W/H< -2 z and/or oedema 11.5%[8.9%-14.1%] NCHS(1977) Severe Acute Malnutrition W/H < -3 z and/or oedema 0.1%[0.0%-0.4%] Global Acute Malnutrition % Median W/H < 80% and/or oedema 6.2%[4.2%-8.2%] Severe Acute Malnutrition W/H < 70% and/or oedema 0.0%[0.0%-0.0%] Global Acute Malnutrition Z-scores W/H< -2 z and/or oedema 12.1%[9.5%-14.7%] WHO(2005) Severe Acute Malnutrition W/H < -3 z and/or oedema 1.3%[0.4%-2.1%] Global Acute Malnutrition % Median W/H < 80% and/or oedema 2.5%[1.1%-3.9%] Severe Acute Malnutrition W/H < 70% and/or oedema 0.0%[0.0%-0.0%] MUAC Height >=65 cm Global Acute Malnutrition (<120mm) 1.1%[0.4%-1.9%] Severe Acute Malnutrition (<110mm) 0.0%[0.0%-0.0%] Total crude retrospective mortality (last 3 months) /10,000/day Under five crude retrospective mortality /10,000/day 0.28[ ] 0.52[ ]

7 7 2 INTRODUCTION Tana River is one of the seven districts in Coast Province. Located between latitudes 38 0, 30, and 40 0, 15 East, it bounders Garissa district to the North East, Isiolo to the North, Ijara and Lamu to the East, Mwingi and Kitui to the West, Malindi to the South and the Indian ocean to the South East. The Tana River which traverses the district from Tharaka District is the only permanent river in the district. Several seasonal rivers flow during the rainy season in a West East direction from Kitui and Mwingi districts, draining into Tana River and ultimately into the Indian Ocean. During the dry seasons the dry river beds (locally known as lagas ) provide water to the population, livestock and wildlife. The Great Tana River District had a total area of 38,782 km2 and was divided into seven administrative Divisions (Kipini, Garsen, Galole, Bura, Madogo and Bangale) whose projected total population was 214,423 persons with an average population density of 5 persons per square kilometre. The District is inhabited by people from the Pokomo, Orma, Wardei, Somalis, Malakote, Bajuni, Mijikenda and Munyoyaya ethnic groups. This was before the district was subdivided into two districts in 2007 i.e. Tana River and Tana Delta districts. Tana River district itself has five administrative divisions namely Bangale, Madogo, Bura, Galole and Wenje while Tana Delta has two administrative divisions namely Garsen and Kipini. Tana River district has estimated population of 142,077 persons 1. Tana River district has one hospital, 2 health centres, 20 private and public dispensaries and 3 clinics, which are not enough for the vast district. People have to walk an average of 50km to access medical care. There are only three doctors and about 100 paramedics with a doctor/ patient ratio of 1:47,359. The lack of electricity supply in the district curtails the operation of medical equipment, mainly available only in the district hospital 2. The district is divided into three livelihood zones namely Pastoral, Marginal mixed farming and Mixed farming zones (see Tana River map). The Mixed farming zone runs next to and along the Tana River and has more reliable rainfall and thus a greater agricultural potential compared to the Marginal mixed farming zones which lie between the Mixed farming and Pastoral zone. The Pastoral zone covers the hinterland areas across the district and is characterised by range land which is ideal for pastoral activities. The Survey covered five divisions of Tana River district, including both Marginal mixed farming and Mixed farming zones, with a projected population of 111,440 persons. The soils range from the sandstone, dark clays in some patches, to alluvial soils along the Tana River basin. The area receives rain in two seasons, the long rain season between April and July and the short rain season between October and December. However, erratic and inadequate rains in the district this year led to crop failure in the farming zones.the rainfall is highly unreliable. Located within the Arid and Semi-Arid Land (ASAL) region, the district has historically been prone to repeated droughts that make the communities increasingly vulnerable to disasters and food insecurity. The last nutrition survey done in Tana river was in October 2005 and revealed GAM rates of 18.5% (CI: 16.1%-21.0%) and SAM rates of 3.3% (2.6%-4.8%). UNICEF and MOH identified Tana River mixed farming livelihood for practical experience for nutrition assessment capacity building program. The area is also of concern since a high number of children attending ACF OTP program in Garissa originate from the region. The SMART survey methodology was used in the planning, training, field data collection and analysis of the anthropometric and mortality surveys. The objectives of the surveys include: To assess the prevalence of acute malnutrition in children aged 6-59 months To estimate the crude and under five mortality rate To identify the underlying causes and factors of malnutrition 1 Tana River district Development plan ( ) 2 Hola district hospital (Medical records office)

8 3 METHODOLOGY Type of Survey and Sample Size Two-stage cluster sampling with probability proportional to size using SMART methodology was employed in the survey. The Emergency Nutrition Assessment (ENA) software used in calculating the sample size required using population estimate obtained from local ALRMP office and based on the October 2005 integrated Nutrition and Health survey results. For the Nutrition survey, the GAM prevalence rate of 18.5%, precision of 4% and a design effect of 2 yielded a sample size of 712 children which was adjusted upwards by 5% to 748 children for non-response cases. For the mortality, the sample size was determined based on October 2005 mortality survey with prevalence of 1.87 deaths per 10,000/day, precision of 0.5 and design effect 1.5 and recall period of 99 days, yielding 4157 persons. Using an average household size of 6 persons from the previous year survey, the mortality survey was the one that determined the number of households visited (720 households). At the second stage, the selection of sample households was done based on the EPI random walk method. The starting point was determined with local chiefs, elders or key informants who assisted in identifying centres of clusters. Once a centre was identified survey teams employed the spin a pen method to randomly assign the direction towards the edge of the cluster then the team walked to the boundary of the cluster. At the edge again a pen was spun until it pointed to the main body of the cluster. From this end of the village survey team counted the number of houses until they reached to the other end of the cluster assigning numbers to each house. Once all the houses in that direction have been counted and assigned consecutive numbers, survey teams used the random number table to select the starting household. In the selected household, all children aged 6-59 months were included in the nutritional survey. If there was more than one wife/caretaker in the household, each wife was considered separately. If there were no children in a household, the house remained a part of the sample that contributed zero children to the nutritional part of the survey. The household was recorded on the nutritional data sheet as having no eligible children. Consecutive sample households were selected to the right of the gate of the interviewed households until the required number of households (at least 16 households per cluster) was reached. The mortality questionnaire was administered to all households that were selected regardless they have eligible children or not. 3.2 Data Collection Data collection period lasted for 9 days with anthropometric and mortality data being collected by the survey teams accordingly with emphasis on accurate and precise measurements so as to minimize errors Anthropometrical survey For each eligible child aged 6-59 months; (Children Born from December 2003 to May 2008 ONLY) information was collected during the anthropometric survey using an anthropometric data entry form. Exceptions were made to some eligible children who were either disabled or completely absent during the time of survey with remarks accompanying those cases. The information collected included (See appendix). Age: determined with the help of birth certificate, road to health card or if the two fails a local calendar of events developed prior during training period (See appendix) Sex: recorded as m for male and f for female Weight: Children were weighed to the nearest 100g with a Salter Hanging Scale of 25 kg or battery operated bathroom scale. All scales were calibrated daily by using a standard weight of 5 kg before the teams commenced their field work and continuously during data collection. Salter hanging scale was always and adjusted (to 0 with an empty weighing pant for boys or empty weighing pant plus standard dress for girls) before each measurement. Boys were measured undressed. Girls were undressed and redressed with a standard dress before measuring the weight. If the caretaker refused to have the child weighed on the Salter hanging scale a battery operated bathroom scale was the alternative. The caretaker would be first weighed on the bathroom scale then adjusted to zero, then child would be

9 9 handed to her were the child s weight will be obtained. For children who were able to stand bathroom scales were used always. 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 in cm at mid-point of left upper arm (between olecranon and the acromion process) to the nearest 0.1 cm with a non-stretchable tape. Bilateral Oedema: assessed by the application of moderate thumb pressure for at least 3 seconds to both feet (upper side). Only children with bilateral oedema were recorded as having nutritional oedema Mortality survey Each randomly selected family within a cluster regardless whether they have children aged 6-59 months, the family head was asked to state all family members and indicate their age and sex. The family was then asked to indicate which of the listed family members were present now and at the beginning of the recall period, which members joined or left during the recall period, and whether there was any birth or death in the family during the recall period of 99 days ( 12 th August 2008, start of school children holiday) Qualitative data The following key informants were interviewed to generate relevant information in their areas of jurisdiction: Drought Monitoring Officer (ALRMP) District Statistics Officer National Irrigation Board officers District Medical Officer of Health District Nutritionist Area Chiefs 3.3 Indicators, Guidelines, and Formula s Used 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 (standard deviation or SD score) and percentage of the median, according to both, NCHS 3 and WHO references 4. The complete analysis is, however, done with the NCHS reference. The expression in Z-scores has true statistical value and allows inter-study comparison. The percentage of the median, on the other hand, is commonly used to identify children eligible for admission to feeding programmes. Guidelines for the results expressed in Z-score: Severe acute malnutrition.wfh < -3 SD and/or existing bilateral oedema Moderate acute malnutrition.wfh < -2 SD and -3 SD and no oedema Global acute malnutrition..wfh < -2 SD and/or existing bilateral oedema Guidelines for the results expressed in percentage of median: Severe acute malnutrition.wfh < 70 % and/or existing bilateral oedema Moderate acute malnutrition.wfh < 80 % and 70 % and no oedema Global acute malnutrition..wfh < 80 % and/or existing bilateral oedema 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 is a useful tool for rapid screening of children at a 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: 3 NCHS: National Center for Health Statistics (1977) NCHS growth curves for children birth-18 years. United States. Vital Health Statistics. 165, WHO reference, 2005

10 10 MUAC < 110 mm MUAC 110 mm and <120 mm MUAC 120 mm and <125 mm MUAC 125 mm and <135 mm MUAC 135 mm severe acute malnutrition and high risk of mortality moderate acute malnutrition and moderate risk of mortality high risk of malnutrition moderate risk of malnutrition adequate nutritional statuses Mortality Mortality data was collected using Standardized Monitoring and Assessment of Relief (SMART). The crude mortality rate (CMR) is determined for the entire population surveyed for a given period. The CMR is calculated using ENA 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 5 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 follows 6 : Crude Mortality Rate (CMR): Alert level: 1/10,000 people/day Emergency level: 2/10,000 people/day Under Five Mortality Rate (U5MR): Alert level: 2/10,000 people/day Emergency level: 4/10,000 people/day 3.4 Field Work The survey was carried out by five teams, each team comprising four members: 1 team leader, 1 data collector and 2 measurers. The teams had one or two MOH/KNBS participants who were trained previously for one week (10 th 16 th Nov 2008) on SMART methodology from different parts of Kenya and two local data collectors who were also trained intensively for 3 days (14 th 16 th Nov 2008) prior the start of the survey. This training included theoretical and practical session (standardization test and a pilot test) on conducting nutritional surveys. Local Data collectors and measurers were recruited for the survey from Tana River district with language balancing ensuring that all different dialects in the district were equally represented in the survey teams. All teams were closely supervised during their field work throughout the whole survey time by ACF Nutritionist. The field data collection was conducted from November 17 th to 25 th, 2008, covering 45 clusters, 16 households per cluster and at least 16 children (6-59 months) per cluster. The actual number of children (6-59 months) measured for the anthropometric survey was 849 children and for the mortality, 720 households were interviewed. 5 See chapter 3.2 for specific recall days used in each survey 6 Health and nutrition information systems among refugees and displaced persons, Workshop report on refugee s nutrition, ACC / SCN, Nov 95.

11 3.5 Data Analysis 11 Data was entered on a daily basis into ENA database and analyzed for plausibility for immediate feedback to the field teams next morning. Data processing and analysis for both, anthropometric and mortality were carried out using ENA for SMART software, October 2007 version with both, NCHS and WHO references. MS Excel was used to carry out analysis on MUAC. 4 RESULTS OF THE ANTHROPOMETRIC SURVEY 4.1 Anthropometric results Overall 793 children were included in the nutritional anthropometric survey, (comprising 412 (52.0%) boys and 381 (48.0%) girls were covered. The majority (56.3%) of the children aged 6-17 months were males as compared to 43.7% with similar age for girls. 20 children were excluded from the final analysis due to incoherence of data. As shown in the table 2 below Distribution by Age and Sex The distribution of the nutrition survey sample by sex and age group shows a total boy/girl sex ratio of 1.1 which is within the normal limits ( ). Similarly, sex ratio within the age groups indicates a normal distribution. Table 2 Distribution by age and sex in Tana River Boys Girls Total Ratio n % n % n % Boy: girl 6-17 months months months months months Total Fig. 1 Distribution by age group and sex Distribution by age and sex, Tana River anthropometric survey, Nov boys girls

12 Anthropometric Analysis Distribution of Acute Malnutrition in Z-Scores In the age groups sample, the prevalence of global acute malnutrition was 11.5%, severe acute malnutrition 0.1%. Table 3 Weight for Height distribution by age groups in Z-scores, Tana River, November 2008 (NCHS Reference) Age (months) N Severe wasting (<-3 z-score) Moderate wasting (>= -3 and <-2 z- score ) Normal (> = -2 z score) Oedema n % N % n % n % Total As shown in Table 4 below no cases of nutritional oedema were found in the surveyed population. Table 4 Weight for height vs. oedema in Tana River, November 2008 (NCHS Reference) Edema present Edema absent <-3 z-score >=-3 z-score Marasmic kwashiorkor Kwashiorkor 0 (0.0 %) 0 (0.0 %) Marasmic Normal 1 (0.1 %) 792 (99.9 %) Figures 2 & 3 show the weight for height distribution curve of the survey sample in Z-scores for both the NCHS and the WHO reference populations. The entire weight for height distribution curves of the sample are shifted to the left, with a mean Z-score of ± 0.80 for the NCHS reference table and a mean of ± These results highlight a suboptimal nutritional status.

13 13 Fig. 2 & 3 Z-scores distribution Weight-for-Height, Tana River, November 2008, WHO and NCHS standards Table 5 Global and Severe Acute Malnutrition in Z-score Global acute malnutrition Severe acute malnutrition NCHS Reference 11.5% (8.9% %) 0.1% (0.0% - 0.4%) WHO Reference 12.1% (9.5% %) 1.3% (0.4% - 2.1%) Distribution of Malnutrition in Percentage of the Median Table 6 Distribution of Weight/Height by age groups in percentage of the median in Tana River (NCHS Reference) Age (months) N Severe wasting (<70% median) Moderate wasting (>=70% and <80% median) Normal (> =80% median) Oedema n % n % n % n % Total

14 Table 7 Global and Severe Acute Malnutrition in % of the median 14 Global acute malnutrition Severe acute malnutrition NCHS Reference 6.2% [4.2%-8.2%] 0.0% [0.0%-0.0%] WHO Reference 2.5% [1.1%-3.9%] 0.0% [0.0%-0.0%] Risk of Mortality: Children s MUAC All children measured with a height >= 65 cm were included in the MUAC analysis (Table 8). According to the MUAC, 0.0 % of the children were found to be severely malnourished, 1.1 % were under acute malnutrition criteria and 18.5% were found at risk of malnutrition (>=120 and <135mm). Table 8 MUAC distribution in Tana River MUAC (mm) >= 65 cm to < 75 cm height >=75 cm to < 90 cm height >=90 cm height Total < % 0 0.0% 0 0.0% 0 0.0% 110<= MUAC< % 5 1.4% 0 0.0% 9 1.1% 120<= MUAC< % 8 2.2% 2 0.7% % 125<= MUAC < % % % % MUAC>= % % % % TOTAL % % % % 4.2 Household Status Overall 720 households were visited during the survey period as outlined in table 9 below. Households without under five were mainly inhabited by elderly or newly wedded couples. Table 9 Household Status Tana River N % Households with children< % Households without children< % Total %

15 4.3 Composition of the households 15 The survey showed a percentage of under fives which is normal for developing countries (Table 10). The average number of people in a household reached 5.6 (Table 11). Table 10 Age group proportion Age groups Tana River n % Less than % More than 5 years 3, % Total 4, % Table 11 Household Composition Average number of people per household Average number of <5 per household Tana River RESULTS OF THE RETROSPECTIVE MORTALITY SURVEY Mortality rates with a recall period of 99 days were calculated from the figures collected from families with or without children under 5 years and are presented in Table 12 below. Table 12 Demographic information Demographic data Tana River Current resident HH 4371 Current resident < 5 years old 997 People who joined HH 88 < 5 years old who joined HH 10 People who left HH 127 < 5 years old who left HH 13 Birth 61 Death 12 Death < 5 years old 5 Recall period (days) 99 CMR (Deaths /10,000 people/day) 0.28[ ] U5MR (Deaths in children<5/ 10,000 / day ) 0.52[ ] Both, CMR and U5MR are below the alert levels of 1/10,000 and 2/10,000 per day, respectively. It could be seen that both, out-migration and in-migration was high which was attributed to residents moving in search of employment and food due to failure of short rains this year.

16 16 6 Discussion and conclusion The measured nutritional status in Tana River can be explained by the following factors. 6.1 Lack of access to adequate health care The number of health facilities in Tana River is low and the few available ones are difficult to reach (lack of movement means and infrastructures), leading to a low access to health care for the population. Existing mobile clinics in rural places run by the MoH are irregular and prone to delay or cancellation when resources are short. In the northern part of the district (Baghale Division), most of the sick cases are referred to Garissa PGH as the only district hospital, located at Hola, is too far for most communities to access. Furthermore, national health facilities are understaffed (only one nurse present in most of the health facilities). Due to the perceived hardship of being posted in the Tana River district, the motivation from medical staffs is low, but the number of patients high. The high prevalence of preventable diseases like malaria can be attributed to low mosquito nets possession rates and is partly responsible for the high under five mortality rates. The high morbidity stress subjected the children to the risk of malnutrition due to synergy between diseases and inadequate dietary intakes. 6.2 Lack of safe drinking water and sanitation facilities The low access to safe water sources for the households in the district is a major problem. This is a result of sharing the available water with animals, which leads to contamination and most of the households do not treat the water they use. The Tana River, on which the district depends for its water sources, experiences floods on a regular basis, which leads to the mixing of sewage and other wastes with drinking water, increasing the risk of water borne diseases. Irrigation canals from the National Irrigation Board also harbors hazardous organisms like snails which from key informants discussions led to many water borne diseases. Water borne diseases contributed to malnutrition as many sufferers were children. 6.3 Persistent food insecurity Scanty rainfall and floods observed for the several past years led to poor agricultural production and pasture growth, exposing the communities to perennial food shortage. As not having other income opportunities, most of the inhabitants of Tana River rely solely on the well-being of their herds and farms and are very vulnerable at times of delayed or poor rains. Since the sampled population mainly derives their livelihood from marginal mixed farming and prolonged period of drought this year, there was minimal farming. Most of the farming land was bare except along the river line areas where there was some evidence of banana and mango farming. 6.4 Lack of knowledge on nutrition/malnutrition and health issues Due to a lack of adequate trained Nutrition personnel in the district, Nutrition and health education is poorly developed among the majority of the population. Adequate breastfeeding practices are hardly followed and knowledge about suitable weaning foods and energy/protein- rich diets for growing infants and children is scarce. Adequate child care remains wanting in the region, since mothers/caretakers do have heavy workload in their daily life. Most mothers are involved in taking care of livestock, fetching water and firewood, thus leaving young children at home unattended. This ultimately leads to poor breastfeeding for infants and younger children and reduces the number of meals and/or amount of food taken during the day. Also, understanding and concerns about malnutrition and its consequences among mothers/caretakers is poorly developed. In an environment which has been prone to chronic malnutrition for many decades, now people seemed to have adjusted to the unhealthy living situation with all its negative consequences.

17 Recommendations In order to improve the nutrition status of the surveyed communities, we recommend the following: Health and nutrition Integration of nutritional treatment activities in the existing health facilities to ensure sustainability in centres where it has not been done Increase of the number of outreach programs to cover other parts of the districts. This will help in screening the children and in early decision taking. Implementation of a provisory mobile clinic in every division to enhance the access of heath services WASH, Nutrition and health education Continuation of health education programs in the communities, schools and other institutions to improve hygiene practices. Increase of the accessibility to safe water by construction/rehabilitation of earth pans and water storage Tanks. Continuation of sanitation programs such as water protection, and increase of safe human waste disposals. Food Security In the long term, there is need to step up poverty reduction strategies in the district to mitigate chronic food insecurity and bring down acute malnutrition rates to acceptable levels which may include improved markets for the already available fruit crops. Diet diversification, rehabilitation of irrigation schemes in the district and provision of reliable water supplies may be a priority. 6.6 Conclusion It is evident that a number of aggravating factors such as persistent food insecurity, poor child feeding practices, high morbidity stress, and poor maternal nutritional knowledge and status adversely affect the children nutritional status in the district. To address this prevailing situation the MOH and other stakeholders in the health sector could consider employing additional Nutritionists as during the survey it was evident in the centres with nutritionist such as Bura Health centre and Madogo Health centre that cases of acute malnutrition were reported. Additionally an integrated approach to management of acute malnutrition should be adopted to address the issues of water and sanitation, health education and food security which further worsen the nutritional situation.

18 18 7 APPENDIX Sample Size and Cluster Determination Tana River district DIVISION CLUSTER POPULATION SIZE ASSIGNED CLUSTER NO. BUWA 2,104 KORATI 1,708 1 ASAKO 1,919 2 PAMBA 814 BISKIDERA 5,327 3, 4 METI 8,659 5,6,7,8 CHEWELE 2,607 9 WADESA 3, HALO DUKANOTU 5,078 12,13 NANIGHI 6,367 14,15 CHEWANI 2, HOLA MISSION 3,081 17,18 KIARIKUNGU 681 BUBU 1, GHOREI 1,242 MASABUBU 1, RHOKA 1,102 LENDA 2, MIKINDUNI 2, BOHONI 1,106 LAINI 2, HOLA 3, KIBUYU 6,955 25,26,27 MADOGO 7,298 28,29,30 MARAMTU 1, MORORO 3, ZIWANI 2, KONORAMADHA 2,694 34,35 MLANJO 5,045 36,37 SALA 1,659 SOMBO 2, HARA 651 MARONI 1, WENJE 2, BUBUBU 2, MAJENGO 1, MASALANI 1,463 GAFURU 1, MAZUNI 552 MKOMANI 611 BONDENI 1, HANDAMPIA 1, BANGALE BURA GALORE MADOGO WENJE

19 ANTHROPOMETRIC SURVEY DATA FORM Ministry of Health Tana River District November 2008 istrict Division Location Sublocation/Village Survey Date: Cluster number: Team number: Include Children Born from December 2003 to May 2008 ONLY Child no. HH. no. Sex (F/M) Age in Months Weight (kg) ±100g Height (cm) ±0.1cm Oedema (Y/N) MUAC ±0.1cm W/H % Remarks Name Signature (Team Leader) Target Number of Children per cluster

20 Mortality Data Sheet Tana River District November 2008 Household enumeration data collection form for a death rate calculation survey (One sheet/household) Survey District: Division: Location: Sub-location/Village: Cluster number: HH number: Date: Team number: ID Present at beginning of recall Age Born Present (include those not present now and Sex in during now indicate which members were not F or M months recall Y or N present at the start of the recall or years period? period ) HH member Died during the recall period Tally (these data are entered into ENA for each household): Current HH members total Y in Col 2 Current HH members - < 5 U5 in col 5 & Y in col 2 Current HH members who arrived during recall (exclude births) Current HH members who arrived during recall - <5 (exclude births) Past HH members who left during recall (exclude deaths) X in col 2 Past HH members who left during recall - < 5 (exclude deaths) Births during recall (exclude those born and died during recall) Birth in col 3 and 6 Total deaths Total deaths Dead in col 2 and 7 Deaths < 5 Name Signature (Team leader)

21 21 Questionnaire for mortality rate calculation (one sheet/cluster) Nutrition Survey Tana River District November 2008 Division: Location: Village: Date: Cluster number: Team number: N Current HH member Current HH members who arrived during recall (exclude births) Past HH members who left during recall (exclude deaths) Births during recall Deaths during recall Total < 5 Total <5 Total < 5 Total < 5 Signature of Team leader: Target of number of people present now:

22 LOCAL EVENTS CALENDAR TANA RIVER DISTRICT NUTRITIONAL SURVEY 2008

23 Plausibility check: Tana river survey Anthropometric Indices out of usual range (mean -3.0, mean +3.0): Age distribution: Month 6 : ############# Month 7 : ############## Month 8 : ######################## Month 9 : ################ Month 10 : ############### Month 11 : ######### Month 12 : ################## Month 13 : #################### Month 14 : ################## Month 15 : ################ Month 16 : ######################### Month 17 : ######### Month 18 : ################## Month 19 : ######## Month 20 : #################### Month 21 : #################### Month 22 : ############ Month 23 : ############## Month 24 : #################### Month 25 : ##################### Month 26 : ############################ Month 27 : #################### Month 28 : ############ Month 29 : ############# Month 30 : ################ Month 31 : ############# Month 32 : ############ Month 33 : ###### Month 34 : ########## Month 35 : ################## Month 36 : ########################## Month 37 : ################## Month 38 : ############# Month 39 : ######## Month 40 : ############# Month 41 : ############# Month 42 : ############ Month 43 : ########## Month 44 : ########## Month 45 : ############## Month 46 : ########### Month 47 : #################### Month 48 : ############## Month 49 : ################# Month 50 : ############# Month 51 : ############# Month 52 : ############## Month 53 : ######### Month 54 : ############ Month 55 : ####### Month 56 : #########

24 24 Month 57 : ########## Month 58 : ############## Month 59 : ############### Digit preference Weight: Digit.0 : ################################################## Digit.1 : ################################## Digit.2 : ###################################### Digit.3 : ######################################## Digit.4 : ########################################## Digit.5 : ############################################ Digit.6 : ########################################### Digit.7 : ################################## Digit.8 : ###################################### Digit.9 : ############################## Digit preference Height: Digit.0 : ################################################ Digit.1 : ################################################## Digit.2 : ################################################ Digit.3 : ################################## Digit.4 : ############################### Digit.5 : ####################################### Digit.6 : ################################################# Digit.7 : ####################################### Digit.8 : #################################### Digit.9 : ###################### Standard deviation of WHZ: Standard Deviation SD: (The SD should be between 0.85 and 1.10) Prevalence (< -2) counted: 11.5% Prevalence (< -2) calculated with current SD: 12.4% Prevalence (< -2) calculated with a SD of 1: 15.7% Standard deviation of HAZ: Standard Deviation SD: (The SD should be between 1.10 and 1.30) Prevalence (< -2) counted: 28.2% Prevalence (< -2) calculated with current SD: 27.5% Prevalence (< -2) calculated with a SD of 1: 23.5% Skewness and Kurtosis of WHZ: Skewness of WHZ: => probably skewed (value > 2*(6/n)½) (Skewness characterizes the degree of asymmetry around the mean, positive skewness indicates a long right tail, negative skewness a long left tail) Kurtosis of WHZ: => probably no kurtosis problem (value < 2*(24/n)½) (Kurtosis characterizes the relative peakedness or flatness compared with the normal distribution, positive kurtosis indicates a relatively peaked distribution, negative kurtosis incidates a relatively flat distribution)

25 25 Detailed Team Evaluation Team Digit preference Weight (%):.0 : : : : : : : : : : Digit preference Height (%):.0 : : : : : : : : : : Global malnutrition (WHZ < -2): SD Prevalence (< -2) counted: % Prevalence (< -2) calculated with current SD: % Prevalence (< -2) calculated with a SD of 1: % Stunting (HAZ < -2): SD Prevalence (< -2) counted: % Prevalence (< -2) calculated with current SD: % Prevalence (< -2) calculated with a SD of 1: % Poisson distribution of clusters for WHZ: number of clusters 1 children/cluster with WHZ < -2: ######### 2 children/cluster with WHZ < -2: ########### 3 children/cluster with WHZ < -2: ######## 4 children/cluster with WHZ < -2: ###### 5 children/cluster with WHZ < -2: 6 children/cluster with WHZ < -2: ##