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TURKANA SMART SURVEYS PRELIMINARY REPORT

TURKANA SMART SURVEYS PRELIMINARY REPORT. 27 th June- 9 th July , 2012 Kabura Ndegwa -Nutrition Survey Consultant NutriBalance Consultancy Services. 1. Background Information.

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TURKANA SMART SURVEYS PRELIMINARY REPORT

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  1. TURKANA SMART SURVEYS PRELIMINARY REPORT 27th June- 9th July , 2012 KaburaNdegwa-Nutrition Survey Consultant NutriBalance Consultancy Services

  2. 1. Background Information • Turkana County lies in the Rift valley province in Kenya and is situated in the arid Northwestern region of the country. It is situated in the northwestern region of Kenya, sharing international borders with Ethiopia, Sudan and Uganda and locally with Baringo, West Pokot and Samburu districts. • The district has an estimated total population of 855,399. Turkana County cover an area of 77,000km2 (Turkana District; Kenya National Bureau of Statistics, May 2009). The area has in the recent past been divided into six districts and comprises of seventeen administrative divisions. • The larger Turkana district is the second poorest district in Kenya with poverty levels of approximately 20% above the national average. Turkana is constrained by the harsh environment, remoteness coupled with the poor infrastructure and low access to essential services in addition to other underlying causes of poverty that are experienced elsewhere in Kenya. It is classified among the Arid and semi arid lands (ASAL). • Turkana consists mostly of low-lying plains with isolated mountains and hill ranges and receives unreliable and erratic rainfall of less than 100mm annually. The rainfall pattern is unreliable and erratic. There are two rainfall seasons, the long rains occurring between April and July and the short rains between October and November. Warm and hot climate with temperatures ranging between 33◦C and 40◦C characterise the County. Being an ASAL district, Turkana is a drought prone area that experiences frequent, successive and prolonged drought and cattle rustling which leads to heavy losses of lives and livestock. • According to Arid land resource management project (ALRMP), the County has four main livelihood zones. Nearly 60% of the population is considered pastoral, 20% agro pastoral, 12% fisher folks and 8% are in the urban/peri-urban formal and informal employments. • Turkana district requires continuous surveillance of nutrition situation due to its vulnerability to adverse climatic conditions such as drought and flooding.

  3. Map of Turkana County-Study Area

  4. 2. Rationale for conducting the surveys 2.1 Survey justification • The nutrition survey conducted in Dec 2011 showed a decrease in levels of malnutrition for the six districts as compared to the May 2011 results. Despite this significant progress, continued focus on sustaining recovery in Turkana and across the Arid and Semi-Arid Lands will be required if these gains are to be maintained(UNOCHA Horn of Africa Situation Report no.27, 16 December 2011). • In the light of this health and nutrition partners in Turkana propose to conduct nutrition surveys covering the 6 districts in Turkana. The survey will seek to estimate the level of malnutrition among children 6-59months and will also estimate the coverage of nutrition programs.

  5. 2.3 Timing of Surveys Start of the short rains Dry Season Long Rains Dry Cool Season Short Rains Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec Increase in quality and quantity of pasture and browse. Increase in milk production. Decrease in number of livestock young. Start of Long Rains Current surveys took place in the same season as 2011 surveys (April 2011). This makes them comparable (for nutrition surveillance)

  6. 2.2 Overall objective of the survey • Turkana district surveys will be conducted to assess the nutritional status of children 6-59 months of age. In addition, the survey will seek to establish infant and young child feeding practices among children 0 to 23.9 months as well the nutritional status of women in the reproduction age (15-49 years). • This will determine the extent and severity of malnutrition among children aged 6-59 months and analyze the possible factors contributing to malnutrition such as illnesses, child care practices, water & sanitation and food security and recommend food and non-food interventions • This assessment will constitute a nutrition surveillance system as well as provide information for the ongoing HINI programme.

  7. 2.2 Specific Objectives 1. Determine the prevalence of acute malnutrition among under five year olds children, pregnant and lactating women 2. Estimate coverage of the current High Impact Nutrition interventions in the districts 3. Determine the Infant and Young Child Feeding Practices (IYCF) among children 0-23 months of age 4. Investigate household food security and food consumption practices. 5. To estimate crude and under-five mortality rates. 6. Estimate morbidity rates of children below five years 7. Determine the proportion of households with access to safe water and sanitation

  8. 3. Survey Methodology3.1 Survey sampling • Two-stage cluster sampling was applied to randomly identify clusters with the probability of being selected proportional to the population size(PPS) in each cluster. • All villages that were accessible by road and will be included in the sample selection. Those currently inaccessible(due to insecurity) were excluded from sampling frame. The required sample size was calculated on the nutritional status of children under five for the children 6-59 months sample, Infant and Young Child (IYCF)feeding practices for the children 0-23.9 month sample and on the CMR for the household sample

  9. Summary of parameters considered for sample size calculation & actual outcome of survey below: [1] Number of households to be visited from anthropometry sample

  10. Turkana Surveys (July 2012)-Plausibility Checks 1. Skewed Poisson: Pockets of malnutrition(previouslyun-surveyedareas 2. Excess of boys

  11. Population Age-Sex pyramids Bottom-heavy pyramids Less children in older age categories

  12. Demographic Indicators for Turkana County (From Individual and HH mortality Data): More than ¼ of population is U5 in al zones Larger female population linked with high migration

  13. Turkana Survey Zones- Comparison of 2011 and 2012 GAM July 2012: 14.3% GAM April 2011: 27.8% SAM July 2012: 2.1% SAM: April 2011: 6.0% p-value= 0.00 GAM July 2012: 15.3% GAM April 2011: 37.4% SAM July 2012: 2.3% SAM: April 2011: 9.4 p-value= 0.00 GAM July 2012: 11.6% GAM April 2011: 24.4% SAM July 2012: 0.7% SAM: April 2011: 4.5% p-value= 0.00 GAM July 2012: 17.1% GAM April 2011: 33.5% SAM July 2012: 4.2% SAM April : 2011: 6.8% p-value= 0.00

  14. Malnutrition and Mortality Rates- Turkana County

  15. Malnutrition and Mortality Rates- Turkana County

  16. Turkana Central GAM and SAM decrease (Significant)

  17. Malnutrition and Mortality Rates- Turkana County

  18. Prevalence of acute malnutrition based on weight-for-height z-scores (and/or oedema) and by sex Chi-square analysis indicates that there is no significant differences between the sexes in 3 surveys. Turkana Central showed a significant difference (p<0.01) between the male and female sample. This suggests that overall, sex is not a risk factor for malnutrition levels.

  19. Prevalence of acute malnutrition by age, based on weight-for-height z-scores and/or oedema Chi-square analysis indicates that there is a significant difference (p<0.01) between the younger (6-23 m) and whole (24-59) sample. This suggests that infant feeding practices are related to malnutrition levels.

  20. Continued: Differences in GAM/SAM prevelance in younger and older age group Chi-square analysis indicates that there is no significant difference (p<0.05) between the younger (6-23 m) and whole (6-59) sample. Exception: Turkana North (Younger age group affected more by SAM)

  21. Nutrition Status of caregivers of < 5 year old children:

  22. Maternal Nutritional Status - Turkana County Vast improvement compared to 2011

  23. Morbidity, immunization and deworming results Included 8 suspected cases from one cluster in Kibish. Reporting protocol was followed 1 unconfirmed case of TB

  24. Vitamin A Supplementation and deworming results

  25. Health and Sanitation

  26. Meal Frequencies

  27. IYCF: Summary of Breastfeeding Practices • Indicators comparable, but EBF rate improved(2011); Some BF practices poor; Community Strategy(CS) to be launched; MtMSG existing- to be up-scaled in community units

  28. IYCF: Summary of Complementary Feeding Practices DD and frequency static from 2011; DD impacts on nutrition status of U24months particularly. Agropastoral zone has introduced new species(traditional vegs; legumes; fruits- but utilization is not known (MoA). MtMSG scale-up may have an impact.

  29. Coverage of feeding programs • Calculated using direct method (SPHERE) to estimate PERIOD PREVALENCE • Lower coverage than Sphere recommendations for rural area coverage (>50%)

  30. Prevalence of MalnutritionContextual Factors: • Trend analysis shows that there has been no significant difference in GAM/SAM reported for Turkana County since December 2011, taking into consideration seasonality: Four surveys were carried out in 2011, in Turkana • The Dec 2011 and July 2012 surveys show no significant difference (overlapping confidence intervals and 2011 comparison with two survey calculator (CDC) also indicates a non-significant difference (p=0.362), with current survey. However, there is a significant difference, in all zones, with May 2011 survey • Sharp increase of malaria incidence • Children who have recently recovered (MAM and SAM) are likely to relapse when the water and food security situation deteriorates because the effect of the on-going hazards is likely to further lower the resilience of vulnerable groups. • Age-verification; over-reporting of illness is major challenge in the survey, despite specifying illness over the preceding 15-day period.

  31. Prevalence of MalnutritionContextual Factors: • Nutrition surveillance data in Turkana since 2011 indicate CRM has consistently exceeded emergency levels (1.0CMR). This has been exacerbated by increasing insecurity. • Poisson distribution WHZ -2, showed a slight significant difference (p=0.000) in cluster heterogeneity which matched the clusters/villages in North zone that have never previously been surveyed, and had high rate of malnutrition. • The statistical analysis of surveyed children (plausibility test) and graphical data below shows that there was a significant difference in age distribution (p=0.000), which suggests that there are currently less children in some age groups than expected. This may be indicative of: • incorrect age given and/or the impact of migration of younger children 0-24 months, due to insecurity. • Older children not being present during the survey day because of pre-school attendance

  32. Factors linked to Malnutrition in the Survey Area

  33. Factors linked to Malnutrition in the Survey Area

  34. Conclusion • With deteriorating NS indicators and estimated programme coverage(SFP and OTP) seemingly inadequate, further investigation is warranted through a coverage survey to determine specific causal factors and areas for IMAM programme implementation improvement • Coverage for Vitamin A supplementation was relatively low with most of the children having received vitamin A only once. • Under five illnesses are high, and fever rate indicative of malarial outbreak. Morbidity and malnutrition rates likely to increase with depletion of water levels from safe sources • Sub-optimal hand-washing practices, poor sanitation practices and minimal treatment of unsafe drinking water at the household level • Household food DD & security low due to increasing food prices and drought. • A prevailing food deficit situation that is set to deteriorate further, pending the performance of the short rains

  35. Recommendations (draft)

  36. Recommendations (Draft)

  37. Recommendations (Draft)

  38. Lessons Learnt: • Training: Coordinated; Joint practical exercises; assessment of use of calendar of events; modified standardization • Questionnaires: Use of HH and summary mortality; Addition of ‘reason for leaving column(triangulation of causal factors);Cluster sheets; • Data Collection: Migrated clusters surveyed in current location; Enhanced and cumulative calendar of events (multi-sectoral-initiate at KNBS office) • Data Entry: Individual mortality (advantages) • Capacity-building: ENA Clinic for Supervisors • Overall(Turkana Surveys): Strong survey planning committee; Supervision of surveys (Min. 2 supervisors daily- each survey)

  39. Acknowledgments • The Turkana District Health Management Team for taking the lead in implementation of the nutrition survey, • UNICEF KCO for technical and financial support • The District Nutritionists for Turkana Central, South, North East and North West for taking an active role in planning the survey, training enumerators, supervising data collection and entry and lastly reporting and dissemination of results. • Partner Support: MERLIN; IRC; WORLD VISION; APHIA PLUS IMARISHA • The MOPHS(Survey Planning Committee) and DNTF for their invaluable support during survey preparations as well as the actual surveillance implementation • The team members (Enumerators, team leaders and Coordinators) involved in ensuring the survey obtained good quality data; not forgetting the drivers who efficiently facilitated teams’ movement to the various locations • The parents and caretakers for providing valuable information by patiently providing their time to be interviewed and allowing their children to be measured.

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