Nutrition Information in Kenya

Sources of Nutrition Information Tools

Nutrition Information systems in Kenya Health facility level: District Health Information System (DHIS) - (monthly) supported by CDC in improving quality Cross sectional nutrition SMART surveys (annually or more frequently if needed in vulnerable areas) Kenya Demographic and Health survey – National & County level. (5years) Integrated FSN sentinel surveillance – monthly MUAC Data – Integrated Nutrition situation analysis (pilot Nutrition Classification) KAP surveys (annually in specific locations) Coverage assessments (SQUEAC, BNA, KPC etc) – Review of methodologies on going Nutrition causal analysis (ACF in 2 counties)

Led by the NIWG chaired by ACF, MOH Secretariat Main focus on the arid and semi arid lands- highly vulnerable Validation team in the NIWG at national level UNICEF 2 full time staff on support Nutrition Surveillance; DHIS NSO network of 11- also support QA Training provide regularly from NIWG team to MoH NGO and NDMA team on SMART, MUAC surveillance and DHIS2 SMART and nut info training done for all NDMA info officers and DISK member of the KFSSG 2 media training session on appropriate food security and nutrition reporting KDHS – Nutrition Module training led by NIWG team- included standardization tests for 350 enumerators- and follow up supervision at county level (worked with CDC) Data collection and validation

The IPC Acute Malnutrition analysis has been formalized and been carried out for all vulnerable areas in the country since the pilot Adopted through national integrated FSN system IPC Acute Malnutrition Pilot - Kenya Aug 2014

Nutrition IPC IndicatorAcceptablePoorSeriousCriticalVery critical GAM among children 6-59 months (%) < ≥30 Mean Weight-for-Height Z (WHZ) scores > to -0.69; Stable/Usual to -0.99; >usual/increasing <-1.00; >usual/increasing <-1.00; >usual/increasing MUAC <125 mm among children 6-59 months (%) <2.0% % with increase from seasonal trends % %, or where there is significant increase from seasonal trends ≥20.0%, Or where there is significant increase from seasonal trends Sentinel Site Data Very low (<5%) and stable levels Low levels (5 to <10%)and one round indicating increase, seasonally adjusted Low (5 to < 10%) & increasing or moderate (10 to <15%) levels based on two rounds (seasonally adjusted) High levels (> 15%) of malnourished children and stable (seasonally adjusted) High levels (> 15%) and increasing with increasing trend (seasonally adjusted) HMIS Data V. low ( 2yr seasonal trends Low proportion (5 to 2yr seasonal trends Moderate (10 to 2yr seasonal trends High (> 15%) and stable proportion in the preceding 3mths relative to >2yr seasonal trends High (> 15%) and increasing proportion in the preceding 3mths relative to >2yr seasonal trends Analysis of contributory factors such as food security, breastfeeding, complimentary feeding practices, morbidity, access to health services and nutrition programmes, sanitation etc are also analysed 6

Nutrition Situation and Caseloads 7 MANDERA SAM=12052 MAM=48208 ISIOLO SAM=641 MAM=2771 GARISSA SAM=1029 MAM=6174 TANA RIVER SAM=277 MAM=4439 MARSABIT SAM=7964 MAM=2488 TURKANA SAM=10257 MAM=35359 WEST POKOT SAM=511 MAM=4359 BARINGO SAM=2345 MAM=9086 WAJIR SAM=2828 MAM=19901 KILIFI SAM=1596 MAM=5676 MOMBASA SAM=917 MAM=3387 KWALE SAM=477 MAM=2147 KAJAIDO SAM=1276 MAM=3509 SAMBURU SAM=1158 MAM=7495 LAIKIPIA SAM=3709 MAM=10369 NAIROBI SAM=10886 MAM=21733 MERU NORTH SAM=1728 MAM=5184 KITUI SAM=600 MAM=4498 MBEERE SAM=94 MAM=1309 KISUMU SAM=2875 MAM=3183 MACHAKOS SAM=1427 MAM=2283 TAITA TAVETA SAM=1306 MAM163 MAKUENI SAM=1933 MAM=2163 NAROK SAM=4390 MAM=4228 SUMMARY ASAL SAM = MAM= PLW= URBAN SAM=14659 MAM=28303 PLW=4445

How nutrition Information analysis benefits programs Nutrition IMAM program & costed contingency and response plans updated x2 per year with new analysis - key planning tool to determine caseload and funding needs for responses and supplies - surge model review admission data and capacity of HF staff to respond (NSO support) Nutrition sector respected in terms of analysis and advocacy - Nutrition analysis define dynamic response approach Arid and Semi Arid lands – for nutrition sector responsiveness – different strategy in each areas- PCA. Influence on national NDMA led long and short rains assessment decisions on resource allocation and type of programming and national Drought Contingency Fund access e.g. for outreach support. Analysis on stunting including inequities by age and region has influenced programming around stunting. Analysis on magnitude of acute malnutrition & urban vulnerabilities in terms of numbers affected has led to increased focus on urban informal settlements.

Contd… Partnerships – decisions on type of partnerships with civil society organization influenced by nutrition situation analysis. Contingency fund embedded within Kenya Red Cross for timely emergency response. Increased collaboration with other institutions - CDC, NDMA, KNBS has led to better nutrition data collection, analysis and utilization. On going nutrition research – spatial determinants of under nutrition and secondary analysis of nutrition survey data to inform programs in 2015/2016. Refocus on KAP and qualitative indicators of MIYCN - and best data sources of data collection Real time learning exercise just started on nutrition integration within health system strengthening - 14months

Clear Monitoring and Evaluation Framework Need for investment in staffing on nutrition information at national and sub national level – longer term Ongoing capacity development and advocacy on importance of quality nutrition info for key partners such as NDMA Strong MoH leadership and commitment Strong NGO partnership – identify those with capacity and support UNICEF as a gentle leader.. Validation validation validation of data quality…. Use of technical partnerships Clarity on difference between routine programing monitoring and response and surge and how to communicate and use this information for appropriate response – more sophisticated analysis Take home thoughts…