Nutrition Information in Kenya

Sources of Nutrition Information Tools

Nutrition Information systems in Kenya

• Health facility level: District Health Information System (DHIS) - http://www.hiskenya.org (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

Indicator Acceptable Poor Serious Critical Very criticalGAM among children 6-59 months (%)

<5 5-9.9 10-14.9 15-29.9 ≥30

Mean Weight-for-Height Z (WHZ) scores >-0.40 -0.40 to -0.69;

Stable/Usual-0.70 to -0.99;

>usual/increasing<-1.00;

>usual/increasing<-1.00;

>usual/increasingMUAC <125 mm among children 6-59 months (%)

<2.0% 2.1-5.5% with increase from

seasonal trends

5.6-8.0%

8.1-19.9 %, 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 DataV. low (<5%) proportion in the preceding 3mths relative to >2yr seasonal trends

Low proportion (5 to <10%) and stable trend in the preceding 3mths relative to >2yr seasonal trends

Moderate (10 to <15%) and stable or low (5 to <10%) but increasing proportion in the preceding 3mths relative 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

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Nutrition Situation and Caseloads

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MANDERASAM=12052MAM=48208

ISIOLOSAM=641

MAM=2771

GARISSASAM=1029MAM=6174

TANA RIVERSAM=277

MAM=4439

MARSABITSAM=7964MAM=2488

TURKANASAM=10257MAM=35359

WEST POKOTSAM=511

MAM=4359

BARINGOSAM=2345MAM=9086

WAJIRSAM=2828

MAM=19901

KILIFISAM=1596MAM=5676

MOMBASASAM=917

MAM=3387

KWALESAM=477

MAM=2147

KAJAIDOSAM=1276MAM=3509

SAMBURUSAM=1158MAM=7495

LAIKIPIASAM=3709

MAM=10369

NAIROBISAM=10886MAM=21733

MERU NORTHSAM=1728MAM=5184

KITUISAM=600

MAM=4498

MBEERESAM=94

MAM=1309

KISUMUSAM=2875MAM=3183

MACHAKOSSAM=1427MAM=2283

TAITA TAVETASAM=1306MAM163

MAKUENISAM=1933MAM=2163

NAROKSAM=4390MAM=4228

SUMMARYASAL

SAM = 51012MAM=188434PLW= 34929

URBANSAM=14659MAM=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…

Questions & Answers