Challenges to improve programming UNICEF 2013 Nutrition Programming Coverage is critical Annual e stimated caseloads of severe acute malnutrition across the Sahel In 2010 Nutrition Cluster in countries described their own methods ID: 814770
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Slide1
Programme Data and Coverage Surveys Challenges to improve programming
UNICEF 2013
Slide2Nutrition Programming - Coverage is critical
Slide3Annual estimated caseloads of severe acute malnutrition across the SahelIn 2010, Nutrition Cluster in countries described their own methods
variations of Annual caseload = Pop 6-59 m * Prevalence SAM *Conversion Factor (X) +
Safety
Margin (X%)
From 2012, a
s
tandard calculation used in all countries
following calculation defined by Mark Myatt
Annual
caseload = Pop 6-59
m * Prevalence
SAM *Conversion Factor
(2.6)
Slide4What information is needed for case load estimation of severe acute malnutrition ?
Accurate incidence data from effective large scale programmes
Accurate population and prevalence estimates
Duration of case of severe acute malnutrition as defined by WHZ and MUAC
Velocity of increase or decrease of new cases following seasonal / temporal variation
Current Cases of
Severe Acute Malnutrition
New cases
Exits
Slide5Mapping of
geographic coverage of northern Nigerian states
100% of targeted severe acute malnutrition caseload achieved in only ~30 % geographic area of northern states
Slide6Comparison of coverage with the severe acute malnutrition caseload in Maradi, Niger in 2011
Prevalence of SAM- WHZ 1.6% in May 2011102,500 SAM cases treated in Maradi in 2011Coverage estimates of 24% in Maradi from 5 region coverage survey in 2011
Assuming no over-reporting the annual caseload corrected by coverage would be – 425,000 cases
Population 6-59m of Maradi ~578,000
Estimated number of children 6-59 months of age with
s
evere
a
cute
m
alnutrition in Niger, May 2011
Slide7Why are there such discrepancies? Inputs to annual caseload estimatesPrevalence of severe acute malnutrition
Population estimatesPrevalence to incidence conversion factorCoverage estimates
Slide8LQAS Sampling MethodsWith coverage estimates, there are no Niger results using other sampling methods to verify those estimates made with S3M methods
National level surveys collecting IYCF indicators with LQAS samplesLiberia IYCF resultsNigeria IYCF results
Slide9Measures of Exclusive Breastfeeding with LQAS in Liberia9
Slide10Measures of Exclusive Breastfeeding with LQAS - Liberia10
Slide11Measures of Exclusive Breastfeeding with LQAS - Nigeria11
Slide12Measures of Exclusive Breastfeeding with LQAS - Nigeria12
Slide13Presentation of data quality indicators into coverage survey reportsAnalysis of number of identified cases by data collection points (min, max, mean, median)
Distribution of cases with MUAC < 115mm, Bilateral Oedema, reported appetiteQuality of MUAC measure (accuracy and precision of anthropometrist measures, digit preference, flagged data, use of colored vs non colored MUAC strips)
Age estimation and sex of child
socio-demographic variables of child and or household – comparison to survey data results in households with children with GAM.
Population size of sampling points
GPS validation of survey sampling points
Verification of child in programme with RUTF in HH, treatment programme follow-up cards
Capture / Recapture data analysis
Slide14Management of severe acute malnutrition programme data
New Admissions, Verification with stocks use
Slide15Stocks and programme exits
Rapid increase of scale of programme often leads to quality issues. Without programme data, these issues are not addressed.Programme data support:
Integration of management of SAM into regular programme delivery
Ensure lives saved by programme (avoid stock-outs, ensure malaria treatment)
Incorporate preventive interventions (WASH/Nutrition minimum package)
Slide16Information Flow
IFP
OTP
SFP
District Health Chiefs
Ministry of Health
Regional Health
Supervisors
Health Management
Information
System
Department of Nutrition
H
H
H
Monthly reports sent by email
or on demand
Programme
d
ata
n
eeds
Real time data on:
New Admissions
Stocks
P
rogramme Exits
Without these data
, there is no
identification or response
to critical events that
cripple programme delivery.
Slide17To address these data challengesAnalysis framework for improved understanding of annual caseloads and programme data compared to coverage estimates
Recommendations for what types of programme evaluations should be conducted when. Timely production of results for critical programme management decisions prior to the hunger season.For Coverage Surveys of large scale programmes (national or regional)
Standardized robust and cost appropriate sampling methods
Data collection in one month
Standardized reporting models including data quality measures
Slide18ConclusionsPrevention
and treatment are two sides of the same coin
Coverage is critical but without quality
programme data,
coverage estimates are less relevant.
Timely accurate regular coverage
estimates should be used
to modify and improve
programme
implementation