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Programme Data and Coverage Surveys Programme Data and Coverage Surveys

Programme Data and Coverage Surveys - PowerPoint Presentation

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Programme Data and Coverage Surveys - PPT Presentation

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

programme data acute coverage data programme coverage acute estimates malnutrition severe cases annual caseload lqas measures sam sampling nutrition

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Slide1

Programme Data and Coverage Surveys Challenges to improve programming

UNICEF 2013

Slide2

Nutrition Programming - Coverage is critical

Slide3

Annual 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)

Slide4

What 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

Slide5

Mapping of

geographic coverage of northern Nigerian states

100% of targeted severe acute malnutrition caseload achieved in only ~30 % geographic area of northern states

Slide6

Comparison 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

Slide7

Why are there such discrepancies? Inputs to annual caseload estimatesPrevalence of severe acute malnutrition

Population estimatesPrevalence to incidence conversion factorCoverage estimates

Slide8

LQAS 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

Slide9

Measures of Exclusive Breastfeeding with LQAS in Liberia9

Slide10

Measures of Exclusive Breastfeeding with LQAS - Liberia10

Slide11

Measures of Exclusive Breastfeeding with LQAS - Nigeria11

Slide12

Measures of Exclusive Breastfeeding with LQAS - Nigeria12

Slide13

Presentation 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

Slide14

Management of severe acute malnutrition programme data

New Admissions, Verification with stocks use

Slide15

Stocks 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)

Slide16

Information 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.

Slide17

To 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

Slide18

ConclusionsPrevention

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