Programme Data and Coverage Surveys

Challenges to improve programming

UNICEF 2013

Nutrition Programming - Coverage is critical

Annual estimated caseloads of severe acute malnutrition across the Sahel• In 2010, Nutrition Cluster in countries described their own methods variations of Annual caseload = Pop 6-59 * Prevalence SAM *Conversion Factor (X) + Safety Margin (X%)

• From 2012, a standard calculation used in all countries following calculation defined by Mark Myatt Annual caseload = Pop 6-59 * Prevalence SAM *Conversion Factor (2.6)

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

Mapping ofgeographic coverage of northern Nigerian states

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

Comparison of coverage with the severe acute malnutrition caseload in Maradi, Niger in 2011

• Prevalence of SAM- WHZ 1.6% in May 2011• 102,500 SAM cases treated in

Maradi in 2011• Coverage 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 severe acute malnutrition in Niger, May 2011

Why are there such discrepancies? Inputs to annual caseload estimates• Prevalence of severe acute malnutrition • Population estimates• Prevalence to incidence conversion factorCoverage estimates

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 samples• Liberia IYCF results• Nigeria IYCF results

9

Measures of Exclusive Breastfeeding with LQAS in Liberia

10

Measures of Exclusive Breastfeeding with LQAS - Liberia

Liberia CFSS 2006 Liberia DHS 2007 Liberia CFSNS 2010

LQAS Bomi LQAS Bong LQAS Lofa LQAS Nimba0

10

20

30

40

50

60

70

80

90

100

21.7

29.1

34

77

8790

93

11

Measures of Exclusive Breastfeeding with LQAS - Nigeria

12

Measures of Exclusive Breastfeeding with LQAS - Nigeria

North west Kano Kaduna Jigawa North east Gombe Bauchi0

5

10

15

20

25

30

35

40

45

50

Percent MICS (March April 2007)

Percent LQAS (October-November 2006)

Presentation of data quality indicators into coverage survey reports• Analysis of number of identified cases by data collection points (min, max, mean,

median)• Distribution of cases with MUAC < 115mm, Bilateral Oedema, reported appetite• Quality 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

Management of severe acute malnutrition programme data

New Admissions, Verification with stocks use

Stocks and programme exitsRapid 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)

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec0

5000

10000

15000

20000

25000

AdmissionsExits

Nigeria Overall 2012

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec0%

20%

40%

60%

80%

100%

Cure

Default

Death

Non- recovered/ Medical Transfer

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 data needsReal time data on:• New Admissions• Stocks• Programme ExitsWithout these data, there is no identification or response to critical events that cripple programme delivery.

To address these data challengesAnalysis framework for improved understanding of annual caseloads and programme data compared to coverage estimatesRecommendations 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

Conclusions• Prevention 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