e4 unicef - Coverage Monitoring Network

Report
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 ?
Current Cases of
Severe Acute Malnutrition
New cases
Exits
• 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
Mapping of
geographic
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 factor
Coverage estimates
LQAS Sampling Methods
With 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
Measures of Exclusive Breastfeeding with
LQAS in Liberia
9
Measures of Exclusive Breastfeeding with
LQAS - Liberia
100
93
90
90
87
80
77
70
60
50
40
34
29.1
30
21.7
20
10
0
Liberia CFSS 2006 Liberia DHS 2007 Liberia CFSNS 2010
LQAS Bomi
LQAS Bong
LQAS Lofa
LQAS Nimba
10
Measures of Exclusive Breastfeeding with
LQAS - Nigeria
11
Measures of Exclusive Breastfeeding with
LQAS - Nigeria
50
45
40
Percent MICS (March April 2007)
35
Percent LQAS (October-November 2006)
30
25
20
15
10
5
0
North west
Kano
Kaduna
Jigawa
North east
Gombe
Bauchi
12
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
Nigeria Overall 2012
25000
Stocks and programme exits
20000
15000
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)
Admission
Exits
10000
5000
0
Jan
Feb Mar Apr May June July Aug Sept Oct
Nov Dec
100%
Cure
80%
Default
60%
Death
40%
Non- recovered/ Medic
Transfer
20%
0%
Jan
Feb Mar Apr May June July Aug Sept Oct
Nov Dec
Information Flow
Programme data needs
Ministry of Health
Health
Management
Information System
Department of
Nutrition
H
Monthly reports
sent by email
or on demand
Regional Health
Supervisors
H
H
District Health Chiefs
IFP
OTP
SFP
Real time data on:
• New Admissions
• Stocks
• Programme Exits
Without these data, there is no
identification or response to
critical events that cripple
programme delivery.
To address these data challenges
Analysis 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
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

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