1. Caseload Estimations- current practices and limitations
UNICEF, GOAL and WFP
GNC
Nairobi 2015

2. Caseload estimates for SAM
Burden =
existing (prevalent) cases + incident (new) cases
Outcome of interagency discussion in recent years
Caveats:
This is a starting point to adapt to context
And also to cross check with programme performance (but limited guidance on the how)
Burden= total estimated affected
Often different from those we target which we then use as our caseload

3. Caseload estimates for SAM
Burden = prevalent cases + incident cases
Caveats:
Seasonal variation common in SAM
For programme planning (not joint estimates of global burden)
Not currently linked systematically to MAM estimates

4. Caseload estimates for SAM
Burden = prevalent cases + incident cases
Prevalent cases = population x prevalence
Current cases based on %WFH as standard. But what about MUAC admissions? MUAC WFH conversion not currently possible, and huge variation between countries and surveys.
Misses burden of oedematous SAM.
How to use subnational data to generate national figures? Comparability is key.

5. Caseload estimates for SAM
population
= Children 6-59 months
But we miss burden in infants.
(SAM burden estimates for other age groups have similar challenges)

6. Caseload estimates for SAM
Burden = prevalent cases + incident cases
Incident cases = pop x prevalence x incidence factor
= pop x Prevalence × 12/7.5 = pop x Prevalence × 1.6
Estimates of mean duration of SAM vary, based on limited studies. Estimate of untreated SAM as 7.5 months. Assumes constant incidence (seasonality however….)
No specific guidance for scenario building.
Some countries using other estimates based on experience. Cohort studies needed to generate incidence and research is forthcoming.

7. Caseload estimates for SAM – for one year
Total Population in Need: 1,200,000
198,000 (16.5% of tot pop) 6 to 59 months with prevalence 7.5%
Prevalent cases
198,000 X 7.5%
=14,850
Incident cases
198,000 X 7.5% X 1.6
=23,760 +
=12 months prog. duration/7.5months
average duration of untreated SAM– we need to plan for worst case scenario, incidence-correction factors will be increased
= 38,610
OR Burden = population 6-59m x prevalence x 2.6
198,000 X 7.5% x 2.6 = 38,610

8. Caseload Estimates for MAM
MAM estimates are basically the same as SAM estimates…
Prevalence will be higher for MAM than SAM
Use 7.5 months average duration as in SAM
Cases to treat=prevalence * (1+period/duration)
Incidence usually set at 1.6 (but variations at national level)
Same key limitations
-> not linked systematically to SAM
->Seasonal variation
-> MUAC screening data versus SMART prevalence data
-> Recall target is based on % of overall caseload

9. Caseload estimates for MAM – for one year
Total Population in Need: 1,200,000
198,000 (16.5% of tot pop) 6 to 59 months with prevalence 16.8%
Prevalent cases
198,000 X 16.8%
=33,264
Incident cases
198,000 X 16.8% X 1.6
=53,223 +
=12 months prog. duration/7.5months
average duration of untreated MAM– we need to plan for worst case scenario, incidence-correction factors will be increased
= 86,487
OR Burden = population 6-59m x prevalence x 2.6
198,000 X 16.8% x 2.6 = 86,487

10. Kutapalong Registered Refugee Camp Context
WHZ v. MUAC
Kutapalong Registered Refugee Camp Context
November 2017

11. Critical issue of WFH and MUAC
Discrepancy between MUAC and WHZ in estimation of acute malnutrition.
MUAC is a better predictor of mortality than WHZ.
In some population, WHZ is influenced by body shape.
MUAC selects younger children than WHZ.
MUAC is also biased against boys.
Over-estimating Vs under-estimating.
What is the context in Cox’s Bazar?

12. Review of assessment reports

13. Review of assessment reports –cont.

14. Program Implications of caseload calculations if using WFH and/or MUAC

15. Issues affected by different anthropometric indices yielding different caseloads
WHO thresholds and SPHERE indicators for the definition of different situations
The identification of priority areas for different services (GFD, BSFP, TSFP, OTP, SC, etc.)
Admission, transition and & discharge criteria for programming
Caseloads affect country capacity to respond: personnel, production provision (RUTF, MAM and BSFP supplies, medecines, NFIs etc.)
Caseloads affect quality of care, geographical & therapeutic coverage (including screening, referrals, compliance) and thus overall ‘met need’.
Exit strategy criteria

16. WHZ v. MUAC – Considerations
MUAC is easier to use, cheaper, more rapid and well suited for community outreach
MUAC is more likely to identify younger children, girls, and is better predictor for SAM than MAM
MUAC appears to be a better predictor of mortality
Global studies (as well as the SSD case example) show a wide discordance between MUAC and WHZ in some populations and a closer correlation in others  Where there is a wide discordance, we need to determine if one measurement is overestimating or the other one underestimating
The overlap would be expected to be higher (at least higher for SAM than MAM), but that is not always the case  What is this telling us?
What about the kids that would be missed if we used only MUAC or only WHZ?
What is the research evidence regarding wasting and MUAC/WHZ?
Need to clearly define our program objectives  Growing understanding of nutrition programming in emergencies suggest focus needs to go beyond mortality and treatment of SAM/MAM, such as stunting, micronutrient deficiencies and prevention of children descending into SAM for better whole life outcomes
Long term effectiveness and cost-efficiency in MUAC v WHZ?
MUAC vs. WHZ debate  What do we lose, what do we gain and is actually the right debate? (health v ill health as marker)