Caseload Estimations- current practices and limitations

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)

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 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. Burden = prevalent cases + incident cases

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 • 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 • MAM estimates are basically the same as SAM estimates…

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. 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, WHZis 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)