1. Learning Unit 8. Feasibility GMS Training Course on Malaria Elimination Chiang Mai, 11 August 2015 Allan Schapira

2. Learning objectives Describe the purpose of a feasibility assessment; Describe the technical, operational and financial factors that should be considered in assessment of feasibility of malaria elimination; Identify the important determinants af feasibility and their most important interactions Carry out and interpret a simple extrapolation of epidemiological trends.

3. 3-way analysis Technical: Is elimination possible with existing tools (but not necessarily limited to tools currently used in the country), assuming that there are no operational or financial constraints? If yes, proceed: Operational are there significant obstacles in the health system including the malaria programme, or in broader contextual determinants? If so, can they be overcome? If yes, proceed: Financial and economic: Would the costs be reasonable and justified? How they could be covered? It is most useful, if information is presented to compare various options. ……………… If yes, eliminate……..

4. Or as 4 questions Can transmission be interrupted? Can malaria- free status then be maintained? Can transmi ssion be reduced to very low? Can the system to interrupt transmiss ion be built?

5. Time is of the essence There must be a time-limited objective Time-frame (to reach 0 cases) could be 5 – 15 years Timing of feasibility analysis: When SPR/TPR approaches 5% in the most affected region/province may be a sensible point to do feasibility study. I.e. the 5% threshold is a good time to do analysis. Depending on it, a programme may enter pre-elimination phase and set time-bound objectives, or decide to continue in control mode.

6. Why should a feasibility analysis be done? What are the risks of embarking on elimination and failing? What are the risks of not embarking on elimination, if it is feasible? The decision is political, but the issues are too serious and too complicated to be left to the politicians.

7. Special situation of GMS An elimination strategy with ambitious time- bound targets has been decided jointly by Governments and international partners because of the compelling need –both in local and international perspective - to contain multi-drug resistant Pf. Meeting the targets will require an extraordinary degree of investment and in some areas the inclusion of novel interventions.

8. Strategic variants Elimination of falciparum malaria only – or first Local elimination in a unit of suitable size, which is not too vulnerable – i.e. it is relatively isolated (unless its receptivity is very low). It should not be an area, where malaria is disappearing by itself. Aim for controlled non-endemic malaria: corresponds to remaining in WHO’s “prevention of reintroduction” category, but not 100% malaria-free. Proceed to pre-elimination, but without an elimination plan. Not ideal.

9. Local elimination In a State, region, province, island. Often an intermediate step. An independent national body must validate (not certify) that local health authority has eliminated malaria and can maintain malaria-free status. Responsibility for maintenance must rest with local authority. WHO takes no responsibility, but may provide technical cooperation and may report on the reported achievement in ITH. (WHO Malaria Policy Advisory Committee and Secretariat Malaria Journal 2013, 12:456. http://www.malariajournal.com/content/12/1/456) In the case of GMS, WHO would surely give high priority to technical cooperation

10. TECHNICAL FEASIBILITY in elimination + prevention of reintroduction phases (1) vectorial capacity/receptivity, (2) duration of infectivity, which depends on the surveillance system and, (3) risk of importation/vulnerability.

11. Elimination phase: Can we get R<1? R 0 =C 0 x D 0 Before control, we have: Ex.: 100 = 5/day x 20 days R c =C c x D c Under best possible control, we get: Transmission will be interrupted, if R c < 1 Vectorial capacity Duration of infection in humans

12. Maintenance Phase: Can we prevent reintroduction? R c =C c x D c Recep tivity Surveillance => D Vulnerability !!!

13. R c and vulnerability If R c =1 and 100 cases are imported, we expect 100 introduced cases; in the next generation 100 indigenous cases and in the next generation…. If R c =1 and there are 3 imported cases… If R c =0.5 and100 cases are imported and, then we expect 50 introduced cases; in the next generation….

14. Local/imported ratio In steady state, after apparent interruption of transmission A ratio local/imported of 1/1, indicates that R c = 0.5. In the face of frequent importation, small outbreaks will continue to occur. This corresponds to controlled non-endemic malaria (Cohen et al. How absolute is zero. Mal.J. 2010)

15. Technical + operational feasibility Review the lists in 8.5 and 8.6 in participants module pp. (149-55). The scheme assesses: Technical and operational determinants of: Vectorial capacity/Receptivity Duration of infection Vulnerability

16. Empiricist approach to feasibility: Plot log incidence by year and extrapolate Within 2-3 years, all 5 of these provinces should reach incidence rate of 0.001/ 1000 = 1 /million, malaria will disappear (as population of each province is about 1 million).

17. Extrapolation in the frontier islands Extrapolation indicates that Occidental Mindoro will get to 10 per million in 3-4 years, but Palawan only by 2020. With such a long time horizon, there is considerable uncertainty.

18. Summary: 2 approaches to assess vulnerability. 1 st : review determinants Select a country or a region, which is sufficiently homogenous, sufficiently large and sufficiently advanced to make a feasibility assessment rational Go through the main technical and operational determinants. Note for each if favourable, unfavourable, neutral or unknown For operational, note to what extent the situation can realistically be ameliorated. Assess vulnerability Assess feasibility of elimination and timeline For GMS: Consider possible novel interventions Weakness: It provides a qualitative overview. A group of experts need to draw the conclusions

19. 2 nd method: Extrapolation For the area under consideration, present case number by year for latest 4-8 years. Graph case number by year with logarithmic y-axis If it is approximately linear, compute the least square regression slope, using the SLOPE function in Excel Through linear extrapolation, find the years, when the number reaches 10 and 1. More details: 8.6.4. Assessing the duration of the final stage on p157-8 in Participants’ module Weakness: Does not consider possible strengthening of the intervention package. Entirely driven by surveillance data. Can be suitable in the late stages of a mature program, where surveillance data is good and intervention package optimized. Stratum-wise.