Cost-effectiveness analysis of malaria rapid diagnostic test incentive schemes for informal private healthcare providers in Myanmar

Table 2 Base case inputs for provider behaviour *

Provider behaviour		No intervention	Arm 1	Arm 2	Arm 3
Probability of clinical diagnosis		0.98	0.98	0.98	0.92
Probability of using RDT		0.02	0.02	0.02	0.08
Diagnosis	Medicine prescribed
Clinical Diagnosis	ACT	0.05	0.12	0.12	0.19
	Other anti-malarial	0.03	0.07	0.07	0.07
	No anti-malarial	0.92	0.81	0.81	0.74
RDT Pan + falciparum +	ACT	0.75	0.78	0.84	0.87
	Other anti-malarial	0.05	0.05	0.05	0.05
	No anti-malarial	0.2	0.17	0.11	0.08
RDT Pan + falciparum -	ACT	0.5	0.10	0.10	0.10
	Other anti-malarial	0.25	0.45	0.45	0.45
	No anti-malarial	0.25	0.45	0.45	0.45
RDT Pan - falciparum +	ACT	0.75	0.78	0.84	0.87
	Other anti-malarial	0.05	0.05	0.05	0.05
	No anti-malarial	0.2	0.17	0.11	0.08
RDT Pan - falciparum -	ACT	0.4	0.057	0.083	0.022
	Other anti-malarial	0.02	0.029	0.056	0.089
	No anti-malarial	0.58	0.914	0.861	0.889

*Source: pilot study data from household survey, mystery client visits, provider demographics from in-depth qualitative interviews, and PSI Myanmar MIS data.
‘No antimalarial’ comprises of the use of antipyretics 70% of the time and antibiotics 30% of the time, as described and rationalized in the Additional File section ‘Assumptions’.
Note: baseline RDT uptake is a conservative lower bound based on household surveys with denominator adjusted to only include care sought from informal providers. Mystery clients were prompted to suggest they have malaria, possibly motivating providers to use RDTs at higher rates than in real-life scenarios.

ISSN: 1475-2875