A framework for evaluating health system surveillance sensitivity to support public health decision-making for malaria elimination: a case study from Indonesia

Table 3 Result of Bayesian models of probability of care seeking (\({P}_{SEEK}\)) and probability of being tested for malaria (\({P}_{TEST}\)), as a function of antimalarial availability at health facilities in Kulon Progo and Magelang Districts

Model	Variable	β	95% LCI	95% UCI
P_SEEK	(intercept)	− 3.501	− 3.589	− 2.640
	antimal_provided	1.093	0.430	1.151
	antimal_stockout	− 1.164	− 1.195	− 0.921
	travel_time	− 0.044	− 0.075	− 0.041
P_TEST	(intercept)	− 6.100	− 6.941	− 5.454
	microscopy_function	1.992	1.290	3.026
	microscopy_meters	0.549	0.532	0.566
	microscopy_staff	3.095	2.717	3.482
	microscopy_stockout	− 1.098	− 1.145	− 1.001
	rdt_provided	0.052	− 0.450	0.314
	rdt_staff	0.015	0.009	0.032
	rdt_stockout	− 0.625	− 4.659	4.593
	supervision	0.060	0.037	0.085
	treatment_sops	0.110	0.088	0.131

β: mean of posterior distribution, LCI: lower credible interval, UCI: upper credible interval

ISSN: 1471-2334