Study site
Jiangsu Province is a coastal area in East China. Its malaria incidence rate was about 250 cases per 1000 population at risk in the 1960s [16]. After decades-long efforts, there has been no indigenous case in Jiangsu since 2011. However, imported Plasmodium infections in this area have been increasing with the development of international trade, which poses tremendous thread for elimination [17–19]. In 2018, there were 243 imported malaria cases reported, which increased by 1.67% compared to 2017 (239 cases). All of them were adults, and the majority were male migrant workers who had been returned from sub-Saharan Africa with P. falciparum infections [18].
Diagnostic Strategies
Three strategies of malaria diagnostic testing (MDT) were compared. In the first strategy (MDT1), all febrile patients would undergo microscopy test, and patients with a positive result would be diagnosed as malaria. In the second strategy (MDT2), RDT were used, and diagnosis would be made based on the test results. In the last strategy (MDT3), patients would be tested using RDT at first, and those with a positive result would be followed by microscopy examination. If the results of microscopy were still positive, they would be confirmed as malaria.
Decision-analytic Model
To compare the three malaria diagnosis strategies, a decision tree model was developed using TreeAge Pro software [20]. Figure 1 presents the basic structure of the decision tree. A hypothetical cohort of 300,000 febrile patients were simulated which is approximately the annual number of febrile patients who need blood tests in Jiangsu. Patients could either have malaria or not. The number of malaria cases was determined by the prevalence of malaria among febrile patients. Patients with malaria and positive diagnosis test results were considered as true positives. Treatment for different malaria status (uncomplicated and severe malaria cases) was assumed to be implemented according to the national malaria treatment guidelines. Specifically, uncomplicated malaria patients caused by P. falciparum would receive artemisinin-based combination therapies (ACTs) such as dihydroartemisinic and piperaquine; uncomplicated malaria patients caused by non-P. falciparum would receive chloroquine alone, or with primaquine (for vivax and ovale malaria); and all severe malaria patients would receive artemisinin injection such as artesunate. A part of uncomplicated malaria patients would be treated as intpatient, but all severe malaria patients would be treated as inpatient.
Measurement Of Effect
Considering that the early detection of malaria cases in areas approaching elimination had a high priority, the effect of the MDT strategies was measured by the number of appropriately diagnosed malaria cases (true-positives, TP) in this study. The terminal nodes marked by TP in the decision tree (Fig. 1) were considered as the effect defined by this study.
Measurement Of Cost
Costs in a year (2018) were measured from the health institution and patient joint perspective. Since all costs occurred within one year, they were not discounted. Costs were presented in Chinese Yuan (CNY) but then converted to US dollar (USD). And this study used 2018 yearly average currency exchange rate: 6.6174 CNY = 1 USD.
Direct costs were categorized as direct medical costs and direct non-medical costs. The direct medical costs included the costs of malaria diagnosis testing (RDT or microscopy), the costs of antimalarial drugs, and other medical costs. The direct non-medical costs were the travel costs for the patients.
The costs of malaria diagnosis included material costs and labor costs of laboratory personnel while the costs of antimalarial drugs differed according to the type of plasmodium and the severity of patient's symptoms.
The costs for false-positive (FP) and false-negative (FN) patients were taken into account according to clinical treatment. FP and FN patients have the same costs (i.e., the costs of malaria diagnosis tests, and antimalarial drugs) compared to those true-positives (TP) or true-negatives (TN) as they shared the same clinical pathways in the decision tree. But a FN patient in one strategy would incur an additional cost, the value of which is equivalent to all medical cost of one severe malaria case in the same strategy, including the cost of diagnosis, antimalarial drugs, and other treatments.
Other medical costs included registration costs, supplementary drug costs (eg, anti-fever medicines, Chinese patent medicines), biochemical diagnosis costs (except the malaria diagnosis test), bedside care costs (only for inpatient).
Data Source
Costs data from multiple sources were used, such as key informant interviews, hospital information systems (HIS), and patient surveys. The costs for the antimalarial drugs and RDT were made based on key informant interviews with healthcare administrators. According to the national health policy in China, both antimalarial drugs and RDT were purchased and distributed to healthcare facilities by health administrative department, and they were all free for patients. So, the key informant interviews with experts were conducted to estimate their costs. And microscopy test had been kept at extremely low price for patients due to government subsidies. Its costs was also estimated using key informant interviews.
Inpatient costs were estimated based on the data of 25 latest malaria cases in 2018 identified through the HIS of a designated hospital for malaria treatment in Jiangsu province. Outpatient costs were collected from confirmed malaria cases reported between the first week and the forty-ninth week of 2018 via telephone surveys conduceted by one researcher. Each phone number was contacted no more than three times. Cost information was used only if the patient answered the call and gave consent to participate. Transportation costs were also collected by telephone surveys. Table 1 shows the cost components and sources.
Table 1
Cost components and unit costs
Items | Base case value (USD) | Range for one-way sensitivity analysis |
Direct medical cost | |
1) Malaria diagnosis | |
RDT - Malaria Pf/Pan Whole Blood Test per test | 1.51 | 1.21–2.27 |
Microscopy - Material cost of thick smear per exam | 0.18 | 0.15–0.30 |
Labor cost of laboratory staff per hour | 6.80 | 3.02–11.33 |
Time spent on RDT per test | 0.1 (hour) | 0.08–0.25 |
Time spent on thick smear test per test | 1 (hour) | 0.5–1.5 |
2) Malaria treatment | |
Chloroquine and primaquine per course of treatment | 6.04 | 6.04–7.56 |
Dihydroartemisinic and piperaquine per course of treatment | 4.53 | 4.53–6.80 |
Artesunate injection per course of treatment | 132.98 | 132.95-151.12 |
3) Other relative diagnosis and treatment | |
Other medical costs for outpatient per uncomplicated malaria case | 31.58 | 30.22–45.34 |
Other medical costs for inpatient per uncomplicated malaria case | 1,167.83 | 824.49-1,511.17 |
Other medical costs per severe malaria case | 17,569.29 | 10,578.17-45,335.03 |
Other medical costs per false positive case | 786.34 | 435.35-1,137.33 |
Other medical costs per false negative case | 11,652.67 | 7,555.84-22,667.51 |
Direct non-medical cost | |
Travel cost of patient visiting health care sector per person | 3.02 | 1.51–4.53 |
Epidemiological data were obtained from malaria surveillance reports, such as the proportion of falciparum malaria, and the proportion of hospitalization for uncomplicated malaria cases. The accuracy of MDT was derived from published literature [21–25]. Table 2 showed the epidemiological parameters used in decision tree.
Table 2
Epidemiological parameters considered in the analytic model
Parameter | Base case value | Range for one-way sensitivity analysis |
falciparum cases per 1000 febrile patients | 0.7073 | 0.3537–1.0610 |
Non-falciparum cases per 1000 febrile patients | 0.1746 | 0.0873–0.2619 |
Probability of conversion of falciparum malaria into severe case | 3% | 1%-5% |
Probability of conversion of non-falciparum malaria into severe case | 0.10% | 0.05%-0.5% |
Proportion of inpatient in all uncomplicated malaria cases | 66% | 20%-80% |
Sensitivity of RDT for falciparum malaria | 93% | 91%-93% |
Sensitivity of RDT for non-falciparum malaria | 91% | 89%-92% |
Specificity of RDT | 99% | 98%-99% |
Sensitivity of microscopy | 90% | 85%-95% |
Specificity of microscopy | 100% | 90%-100% |
Cost-effectiveness Analysis And Sensitivity Analysis
In deterministic cost-effectiveness analysis, total costs of the cohort were estimated for each strategy and based on base case value in Table 1 and Table 2. Incremental cost-effectiveness ratio (ICER) compared the incremental costs that one strategy would incure over another for one additional malaria case that have been appropriately diagnosed and treated.
To examine the uncertainty brought by the underlying assumptions, a series of one-way sensitivity analyses, including all parameters, were conducted [26]. Moreover, a two-way sensitivity analysis based on the table of value sets in TreeAge software was undertaken to reveal the impact of different proportion of falciparum malaria among all malaria cases. This method was different with normal two-way sensitivity analysis (two variables change in the same direction). The value sets (Table 3) would keep the total incidence of malaria fixed while the proportion of falciparum changed from 50–100%. That meaned when the incidence of falciparum increased, the incidence of non-falciparum decreased by the same value. This range include all fluctuations in the proportion of imported malaria species in China in the past decade [3].
Table 3
Value set of incidence in sensitivity analysis for the proportion of falciparum malaria
The proportion of falciparum malaria simulated in model | 50% | 60% | 70% | 80% | 90% | 100% |
The incidence of falciparum malaria | 0.441 | 0.529 | 0.617 | 0.706 | 0.794 | 0.882 |
(per 1000 febrile patients) |
The incidence of Non-falciparum malaria | 0.441 | 0.353 | 0.265 | 0.176 | 0.088 | 0 |
(per 1000 febrile patients) |
In order to reflect the real situation, total costs and effects for each strategy were estimated by Monte Carlo simulation, a probabilistic sensitivity analysis (PSA) method. Monte Carlo simulation was conducted to incorporate uncertainties of multiple parameters into an analysis by assigning statistical distributions to all relevant parameters. The distributions (Table 4) were assigned to parameters considering the data uncertainty caused by statistical methods and the forecast of cost fluctuations [27]. Beta distribution was assigned to the sensitivity of RDT to make sure it could be constrained between zero and one [26, 28]. Triangular distribution was used for the sensitivity of microscopy, as it was not likely to follow a normal or beta distribution. Gamma distribution was specified for selected cost parameters to capture their strictly-positive and right-skewed nature [15, 28]. Uniform distribution was also used to costs parameters according to the intervals estimated by the key informants [15]. Monte Carlo simulation would generate random draws from these distributions and run 1,000 iterations. Uncertainty would be presented in a figure of incremental cost-effectiveness plane.
Table 4
Parameters and distributions for Monte Carlo simulation
Parameters | Distribution | PSA parameters in Treeage |
RDT - Malaria Pf/Pan Whole Blood Test per test | Uniform | Low = 1.21 | High = 1.51 | - |
Travel cost of patient visiting health care sector per person | Triangular | Min = 0.30 | Likeliest = 3.02 | Max = 6.35 |
Labor cost of laboratory staff per hour | Uniform | Low = 6.80 | High = 7.56 | - |
Other medical costs for outpatient per uncomplicated malaria case | Gamma | Mean = 31.58 | SD = 88.4 | |
Other medical costs for inpatient per uncomplicated malaria case | Gamma | Mean = 1167.83 | SD = 604.00 |
Sensitivity of RDT for P. falciparum | Beta | Mean = 0.93 | SD = 0.02 | |
Sensitivity of RDT for non-P. falciparum | Beta | Mean = 0.91 | SD = 0.03 | |
Specificity of RDT | Beta | Mean = 0.99 | SD = 0.005 |
Sensitivity of microscopy | Triangular | Min = 0.85 | Likeliest = 0.90 | Max = 0.95 |
Specificity of microscopy | Triangular | Min = 0.99 | Likeliest = 1.00 | Max = 1.00 |
Time spent on RDT per test | Triangular | Min = 0.08 | Likeliest = 0.1 | Max = 0.25 |
Time spent on thick smear test per test | Triangular | Min = 0.50 | Likeliest = 1.00 | Max = 1.50 |
Note: PSA = Probabilistic Sensitivity Analysis. |