Economic Evaluation of Implementing SD Biosensor Antigen Detecting SARS-COV-2 Rapid Diagnostic Tests in Kenya

Economic Evaluation of Implementing SD Biosensor Antigen Detecting SARS-COV-2 Rapid Diagnostic Tests in Kenya | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Economic Evaluation of Implementing SD Biosensor Antigen Detecting SARS-COV-2 Rapid Diagnostic Tests in Kenya Brian Arwah, Samuel Mbugua, Jane Ngure, Mark Makau, Peter Mwaura, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6854403/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The COVID-19 pandemic has created a need to rapidly scale-up testing services. In Kenya, services for SARS-CoV-2 nucleic acid amplifying test (NAAT) have often been unavailable or delayed, precluding the clinical utility of the results. The introduction of antigen-detecting rapid diagnostic tests (Ag-RDT) has had the potential to fill at least a portion of the ‘testing gap’. We, therefore, evaluated the cost-effectiveness of implementing SD Biosensor Antigen Detecting SARs-CoV-2 Rapid Diagnostic Tests in Kenya. We conducted a cost and cost-effectiveness analysis using a decision tree model following the Consolidated Health Economic Evaluation Standards (CHEERS) guidelines under two scenarios: first comparing Ag-RDT as a first-line diagnostic followed by NAAT confirmation of negatives versus delayed NAAT testing only; second comparing Ag-RDT to clinical judgment where NAAT was unavailable. We employed a societal perspective with a time horizon of patient care episodes. Cost and outcomes data were obtained from primary and secondary sources, with robustness assessed through one-way and probabilistic sensitivity analyses. At 10% COVID-19 prevalence, implementing Ag-RDT with confirmatory NAAT for negatives was more costly (US $ 1,336,231.13 vs US $ 1,107,117.83) but more effective in averting DALYs (1998.97 vs 2236.49) than delayed NAAT testing alone, yielding an ICER of US $ 964.63 per DALY averted—below Kenya's cost-effectiveness threshold of US $ 1003.4. In settings without NAAT access, Ag-RDT was less costly (US $ 998,260.67 vs US $ 1,261,230.78) though less effective than clinical judgment at prevalence levels below 16.25%. These findings suggest that implementing Ag-RDTs represents a cost-effective strategy in settings with delayed NAAT access and a cost-saving approach where NAAT is unavailable, providing evidence-based guidance for diagnostic resource allocation in resource-limited settings. Cost-effectiveness SARS-CoV-2 Ag-RDT NAAT assay ICER Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction The COVID-19 pandemic has created a need to rapidly scale-up testing services and provide diagnoses to implement test-trace-isolate strategies, essential to treat and care for patients and to control the spread of the virus. Hundreds of diagnostic products are now available on the market, targeting the detection of viral RNA, viral antigens, and host antibodies against SARS-CoV-2. Services for SARS-CoV-2 Nucleic acid amplification testing (NAAT) assays have often been unavailable or backlogged for several days to weeks, precluding the clinical utility of the results. NAAT, a reverse transcription polymerase chain reaction (PCR) molecular testing of respiratory tract samples, is the recommended method for confirmation of COVID-19. In low and middle-income countries, however, the availability and health impact of PCR testing can be jeopardized by lack of testing capacity, insufficient trained personnel, shortages of reagents, long turnaround times (TAT), and high costs [ 1 ]. Lateral flow antigen-detecting rapid diagnostic tests (Ag-RDTs), which are easy to perform and provide results within 15–30 minutes, have recently been commercialized and have the potential to fill at least a portion of the ‘testing gap’. Under certain conditions, Ag-RDTs that meet minimum performance requirements are recommended, and some have WHO Emergency Use Listing authorization [ 2 ]. These simple-to-use tests offer the possibility of rapid case detection, especially of the most infectious patients in the first week of illness, at or near the point of care. WHO released an interim guidance on the use of Ag-RDTs for SARS-CoV-2, and the use of Ag-RDTs is recommended when PCR is either unavailable or long TAT of PCR which delays its clinical utility. This is particularly the case in less privileged countries in Africa, especially in Sub-Saharan Africa [ 3 ]. National norms and policies are being adopted in Kenya and many countries to allow and encourage targeted use of these Ag-RDTs. The decision to fully implement rapid diagnostic kits for detecting SARS-CoV-2 in Kenya relies on the field performance, feasibility, acceptability, and cost-effectiveness of the RDT compared to other diagnostic methods in the different settings which involve point-of-care diagnosis of COVID-19. Several studies have evaluated the cost-effectiveness of Ag-RDTs for SARS-CoV-2 detection in different contexts. A prospective study[ 4 ] comparing Ag-RDTs with PCR testing concluded that despite lower sensitivity, rapid tests offered cost-effective benefits through faster turnaround times and reduced resource requirements. Similarly, research assessing various COVID-19 screening strategies in college settings[ 5 ] demonstrated that testing frequency and result availability often outweighed sensitivity concerns for effective disease control. These findings suggest that in resource-constrained environments like Kenya, the implementation value of Ag-RDTs may extend beyond their technical specifications. A published study by Ricks et al. analyzed the health system cost and health impact of using RDTs among hospitalized and symptomatic patients with SARS-COV2 and confirmed that despite the low sensitivity of RDTs compared with RT-PCR, the Ag-RDTs have the potential to be more impactful with less cost per death and more infections averted [ 6 ]. Studies on the cost-effectiveness of rapid tests have been carried out. In 2022, a study[ 7 ] on cost-effectiveness analysis of antigen rapid test of influenza virus in Iran compared to polymerase chain reaction (PCR) in patients with acute respiratory syndrome to analyze the cost-effectiveness of rapid tests and PCR in patients with suspected influenza. concluded that a rapid test is less costly and effective than PCR, but the cost difference was greater than the difference in effectiveness, and in terms of effectiveness indicators, both effectiveness tests were almost similar, and the cost difference has led to the choice of the rapid test as a cost-effective option. A cost analysis[ 8 ] of diagnostic tests for SARS-CoV-2 using different Ag-RDTs and RT-PCR technologies in Mozambique and found that Ag-RDT was three times lower than PCR testing. Although the study did not assess the effectiveness of the antigen test but in terms of costs, Ag-RDT was concluded to be cost-effective in Low and Middle-Income Countries Our study builds upon this foundation by specifically evaluating the cost-effectiveness of SD Biosensor Ag-RDTs within the Kenyan healthcare context, considering both scenarios with delayed or absent NAAT testing capabilities. The need to examine both the cost and health outcome (effectiveness) of the test kits is critical in determining whether the value of the test kit justifies its cost and is affordable to the payer. A specific approach to assess cost-effectiveness of health interventions suggested by the commission on macroeconomics and Health (WHO,2001) is that interventions costing less than the per capita gross domestic product (GDP) in Low and Middle-Income Countries (LMICs) are “very cost-effective”, and those costing less than triple the per capita GDP are “cost-effective [ 9 ]. This, therefore, raises one question, under which scenario in the point-of-care diagnosis is RDT cost-effective? The objective of this study was to evaluate the cost effectiveness of SD Biosensor Antigen Detecting SARs-CoV-2 Rapid Diagnostic Tests in Kenya. Methods Study Design We developed a decision tree model in TreeAge Pro Healthcare 2021 to evaluate the cost-effectiveness of implementing SD biosensor antigen detecting SARS-CoV-2 rapid diagnostic tests in Kenya from a societal perspective. We modeled the costs and outcomes for diagnosing and treating COVID-19 patients in line with WHO interim guidance on antigen detection for COVID-19 using rapid immunoassays [ 10 ] and the Kenya ministry of health COVID-19 case management guidelines [ 11 ]. The diagnostic and treatment pathway followed the cases where symptomatic patients with suspected COVID-19 and asymptomatic contacts of COVID-19 cases attend health facilities with i) no access to NAAT for diagnosis or ii) limited access with prolonged turnaround times precluding clinical utility of results. Evaluation Scenarios We assessed two scenarios: Point of care Ag-RDT use for case management in settings with access to delayed confirmatory NAAT testing scenario. The scenario represented a health facility that sends samples to an external lab for NAAT, often with delayed result reporting. In this situation, Ag-RDT would be the first-line test to allow for case detection and rapid implementation of isolation procedures amongst positives and prioritization of negatives for confirmatory testing by NAAT at a designated laboratory facility. We compare the scenario with patients subjected to NAAT test, which is associated with a long turn-around time but obviates the need for confirmatory testing of negatives or a case whereby there is no testing. The diagnosis only relies on the clinical presentation of COVID-19 symptoms as per WHO case definitions [ 12 ]. Point of care Ag-RDT use for case management in settings with no access to confirmatory NAAT testing scenario. The scenario differs from the first one since the target location involves a health facility with no access to NAAT and no secure means for the safe and timely transport of samples to centralized facilities. The scenario presents a case whereby Ag-RDT is the only feasible tool to aid in the diagnosis or a choice of not conducting a COVID-19 test. Sampling and sample size We selected two counties in Kenya, Kiambu and Nairobi counties, to assess the field performance and impact of SD biosensor antigen detecting SARS-CoV-2 rapid diagnostic tests. The two counties were chosen since they had the highest prevalence of COVID-19 in Kenya (average at > 3% over the past three months), they also had different levels of government-owned facilities, and they had communities that were at high risk of outbreaks. We sampled four facilities that captured the diversity of access for COVID-19 testing and drew a sample size of 18 patients to capture the patient cost perspective. The patients’ sample size was selected to achieve balance in the facilities chosen, and we settled on 18 patients after reaching saturation. The initial sample size needed for the COVID-19 RDT assumed the following expected values: test sensitivity of 80%, confidence interval of 5%, and COVID-19 prevalence of 10% to yield an estimated sample size of 2459 participants. Due to low turnout in daily tests conducted, a sample size of 506 participants was included in the study, which was still a representation of the targeted population assuming a test sensitivity of 85%, confidence interval of 5%, a width of 10%, and COVID-19 prevalence of 5–10%. To achieve the necessary accuracy on performance estimates, we determine data for negative cases (by NAAT) using a value of 50% for each estimate. Data Collection We used primary and secondary data to determine the cost components of diagnosis and case management of suspected COVID-19 cases. Questionnaires were administered at the facility level and to individuals seeking COVID-19 testing services for cost data. For effectiveness measure, we relied on endpoint data on project-specific reporting forms that included COVID-19 testing registries, laboratory report forms, patient history, case management forms, contact history forms, competency assessments, and Ag-RDT ease of use assessments. Model Structure Figure 1 and 2 depicts the intervention strategies applied. We applied three strategies for the study scenarios. The first strategy involves the use of Ag-RDT followed by a different diagnosis pathway. Under the first scenario, Ag-RDT was used as the first-line diagnosis method, followed by the prioritization of negatives for confirmatory testing by NAAT. The second scenario involved clinical judgment as the comparator. The first strategy pathway of Ag-RDT diagnosis and case management did not include a confirmatory test of negatives, making the diagnosis pathway shorter than in the first scenario where there is access to NAAT services. Costing Methods The costing followed the Global Health Cost Consortium’s (GHCC) reference guidelines [ 13 ] to evaluate the cost of implementing SD Biosensor antigen detecting SARS-Cov-2 diagnostic tests in Kenya. We applied an ingredient-based approach from a societal perspective to analyze costs for diagnosing COVID-19 cases using antigen RDT. Under the healthcare system, we costed both the direct and ancillary costs, which included physical costs and overheads, costs for personal protective equipment (PPE), staff time, and costs for non-pharmaceuticals. We also computed the direct and indirect costs from the patient's perspective. For the direct cost, we included the cost of testing, the cost of treatment, the cost incurred for related healthcare services, and the cost of isolation/quarantine. As for the indirect costs, we considered the travel cost; we valued time spent away from normal activities to visit the healthcare facilities; we valued informal care, and using the human capital approach, we valued productivity loss due to absenteeism (See Additional file 1). The costs for treatment, quarantine, and isolation, such as accommodation and overheads, pharmaceuticals, non-pharmaceuticals, staff, PPE, ICU equipment, oxygen therapy, other laboratory tests associated with COVID-19 case management in hospitals, and the cost for the diagnostic of patients using PCR were derived from a previous study [ 14 ]. We also estimated the costs for clinical diagnosis by considering input costs for physical and overhead costs, PPEs, and staff costs, as detailed in the supplementary file. The per-day costs overheads were obtained from median costs reported in a cost analysis of 20 healthcare facilities in Kenya in 2018 (the VALUE TB Study). The overhead costs included electricity, water, incinerator, fuel, communication, and cleaning services. The cost for diagnosis of patients using NAAT included the input cost of equipment and consumables as provided in the supplementary document. For each of the unit costs for case management, the estimate included costing all the direct and ancillary inputs for the unit cost that go into the delivery of the case management per patient. They included accommodation and overhead, staff, pharmaceuticals, non-pharmaceuticals, oxygen therapy, ICU equipment cost and monitoring, radiology, other laboratory test costs, and PPE costs[ 14 ]. Physical cost and overheads We obtained outpatient cost overheads from the study on case management of COVID-19 patients [ 14 ], which collected primary data from three public health facilities. We computed the physical cost incurred per test by collecting data on the estimated cost of the COVID-19 test room, the size of the facility, and the size of the space the COVID-19 test was being conducted. We later annuitized the estimated cost using the respective useful life years of the housing facility. To estimate the cost of the testing space after annuitizing the cost of the housing facility, we first computed the cost per square meter of the housing facility by dividing the annuitized cost by the size of the housing facility. Second, we multiplied the specific space size for COVID testing by the cost per square meter of the area housing the test. Finally, we divided the cost of the COVID space by the number of tests per day and the number of working days in a year, assuming the daily average test conducted within the last six months and the facility operating every day. Non-pharmaceuticals and Personal Protective Equipment (PPE) Data was collected on the non-pharmaceutical and PPE items used during the testing of suspected COVID-19 cases. We obtained cost data for NAAT testing from a recent study by Barasa et.al. (2021) on examining unit costs for COVID-19 case management in Kenya. The other cost of items for Ag-RDT and quantity required per test were obtained from the sampled health facilities. Staff cost Data was collected on the type of staff, gross salaries, and time spent on testing from three public health facilities. We computed the amount of time allocated on a test as follows. First, we estimated the total time allocated to testing in a day by obtaining the number of shifts in a day, the number of the specific cadre of staff conducting the test, and the length of each shift in minutes. Second, we estimated the amount of time allocated to a test per day by dividing the number of tests per day, assuming a daily average of tests conducted within the last six months and equal allocation of testing time. Finally, we computed the average staff cost per test by multiplying the staff time allocated to COVID-19 testing in a day in minutes by the gross salary of that cadre of staff per minute. Valuing Time Cost The time patients lost from routine activities was estimated by adding the travel time and the time spent at the health facility as per the patient’s and companion’s response. Using data from Kenya’s minimum wages [ 15 ]. The time lost was subsequently valued at the average hourly pay of the different categories of paid work the patient and companion would have engaged. For the unpaid work, a proxy value of the cost of a close market substitute was used. Valuing Productivity Loss The study considered productivity loss from both paid and replaced unpaid work. Using the human capital approach, the number of hours worked per working day was calculated based on the average number of hours a week the patient worked over the last four weeks, assuming the patient worked for five days in a week. Subsequently, the gross daily wage was estimated by multiplying working hours per day by estimated hourly salaries for different categories of work [ 15 ]. Next, the total number of lost productive days from paid work was multiplied by the gross daily wage. The cost of replacing unpaid work was considered by analyzing the time spent by an informal giver to replace the patient's missed unpaid work. Pricing and Valuation We identified the cost of building as the only capital good, and annuitized it, assuming a useful life of 5 years. We obtained price data from a previous study [ 14 ] and presented the costs in Kenya shillings (KES) and US dollars. We used an exchange rate of US $ 1 = KES 112.52 derived from Xe.com and accessed on 30th November 2021, to convert KES to US $ . We obtained shadow prices for unpaid work and the opportunity cost of time from Kenya minimum wages reported by the africanpay.org database accessed on 30th December 2021. Effectiveness and Cost-effectiveness measurement The impact of case management of COVID-19 was dependent on the diagnostic performance of the different diagnostic tests used (Ag-RDT or NAAT), the timing of the test, and the adherence to COVID-19 case management guidelines. The intermediate outcome was measured in terms of the diagnostic performance of the antigen RDT, which was measured by its sensitivity and specificity compared to the PCR test. Based on the results of 506 test samples, the estimated sensitivity of Ag-RDT is 73%, and the estimated specificity is 93%. Using the diagnostic test confidence interval formula [ 16 ], we obtained a 95% confidence interval for the Ag-RDT sensitivity as [59%-87%], and the confidence interval for the specificity as [91%-96%]. The primary health outcome was measured in terms of the cost per disability-adjusted life years (DALYs) averted. We factored in both the mortality and morbidity to obtain DALYs by summing up years of life lost (YLL) and years of life with disability (YLD) [ 17 ]. A discounting rate of 3% was used to calculate DALYs and applied Kenya’s life expectancy of 66.34 [ 18 ], disability weights as reported [ 19 ] and captured in Table. 1 The incremental cost-effectiveness ratio (ICER) comparing the use of Ag-RDT and confirmatory testing of negatives by NAAT and the use of NAAT as the only diagnostic test conducted was calculated as the difference in costs and DALYs averted of diagnostic and case management in the compared groups. $$\:ICER=\:\frac{({C}_{ST1}-{C}_{ST2})}{({DALYs}_{ST1}-{DALYs}_{ST2})}$$ Where ICER = Incremental cost effectiveness ratio; ST1 = RDT as first-line diagnosis followed by NAAT, ST2 = NAAT diagnosis, C ST1 = Cost of strategy 1; C ST2 = Cost of strategy 2; DALYs ST1 = DALYs averted in strategy 1; and DALYs ST2 = DALYs averted in strategy 2. We compared the ICER with an opportunity cost of USD 20.07 to USD 1023.47 (1–51% GDP per capita) based on Kenya’s cost-effectiveness threshold as estimated by Woods et al. [ 20 ] and Ochalek et al. [ 21 ]. Assumptions and Parameters Table 1 . present the model parameters. The model also used some assumptions that are key to note. First, we assumed that patients who test positive and show no clinical symptoms of COVID-19 are given home-based standard care, equivalent to isolation and routine care given to mild COVID-19 patients. Second, although there is little data to show the correlation of late diagnosis and severity or mortality of COVID-19 patients, according to PHOF et al. (2020) “a longer time to confirm COVID-19 diagnosis after initial symptoms was found to be a predictor of hospitalization.” As a result, an assumption was made that the proportion of severe cases progressing to critical cases could be higher on the verge of the health system collapsing due to an increase caregivers’ risk of infection as identified.[ 22 ]. Third, we relied on a COVID-19 study on an outpatient setting [ 23 ] to analyze the outcomes of COVID-19 untreated patients. Lastly, we assumed that all patients who test positive and no further confirmatory diagnostic tests conducted are isolated and provided standard care even though the results could be false positive. Table 1 Key model parameters (Line 271 in the main text) Description Value (Lb;Ub) Source Population Study cohort population 4918 Author COVID-19 Prevalence 10% Author Cost Cost (USD) for asymptomatic care episode 16.39 [ 14 ] Cost (USD) for conducting a PCR test 21.84 (21.60;22.87) [ 14 ] Cost (USD) for critical care episode 472.02 [ 14 ] Cost (USD) for severe care episode 121.88 [ 14 ] Cost (USD) for conducting a rapid diagnostic test 4.68 (4.83;7.25) Author Cost (USD) for clinical diagnosis 3.85 (5.06;5.36) Author DALYs Disability weight for mild COVID-19 0.006 [ 19 ] Disability weight for critical COVID-19 0.655 [ 24 ] Disability weight for severe COVID-19 0.133 [ 19 ] Average age at death 55.5 [ 25 ] Life expectancy 66.99 [ 18 ] Characteristics of Patients Proportion of critical patients hospitalized 0.14 [ 26 ] Proportion of critical COVID-19 who die 0.892 [ 27 ] Proportion of critical COVID-19 who recover 0.108 [ 27 ] Proportion of infected patients with SARS-CoV-2 0.10 Author Proportion of patients not given asymptomatic care 0 Author Proportion not infected with SARS-CoV-2 0.90 Author Proportion of severe patients hospitalized 0.86 [ 26 ] Proportion of severe COVID-19 who progress to critical 0.424 [ 28 ] Proportion of severe COVID-19 who recovered 0.576 [ 28 ] Proportion of patients given asymptomatic care 1 Author Proportion of severe COVID-19 untreated patients who progresses to critical 1 Author Proportion of untreated severe COVID-19 patients who recovered 0 Author Proportion of untreated critical COVID-19 patients who dies 1 Author Proportion of untreated critical COVID-19 patients who recovered 0 Author Length of stay asymptomatic care 12 [ 29 ] Length of stay critical 7 (4;10) [ 27 ] Length of stay severe 6 (3;9) [ 17 ] Diagnostic Accuracy PCR Specificity 0.998 (0.992;0.999) [ 30 ] PCR Sensitivity 0.86 (0.547;0.994) [ 30 ] RDT Specificity 0.93 (0.91;0.96) Author RDT Sensitivity 0.73 (0.59;0.87) Author Probability of correctly excluding SARS-CoV-2 0.683 (0.60;0.758) [ 31 ] Clinical Judgement Sensitivity 0.806 (0.729;0.869) [ 31 ] Dealing with uncertainty We performed a sensitivity analysis on the following parameters: COVID-19 prevalence level; sensitivity of RDT and PCR; the proportion of treated and untreated hospitalized cases; and cost of RDT, NAAT, and treatment of severe and critical cases. We implemented a 20% increase or decrease in the cost of RDT, NAAT, and cost of treatment. The sensitivity analysis of RDT was based on +/- 5% confidence bounds while the bounds of PCR and clinical judgement were provided [ 30 ], [ 31 ]. We conducted a probabilistic sensitivity analysis to check for the collective uncertainty on the probability of cost-effectiveness using second-order Monte Carlo simulation (n = 1000). We used beta distributions to calculate the probability range of the study parameters and gamma distribution on the cost parameters [ 32 ]. Finally, we presented the ICE scatterplot to illustrate the uncertainty in the cost-effectiveness results. Results Table 2 . summarizes the key findings from the patient questionnaire administered. As per the results, the primary mode of transport was public transport, with 13 (72%) of the 18 sampled patients preferring public transport to get to the health facility, and the second most popular means was walking on foot, 3 (17%). It was also noted that most (89%) of the patients went alone to the health facility, and only 11% were accompanied. The table also details the patient’s usual activities foregone by visiting a health facility. Most of the patient’s main activities would be, attending to paid work at 28% or attending to a business activity at 28%. Housework activities took 17%, whereas only 6% of patients forego childcare activity. It also shows the treatment cost, travel cost, time lost per hour, and time cost from the foregone activity the patient would have engaged in during the health facility visit, and the productivity loss. The median travel cost for a one-way visit for a patient was US $ 0.27. The study findings also report that out of the patients accompanied to the health facility, there was no cost incurred by the patient’s companion while visiting the health facility. Applying the values per hour of paid and unpaid work foregone the median time cost per hour of both patient’s and companion’s usual activities lost was US $ 1.57. For the productivity loss, the median productivity cost of absenteeism from both paid and unpaid work was US $ 1.08. Table 2 Key findings from the patient’s questionnaire Patient cost Mode of transport (no. (%)) Number Percentage Public transport 1 6% Walk on foot 13 72% Motorcycle and public transport 1 6% Taxi 3 16% Patients accompanied (no. (%)) 2 11% Traveling cost (KES) Mean (SD) 43.33 40.58 Median (IQR) 30 (20–70) Cost of related healthcare services (KES) Mean (SD) 163.89 387.22 Median (IQR) 0 (0-100) Patient’s usual activities Number Percentage Housework 3 17% Childcare 1 6% Attending school 2 11% Seeking work 1 6% Paid work 5 28% Business activity 5 28% Other 1 6% Companion’s usual activities Attending school 2 11% Time cost (KES) Mean (SD) 247.18 247.51 Median (IQR) 176.89 (50.26-433.42) Productivity loss (KES) Mean (SD) 1000.95 1469.42 Median (IQR) 121.79 (0-2336.4) Table 3 . details the unit cost for rapid diagnostic tests compared to NAAT for a COVID-19 suspected case. The results showed the unit cost per test for NAAT and Ag-RDT tests in the healthcare system was US $ 18.93 and US $ 1.76, respectively. There is a considerable cost difference between the two tests, mainly because of the laboratory cost incurred when conducting the NAAT test. The table also showed the patient cost incurred for a diagnostic test was US $ 2.92; the major cost driver was the patient time cost. Summarizing the results, we found that the societal cost for PCR was higher at US $ 21.84 than the Ag-RDT cost of US $ 4.68. Table 3 Unit costs for antigen RDT and PCR test for SARS-COV-2 detection Inputs Ag-RDT Test median cost NAAT Test (PCR) cost [ 14 ] Clinical Diagnosis (KES) US $ (KES) US $ (KES) US $ Healthcare perspective Physical cost & overheads per test 0.23 0.00 6.66 0.06 0.23 0.00 Equipment per test - - 2.25 0.02 - - Personal protective equipment (PPE) cost per test 157.72 1.40 151.76 1.35 97.19 0.63 Consumables cost per test 31.96 0.28 - - - - Lab supplies - - 1959.5 17.41 - - Staff cost per test 7.57 0.07 8.98 0.09 7.57 0.07 Healthcare estimated cost 197.49 1.76 2129.15 18.93 104.99 0.70 Patient perspective Patient direct cost 0.00 0.00 0.00 0.00 0.00 0.00 Patient travel cost 30.00 0.27 30.00 0.27 30.00 0.27 Patient time cost 176.89 1.57 176.89 1.57 176.89 1.57 Companion travel cost 0.00 0.00 0.00 0.00 0.00 0.00 Informal care cost 0.00 0.00 0.00 0.00 0.00 0.00 Patient productivity loss 121.79 1.08 121.79 1.08 121.79 1.08 Patient estimated cost 328.68 2.92 328.68 2.92 328.68 2.92 Societal cost 526.17 4.68 2,457.83 21.85 433.67 3.85 Antigen-RDT and PCR test results Out of 506 patients recruited, 72 (14.2%) patients tested positive with antigen RDT, 52 (10.3%) patients tested positive with PCR test, 38 (7.5%) were positive for both RDT and PCR test, 34 (6.7%) were positive for RDT and negative for PCR, 14 (2.8%) were positive to PCR and negative to RDT, and 468 (92.5%) were both negative for RDT and PCR test. Base case results The costs, DALYs, and the ICER at 10% COVID-19 prevalence level associated with the three strategies are presented in Table 4 . Under the first scenario, where we apply Ag-RDT as the first-line test and prioritization of negatives for the confirmatory test by NAAT in comparison to delaying and conducting NAAT, the findings show no-test strategy is dominated. The results show that the RDT strategy is the costliest (US $ 1,336,231.13), followed by the no-test strategy (US $ 1,261,230.4), and NAAT test strategy was relatively less costly (US $ 1,107,117.83) compared to the other two strategies. Although the RDT strategy was costly, it is most effective in averting DALYs (1998.97 DALYs) compared to both NAAT diagnostic strategy (2236.49 DALYs) and no-test strategy (2361.35 DALYs), with lower values representing better health outcomes. The incremental cost-effectiveness ratio (ICER) of the Ag-RDT strategy compared to the NAAT diagnostic strategy was US $ 964.63 per DALY averted (US $ 229,113.3 additional cost / 237.51 DALYs averted), which falls below Kenya's maximum cost-effectiveness threshold of US $ 1003.4, making it a cost-effective strategy. When comparing all three strategies, the results showed the no-test strategy was absolutely dominated (both more costly and less effective than NAAT), and it would be more efficient to apply the Ag-RDT strategy in scenarios with delayed NAAT testing rather than switching to the no-testing approach. Table 4 : Cost-effectiveness results for Ag-RDT implementation (USD 2021) Under the second scenario, where Ag-RDT is the only feasible tool to aid testing, the no-test strategy is costly compared to Ag-RDT diagnostic strategy. As for effectiveness, the results show no-test strategy is more effective in averting DALYs than the RDT strategy but with an ICER of US $ 1490.34 no-test strategy was not cost-effective in Kenya. Sensitivity Analysis Difference prevalence level from 5–20% A one-way sensitivity analysis showed that the ICER was sensitive to the COVID-19 prevalence level. The results showed that at less than 5% COVID-19 prevalence level and under a case where there was access to delayed NAAT, the use of RDT and further confirmation by NAAT strategy was not cost-effective compared to the delayed NAAT strategy. At a prevalence rate of more than 5–20%, the results showed that the use of RDT and further confirmation of negatives by NAAT was cost-effective compared to the delayed NAAT strategy as presented in Table 5 . Table 5 Different prevalence levels from 5–20% sensitivity report scenario 1 Prevalence Strategy Cost (USD) Incremental Cost (USD) Effectiveness (DALYs) Incremental Effectiveness (DALYs) Incremental cost per DALY averted Dominance 0.05 Delayed test-NAAT 616103.08 0.00 1118.24 0.00 0.00 Ag-RDT 757680.81 141577.72 999.49 118.76 1192.17 No test, Clinical Judgement 804796.71 47115.90 1180.67 -181.19 -260.04 Absolute 0.0875 Delayed test-NAAT 984364.14 0.00 1956.92 0.00 0.00 No test, Clinical Judgement 1147121.98 162757.83 2066.18 -109.25 -1489.71 Absolute Ag-RDT 1191593.55 207229.40 1749.10 207.82 997.14 0.125 Delayed test-NAAT 1352625.20 0.00 2795.61 0.00 0.00 No test, Clinical Judgement 1489447.25 136822.04 2951.68 -156.08 -876.63 Absolute Ag-RDT 1625506.29 272881.08 2498.72 296.89 919.13 0.1625 Delayed test-NAAT 1720886.26 0.00 3634.29 0.00 0.00 No test, Clinical Judgement 1831772.51 110886.25 3837.19 -202.90 -546.50 Absolute Ag-RDT 2059419.03 338532.76 3248.33 385.96 877.12 0.2 Delayed test-NAAT 2089147.32 0.00 4472.97 0.00 0.00 No test, Clinical Judgement 2174097.78 84950.46 4722.70 -249.72 -340.18 Absolute Ag-RDT 2493331.76 404184.44 3997.94 475.03 850.87 In a scenario with no access to NAAT assay, at a lower prevalence rate of 5–16.25%, the no-test strategy was still not cost-effective compared to the RDT strategy as presented in Table 6 . The results showed that at a higher prevalence rate of 20%, the no-test strategy was more costly and less effective than the Ag-RDT strategy, and the ICER was US $ 989.15, hence a cost-effective strategy. Table 6 Different prevalence levels from 5–20% sensitivity report scenario 2 Prevalence Strategy Cost (USD) Incremental Cost (USD) Effectiveness (DALYs) Incremental Effectiveness (DALYs) Incremental cost per DALY averted Dominance 0.05 Ag-RDT 584876.96 0.00 1268.90 0.00 0.00 No test, Clinical judgement 804796.90 219919.94 1180.67 88.22 2492.72 0.0875 Ag-RDT 894914.74 0.00 2220.57 0.00 0.00 No test, Clinical judgement 1147122.31 252207.57 2066.18 154.39 1633.54 0.125 Ag-RDT 1204952.52 0.00 3172.25 0.00 0.00 No test, Clinical judgement 1489447.73 284495.21 2951.68 220.56 1289.86 0.1625 Ag-RDT 1514990.29 0.00 4123.92 0.00 0.00 No test, Clinical judgement 1831773.14 316782.85 3837.19 286.73 1104.81 0.2 Ag-RDT 1825028.07 0.00 5075.60 0.00 0.00 No test, Clinical judgement 2174098.55 349070.48 4722.70 352.90 989.15 RDT and PCR Sensitivity When we varied the sensitivity of RDT by increasing or reducing RDT sensitivity, we found applying RDT as the first-line tool to aid in testing, followed by prioritization of negatives for confirmatory testing by NAAT was still costly and more effective up to a sensitivity level ≥ 87% to delayed NAAT diagnostic strategy as presented in Table 7 . Table 7 RDT Sensitivity Report Scenario 1 RDT_TP Strategy Cost (USD) Incremental Cost (USD) Effectiveness (DALYs) Incremental Effectiveness (DALYs) Incremental cost per DALY averted Dominance 0.59 Delayed test-NAAT 1107117.83 0.00 2236.49 0.00 0.00 Ag-RDT 1177698.54 70580.71 1731.44 505.04 139.75 No test, Clinical Judgement 1261230.40 83531.86 2361.35 -629.91 -132.61 Absolute 0.66 Delayed test-NAAT 1107117.83 0.00 2236.49 0.00 0.00 Ag-RDT 1256964.83 149847.00 1865.21 371.28 403.60 No test, Clinical Judgement 1261230.40 4265.57 2361.35 -496.14 -8.60 Absolute 0.73 Delayed test-NAAT 1107117.83 0.00 2236.49 0.00 0.00 No test, Clinical Judgement 1261230.40 154112.57 2361.35 -124.86 -1234.26 Absolute Ag-RDT 1336231.13 229113.30 1998.97 237.51 964.63 0.8 Delayed test-NAAT 1107117.83 0.00 2236.49 0.00 0.00 No test, Clinical Judgement 1261230.40 154112.57 2361.35 -124.86 -1234.26 Absolute Ag-RDT 1415497.42 308379.59 2132.74 103.75 2972.40 0.87 Delayed test-NAAT 1107117.83 0.00 2236.49 0.00 0.00 No test, Clinical Judgement 1261230.40 154112.57 2361.35 -124.86 -1234.26 Absolute Ag-RDT 1494763.72 387645.89 2266.50 -30.02 -12913.78 Absolute In a scenario where there was no access to the NAAT assay, RDT was still less costly and less effective than the no-test strategy, and in the two scenarios, we found the ICER was sensitive to changes in RDT sensitivity as presented in Table 8 . Table 8 RDT Sensitivity Report Scenario 2 RDT_TP Strategy Cost (USD) Incremental Cost (USD) Effectiveness (DALYs) Incremental Effectiveness (DALYs) Incremental cost per DALY averted Dominance 0.59 Ag-RDT 839821.71 0.00 2270.27 0.00 0.00 No test, Clinical judgement 1261230.78 421409.07 2361.35 -91.08 -4626.74 Absolute 0.66 Ag-RDT 919041.19 0.00 2404.03 0.00 0.00 No test, Clinical judgement 1261230.78 342189.60 2361.35 42.68 8016.74 0.73 Ag-RDT 998260.67 0.00 2537.80 0.00 0.00 No test, Clinical judgement 1261230.78 262970.12 2361.35 176.45 1490.34 0.8 Ag-RDT 1077480.14 0.00 2671.56 0.00 0.00 No test, Clinical judgement 1261230.78 183750.64 2361.35 310.22 592.33 0.87 Ag-RDT 1156699.62 0.00 2805.33 0.00 0.00 No test, Clinical judgement 1261230.78 104531.17 2361.35 443.98 235.44 When we varied the PCR sensitivity by increasing it, we found that PCR was less costly and less effective than RDT. Reducing the PCR sensitivity also led to a reduction in the costs of PCR diagnostic strategy and was attractively effective under the three strategies as presented in Table 9 . Table 9 PCR Sensitivity Report PCR_TP Strategy Cost (USD) Incremental Cost (USD) Effectiveness (DALYs) Incremental Effectiveness (DALYs) Incremental cost per DALY averted Dominance 0.547 Delayed test-NAAT 750252.10 0.00 1638.36 0.00 0.00 Ag-RDT 1239626.35 489374.24 1837.48 -199.12 -2457.73 Absolute No test, Clinical Judgement 1261230.40 510978.30 2361.35 -722.99 -706.76 Absolute 0.65875 Delayed test-NAAT 877663.43 0.00 1851.91 0.00 0.00 No test, Clinical Judgement 1261230.40 383566.97 2361.35 -509.44 -752.92 Absolute Ag-RDT 1274117.03 396453.60 1895.14 -43.23 -9171.36 Absolute 0.7705 Delayed test-NAAT 1005074.76 0.00 2065.46 0.00 0.00 No test, Clinical Judgement 1261230.40 256155.64 2361.35 -295.89 -865.71 Absolute Ag-RDT 1308607.72 303532.96 1952.79 112.66 2694.19 0.88225 Delayed test-NAAT 1132486.08 0.00 2279.00 0.00 0.00 No test, Clinical Judgement 1261230.40 128744.32 2361.35 -82.34 -1563.50 Absolute Ag-RDT 1343098.40 210612.32 2010.45 268.55 784.25 0.994 Delayed test-NAAT 1259897.41 0.00 2492.55 0.00 0.00 No test, Clinical Judgement 1261230.40 1332.99 2361.35 131.20 10.16 Ag-RDT 1377589.08 116358.68 2068.11 293.24 396.81 According to Fig. 4 . the key parameters that had the most significant effect on the ICER when we compared the RDT diagnostic strategy to the delayed NAAT diagnostic strategy are 1) Proportion of severe patients hospitalized 2) Proportion of critical patients hospitalized (both of which fewer cases improves cost-effectiveness); 3) Probability of critical patient dying (lower mortality for critical patients improves cost-effectiveness); 4) Length of stay for critical patients (shorter length of stay in the hospital improves cost-effectiveness). Comparing RDT diagnostic strategy and no-test strategy, Fig. 5 . summarizes the three parameters that had the most significant effect on the ICER. These are: 1) Clinical true positive; (reduction in true positive cases improves cost-effectiveness); 2) Clinical false positive (reduction in false-positive diagnosed cases improves cost-effectiveness, and 3) Proportion of infected SARS-Cov-2 (reduction in SARS-CoV-2 infection improves cost-effectiveness). Probabilistic Sensitivity Analysis The results of the Monte Carlo simulation of 1000 samples under the first scenario (Fig. 6 ) show that at a cost-effectiveness threshold of US $ 1003.4 per DALYs averted, the probability of antigen rapid diagnostic test being the more cost-effective strategy was 52.5%. Under the second scenario, the results for PSA (Fig. 7 ) show that at a cost-effectiveness threshold of US $ 1003.4 per DALYs averted, the probability of the no-test diagnostic strategy being more cost-effective was 28.7%. Figures 8 and 9 present cost-effectiveness acceptability curves under scenario one and scenario two, respectively, based on a range of cost-effectiveness thresholds. Under a scenario where there is delayed NAAT diagnosis and given a willingness to pay of US $ 900 per DALYs averted, there was a 40% probability of the Ag-RDT strategy being cost-effective. The cost-effectiveness acceptability curve shows the probability of the Ag-RDT strategy being more cost-effective as the decision maker was willing to increase their willingness to pay (Fig. 8 ). Under a scenario where there is no access to NAAT assay in a resource-limited setting and a decision maker is not willing to pay for any DALYs averted, the probability of the no-test strategy being cost-effective compared to Ag-RDT strategy was approximately 75% (Fig. 9 ). Discussion This study presents the cost and cost-effectiveness of SD Biosensor Ag-RDTs compared to PCR and clinical judgment for SARS-CoV-2 detection in Kenya. We evaluated three diagnostic strategies under different scenarios, building on existing research on rapid testing approaches. Our findings indicate that when comparing the use of Ag-RDT as a first-line tool with subsequent confirmatory PCR testing of negatives to the strategy of delayed NAAT testing, the Ag-RDT diagnostic strategy is more costly but also more effective. The higher costs are primarily driven by increased detection of true positives through confirmatory PCR testing of RDT-negative results, which consequently increases case management costs for diagnosed positive cases. Importantly, we found that this strategy averted more DALYs (1998.97 versus 2236.49) among infected SARS-CoV-2 patients not initially detected by RDT. At a COVID-19 prevalence level of 10%, our analysis determined that using Ag-RDT as a first-line tool followed by confirmatory PCR testing of negative results was cost-effective (ICER = US $ 964.63 per DALY averted) when compared to Kenya's cost-effectiveness threshold of US $ 1003.4. This finding aligns with Jakobsen et al. (2021), who similarly concluded that despite lower sensitivity, rapid tests offered cost-effective benefits through faster turnaround times. When evaluating a scenario with no access to NAAT, comparing Ag-RDT as the only testing option versus clinical judgment, our results showed that at prevalence levels ≤ 16.25%, the no-test strategy was not cost-effective compared to Ag-RDT. The Ag-RDT strategy was less costly (US $ 998,260.67 versus US $ 1,261,230.78) though less effective in averting DALYs than the clinical strategy. The high costs associated with the clinical diagnostic approach can be attributed to the treatment of presumptive cases with symptoms resembling SARS-CoV-2 infection. While the clinical strategy averted more DALYs by subjecting more symptomatic cases to treatment, in resource-limited settings like Kenya, this approach may not be cost-effective due to its substantial costs. This supports Paltiel et al.'s (2020) assertion that testing frequency and result availability often outweigh sensitivity concerns for effective disease management in resource-constrained environments. Our sensitivity analysis revealed that the proportion of severe and critical cases hospitalized significantly impacts the cost-effectiveness of the Ag-RDT strategy. This can be explained by the fact that lower proportions of severe and critical hospitalizations correlate with lower COVID-19 prevalence levels, which emphasizes the need for accurate diagnosis to avert more DALYs and reduce costs associated with misdiagnosis. Previous studies have shown that delayed diagnosis may not necessarily be associated with ICU admission or death[ 22 ], suggesting that delays in obtaining NAAT results do not directly link to disease progression and recovery. However, implementing an Ag-RDT diagnostic strategy in settings with delayed access to NAAT would avert more DALYs than relying solely on delayed NAAT testing Our findings complement those of a study conducted in Mozambique[ 8 ] who conducted a cost analysis of diagnostic tests for SARS-CoV-2 using different Ag-RDTs and RT-PCR technologies in Mozambique and found that Ag-RDT was three times lower than PCR testing. Although the study did not assess the effectiveness of the antigen test but in terms of costs, Ag-RDT was concluded to be cost-effective. Similarly, another study[ 7 ] concluded that rapid tests were less costly but still effective compared to PCR for influenza, with the cost difference making rapid tests the cost-effective option. These studies reinforce our conclusion that Ag-RDTs represent a cost-effective approach in Low- and Middle-Income Countries, particularly in settings with limited or delayed access to molecular testing. A major limitation of this study is the scarce data on the outcomes of COVID-19 patients with false-negative diagnosis results. However, we made assumptions regarding disease progression and outcome during the peak of the COVID-19 pandemic for the COVID-19 cases that received no care. One strength of this analysis is the comprehensive inclusion of both diagnostic costs and treatment costs associated with false-positive cases. This research represents one of the first cost-effectiveness analyses of Ag-RDTs specifically conducted in low- and middle-income countries, providing valuable guidance for resource allocation in similar settings. Conclusion Our cost-effectiveness analysis provides clear guidance for SARS-CoV-2 diagnostic strategy selection in resource-limited settings like Kenya. The findings demonstrate that at a COVID-19 prevalence level of 10%, implementing Ag-RDT as a first-line tool followed by confirmatory NAAT testing of negative results is a cost-effective strategy (ICER = US$964.63 per DALY averted) compared to delayed NAAT testing alone, falling below Kenya's cost-effectiveness threshold of US$1003.4. In scenarios with delayed access to NAAT, the Ag-RDT strategy, while more costly, averts significantly more DALYs (1998.97 versus 2236.49) than relying solely on delayed molecular testing. This strategy becomes cost-effective at prevalence levels exceeding 5%, offering a practical solution for timely diagnosis and appropriate patient management. Sensitivity analyses further revealed that the cost-effectiveness of this approach is particularly influenced by the proportion of severe and critical cases requiring hospitalization. In settings where NAAT is unavailable, our results indicate that Ag-RDT implementation is less costly (US$998,260.67 versus US$1,261,230.78) than clinical judgment alone at prevalence levels below 16.25%. While the clinical strategy averted more DALYs, its substantially higher costs make it an unsustainable approach in resource-constrained environments. These findings have important policy implications for Kenya and similar low- and middle-income countries. Policymakers should prioritize the implementation of Ag-RDTs as either a complement to delayed NAAT testing or as a standalone diagnostic tool in areas without molecular testing capabilities. This strategic approach would reduce the financial burden associated with presumptive treatment based on clinical judgment while ensuring timely diagnosis to limit disease transmission. The implementation of Ag-RDTs represents a practical, cost-effective solution for COVID-19 diagnosis in resource-limited settings, balancing economic constraints with the need for effective disease management. Declarations Ethical considerations This research was approved by the Ethics committee of Kenya Methodist University and all participants signed a written consent form to participate to the study Policy implication This paper will give important insight on cost effective tests to use in a pandemic and will help decision makers to use efficiently the scarce healthcare resources Funding The project that generated data used in this study was made possible by the generous support of the World Health Organization. The study was an implementation Research on the use of Antigen Rapid Diagnostic Tests for Coronavirus Disease 2019 (COVID-19). The study entailed the assessment of field performance, feasibility, acceptability, ease of use and impact of Ag-RDTs for the diagnosis of SARS-CoV-2 infection in Kenya. The funding was awarded to JG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgement We are grateful to the Mount Kenya University study team fieldworkers who collected effectiveness data. 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Oct., Model parameter estimation and uncertainty analysis: a report of, Med. Decis. Mak. Int. J. Soc. Med. Decis. Mak. , vol. 32, no. 5, pp. 722–732, 2012, 10.1177/0272989X12458348 Additional Declarations No competing interests reported. Supplementary Files Additionalfile.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6854403","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469535455,"identity":"77caa27c-5e9d-4d7a-8d60-1dcd81e7c370","order_by":0,"name":"Brian Arwah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYBACAxDB2GADpg8AsQwfkVrS4Fp42IjUctgAJkBYizn72WcSP3ecN+YXO/vwAGObDQ8b++GjGz4w2OTLO2DXYtmTbibZe+a2meTsdAOgljQeNp60tJszGNIsNx7A4bADaWw3eNtu2xjcTmM4/HfbYR42CR6z2zwMhw0MG3BoOf+M7ebftnM29kAtBxi3/Ydo+YNPy400ttu8bQfMDKTBWg5AtDAAtcjj8L7ljGfsv2Xbko0lwLb8S4b4pccgzcAAhxZz/jRmw7dtdob9s9OYPzCcsZPjZz987MaPChsDeRwOwwUMQCFDmhYgINWWUTAKRsEoGLYAAF7nVxIF8c14AAAAAElFTkSuQmCC","orcid":"","institution":"Health Economics Research Unit - KEMRI Wellcome Trust Research Programme","correspondingAuthor":true,"prefix":"","firstName":"Brian","middleName":"","lastName":"Arwah","suffix":""},{"id":469535456,"identity":"0d4035ec-460d-4ace-ad5e-018b3800eb54","order_by":1,"name":"Samuel Mbugua","email":"","orcid":"","institution":"Mount Kenya University","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Mbugua","suffix":""},{"id":469535459,"identity":"1436ea09-0d70-443f-b77d-ca3e01fe6a61","order_by":2,"name":"Jane Ngure","email":"","orcid":"","institution":"Mount Kenya University","correspondingAuthor":false,"prefix":"","firstName":"Jane","middleName":"","lastName":"Ngure","suffix":""},{"id":469535463,"identity":"3aea5554-5933-4bc0-8e4c-b283fea50465","order_by":3,"name":"Mark Makau","email":"","orcid":"","institution":"Mount Kenya University","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Makau","suffix":""},{"id":469535464,"identity":"0d491517-b944-44ce-860f-33aa2534ec88","order_by":4,"name":"Peter Mwaura","email":"","orcid":"","institution":"Mount Kenya University","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Mwaura","suffix":""},{"id":469535467,"identity":"50a1bb21-bdac-473a-be3c-2ecd0a45134c","order_by":5,"name":"David Kamau","email":"","orcid":"","institution":"Mount Kenya University","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Kamau","suffix":""},{"id":469535468,"identity":"d4f788b2-44b3-4133-8769-06643f435061","order_by":6,"name":"Desire Aime Nshimirimana","email":"","orcid":"","institution":"Grand Canyon University","correspondingAuthor":false,"prefix":"","firstName":"Desire","middleName":"Aime","lastName":"Nshimirimana","suffix":""},{"id":469535472,"identity":"76060841-9282-42c6-8ae8-03f46e53d0e3","order_by":7,"name":"Edwine Barasa","email":"","orcid":"","institution":"Health Economics Research Unit - KEMRI Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Edwine","middleName":"","lastName":"Barasa","suffix":""},{"id":469535473,"identity":"ee4b2d2b-d911-4d8a-a665-c1a2d37c6f28","order_by":8,"name":"Jesse Gitaka","email":"","orcid":"","institution":"Mount Kenya University","correspondingAuthor":false,"prefix":"","firstName":"Jesse","middleName":"","lastName":"Gitaka","suffix":""}],"badges":[],"createdAt":"2025-06-09 12:23:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6854403/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6854403/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84665409,"identity":"ac981c29-cc06-4946-b918-70ee8cc5e84f","added_by":"auto","created_at":"2025-06-16 05:37:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":197636,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic illustration of the decision tree mode under scenario 1. +ve, Positive; -ve, Negative; TP: True Positive; FN: False Negative; FP: False Positive; TN: True Negative\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6854403/v1/99d3f24eb180bb0550ccbb11.png"},{"id":84666951,"identity":"041bba13-51c8-4123-ba75-f36dd169767c","added_by":"auto","created_at":"2025-06-16 05:53:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":158324,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic illustration of the decision tree mode under scenario 2. +ve: Positive; -ve: Negative; TP: True Positive; FP: False Positive; FN: False Negative; TN: True Negative\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6854403/v1/2de9a890e1a579dc21d07be9.png"},{"id":84666522,"identity":"fe2b5579-f06d-434f-840e-6da4f8184e4b","added_by":"auto","created_at":"2025-06-16 05:45:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":194808,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAntigen rapid diagnostic test cost component\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6854403/v1/f852853e15e285333e5948b5.png"},{"id":84665416,"identity":"4ef509e6-7e0e-4318-8b30-219a7382eb5a","added_by":"auto","created_at":"2025-06-16 05:37:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":577620,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTornado diagram of one-way sensitivity analysis of the parameters affecting the ICER under scenario 1. YLL, years of life lost; YLD, years of life lived with disability; RDT, rapid diagnostic test; DALYs, disease life adjusted years\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6854403/v1/9d6e33841f2335909b6624c6.png"},{"id":84665420,"identity":"47e75a3d-5ef2-491e-ab41-07a03145bf91","added_by":"auto","created_at":"2025-06-16 05:37:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":478189,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTornado diagram of one-way sensitivity analysis of the parameters affecting the ICER under scenario 2. YLL, years of life lost; YLD, years of life lived with disability; PCR, polymerase chain reaction; RDT, rapid diagnostic test; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6854403/v1/f966aaf7efdf8cf9a79dfbb0.png"},{"id":84666525,"identity":"0bac0a38-3436-4086-be29-1b9e3817b874","added_by":"auto","created_at":"2025-06-16 05:45:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":299094,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProbabilistic sensitivity analysis of Ag-RDT diagnostic strategy versus delayed nucleic acid amplifying test diagnostic strategy under scenario 1. Green dots representing the points that are cost-effective (below the willingness to pay (WTP)). While the red dots represent the points that are not cost-effective (above the WTP)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6854403/v1/6ca06b20a9d4892a8dff09be.png"},{"id":84666524,"identity":"72db8af6-5e60-4c7d-b290-177931367c64","added_by":"auto","created_at":"2025-06-16 05:45:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":241775,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProbabilistic sensitivity analysis of Ag-RDT diagnostic strategy versus delayed nucleic acid amplifying test diagnostic strategy under scenario 1. Green dots representing the points that are cost-effective (below the willingness to pay (WTP)). While the red dots represent the points that are not cost-effective (above the WTP)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6854403/v1/8e2469568cc937203e46839d.png"},{"id":84665415,"identity":"f2f22605-2451-4342-a7b0-372ea4cbe91c","added_by":"auto","created_at":"2025-06-16 05:37:35","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":118229,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCost-effectiveness acceptability curve showing the probability of cost-effectiveness of Ag-RDT strategy compared to Delayed test NAAT strategy and No-test Strategy over a range of willingness-to-pay values.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6854403/v1/82d11851a1904e4920e1b91a.png"},{"id":84666962,"identity":"e6a19d18-f629-4015-9189-b7f2ef42c6da","added_by":"auto","created_at":"2025-06-16 05:53:36","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":106681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCost-effectiveness acceptability curve showing the probability of cost-effectiveness of Ag-RDT strategy compared to No-test Strategy over a range of willingness-to-pay values.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6854403/v1/d2a53dda595ba10ecfd3ec4a.png"},{"id":87147075,"identity":"11004b03-a421-441f-bc15-c576bb27bfd1","added_by":"auto","created_at":"2025-07-20 20:46:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4703217,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6854403/v1/c38525a9-60ff-4991-b1b7-aba1955f33c7.pdf"},{"id":84665411,"identity":"525c578d-2c26-4584-a6da-4b4e56d86b91","added_by":"auto","created_at":"2025-06-16 05:37:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":27866,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-6854403/v1/5331f835ebcc9984ee444870.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Economic Evaluation of Implementing SD Biosensor Antigen Detecting SARS-COV-2 Rapid Diagnostic Tests in Kenya","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe COVID-19 pandemic has created a need to rapidly scale-up testing services and provide diagnoses to implement test-trace-isolate strategies, essential to treat and care for patients and to control the spread of the virus. Hundreds of diagnostic products are now available on the market, targeting the detection of viral RNA, viral antigens, and host antibodies against SARS-CoV-2.\u003c/p\u003e \u003cp\u003eServices for SARS-CoV-2 Nucleic acid amplification testing (NAAT) assays have often been unavailable or backlogged for several days to weeks, precluding the clinical utility of the results. NAAT, a reverse transcription polymerase chain reaction (PCR) molecular testing of respiratory tract samples, is the recommended method for confirmation of COVID-19. In low and middle-income countries, however, the availability and health impact of PCR testing can be jeopardized by lack of testing capacity, insufficient trained personnel, shortages of reagents, long turnaround times (TAT), and high costs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Lateral flow antigen-detecting rapid diagnostic tests (Ag-RDTs), which are easy to perform and provide results within 15\u0026ndash;30 minutes, have recently been commercialized and have the potential to fill at least a portion of the \u0026lsquo;testing gap\u0026rsquo;. Under certain conditions, Ag-RDTs that meet minimum performance requirements are recommended, and some have WHO Emergency Use Listing authorization [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These simple-to-use tests offer the possibility of rapid case detection, especially of the most infectious patients in the first week of illness, at or near the point of care.\u003c/p\u003e \u003cp\u003eWHO released an interim guidance on the use of Ag-RDTs for SARS-CoV-2, and the use of Ag-RDTs is recommended when PCR is either unavailable or long TAT of PCR which delays its clinical utility. This is particularly the case in less privileged countries in Africa, especially in Sub-Saharan Africa [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNational norms and policies are being adopted in Kenya and many countries to allow and encourage targeted use of these Ag-RDTs. The decision to fully implement rapid diagnostic kits for detecting SARS-CoV-2 in Kenya relies on the field performance, feasibility, acceptability, and cost-effectiveness of the RDT compared to other diagnostic methods in the different settings which involve point-of-care diagnosis of COVID-19.\u003c/p\u003e \u003cp\u003eSeveral studies have evaluated the cost-effectiveness of Ag-RDTs for SARS-CoV-2 detection in different contexts. A prospective study[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] comparing Ag-RDTs with PCR testing concluded that despite lower sensitivity, rapid tests offered cost-effective benefits through faster turnaround times and reduced resource requirements. Similarly, research assessing various COVID-19 screening strategies in college settings[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] demonstrated that testing frequency and result availability often outweighed sensitivity concerns for effective disease control. These findings suggest that in resource-constrained environments like Kenya, the implementation value of Ag-RDTs may extend beyond their technical specifications.\u003c/p\u003e \u003cp\u003eA published study by Ricks et al. analyzed the health system cost and health impact of using RDTs among hospitalized and symptomatic patients with SARS-COV2 and confirmed that despite the low sensitivity of RDTs compared with RT-PCR, the Ag-RDTs have the potential to be more impactful with less cost per death and more infections averted [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies on the cost-effectiveness of rapid tests have been carried out. In 2022, a study[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] on cost-effectiveness analysis of antigen rapid test of influenza virus in Iran compared to polymerase chain reaction (PCR) in patients with acute respiratory syndrome to analyze the cost-effectiveness of rapid tests and PCR in patients with suspected influenza. concluded that a rapid test is less costly and effective than PCR, but the cost difference was greater than the difference in effectiveness, and in terms of effectiveness indicators, both effectiveness tests were almost similar, and the cost difference has led to the choice of the rapid test as a cost-effective option. A cost analysis[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] of diagnostic tests for SARS-CoV-2 using different Ag-RDTs and RT-PCR technologies in Mozambique and found that Ag-RDT was three times lower than PCR testing. Although the study did not assess the effectiveness of the antigen test but in terms of costs, Ag-RDT was concluded to be cost-effective in Low and Middle-Income Countries\u003c/p\u003e \u003cp\u003eOur study builds upon this foundation by specifically evaluating the cost-effectiveness of SD Biosensor Ag-RDTs within the Kenyan healthcare context, considering both scenarios with delayed or absent NAAT testing capabilities. The need to examine both the cost and health outcome (effectiveness) of the test kits is critical in determining whether the value of the test kit justifies its cost and is affordable to the payer. A specific approach to assess cost-effectiveness of health interventions suggested by the commission on macroeconomics and Health (WHO,2001) is that interventions costing less than the per capita gross domestic product (GDP) in Low and Middle-Income Countries (LMICs) are \u0026ldquo;very cost-effective\u0026rdquo;, and those costing less than triple the per capita GDP are \u0026ldquo;cost-effective [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This, therefore, raises one question, under which scenario in the point-of-care diagnosis is RDT cost-effective? The objective of this study was to evaluate the cost effectiveness of SD Biosensor Antigen Detecting SARs-CoV-2 Rapid Diagnostic Tests in Kenya.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Design\u003c/h2\u003e\n \u003cp\u003eWe developed a decision tree model in TreeAge Pro Healthcare 2021 to evaluate the cost-effectiveness of implementing SD biosensor antigen detecting SARS-CoV-2 rapid diagnostic tests in Kenya from a societal perspective. We modeled the costs and outcomes for diagnosing and treating COVID-19 patients in line with WHO interim guidance on antigen detection for COVID-19 using rapid immunoassays [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e] and the Kenya ministry of health COVID-19 case management guidelines [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe diagnostic and treatment pathway followed the cases where symptomatic patients with suspected COVID-19 and asymptomatic contacts of COVID-19 cases attend health facilities with i) no access to NAAT for diagnosis or ii) limited access with prolonged turnaround times precluding clinical utility of results.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eEvaluation Scenarios\u003c/h3\u003e\n\u003cp\u003eWe assessed two scenarios:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePoint of care Ag-RDT use for case management in settings with access to delayed confirmatory NAAT testing scenario.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe scenario represented a health facility that sends samples to an external lab for NAAT, often with delayed result reporting. In this situation, Ag-RDT would be the first-line test to allow for case detection and rapid implementation of isolation procedures amongst positives and prioritization of negatives for confirmatory testing by NAAT at a designated laboratory facility. We compare the scenario with patients subjected to NAAT test, which is associated with a long turn-around time but obviates the need for confirmatory testing of negatives or a case whereby there is no testing. The diagnosis only relies on the clinical presentation of COVID-19 symptoms as per WHO case definitions [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePoint of care Ag-RDT use for case management in settings with no access to confirmatory NAAT testing scenario.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe scenario differs from the first one since the target location involves a health facility with no access to NAAT and no secure means for the safe and timely transport of samples to centralized facilities. The scenario presents a case whereby Ag-RDT is the only feasible tool to aid in the diagnosis or a choice of not conducting a COVID-19 test.\u003c/p\u003e\n\u003ch3\u003eSampling and sample size\u003c/h3\u003e\n\u003cp\u003eWe selected two counties in Kenya, Kiambu and Nairobi counties, to assess the field performance and impact of SD biosensor antigen detecting SARS-CoV-2 rapid diagnostic tests. The two counties were chosen since they had the highest prevalence of COVID-19 in Kenya (average at \u0026gt;\u0026thinsp;3% over the past three months), they also had different levels of government-owned facilities, and they had communities that were at high risk of outbreaks. We sampled four facilities that captured the diversity of access for COVID-19 testing and drew a sample size of 18 patients to capture the patient cost perspective. The patients\u0026rsquo; sample size was selected to achieve balance in the facilities chosen, and we settled on 18 patients after reaching saturation.\u003c/p\u003e\n\u003cp\u003eThe initial sample size needed for the COVID-19 RDT assumed the following expected values: test sensitivity of 80%, confidence interval of 5%, and COVID-19 prevalence of 10% to yield an estimated sample size of 2459 participants. Due to low turnout in daily tests conducted, a sample size of 506 participants was included in the study, which was still a representation of the targeted population assuming a test sensitivity of 85%, confidence interval of 5%, a width of 10%, and COVID-19 prevalence of 5\u0026ndash;10%. To achieve the necessary accuracy on performance estimates, we determine data for negative cases (by NAAT) using a value of 50% for each estimate.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eWe used primary and secondary data to determine the cost components of diagnosis and case management of suspected COVID-19 cases. Questionnaires were administered at the facility level and to individuals seeking COVID-19 testing services for cost data. For effectiveness measure, we relied on endpoint data on project-specific reporting forms that included COVID-19 testing registries, laboratory report forms, patient history, case management forms, contact history forms, competency assessments, and Ag-RDT ease of use assessments.\u003c/p\u003e\n\u003ch3\u003eModel Structure\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e depicts the intervention strategies applied. We applied three strategies for the study scenarios. The first strategy involves the use of Ag-RDT followed by a different diagnosis pathway. Under the first scenario, Ag-RDT was used as the first-line diagnosis method, followed by the prioritization of negatives for confirmatory testing by NAAT.\u003c/p\u003e\n\u003cp\u003eThe second scenario involved clinical judgment as the comparator. The first strategy pathway of Ag-RDT diagnosis and case management did not include a confirmatory test of negatives, making the diagnosis pathway shorter than in the first scenario where there is access to NAAT services.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eCosting Methods\u003c/h2\u003e\n \u003cp\u003eThe costing followed the Global Health Cost Consortium\u0026rsquo;s (GHCC) reference guidelines [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] to evaluate the cost of implementing SD Biosensor antigen detecting SARS-Cov-2 diagnostic tests in Kenya. We applied an ingredient-based approach from a societal perspective to analyze costs for diagnosing COVID-19 cases using antigen RDT. Under the healthcare system, we costed both the direct and ancillary costs, which included physical costs and overheads, costs for personal protective equipment (PPE), staff time, and costs for non-pharmaceuticals. We also computed the direct and indirect costs from the patient\u0026apos;s perspective. For the direct cost, we included the cost of testing, the cost of treatment, the cost incurred for related healthcare services, and the cost of isolation/quarantine. As for the indirect costs, we considered the travel cost; we valued time spent away from normal activities to visit the healthcare facilities; we valued informal care, and using the human capital approach, we valued productivity loss due to absenteeism (See Additional file 1).\u003c/p\u003e\n \u003cp\u003eThe costs for treatment, quarantine, and isolation, such as accommodation and overheads, pharmaceuticals, non-pharmaceuticals, staff, PPE, ICU equipment, oxygen therapy, other laboratory tests associated with COVID-19 case management in hospitals, and the cost for the diagnostic of patients using PCR were derived from a previous study [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. We also estimated the costs for clinical diagnosis by considering input costs for physical and overhead costs, PPEs, and staff costs, as detailed in the supplementary file. The per-day costs overheads were obtained from median costs reported in a cost analysis of 20 healthcare facilities in Kenya in 2018 (the VALUE TB Study). The overhead costs included electricity, water, incinerator, fuel, communication, and cleaning services. The cost for diagnosis of patients using NAAT included the input cost of equipment and consumables as provided in the supplementary document. For each of the unit costs for case management, the estimate included costing all the direct and ancillary inputs for the unit cost that go into the delivery of the case management per patient. They included accommodation and overhead, staff, pharmaceuticals, non-pharmaceuticals, oxygen therapy, ICU equipment cost and monitoring, radiology, other laboratory test costs, and PPE costs[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePhysical cost and overheads\u003c/h3\u003e\n\u003cp\u003eWe obtained outpatient cost overheads from the study on case management of COVID-19 patients [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e], which collected primary data from three public health facilities. We computed the physical cost incurred per test by collecting data on the estimated cost of the COVID-19 test room, the size of the facility, and the size of the space the COVID-19 test was being conducted. We later annuitized the estimated cost using the respective useful life years of the housing facility. To estimate the cost of the testing space after annuitizing the cost of the housing facility, we first computed the cost per square meter of the housing facility by dividing the annuitized cost by the size of the housing facility. Second, we multiplied the specific space size for COVID testing by the cost per square meter of the area housing the test. Finally, we divided the cost of the COVID space by the number of tests per day and the number of working days in a year, assuming the daily average test conducted within the last six months and the facility operating every day.\u003c/p\u003e\n\u003ch3\u003eNon-pharmaceuticals and Personal Protective Equipment (PPE)\u003c/h3\u003e\n\u003cp\u003eData was collected on the non-pharmaceutical and PPE items used during the testing of suspected COVID-19 cases. We obtained cost data for NAAT testing from a recent study by Barasa et.al. (2021) on examining unit costs for COVID-19 case management in Kenya. The other cost of items for Ag-RDT and quantity required per test were obtained from the sampled health facilities.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eStaff cost\u003c/h2\u003e\n \u003cp\u003eData was collected on the type of staff, gross salaries, and time spent on testing from three public health facilities. We computed the amount of time allocated on a test as follows. First, we estimated the total time allocated to testing in a day by obtaining the number of shifts in a day, the number of the specific cadre of staff conducting the test, and the length of each shift in minutes. Second, we estimated the amount of time allocated to a test per day by dividing the number of tests per day, assuming a daily average of tests conducted within the last six months and equal allocation of testing time. Finally, we computed the average staff cost per test by multiplying the staff time allocated to COVID-19 testing in a day in minutes by the gross salary of that cadre of staff per minute.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eValuing Time Cost\u003c/h2\u003e\n \u003cp\u003eThe time patients lost from routine activities was estimated by adding the travel time and the time spent at the health facility as per the patient\u0026rsquo;s and companion\u0026rsquo;s response. Using data from Kenya\u0026rsquo;s minimum wages [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. The time lost was subsequently valued at the average hourly pay of the different categories of paid work the patient and companion would have engaged. For the unpaid work, a proxy value of the cost of a close market substitute was used.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eValuing Productivity Loss\u003c/h2\u003e\n \u003cp\u003eThe study considered productivity loss from both paid and replaced unpaid work. Using the human capital approach, the number of hours worked per working day was calculated based on the average number of hours a week the patient worked over the last four weeks, assuming the patient worked for five days in a week. Subsequently, the gross daily wage was estimated by multiplying working hours per day by estimated hourly salaries for different categories of work [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. Next, the total number of lost productive days from paid work was multiplied by the gross daily wage.\u003c/p\u003e\n \u003cp\u003eThe cost of replacing unpaid work was considered by analyzing the time spent by an informal giver to replace the patient\u0026apos;s missed unpaid work.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003ePricing and Valuation\u003c/h2\u003e\n \u003cp\u003eWe identified the cost of building as the only capital good, and annuitized it, assuming a useful life of 5 years. We obtained price data from a previous study [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e] and presented the costs in Kenya shillings (KES) and US dollars. We used an exchange rate of US\u003cspan\u003e$\u003c/span\u003e1\u0026thinsp;=\u0026thinsp;KES 112.52 derived from Xe.com and accessed on 30th November 2021, to convert KES to US\u003cspan\u003e$\u003c/span\u003e. We obtained shadow prices for unpaid work and the opportunity cost of time from Kenya minimum wages reported by the africanpay.org database accessed on 30th December 2021.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eEffectiveness and Cost-effectiveness measurement\u003c/h2\u003e\n \u003cp\u003eThe impact of case management of COVID-19 was dependent on the diagnostic performance of the different diagnostic tests used (Ag-RDT or NAAT), the timing of the test, and the adherence to COVID-19 case management guidelines.\u003c/p\u003e\n \u003cp\u003eThe intermediate outcome was measured in terms of the diagnostic performance of the antigen RDT, which was measured by its sensitivity and specificity compared to the PCR test. Based on the results of 506 test samples, the estimated sensitivity of Ag-RDT is 73%, and the estimated specificity is 93%. Using the diagnostic test confidence interval formula [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e], we obtained a 95% confidence interval for the Ag-RDT sensitivity as [59%-87%], and the confidence interval for the specificity as [91%-96%].\u003c/p\u003e\n \u003cp\u003eThe primary health outcome was measured in terms of the cost per disability-adjusted life years (DALYs) averted. We factored in both the mortality and morbidity to obtain DALYs by summing up years of life lost (YLL) and years of life with disability (YLD) [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. A discounting rate of 3% was used to calculate DALYs and applied Kenya\u0026rsquo;s life expectancy of 66.34 [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e], disability weights as reported [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e] and captured in Table. 1\u003c/p\u003e\n \u003cp\u003eThe incremental cost-effectiveness ratio (ICER) comparing the use of Ag-RDT and confirmatory testing of negatives by NAAT and the use of NAAT as the only diagnostic test conducted was calculated as the difference in costs and DALYs averted of diagnostic and case management in the compared groups.\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:ICER=\\:\\frac{({C}_{ST1}-{C}_{ST2})}{({DALYs}_{ST1}-{DALYs}_{ST2})}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere ICER\u0026thinsp;=\u0026thinsp;Incremental cost effectiveness ratio; ST1\u0026thinsp;=\u0026thinsp;RDT as first-line diagnosis followed by NAAT, ST2\u0026thinsp;=\u0026thinsp;NAAT diagnosis, C\u003csub\u003eST1\u003c/sub\u003e = Cost of strategy 1; C\u003csub\u003eST2\u003c/sub\u003e = Cost of strategy 2; DALYs\u003csub\u003eST1\u003c/sub\u003e = DALYs averted in strategy 1; and DALYs\u003csub\u003eST2\u003c/sub\u003e = DALYs averted in strategy 2.\u003c/p\u003e\n \u003cp\u003eWe compared the ICER with an opportunity cost of USD 20.07 to USD 1023.47 (1\u0026ndash;51% GDP per capita) based on Kenya\u0026rsquo;s cost-effectiveness threshold as estimated by Woods \u003cem\u003eet al.\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] and Ochalek \u003cem\u003eet al.\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eAssumptions and Parameters\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. present the model parameters. The model also used some assumptions that are key to note. First, we assumed that patients who test positive and show no clinical symptoms of COVID-19 are given home-based standard care, equivalent to isolation and routine care given to mild COVID-19 patients. Second, although there is little data to show the correlation of late diagnosis and severity or mortality of COVID-19 patients, according to PHOF et al. (2020) \u0026ldquo;a longer time to confirm COVID-19 diagnosis after initial symptoms was found to be a predictor of hospitalization.\u0026rdquo; As a result, an assumption was made that the proportion of severe cases progressing to critical cases could be higher on the verge of the health system collapsing due to an increase caregivers\u0026rsquo; risk of infection as identified.[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Third, we relied on a COVID-19 study on an outpatient setting [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] to analyze the outcomes of COVID-19 untreated patients. Lastly, we assumed that all patients who test positive and no further confirmatory diagnostic tests conducted are isolated and provided standard care even though the results could be false positive.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eKey model parameters (Line 271 in the main text)\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue (Lb;Ub)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudy cohort population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOVID-19 Prevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost (USD) for asymptomatic care episode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost (USD) for conducting a PCR test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.84 (21.60;22.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost (USD) for critical care episode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e472.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost (USD) for severe care episode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost (USD) for conducting a rapid diagnostic test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.68 (4.83;7.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost (USD) for clinical diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.85 (5.06;5.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDALYs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisability weight for mild COVID-19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisability weight for critical COVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisability weight for severe COVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage age at death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLife expectancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics of Patients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of critical patients hospitalized\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of critical COVID-19 who die\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of critical COVID-19 who recover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of infected patients with SARS-CoV-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of patients not given asymptomatic care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion not infected with SARS-CoV-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of severe patients hospitalized\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of severe COVID-19 who progress to critical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of severe COVID-19 who recovered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of patients given asymptomatic care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of severe COVID-19 untreated patients who progresses to critical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of untreated severe COVID-19 patients who recovered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of untreated critical COVID-19 patients who dies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of untreated critical COVID-19 patients who recovered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLength of stay asymptomatic care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLength of stay critical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (4;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLength of stay severe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (3;9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnostic Accuracy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCR Specificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998 (0.992;0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCR Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86 (0.547;0.994)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDT Specificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93 (0.91;0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDT Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73 (0.59;0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbability of correctly excluding SARS-CoV-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.683 (0.60;0.758)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinical Judgement Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.806 (0.729;0.869)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eDealing with uncertainty\u003c/h2\u003e\n \u003cp\u003eWe performed a sensitivity analysis on the following parameters: COVID-19 prevalence level; sensitivity of RDT and PCR; the proportion of treated and untreated hospitalized cases; and cost of RDT, NAAT, and treatment of severe and critical cases. We implemented a 20% increase or decrease in the cost of RDT, NAAT, and cost of treatment. The sensitivity analysis of RDT was based on +/- 5% confidence bounds while the bounds of PCR and clinical judgement were provided [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eWe conducted a probabilistic sensitivity analysis to check for the collective uncertainty on the probability of cost-effectiveness using second-order Monte Carlo simulation (n\u0026thinsp;=\u0026thinsp;1000). We used beta distributions to calculate the probability range of the study parameters and gamma distribution on the cost parameters [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. Finally, we presented the ICE scatterplot to illustrate the uncertainty in the cost-effectiveness results.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. summarizes the key findings from the patient questionnaire administered. As per the results, the primary mode of transport was public transport, with 13 (72%) of the 18 sampled patients preferring public transport to get to the health facility, and the second most popular means was walking on foot, 3 (17%). It was also noted that most (89%) of the patients went alone to the health facility, and only 11% were accompanied. The table also details the patient\u0026rsquo;s usual activities foregone by visiting a health facility. Most of the patient\u0026rsquo;s main activities would be, attending to paid work at 28% or attending to a business activity at 28%. Housework activities took 17%, whereas only 6% of patients forego childcare activity.\u003c/p\u003e \u003cp\u003eIt also shows the treatment cost, travel cost, time lost per hour, and time cost from the foregone activity the patient would have engaged in during the health facility visit, and the productivity loss. The median travel cost for a one-way visit for a patient was US\u003cspan\u003e$\u003c/span\u003e0.27. The study findings also report that out of the patients accompanied to the health facility, there was no cost incurred by the patient\u0026rsquo;s companion while visiting the health facility. Applying the values per hour of paid and unpaid work foregone the median time cost per hour of both patient\u0026rsquo;s and companion\u0026rsquo;s usual activities lost was US\u003cspan\u003e$\u003c/span\u003e1.57. For the productivity loss, the median productivity cost of absenteeism from both paid and unpaid work was US\u003cspan\u003e$\u003c/span\u003e1.08.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKey findings from the patient\u0026rsquo;s questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePatient cost\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMode of transport (no. (%))\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWalk on foot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotorcycle and public transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaxi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatients accompanied (no. (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTraveling cost (KES)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(20\u0026ndash;70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCost of related healthcare services (KES)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e387.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0-100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatient\u0026rsquo;s usual activities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNumber\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePercentage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildcare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttending school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeeking work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaid work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCompanion\u0026rsquo;s usual activities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttending school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime cost (KES)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e247.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(50.26-433.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProductivity loss (KES)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1469.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0-2336.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. details the unit cost for rapid diagnostic tests compared to NAAT for a COVID-19 suspected case. The results showed the unit cost per test for NAAT and Ag-RDT tests in the healthcare system was US\u003cspan\u003e$\u003c/span\u003e18.93 and US\u003cspan\u003e$\u003c/span\u003e1.76, respectively. There is a considerable cost difference between the two tests, mainly because of the laboratory cost incurred when conducting the NAAT test. The table also showed the patient cost incurred for a diagnostic test was US\u003cspan\u003e$\u003c/span\u003e 2.92; the major cost driver was the patient time cost. Summarizing the results, we found that the societal cost for PCR was higher at US\u003cspan\u003e$\u003c/span\u003e21.84 than the Ag-RDT cost of US\u003cspan\u003e$\u003c/span\u003e4.68.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnit costs for antigen RDT and PCR test for SARS-COV-2 detection\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInputs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAg-RDT Test median cost\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNAAT Test (PCR) cost [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eClinical Diagnosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(KES)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUS\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(KES)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUS\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(KES)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUS\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHealthcare perspective\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical cost \u0026amp; overheads per test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquipment per test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal protective equipment (PPE) cost per test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsumables cost per test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLab supplies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1959.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStaff cost per test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealthcare estimated cost\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e197.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2129.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e18.93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e104.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatient perspective\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient direct cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient travel cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient time cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e176.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e176.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompanion travel cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformal care cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient productivity loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e121.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatient estimated cost\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e328.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e328.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e328.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocietal cost\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e526.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2,457.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e21.85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e433.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e3.85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAntigen-RDT and PCR test results\u003c/h2\u003e \u003cp\u003eOut of 506 patients recruited, 72 (14.2%) patients tested positive with antigen RDT, 52 (10.3%) patients tested positive with PCR test, 38 (7.5%) were positive for both RDT and PCR test, 34 (6.7%) were positive for RDT and negative for PCR, 14 (2.8%) were positive to PCR and negative to RDT, and 468 (92.5%) were both negative for RDT and PCR test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eBase case results\u003c/h2\u003e \u003cp\u003eThe costs, DALYs, and the ICER at 10% COVID-19 prevalence level associated with the three strategies are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Under the first scenario, where we apply Ag-RDT as the first-line test and prioritization of negatives for the confirmatory test by NAAT in comparison to delaying and conducting NAAT, the findings show no-test strategy is dominated. The results show that the RDT strategy is the costliest (US\u003cspan\u003e$\u003c/span\u003e1,336,231.13), followed by the no-test strategy (US\u003cspan\u003e$\u003c/span\u003e1,261,230.4), and NAAT test strategy was relatively less costly (US\u003cspan\u003e$\u003c/span\u003e1,107,117.83) compared to the other two strategies. Although the RDT strategy was costly, it is most effective in averting DALYs (1998.97 DALYs) compared to both NAAT diagnostic strategy (2236.49 DALYs) and no-test strategy (2361.35 DALYs), with lower values representing better health outcomes. The incremental cost-effectiveness ratio (ICER) of the Ag-RDT strategy compared to the NAAT diagnostic strategy was US\u003cspan\u003e$\u003c/span\u003e964.63 per DALY averted (US\u003cspan\u003e$\u003c/span\u003e229,113.3 additional cost / 237.51 DALYs averted), which falls below Kenya's maximum cost-effectiveness threshold of US\u003cspan\u003e$\u003c/span\u003e1003.4, making it a cost-effective strategy. When comparing all three strategies, the results showed the no-test strategy was absolutely dominated (both more costly and less effective than NAAT), and it would be more efficient to apply the Ag-RDT strategy in scenarios with delayed NAAT testing rather than switching to the no-testing approach.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e: Cost-effectiveness results for Ag-RDT implementation (USD 2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg 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vridrvgAAAAAAMwxAnsAAAAAAOYYgT0AAAAAAHOMwB4AAAAAgDlGYA8AAAAAwBwjsAcAAAAAYI4R2AMAAAAAMMcI7AEAAAAAmGME9gAAAAAAzDECewAAAAAA5hiBPQAAAAAAc4zAHgAAAACAORakaZpmfwRBfioAAAAAADhxrFCeFnsAAAAAAOaZt8Xejvwxv8hPYL5xDp9N5DtslAeUQTk5/chj2HzlgRZ7AAAAAADmGIE9AAAAAABzjMAeAAAAAIA5RmAPAAAAAMAcI7AHAAAAgGNy7tw5BUGg4XDoTgKeGwJ7AAAA4AxaXV1VEARqt9vuJBzB/v6+Owp47gjsAQAAgDPoV7/6lTsKY+ahx40bN9xJwIlEYA8AAAAAFvPQ49e//rU7CTiRCOwBAAAA6MmTJwqCQKurq+r1eqpWqwqCQNVqVffv359Ia1q1gyBQo9HQ3t6eNH7H/Ny5c7p//372vvmTJ0+88128eDG37Bs3bmSvB3z00UfZ/PV6XU+ePNGNGzey7bp48WK2XKPdbmfTz507l9suWS3x9+/fV6PR8G5/EATq9/uSpGazmW2Pxtt38eLFbD7fOoCXIrVISp1RmGPkJzDfOIfPJvIdNsoDyjhsOYmiKJWUtlqtNE3TdDAYZMvyDUmSpGmapo8fP04XFhYmppvluOMlpYPBIE2SxDufpPTevXtpmqZpq9WamGYG37xhGGb7c/Xq1YnpktLFxcUsjdln3xDHceH2X79+vXCaPa+dZjAYZOOOyiwTSAvKAy32AAAAAHLiONbu7q4eP36cjfvqq6+kcav1/v6+FhcXlSSJdnd31Wq1rLm/ZqYnSaJaraaf/OQn2t/f18rKinZ3d5WmaTbfT3/6U3d2Xb9+PZdmf38/267r169LkpIkkSTt7e3pk08+kSQNBgOlaaokSRSGoba3tyda9hcWFrJ0KysrkqTRaCR9HS0piiJJUqvVUpqmev/99yVJ3W43my9NU129ejU3L/CyENgDAAAAyNnc3NT58+d14cKFLMj9p3/6J8kKYn/4wx+qUqno/Pnz2tjY0MbGRm4Zf/M3f6NKpaJKpSJJ+sd//EdJ0p07d/TKK68oCAI1m03JCtCNKIqyYPqtt97KxpvtMtMkaTgc6pe//GX299LSkoIgUBiG2XL/+Z//OZsuSR9++KFqtZok6Y//+I8lSZ9//nkujc9rr72mH//4x9krAuZhAvCyEdgDAAAAmOl5fkju/Pnz7qhj9c1vftMdNWHWz9SNRiMtLS3pzp07M9MCLxqBPQAAAIDSvv3tb0uSfv7zn2et9zdu3Mg+MFfEtNzbXfHNsLW15SY/kFdffTX7v91V3gymdf6gzMOM0WiUvYoQhmG2/XEcO3MALweBPQAAAIDSTNf17e1thWGoIAj0R3/0R26yCe+++67kdMU3w6yHArNUKpXslQHTFd8eDso8hPjkk08UBIHu3r2rb3zjG9L4tQGz/Z1Ox5kTeDkI7AEAAIAzyLS8GyZwLfKtb31LklSr1XTv3j0tLi5K4w/RxXGs9957L/vbp1araTAYZB+rs5llm39ts7bLTP/Zz36mVqulMAxz0+3tcffZZqf74IMPsgcFCwsL+u53v6sLFy6o2+1myw/D0PvRwKL9B56nYPy5/K//GD/NskZhjpGfwHzjHD6byHfYKA8og3Jy+pHHsPnKAy32AAAAAADMMQJ7AAAAAADmGIE9AAAAAABzjMAeAAAAAIA5RmAPAAAAAMAcI7AHAAAAAGCOEdgDAAAAADDHCOwBAAAAAJhjBPYAAAAAAMwxAnsAAAAAAOYYgT0AAAAAAHOMwB4AAAAAgDlGYA8AAAAAwBwjsAcAAAAAYI4R2AMAAAAAMMcI7AEAAAAAmGME9gAAAAAAzDECewAAAAAA5hiBPQAAAAAAc4zAHgAAAACAOUZgDwAAAJRw7tw5BUGg4XDoTgKAl4rAHgAA4JRZXV1VEARqt9vuJBzB/v6+OwoATgQCewAAgFPmV7/6lTsKY+ahx40bN9xJeA5ML4cnT564kwAcIwJ7AAAAnBnmocevf/1rdxKeA9PL4Te/+Y07CcAxIrAHAAA45Z48eaIgCLS6uqper6dqtaogCFStVnX//v2JtKZVOwgCNRoN7e3tSePW13Pnzun+/fsTLbHufBcvXswt+8aNG9nrAR999FE2f71e15MnT3Tjxo1suy5evDjRwttut7Pp586dy22XrJb4+/fvq9FoeLc/CAL1+31JUrPZzLZH4+27ePFiNp9vHaeRfVzd8mDy1OSVOSaj0WgijXu8h8OhgiDI0i0tLSko+D6BXT7v37+f255er5dLe//+/Vw+ra6u5sqKXc7Mdq2uruaWAZxKqUVS6ozCHCM/gfnGOXw2ke+wHbY8RFGUSkpbrVaapmk6GAyyZfmGJEnSNE3Tx48fpwsLCxPTzXLc8ZLSwWCQJkninU9Seu/evTRN07TVak1MM4Nv3jAMs/25evXqxHRJ6eLiYpbG7LNviOO4cPuvX79eOM2e104zGAyycSeB2a6DiuN4Yn/NcqblaRiG6e7ubpqmaRqG4cT069evF5a5x48fO1vx2/JZtD5zvO/duzcxzcxnyrCvnEVR5Kxx/ph9AdKC8kCLPQAAwBkSx7F2d3f1+PHjbNxXX30ljVs79/f3tbi4qCRJtLu7q1arZc39NTM9SRLVajX95Cc/0f7+vlZWVrS7u6s0TbP5fvrTn7qz6/r167k0+/v72XZdv35dkpQkiSRpb29Pn3zyiSRpMBgoTVMlSaIwDLW9vT3Rsr+wsJClW1lZkaSshTlNU0VRJElqtVpK01Tvv/++JKnb7WbzpWmqq1ev5uY9bUajkTqdjmTlx2Aw0OLioiTp008/zZUFM13jvPnss8+y/0vS48ePc+WlVqvp6/jja+bYXrhwIRvn2t/fV6vV0u7urpIk0cLCgiTp9u3bkqQf/ehHkpV3u7u7WllZ0f7+frY9NrNfm5ub7iTg1CGwBwAAOEM2Nzd1/vx5XbhwIQty/+mf/kmygtgf/vCHqlQqOn/+vDY2NrSxsZFbxt/8zd+oUqmoUqlIkv7xH/9RknTnzh298sorCoJAzWZTsgI/I4qiLJh+6623svFmu8w0SRoOh/rlL3+Z/W26c4dhmC33n//5n7PpkvThhx+qVqtJkv74j/9YkvT555/n0vi89tpr+vGPf5x1OzcPE04r8zBHUnbMa7Wa/uEf/kHylAUz3ZSZL774Qho/KJKk733ve6rX63r99ddzeXhQGxsbOn/+vCqVSrZssy3b29uS9RrFK6+8ojt37kjW9hh2OTPbD5xmBPYAAABn3PP8kNz58+fdUcfqm9/8pjtqwqyfqRuNRlpaWtKdO3dmpkXe5uamut2uoijS9va21tbW9NFHH7nJDuUgefGtb33LHQWcKQT2AAAAkCR9+9vfliT9/Oc/z1pJb9y4kX1grohpEbW74ptha2vLTX4gr776avZ/u6u8GUzr/EGZhxmj0ShrvQ7DMNt+01p8Wn3jG9/I/m9++m80GunixYtSQVkYDofZxwe/+93vSpIuXryo1157TVtbW+p2u5LVg8P2L//yL9rb25v5McJ2u629vT09efIk+3De7/7u70rj/JHVFd8e3F4lwFlDYA8AAADJ6rq+vb2tMAwVBIH+6I/+yE024d1335WcrvhmmPVQYJZKpZJ1/zZd8e3hoMxDiE8++URBEOju3btZkJskSbb95v3z08p+FeOP/uiPslccTHd3X1lYWlqSxt9YeOedd7LpJl/W1tYkp+u7CcZ/8IMf6JVXXsm9WuHTbDb1yiuv6Hvf+17WYv/ee+9Jkq5cuZKlccuB72v7wFlCYA8AAHDKmNZWw26d9THdmGu1mu7du5d9QG1hYUFxHGeBlfmYmatWq2kwGGQfq7OZZfu6Ss/aLjP9Zz/7mVqtVhYkGvb2uPtss9N98MEHWUC7sLCg7373u7pw4YK63W62/DAMvR8NLNr/efWzn/1McRxn+7W4uKh79+5JBXlqjovdC8POF1Ne/uIv/iKbfuvWrdxxtXtg+Ny7dy9Lv7i4qMFgkD0o2NjYULfbzcqnzZQVXzkDzoJg/Ln8r/8YP/W0RmGOkZ/AfOMcPpvId9goDyhj3svJcDjMegPM6z48b/OexzhevvJAiz0AAAAAAHOMwB4AAADASzPrlQwAs9EV/xQjP4H5xjl8NpHvsFEeUAbl5PQjj2HzlQda7AEAAAAAmGME9gAAAAAAzDECewAAAAAA5hiBPQAAAAAAc4zAHgAAAACAOUZgDwAAAADAHCOwBwAAAABgjhHYAwAAAAAwxwjsAQAAAACYYwT2AAAAAADMMQJ7AAAAAADmGIE9AAAAAABzjMAeAAAAAIA5RmAPAAAAAMAcI7AHAAAAAGCOEdgDAAAAADDHCOwBAAAAAJhjBPYAAJRw7tw5BUGg4XDoTporH330kYIgUBAE6vV6kqS9vT3V63UFQaBz587pyZMn7mw4pSgPAHA6ENgDwCmzurqqIAjUbrfdSTiC/f19d9SJYvLdHc6dO5el6fV6+uSTT7K/v/jiC0nSn//5n6vf70vj/fzNb36TpXkezEOS+/fvu5NwTCgPKMNXTqrVqhqNxtQHOtVqNUvve9h548YNBUGger3uTsrmc69R5iHTxYsXc+MBlENgDwCnzK9+9St3FMbMTeyNGzfcSXOvTL7/3d/9nSRpZWVFaZrq8uXL0jjAk6Tr169rd3dX3/nOd3LzHdZpPt4nHeUBZfjKSZIk6nQ6+t73vpeVBdtwOFSSJFpYWJAk/fjHP3aT6Ne//rU7KtNqtSRJf/VXf6W9vT1J0mg0yh4y/dmf/VkuPYByCOwBAGeGuYmddtM571qtltI0zYZnz55l08z+/87v/I4kqVKpSFZvhH/1r/6Vzp8/r/Pnz2fzHEXR8X727JnSNNU777yTG4/jR3lAGXY5uXfvnhYXFyVJjUZDo9Eol/b27duSpL/8y7+UJN25cycL0Mt47733tLCwoP39fd29e1eSsn/DMNTq6qozB4AyCOwB4JR78uSJgiDQ6uqqer1e1oWyWq1OdH198uRJrmtmo9HIbtjOnTunc+fO6f79+1nXWdNV053v4sWLuWWbbpntdlsfffRRNn+9XteTJ09048aNbLsuXrw40QW03W5n08+dO5fbLlktgffv31ej0fBufxAEWffiZrOZbY/G23fx4sVsPt865l3R/gdBkKVZWlrK/X3//v3ccVldXZ3IGzvNuXPnsmNatD453ysYDofZ8u3j3ev1FDhdeWeVA1NGh8Nh7h1xt8vv3t6eGo1Gth3VanUijb0u91wp2ud5UpQ/AeXhTJYH2zvvvKOtrS1p/JDns88+y003rfiXL1/OHgCYwLyM8+fP68MPP5Qk/fVf/7X29vb0V3/1V5KkW7duOakBlJZaJKXOKMwx8hOYb4c9h6MoSiWlrVYrTdM0HQwG2bJ8Q5IkaZqm6ePHj9OFhYWJ6WY57nhJ6WAwSJMk8c4nKb13716apmnaarUmppnBN28Yhtn+XL16dWK6pHRxcTFLY/bZN8RxXLj9169fL5xmz2unGQwG2bjnwaznoIqOgdle33G+fv36xDiz7nv37k2M1zi/TJnpdrsT0zVepztOnuPtblu32832Z2VlJZWUXr16NU1LlgN3mj3Yy15cXJyYLmtdvv1aWFhI0zRNkySZmCYpffz4cbb842SWf1CUh9NZHoqY9R6Ue73wTVtZWcnGmXJgxpkyYx/31KrzoyjKjTd2d3ezfDbrKUqLrx02j3E6+coDLfYAcIbEcazd3V09fvw4G/fVV19J41br/f19LS4uKkkS7e7uZu9C2sz0JElUq9X0k5/8RPv7+1pZWdHu7q7SNM3m++lPf+rOruvXr+fS7O/vZ9t1/fp1afyOp8Ytaea9y8FgoDRNlSSJwjDU9vb2RGvhwsJClm5lZUUav7upr69+iqJIsrqdvv/++5KkbrebzZemqa5evZqb9zR49uyZd/+/vj/4mjkGkvSjH/0ol3Z3d1crKyu5Frxr165JVrlKkiQ77tOOt8t0vTXvfO/t7enOnTuSpD/8wz88cDmwy3AYhpL1Ybj79+9re3tbYRgqSRKlaarBYCBJ6nQ6ubTm3fPBYKA333xTss6XMAwn9nmeUB4oD2XZ7+GbOv0P/uAPJEnf//73JUnb29sHqi/tVnvTk8OUHwCHZEf5vsgf84v8BObbYc9htwXGbi3zpTOtZu58LrMMt8W6qGVQViuOr/WmaLvs9RS19JnB9AjwbbuZ17SsFaUzaVdWViZaMe3ttbfreTLrOaiifbMVpfHtm30c3MG0ZPrms5Vdn53Pu7u7WQupKT9ly4G73NQqe6aFcVrvEY3Xb/deWVhYSOM4zlqld3d3cy28Kysrhft/HMx6Dqro2NuK0ph1Uh5OXnkoYtZ/UEV5Yk8zPZd2d3dzx8Uw+2/KQVpQ5/uEYVgqHQ6fxzidfOWBFnsAOOPcD1kdp+P66FaRb37zm+6oCbN+pm40GmlpaUl37tyZmRZf+9a3vuWOOpJarZa1pH722WdZS+2///f/3knpV6Yc+L7+7TJf+b5w4YK2t7eznhudTkeLi4sajUY6f/68tra2dP36dYVhqDt37mhpaWniexVnCeXh9JWH0WiUtaRfuHBBGueF8corr2TfQtje3pbGH9E7qGq1Kkm6dOmSOwnAARHYAwAkSd/+9rclST//+c+zLpU3btyY+SEo8yVtuyu+GcwHmA7r1Vdfzf5vd5U3Q61Wy6UvyzzMGI1GE11p0zRVHMfOHGePCazcr6qnaaqNjQ3JCnxu376tvb297CNk7u9a28e7iOm+fOvWrSxAMN18j7McvP7669J4/0zXazM8e/ZM58+fV7vd1t27d/Wnf/qnevbsmcIw1P7+vr766isNh0P9h//wH/T9739fOzs7WVn5v//3/zprOl0oD2enPNy/fz87/gsLC9nPIP6n//SfnJR5SZJM5DWAF4fAHgAgSfrjP/5jafyuZBiGCoJAf/RHf+Qmm/Duu+9K49YauxXH/ur1YVUqley9XPOFbns4KPMQ4pNPPlEQBLp7966+8Y1vSOObUrP95t3aeWS+OG6Gc+fOuUlKuXLliuRZXjD+ermk7B3ZTqejV155Ra+88kru2PmOd5E//MM/lKz3baMoyuY/znLw/e9/XwsLC9k72fZy7C+uN5vNrDwk428+mIDyzp072bxmf02AeNK4+Ud5yDtr5aGIna8/+MEPtL29rYWFBW1ubur8+fMajUZZy7z7ACRN0+zBj/kpPKPf7xceUwDHi8AeAE4Z0/JumMC1iOlGW6vVcr9fvLCwoDiO9d5772V/+9RqNQ0GA+8Ho8yyfV11Z22Xmf6zn/1MrVYru3E07O1x99lmp/vggw+ygGBhYUHf/e53deHCBXW73Wz5YRh6PxpYtP8nxbRjYJg0bn6YfbPzZGNjQ91uNysPNpNuY2Mjlzfm2JkWU9/xNv93XbhwIbeu9fX13PQy5cC3XMPs+/nz57W9va04jifSmzRRFOXK8+Liou7du6dKpaLvfOc7uXnNPp+0396mPPiXa5y18lDEV04WFxcVx7G2t7ez/TDd8O0HLDbz4Mf8FJ5bpgx3fe7fAA4vGL98//Uf46ec1ijMMfITmG+cw2cT+Q4b5QFlUE5OP/IYNl95oMUeAAAAAIA5RmAPAAAAAMAcI7AHAAAAAGCOEdgDAAAAADDHCOwBAAAAAJhjBPYAAAAAAMwxAnsAAAAAAOYYgT0AAAAAAHOMwB4AAAAAgDlGYA8AAAAAwBwjsAcAAAAAYI4R2AMAAAAAMMcI7AEAAAAAmGME9gAAAAAAzDECewAAAAAA5hiBPQAAAAAAc4zAHgAAAACAOUZgDwAAAADAHCOwBwAAAABgjhHYY26srq4qCAL1ej130gu3t7enIAh07tw57e3tuZMBeHAOP383btxQEARqNBrupLlyWvPnRaM8AMDZQWCPuTAajXTnzh1J0ve//3138gt3/vx5raysaH9/X5999pk7GYCDc/jF+Ou//mtJ0u///u+7k+bKac2fF43ygLOo1+spCALV63V3EnCqHTmw39vbU6PR0Llz5xQEgarVqtrtNk9U59ze3p7a7bYuXryoIAiyCvL+/ftu0hfCXMhXVlZ0/vx5yWqJ8FXcvmmzyuqTJ0+yfbWHer2uGzduWEv/2h/8wR9Ikv7Tf/pP7iTgpeMcPnvn8JMnT5QkiRYWFvTOO+9k49zjMe2YGKPRKEtb1Epq8mE4HObGD4fDbF53mimPH330UW68z2nLnxeN8oCz6osvvnBHnWmmt5w9VKtVNRoNPXnyxE3+XJk66Ny5cxqNRu5kHFVqkZQ6o2ZaXFzM5rOHwWCQpVlYWEglpY8fP87NexTXr19PJaUrKyvuJIwdJj/TNE13d3cL8/Uwy5umbNmIoiiVlF6/fj0b12q1UklpFEW5tEXTivbJlNXBYDAxzR4WFxfT3d3dbHlJkmTT7PHAcTHl66A4h/3DvJzDZpsOyhwz+7p40GNimGWZ/LXzzTDLsK/3hslvO/+63W42T5IkufQ+JzV/XjRzDA6K8nC2mGMzj1ZWVgrL1WH4rh+nwWHz2Jx/RUO323VneW4eP36calyXlDnvUcxXHo7UYv/kyRNtb29LkgaDgdI0VZIkWllZyaXb39+XJP3mN7/JjT+KX//615KkX/3qV+4kHNGf//mfZ/l6/fp17e7uKk1T3bt3T2EYusmPpGzZ6Pf7kqR/9a/+lTuplLJl1UjTNEvTarUkSdvb2/rP//k/Z2kqlYoWFhYkSb/85S+z8cDLxjl8Ns/hhw8fSpJ+53d+x50klTwmxs2bNyVJf/mXfylJ+m//7b85Kaa7du2aNM530yJ069YtSVIcx6pUKrn0Pqctf140ygPmhbmXN/f2eD5arVZ23t+7d0+Li4uSpEaj8cJazy9cuKA0TfXs2bNS5z0OyI7yfZH/NPaTX9/TnqInw4PBIHtis7KyknsSbOZzny6trKxkLULuNDlP5e7du5dr2bHnTcetWXEcTyxD4/0w/7937142j/2keF4cdnvNfFevXnUn5Tx+/DhdWVnJnuAvLCykcRznnsCZNGaZcRynu7u7U8uGq+gp/bQnsu60WWU1ddK4rl69mu2jzZTFVquVGw8ch6LyOIuZj3P4t+bpHC7ah1nCMEzlXLsOc0zM9dm03pr53V4ZZrwvz1Pr2MZxnG3HwsLCRBkw2x2GYW7b7WWcpPx50YrybxbKw9lSlK+zXL9+PXfPbK4DJl/MMXevJyafTH6714rFxcVc/pmetq1WK0tnp7cHO3/tMuFum53GXMeiKMqW67u2zDNzfA6q6Lyxz2e7t8S9e/dysVYYhmmr1cod94WFhXRhYSEdDAZZ+VlYWEhbrVaaJEmWBwsLC7myY+oTez/sZZn1mmXZ4jjOyoK9XYYdW3a73al1iR0ruusycaIpU+56Tgr3OKbjpza//cOTYBa7MoiiKHfDZV9A7OHx48eF09KCwF3jA1s03XQ1u3fv3sQ0OV0+7Eqq1Wrl9qHb7WZBv919zVRIi4uL2biTzuzTQdj5UnRxTsc36qbAu0MYhtnJb5+AZrh+/Xph/rs3CumUG5HWAYKCdEZZTaesx51mb2NRZQkch6LyOA3n8OR63Gkn/Rwu2odZzHx2vh/mmJgAz9zomRs098betz6bvXzfcbYfpJthYQ4evLxoRfk3iy9/KA+nV1G+zuIeczPEcZym1v2vuQdPneBsd3d36vXEBFSmTrcH3/28rLJmyp472PfiZvt8g+/aMs/Mfh3UtPPGTJsVR9lp0inlRuPz1h1nrte+OshNO2s+dyiTRtYrP766RlbdZd9r2INb571sZrtsR+qKL0lbW1uKokgad7FaW1vTxYsX9eTJE9VqNX293q+Z7pMXLlywlvB1N6zd3V0lSSKNu2zdu3cv131UUjZ9a2sr6zYWRZHSNM1+PulHP/qRZHU32d3dnfiSqukS+h//43/UxsaGfvjDH0rjLqurq6t69913JUl37tzJuqaYbmd/9md/Jkiffvqp9vf3tbi4qCRJlKapBoOBNM4nc6xNnj1+/Fi7u7tZvpUtG8dpWlk9iFldjoF5wDkMm31MOp2OZP16gflomflVg7JqtVouvxYWFvTee+9l080HrlZWVrLy9+abb2bT8fJQHs6Obreb1d9pmurq1avS+IOJsvI9SZKsrv3bv/1byfoY6k9+8hPt7+9rZWUlu3c314qf/vSn4zX91vXr15WmqTY3N5WmaVYuzL37+++/r729PX3yySeS8/pVGIba3t7OtsX88oNZd5Ik2fJQnnkd4k/+5E8kKzaz89KOiwxzD7G7u5u9KqNxnu3u7mav/5X5oKG9LHe+Wq2m69ev6/Hjx1lZNa8S+JZttv/x48fZuK+++kqyXg2y40/zWt/9+/e1vb2tMAwn7o1MXXiSHTmwP3/+vLa2tvT48WPFcSyN39FqNptu0kKbm5s6f/589q7Fq6++qr//+7/X7/3e7ykIAv3gBz9wZylk3sFsNpsKgkCvvPJKdvExGW9O+I8++kjtdls///nPJUn/z//z/0jjwmMK1GeffabRaJQt9yT8TNNJYE7sH/7wh1m+2Rdtc6xNmfje976ner2u119/Xe+//362nBfpOMqqxuUTmHecw7CZY3L//v3sgY8pF/aN/UF/VWFzczP7/4cffpj9IoLG1+KFhQXduXNH586d0+3bt3Pp8fJQHs6O1157TT/+8Y+zXzUwwbRRqVSy64IJ6M199b/7d/9OkvSP//iP2fhXXnlFQRBk9bJ5OGxEUZRdQ6a9Y21/R2FpaUlBEGTBliT98z//s2Qt/4//+I+zWOLSpUvZvCjH5IU5nu+++252fm5sbGTpTHBs/M3f/I0qlYrOnz+fPYj78MMPVavVdP78eV25ckWyvvkxjb0sM9//+T//J5v+rW99K4vvgiDIYjMfE1teuHAhK7//9E//JHn2sVKpqNfrqVar6f/+3/+bpQnDUEEQaGlpSRp/U8j3iyAnyZEDe+PChQva3NzU9evXJatV/DCWl5fV6XQmKoOj+ta3viVZhffOnTtqNpva39/X9evXs5+DkaT/9//9f6VxS73vZ5pOK/uG9xe/+EVu2mFsbm6q2+0qiiJtb29rbW2t1M/aHMTnn3/ujtLTp0/dUZnDlNXbt29LkhYWFqZeiICXjXPYj3N4ku+Y/P3f/700fmBibp5eeeWVbB4zvSz7WL/11lu5aRcuXND29nbWQtjpdLS4uDjRIoQXg/Jw9oxGIy0tLenOnTvZh1B91tfXpfF9s+9nFKd5XvfN3/zmN91ROKDRaJRdP59nb7ujMD0Jer2e1tbWSl3vixz144x2j4ST6kiB/Y0bN7S6upp1h9nb28v+7/vy8r/8y79ob29v5tMOE9Cb7hbdbtdNkjHLMhW/Wa/95UczbGxsaDQaZReLnZ0dpWmqnZ2diRaoy5cvS+OL2f/3//1/kvVk8jSzn8w2m03duHEjO8b379/XxYsXJUnf/va3JUk///nPs2M/HA6zE+673/2uNP6N2tdee01bW1tZPponu7ZpZcMOVOzpZjv39/fVaDSyab1eL+su87u/+7vSIcqqMRqN1G63s+V9+OGHbhLgROEczjtL57A5Dv/yL//iTsopOiZ7e3szuxr2ej1vHh9Gu93W3bt39ad/+qd69uyZwjDU/v7+RIsQDofygFnMsQ3DMOt2bXpD2exeGqYlfnV1NZtuHtjYXfHNsLW1laWbxQReo9Eod92wXxUwQ61Wk6xy/uMf/zib1/TERbH79+9n3c8XFhayuMcEr7dv387O7Xa7nc33ne98J/v/i+S+qrO7u5t1xT8odx/39vbUaDQ0HA71+uuvS+NyZbrim+HZs2fP7UHVsbFfuPe9hD/NtA9W2B81cj++NBgMvB9PMHwfXXDTuh8+MB/I8H2cwwyDwSDd3d31Lt/9emc6/vqime5+wGUemG0/qMePH3uPkb28aR+osD9q4k6T9UGWtKBs+BRNL/rwipwPgJUpq9P2SeMy5n6J1Uxztws4DqZ8HRTnsH+Yl3PYbNNB+T6QdJBjYq6r9keyDPuXDczxdpdlBvdYFo0vul7bv8pQNO9ZYo7BQVEezhZzbA7C/gieO7gfnnO/YG/nwbRyZcqf74OohvtLVWaeadcHw1duzPXPt6555u57WdOO48LCQi5m8x1PM9gfjzPj7HLgq3PcfLfLiuFbljvftHsAsz7fslPPdhXt42BKnKgTWJ58+3qkwH53dzdtWT9DYXbaDZAHg0GWJgzDNEkS788dGI8fP879BEEcx9nfNrsisAtRt9v1ftHQfOV1WgG3LyB2AbFvZOeF2fbDSJJk4mcl3LwdDAa5ij70/ByGXT5MXtrTfWXDxz0pbS3nlw3CMJxYT5my6rvALSwspJHn69tuejdYAI6DKV+HwTk8v+ew2aaDcm+E0gMeE1MWfHmUer6eXHTz4/4ygkln37SlJX4e66Tmz4tmjsFBUR7OFnNsDqrr/CyYr9ykztfSQ8/DHvd6YgbzhXsTmLnLTcfXK1OeFhYWsnz31fsmje3q1au5YN6UFd+65pnZ/4Py5cvi4mIaOz9va7hx1OLi4kT94DuPfXWGKU+mnvDFf75lufOlTj4vLi5mv5pg1udbdmrVVfZP+tnlypR7w9w/uXWavS0ngW9fjxTYz6NZP9thFyr7ibR7AZoHpyk/p10QXhazTXbrJnCcOIefr5N6Dh8234tuaubVSc2fF+2weUp5OFtOU17DjzyGzVcejvSO/TxLkiT7EMz3vvc9afzOhf3uyN27d6XxexbmXR68HOb9rn6/f2zv8x0VP4EIlMc5/PxduHAhe9/0oF8rP4lOW/68aJQHADhbzlxg//7776vVauU+uLCwsKA4jrW9vZ37KMLNmzclKfvJBbw8lUol+6CL+ZWCl2lvb0/b29taWFjgJxCBEjiHX4yPP/5YOsTXyk+a05o/LxrlAQDOjmDclP/1H0Egfd2mb6fBnCI/gfnGOXw2ke+wUR5QBuXk9COPYfOVhzPXYg8AAAAAwGlCYA8AAAAAwBwjsAcAAAAAYI4R2AMAAAAAMMcI7AEAAAAAmGME9gAAAAAAzDECewAAAAAA5hiBPQAAAAAAc4zAHgAAAACAOUZgDwAAAADAHCOwBwAAAABgjhHYAwAAAAAwx4I0TdPsjyDITwUAAAAAACeOFcrTYg8AAAAAwDzzttjbkT/mF/kJzDfO4bOJfIeN8oAyKCenH3kMm6880GIPAAAAAMAcI7AHAAAAAGCOEdgDAAAAADDHCOwBAAAAAJhjJy6wbzQaqlar7uiXotfrKQgCjUYjdxLwQrXbbdXrdXc0AAAA8NK86Hip3W6/0PUdRb1ef6H370cO7M3BdYd2u+0mxRxqNBoKgkC9Xs+dVMiUiaIy4JaZRqORTRuNRhNlyR6Gw2FuWT4m7TTValWBp1Iw++sbZp2Y7n75tqFer+em2/s+TbPZ1Pr6ujs6q0zLLkfWMS56gObLA5d7nGYdG98yi7bBTVNUjkwemqHMMfBtR1HZNnlVZrkAjp97jvvqgjJ1atn66iD1g2s4HGbzuNcVU0+bwVfvAQBwVEcO7I00TbMhSRI1m00uXnPM3OB0Oh130lRBEKjZbLqjM41GQ81mM1dWOp1OdjNWqVRyZckMrVZLklSr1Zwl/pYJrKcxN1hJkriTJEmbm5sT6zY/I3Hp0iU3eabdbqvZbCpJkmyeKIpy29Nut3P7NxgMcvtexNxYrq6u5sZXq1Wtra3lxs3SaDQUhqE7OjMcDhWGobrdbuF+9Ho9jUaj3H70+/3Cm2Wbvdw0TbWzs5ObHgSB4jjOLbvZbOZu6E3ZXF5ezi1rc3MztyxXr9eb2Lc0TSeOq8Z5tbOzM/VYGSeplxFwWjQaDV25ciU7T1ut1kRd4Nap3W53ok5166skSbz11UHqB5+lpSVFUeSOVq/X09ramgaDQW4b3PUDAMrZ2NhQmqaqVCrupCOrVqsz78tPtNQiKXVGzdRqtbzzJEmSSkrjOHYnTRXHcRqGoTv6peh2u6mkNEkSd9JcOEx+GmEYplEUZfnY7XbdJBOiKMryTlLaarVy04uWVVSGbLPK0mAwyJZdtDyz/lardaC8NcubljaKojSKotw4s03T5jPHeRrfsuM4zvYxDMOpx8Yw+zwYDArPM9+6ivLNFkWR95gbZZZhjtdgMMiNj6Iot3++bSzDVyZ9zLYOBoNSx7boWB4HHeEcxvwi3/0kzTz3wzCceT7a1yqjbP3gE8dxGkWR97riq+PturgMygPKoJycfic1j31137wqc993EIe9Zy3DVx6OrcXeValUFMfxRIuv3V2tbJc0txud/cS+Wq16n3y76dxu0r6nMXaXvjLbdZrt7Oxoa2vLHT3V1tbWRAus7fPPP5ckvfnmm7nxb731ljQuGz4mHz/44AN3UqZWqymd0bpiWnY2NjbcSVM1m03FcTz1yWClUlG/38+Nu337djbtKPr9/kRvAdOz4CBWV1eVpunUXg++dVUqFYVhqEePHuXGH7dXX31VkvSLX/wiGzcajdTv9/XGG29k4/r9vve1hGlM2bp8+bI7aUKj0VAcx1OPkxGMe7UkSZLVHXY5dusuX10F4OU6SP3gGg6H6nQ6hT2GkiSZqFPNNfDLL7/MjQeA58H3nrf7XrzpDTkcDnOvQR0lXrLvfwLPq031el2NRiMXo5nlua8w2dxtN7Gl+zqV7/Ut9xUvU/+bZZiexL71Fs1rlFn/8/bcAntJevvttyXrotnr9bS0tJTrrlytVqcWikajoTfeeCNLb7rmmmVeuXJF/X4/906bKTjmIt1oNHTz5s1sGWmaTnTXazQa6vf72fSPP/74wN2cMd0XX3zhjsopusm5efOmoig6coB8GKYsvfvuu+6knM3NTYVhqGBccdXr9SzgK9JoNJQkia5du+ZOyphy/frrr7uTjp37Xqhr2vR+v+/thupaW1vLVXr2MiuVirrdbvYaj3ktII7j7GGMOe8fPXqUW45b+brMw4JPP/00N4/vQtfv9wtv0l1pmiqOY4VhmNUd5oGAKQN2vbOzszO1vgPgZ859N0i2jUYjJUmi5eVld1Km3W6r3+/r448/zsaVrR981tfX1Wq1pl6fnj59mvvbpJ11TQSAF21paUm3bt3KYq5Op5MLyMvESybAdV+tXFtbmwh2O52Onj59mqUzDSXXrl3LxoVhWKo+DsMwizHN/aQdfFer1Wzf0vErXktLS5LVQGjuO00aIwiC3Oth3W5XS0tLueW78z58+HCi0e95e66BvevatWsTF8Br164pSZKJpx7G5uZmroXV3DSbINAE73fv3s3S3Lp1KwsER6OROp2Obt26lU2XpFarletN0Ol0sve4NW7d7Ha72d84Pu4NkGmp9en1ejOD3+fp2rVriqKoVOvtzs6OoijS2tqa+v3+RFmX04Lb6XQ0GAymLvurr76SJL322mvupOfG9xBhWjBqKulpeWS/B2tX1GEY5oL71dVVDQYDJUmSVbZFQba7rGnbaLz99tvZPOZdW/sB39ra2rGc98PhUP1+X4PBIDf+1q1bU+s7AH4mEJ/W4+rTTz+VPL277NagZrOpbrfr7d01q35wmbpv2jaZnot2Pefe2ALASdHtdrP70lqtNtFjs0y89OmnnyoMw9z9W61WUxzHunnzZi5tFEW5dKaxxO4BbBpxZxkMBtl9t6nj7V6gOzs7uXtu02N4WsOVxnV2GIa5un51dVVhGGa9c83DD/v6s7W1VarR6zi9kMDeBG7J+KN69hNxc/M+jd3lIxi3zJkn3ZVKRVEU5QqK3VXXBEZLS0u5ZdgfeDM32SaD8Xy5J5DJI59bt24pDMOpwe/zMhwOlSRJqW7fpguPCWBNzxI32LQ/zmeCVzeNragXw/Pka0UqesViOByq2Wyq1WodOI8ePHggOQ/l6vV6rleP+XCf233L7UHx8ccfK0mSibLlsm/mK+PXhcx2NBoNRVHkveE/KJNv7kMr8/fLyFdgXplWdvdBma3X66nT6ajb7U48UDUfWjKD6TnkmlY/uEajkZrN5kSjgWtzc1NRFGU9uoIgyFrwfQ9RAeAkqVar2b1V2XhpNBp5723feOONqT1Zj1sYhhM9pnwx6LQ4RONeV6YngT3Y+/Lo0SOFYThx/XnRnmtgb57w2DvZarVyF1gzFAUF1WpVOzs7ubSu9fX1rBXMBADuzbn9Rdppy8LzM+smxm2VNq2edpfJF+njjz9WGIYTZclnaWlJcRxnTx1rtZqSJFGSJIWtM5VKRa1Wa2oLrntMnqdZlZE7fTgcZvs9rcWqiLs8c/OeJEk2bWtrS3EcT3TzOmxgPC3wf/Dggfr9/kSlbd61KsojAM9Pe/yLI3Yrkst8eb7VapWqr03rkvvAcFr94DLfjLEbDUw9FTrdRre2tnL3HaZF50XW7wBwlhW9HlBWaL1yaQ9FvUpfluca2Hc6HcVxnP3te3IyS5IkMwM7uzvErVu3cuss00JWlMbXconDMx8MMjdEhukm4960mXwvc6N23MxDhStXrriTJpibQfNNCcMNXH1mPewoKpvPSxRFevjwYW6ceW/V3r/RaDTxMOOgTKBsjsHTp0+9TzvtD+eZMuKem3YPHh/zdNl9Kms/VXYfIKbOu1Zu+TTs7TPMDbu7vpfxagUwr3q93tSu8xrXIyaoL/uA0T3/ytQPLvMxUnswDwySJJn68dm7d+++tJ5oAM6eSqVS2PPyIIruSd17sqL1mfu8l8HU7+6rWi5fnV+mp0FRGt9xeK7sT+T7Pps/i++nxczPRbk/J2PS2j95lSRJLp3701HuT8WYn9Vyf5bGLFuen5Ax89g/w9Dtdid+QsveD/PTDe588+Qw+ekq8zNlPr48Sj3H2fzEmZvW/mm6g/KVSdesn+awf07OR87PLvl+YslshymP5ueQbPKcJ65Zx+EwP83hnmeGOS72drr7ZvLmID/fEcfxzH33rduks9dl8sbkna+suOUs9exHmZ+cKnNsi34O0V1f6tmXWXQM5zDmD/n+2/NzWt1nrh/TztE4jieWEYbhxPF1z1df/TDr/J11XUk914UyKA8og3Jy+h02j936zBfjFN3vuz/X5t5f+Zblu7f33eO5y04L7k/d+3q3ri36uWT7Hs53r2i2254v8vwcatF9bxRF2bwmjS+2dOc7Lmb7c+Nyf3gSzGIH1PbgFgzDLgC+9bkZag6UGVqtVhqG4cSF2qRzM8MwwYAZfAfZXOzNdFNQpl2kTzLf8S3LPhazjpthCrA7uHnipvOVFTd4m8XklW8wy3fLkj3Y5cl3crrkORa+Y2Zvv2/97jJ8iio+d1lmKOI798xgV2puuoOs2z0vDd+++46vu+6idLPKkJnucpc9q3yVCexTJ+/tZbrbWWZZNjMfzhby3V+fmsF+WOpOM4NdJ7jT3GuS4aZz6wd56kObe7OZFtSXB3XY+XC2UE5Ov6PksV0XhWE4EeOY+zTf/ZRb75WJl3z3fW7g7Vu2GwemxxTYp557TPO3PZ+93e52uPvjHis3FumOG5HdfTwuZj22YDxBGn9QYNyKbzfqz40gCA7UHe+0m/f8xNfM+6Pk49nDOXw2ke+wUR5QBuXk9COPYfOVh+f6jv2LZD5QZn7+DjgtzLul7oeeAAAAAECSTk2LfbVaVbVanfrBmrNmnvMTee12Ww8fPqR8nzGcw2cT+Q4b5QFlUE5OP/IYNl95ODWBPSaRn8B84xw+m8h32CgPKINycvqRx7D5ysOp6YoPAAAAAMBZRGAPAAAAAMAcI7AHAAAAAGCOEdgDAAAAADDHCOwBAAAAAJhjBPYAAAAAAMwxAnsAAAAAAOYYgT0AAAAAAHOMwB4AAAAAgDlGYA8AAAAAwBwjsAcAAAAAYI4R2AMAAAAAMMcI7AEAAAAAmGME9gAAAAAAzDECewAAAAAA5hiBPQAAAAAAc4zAHgAAAACAOUZgDwAvQLVaVa/Xc0cDc4syDQDAyXFqAvtqtaogCNRoNNxJqtfrqtfr7mhpPC0IArXbbXdSznA4VBAECoLAnZStu2jwbRPwMpnyXHReFCk6B2zmfBiNRu4kyXO+DIdDN8kEc56aoSiYaLfbCkqczyZdtVp1J01ltmPaOd1oNLzH9sqVK7p27VpuHDCver2ekiTR6upqNs6cV2bwnV+9Xi+XJvDUJ/b11gy++sStF2ad94Y7X1F9Ys7loukAAJwkRw7szUXad9E9isMut9PplJ5nNBqp3+8riiLdvHnTnZxz+/ZtRVEkjbfNtrOzozRNlaaput2uJClJkmzc5uZmLj3wMtXrdS0tLbmjpzI37NOYczZJEndSJggCLS8vZ+dGq9XS0tLS1ODeBAf2Oba2tjZxHgZBoGazmRvnMxwO1Ww2s/O5rHa7rZ2dHYVh6E7K9Ho9dTod77IvX76sJEmm7iswL27duqU4jrO/h8Ohbt68mZ2nSZIoSZJccD8ajbS2tpalSdNUGp+7tqWlpdw1NAxDhWGYu7bX6/WJa2+z2ZyoF1xl6pPRaKQgCNTpdKw5pytTRwIA8DwdObA/SeI4VhiGU1vTbHfv3lUURbp27drMG+5Op6Nr164piiLdunXLnQzMhXa7rX6/ryRJvMGnjwmEu92uWq2WO1mybthbrVb2cMtlWtPsB10bGxsKw1C3b9+2Uv6WaRW051ldXZ04D+v1usIwzAKFadbX19VqtVSpVNxJhUajkZrN5sxzf21trXD/K5XK1H0F5oV5KP72229n42q1mnZ2drK/K5WK4jhWkiRZQF6pVCbOUVOn2NffNE1z5+fHH38sSfr888+ztP1+Pxsvq16Y1iumbH2yvLysKIqmPqQEAOCkOVJgX6/Xtba2JkkKw3CiK5zb5c7tmmqeipvBPDGftdxpPv74Y/X7/alBunHz5k2tr6+rVqtNveHu9XoKw1C1Wk3r6+vq9/ulewUAJ8nGxsbETfMstVpNaZrmuty6zA37xsaGOynz8OFD78OE5eVlPXjwwB0tSXr06JHCMJzY3kuXLqnf72d/b21t5YKKIqYembadPo1GQ3Ecq1aruZMyjUZDURRNPU7T9hWYFybAfvPNN91JL8SXX34pedZ/6dKlqcF42fpkZ2dHW1tbuTTTVKvVrLeQez+jkq8oAABwVEcK7Le2tia6npsb5na7PdHlbmdnJxfch2GoVquVTTdP2qctd5bV1VWFYaj19XV3Us5wOFSSJNmNwfLycmG3u1u3bml5eVmybiTu3r3rpAIwzbTAu+hmfNYDtFnTbWVb3V29Xk/9fn/qKzXD4VCdTmdqGkl64403CvcVmBdffPGFNH6gN82DBw+8gbTt4cOH0vgBYpFHjx5JnkD+q6++yv39+uuvS1PqhaLxxqzpRXZ2drKeB+Z+xjzgazQaajabuVcLzHdGAAA4TkcK7KcxXXdtpjW96OI57cb/IG7duqUkSaa+a2femTc3HO+++67keX/edDk00yuVSql38gFM8t3gv/HGG+6oHF/rlrmBP4gyre4+07rXG2W7988KPIB58PTp06nfmpDV7d3uLu8yXeqLXvHR+FzpdDqK4zg7v0zQ7C57Wjd847jqkzLMtne73VzdYB4AuvcbAAAcxXMJ7M1N69raWq77meleb7RaLTWbTQUlv4ztdu0veuJdq9UURdHE+mydTkeXLl3K/jbd8d3WPNMybwcD6+vrM9/JBzDJF9A+ffrUHZXje+BnWgzLKtPq7lOme/1huve7LY3APPGdx7bR+JsbcRxPPXeWlpYURdHUc2d5eVlhGE6cu0mSqN/v5+4HTM+6aQ/YjqM+Kcuc56+99lpuvNm+57VeAMDZ9FwCe6Pb7ea64pvBXNTM+75xHGtpaakwUDdWV1cnllXE3AT43s03T8nNQwUzmBsF+6bFtMz7HlAUvZMPYJKvpcwoav2bdoOuEtMN05XXPo87nY6SJFEw5RseDx48mAgekiRRp9NRMH4g+fDhw2w5Zuj3+9l8vla5V1991R0FzI1p591oNFIYhoqiaCIYtwVBoDAMp77LXq1WlSSJNxivjL/rYQ9vvPFGYV2iGdutEtMBADjJjhzYu0+idYin0Zubm9l7p+Ym2Lfcg6iMv8jr+/mrW7duKYqiiZsCsw2mld68hz8YDCbSxnFc+E4+gEnuB6qMBw8eZC1trrfffluJ9VVto+hDfEU2Nze953A4/pJ+UYuh/XNaZgjDUHEcK01T1Wo1bW1tTaSJoiirY+wWy7LvJgMn2bRvRZigflbAHoahN2A36vV69l56WTdv3tSVK1fc0Znjqk98fN35zQM887E/w6zfNw8AAId15MDeMF/JNUxQbXdXHw6H2cfz7P/Lmt8N6N3lHoRpLbCDCfPOvO/jepXxz1GZj/ncvn1b4fhr+C7zMz90xwfKuXz5sjTu3m40Gg0lSaIPPvjASvlb9geoDPOTfWXepz2oRqOhIAgmbvyPS5l3k4GTruhbEUdthTfq9br645/lLMv0CLIf0rnn84uoT+x7gsr4mzzua4Hm9YJprykAAHBgqUVS6owqJY7jbN5ut5uNb7Va2XhJaRiGhfO587rT3WmuMAzTOI7d0Wm3200lpVEUpam1TUXM9CRJUklpq9Vyk2QkTazTrC9Jktz4l8EcO8Bwz0l7KCqzg8FgIq0ZzHlpzhffYJ9DvnRluPO42xpF0UQaeeocWxzHE9NNneMu31ZU19iiKMrqHFuZeW0HOUY4PU56vpvzeNr13h5MmTfXR99gzpdp9Y19vrrTfOdV0fnszutOD8NwIo2sbSxi10ODwSAb797rzFqOy8wHTEM5Of3IY9h85SEYT5DGT9vHwb4d+2NOkZ/AyWHePR4MBt5eQD6cw2fTPOR7vV5XpVKZ+h49jsc8lAe8fJST0488hs1XHo6tKz4AoNjdu3cLX+0B5s36+jrfmQEA4AQhsAeAF+DmzZsTv7sNzKvV1VWFYej91QcAAPDi0RX/FCM/gfnGOXw2ke+wUR5QBuXk9COPYfOVB1rsAQAAAACYYwT2AAAAAADMMQJ7AAAAAADmGIE9AAAAAABzjMAeAAAAAIA5RmAPAAAAAMAcI7AHAAAAAGCOEdgDAAAAADDHCOwBAAAAAJhjBPYAAAAAAMwxAnsAAAAAAOYYgT0AAAAAAHOMwB4AAAAAgDlGYA8AAAAAwBwjsAcAAAAAYI4R2AMAAAAAMMcI7AEAAAAAmGME9gAAAC9AvV5Xu912RwMAcGQE9gAA4KXp9XoKgiA3uIbD4USa0WiUSzMajSbSDIfDXBpJajQauTSNRsNN4lWtVkvNZ/bHN319fV3NZtMdDQDAkRHYAwCAl2I0GmltbU1pmmaDpIngfmlpSUmSZGnCMFQYhrngfnl5WYPBIEsTx7GWlpZywX2j0VCn08nSDAYDdTqdma3o1WpV1Wo1my9JEnU6nYngvVqtam1tLTfOtrq6Ko2DfwAAjhOBPQAAeCkqlUoWzButVksat9IbaZqqUqlkf3/88ceSpM8//zwbt7Ozo1qtlv397rvvSpK+/PJLafwQodPpZMuXpFqtpjiOp7aij0YjJUmi9fX1bFylUlEcx7kHC41GI3v4EIZhNt4VRZFu3brljgYA4EgI7AEAwKn31VdfSZLeeuut3Pi3335bGgfwPuaBwqNHj3LjO51O7mHD5ubmxEMKn0uXLqnf77ujAQA4EgJ7AABwYjx8+FAat6YXMUH2m2++6U7KLC0tKQzDrPu7YVrwjddee02yAn8f02XfvNsfBIGiKNLm5qabdKbXX39dmvIgAQCAwyCwBwAAJ8JwOFS/3891l3eZLvVxHOdazDV+N98MYRhqZ2cnm1ar1RSGoa5du5abx3Trn6ZWqylJEknKutkfJqhXyQcJAAAcFIE9AAA4EZaWlhRFkTY2NtxJmeXlZYVh6A2s7Y/wXblyRUEQ5D6Mt7OzoyRJcg8AzMOBV1991VpSXqPRUBiG2cf54jhWGIYzP7o3jdtzAACAoyCwBwAAL51pZd/a2nInZarVqpIkybXEF9nY2FAYhrp582ZuvB38p2mavWPvtv4bvV5PnU5Hg8Egez1gc3NTrVZLzWbz0F3qTcs9AADHgcAeAAC8VNVqdaLrvKter2dfnS+rWq26oybcunVLcRy7ozNffPGF5Hnn37wrf1CmpX5aDwEAAA6KwB4AALw0ZVrh6/W6+v1+9p67y3zQztbr9dTv93XlypXceJtZrt2tv91uKwiC7Of2zFf03W73165dUxiGhS39RcyDgoPOBwDANAT2AADgpej1elmwbr/3HgSB6vW6ZH1QT+MP19lpTIt8pVJRq9XKTVtbW1O32829r1+tVnNpNO6aP02tVtNgMFCz2ZxYt/0wotFoZNOSJMm+om/WYzx8+FBRFOXGAQBwVEFqXdHKXuQwH8hPYL5xDp9N5PvpFgSBut3uxM/wFaE8oAzKyelHHsPmKw+02AMAALwAvV5PkkoH9QAAlEVgDwAA8ALcunVLrVbLHQ0AwJHRFf8UIz+B+cY5fDaR77BRHlAG5eT0I49h85UHWuwBAAAAAJhjBPYAAAAAAMwxAnsAAAAAAOYYgT0AAAAAAHOMwB4AAAAAgDlGYA8AAAAAwBwjsAcAAAAAYI4R2AMAAAAAMMcI7AEAAAAAmGME9gAAAAAAzDECewAAAAAA5hiBPQAAAAAAcyxI0zTN/giC/FQAAAAAAHDiWKE8LfYAAAAAAMwzb4u9HfljfpGfwHzjHD6byHfYKA8og3Jy+pHHsPnKAy32AAAAAADMMQJ7AAAAAADmGIE9AAAAAABzjMAeAAAAAIA5RmAPzIF2u616ve6OBgAAAI4kCAK12213NObMkQP7drutIAiyL/PZer2egiDQaDRyJ+EUMWWgTIUwGo2y8mKG4XDoJpMkNRqNXLpqteomyZbnm+bjLtMXLNtletqy6/V6Ll2v13OTFLKPQ9H+25rNptbX193R2TnWaDTcSV7VajW3zW6emeW5g7v84XA4kWbWfsw6ru70aWnl2Rd3G12+suerm9w0s/brLDPHyOUrH0HB+WaY/HTLZJEy9c6s88PdvjLnsDuPGYyic8hNh9ncc9yX1249XJTXvjLpO7fN8nzTfGZtY1F5KNpOPD/HWR/Y5cl3HXG55aRoG9zyPK3OPGuOI//cfHDPV9tB79PKrF8F9zrAsUktklJn1EytViubr9Vq5aZ1u91UUpokSW78QQ0Gg1RSOhgM3ElHkiRJKintdrvupFPhMPl5UGYdvvz3CcMwl49RFHnzNgzDNAzD3DhXHMfZumelTcflMYqi7G9Trtxx7rJ8yw/DMDefKetly5KZ37fvLrNsVxiG2f7HcexOnhDHcS6PzLlrjytzzprjZu+rWVbRvrjH1Zx77nH1CcMwt39m3jL7bJh57H01x9/mLteUsWnHI47jUvtxGCZ/Txq73vdt30Hr7DiOs/woU4/Y6y5KP+v8iKIoV4ZNXs86h6etc5ooinJ1xjRFx/UsKVNftVqtXN6acufm96z6KbXmNcO0tIa7jab+9I2bVofMQnk4uuOuDzS+dyiTt+79QtE1zCzzsE5zOTmO/CtzvtoOcp9WZv0m3VHy6Hnm8bRjgZPJVx6OLbA3/9oV3HFc0NJD3CSWRWB/NFEUZQHNYSsEk7fuzdqs7TZlazAYHCmwKlPJmgraKCrXZW/cW61WGo4fcJQp177l2tvkBr4HIedGomjfbEX5c9BzyezDtHXZ+Wz4jscsbh6mnmDfV4el4/2adnyPUv5m0XM+hw/DlNtut1tYFsqW7dRK6+ZHkTL1zmHOj6KbbVfROqexj1kZJzHfTwKVCHyKgqhZx97k/UHKro9bhsrUqbNQHo7muOuDePwgskzeFpU/swz776NeR05rOTnu/LMVpTnofZrLt/4y5WWW55nHOsS1DS+XrzwcuSu+sbGxIUn69NNP3UkT3K5GRd1qNO46vbS0JElaWlqaSO/rXuuyp5luN+12W2EYSpLW1tYU0OXpwLa2trSzs+OOPrKHDx8qjmN3dM7q6qrSNFWtVnMnPXePHj1SGIaqVCq58ZcuXVK/38+Nc41GIzWbTd26dcudVKjf7+vSpUu5cZubm/r6nH7xXn/9dWncFdEwXc5ee+21bNxxuHbtmqIoyuVzv9/3vpYwzWg0UhRFuXGVSkVhGOrp06eSpKdPn3rzNYqiwq6WQRCo0+koSZKsjrGPS5lXP+ZNrVZTmqZaXV11Jx3K+vq6Wq3WxHEvUqbeeZnnh8/HH3+sMAyP7ZihvM8//1waXzOmSdNUm5ub7micAsdZHwyHQ3U6ndJlxdRrjx49yo3vdDq5Ou/Bgwe6cuVKLg2+dpz5V8Zh7tPKePTokaIoKn2tO25u7FX0qoB93+J7DdIXU+HkOLbAXpJarZY6nc7Ud1Gq1ap2dnaUft1bQGmaqtPpFN7wbm5uajAYSJIGg0Hu4jscDhWGYTY+TVPFcazACu6r1ariOM6mP3z4UKPRSBsbG0qSRJLU7XaVpqm2tray+fBi/OIXv5AkvfXWW9k4ExzblYcbMB2Xfr8/EfDZer2eOp2OWq1WNq4oyDOmTW80GorjuPQDCbMsE0wfJ3M83YcGkhSGYWHFvrq6qjiOtbS0pEajoXa7rbW1NXW73dL7pfGNjC+QNobDoZIkyQXxZpsfPXo0UT5m8QWD1Wo1l1+mTrBVKhXvvBoHA3EcKwzDrI4xx6Ber6vT6eTqup2dnYnjeZqZh7FFeWRuCsyD4ZfJBIBvv/22O2lCs9ksXTeNRiP1+31u2o9oWn1lNBoNJUmia9euZePMg1j3pnZag8JhmfuYDz74wJ00tU7FyVNUHxz0QaTG966dTkfB+H38IAgURVF2LzsajZQkiZ4+fZoroyY9Dq4o/2xF5+tB79N8fOt/8OCBKpXKRB4XBdjHqV6va319PbsX6Xa7WltbmyhfzWZTb7zxRpbOzGsUxVQ4Qezme1+T/ixuV0y7G5zb7cT92ygabxR1hYmiaKILjdvtaVq3HTftaXOY/DwsHbILjzxdK+V599p0xfKVkcN2YTNl1y1Xpjyawd2voq7gs8qxmW4UlWtbmTTTyvg0UYnXEMw54ju+5vj58nAWcyymnXt2l2vDHA93f8MZ32Twrc8sy2y7+dvO72n7b/jKX1G+FY0vYo7vSeXW/9O4+2KOrX0s3OM/S5n0Zc+PWWWoiDmPivLU7kZalnusUFxfmeNrBjcfzHR7vO9cd6e5yylitssMRfW/UaZOcVEejs9R6gPTPduYdc23mXz3lRMzzb2OFpX5ImehnBwl/9IS5+th7tN8fOsPx98JsBXdhxY5rjwue/11y3jZ448Xw1cejrXFXuMuh/1+3/sE6osvvpCsrkmG6b771Vdf5cbPsrOzkz0FNYPpXm98/PHHWRrfNuHlMU8BfT0l3NYt80TVPAU9quFwqGazqVarNfFU1nTzN8PNmzcnnpz7WnBN+S5iWrUP4ssvv3RHHYt2u61+v5/1hilSqVTU7XaVJEmuVTIIAt28eTM7Rv1+f+IYFRmNRlpbW1Mcx4XdY2e1cr777ru5vz/++GMlSVK4/tXVVbVarey1myAItL6+nusWV6vV1O12c62xYRgqjuMDt7CZfHv11Vdz483fzytfTzJT1kw9fBytIsfFtPQ+ePDAnTSTqb9u377tTpLGXW5nvVqE6abVV6abbpqmSpJES0tLE+drGIa5clar1RRFkR4+fJhLdxhbW1vZ+geDQdY7oEhRnYqjO66WUF99MDpC9+xGo5HrXWp6ebndmN1XzEzPk7NSTp5n/hmzztfD3Ke5pq3fvRaY3mqm9+rzZP8igImVZt2LuPEZMdXJd+yB/erqqqIoynWFe57sLiH2YAIGE6TZN/VFN/94cer1uvr9vrfrs8bvOz8vw+FQS0tLiuO4VBdgcyG/e/eu5Hkw5fJNNxdmO7C0vx3h3ogax/3OusY3yc1ms3TXeXcb6vW6wjDMPdxI01RhGM7s3joajRSGYa4boo/5VkdR/sy6GPlsbGzk6oidnR3t7OzojTfeyNK4D3XSNNVoNPLmKQ7GfcjR7/cnHszK6ub+ourpRqOhTqejwWBw6Hx2Hygb5sbd7eqJ8g5SX1UqFbVarYmgueg6c9xqtZriOFa/359aft06FcfDrbuLHhxPU1QfmEYF+/WitbU1aXz+Fz3MMa/zDQaDrPxubm6q1Wqp2WzmysmsxoHT7nnmn497vh72Ps02a/3T6oXnKRi/AmSO7WHrRGKqk+/YA3uNnzImSTLxZNO8J+wWgqLWLaNovPt+7DTmpl4HCNDwfDQajSyo9+WB72Nl5mnhUW+IRqNRFtRPCyxtbvl7++23va3DDx8+LHxf33xwzB7sb0f4egDoObTw9nq97Ca57EXTPEk227Kzs6Pl5WUn1dfn4ywmqPf10jBGo1FhK6e5MXJvgIp6A01j3uG/fPmyOykzGvcccHsI2OwHA4b7lNs4rnI8j8yNsdl393ww9XOr1VKapgfKy8Nqt9sTN90HNRq/H+srB81m86V+LGneHaa+cr9HYt5xdevrnZ2d55IvvnLgcutUnAzT6gPfQ1/TspskSeE1zVyb3OXZ5bTifMjVmHVvjLxp+VfEPl8Pe59mzFr/8vLyxDJMveTWW8fJrOMwDa5FZdAXU+GEsPvl+/rqz1L0jqX93pv9Douc94jMex7T3tkwaYre/XDHm+1JkiT3jot5V8Z+z9a37qL1zZvD5OdhlT1evvcdXb73oUPnJ4xsvnecfYreY7O1nN9FTq13sqaVY997Ur53rGxl392adWzLvvNUdL7YwjDM7afZRnv5Jg996aYtWyXfKzXH0n33zXDX7ztfZ72bWGZ7y9RN6ZTt9eW/W25m0Qs8hw+jqP6PoihXrsuce2mJsu4qk77o/DDbPu07D+553Wq1Jtbne28ytc63Wee3z0nP9xehTH0VOb8dnRbUM27ZKzpn0xn1sruccPxzWIavnJepU2ehPByfo9QHLvf9Y3ucWU7Rtca9Prh1TVpwfzrNWSgnR8m/Muery1cf+PK0zPp980WebwlNc9g8dsuSuW658ZC7ffbxSWbEVL4yjOfLVx6eW2CfWstzL56mQPkKURGzHje9KVj2YK/Pns+dN3U+lGYKvS9QmEdmv54XEzy5Q1ElZY5r0WBz882tyO18c4eiSsUEhL7Bzmu3fKrgGLppfOW86FikBRcMn8jzsb5p+1LEt19mMNvgO66+C5Vv/b50hpuf9uDmrWZcaFNP2XPX7Qb2vv1yj7uvLnGXW8Q+tnY5cLfT3ddZzHwnie84ucfLl6ZMfVomnXtMzWCfa77yaQbDHW8PJg99Nypu2qKyOuv8n8bd1rOoTH3lu6b48sOXzuVON4N9zrrL95Vz9xz31T1l6xXDzIfDOa76wGXydlpgnxaUE185dbdzVl3oMvOdNu5xsQfDHW8PJn98+eCery7ffZoZ5wbARYOvfJjBVw6mMfMdlLvvvnKq8X7a11h3+9x7OfsY+K6XeL585SEYT5DG72CMW/HtRv0zp9fraW1trbCr+LwgP08HUx7Jx7OHc/hsIt9hozygDMrJ6Ucew+YrDwT2HtVqVVeuXCn8cNe8ID9PjyAIDvSeKU4HzuGziXyHjfKAMignpx95DJuvPDyXj+fNM/NVzHkP6nG6tFqtiY9RAgAAAIBosT/dyE9gvnEOn03kO2yUB5RBOTn9yGPYfOWBFnsAAAAAAOYYgT0AAAAAAHOMwB4AAAAAgDlGYA8AAAAAwBwjsAcAAAAAYI4R2AMAAAAAMMcI7AEAAAAAmGME9gAAAAAAzDECewAAAAAA5hiBPQAAAAAAc4zAHgAAAACAOUZgDwAAAADAHCOwBwAAAABgjhHYAwAAAAAwxwjsAQAAAACYYwT2AAAAAADMMQJ7AAAAAADm2AsL7IMgULvdzv3daDRyaWbp9XoKgkDD4dCddGjudgHA81CtVtXr9dzRwNyiTAPAfBoOh8ceU03zPGK456Ver6ter7uj58KxBfYmw+xhNBq5yQCcAKZCL1tx1ev13Lld9FDOLNceiirxarVa+sGau35fMGGW50vj2y57mFVXzVq/b/nuMq9cuaJr167lxgHzqtfrKUkSra6uZuPa7XbuHKhWq7l5VHCvUMQ9p916x12fO30Wc1775pt1zgMAcNIcS2Bfr9e1tramJEmUpqnSNFWSJArDcOLm1kjTVJubm+7oqVZXV5WmqWq1mjsJQEn1el1LS0vu6ELtdluVSiU7t7vdrjqdzsTNcLvd1tLSkgaDQZa26HxtNBrem34fk85e/9raWu5Gu1qtqlqtZmlarZbW1tayhwq1Wi23TWaI41hhGKpSqWTLcpVZ/9LSUq7+i6Joov67fPmykiQpfNABzJNbt24pjuPs7+FwqJs3b2bnQJIkSpIkd56PRiOtra3lzsEwDCeC+9FopCAItLy8nEtr3zM0Gg01m83c+nz1UpF2u62dnR2FYehOKnXOu8xDBgDApOcZw1Wr1dJ1/6mXWiSlzqiZut1uKikdDAbupBxJaavVcke/dCd1u47DYfITp1ur1UolpUmSpFEUpVEUuUlKCcMwDcMw+ztJklRS2u12c+l8BoNBtg2zzj9TvyRJkhtvb3tRHRSG4dT9O671+0zbpjiOc+Om4Rw+m056vpc93+M49p4/NrtOMmadX0XrN8uaxcw/GAwmzsnDnvNl130YJ7084GSgnJx+x5nH5l7MvU+ZR249flSz6vuTwlcejtxif+3aNUVRdOAnMO7TlXq9rkajMdG1zlb0Poid3m0FpDsd8FsbGxtK03RqC/VhfP7559L4iews6+vrarVapbbh0aNH3hb1S5cuqd/vS5K++OILadwqb1teXtbOzk5unO3TTz+VxsekSJn1H8Ty8rIePHjgjgbmijnf33zzTXfSsej3+1pfX3dHZ4rW/9Zbb0nje4VpGo2G4jieqDN0yHO+Wq2q2WxK1v2Ifa/h3te49ykA8Lw1Go3C+MrmvgLlvi7ZaDRUr9cnXquS57VEu9eiG8OZnlnD4TC3Tl/Lu7tNZhlmmabHlr0txqw40GyHGdz9nTdHDuyTJJm4AB5Wp9PR06dPs+5vYRjOfAc4CALFcZzNc+XKlSxT6vW61tfXs2mmO13R6wEAZhuNRkqSRMvLy9k4czPsVqBuBW3OzWnBtG3WuWpPd9O+8cYbSpIkN87W6XRyXYl93GW6fNNNd2PfA89Z2wTMA/Mwbda1/8GDB94g2RgOh2o2m4rjOEtjbtgePXqUq0vsmzWz/iJffvmlOyrT6/XU7/cLXwX0ndM23/SdnR21Wi3p66YTpWmaPeQ0rwzYr+qYm1QAeBHa7bY6nU5WDw0GA+8rmcH4waOpq5IkUbPZnLiX6/f7unXrVi5eC4JAS0tL2bgoinL3iUWWlpayZQ0GA3U6nYlXLe11tVqtbNvNa5ZhGOZiQXte+1XSwWAw8VqVO+/Dhw8LH+LOgyMF9uYC98Ybb7iTDiWKotzF9sqVK1MPrgkS7Hk2NjayoGFrayvXgmie7n/11VfZOAAHY1q6P/jgg9z4JEl07dq1XAXa6XSy83Q0GqnZbOrWrVu5+WbxtW69/vrr2f8vX74sWdtlmBY0H7NN7j74zFq/nCe+YRgqiiJtbW3l0siazxccAPPi6dOn3nfTbebjeh9//HFuvN2is7S0pDiOC4NsU5eYGzf3XHQfGLz66qu5v33W1tbU7Xbd0TnueuQ558sYjUbqdDrqdru5bTX767YcAcDzcPPmzdwD1FqtpsFgkEtj7ovse5dKpaJWq6VOp2Ol/DoYttNduXJFGtfZxvr6upIkmXm/0+12s0aQWq2mMAz16NGjbPrOzk6ukcT0zJq1XHMNsq8vtVpNURRl96GmDrbvBbe2thRFUfb3vDlSYP+8zboJLnNzYXffMGmnPc0HUKzX63lvVDWu6O3K11SgDx8+lGZ0f53G153ebrGrVCrZQwS7dc98GM/n5s2biqJoYh98Zq1f422wg5BKpaLA0+XL4OEi5lnRNdkwvVbiOJ54Pcf9kKV5KOZ2n3/33Xdzf3/88ccTN4nudsw6rxqNhqIomtgmV5lzvgyzPa+99lpuvKl3DrNMADiI0biX5dtvv+1OyimKqWbFYsetWq1OrMu+tzOt9bPqe1O/2vMGQZBrMC569WqeHSmwr1QqCsMwu3E/aQJPlxIAh9Pr9bS2tqZWq+W9MZ51fvX7/YngW+OW9cDz83DytMi57KfPdrBgnhr7Wt7MU9wyPz1Xdv0u84S4qHdCmZZF4KQqKvca3/yZXitFLfE2k+b27du58dMewM9qPXcDaePBgwfq9/u5Oiix3s0cDodT900z9h0AcHzMg1+7q7zb02AW994wTVNvj8rT4kiBvcYfg+r3+96b8udt2vuqZnvK3LwDmG44HGZBve/9ePMk2K0HdnZ2shtht2I1wXer1VJa8EG/t99+e6KVTpIePnw4s6tUp9PxfoDr2rVrE70Lihxl/b4n32XfTQZOsmnX3mmvovi454I5L93WbPvcMa/VmY/oGb/4xS8kz4c0jZ2dnYk6KLTer6zVaoc+530PG8wDPPchhVm2bx4AOE5FPYTceqmoXn+Z9y2mVX7Wa5O+RpwyPQ2K9tnXa2teHDmw39zcVBiGCj2/WV/UCndczLu19kcd2u129rvbcloBynzEAUDecDjM3oX1BfWyvobvnotJksyskKcpWm6/35/60C4IAm+X2+FwqMTz3q9hvhpr6q0y6zdfdLWZfXcfLBR1dQPmSdENUzB+5a0oqO/1ehMfxDXnlt31Po5jNZvNbPmj8fc5zAfqKpWKoijS2tpaNo/5EJ9JI8/5XEaZc34a+5UC33ZqfC8ShuFE/QQAz4Nbp5rGGpsvpvLVqy+SeTh69+7dbJzvo38a98iymfrVjf0ajUb2mqRvn+v1ujfYnxv2b9/5fg+vLPN7tfZg/w6snN+Ldn9z0Pebge7vyfp+c9H8Hq0Z7N/WNunNYJZn//atu12nidlvwDC/tewb3N9tNqIomkhrn1OGey6WKXsqef65y7W31bfeomWafSlS9Lvb7vLd6b76z/fbsG69N4tZFs6Wk57vvt+Rn1a3uNd6d7p7PvnSub9ZXyZN0flsKzony2yjy94e+/x36wf3XmcWMx8wDeXk9DtKHtv1UxiGWT3u3qvYdZUK6lU71kqt+t82K4bzXUdSTzxolmNvj7vd9n2gu21hGObmd+8PfbFiHMcHrqdfBrPNtmA8QRo/bR8H+3bsjzlFfgInx2j87vFgMCjsKuziHD6b5iHf6/W6KpVKqffocTTzUB7w8lFOTj/yGDZfeThyV3wAwGx3794t/W4/cNKtr69P/AQSAAB4eQjsAeAFuHnzZuG7/cC8WV1dVRiGhT/pCAAAXiy64p9i5Ccw3ziHzybyHTbKA8qgnJx+5DFsvvJAiz0AAAAAAHOMwB4AAAAAgDlGYA8AAAAAwBwjsAcAAAAAYI4R2AMAAAAAMMcI7AEAAAAAmGME9gAAAAAAzDECewAAAAAA5hiBPQAAAAAAc4zAHgAAAACAOUZgDwAAAADAHCOwBwAAAABgjhHYAwAAAAAwxwjsAQAAAACYYwT2AAAAAADMMQJ7AAAAAADmGIE9AAAAAABz7EQG9kEQqN1uu6MBAADmVr1e5/4GAPBcHEtgPxwOFQSBhsOhOwkAAKBQr9dTEAS5wWXuM+xhNBq5yaRx8Gynq9fruenucnq9Xm76LI1Gw7tcW7vdVhAEqlarufHr6+tqNpu5cQAAHIdjCexRjrnQAwAAaTQaaW1tTWmaZoPGwbdtaWlJSZJkaaIoUhiGE8G9mc9e3tbWVja9Xq+r2+1m0+I41traWungvtfrqdPpKIoid1JmOByq2Wx606yurkrj5QAAcJwI7AEAwEtRqVSyYN5otVrSOEA20jRVpVLJ/l5fX5ckffXVV9m4RqOhMAxzgbxra2srC64l6YMPPpAkPXr0yEpVbG1tTd1u1x2ds76+rlarldteWxRFunXrljsaAIAjeW6BfeB5T77RaEx0S3O74Pm68/u64AVBoEajUZjGXU+73Va1Wp1INxqNNBqNZm5DtVotTGOW6S7H3v9qtZp1vzPTeWIPAMDxePDgga5cueKOPjaNRkNRFOUeDLjMdX9jY8OdlLl06ZL6/b47GgCAI3lugX0Zw+Ewe/ptusUtLS3l0oxGIy0tLeXSaPxEf3NzUxo/HHC76VWr1YngPkkSra+vT3TlC8MwmzeO44ltCIJAV65cyebrdrtaWlqaeABgL6fb7arZbGZpdnZ2slYIs5xpNwcAAJxFDx8+lCTVajV3kmR134+iKEszGo2UJImePn2ae8BuHroX+fzzzyVJb7/9tjspZzgcqtPpZPcdPqPRSM1mc2Zr/Ouvvy6N0wMAcFxeamB/+/ZthWGYC3DdLnnmomunieM4u/BL0rVr1ya6vV27dk1JkkwE3zs7O9n/TVe+JEmyed99913J6gLYbrcVhmHu6fvq6qrCMNTt27ezcZI0GAyy5Zjt/cUvfpFLAwAA/IbDofr9fvYg3LB7xIVhqCiKvF3uR6NR9vDcfoBf5Nq1axP3IT6zutdr3KIfx3HhAwnjtddek5zXCAAAOKqXGtg/ePBAy8vL7uicN998U3I+NNPpdHTp0qXs7yRJ1Gw2c0/o3Vb3w3r69KmSJJloAUiSxE06IQxDPX361B0NAAA8lpaWFEXRRFd28y6+GSqViveVNvPA3rh27ZrkvK9vNBoNJUmiBw8euJNyynSv7/V66vf7U1v0XV9++aU7CgCAQ3upgf1BrK2tZUF1HMcTF9hWq5W76Jth1pPzMsIwnFhumqYHuoADAIBipjXe1xLvMtdft9v7F198kfu7SKPRUKfTyfW0K/Lw4cOJB/z9fl/9fj97uGA+vmen6XQ62XzuN4dktdwDAHAcnltgX6a1ulqtTrxj5v599+5dRVE0NaAus67DeuONN0q1zpdh3qsDAAC/Va1WFYZh7nW5Wewu9pVKxXsvYFrFX3311Wxcu93OgvoyD/+3trYmHuxHUZTdm6yurmpzc3MiTRzHWcOA3Rjh2yYAAI7qWAJ78x65fYFcXl5Wp9PJ/jZPx23r6+vq9/u5rnTuu3Cvv/569lTcHuyn31euXFGn08ktZzQaTXw87zAuX74sjX/71lav171d+8o47HwAAJw21WpVSZIUBvXD4XDiet5ut7MP4hrmXsC+xq6trSmO46xVvt1uq9lsqtvtFgb17XZbQcEv5BwH06tgVk8BAAAO4kiBvfkJOHORtG1ubioMwywQl/XbtMbq6qparVaum32SJLng3nRVs5+Cm3fqTSC/sbGhbrebW85Bn/wXMe/1uQ8X1tfXC28KiqyuriqKIi0tLT3XmwYAAOZBr9fLesW5D/DNA/Varabl5eXctGazqcFgkPvo3cbGRvbLNiad/Qs6krKfnbXvF8zg9hh8Xh4+fKgoitzRAAAcSZCmv/0MfTAOwK1RL121WtXy8vJE9/tqtaorV65MvGuP3zqJ+QmgPM7hs4l8P92CIFC32535JX6D8oAyKCenH3kMm688HKnF/kWoVqsTX6w1T/jfeuut3HgAAICTyvQ0LBvUAwBQ1olvsZf1/p3N/u15+J3U/ARQDufw2US+n171el2XLl06UG9DygPKoJycfuQxbL7yMBeBPQ6H/ATmG+fw2US+w0Z5QBmUk9OPPIbNVx5OfFd8AAAAAABQjMAeAAAAAIA5RmAPAAAAAMAcI7AHAAAAAGCOEdgDAAAAADDHCOwBAAAAAJhjBPYAAAAAAMwxAnsAAAAAAOYYgT0AAAAAAHOMwB4AAAAAgDlGYA8AAAAAwBwjsAcAAAAAYI4FaZqm2R9BkJ8KAAAAAABOHCuUp8UeAAAAAIB55m2xtyN/zC/yE5hvnMNnE/kOG+UBZVBOTj/yGDZfeaDFHgAAAACAOUZgDwAAAADAHCOwBwAAAABgjhHYAwAAAAAwxwjsgTnQbrdVr9fd0QAAAMBL0+v1FASBRqORO+m5aLfbL3R9R1Gv11/o/fuxBvbValVBEKjX67mTDm00GikIgomh3W7n0plC5Q6NRiOXrtFoTKSxh2q1mkt/1pnjVSZPh8PhxPF0Tzpffg6Hw1waw82rWXlTr9dz6d28l2eZ0042s7yi7fMx+1e0rWW20afZbGp9fd0dnZX7sssx56gZ3PNIJbfxIMfRMBWxGXzHyHceT2O2o8z6beY4+PbfXb8vDWYrU0bKlAl58qTsOVnm/DD1lm/7VPKccZWpC1HOrDrVrTN86Xz54fJdm8pc99wy7JYjt/zYQ5myhKPz5a2vDnHTuPnvTp+W1lZUBnx1Qpk6C37uNafoGLrnbOCpD+SUG1958XG3wa2P3LJAHYBjl1okpc6o0gaDQSopjaIojaLInXxoSZKkktJut5uN63a7qaS01WpNjEuSJBuXztgn37JPk2n7Pos5NmYoc4zc4x9F0cS4MAzTwWCQ/R3HcSopN86kC8MwN26aVquVxnGc/W3KgzvOLptmH93yasqyGdxtK2L2RZJ3291tNOuxx/mYfXGFYZitb9Yy0vH2+c4Ze1yZbXSPo33uFxkMBrljYo69b5zN7J+P2f6D1jlxHGdl0973dLw+3/676WxxHHvz+zhM2/+TrEwZKVMmUk+emPPMretdZc4PUw7cbTPKnDM+7vb56sJp5jXfj9usOtXkh11Hu3lpyp59DXOvL6bs+fJ62rWv1Wrl8tUsp6i8GWabyl5bKA9H4953mPPRHWfntcnbafmfTrn3tIVhOLNMpCXrrGnOcjkxdYVRdO02eV9GGIbeslLErVdch72e2E5qHpc5D+ZF2fO1rIPeox6ErzwcW2Afx3Eax/GxZ665ULqVqznhjGnrVcFNW9GyT4uj5Kc5vkc5Rr6bLpfvpstcUI9qViWbjk84N43GF9WD3HzZ+3qQQM8txz6+SsG+iB2lEjL7Ok3ZbTxonpl98J2zhikLvjww5cZ3fIqYPPXdyBfldxRFU4/RQfL7oHSEc/ikKVNG3DLhBk7GrHJb5vywl32QMjRr3T5l6kLbacr3wypTp/rqJvdY++p497pTdP8QhuHUm25fWSgqszbfNk1DeTheRUGfzXeN8PGVQVdRHWQrU2fNclbLSVFe2cc0nXKe+7RarTQcPxCy65Mih71v9dUh05zUPD7IsT3pDnv+FTnI/cVB+crDsXXF73Q6evfdd7W6uipJunv3rpvE2/0lmNGF6Ti0Wi31+31vtyf47ezsaGtryx39Qjx8+FBxHLujX5g0TbW5uemOnmp1dVVpmqpWq7mTjqzf7+vSpUu5cZubm/r6nD6bGo2GoijK6puy1tfX1Wq1VKlU3El69dVXJUm/+MUvsnGj0Uj9fl9vvPGGlfK3giBQp9NRkiTeLntutzy3my6me/r0qcIwnMivKIqm1udlzo+NjQ2laTqxbJwMZerUJEkm6sY333xTkvTll19K42vZ8vJyLo1Z5hdffCFJeu211yRJn3/+eZZmOBwqSRK9/vrr2TibKX9vv/12bvxbb70lSfrqq69y443hcKh+v6+PP/7YnYQ50+v1lCSJrl275k46sDJ1FvzMuWbOPcOcm+ZcffTokaIomlnnj0YjNZtN3bp1y51U6GXft87ie8/bfS/evHowHA5zrwz4XmmwX9l0XzewzYr36vW6Go1GLj40y3Nfs7K5225et3Jfu/G96uC+DmHu2cwykiRRp9PxrrdoXqPM+p+3YwnsTUaZi2UURbp58+ZEmmazqSRJlKapBoOBJGkwGBz45rzdbitJEu87xz7mZLcv2ni+RqOR1tbWFEXR1BuzpaUlhWGYKwP9fl/yVAjuCTTNaDRSkiQTN3S2drv9Um+wGo3GzJsCU2kV3VwehankP/jgA3dSpsw2apxnURS5o6d68OCBN2izPXz4ULLqFo0r306nc+CHL6aC3djYcCdJkiqVirrdrprNpqrVqobDocIwVBzHhfOkaao4jhWGodKve0Bl21qv19XpdLLxaZpqZ2dn6kXwNCtTRnxlIkmSXBqN82pnZ8cd/dyVOWdcZetCHM7Tp09zf5uyY4J2WfWoLQzDbN5araZWq6W1tTXV63X1ej0tLS2p1WrNvD+x1yPrAaF5sOC6ffu2NH5wgZfDPLx1A0GbuXcoqvsl6datWwrDsNR5bQcKgSfAwdG555x5YGcC/wcPHqhSqUzcW7p50Wg0FMdxqXw1DnPfepjryYuwtLSkW7duZbFap9PJHaNGo6F+v5/d13z88cdaW1vLLcMEuHEcZ+kGg4HW1tYmgt1Op6OnT59m6UxDybVr17JxYRhOPJjwCcMwizPN/ZydB9VqNdu3NE3VarW0tLQkja8DZl32dhtBEOjKlSvZ+G63q6Wlpdzy3XkfPnyYlY0Xxm6+9zXpl+F2VXW7wqXWe622Wd3cUs+73kXbOK0biNvtzjhKN/N5UHSsDuIgx8jNKze/DTuNrzuib7zbRXeWovSmu5QZpu1X2S5YrqJuo6nzzmiZZZfZhoN0GzLdoc3gHp/0ENs4rbt8EXO+ljn+bh3h1htlujmZsmlvo2/ZqbVeM8ziy++ifCsaX6TsNpx0ZcqIr0z4yoDJS/eYFylzfkwrQ2XOGVfZurDIacn34+I7x8x4N09MWTNlxlf2TFlzy4UZX7Z8mbJhM9vkq9uKugzPQnk4XkXnZGi94z7reJu6yZfPs0wrI2nJOsunzHafVqHn1Utzfppz3+Svza0fTB1glL1my1Nn+Oqnw1xPbIfNY981zo2biu733fIoTx3mLmtanW2P922Xmyb1vOrgrq8on3zbajPz2fng7m9qvZrhstO622T49vG4+MrDkVvsTVdVuzuaeRJtnkxr3CXG7g5vurmZJ6ZuN323Vavb7SodP8nR+InRQZmnd3g+KpWKUqt10jwZdZ+G2mmuXLmiwNNd5cqVK7m/zRPNMr0uer2eOp2Out3uRGuw6X5rhrW1NQUFX0R9Hkx3O1OWl5aWJsq6zX0CfVRbW1vZ+geDgfcp6EG2cTgcqtlsqtVqlX66bVow4zie2mq1tLSkKIpyLSazWt2LlH0CX6/XtbS0lD3xjaLIW4ZnMflmWu+MWa15p1GZMlJUJmq1WvbU3VwbzBPxojJ53MqcM66ydSGOZnNzU1EUKQzDrHyYVnjTy2ljY0NxHGtpaSlLY1pa7VdsqtWq1tbWsjzTuIVmWovb1tZWbt32tcR3v2FeUbx8+bI7CS+IOXd9rxru7Oxk+d/tdhUUdEPWEXtebG5uKgzDA3X1xnQ7OztZS68ZzP2ffR12u8ubewnTi2NtbU3dbjeXpqwy962HuZ68bNVqNRe7aUZvF42v6b5r9BtvvOHthfe82D2zDLuMmNb6olenjKdPn06Ur2Dcbd949OjRRI/Dl+HIgb25UJkAyb64dTodJ/XXB9kczG63m93ouQFXUTfLSqWiVqulTqfj7V7nY05Y9yYbz5fpKj3t4rWxsaEwDCde3XBPxLJ6vZ7W1tZKdaHU+IGRrNdJXiRTlpMkKbx59N0cHpdaraY4jqd+f2LaNg6HQy0tLU3tqu4ajUYKw1BRFE3tSh+MAzj35uvhw4cTlWu/31e/31cwJXDq9/sTXSE1/hnBYPxelnk1I0mSrGLe2tpSHMcT3cxQTpkyMqtMmHet7WE0Gr2Ui2eZc8anTF2Iw7FvlNM0zW6m7brTfliZpqm2traUWO/Pm1eO0nFAr3GgEEXRzFf+7GAwTVO9++67UsH9RrPZVBzHL6Xs4uug3tTxs6yuriqKIu997Gg0UqfTUavVcieV5gt6cDTudcI0ONrn27R629zj2PGMCfymNXAYB71vPez1BIczKng9oKzQeuXSHnz3LS/TkQP7mzdv5g6Se7DMjfatW7fUarVyacoEXj7mBvHTTz91J3ndvHmz1AczcPzCMHRHTXArS9+HsczTtGmB7nA4zIL6oiDCNW15L8Ksd+efdwtv0UfhbL5tHI1GWcB2kErNBHBuwG6rVqsKw9D7cM+9iU/HrepRFE2tU9x50vENvKmTKpVK4YfaZh0j33T33T6jTDk+LcqWkTJlwjYa9xIzAdSL5svvMsrUhTi6u3fvKpzx3rO5LzH1xWg08n7/wa0Lyrh9+7b3fsP0NnpZ5fasM+8F2w9uZylKZ+49j9Lzot/vFy4fx+PWrVu5Fvrl5eWJ+wpzr/n6669n71jbg/09MHde22HvWw97PTmoyjF9l6bontT91kjR+sx91stg8mPWNw3cmEQlexoUpfEdh+fK7pfv66s/TdE7DYasd9jMuybuUDSvUfTOh1meUfRuw7R9Klr2aTFt38sqe4wGzm9Sp57fgTXLspl8s9+BMePsdYYzflLGlEX3vRhb7PyGaFrwzpUxrXyr4B29tOD9oHT8no17HOV5L8vlHh+X730gn9D5LV+TH/Z+lNlG33xluMvxmZYfRXzvL5n32KZxj6uv3Jl07vJtppy7dU/oeedv1rJcOoZz+GUoW0bKlAmbWW6Z8m6UOT98ZSgtec645aZMXTjLvOb781JUp7rc92V93PxKC+bzlTU3713mvsStC9IS17BpKA9HY/JlWrlw823aPUXR+NRTvrrd7kRac33ylZO0ZJ3lQzn5Ld89gMlT+7ofzfjpSd99oG85br6nnnO+zPVklsPmsdk+s37zt10Oi+733euje2x9yyp7jNxlpwX1/WHfsbfPJbN/9jaZ7bbn85WJoryKoiib13fNMMfKne+4mO3Pjcv94Ukwje/g20xlmo4zxd0xN2N8igqayUS78jTbbw/TAqKiZZ8WB81PW+h8RMYMbh7aTH7bg3uSmZPTHnzH30036yJnTh7fYC/fneYrv24a3zao4ObeN7gnvT1MO55GUcXnLssMRcw5Yw/ucS2zjdPWXXS+ufnp24Zpx9DdBpvv+JjyMI1ve33b4B4jH/t8seszt1yWWZbNzDdvypSRMmXCV2Z99YVr2vqNaet3bxbswc1D382Kb/1uXTiNmecs852L7rH0HWeXL5999xy+dG79IM+DUDu973qSFpSRgyjaN8zmu6bZg+Erb27+pwUPgWy+vHbvp3zlxFeW3W2c5aDpTxP3GLv3BIabz0XpDF/A6AtaU08d4l4rylxPZjHzHYZdxsxDBh0isE+d4x2Ng1t7WWnBueeeN75lx57Y0hxbw+Sje612l28H9qkn/83f9nz2drvb4e6Pe6zcPO6OH+y5+3hczHpswXiCNH6vddyKbzfqH5l5h7Lb7ea6yprxg8Fgarc5HM7zyk+8WOa7AeTj2cM5fDaR77BRHlAG5eT0I49h85WHI79jX4Z5j+jRo0e58eY9JYJ6oJh5GFb0YTgAAAAAZ9sLabGX1TpvK/pAFo7H88xPvFjtdlsPHz4s/YExnA6cw2cT+Q4b5QFlUE5OP/IYNl95eGGBPV488hOYb5zDZxP5DhvlAWVQTk4/8hg2X3l4IV3xAQAAAADA80FgDwAAAADAHCOwBwAAAABgjhHYAwAAAAAwxwjsAQAAAACYYwT2AAAAAADMMQJ7AAAAAADmGIE9AAAAAABzjMAeAAAAAIA5RmAPAAAAAMAcI7AHAAAAAGCOEdgDAAAAADDHCOwBAAAAAJhjBPYAAAAAAMwxAnsAAAAAAOYYgT0AAAAAAHOMwB4AAAAAgDlGYA8AL0C1WlWv13NHA3OLMg0AwMlBYA+cQcPhUEEQqF6vu5O86vW6giDIhkaj4SZRo9HIpfEtezQa5dIEQaDRaOQmy7Tb7Yn0ZqhWq1k6sz9lltvr9RQU7IOPSW8PPu7+t9vt3PQrV67o2rVruXHAvOr1ekqSRKurq+6krL4YDofuJEmaOJ986arVai5N0QMEt27y1TuuWet3p5sBAICTjMAeOGPq9bqWlpbc0YXa7bYqlYrSNFWaphoMBup0OrnAuNfraTQaZWmSJFG/38/dZI9GI4VhqFarlaWLokhhGGZpXBsbG1laewjDUMvLy1m6paUlJUmSmx6G4URwX61Wtba2lhs3zWg00tra2sS63Zv8er2uBw8e5I5Rs9nMBfeXL19WkiQTQQQwj27duqU4jnPjzAO2fr+fG28LgkBxHGfnShzHWlpayp2r1WpV1Wo1S9NqtbS2tjZx7pjz0D4/t7a2cmlcZdYvKVdPmWEa8xASAICXJrVISp1RmGPkJ1ytViuVlCZJkkZRlEZR5CYpJQzDmfNGUZSGYZj9HcfxRHlMkiSVlLZardz4abrdbiopHQwG7qSMSdPtdrNx9vrDMEzjOLbmKM8+hmmapoPBwLs9cRzn9j89xHo5h8+mk57v5ry1z690vN1xHBeeE+65Y5j50innt1vn+M6vWcqs3/x9kDoptZb9PJz08oCTgXJy+pHHsPnKAy32wBliWsArlYo76bkbjUaKoig3rlKpKAxDPX36NDd+mmvXrimKItVqNXfSVJubmzNb3Q7jF7/4hSRNbM/bb7+tJElyLYHLy8t68OBBLh0wbz7//HNJ0ptvvpkbn6apNjc3c+NsT58+VRiGE/VPFEXZefLFF19InvNpeXlZOzs72d8PHjzQlStXcmlmKbP+w6hWq2o2m5LVjd9+dcB9pch+jQgAgONCYA/gQBqNhpIkmfq+eLvdVr/f18cff5wbb9+YG9VqtfRN9XA4VJIkWl9fdyflPHr0SPIEHkc1HA7VbDYVx3EWHMx6KPHVV19l/3/jjTeUJEluOjBvTPDtBshl+Mp/pVKZqBvcOsE+d0ajkZIk0dOnTyfeg3fnc5Vdf7PZzC3XfQ3AtrOzo1arJY0fbqRpmn17oNFoqNls5l4VMt8PAADgOBHYA5jJ/jBcp9PRYDCYaFGzW6Wazaa63W7uw1rr6+tKkiTXkjUcDqe+j+v6+OOPFYah94Ndxmg0UqfTyQXfR2F/mG9paUlxHE+0Svq+E/Daa6+5o/T6669LnqAFmCem5fug3n33XWlcVxjmfDUuX74sSfr000+zcRoH2q6R9V2PtMQ3O8qsX1Zwbi93aWlpanDvY5bd7XZzdZGpP4o+CAgAwGEQ2AOYyXRjT8cfxltaWproTup+6G5tbS3XKrW6upp9BMsEyuvr64qiqFQAPhqN1O/3Z3a/XV5eVhiGE8H3YdVqtdx+jcZf9rdv8n2tgF9++aU7KmO34gPz5rAPpmq1mrrdbq41PAxDxXGc1SeVSiX7QKfdYh7H8UTQ7vbcMb2IigLwMuv3MR/ku337tjtpKnOeuw/5TH1nej4AAHAcCOwBHEilUlGr1Zr5hfdutys5rVJu8L+zs6OdnR298cYb1px+pgVvY2PDnZSpVqtKkmSiW+1xMg8MzE3+rG1/9dVX3VHeccC8KPMgrsjq6upEi/hoNMot032Ylo6/jeEG34cJjMus38d9qAAAwElDYA/gwEyX8mncViof88686X5bxO5eX6Rer2fvsT5PbgDw1ltvSZ5WwkePHk18qOso7yYDJ8VxfivC9MQx3eSLdDqdrIW+UvDRTdNL5iAPzsqs37zTP+0hnq9ONNvh9t4xPR588wAAcFgE9gCmqtfrE++Crq2tKQzD7D37RqORe29VVjfZovfhh8OhlpaW1Gq1Zga6d+/elSR98MEH7iRpvI39fv/IwYb5loC58e71eqrX6xNpZL2vW6vVFIZhrltwr9dTp9OZ+HjgYd9NBk6S4/pWxGg0yrrCu9/ssAVBoCiKcnXJlStX1Ol0cg/U1tbWct/WcM9nl2/97XZ7oi5bXl6WZvQWMuztqVQqiqJIa2truTTmdaGiuhEAgEOxf/vO93t4mF/kJ1zmt5Z9g/vbzob5zWp78P2GvZvG/Y1p8/vU9uD+VnWRonWm1u/I+wZ7G8zv2PsGN419LKIomkjvO1ZhGObS+PaN37FHGSc936f9jr1vMGXed666y/DVN0W/Ke+e024693wus/7Usx9FdY/Lrivs89/dzrLLM8x8wDSUk9OPPIbNVx6C8QRp/FR8HOzbsT/mFPkJnBymddD3iwJFOIfPpnnI93q9rkqlcmwfqUSxeSgPePkoJ6cfeQybrzzQFR8AXoC7d+/mXl8A5tn6+vrEz8QBAICXh8AeAF6AmzdvTrxzD8yr1dVVhWE48f0NAADwctAV/xQjP4H5xjl8NpHvsFEeUAbl5PQjj2HzlQda7AEAAAAAmGME9gAAAAAAzDECewAAAAAA5hiBPQAAAAAAc4zAHgAAAACAOUZgDwAAAADAHCOwBwAAAABgjhHYAwAAAAAwxwjsAQAAAACYYwT2AAAAAADMMQJ7AAAAAADmGIE9AAAAAABzjMAeAAAAAIA5RmAPAAAAAMAcI7AHAAAAAGCOEdgDAAAAADDHCOwBAAAAAJhjBPYAAAAvQL1eV7vddkcDAHBkBPYAAOClazQaCoJAvV7PnSRJqlarCoIgG3zp6vV6Lo0viDbrMUOj0XCTeLnrt+cbDoe5ae4wGo0kSevr62o2m9ZSAQA4HgT2AADgpRmNRgqCQJ1Ox52UqVarqlarStNUaZqq1WppbW1Nw+EwS1Ov17Wzs5Ol6Xa7ajabuQcAjUZDnU4nSzMYDNTpdLwPAGzu+pMkUafTyYL7Wq2WTbOHOI4VhqEqlYokaXV1VZK8DyUAADgKAnsAAPDSLC8vK4oiJUniTpLGQXCSJLp27Vo2bmNjQ2EY6uOPP5bGLeb9fj/7W+MgOoqibL7RaKROp6NWq5WlqdVqiuN4aiv6aDRSkiRaX1/PxlUqFcVxnLXE+5j1XblyJTc+iiLdunUrNw4AgKMisAcAAC/Nzs6Otra23NGZL774QhoH4bbl5WXt7OxIkr788ktJ0ptvvplLc+nSpeyBwVdffSVJeuutt3Jp3n77bWkciPuY1vZHjx7lxnc6nWyaz6effiqNH0LYLl26pH6/nxsHAMBREdgDAIATzw2833jjjYlWfhO8G6+//rrkzGseAhivvfaa5JnXZrrsm/flgyBQFEXa3Nx0k2Y6nY7iOHZHe7cJAICjIrAHAAAn1uXLlyWrBdywu8+bd9ftrviSct33a7WawjDMjZNnHp9arZY9RAjDUJKmBvXmnf0PPvjAnVTqQQIAAAdFYA8AAE6sSqWSazE3g/kwnZEkifr9fi7N8vJytgyNu/0nSZJLY6a9+uqr2bJcjUZDYRhqMBjkPopX9NG9mzdvKoqiqV313Z4DAAAcBYE9AAA40Xxfndf4a/VGpVKZSPPGG2/kgn9JE2nMO/ZFQXiv11On09FgMMje89/c3FSr1VKz2ZzoUu/72J+PabkHAOA4ENgDAIC50+l0cl+q97l58+bEV+ldt27d8r4LbxR9vM+8K++6du2awjCcSG+YlvppPQQAADgoAnsAADBXzMfrzLv1PqY13/0qva1er6vf7+fel2+32wqCQMPhULK+ou92uzcBvN3SPxwOlSTJ1Pf2zYOCoh4CAAAcBoE9AAB4aarVqoIgyLrMr62tKQgC1et1afz1ePud+CAI1Gq1Jn4iz01j/xyeYdZlBo275k9Tq9U0GAzUbDZz81ar1Ynlm4B+2gOHhw8fKooidzQAAEcSpNYVrexFDvOB/ATmG+fw2US+n25BEKjb7U4N/m2UB5RBOTn9yGPYfOWBFnsAAIAXoNfrSTNa9AEAOAwCewAAgBfg1q1barVa7mgAAI6MrvinGPkJzDfO4bOJfIeN8oAyKCenH3kMm6880GIPAAAAAMAcI7AHAAAAAGCOEdgDAAAAADDHCOwBAAAAAJhjBPYAAAAAAMwxAnsAAAAAAOYYgT0AAAAAAHOMwB4AAAAAgDlGYA8AAAAAwBwjsAcAAAAAYI4R2AMAAAAAMMeCNE3T7I8gyE8FAAAAAAAnjhXK02IPAAAAAMA887bY25E/AAAAAAA4GXxxOy32AAAAAADMMQJ7AAAAAADmGIE9AAAAAABzjMAeAAAAAIA5RmCPF+6jjz5SEAQKgkC9Xs+dfObcuHFDQRBodXXVnQQAAAAAMx05sN/b21Oj0dC5c+cUBIGq1ara7bb29vbcpCea2f779++7kw5kb29P7XZb1Wo1C15XV1f15MkTN+lUJtizh2q1qo8++ujAx/bJkycKgkDnzp3TaDRyJ79QvV5Pn3zySfb3F198kZt+WhzkmP/617+WJP3qV79yJ+GEWl1dzc7LdrudmzbtQc1oNMrmO3fu3NRzudfrZWnr9bo7eaJ+cAd3uwAAAHB6HTmwr9fr6nQ62t/flyQlSaJms6lf/vKXbtIz4T/8h/+gZrOpJEmycXfu3NG/+Tf/JpduFhPs2ZIk0SeffOK9yX9eTABz48YNd9Kh/N3f/Z0kaWVlRWma6vLly26SU8sO9vFiTQu2D8N+CPNXf/VXuQB92oOau3fvSpIWFha0v7+f/e1z69YtaZy23+8f+OEgAAAAzo4jBfZPnjzR9va2JGkwGChNUyVJopWVFTfpiffs2TOlaap33nnHnXRg169f1+7urnZ3d7W4uCiNg/LhcOgmnSmKIqVpqjRN1Wq1JEnb29sHWtaFCxeUpqmePXumSqXiTp7KBCe+Bw2HYZb3O7/zO5J04O2ZF75j/pvf/EaSsodgeHGmBdtHsbi4qP39ff3kJz9xJ3ndvHlTkvSXf/mXkqT/9t/+m5Pia6PRSP1+X4uLi9nDiL/927/NpTH1gv37pa1WKxu3sbGRSw8AAIDT60iBvQlUJOnLL7+UxoFar9dTrVbLpj158iTXdbXRaORauOyu6+fOnZuYfu7cOZ07d07D4VD1ej1L53Y1bTQauS7w5rUAw7SYrq6uqt1u51pPTVd8O2A2222mmW2b1rW61+vp/fff1/nz53X+/Hn98Ic/dJNoNBrp3Llzqlar7qSp3nrrLXeUND5+Fy9ezPb74sWLuXfXzX4HQZCNK3NMgyBQv9+XJDWbTQUzuvfO2g7f8ooeUEwrMzdu3MitZ1qZuX//fpZ/psXz/v37ufmnvSphd512lx843whoNBrZMXKPebvd1tLSUpbWnmZrt9vZsuv1euF2yepNcf/+/Wzdgef8Mq/LuK+HFB17jffbLvvBOD/dV1Wm5ZOcc7tarebmd+d1l+++5uMej2nLttXrdTWbTUlSv9/PlmXcv38/OwfMstolXyf6sz/7M8nTau/z5MkTJUmixcXFrKfK9va2N48/++wzSdLy8rJ+//d/X5LU6XScVAAAAMBYapGUOqNmWlxczOaLoijtdru56Y8fP04XFhayNGZotVppmqbp1atXJ6ZJShcXF7NluNPswaxvMBhMTCubxl7HYDBI0zRNkyTxbrekNAzDdHd3N9u+aczxCcMwG2dvR5FWq5VqfEzTNE13d3fTOI6z+ZIkSdMpx09Sev369TQtWJ+b1h7M8XLH28t0ldkO3/E0x9s2q8y4480Qx3G2DHeaWde9e/cmxktKFxYWsmPqCsMwlXVcHj9+nM23srIyke7evXsTx9zkp7vOomlmsMuNK4qiifRmsI+FfY66w+PHj3PLNKZtkzlOs/LJLq/2kM44v+7du1c4vzne3W53Ypo5ni7fcTLLKSoPdhofs8zBYJBtp9lv99w1zDlizoeVlZVUUnr16tVcutTKM5M/5liZY+My22y2AQAAAKeXufezHanFXpK2trYURZE0bg1bW1vTxYsXs1aoGzduaH9/X4uLi0qSRLu7u1mX8r29vexDanZX/jAMvS1Z9jLCMJSsj6/VajVdv35djx8/zrqimm7wvg+0xXGs3d3d3Lvwtk8//TS33WmaajAYSONu9aZFbZobN25kryr8l//yX7Lxr776qhYWFrJ9mMa0ML7yyitZi10cx6pUKhqNRtnxu379utI01e7ubvYqxF//9V/nluUz7ZimaZrlreni+/777+fm17h1t8x2PHv2bGJ5ds8OY1qZkaRut5uVlzRNdfXqVWm8HS6zjCRJVKvV9KMf/Uiy1m+2c39/vzBPl5eXJev7AP/7f//vbNqDBw8kqzV2YWHB+zrHxsZGVn40PrbPnj3LpZF1/K5fvy6Ny9osCwsL2fEwx9wci16vp+3t7Vwac45pfKx9Ll++rG63m5X93d3dbNpXX30lzcin0WiUlVezT4PBIDsnf/KTn2h/f18rKyva3d1Var1q8tOf/jRbhj1/t9vNetiYMmq+1TAYDPTmm2/KZ2trK1u2ebXF9LT4kz/5E8mqD+ztuHPnjrdMuf7iL/5CCwsLM1vtzfH4/ve/L0n6gz/4A2m8HtuT8StOYRjqwoUL0rh3hiT9/d//fS4tAAAAIDlhvi/yL+vx48e5FjbTWmVatnwtSUUt6GYwrVPmb7t117SK2a1q3W7X2zpn1u22otrcdRRtd9F4l92i6PZiKMPXYrq4uJhrMS/aH3e8+3fq2d+04JiW2V/f8ovGl1nerDSDwSBdWVmZaPG1W0h9+2eP9w2+ltPUatU1LcKmpdVutb1+/Xoqq6Xct+++cemUFl6TtqhV3XeczDrc3gDusovGG7u7u2mr1fK29s86R9Ip+2r4zlMzmN469jkUhmHaarWynjJ2b4GFhYU0juPCHhfplP1192nWeMPO+9RafqvV8q7LlCG7J9Lu7m62Hrsl3rTs2+XR7lng6y1kpvnyAgAAAKeLufezHbnF3rhw4YI2NzezlkbzLvVRfPOb33RHTTAfw+r1elpbWzuW9R6V2RaNW5eP8iVu++N5//AP/+BtMT9ux/2BseM0Go20tLSkO3fuHPtH6L71rW+5oyRJ77zzTvYV8/v37+vOnTuKokj/+l//a0nS//yf/1P/43/8D0nS22+/7cx9dPa3LMo66rH58z//czWbzazHyYt0/vx5adxKPRgMFMexkvGvbZh34y9cuKDt7e2st0an09Hi4mKpFvbn4b333sta7X0fmjQt7dvb29m7/K+88srEdFkt+J988kmW9gc/+EE2vahnCQAAAM6uIwX2N27cyH14bG9vL/u/6er77W9/W5L085//PLvpvnHjhtrttl599dVsWXbXajP4umkXcbvm2l+kPwzfdg+Hw+zBwXe/+91ceqNMUH/Yj+e5vvGNb2T/N12q9/b29OMf/1gad0M/LiZY8QVOx70dvmNvyozpBh6GYdZ1Oo7j3PzTmHJpfz3cDNO+Im7y0XTlv3TpUtal+sGDB1m5MONm2dvb8x7L42YeVnz++efZx/JGo1H2dfbf/d3fzaU33G7wvlcCpuWTr0yMRiNdvHhRsn4Nwe6Kb4atrS1pfMy//PJLbW5uZus3Dxra7bbu3r2rP/3TP9WzZ88UhqH29/ez8lHEdJU327uwsCBJun37djbN/kDkd77znez/05w/f14ffvih9vf3J7rW7+3tzfzwXa/X097enu7fv+891jbzM3gAAABAxm6+9zXpT2O6H/uGWR+sM11Gp3XJNczfvm7jprvrtG0x65rWPdhdR9F2y+lO63K7h9uD6eI+bTsMd/+KTDt+pnuvb33m72nHNPV8wKyoq2+Z7bDTFS0nnXLsW61W7sN17mBvtxnndqU2++gb3LQ29yNrpnu83VXdfoXBd8yTJJlYZ1pw3NMp+2D4jqW73t3d3cIyOe2DgeZ1A98w6xyZdW5Pm3fW/OZjgkX5WLQ/7sf2zLEuWo6mvJqRerriG+YDivY6zLp9H0K0y0S3283ON/vjh4a9D+5+mvHTzisAAACcDubez3akFvvLly+r1WrlPgIXRZHu3buXtXDWajXdu3cva7VdWFhQHMd67733JEk/+9nPJpZh0vn+7zKthu+//76uXr2apV1cXMy66Rp2K6LLXUetVtNgMMg+RqZxa2+r1cpaFH3Mx72mKfPxvKJu4S7f8VtZWdFgMMg+4ubbb3d/beaYStIHH3yQffBuYWGhsKdCme2Qtexp+zetzFy4cEHdbjdbj8kTV9H+bWxsqNvtensR+I6T8Xu/93vZMhcWFrKPmtk/Z2g+hqaCZVUqFV2/fj1bjjmu046FCpYlJ59cZh3nz5/X9va24jjObX8cx9re3s5azl3/9b/+11zZ//+3dzc9chx3nsd/+QL80CRPglYQKutijAAaM00tIHTrQMDMhr2HOcjT3TwsCHhBuWqNOcxou7QSBz4sRzPVo1lfjC6SWAOEAauaay4whyHRRQM8sAo6aOkFCYzhS2eBEGSemmo/vIDcg+qfExkVWVX9QLKz+/sBEmRnRj5G5MM/IyJreXm58Lem5JNGZcJd7/z8vO7cuZPP659fxo7HX//1XxfyaXl5Wf/n//wfaXTs3Hlt2WX7s7KyUmjZcf78eamkPMzPz6vb7erv//7v83G+smPvfiTT1mEfXbx8+XI+zdRqtbwc/Mu//Ev+Ub+LFy96Kb9qDWLH0m+Ob+OnlSUAAAAcT9Eo4v/qj9HvajujAAAAAADAERGK2w9UYw8AAAAAAF6uA9XYW3oAwNEz67UcAAAA1RGK2w8U2AMAAAAAgBcnFLfTFB8AAAAAgAojsAcAAAAAoMII7AEAAAAAqDACewAAAAAAKozAHgAAAACACiOwBwAAAACgwgjsAQAAAACoMAJ7AAAAAAAqjMAeAAAAAIAKI7AHAAAAAKDCCOwBAAAAAKgwAnsAAAAAACosyrIsy/+IouJUAAAAAABw5DihPDX2AAAAAABUWbDG3o38UV3kJ1BtnMMnE/kOF+UBs6CcHH/kMVyh8kCNPQAAAAAAFUZgDwAAAABAhRHYAwAAAABQYQT2AAAAAABUGIE9AAAAABySU6dOKYoiDQYDfxLw3BDYAwAAACfQysqKoijS+vq6PwkHsLu7648CnjsCewAAAOAE+v3vf++Pwoi99Lh+/bo/CTiSCOwBAAAAwGEvPf7whz/4k4AjicAeAAAAgB4/fqwoirSysqLNzU3V63VFUaR6va67d++OpbVa7SiK1Gw29ezZM2nUx/zUqVO6e/du3t/88ePHwfnOnTtXWPb169fz7gEffvhhPv/S0pIeP36s69ev59t17ty5fLlmfX09n37q1KnCdsmpib97966azWZw+6MoUq/XkyS1Wq18ezTavnPnzuXzhdYBvBSZQ1LmjUKFkZ9AtXEOn0zkO1yUB8xiv+UkSZJMUtZut7Msy7J+v58vKzSkaZplWZY9evQom5ubG5tuy/HHS8r6/X6WpmlwPknZnTt3sizLsna7PTbNhtC8cRzn+/PBBx+MTZeUzc/P52lsn0NDo9Eo3f5r166VTnPnddP0+/183EHZMoGspDxQYw8AAACgoNFoaGdnR48ePcrHPX36VBrVWu/u7mp+fl5pmmpnZ0ftdtuZ+ys2PU1TLSws6Gc/+5l2d3e1vLysnZ0dZVmWz/fzn//cn13Xrl0rpNnd3c2369q1a5KkNE0lSc+ePdNHH30kSer3+8qyTGmaKo5jPXz4cKxmf25uLk+3vLwsSRoOh9JX0ZKSJJEktdttZVmmd999V5LU7Xbz+bIs0wcffFCYF3hZCOwBAAAAFGxsbOj06dM6e/ZsHuT+5je/kZwg9vvf/75qtZpOnz6ttbU1ra2tFZbxk5/8RLVaTbVaTZL061//WpJ069YtnTlzRlEUqdVqSU6AbpIkyYPpt956Kx9v22XTJGkwGOi3v/1t/vfi4qKiKFIcx/lyf/e73+XTJen999/XwsKCJOlHP/qRJOmzzz4rpAl59dVX9dOf/jTvImAvE4CXjcAeAAAAwFTP80Nyp0+f9kcdqq9//ev+qDHTfqZuOBxqcXFRt27dmpoWeNEI7AEAAADM7Jvf/KYk6Ze//GVee3/9+vX8A3NlrObebYpvw9bWlp98T1555ZX8/25TeRusdn6v7GXGcDjMuyLEcZxvf6PR8OYAXg4CewAAAAAzs6brDx8+VBzHiqJIP/zhD/1kYy5evCh5TfFtmPZSYJparZZ3GbCm+O6wV/YS4qOPPlIURbp9+7a+9rWvSaNuA7b9nU7HmxN4OQjsAQAAgBPIat6NBa5lvvGNb0iSFhYWdOfOHc3Pz0ujD9E1Gg394Ac/yP8OWVhYUL/fzz9W57Jl27+uadtl03/xi1+o3W4rjuPCdHd7/H12uenee++9/EXB3Nyc3njjDZ09e1bdbjdffhzHwY8Glu0/8DxFo8/lf/XH6G2WMwoVRn4C1cY5fDKR73BRHjALysnxRx7DFSoP1NgDAAAAAFBhBPYAAAAAAFQYgT0AAAAAABVGYA8AAAAAQIUR2AMAAAAAUGEE9gAAAAAAVBiBPQAAAAAAFUZgDwAAAABAhRHYAwAAAABQYQT2AAAAAABUGIE9AAAAAAAVRmAPAAAAAECFEdgDAAAAAFBhBPYAAAAAAFQYgT0AAAAAABVGYA8AAAAAQIUR2AMAAAAAUGEE9gAAAAAAVBiBPQAAAAAAFUZgDwAAAABAhRHYAwAAADM4deqUoijSYDDwJwHAS0VgDwAAcMysrKwoiiKtr6/7k3AAu7u7/igAOBII7AEAAI6Z3//+9/4ojNhLj+vXr/uT8BxYK4fHjx/7kwAcIgJ7AAAAnBj20uMPf/iDPwnPgbVy+NOf/uRPAnCICOwBAACOucePHyuKIq2srGhzc1P1el1RFKler+vu3btjaa1WO4oiNZtNPXv2TBrVvp46dUp3794dq4n15zt37lxh2devX8+7B3z44Yf5/EtLS3r8+LGuX7+eb9e5c+fGanjX19fz6adOnSpsl5ya+Lt376rZbAa3P4oi9Xo9SVKr1cq3R6PtO3fuXD5faB3HkXtc/fJgeWp5ZcdkOByOpfGP92AwUBRFebrFxUVFJd8ncMvn3bt3C9uzublZSHv37t1CPq2srBTKilvObLtWVlYKywCOpcwhKfNGocLIT6DaOIdPJvIdrv2WhyRJMklZu93OsizL+v1+vqzQkKZplmVZ9ujRo2xubm5sui3HHy8p6/f7WZqmwfkkZXfu3MmyLMva7fbYNBtC88ZxnO/PBx98MDZdUjY/P5+nsX0ODY1Go3T7r127VjrNnddN0+/383FHgW3XXjUajbH9teVMytM4jrOdnZ0sy7IsjuOx6deuXSstc48ePfK24t/LZ9n67HjfuXNnbJrNZ2U4VM6SJPHWWD22L0BWUh6osQcAADhBGo2GdnZ29OjRo3zc06dPpVFt5+7urubn55WmqXZ2dtRut525v2LT0zTVwsKCfvazn2l3d1fLy8va2dlRlmX5fD//+c/92XXt2rVCmt3d3Xy7rl27JklK01SS9OzZM3300UeSpH6/ryzLlKap4jjWw4cPx2r25+bm8nTLy8uSlNcwZ1mmJEkkSe12W1mW6d1335UkdbvdfL4sy/TBBx8U5j1uhsOhOp2O5ORHv9/X/Py8JOnjjz8ulAWbrlHe/OpXv8r/L0mPHj0qlJeFhQV9FX98xY7t2bNn83G+3d1dtdtt7ezsKE1Tzc3NSZI++eQTSdKPf/xjycm7nZ0dLS8va3d3N98el+3XxsaGPwk4dgjsAQAATpCNjQ2dPn1aZ8+ezYPc3/zmN5ITxH7/+99XrVbT6dOntba2prW1tcIyfvKTn6hWq6lWq0mSfv3rX0uSbt26pTNnziiKIrVaLckJ/EySJHkw/dZbb+XjbbtsmiQNBgP99re/zf+25txxHOfL/d3vfpdPl6T3339fCwsLkqQf/ehHkqTPPvuskCbk1Vdf1U9/+tO82bm9TDiu7GWOpPyYLyws6P/+3/8rBcqCTbcy8/nnn0ujF0WS9O1vf1tLS0t67bXXCnm4V2trazp9+rRqtVq+bNuWhw8fSk43ijNnzujWrVuSsz3GLWe2/cBxRmAPAABwwj3PD8mdPn3aH3Wovv71r/ujxkz7mbrhcKjFxUXdunVraloUbWxsqNvtKkkSPXz4UKurq/rwww/9ZPuyl7z4xje+4Y8CThQCewAAAEiSvvnNb0qSfvnLX+a1pNevX88/MFfGakTdpvg2bG1t+cn35JVXXsn/7zaVt8Fq5/fKXmYMh8O89jqO43z7rbb4uPra176W/99++m84HOrcuXNSSVkYDAb5xwffeOMNSdK5c+f06quvamtrS91uV3JacLj++Mc/6tmzZ1M/Rri+vq5nz57p8ePH+Yfz/uIv/kIa5Y+cpvju4LcqAU4aAnsAAABITtP1hw8fKo5jRVGkH/7wh36yMRcvXpS8pvg2THspME2tVsubf1tTfHfYK3sJ8dFHHymKIt2+fTsPctM0zbff+p8fV25XjB/+8Id5Fwdr7h4qC4uLi9LoGwvf/e538+mWL6urq5LX9N2C8e9973s6c+ZMoWtFSKvV0pkzZ/Ttb387r7H/wQ9+IEm6fPlynsYvB6Gv7QMnCYE9AADAMWO1rcatnQ2xZswLCwu6c+dO/gG1ubk5NRqNPLCyj5n5FhYW1O/384/VuWzZoabS07bLpv/iF79Qu93Og0Tjbo+/zy433XvvvZcHtHNzc3rjjTd09uxZdbvdfPlxHAc/Gli2/1X1i1/8Qo1GI9+v+fl53blzRyrJUzsubisMN1+svPyP//E/8uk3b94sHFe3BUbInTt38vTz8/Pq9/v5i4K1tTV1u928fLqsrITKGXASRKPP5X/1x+itpzMKFUZ+AtXGOXwyke9wUR4wi6qXk8FgkLcGqOo+PG9Vz2McrlB5oMYeAAAAAIAKI7AHAAAA8NJM65IBYDqa4h9j5CdQbZzDJxP5DhflAbOgnBx/5DFcofJAjT0AAAAAABVGYA8AAAAAQIUR2AMAAAAAUGEE9gAAAAAAVBiBPQAAAAAAFUZgDwAAAABAhRHYAwAAAABQYQT2AAAAAABUGIE9AAAAAAAVRmAPAAAAAECFEdgDAAAAAFBhBPYAAAAAAFQYgT0AAAAAABVGYA8AAAAAQIUR2AMAAAAAUGEE9gAAAAAAVBiBPQAAAAAAFUZgDwDADE6dOqUoijQYDPxJlfLhhx8qiiJFUaTNzU1J0rNnz7S0tKQoinTq1Ck9fvzYnw3HFOUBAI4HAnsAOGZWVlYURZHW19f9STiA3d1df9SRYvnuD6dOncrTbG5u6qOPPsr//vzzzyVJf/d3f6deryeN9vNPf/pTnuZ5sJckd+/e9SfhkFAeMItQOanX62o2mxNf6NTr9Tx96GXn9evXFUWRlpaW/En5fP49yl4ynTt3rjAewGwI7AHgmPn973/vj8KIPcRev37dn1R5s+T7v/zLv0iSlpeXlWWZ3nnnHWkU4EnStWvXtLOzo29961uF+fbrOB/vo47ygFmEykmapup0Ovr2t7+dlwXXYDBQmqaam5uTJP30pz/1k+gPf/iDPyrXbrclSf/4j/+oZ8+eSZKGw2H+kulv//ZvC+kBzIbAHgBwYthD7KSHzqprt9vKsiwfvvzyy3ya7f+f//mfS5JqtZrktEb4sz/7M50+fVqnT5/O5zmIsuP95ZdfKssyffe73y2Mx+GjPGAWbjm5c+eO5ufnJUnNZlPD4bCQ9pNPPpEk/cM//IMk6datW3mAPosf/OAHmpub0+7urm7fvi1J+b9xHGtlZcWbA8AsCOwB4Jh7/PixoijSysqKNjc38yaU9Xp9rOnr48ePC00zm81m/sB26tQpnTp1Snfv3s2bzlpTTX++c+fOFZZtzTLX19f14Ycf5vMvLS3p8ePHun79er5d586dG2sCur6+nk8/depUYbvk1ATevXtXzWYzuP1RFOXNi1utVr49Gm3fuXPn8vlC66i6sv2PoihPs7i4WPj77t27heOysrIyljdumlOnTuXHtGx98r5XMBgM8uW7x3tzc1OR15R3WjmwMjoYDAp9xP0mv8+ePVOz2cy3o16vj6Vx1+WfK2X7XCVl+RNRHk5keXB997vf1dbWljR6yfOrX/2qMN1q8d955538BYAF5rM4ffq03n//fUnSP/3TP+nZs2f6x3/8R0nSzZs3vdQAZpY5JGXeKFQY+QlU237P4SRJMklZu93OsizL+v1+vqzQkKZplmVZ9ujRo2xubm5sui3HHy8p6/f7WZqmwfkkZXfu3MmyLMva7fbYNBtC88ZxnO/PBx98MDZdUjY/P5+nsX0ODY1Go3T7r127VjrNnddN0+/383HPg61nr8qOgW1v6Dhfu3ZtbJyt+86dO2PjNcovKzPdbndsukbr9McpcLz9bet2u/n+LC8vZ5KyDz74IMtmLAf+NHdwlz0/Pz82Xc66Qvs1NzeXZVmWpWk6Nk1S9ujRo3z5h8mWv1eUh+NZHsrYevfKv1+Epi0vL+fjrBzYOCsz7nHPnGt+kiSF8WZnZyfPZ1tPWVp8Zb95jOMpVB6osQeAE6TRaGhnZ0ePHj3Kxz19+lQa1Vrv7u5qfn5eaZpqZ2cn7wvpsulpmmphYUE/+9nPtLu7q+XlZe3s7CjLsny+n//85/7sunbtWiHN7u5uvl3Xrl2TRn08NapJs36X/X5fWZYpTVPFcayHDx+O1RbOzc3l6ZaXl6VR3019dfdTkiSS0+z03XfflSR1u918vizL9MEHHxTmPQ6+/PLL4P5/9XzwFTsGkvTjH/+4kHZnZ0fLy8uFGrwrV65ITrlK0zQ/7pOOt8+a3lqf72fPnunWrVuSpL/6q7/aczlwy3Acx5LzYbi7d+/q4cOHiuNYaZoqyzL1+31JUqfTKaS1vuf9fl9vvvmm5JwvcRyP7XOVUB4oD7Ny++HbNf0v//IvJUnf+c53JEkPHz7c0/XSrbW3lhxWfgDskxvlhyJ/VBf5CVTbfs9hvwbGrS0LpbNaM38+ny3Dr7EuqxmUU4sTqr0p2y53PWU1fTZYi4DQttu8VrNWls7SLi8vj9ViutvrbtfzZOvZq7J9c5WlCe2bexz8wWoyQ/O5Zl2fm887Ozt5DamVn1nLgb/czCl7VsM4qfWIRut3W6/Mzc1ljUYjr5Xe2dkp1PAuLy+X7v9hsPXsVdmxd5WlsXVSHo5eeShj69+rsjxxp1nLpZ2dncJxMbb/Vg6ykmt+SBzHM6XD/vMYx1OoPFBjDwAnnP8hq8N0WB/dKvP1r3/dHzVm2s/UDYdDLS4u6tatW1PT4ivf+MY3/FEHsrCwkNek/upXv8prav/Lf/kvXsqwWcpB6OvfPvvK99mzZ/Xw4cO85Uan09H8/LyGw6FOnz6tra0tXbt2TXEc69atW1pcXBz7XsVJQnk4fuVhOBzmNelnz56VRnlhzpw5k38L4eHDh9LoI3p7Va/XJUnnz5/3JwHYIwJ7AIAk6Zvf/KYk6Ze//GXepPL69etTPwRlX9J2m+LbYB9g2q9XXnkl/7/bVN6GhYWFQvpZ2cuM4XA41pQ2yzI1Gg1vjpPHAiv/q+pZlmltbU1yAp9PPvlEz549yz9C5v+utXu8y1jz5Zs3b+YBgjXzPcxy8Nprr0mj/bOm1zZ8+eWXOn36tNbX13X79m39zd/8jb788kvFcazd3V09ffpUg8FA//W//ld95zvf0fb2dl5W/u3f/s1b0/FCeTg55eHu3bv58Z+bm8t/BvGf//mfvZRFaZqO5TWAF4fAHgAgSfrRj34kjfpKxnGsKIr0wx/+0E825uLFi9KotsatxXG/er1ftVot75drX+h2h72ylxAfffSRoijS7du39bWvfU0aPZTa9lvf2iqyL47bcOrUKT/JTC5fviwFlheNvl4uKe8j2+l0dObMGZ05c6Zw7ELHu8xf/dVfSU5/2yRJ8vkPsxx85zvf0dzcXN4n212O+8X1VquVl4d09M0HCyhv3bqVz2v7awHiUePnH+Wh6KSVhzJuvn7ve9/Tw4cPNTc3p42NDZ0+fVrD4TCvmfdfgGRZlr/4sZ/CM71er/SYAjhcBPYAcMxYzbuxwLWMNaNdWFgo/H7x3NycGo2GfvCDH+R/hywsLKjf7wc/GGXLDjXVnbZdNv0Xv/iF2u12/uBo3O3x99nlpnvvvffygGBubk5vvPGGzp49q263my8/juPgRwPL9v+omHQMjKXx88P2zc2TtbU1dbvdvDy4LN3a2lohb+zYWY1p6Hjb/31nz54trOvSpUuF6bOUg9Byje376dOn9fDhQzUajbH0liZJkkJ5np+f1507d1Sr1fStb32rMK/t81H77W3KQ3i55qSVhzKhcjI/P69Go6GHDx/m+2HN8N0XLC578WM/heeXKeOvz/8bwP5Fo873X/0xesvpjEKFkZ9AtXEOn0zkO1yUB8yCcnL8kcdwhcoDNfYAAAAAAFQYgT0AAAAAABVGYA8AAAAAQIUR2AMAAAAAUGEE9gAAAAAAVBiBPQAAAAAAFUZgDwAAAABAhRHYAwAAAABQYQT2AAAAAABUGIE9AAAAAAAVRmAPAAAAAECFEdgDAAAAAFBhBPYAAAAAAFQYgT0AAAAAABVGYA8AAAAAQIUR2AMAAAAAUGEE9gAAAAAAVBiBPQAAAAAAFUZgDwAAAABAhRHYozJWVlYURZE2Nzf9SS/cs2fPFEWRTp06pWfPnvmTAQRwDj9/169fVxRFajab/qRKOa7586JRHgDg5CCwRyUMh0PdunVLkvSd73zHn/zCnT59WsvLy9rd3dWvfvUrfzIAD+fwi/FP//RPkqT/9J/+kz+pUo5r/rxolAecRJubm4qiSEtLS/4k4Fg7cGD/7NkzNZtNnTp1SlEUqV6va319nTeqFffs2TOtr6/r3LlziqIov0DevXvXT/pC2I18eXlZp0+flpyaiNCFOzRtWll9/Phxvq/usLS0pOvXrztL/8pf/uVfSpL++Z//2Z8EvHScwyfvHH78+LHSNNXc3Jy++93v5uP84zHpmJjhcJinLasltXwYDAaF8YPBIJ/Xn2bl8cMPPyyMDzlu+fOiUR5wUn3++ef+qBPNWsu5Q71eV7PZ1OPHj/3kz5Vdg06dOqXhcOhPxkFlDkmZN2qq+fn5fD536Pf7eZq5ublMUvbo0aPCvAdx7dq1TFK2vLzsT8LIfvIzy7JsZ2enNF/3s7xJZi0bSZJkkrJr167l49rtdiYpS5KkkLZsWtk+WVnt9/tj09xhfn4+29nZyZeXpmk+zR0PHBYrX3vFORweqnIO2zbtlR0z976412NibFmWv26+GVuGe783lt9u/nW73XyeNE0L6UOOav68aHYM9orycLLYsami5eXl0nK1H6H7x3Gw3zy2869s6Ha7/izPzaNHjzKNriWznPcoFyoPB6qxf/z4sR4+fChJ6vf7yrJMaZpqeXm5kG53d1eS9Kc//akw/iD+8Ic/SJJ+//vf+5NwQH/3d3+X5+u1a9e0s7OjLMt0584dxXHsJz+QWctGr9eTJP3Zn/2ZP2kms5ZVk2VZnqbdbkuSHj58qP/5P/9nnqZWq2lubk6S9Nvf/jYfD7xsnMMn8xy+f/++JOnP//zP/UnSjMfE3LhxQ5L0D//wD5Kk//W//peXYrIrV65Io3y3GqGbN29KkhqNhmq1WiF9yHHLnxeN8oCqsGd5e7bH89Fut/Pz/s6dO5qfn5ckNZvNF1Z7fvbsWWVZpi+//HKm8x575Eb5och/EvfNb+htT9mb4X6/n7+xWV5eLrwJtvn8t0vLy8t5jZA/Td5buTt37hRqdtx5s1FtVqPRGFuGRvth/79z504+j/umuCr2u7023wcffOBPKnj06FG2vLycv8Gfm5vLGo1G4Q2cpbFlNhqNbGdnZ2LZ8JW9pZ/0RtafNq2sZl4a3wcffJDvo8vKYrvdLowHDkNZeZzG5uMc/ndVOofL9mGaOI4zefeu/RwTuz9b7a3N77fKsPGhPM+cY9toNPLtmJubGysDtt1xHBe23V3GUcqfF60s/6ahPJwsZfk6zbVr1wrPzHYfsHyxY+7fTyyfLL/9e8X8/Hwh/6ylbbvdztO56d3BzV+3TPjb5qax+1iSJPlyQ/eWKrPjs1dl5417PrutJe7cuVOIteI4ztrtduG4z83NZXNzc1m/38/Lz9zcXNZut7M0TfM8mJubK5Qdu564++Euy9Zry3I1Go28LLjbZdzYstvtTryWuLGivy6LE61M+es5KvzjmI3e2vz7H4EE07gXgyRJCg9c7g3EHR49elQ6LSsJ3DU6sGXTranZnTt3xqbJa/LhXqTa7XZhH7rdbh70u83X7II0Pz+fjzvqbJ/2ws2XsptzNnpQtwLvD3Ec5ye/ewLacO3atdL89x8UsgkPIu09BAXZlLKaTViPP83dxrKLJXAYysrjJJzD4+vxpx31c7hsH6ax+dx8388xsQDPHvTsAc1/sA+tz+UuP3Sc3RfpNsxV4MXLi1aWf9OE8ofycHyV5es0/jG3odFoZJnz/GvP4JkXnO3s7Ey8n1hAZdd0dwg9z8spa1b2/MF9FrftCw2he0uV2X7t1aTzxqZNi6PcNNmEcqPReeuPs/t16Brkp502nz/MkkZOl5/QtUbOtct91nAH/5r3stl2uQ7UFF+Stra2lCSJNGpitbq6qnPnzunx48daWFjQV+v9ijWfPHv2rLOEr5ph7ezsKE1TadRk686dO4Xmo5Ly6VtbW3mzsSRJlGVZ/vNJP/7xjyWnucnOzs7Yl1StSejf//3fa21tTd///velUZPVlZUVXbx4UZJ069atvGmKNTv727/9W0H6+OOPtbu7q/n5eaVpqizL1O/3pVE+2bG2PHv06JF2dnbyfJu1bBymSWV1L6Y1OQaqgHMYLveYdDodyfn1Avtomf2qwawWFhYK+TU3N6cf/OAH+XT7wNXy8nJe/t588818Ol4eysPJ0e128+t3lmX64IMPpNEHE+Xke5qm+bX2f//v/y05H0P92c9+pt3dXS0vL+fP7nav+PnPfz5a07+7du2asizTxsaGsizLy4U9u7/77rt69uyZPvroI8nrfhXHsR4+fJhvi/3yg607TdN8eZiddYf467/+a8mJzdy8dOMiY88QOzs7eVcZjfJsZ2cn7/43ywcN3WX58y0sLOjatWt69OhRXlatK0Fo2bb9jx49ysc9ffpUcroGufGndeu7e/euHj58qDiOx56N7Fp4lB04sD99+rS2trb06NEjNRoNadRHq9Vq+UlLbWxs6PTp03lfi1deeUX/+q//qv/4H/+joijS9773PX+WUtYHs9VqKYoinTlzJr/5WMbbCf/hhx9qfX1dv/zlLyVJ/+E//AdpVHisQP3qV7/ScDjMl3sUfqbpKLAT+/vf/36eb+5N2461lYlvf/vbWlpa0muvvaZ33303X86LdBhlVaPyCVQd5zBcdkzu3r2bv/CxcuE+2O/1VxU2Njby/7///vv5LyJodC+em5vTrVu3dOrUKX3yySeF9Hh5KA8nx6uvvqqf/vSn+a8aWDBtarVafl+wgN6eq//zf/7PkqRf//rX+fgzZ84oiqL8umwvh02SJPk9ZFIfa/c7CouLi4qiKA+2JOl3v/ud5Cz/Rz/6UR5LnD9/Pp8Xs7G8sON58eLF/PxcW1vL01lwbH7yk5+oVqvp9OnT+Yu4999/XwsLCzp9+rQuX74sOd/8mMRdls33//7f/8unf+Mb38jjuyiK8tgsxGLLs2fP5uX3N7/5jRTYx1qtps3NTS0sLOjf/u3f8jRxHCuKIi0uLkqjbwqFfhHkKDlwYG/Onj2rjY0NXbt2TXJqxffjwoUL6nQ6YxeDg/rGN74hOYX31q1barVa2t3d1bVr1/Kfg5Gk//bf/ps0qqkP/UzTceU+8H766aeFafuxsbGhbrerJEn08OFDra6uzvSzNnvx2Wef+aP05MkTf1RuP2X1k08+kSTNzc1NvBEBLxvncBjn8LjQMfnXf/1XafTCxB6ezpw5k89j02flHuu33nqrMO3s2bN6+PBhXkPY6XQ0Pz8/ViOEF4PycPIMh0MtLi7q1q1b+YdQQy5duiSNnptDP6M4yfN6bv7617/uj8IeDYfD/P75PFvbHYS1JNjc3NTq6upM9/syB/04o9si4ag6UGB//fp1rays5M1hnj17lv8/9OXlP/7xj3r27NnUtx0W0Ftzi2636yfJ2bLswm/rdb/8aMPa2pqGw2F+s9je3laWZdre3h6rgXrnnXek0c3sv//3/y45byaPM/fNbKvV0vXr1/NjfPfuXZ07d06S9M1vflOS9Mtf/jI/9oPBID/h3njjDWn0G7Wvvvqqtra28ny0N7uuSWXDDVTc6badu7u7ajab+bTNzc28ucxf/MVfSPsoq2Y4HGp9fT1f3vvvv+8nAY4UzuGik3QO23H44x//6E8qKDsmz549m9rUcHNzM5jH+7G+vq7bt2/rb/7mb/Tll18qjmPt7u6O1QhhfygPmMaObRzHebNraw3lcltpWE38yspKPt1e2LhN8W3Y2trK001jgddwOCzcN9yuAjYsLCxITjn/6U9/ms9rLXFR7u7du3nz87m5uTzuseD1k08+yc/t9fX1fL5vfetb+f9fJL+rzs7OTt4Uf6/8fXz27JmazaYGg4Fee+01aVSurCm+DV9++eVze1F1aNwO96FO+JNM+mCF+1Ej/+NL/X4/+PEEE/rogp/W//CBfSAj9HEOG/r9frazsxNcvv/1zmz09UWb7n/ApQps2/fq0aNHwWPkLm/SByrcj5r40+R8kCUrKRshZdPLPrwi7wNgs5TVSfukURnzv8Rq0/ztAg6Dla+94hwOD1U5h22b9ir0gaS9HBO7r7ofyTLuLxvY8faXZYN/LMvGl92v3V9lKJv3JLFjsFeUh5PFjs1euB/B8wf/w3P+F+zdPJhUrqz8hT6IavxfqrJ5Jt0fTKjc2P0vtK4q8/d9VpOO49zcXCFmCx1PG9yPx9k4txyErjl+vrtlxYSW5c836RnA1hdadhbYrrJ97E+IE3UEy1NoXw8U2O/s7GRt52cobKf9ALnf7+dp4jjO0jQN/tyBefToUeEnCBqNRv63y70QuIWo2+0Gv2hoX3mdVMDdG4hbQNwH2aqwbd+PNE3HflbCz9t+v1+40MeBn8Nwy4flpTs9VDZC/JPS1fZ+2SCO47H1zFJWQze4ubm5LAl8fdtP7wcLwGGw8rUfnMPVPYdtm/bKfxDK9nhMrCyE8igLfD257OHH/2UES+c+tGUz/DzWUc2fF82OwV5RHk4WOzZ71fV+FixUbjLva+lx4GWPfz+xwb5wb4GZv9xsdL+y8jQ3N5fne+i6b2lcH3zwQSGYt7ISWleV2f7vVShf5ufns4b387bGj6Pm5+fHrg+h8zh0zbDyZNeJUPwXWpY/X+bl8/z8fP6rCba+0LIz51rl/qSfW66s3Bt7fvKvae62HAWhfT1QYF9F0362wy1U7htp/wZUBccpPyfdEF4W2ya3dhM4TJzDz9dRPYf3m+9lDzVVdVTz50Xbb55SHk6W45TXCCOP4QqVhwP1sa+yNE3zD8F8+9vflkZ9Lty+I7dv35ZG/SysLw9eDuvf1ev1Dq0/30HxE4jA7DiHn7+zZ8/m/U33+rXyo+i45c+LRnkAgJPlxAX27777rtrtduGDC3Nzc2o0Gnr48GHhowg3btyQpPwnF/Dy1Gq1/IMu9isFL9OzZ8/08OFDzc3N8ROIwAw4h1+Mq1evSvv4WvlRc1zz50WjPADAyRGNqvK/+iOKpK/q9N00qCjyE6g2zuGTiXyHi/KAWVBOjj/yGK5QeThxNfYAAAAAABwnBPYAAAAAAFQYgT0AAAAAABVGYA8AAAAAQIUR2AMAAAAAUGEE9gAAAAAAVBiBPQAAAAAAFUZgDwAAAABAhRHYAwAAAABQYQT2AAAAAABUGIE9AAAAAAAVRmAPAAAAAECFRVmWZfkfUVScCgAAAAAAjhwnlKfGHgAAAACAKgvW2LuRP6qL/ASqjXP4ZCLf4aI8YBaUk+OPPIYrVB6osQcAAAAAoMII7AEAAAAAqDACewAAAAAAKozAHgAAAACACiOwBypgfX1dS0tL/mgAAADgpdnc3FQURRoOh/6k52J9ff2Fru8glpaWXujz+6EG9vV6XVEUaXNz05+0b8PhUFEUjQ3r6+uFdFao/KHZbBbSNZvNsTTuUK/XC+mxN1YGbAiVhaWlpUIaPy8VyKdZ8sVfrp/3ZjAYjOX7YDAopPH3w59exi427mD8ZbpD6Bi4Wq2WLl26VBjn76+7jLLzxgZ3f/zjMcux9ueJnP0MsX0P7ae/nFCaMpPW7ZehF3lhdVlezHJcAeyNfx30r/uha1XoeuBfu/3z1Z8+Ka0v9HxShQdSAEDFZA5JmTdqZv1+P5OUJUmSJUniT963NE0zSVm3283HdbvdTFLWbrfHxqVpmo/LpuxTaNnHyaR9fx7iOC7kfbvdziRl/X4/H5ckSRbHcf635ZubB41GY2y74zguzOdrt9tZo9HI/7bluuMsnb9NPn++WebJRvvmb/c0dt5MWrbti2uW4xhi+2JsPnf90461bbO7rknzNBqN/Ni452wWONa2bD+dz/bDBl+32y2URff6NMlhX7+sLEsqPT6TlO0fjjfyfTazXPdnucb2+/2x83PWczaO47H7jCt0jbXrgv+8UobygFlQTo6/o5rHZTFYFU27pu/VYT9XukLl4dAC+0ajkTUajUPP3LLg2w8iJ61XJQ/0Zcs+Lg6Sn3sVenjJvHwKBYRZIEgNnVSh4HYaP9icJb/9wNeEtsk1qfxN4u97iH9RmPU4hsh76A3t17QH4dB6yrbJxtuxdwP2svUkSTK2TS53XWX5FZLM8OLFP9YH4Z4TjUZj7JjNQi/wHMbRQb7vn3/dL7vOTGPB9yRl9z1X6Ny36+Gk+VyUB8yCcnL8HdU83u8z8FEUei4+iMN8rvSFysOhNcXvdDq6ePGiVlZWJEm3b9/2k5Q2ZQs11z5M7XZbvV6Ppm/P0eeffy5JWlhYKIy/cOGCtre3JUlffPGFJOnNN98spDl//rzSNM3/rtfrunfvXiHNzZs3FcdxYdxeffbZZ5KUl9GQ+/fvK0kSf7QuXLgwtk2uBw8eKEkS1Wo1f1KpwWCgXq+nq1ev+pMKer2ezp8/n/8963H0WRP39957Txo1EU/TVG+//XYhneXhp59+Whhvtre3deHChcI4m8fKgbl06ZLa7XbwuLzyyiuSt57hcKher6fXX3/dSVm0sLCgLMsm5uNeWXP5Xq+nXq8XbC47rcmvb2VlRVmWjZ0TAI6HK1euKEmSief466+/rjRNC9cSez6yayAAPE+hft5+v3h7DhoMBoWuo6FnHfd5aFJXpGnx3tLSkprNZiE+tOX5XZhc/rZblyu/G2qoa6ffLda6ptoy0jRVp9MJrrdsXjPL+p+3QwnsLaPs5pYkiW7cuDGWptVqKU1TZVmmfr8vSer3+3t+QF9fX1eapmN9jsu89dZbkhPY4fnxX57YQ43r6dOnhb9fe+01yZl3a2tLaZrmJ029Xlev18tfEMzCglY3AH3w4IHiOJ4YoE1ah78frnv37qlWqxWWGwUuYq5PPvlEmvKiwY6JHSPXtOPou3HjRuHlgz+/78mTJ/6oXGgdcRwX5rEL2trampPq39VqNXW7XbVaLdXrdQ0GA8VxrEajUTrPQfR6veBLG422JcsyJUmiJEk0as2UH6t6va7t7e18fJZl6nQ6YzdKAC9P6LpvFhcXC9fmSTY3N9XpdNRut/1JucFgMNNzyNrampIkURzHWl9fV7PZVKvVUr/fD77wBICXaXFxUTdv3sxjtU6nU3iWbTab6vV6+bPQ1atXtbq6WliGBbiNRiNP1+/3tbq6OhbsdjodPXnyJE9nz/9XrlzJx9mz+zRxHOdxpj1fusF3vV7P9y3LMrXbbS0uLkpOpZE9h1oaE0WRLl++nI/vdrtaXFwsLN+f9/79++r1evn0F8Ktvg9V6c/Cbzobap7WGPWzdcVxPLUvrTVZ8wffpGYgZc2EZ2maXWVlx+p5sGPpN1/xt0GBbhFxHAfzzsbvJ49CfRhtnFsurWxYOSxrgjOtybdtq2tS3/xQ0/SQsmakezmOWck5WbbsbMJxyEr2y5Zv84Sampbtr22HDXsxLV9MaJtDQk2myq4tZeNDQs1xZ7GfY4LqI9/3J3TdDwkdXzufbQhdq1yhLkmT2LYpcJ+cJrS9gI9ycvztN49nebYpi4n858HQ9dFfVtkzjz8+tF1+mizwrOevr+x5NrStLpvPvWf4+5uN1u9vU+al9bfJhPbxsITKw4Fr7IejprNuc16rgbQaSUl6++23C83h7W231ab7zfT9ph3dblfZ6E2ORm+M9urVV1/1R+GQ1Gq1/M2em4+NRqPQhD5N00JT5yiK8toVq72wJjb2Zqzb7Wp1dXWmt3Vyalu63e5YjUgcx4VmkwsLC0qSRPfv38/HhWqjJ9Vem0ajUfjbap1DTdqtKeY777zjTyqwZve+WY6jy7oyhJqMhtYxqXXC2tqaGo1GoQbMlm9N6JvNphqNRnB9rqWlJS0uLuZvWJMkUTSlpcNeDQYDtVottdvtqdsTYt0L/ONq15NpLR8APH+Trvs+azHoXmes64wNN27cUFTy9Xp77rl8+bI/aYzVXA2Hw/wZxu6TAHDU1ev1QuwmpyV0meFwOBbHqaQV7/PktySV1z3AauunPcc9efIkb0ngDu6+WKvgafef5+3Agb0FKKurq4Wd1ah5hS+O4/xgdrvd/EF7bW2tcFMtaxJdq9XUbrfV6XSCN9wQC6zoz/Z8WTMWd9DoomCsybM7vP7663nwPxwOtbq6qna7nQfGKysr6vf76vV6UwO+zc3NfP5QE/dpF5TQhci4LyhCZi2PGv18XaPRmHoBKHsZNe04usr68k87Hyb1c9/Y2Cis27pPWHeAXq839pJHo/22h9z19XX1ej2laZofh62tLTUajbFmXfs1GAy0uLj43Jr3A3j5pl33fdOufRq9DFXJ94I+/vhjaUI3I9eFCxeUJIm2trYk59qtfVZQAAD2rqx7wKziOB577s6yTBsbG37Sl+rAgf2NGzcKB8k/WBaI3bx5U+12u5BmlhtwiN1M7eY6jd+3GC9Op9OZ2gfxxo0bec2HvTXz3wbO8iA2GAzGXgq4rFWJH4Bvb2/nZeP8+fPB/jD37t0L9ts07kcCja3H7x9v/YsuXrxYGB9i+x2qVfe5x9FlAb1/vtVqNcVxrAcPHhTGz/pG1mXnua3Dvx5kowdZuwbUajU9efIk+HZz0guFvRgOh3lQP+uF198WTfh2geXJLGUTwPMx7bofYt/bKXtxqgnn9XA4VKfTGWuhVSZN08LHT03oJSwAPA+1Wm3sGXU/yp5J/Q8nl63PnvteBosv7APSZUIVfLO0NChLEzoOz5XbLj/UVn+Ssj4NRk5fMrd/mTuUzWvK+nzY8kxZ34ZJ+1S27ONi0r6/CAr0A/fFgd8/D803re+klUW/X4zPX7b12/H7GLnLCa3bX46t3+3LU9YHM/Z+qnEaf7khoeOYzdCXP9T33N+3aezcm3Ye+dtRNp+//rJ9ywL9rozt9172IwtcV4y/rFA5mSTUZ2wWesnnMF4O8n02s1z3kyQpXN9C14Z2uz22jGT085j+Pce/Z/j865Utx1V27StDecAsKCfH337z2K45di10vyniP//61yW/j7h/TQstK/RMHLru+cvOSp6X/Gc9P+Yri0fdPvCh52Hbbne+0LN76L6RefeX0HOhHSt/vsNi218YV/gjkGCS0MF3uQ/J7XZ7bMf8jAkpK2iWiTbeLVjuUBbQZBOWfVzsNT8Pwo7ltGPvp/Efpoyfbtp+2MkTGtz8DW2nb5Y0Cpyofhn0p7tp9lLmQhc+f/vKjmPopYTPLpjTlmX89NOWbxQoE/4xC63ff1C2cz802HG1/Q4N/jb43LSu2PmY4yzLCe2bDf7Np0xoO3D8ke+zmeW6H7pehM5d//wuO/4qubYb/3qVlWznrNeAjPKAGVFOjr+D5LH7XBTHcX5t3Gtgn3nXSwtu3WVlJc/S/nUvtOxQbHkYgX0WeC6zv9353O32t8PfH/9Y+febbrebNQIfjz8sth5XNJogjT4oMKrFdyv1D2w4HCqOY3W73UJzYBvf7/f39VErTPa88hMvlvUfJR9PHs7hk4l8h4vygFlQTo4/8hiuUHk4cB/7WVi/Vb8vr/WRJ6gHytnLsGkfDgQAAABwMr2QGns5tfOuOI5f/EcFTpDnmZ94sdbX13X//v38y8o4GTiHTybyHS7KA2ZBOTn+yGO4QuXhhQX2ePHIT6DaOIdPJvIdLsoDZkE5Of7IY7hC5eGFNMUHAAAAAADPB4E9AAAAAAAVRmAPAAAAAECFEdgDAAAAAFBhBPYAAAAAAFQYgT0AAAAAABVGYA8AAAAAQIUR2AMAAAAAUGEE9gAAAAAAVBiBPQAAAAAAFUZgDwAAAABAhRHYAwAAAABQYQT2AAAAAABUGIE9AAAAAAAVRmAPAAAAAECFEdgDAAAAAFBhBPYAAAAAAFQYgT0AvAD1el2bm5v+aKCyKNMAABwdBPYAphoOh4qiqDCUqdfriqJIw+HQn5RbWlpSFEUaDAb+pDGbm5t7XrcNzWYzn2br9Idp2+CnL9uGSeuWpMuXL+vKlSuFcUBVbW5uKk1Trays5OPW19cL50C9Xi/MoxnP58FgMJYmdD3x1+efcz4/vTv42+pfL3iBAQA46gjsAUw0GAwUx7G63a6yLFOWZUqSRJH3QG4P7GmaFsa77IG91+v5k4KGw6FWV1fz9WZZJo2CbT9dFEW6cOFCIe3GxkYhXZIkhelZlmlhYaGQJqTdbo/N56rX66rX6/m0NE3V6XQKgcY777yjNE2nvkgAquDmzZtqNBr534PBQDdu3CicA2maFgLm0Pkcx/HY+by4uKg0TfM0SZIojuNCcN9sNtVqtSaec761tbWx89i24cKFC3k622ab3u12tbq6OjG4t5cGAAC8LAT2ACa6evWqkiQp1MxZwGwPuvbA3m631e1283S+xcVFNRoN9ft9f1JQrVZT5gXR7XZbGgUSptlsKkmSsUD+RRgOh0rTVJcuXcrH1Wo1NRqNQiBSq9UUx7E++eSTfBxQRcPhUL1eT2+//XY+bmFhQdvb2/nfdg6kaZqfB6Hz+fLly9JomSbLMtVqtfxvO7eePn2ap+10OoVrTa1WU7vdVqfTycfNwloeXLx4sfC3ey1ZWVlRkiS6efOmMycAAEcLgT2AiXq9ns6fP18YZ0HqgwcP8r+zLNPa2lohnS8L1KIfhl6vVwisXyQLQOxYmE6nUwhOJOnChQu6d+9eYRxQNZ999pkk6c033/QnvRBl63/rrbck76XfNFeuXFGSJHnLnQcPHiiO47Fz9/z586Utjer1ulqtluR03XFr9/0uAH6zfwAADgOBPYBSoX6trmnTn4f79+9LoxpCOQ/xDx48KDw8h5rF9nq9wvT19XU/SVCr1SrM5wcO/X5fnU5H0agvcBRFwRYEr7/++sSuCkAVfP7555LzUqvMvXv3gkGyGQwGarVaajQapWmsNZAbfNv6y3zxxRf+qKDBYDDW2mbaNS00fXt7O29JZM33rYWTdRlwuxbY9zgAADhMBPYApnrttdf8US+l1mkwGKjX6+UP0T6/36y7jVtbW4Xp7XZbrVZranDvzpON+vsuLi4WgvuFhYU8YI/jWHK6K7jsOIaCA6Aqnjx5kpfzMtak/erVq4Xx7ofxrGuOf67Yy7EoihTHsZIk0dbWViGNAi8WXnnllcLf01y9elVxHBe6Gank2ha6Bk7jdhlwt9XvygQAwGEgsAcwVaiGzO1P+6IsLi4qSZJgk3/rI2uuXr1a6N/rW1tbU5IkunHjhj9pIgsw3L7yzWZTcRyr3+8ryzI1Gg3FcVz60sD6CgNVVHZOGatlbzQaY0HzwsJC4UWZBfHuizLr2mNDrVZTFPgyvb8dezmvhqPvBFgff1fo2ha6Bk5j2/Pqq68WxluQv59lAgBQhsAeQCm/Rsw3bfphstq7UM2d9tD81rXf7XdrKzc3N9XpdNTv9/OmwhsbG3mLAD/40D5qFoGjZNJ5MxwO81p2vyY+xNJM+qikpbGP102rPfcD6ZCPP/5YGr3gc03aN80wHQCAl4XAHsBESZLk/drNcPQlePer2M9TvV5XHMfBmrSyfrez9AO+d+9esNntJLbvr7/+uuSsx//ZvFDwMcs2AUfdpG9FTGo6HzLrueC+TLOP5tlH9Mynn34qBc5FnzWRd3+uz7z99tvBlj73799XkiSFca7Q+W4v8PyXjrbs0DwAAOxb5pCUeaNQYeQnDkO3280kZd1uNx8Xx3EWx3EhnbH0aZr6k3L9fj+TlPX7fX/SmDiOp5bjRqNRWGeappmkrN1uZ9lofUmSBOdxt8FfTrvdzpdh/O2xfQml849Ro9EYGzcJ5/DJdNTzvewclzSxfHe73annYb/fH1tGu93O5F2DkiSZeh7657Ox5fnjjaTCdlr6SdcrOyZ+Gn87s5JrwyRHvTzgaKCcHH/kMVyh8kBgf4yRnzgs9tBqg/9wboF0aHAftP1pNjQajcLyjL9ed/C3wR6gbXCDgNB0Bc6NUCDgz+OvN3OCimnp4jgu3deQsu3E8XbU893Od/ccs+A3NLhlPnQe+gG2nYfu4AfMWWBZ/jkfOp+zQOAe4q/fX0aIuz2hF4Y2TFu3z+YDJqGcHH/kMVyh8hCNJkijPqyjWny3Uh8VRX4CR4f1PXb74k/DOXwyVSHfl5aWVKvVZupHj4OpQnnAy0c5Of7IY7hC5YE+9gDwAty+fVtxHM8c1ANH2aVLl9TpdPzRAADgJSGwB4AX4MaNG2O/6Q1U1crKiuI4HvsJOgAA8HLQFP8YIz+BauMcPpnId7goD5gF5eT4I4/hCpUHauwBAAAAAKgwAnsAAAAAACqMwB4AAAAAgAojsAcAAAAAoMII7AEAAAAAqDACewAAAAAAKozAHgAAAACACiOwBwAAAACgwgjsAQAAAACoMAJ7AAAAAAAqjMAeAAAAAIAKI7AHAAAAAKDCCOwBAAAAAKgwAnsAAAAAACqMwB4AAAAAgAojsAcAAAAAoMII7AEAAAAAqDACewAAgBdgaWlJ6+vr/mgAAA6MwB4AALw0m5ubiqKoMJSp1+uFdM1mM582HA7HljMYDArzS1Kz2SykqdfrfpKC0Pa5g2t9fb0wzd0+Sbp06ZJarVZhHAAAh4HAHgAAvBTD4VCrq6vKsiwfJI0FzBa0X7hwoZB2Y2MjT3PhwgX1+/18WqPR0OLiYiG4bzab6nQ6Y+ubFNyvrKwU0tuQJImSJMnTNZtNtVqtfHqapup0OoXgfmVlRRq9LAAA4DAR2AMAgJeiVqvlwbVpt9uSNBaQJ0lSCOR929vbWlhYyP++ePGiJOmLL77Ix927d0+NRiP/W5KuXr2qNE0L46YZDAbq9Xq6dOmSNHrx0Ol01O128zS1Wk3tdludTseZU0qSRDdv3iyMAwDgoAjsAQDAkeYG0QdRr9d17969wribN28qjuPCuGmuXr2qOI7zGvjPPvtMkvTmm28W0r311luS95Li/Pnz6vV6TioAAA6OwB4AABwZ9+/fl6S89t2C4gcPHkzs3+5bXFwsBN+StLW1pTRNFY3639frdfV6PW1vbxfmnWQ4HKrX6+ny5cv5uM8//7yQxue2Gnjttdek0XIAADgsBPYAAOBIsCbu1hzf5/Zxj+N4rG+8G/THcRwM2G3excVFpWlaaD4/i48//liStLa25k9SrVYr/P3KK68U/pakV199VZL09OlTfxIAAPtGYA8AAI6ExcVFJUkSDJqtz7yxvvFuzbcb+F++fFlRFBV+Xs6+cH/58mVlWaZut6vV1VUtLS3laabpdDpj/fSNXws/KXh3a/EBADgoAnsAAPDSWS371taWP0naRyC8tramOI5148YNyfkCf7vdzl8crKysqN/vq9frzfSlentJ8N577xXGW/P6MlZLP20cAAD7RWAPAABeqnq9Xtp03vra+/3Y7W+/+bvLbapvtef2QTsTai5fptVqKUmSsXXaR/PsI3rm008/lZx9kPOCYi/rBQBgGgJ7AADw0tTrdaVpGgzqTaPRUKvVypu6D4dDtVqtvC++/c69a3Nzs/CROwuur169WkhnfeYtOF9fX88/rueyGv0rV64Uxmv0ciFJEq2urubjBoNBYRvNLC8kAADYqyhzfkDWbor+b8qimshPoNo4h0+mk5Tvm5ubhWDYlSRJoVn+0tJS4Wfiut1u4Yv36+vrarVa+d+hNHKOr8s91racfr9fqGm32v9JLyCmbaOl0egL/bM4SeUB+0c5Of7IY7hC5YHA/hgjP4Fq4xw+mcj34y2KomDAX4bygFlQTo4/8hiuUHmgKT4AAMALYM35Zw3qAQCYFYE9AADAC3Dz5s2xPvcAABwGmuIfY+QnUG2cwycT+Q4X5QGzoJwcf+QxXKHyQI09AAAAAAAVRmAPAAAAAECFEdgDAAAAAFBhBPYAAAAAAFQYgT0AAAAAABVGYA8AAAAAQIUR2AMAAAAAUGEE9gAAAAAAVBiBPQAAAAAAFUZgDwAAAABAhRHYAwAAAABQYQT2AAAAAABUWJRlWZb/EUXFqQAAAAAA4MhxQnlq7AEAAAAAqLJgjb0b+aO6yE+g2jiHTybyHS7KA2ZBOTn+yGO4QuWBGnsAAAAAACqMwB4AAAAAgAojsAcAAAAAoMII7AEAAAAAqDACe6AC1tfXtbS05I8GAAAAXprNzU1FUaThcOhPei7W19df6PoOYmlp6YU+vx84sLeDa1/mc73ojMbhazabiqJIm5ub/qQxg8EgLws2lOX90tJSIV2o0NvyQtNC/GU2m00/Sb4/k9YrSfV6vZBuMBj4SQrctP7gHjs7J9yh7Bi5Wq2WLl265I/Olxfa1xB/v9bX1wvTQ9sXWn4or6cdI/daEUWR6vX6xOmT0iqwL/42+obD4dhyQ8feTzNtv06CWc6tkGnlxM/zUD6H+HnvnmOhdbqDm+d+eS+7HoRMujaGtiFU1jCZn8/+9Up7KJuhPAmd27a80LSQadvol7Fp24nnp+x+Gbo3uINfFvzrVhR4/i1j1w3/WuOX47J1n2Rl+edzzzmfn3d+PqikPMzCnyd0bzDu9Yh7Aw5V5pCUeaOmarfb+XztdrswrdvtZpKyNE0L4/eq3+9nkrJ+v+9POpA0TTNJWbfb9ScdC/vJT2PHxoZZjpGf13Ecj42zdEmSFMb5kiTJ1z0tbTYqh41GI//byp4/zl2W7aO/fH8+K+N7LX9++be/3eU0Go3gMXLZfD47vv72lmk0GoVz1PbLHedvc4idj26ZmHaM+v1+Fsdx/rcde3dcmTiOC/tn886yz8bmcffVypjLX+4s+dNoNGbaj/2w/H2ZZjm3Qp5XmYjjuHDOTluP8fPJ5nPzVoHrgW+Wa6O/3LJrYZmjkO8v2yzXK79s2rXJL5uzlBGb14ZJaY2/jXZuhMbNmvchlIeD2+v9MnPKjSt035iVlYUkScauM6Fxe3Wcy8ms+ec+1/nHwr/mh54lQs83oWX5kiQJPhOF7g+Zc69xt2cWRzWPD+M6d1T4z5wHdRjndplQeTi0wN4/YbJDzGgC+/3ZT34ae3g+yDGy/Hfn9R+uQ9yydJATIo7jqetKkiT4sO/bz4nuByChfbfjO6lsh46BBZzZPrfNyAtkZjlny46Rn9fT2D5MWpdtj3t8QsdjGvd4GTv29hAeuoZlgWDfF8rXw6IDnMPP07Rza7/XjWllIlQessC55vPzOhsdW/fvbMbyv59rY+haOMlRzfeXTTO8ePHLwqz5ZOf5QZ83bDlmljI1DeXhYPZ7vzzsvLRyGLqHhcbt1XEtJ3vJvyRJssbohZt/LPz8zAL3/VA+zHoNcYXuOabRaGTJ6EXAXsvTUc3j/ezLUTWtjO1VqEwdllB5OHBTfLO2tiZJ+vjjj/1JY/wmR5Oa1TSbTS0uLkqSFhcXx9LP0mTGnWbN5NbX1xXHsSRpdXVVUUmTnJNqe3tbW1tb/ugDu3fvni5fvuyPLlhbW1OWZarVav6k5+7+/ftKksQfrQsXLujevXv+6FKbm5tK01RXrlzJx73++utK07TQ7Or27duSpFdeeSUf5+v1ejp//nxh3MbGhr46p1+81157TRo1JTPW5OzVV1/Nxx2GK1euKEkSLSws5ON6vV6wW8Ikw+FwLF9rtZriONaTJ08kSU+ePFEcx2PlLkmS0qZyURSp0+koTdP8GuMel1m7fhw3n332mSRpZWXFn3Qgn3/+uSQVyoNG5+f29nZhnMvuS3afsjx66623CunefPNNydn+kOd1bcTzMWtZzLJMGxsb/mgcA/u5X9qz4nvvvZePe/DggZIkGbtHzKLZbCpJkqnlEONmzb/NzU31er3geWz38Lfffrsw3u4BT58+lUqet+xZ4cGDB4Xx+zEYDNTpdILb+DwtBfp5W5cFOzYWTw0Gg0IXo1CM5sZxk7rQuc8/UaB7wtLSkprNZqGLhC3P78bk8rfdujb4MaHfNUqB7lP2PGDLSNNUnU4nuN6yec0s63/eDi2wl6R2u61OpzO2o656va7t7W1lX7UWUJZl6nQ6YwXObGxsqN/vS5L6/X7h5jsYDBTHcT4+yzI1Go1CRtTrdTUajXz6/fv3NRwOtba2pjRNJUndbldZlvGwdsjsImgPy8PhUGma6smTJ4WCHz2HPka2rgsXLviTcuvr6+r1erp69Wo+blJwYOVlFjdv3lQcx4UAZG1tTUmSKI5jra+vq9lsqtVqqd/vlz4o2HGxYPow2Xnq38QkKY7jPG/8i/bKyooajYYWFxfzC/Lq6qq63e5YwDXJvXv3goG0GQwGStO0EMTbNj948GCsDE0Tytt6vV4oe6E8rtVqwXk1CgYajYbiOM6vMXYMlpaW1Ol0Cte67e3tseNZNbOcWw8ePFAcx3t6iasZyoTxrxf20qxMp9NRo9HI//7iiy8K0332AuGw+NdC7N2k65VpNptjL1T3Wxb3w55j3GDQTLqm4ui5cePGWBB/79491Wq1sXuPH6z4Zg3mer1eYbkvIyioMnsOmcS/tlulyhdffDF2X/FNm+6y+6O9TDaXLl1Su92eeo97mRYXF3Xz5k1lWaZ+v69Op1Mo481mU71eL3+uuXr1qlZXVwvLsADXjb/6/b5WV1fHynWn09GTJ0/ydFZRcuXKlXycXcOnieNYaZoqyzJ1u121Wq1CTFqv1/N9y7JM7XY7rzxeWFjI1+Vut4miSJcvX87Hd7tdLS4uFpbvz3v//n31er18+gvhVt+HqvSn8Zu7uM3g/KYZ/t+mbLwpaxpnTW5cfpOZSU0q/LTHzX7y07ffY2TzucfexvlNUpIJ/Zf224SlrEmvlVcb/P0qKy9+OZ8k1EfLZdvmH5+QsrLvKtvmaSYdd2N5Fmp27R7LveaRnfNlxygLdJPIJvShndY0PLQ+W5Ztu/3tNp2btP+mEWiKX5ZvZePL2PE9SsrOLZelcfczdHxdoTzyha4r2ZTj5De1zKbccyZto2vWa2PZNk8yaX9OqrLrlXs99cucO33WsrjXc9S2y4ZQmXLNck3xUR4Ozyz3S7s++GUgHvXzdtn1xU/riuO4UNZmea6x5YbKaJmTUE7K8q8xat5uQs9soWuIXR+63e7Ea/oseWbloywf2u124byfdB8qU7bsaULb76+/bP/9Yx4ql/6yQs9FofGh7fLTZIH89NdXdt0ObavL5nPzwN/fLJB3xk3rb5MJ7eNhCZWHQ62xl6SrV6+q1+sF32DamzL/TZU137WmMLPa3t4uNJeIokjxqHm9uXr1ap4mtE14Pi5cuKA4joNvqP1m1Fa7Mqmlx15sbm6q0+mo2+2OlTVr5m+DdcNwhd7KWnPtWXzyySdSoOmnvcEcDofKRm8lrWyWmVazuF/WWsFaw5Sp1WrqdrtK07SQP1EU6caNG/lxtJqG0LHzDYdDra6uqtFojB0jMxwO1ev1SrttXLx4sfD31atXx7o5uFZWVtRut/P8jqJIly5dKtTILCws5G943etJo9HYcw2b5ZvfxcKtHaiiSeeWz2+xsrCwoCRJdP/+/UI6zVgmNCqPVoPgXvet1URIqObN+PecsvJzEJOuhZjNpOuVNdO1a+ri4uLY+bqXsrhXW1tb+fr7/f7UmqWyayqOjlCLO+O2/JFTI/vpp58WxhurnfRrbqexFn43btzwJ8Eza4uIra2tQssZ99nL7Ubo1+qrpMWfz22N3O12FTktg4bDoVqtlm7evOnPduS5LRvtmuV3Y/MNh8Ox67BmaF132GKnu6Vx899q6/1nAd+TJ0/ylgTu4O6LtQ4LPWu8SIce2K+srChJkkJTuOfJbfLgDvZwuLKyomzU3MIe6p/Hwxv+Xb1eV5qmpRfC0EXzsGxubmp1dVXtdntigGCs2Za99AldiExZ4OAaDofqdDpqt9v+JF24cEFJkuRdPmq1mrJRM5+yZqGH3WddoweNVqs1c9N5fxuWlpYUx3Ehf7NR86Wy/TDD4VBxHCtJkok3Yb9PtG8/gbH/Umd7e1vb29t6/fXX8zR2vXCH4XD40i/UR8Fez61Zb96zlgljzeXcQSXnbuhbFwqUad9hdX2Zdi3EdHu5XtVqNbXb7bGgedayeFALCwtqNBrq9XoTnzOmlT+8PIPBYKyLnmtSvobcv39/LCDo9Xr5y/BJFU7cd2ZjL1XcoL3VakmjIM59LvG7AlslwSuvvDL1eE+b7rJYqNPpSM63PuxbYVEU5c3Xp70MxOEYlnQPmFXsdLl0h1meW16kQw/sNaqBTdN07M2UPSz5F8ay2i1TNt7vHzuJPdTL+WDZXk5SzGZpaUnpqH+Lr+Z9rMxMy/9ZDQaDPPAoCwh9/gPW+fPng/1h7t27N7FPsbGA9J133vEnKU3TYP/QSS8MDruGd3NzM39IniU4k3PTtG3Z3t4OHotQYOWzAG7S9yzs5YhfMyLno2n+y6Gy1kCTWB/+UF6Z4ajlgN9CwOW+GDBlrZDsb7/cHXV7PbfsA0X+9Xl7e3ssj2YpE9N0Op2xlkAa3YtCNW/2t1/LZg9fh9EXftK1ELPZz/XKfymzl7J4GELXA59/TcXRYQF9qLyFPtJp5covd8Zt0WFDkiRKkkSZUwkVcu/evZnuqyed/9I+G1XmaVTpMCnw+uSTTwotukIteYaj78r4H96bxr2+hCoNrGIpTdMD3f9mUZvwraC9KHsm9Z/JytZnHyl+Gez5K/QNFFfonJulpUFZmtBxeK7cdvmhtvrT+P0ejNvvze1vIK8/7iz9Dy2N30/C+jP442170jQt9ImwvhRu/5HQusvWVzX7yU9fWZ+bEOu/5PcvcYX6o4XywMzaN8Xytmw52ahM+nnq95kLlUcry5PKsTu+bBtC/btm6Vc8rSyG+gOFlJ0vrjiOC/sZOq6h4zGpz6rRjP1KrYyUlSN//aHzNXSsXbNsb6gshJRtbxzo96+SclNGh3AOH1SoDMzC39fQcZq1TEzir8eErveusnLk7mfoemUmXRtnuRZOchTy/WWb5XqVeL8dnZWUKb+MhMqiKeurmQWWE8dxIZ2VCT9N6Fq5l/OJ8nB4Jt0vQ/cSV+i+kXjfgpnlnu4/1/T7/bFrmF2fQuWwzEkoJ5Pyz2Xn+CT+PSAryT//Xh4qB/5yZjnPy/pkT7LfPLZ1WXmyv931l93T/PLqP1+FlhU6RqFj6y87O+Q+9m55CZ3ftt3ufP45nZVc27NRWps39Axhx8qf77DY9hfGFf4IJJjGP9guW55faGPvAxNlF1GXrcdPb5npDu763Pn8eTOvQE7K/Cqy/doPP49sKCucoXywwT9B7GJqg3+c/TxzB78sGTt5QoN7EfGn+duWOfnvDj4FjoVtt39hcYW2c1L6bMKFz19O2baasjx1t8E9H2zwL/JZyfpD6cykPPVvfAocW59/HP11T7rx2OAf91AZ9pdbxj22bhn1t9Pf12lsvpfJ34dZj8+082gvZcKElulfP4xfBkL8cuwvK3ROl51HVmZD5ciG0PUmxNKfZGXHWU5+hMpD6NoRSufzp9vglkV/+aG89stu6Noz6bwJsfmwP/557g6hdGXPGVkgP/3yFgpefKF7eug6u1f7ne+omzX/XKHYxD/GZdfjaXk8KWh1B/9+4rN5JpU337T9nsQ9jvHopaS7frtO+mU3VF7d63MyCm79fQldd/3nrtCyG88psM8C+WR/u/O52+1vh78//rHy7wndbjdreB92PEy2Hlc0miCN+qKMavHdSv0Tx/qSpmn6XJrqvSjk5/Fg5ZF8PHk4h08m8h0uygNmQTk5/shjuELlgcA+oF6v6/LlyzP1JT3KyM/jI4qiPfUzxfHAOXwyke9wUR4wC8rJ8UcewxUqD8/l43lVZl/SrXpQj+Ol3W6PfYwSAAAAAESN/fFGfgLVxjl8MpHvcFEeMAvKyfFHHsMVKg/U2AMAAAAAUGEE9gAAAAAAVBiBPQAAAAAAFUZgDwAAAABAhRHYAwAAAABQYQT2AAAAAABUGIE9AAAAAAAVRmAPAAAAAECFEdgDAAAAAFBhBPYAAAAAAFQYgT0AAAAAABVGYA8AAAAAQIUR2AMAAAAAUGEE9gAAAAAAVBiBPQAAAAAAFUZgDwAAAABAhRHYAwAAAABQYS8ssI+iSOvr64W/m81mIc00m5ubiqJIg8HAn7Rv/nYBwPNQr9e1ubnpjwYqizINAMDRcWiBvQXd7jAcDv1kAI6AwWCgKIq0tLTkTwpaWloqnNtlL+Vsue5Q9iKuXq/P/GLNX38omLDlhdKEtssdpl2rpq0/tHx/mZcvX9aVK1cK44Cq2tzcVJqmWllZycetr68XzoF6vV6YRyXPCr5ZzicF1ld2XTJ+enfwt3XaOQ8AwFFzKIH90tKSVldXlaapsixTlmVK01RxHAdvxpKUZZk2Njb80ROtrKwoyzItLCz4kwDMaGlpSYuLi/7oUuvr66rVavm53e121el0xh6i19fXtbi4qH6/n6ctO1+bzebYg3QZS+euf3V1tfCgXa/XVa/X8zTtdlurq6v5S4WFhYXCNtnQaDQUx7FqtVq+LN8s619cXCxc/5IkGbv+vfPOO0rTtPRFB1AlN2/eVKPRyP8eDAa6ceNGfg6kaao0TQvn+XA41OrqauEcjONYkRfcz3I+NZtNtVqtwvpC1yXX2tpaYd3uNly4cCFPN8s577OXBgAAvDSZQ1LmjZqq2+1mkrJ+v+9PKpCUtdttf/RLd1S36zDsJz9xvLXb7UxSlqZpliRJliSJn2QmcRxncRznf6dpmknKut1uIV1Iv9/Pt2Ha+WfXlzRNC+PdbS+7BsVxPHH/Dmv9IZO2qdFoFMZNwjl8Mh31fJ/1fG80GsHzx+Vek8r451PZ+m1Ze+Eve7/n/H7WPaujXh5wNFBOjj/yGK5QeThwjf2VK1eUJEmwVm6Ser1eeLO+tLSkZrM51lTOZc3z/BovmtMBs7Eaq0k11Pvx2WefSaNWNdNcunRJ7XZ7pm148OBBsEb9/Pnz6vV6kqTPP/9cGtXKuy5cuKDt7e3CONfHH38sjY5JmVnWvxcXLlzQvXv3/NFApdj5/uabb/qTXoiy9b/11lvS6FlhVv4zzH7O+Xq9rlarJTnPI+6zhv9c4z+nAABwGA4c2KdpOnYD3K9Op6MnT54UmsdN6wMcRZEajUY+z+XLl/M+u0tLS7p06VI+zZrTlXUPADDdcDhUmqaFpqv2MOy/SPObxdq5OSmYdk07V93pftrXX39daZoWxrk6nU6hKXGIv0xfaLo1Nw698Jy2TUAV2Mu0aff+e/fuBYNkMxgM1Gq11Gg0StOEzidbf5kvvvjCHxU0GAyUpqkuXbqUjwud067Q9O3tbbXbbemrqhNlWZa/5LQuA27XAvseCAAAh+lAgb3d4F5//XV/0r4kSVLod3/58uXSN+RyggR3nrW1tTxo2NraKtQg2tv9p0+f5uMA7I3VdL/33nuF8Wma6sqVK/nDa7/fV6fTyc/T4XCoVqulmzdvFuabJlS79dprr+X/f+eddyRnu4zVoIXYNvn7EDJt/Rrtm73MiONYSZJoa2urkEbOfKHgAKiKJ0+eKI5jf3SBfVzv6tWrhfHuh/EWFxfVaDTGvrcz6/nkvwx45ZVXCn9Pc/XqVcVxPNbSaJZzfhbD4VCdTkfdbrewrba/tCAEABymAwX2z9u0h+BZHi7szbg9IGgPb/MBFG1ubgYfVCUpjuNCDfXCwoKSJNH9+/elUc1Vo9EYq8WeJtSc3q2xq9Vq+UsEt7WAfRgv5MaNG0qSZGwfQqatX6NtsBca1tUhmtD1h5eLqLKye7KxWvZGozEWNPsfsrQg3m0+P+v55G/HXs6r4XCoXq+ny5cv+5NmOudnYdvz6quvFsbbdWc/ywQAoMyBAvtaraY4jvMH96MmGvVls4cDmsAC+7e5uanV1VW12+2xh3WNauwn6fV6Y8G3RjXrUcnPWU0LvG26HyxkWSaV1LxZTeIsPz036/p9ViNX1jphrzWLwFFSVu41Cpitlt2viQ+xNJ988ok/KeefT9Nqz/1AOqTsGxuT9k0zTAcA4GU5UGCv0ceger1e8KH8eZvUX9W2Z5aHdwCTDQaDPKj3H4Ql6e2335YCNWjb29v5g7AfeFvw3W6381o539tvv600TceWe//+fSVJUhjn63Q6hb6z5sqVK2OtC8ocZP2h1gKz9k0GjrJJ995JTedDZj0X3PPJutXZR/TMp59+KgU+pOmzJvKhb2zs95wPvWywF3h+K0FbdmgeAAD268CB/cbGhuI4Vhz4zfqyWrjDYn1r3Q90ra+v57+7La8WwP3YF4DZDAaDvC9sKKiX8zV8/1xM03Smfuxlypbb6/UmvrSLokhJkoy1LLCPZfn9fk2z2Sxct2ZZ/2AwGGsZYPvuv1iYpfsQcNSVdZOzLm9lQf3m5ubYB3Ht3Lp48aI04/lUq9WUJIlWV1fzNPYhPvuInQLns7l9+7ZU8o2NWc75SfwuBf52avQsEurbDwDAgbi/fRf6PbxZ2e/VuoP7O7Dyfi/a/z3n0G/E+r8na79/7f42tP2erQ3ub2tbehtsee5v3/rbdZzYfgPGfms5NPi/22ySJBlL655Txj8XZyl7mvH885frbmtovWXLtH0pU/a72/7y/emh65//G/ZZ4Lo3jS0LJ8tRz/fQ78hPurb493p/+n7PJ39Z/u/aTzqf/ecNn79+fxkh7va42+vvz7R1+2w+YBLKyfFHHsMVKg/RaII0ets+Cvbd2B8VRX4CR4f1Pe73+1ObChvO4ZOpCvm+tLSkWq02Uz96HEwVygNePsrJ8UcewxUqDwduig8AmO727dsz9+0HjrpLly6p0+n4owEAwEtCYA8AL8CNGzdK+/YDVbOysqI4jsd+gg4AALwcNMU/xshPoNo4h08m8h0uygNmQTk5/shjuELlgRp7AAAAAAAqjMAeAAAAAIAKI7AHAAAAAKDCCOwBAAAAAKgwAnsAAAAAACqMwB4AAAAAgAojsAcAAAAAoMII7AEAAAAAqDACewAAAAAAKozAHgAAAACACiOwBwAAAACgwgjsAQAAAACoMAJ7AAAAAAAqjMAeAAAAAIAKI7AHAAAAAKDCCOwBAAAAAKgwAnsAAAAAACrswIF9s9lUFEX+6H1ZX18/tGUBAAAcJUtLS1pfX/dHAwBwYAcO7AEAAPZrc3NTURQVhjL1er2Qrtls5tOGw+HYcgaDQWF+ORUSNtTrdT9JQWj73MFlFRQ2uNsnSZcuXVKr1SqMAwDgMBw4sN/Y2FCWZf5oeOr1+tgNHgCAk2w4HGp1dVVZluWDpLGA2YL2CxcuFNJubGzkaS5cuKB+v59PazQaWlxcLAT3zWZTnU5nbH2TgvuVlZVCehuSJFGSJHm6ZrOpVquVT0/TVJ1Op3DvX1lZkUYvCwAAOEwHDuwBAAD2o1ar5cG1abfbkjQWkCdJUgjkfdvb21pYWMj/vnjxoiTpiy++yMfdu3dPjUYj/1uSrl69qjRNC+OmGQwG6vV6unTpkjR68dDpdNTtdvM0tVpN7XZbnU7HmVNKkkQ3b94sjAMA4KAOHNj7/eL9v+W8afffULtN6paWlgrTjN9kzgb/hu9O8/uv2bqXlpbyNPYG3Z03tA1+Ezw/TbPZ1NLS0li64XAojW7+URTlb+5tOgAAmI0bRB9EvV7XvXv3CuNu3rypOI4L46a5evWq4jjOa+A/++wzSdKbb75ZSPfWW29J3kuK8+fPq9frOakAADi4Awf2+2UBsjVZO3/+/Fi/s/X19UKTOXsTnqZp/lZ+aWlJw+Gw0PSt1WqNBferq6u6dOmSsixTv9/Pg2yNtiFNU/V6vcJ86+vrY00Et7e3x4L7Xq+nmzdvFprnXbhwQZK0sLCgLMsUx7EajUaeBgAAjLt//740un/KCYofPHhQeIE+7SX54uJiIfiWpK2tLaVpqmhUQVCv19Xr9bS9vV2Yd5LhcKher6fLly/n4z7//PNCGp/bauC1116TRssBAOCwvJTA3m6KV69ezcetra3lze/M/fv3C03m/Dfj1hTObZpXq9XUaDR048aNfJxGTfts/oWFhTzQtnlrtZqSJMkfKCSp1WoVmtVp9Ja+1+sVbshxHGtrayv/+9KlS0rTlJs2AAB7YPd1/3nAuC/a4zge6xvvBv1xHAcDdpt3cXFRaZqO3een+fjjj6XRc4uvVqsV/n7llVcKf0vSq6++Kkl6+vSpPwkAgH17KYF9WZM13/nz5wt906wpv81nb8DjOC7czP3+bPthQfnq6mph2aurq37SMdy0AQDYu8XFRSVJEgyarc+8sb7x7kt0N/C/fPmyIq97nnWbu3z5srJRS8DV1dWxlniTdDqdsX76xn+hP+k5wK3FBwDgoF5KYL9XblDd7/fH3oinaVq4mWejJvOHodvtji07y7KxbQAAAPtntexuCzjXXgPhtbU1xXGct+CzL/C32+38xcHKyor6/b56vd7Yd4BC7CXBe++9VxhvzevL2Av/aeMAANivQw/sZ+k7Vlaj/eTJk8LfN27cGAus3S/eli3nMFjgPq3f3Kz85oIAAOAr9Xq9tOm83ff9+7H9PelFu3vvtWcF+6CdCTWXL9NqtZQkydg6rSWhtUg0n376qeTsg5wXFHtZLwAA0xx6YG83t9u3b0ujAN//2qz1cXe/cGsfynPV6/WxpvCR88V5W87i4mJhvvX19bGP5+1Ho9FQq9UqfM12MBjsqcmey/8SLwAAJ129XleapsGg3tj92O7/w+FQrVYr74s/HP36jmtzc7PwkTsLrt3v+8jpM2/PL/brPu69X053wCtXrhTGy/lOj9tdbzAYFLbRzPJCAgCAvTr0wL5Wq6nb7arVauXN6kK/D7u9vZ1/mTaKIj158mTsAzb2ITy3xr7dbhdeFGxvbytJkkLg/+TJk2D/vL3a2NhQu93W4uJivuxLly6VNhOcZGNjI99fau8BAPgqWLZnBP8lvvsSfWNjQ0mS5N/UieNY3W43v9fbb8a786+urhbSaNQHv9frFdLZr+9MC7SvXLmiOI4Lte+ura2twvPI4uLi2Po1+jBwkiSFcQAAHFSUZf/+22uR8/Nvs1pfX1er1drTPLMYDAZaXFxUv98v3ERtfJqmU2/CJ91+8hPA0cE5fDKR78dbFEXqdruFn+GbhPKAWVBOjj/yGK5QeThwjf3zevNsfc+sf5q5evWq4jgmqAcAAJVizflnDeoBAJjVvgN7+8mYXq8X7G92ULVaTf1+P2/Sb4NGze8BAACq5ObNm2N97gEAOAwHboqPo4v8BKqNc/hkIt/hojxgFpST4488hitUHvZdYw8AAAAAAF4+AnsAAAAAACqMwB4AAAAAgAojsAcAAAAAoMII7AEAAAAAqDACewAAAAAAKozAHgAAAACACiOwBwAAAACgwgjsAQAAAACoMAJ7AAAAAAAqjMAeAAAAAIAKI7AHAAAAAKDCoizLsvyPKCpOBQAAAAAAR44TylNjDwAAAABAlRVq7AEAAAAAQLVQYw8AAAAAQIUR2AMAAAAAUGEE9gAAAAAAVNj/B1fsUqM20KMiAAAAAElFTkSuQmCC\" style=\"width: 874px; height: 735.229px;\" width=\"874\" height=\"735.229\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003eUnder the second scenario, where Ag-RDT is the only feasible tool to aid testing, the no-test strategy is costly compared to Ag-RDT diagnostic strategy. As for effectiveness, the results show no-test strategy is more effective in averting DALYs than the RDT strategy but with an ICER of US\u003cspan\u003e$\u003c/span\u003e1490.34 no-test strategy was not cost-effective in Kenya.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analysis\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003eDifference prevalence level from 5\u0026ndash;20%\u003c/h2\u003e \u003cp\u003eA one-way sensitivity analysis showed that the ICER was sensitive to the COVID-19 prevalence level. The results showed that at less than 5% COVID-19 prevalence level and under a case where there was access to delayed NAAT, the use of RDT and further confirmation by NAAT strategy was not cost-effective compared to the delayed NAAT strategy. At a prevalence rate of more than 5\u0026ndash;20%, the results showed that the use of RDT and further confirmation of negatives by NAAT was cost-effective compared to the delayed NAAT strategy as presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferent prevalence levels from 5\u0026ndash;20% sensitivity report scenario 1\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevalence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrategy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCost (USD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncremental Cost (USD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffectiveness (DALYs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncremental Effectiveness (DALYs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIncremental cost per DALY averted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDominance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed test-NAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e616103.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1118.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e757680.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e141577.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e999.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e118.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1192.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical Judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e804796.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47115.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1180.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-181.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-260.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed test-NAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e984364.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1956.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical Judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1147121.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e162757.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2066.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-109.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1489.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1191593.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e207229.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1749.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e207.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e997.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed test-NAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1352625.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2795.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical Judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1489447.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e136822.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2951.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-156.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-876.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1625506.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e272881.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2498.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e296.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e919.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.1625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed test-NAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1720886.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3634.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical Judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1831772.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110886.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3837.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-202.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-546.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2059419.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e338532.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3248.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e385.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e877.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed test-NAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2089147.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4472.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical Judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2174097.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84950.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4722.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-249.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-340.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2493331.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e404184.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3997.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e475.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e850.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn a scenario with no access to NAAT assay, at a lower prevalence rate of 5\u0026ndash;16.25%, the no-test strategy was still not cost-effective compared to the RDT strategy as presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The results showed that at a higher prevalence rate of 20%, the no-test strategy was more costly and less effective than the Ag-RDT strategy, and the ICER was US\u003cspan\u003e$\u003c/span\u003e989.15, hence a cost-effective strategy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferent prevalence levels from 5\u0026ndash;20% sensitivity report scenario 2\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevalence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrategy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCost (USD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncremental Cost (USD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffectiveness (DALYs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncremental Effectiveness (DALYs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIncremental cost per DALY averted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDominance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e584876.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1268.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e804796.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e219919.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1180.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e88.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2492.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.0875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e894914.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2220.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1147122.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e252207.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2066.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e154.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1633.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1204952.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3172.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1489447.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e284495.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2951.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e220.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1289.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.1625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1514990.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4123.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1831773.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e316782.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3837.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e286.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1104.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1825028.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5075.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2174098.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e349070.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4722.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e352.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e989.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eRDT and PCR Sensitivity\u003c/h2\u003e \u003cp\u003eWhen we varied the sensitivity of RDT by increasing or reducing RDT sensitivity, we found applying RDT as the first-line tool to aid in testing, followed by prioritization of negatives for confirmatory testing by NAAT was still costly and more effective up to a sensitivity level\u0026thinsp;\u0026ge;\u0026thinsp;87% to delayed NAAT diagnostic strategy as presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRDT Sensitivity Report Scenario 1\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDT_TP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrategy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCost (USD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncremental Cost (USD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffectiveness (DALYs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncremental Effectiveness (DALYs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIncremental cost per DALY averted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDominance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed test-NAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1107117.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2236.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1177698.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70580.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1731.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e505.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e139.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical Judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1261230.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83531.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2361.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-629.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-132.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed test-NAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1107117.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2236.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1256964.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e149847.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1865.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e371.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e403.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical Judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1261230.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4265.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2361.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-496.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-8.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed test-NAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1107117.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2236.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical Judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1261230.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e154112.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2361.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-124.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1234.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1336231.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e229113.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1998.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e237.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e964.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed test-NAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1107117.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2236.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical Judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1261230.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e154112.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2361.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-124.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1234.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1415497.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e308379.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2132.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e103.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2972.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed test-NAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1107117.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2236.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical Judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1261230.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e154112.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2361.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-124.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1234.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1494763.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e387645.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2266.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-30.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-12913.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn a scenario where there was no access to the NAAT assay, RDT was still less costly and less effective than the no-test strategy, and in the two scenarios, we found the ICER was sensitive to changes in RDT sensitivity as presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRDT Sensitivity Report Scenario 2\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDT_TP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrategy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCost (USD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncremental Cost (USD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffectiveness (DALYs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncremental Effectiveness (DALYs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIncremental cost per DALY averted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDominance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e839821.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2270.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1261230.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e421409.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2361.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-91.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4626.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e919041.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2404.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1261230.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e342189.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2361.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8016.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e998260.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2537.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1261230.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e262970.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2361.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e176.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1490.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1077480.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2671.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1261230.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e183750.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2361.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e310.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e592.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1156699.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2805.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1261230.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e104531.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2361.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e443.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e235.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen we varied the PCR sensitivity by increasing it, we found that PCR was less costly and less effective than RDT. Reducing the PCR sensitivity also led to a reduction in the costs of PCR diagnostic strategy and was attractively effective under the three strategies as presented in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePCR Sensitivity Report\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCR_TP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrategy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCost (USD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncremental Cost (USD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffectiveness (DALYs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncremental Effectiveness (DALYs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIncremental cost per DALY averted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDominance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed test-NAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e750252.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1638.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1239626.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e489374.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1837.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-199.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2457.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical Judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1261230.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e510978.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2361.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-722.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-706.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.65875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed test-NAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e877663.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1851.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical Judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1261230.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e383566.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2361.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-509.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-752.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1274117.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e396453.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1895.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-43.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-9171.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.7705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed test-NAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1005074.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2065.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical Judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1261230.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e256155.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2361.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-295.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-865.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1308607.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e303532.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1952.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e112.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2694.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.88225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed test-NAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1132486.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2279.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical Judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1261230.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e128744.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2361.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-82.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1563.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsolute\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1343098.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e210612.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2010.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e268.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e784.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed test-NAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1259897.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2492.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo test, Clinical Judgement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1261230.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1332.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2361.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e131.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAg-RDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1377589.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116358.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2068.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e293.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e396.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eAccording to Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. the key parameters that had the most significant effect on the ICER when we compared the RDT diagnostic strategy to the delayed NAAT diagnostic strategy are 1) Proportion of severe patients hospitalized 2) Proportion of critical patients hospitalized (both of which fewer cases improves cost-effectiveness); 3) Probability of critical patient dying (lower mortality for critical patients improves cost-effectiveness); 4) Length of stay for critical patients (shorter length of stay in the hospital improves cost-effectiveness).\u003c/p\u003e\n\u003cp\u003eComparing RDT diagnostic strategy and no-test strategy, Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. summarizes the three parameters that had the most significant effect on the ICER. These are: 1) Clinical true positive; (reduction in true positive cases improves cost-effectiveness); 2) Clinical false positive (reduction in false-positive diagnosed cases improves cost-effectiveness, and 3) Proportion of infected SARS-Cov-2 (reduction in SARS-CoV-2 infection improves cost-effectiveness).\u003c/p\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003eProbabilistic Sensitivity Analysis\u003c/h2\u003e\n \u003cp\u003eThe results of the Monte Carlo simulation of 1000 samples under the first scenario (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) show that at a cost-effectiveness threshold of US\u003cspan\u003e$\u003c/span\u003e 1003.4 per DALYs averted, the probability of antigen rapid diagnostic test being the more cost-effective strategy was 52.5%. Under the second scenario, the results for PSA (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e) show that at a cost-effectiveness threshold of US\u003cspan\u003e$\u003c/span\u003e 1003.4 per DALYs averted, the probability of the no-test diagnostic strategy being more cost-effective was 28.7%.\u003c/p\u003e\n \u003cp\u003eFigures \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e present cost-effectiveness acceptability curves under scenario one and scenario two, respectively, based on a range of cost-effectiveness thresholds. Under a scenario where there is delayed NAAT diagnosis and given a willingness to pay of US\u003cspan\u003e$\u003c/span\u003e 900 per DALYs averted, there was a 40% probability of the Ag-RDT strategy being cost-effective. The cost-effectiveness acceptability curve shows the probability of the Ag-RDT strategy being more cost-effective as the decision maker was willing to increase their willingness to pay (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eUnder a scenario where there is no access to NAAT assay in a resource-limited setting and a decision maker is not willing to pay for any DALYs averted, the probability of the no-test strategy being cost-effective compared to Ag-RDT strategy was approximately 75% (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e "},{"header":"Discussion","content":"\u003cp\u003eThis study presents the cost and cost-effectiveness of SD Biosensor Ag-RDTs compared to PCR and clinical judgment for SARS-CoV-2 detection in Kenya. We evaluated three diagnostic strategies under different scenarios, building on existing research on rapid testing approaches. Our findings indicate that when comparing the use of Ag-RDT as a first-line tool with subsequent confirmatory PCR testing of negatives to the strategy of delayed NAAT testing, the Ag-RDT diagnostic strategy is more costly but also more effective. The higher costs are primarily driven by increased detection of true positives through confirmatory PCR testing of RDT-negative results, which consequently increases case management costs for diagnosed positive cases. Importantly, we found that this strategy averted more DALYs (1998.97 versus 2236.49) among infected SARS-CoV-2 patients not initially detected by RDT.\u003c/p\u003e \u003cp\u003eAt a COVID-19 prevalence level of 10%, our analysis determined that using Ag-RDT as a first-line tool followed by confirmatory PCR testing of negative results was cost-effective (ICER\u0026thinsp;=\u0026thinsp;US\u003cspan\u003e$\u003c/span\u003e964.63 per DALY averted) when compared to Kenya's cost-effectiveness threshold of US\u003cspan\u003e$\u003c/span\u003e1003.4. This finding aligns with Jakobsen et al. (2021), who similarly concluded that despite lower sensitivity, rapid tests offered cost-effective benefits through faster turnaround times.\u003c/p\u003e \u003cp\u003eWhen evaluating a scenario with no access to NAAT, comparing Ag-RDT as the only testing option versus clinical judgment, our results showed that at prevalence levels\u0026thinsp;\u0026le;\u0026thinsp;16.25%, the no-test strategy was not cost-effective compared to Ag-RDT. The Ag-RDT strategy was less costly (US\u003cspan\u003e$\u003c/span\u003e998,260.67 versus US\u003cspan\u003e$\u003c/span\u003e1,261,230.78) though less effective in averting DALYs than the clinical strategy. The high costs associated with the clinical diagnostic approach can be attributed to the treatment of presumptive cases with symptoms resembling SARS-CoV-2 infection. While the clinical strategy averted more DALYs by subjecting more symptomatic cases to treatment, in resource-limited settings like Kenya, this approach may not be cost-effective due to its substantial costs. This supports Paltiel et al.'s (2020) assertion that testing frequency and result availability often outweigh sensitivity concerns for effective disease management in resource-constrained environments.\u003c/p\u003e \u003cp\u003eOur sensitivity analysis revealed that the proportion of severe and critical cases hospitalized significantly impacts the cost-effectiveness of the Ag-RDT strategy. This can be explained by the fact that lower proportions of severe and critical hospitalizations correlate with lower COVID-19 prevalence levels, which emphasizes the need for accurate diagnosis to avert more DALYs and reduce costs associated with misdiagnosis. Previous studies have shown that delayed diagnosis may not necessarily be associated with ICU admission or death[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], suggesting that delays in obtaining NAAT results do not directly link to disease progression and recovery. However, implementing an Ag-RDT diagnostic strategy in settings with delayed access to NAAT would avert more DALYs than relying solely on delayed NAAT testing\u003c/p\u003e \u003cp\u003eOur findings complement those of a study conducted in Mozambique[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] who conducted a cost analysis of diagnostic tests for SARS-CoV-2 using different Ag-RDTs and RT-PCR technologies in Mozambique and found that Ag-RDT was three times lower than PCR testing. Although the study did not assess the effectiveness of the antigen test but in terms of costs, Ag-RDT was concluded to be cost-effective. Similarly, another study[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] concluded that rapid tests were less costly but still effective compared to PCR for influenza, with the cost difference making rapid tests the cost-effective option. These studies reinforce our conclusion that Ag-RDTs represent a cost-effective approach in Low- and Middle-Income Countries, particularly in settings with limited or delayed access to molecular testing.\u003c/p\u003e \u003cp\u003eA major limitation of this study is the scarce data on the outcomes of COVID-19 patients with false-negative diagnosis results. However, we made assumptions regarding disease progression and outcome during the peak of the COVID-19 pandemic for the COVID-19 cases that received no care. One strength of this analysis is the comprehensive inclusion of both diagnostic costs and treatment costs associated with false-positive cases. This research represents one of the first cost-effectiveness analyses of Ag-RDTs specifically conducted in low- and middle-income countries, providing valuable guidance for resource allocation in similar settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur cost-effectiveness analysis provides clear guidance for SARS-CoV-2 diagnostic strategy selection in resource-limited settings like Kenya. The findings demonstrate that at a COVID-19 prevalence level of 10%, implementing Ag-RDT as a first-line tool followed by confirmatory NAAT testing of negative results is a cost-effective strategy (ICER = US$964.63 per DALY averted) compared to delayed NAAT testing alone, falling below Kenya's cost-effectiveness threshold of US$1003.4.\u003c/p\u003e\n\u003cp\u003eIn scenarios with delayed access to NAAT, the Ag-RDT strategy, while more costly, averts significantly more DALYs (1998.97 versus 2236.49) than relying solely on delayed molecular testing. This strategy becomes cost-effective at prevalence levels exceeding 5%, offering a practical solution for timely diagnosis and appropriate patient management. Sensitivity analyses further revealed that the cost-effectiveness of this approach is particularly influenced by the proportion of severe and critical cases requiring hospitalization.\u003c/p\u003e\n\u003cp\u003eIn settings where NAAT is unavailable, our results indicate that Ag-RDT implementation is less costly (US$998,260.67 versus US$1,261,230.78) than clinical judgment alone at prevalence levels below 16.25%. While the clinical strategy averted more DALYs, its substantially higher costs make it an unsustainable approach in resource-constrained environments.\u003c/p\u003e\n\u003cp\u003eThese findings have important policy implications for Kenya and similar low- and middle-income countries. Policymakers should prioritize the implementation of Ag-RDTs as either a complement to delayed NAAT testing or as a standalone diagnostic tool in areas without molecular testing capabilities. This strategic approach would reduce the financial burden associated with presumptive treatment based on clinical judgment while ensuring timely diagnosis to limit disease transmission. The implementation of Ag-RDTs represents a practical, cost-effective solution for COVID-19 diagnosis in resource-limited settings, balancing economic constraints with the need for effective disease management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical considerations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was approved by the Ethics committee of Kenya Methodist University and all participants signed a written consent form to participate to the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolicy implication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper will give important insight on cost effective tests to use in a pandemic and will help decision makers to use efficiently the scarce healthcare resources\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project that generated data used in this study was made possible by the generous support of the World Health Organization. The study was an implementation Research on the use of Antigen Rapid Diagnostic Tests for Coronavirus Disease 2019 (COVID-19). The study entailed the assessment of field performance, feasibility, acceptability, ease of use and impact of Ag-RDTs for the diagnosis of SARS-CoV-2 infection in Kenya. The funding was awarded to JG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the Mount Kenya University study team fieldworkers who collected effectiveness data.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eB.A. developed the cost data tool, collected costing data, analyzed the cost and conducted the cost-effectiveness, and developed the manuscriptS.M., J.N, M.M. supported the data collection and analysis of effectiveness dataP.M., D.K., D.N., and J.N. contributed to the design and implementation of the projectJ.N. and E.B. developed the proposal, contributed to the designed of the study, developed the conceptual framework and supervised the Effectiveness and Health Economics work package. All authors discussed the results and reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided in the supplementary information file\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLai CKC, Lam W. Laboratory testing for the diagnosis of COVID-19. Biochem Biophys Res Commun. Jan. 2021;538:226\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbrc.2020.10.069\u003c/span\u003e\u003cspan address=\"10.1016/j.bbrc.2020.10.069\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO Emergency. Use Listing for In vitro diagnostics (IVDs) Detecting SARS-CoV-2. Accessed: Mar. 17, 2022. [Online]. 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Mak.\u003c/em\u003e, vol. 32, no. 5, pp. 722\u0026ndash;732, 2012, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0272989X12458348\u003c/span\u003e\u003cspan address=\"10.1177/0272989X12458348\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cost-effectiveness, SARS-CoV-2, Ag-RDT, NAAT assay, ICER","lastPublishedDoi":"10.21203/rs.3.rs-6854403/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6854403/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe COVID-19 pandemic has created a need to rapidly scale-up testing services. In Kenya, services for SARS-CoV-2 nucleic acid amplifying test (NAAT) have often been unavailable or delayed, precluding the clinical utility of the results. The introduction of antigen-detecting rapid diagnostic tests (Ag-RDT) has had the potential to fill at least a portion of the \u0026lsquo;testing gap\u0026rsquo;. We, therefore, evaluated the cost-effectiveness of implementing SD Biosensor Antigen Detecting SARs-CoV-2 Rapid Diagnostic Tests in Kenya.\u003c/p\u003e \u003cp\u003e We conducted a cost and cost-effectiveness analysis using a decision tree model following the Consolidated Health Economic Evaluation Standards (CHEERS) guidelines under two scenarios: first comparing Ag-RDT as a first-line diagnostic followed by NAAT confirmation of negatives versus delayed NAAT testing only; second comparing Ag-RDT to clinical judgment where NAAT was unavailable. We employed a societal perspective with a time horizon of patient care episodes. Cost and outcomes data were obtained from primary and secondary sources, with robustness assessed through one-way and probabilistic sensitivity analyses.\u003c/p\u003e \u003cp\u003eAt 10% COVID-19 prevalence, implementing Ag-RDT with confirmatory NAAT for negatives was more costly (US\u003cspan\u003e$\u003c/span\u003e1,336,231.13 vs US\u003cspan\u003e$\u003c/span\u003e1,107,117.83) but more effective in averting DALYs (1998.97 vs 2236.49) than delayed NAAT testing alone, yielding an ICER of US\u003cspan\u003e$\u003c/span\u003e964.63 per DALY averted\u0026mdash;below Kenya's cost-effectiveness threshold of US\u003cspan\u003e$\u003c/span\u003e1003.4. In settings without NAAT access, Ag-RDT was less costly (US\u003cspan\u003e$\u003c/span\u003e998,260.67 vs US\u003cspan\u003e$\u003c/span\u003e1,261,230.78) though less effective than clinical judgment at prevalence levels below 16.25%.\u003c/p\u003e \u003cp\u003eThese findings suggest that implementing Ag-RDTs represents a cost-effective strategy in settings with delayed NAAT access and a cost-saving approach where NAAT is unavailable, providing evidence-based guidance for diagnostic resource allocation in resource-limited settings.\u003c/p\u003e","manuscriptTitle":"Economic Evaluation of Implementing SD Biosensor Antigen Detecting SARS-COV-2 Rapid Diagnostic Tests in Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 05:37:30","doi":"10.21203/rs.3.rs-6854403/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b2a976cc-3744-4d35-a719-55de8606f912","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-20T20:38:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 05:37:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6854403","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6854403","identity":"rs-6854403","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}