Assessing the economic burden and financial protection gaps of Lassa fever in Nigeria using a multicomponent cost of illness analysis 2019 to 2024

Assessing the economic burden and financial protection gaps of Lassa fever in Nigeria using a multicomponent cost of illness analysis 2019 to 2024 | 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 Assessing the economic burden and financial protection gaps of Lassa fever in Nigeria using a multicomponent cost of illness analysis 2019 to 2024 Joseph Gbenga Solomom, Adamu Ishaku Akyala, Joseph M. Ibbih, Jide Idris, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9093577/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Lassa fever remains one of the most persistent zoonotic and epidemic-prone diseases in West Africa, causing recurrent fiscal and health shocks to Nigeria’s public health system. Despite ongoing control efforts, the disease imposes substantial direct and indirect economic costs on households and government institutions. This study quantified the economic burden of Lassa fever in Nigeria, focusing on out-of-pocket expenditure (OOP), health system cost (HSC), and productivity loss (PL) from illness between 2019 and 2024. Objective To estimate the mean cost per patient and the aggregate national economic burden of Lassa fever, while identifying major cost drivers and policy gaps in financial protection. Methods A quantitative multi‑method approach was used, combining primary survey data with secondary macro‑economic analysis. An expert‑validated questionnaire (n = 50) was administered. The instrument captured out‑of‑pocket (OOP) expenses, public‑health system expenditures, and productivity losses attributable to illness, enabling calculation of the Cost‑of‑Illness (COI). Furthermore, annual foreign‑exchange rates, purchasing‑power parity (PPP) figures, and dollar index values were obtained from the International Monetary Fund (IMF) and the Central Bank of Nigeria (CBN). These macro‑economic indicators were applied to convert COI estimates into Nigerian Naira (₦) and United States Dollars (US $ ). All analyses were performed in R. Descriptive statistics summarized the primary data, while probabilistic sensitivity analysis, Monte Carlo simulation, and scenario modeling examined the impact of exchange‑rate fluctuations driven in part by U.S. Federal Reserve monetary policy—on the COI. Results The study quantified the economic burden of Lassa Fever in Nigeria (2019–2024), encompassing out-of-pocket (OOP) costs, health system costs (HSC), and productivity losses. Mean per-case costs were ₦48,525 (US $ 33.77) for OOP, ₦3,334,806 (US $ 2,320.68) for HSC, and ₦130,791 (US $ 87.98) for morbidity-related productivity loss. Including mortality, per-case loss reached ₦3,259,846 (US $ 2,192.86, discounted). Annual total costs rose from US $ 1.6 million in 2019 to US $ 5.96 million in 2024, cumulating US $ 13.7 million. Sensitivity analysis highlighted productivity loss per case as the main driver. Findings underscore substantial fiscal impact, fragile financial protection, and urgent need for strengthened interventions and health system preparedness. Conclusion Lassa fever imposes a substantial and preventable economic burden on Nigeria’s health system and households. Strengthening insurance coverage is critical to mitigating future financial shocks. Lassa fever cost-of-illness economic burden productivity loss out-of-pocket expenditure health system cost Nigeria. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Lassa fever is a severe viral hemorrhagic disease endemic in Nigeria, posing a recurrent threat to public health and economic stability (Adetunde & Olalubi, 2018 ). The annual outbreaks frequently lead to high case fatality rates, with recent data indicating mortality among confirmed cases in Nigeria ranging between 13.5% and 18.3% (Olayinka et al., 2023 ; Nyinoh, Utume & Bob-Echikwonye, 2021 ). The disease exerts immense strain on the health system, particularly during peak seasons, and challenges infection prevention and control in hospitals (Adetunde & Olalubi, 2018 ; NCDC, cited in Science Nigeria, 2025 ). Economically, Lassa fever inflicts a heavy burden across households and the health infrastructure. Though ribavirin—the mainstay of Lassa fever treatment—is said to be subsidized in some centers, patients still report paying large sums for other care components such as bed space, blood transfusion, diagnostics, and ancillary medications (Obiejisi, 2018 ; Osakwe, 2018 ). Reports from treatment centers suggest that total patient expenses may reach ₦180,000 even when ribavirin is provided "free" (Osakwe, 2018 ). These out-of-pocket (OOP) costs may drive catastrophic health expenditure, eroding the financial resilience of affected families. On the public side, the Nigerian health system shoulders substantial costs during Lassa fever outbreaks. The demands of isolation wards, intensive care, including dialysis, diagnostics, personal protective equipment, contact tracing, and case investigation place a heavy toll on already stretched health budgets (PMC, 2025; ICIR, 2023 ). The infrastructure gaps, budget underfunding, and poor state-level fiscal ownership exacerbate the financial burden (ICIR, 2023 ). Despite these substantial economic implications, there remains a lack of rigorous, country-specific cost-of-illness studies of Lassa fever in Nigeria that jointly quantify household OOP costs, public system expenditures, and productivity losses. Much of the existing literature focuses on epidemiology (Nyinoh, Utume & Bob-Echikwonye, 2021 ), health system challenges (Science Nigeria, 2025 ), and burden projections (Heckert et al., 2022 ) but rarely integrates real-world cost data. Therefore, this study aims to address this gap by estimating the economic burden of Lassa fever in Nigeria using a cost-of-illness framework. We will quantify out-of-pocket costs borne by households, recurrent and capital costs to the public health system, and losses in productivity. Our findings will provide critical evidence to guide policy makers in designing efficient interventions, prioritizing resource allocation, and strengthening financial risk protection mechanisms for vulnerable populations. Methods Research Design This study used a quantitative multi-method design combining primary data from a Structured Expert Elicitation (SEE) survey and secondary macroeconomic and epidemiological datasets. The SEE component captured cost-of-illness (COI) information from health-system experts, caregivers, and recovered patients. Secondary data included exchange-rate, purchasing-power-parity, and dollar-index indicators from the International Monetary Fund (IMF), Central Bank of Nigeria (CBN), U.S. Federal Reserve, and lassa fever cases reported from 1019- 2024b from the Nigeria Centre for Disease Control and Prevention (NCDC) and World Health Organization (WHO). These datasets were used to generate inflation-adjusted, internationally comparable economic estimates. The used multiple quantitative datasets approach allows for robust triangulation, improves internal validity, and provides a more nuanced understanding of the economic dimensions of health‑system costs than a single‑source analysis would permit (Johnson & Onwuegbuzie, 2004; Creswell & Plano Clark, 2018). Study Setting and Population The study considered nationwide pool of data in Nigeria, the most populous African country with an estimated 223 million residents in 2023 (National Population Commission, 2023; United Nations, 2022). Nigeria is divided into six geopolitical zones, 36 states, and the Federal Capital Territory, a structure that facilitates region‑specific health‑system analyses. Two distinct populations are addressed within the quantitative multi‑method design. Surveillance cohort (secondary data) – All Lassa fever cases reported to the Nigeria Centre for Disease Control (NCDC) from 2019 through 2024 are included. These data were extracted from the Integrated Disease Surveillance and Response (IDSR) and weekly situation reports uploaded on the NCDC website, providing complete national coverage. Using the full surveillance dataset enables robust assessment of temporal and spatial trends at the national level. Expert cohort (primary data) – Fifty-one (50) health‑system experts and frontline practitioners are recruited to complete a structured expert‑elicitation questionnaire with assistance of trained enumerators. The instrument gathers individual out‑of‑pocket expenditures, public‑health spending, and productivity‑loss estimates needed to calculate the Cost‑of‑Illness (COI). Sample size, Sample technique and Methods of Data Collection Sample‑size determination A conventional power‑based formula for estimating a proportion was used because the primary outcome (cost‑of‑illness estimate) is derived from expert-elicited quantitative responses. [ n={Z 2 p (1-p)}/{d 2 } (Z) – Z‑value for the desired confidence level (1.96 for 95%). (p) – Expected proportion of “agreement” among experts; a conservative value of 0.5 maximises the required sample. (d) – Margin of error (precision) set at 0.14 (± 14%). [n={(1.96) 2 x 0.5 x (1-0.5)}/{(0.14) 2 } =49] To accommodate possible non‑response and to obtain an even distribution across the six geopolitical zones, the target was marked up to 55 participants (a 10% increase). Sampling technique The study was able to secure a total of 50 participants valid for this study from treatment centre in each of Nigeria’s six geopolitical zones, yielding six centres. Six Lassa fever treatment centres—one from each geopolitical zone—were selected using simple balloting, introducing randomisation into the site-selection process. Within each centre, Health‑system experts—including case‑managers, and members of the Lassa fever Technical Working Group with 50 individuals (approximately eight to nine per zone). These experts were identified through purposive sampling, targeting persons known to be directly involved in Lassa fever case management and surveillance. In addition, two caregivers were recruited via convenience sampling of the next‑of‑kin of recently recovered Lassa fever patients identified through facility registers. From these caregiver contacts, five recovered patients were enrolled using snowball sampling; all patients had completed treatment at the selected facilities. Data‑collection procedures Primary quantitative data (expert elicitation) – A Structured Expert Elicitation (SEE) form hosted on KoboCollect was used to gather information on out‑of‑pocket expenses incurred by patients, public‑health system expenditures related to Lassa fever case management (including drugs, consumables, and staff time), and productivity losses measured as work‑days missed due to illness. The SEE form were administered face‑to‑face to the respondents (the two caregivers, five recovered patients, and two state officials). All responses were exported directly into R for subsequent analysis. Techniques for Data Analysis and Model Specification Quantitative data from secondary surveillance records and primary expert surveys will be processed in R using a suite of advanced statistical tools. First, descriptive statistics will summarize the expert‑reported cost components. Next, a probabilistic multivariate sensitivity analysis will be performed via Monte Carlo simulation (10 000 draws), assigning probability distributions to all key parameters such as hospitalization rate, unit costs, work‑days lost. Correlation and tornado plots will identify the strongest cost drivers. Economic model specification follows a cost‑of‑illness (COI) structure that distinguishes household out‑of‑pocket (OOP) spending, health‑system costs (HSC), and productivity loss (PL). This mathematical framework quantifies the full economic burden of Lassa fever by integrating the following: Mathematical Economic Model Specification for Lassa Fever Let: C = number of cases D = number of deaths A death = average age at death LE = life expectancy at birth r = discount rate (per annum) GDP pc = GDP per capita (USD) DW = disability weight d inpatient ,d recovery ,d caregiver ,d pres =days lost per case Per-case Cost Components Per-Case Cost = OOP+HSC+PLmorb+PLdeath Total Economic Burden Total Cost = Per-Case Cost×C DALY Computation DALY = YLL (years of life lost, discounted) + Years Lived with Disability (YLD) Cost per DALY Cost Per DALY =(Total Cost/DALY) Probabilistic Sensitivity Analysis (PSA) Inputs GDP pc , r, d, DW, OOP, HSC are treated as random variables drawn from appropriate distributions (PERT), and Monte Carlo simulation propagates uncertainty to: Per Case Cost, Total Cost, DALY, Cost Per DALY Scenario Analysis Vary parameters like D, C, r, GDP pc , HSC, OOP, and hospitalization rate to quantify their impact on Total Cost and Cost Per DALY. This model captures direct medical costs, indirect costs, productivity losses, and health outcomes, while supporting probabilistic and scenario-based sensitivity analyses. When estimating the economic burden of Lassa fever, the hospitalisation‑rate (HR) is still included. but because virtually every confirmed case is admitted, HR is set to 1 (or to a distribution tightly centred on 1, e.g., Beta(9, 1) with a mean of ≈ 0.9) to reflect the very small chance of mild conditions possibly managed for very few days or in clinic outside the health facility as outpatient probably while preserving probabilistic uncertainty. Retaining HR—even when it effectively equals 1—ensures model consistency across diseases, allows severity‑weighting, supports “what‑if” policy scenarios (e.g., shifting care to community isolation units), and enables Monte Carlo sensitivity analysis to propagate any residual uncertainty into the final cost estimates. Results Lassa Fever imposes a substantial economic burden on both households and the public health system in Nigeria. In addition, the mean costs per patient are considerably higher than other outbreaks like cholera, reflecting the intensive clinical care, specialized personnel, and resources required to treat and manage Lassa Fever cases. Out-of-pocket costs represent the direct financial burden borne by households when seeking treatment for Lassa Fever. These expenses include consultation fees, transportations, PPE, phone calls, feeding and other minor healthcare expenditures within and outside the treatment centres. The mean OOP cost per patient is ₦48,525 (US$33.77). This low per-patient figure reflects the fact that many Lassa Fever cases may seek care at public facilities where subsidized care is available or free medical cost, yet it still captures the variability in household expenditures depending on severity, care-seeking behavior, and accessibility to healthcare services. Health system costs for Lassa Fever are substantial due to the need for specialized personnel, medical supplies, diagnostics, inpatient operations, contact tracing, and logistics. The mean HSC per patient is ₦3,334,806.09 (US$2,320.68). These costs represent the public health system expenditure on Lassa Fever management and illustrate the intensive resource allocation required to provide adequate care, maintain isolation wards, ensure biosafety, and carry out community interventions such as contact tracing and logistical support. The high HSC emphasizes the fiscal pressure on Nigeria’s healthcare system during Lassa Fever outbreaks. Productivity losses account for indirect costs due to morbidity and include patient illness, caregiver time, recovery periods, and reduced productivity while working sick (presenteeism). On average, patients lose 38.07 days (95% CI: 24.79–49.16 days) per Lassa Fever episode. Using Nigeria’s GDP per capita per day (₦3,434/day for 2024), this translates to a mean indirect cost of ₦130,790.76 per patient (US$88). This metric captures the broader societal impact of illness, reflecting lost income and reduced productivity for both patients and caregivers during acute illness and recovery. Summing the OOP, HSC, and PL components gives the total economic burden per Lassa Fever patient. The mean total cost is ₦3,514,121.44 (US$2,442.43), with a 95% confidence interval of ₦3,150,246.21–₦3,838,131.56 (US$2,191.78–$2,665.62). This figure represents the comprehensive per-patient cost, providing policymakers with a clear estimate of the financial implications of Lassa Fever at the individual level. Table 1 Cost Components of Lassa Fever per Patient (₦ and US$) Component Mean (₦) 95% LCI (₦) 95% UCI (₦) Mean (US$) 95% LCI (US$) 95% UCI (US$) Out-of-Pocket (OOP) Costs 1.OOP Consultation 4,414.33 3,201.67 5,508.33 3.07 2.23 3.83 2.OOP Feeding &Transport 44,110.26 42,966.67 45,366.67 30.70 29.90 31.57 Total OOP 48,524.59 46,168.34 50,875.00 33.77 32.13 35.40 Health System Component (HSC) 3.HSC Personnel (salaries, overtime, hazard pay) 687,748.40 565,166.67 762,104.20 478.60 393.30 530.34 4.HSC Medical Supplies (PPE, Ribavirin, IVfluids, consumables., oxygen 2,063,759.94 1,985,841.67 2,141,760.40 1,436.16 1,381.94 1,490.44 5.HSC Diagnostics & Laboratory (kits, reagents, biosafety) 240,121.47 236,922.92 244,472.10 167.10 164.87 170.13 6.HSC Inpatient Operations (isolation ward, dialysis, WASH) 144,557.69 99,637.50 204,725.00 100.60 69.34 142.47 7.HSC Contact Tracing (staff time, transport, allowances) 143,309.62 137,221.67 149,335.80 99.73 95.49 103.92 8.HSC Logistics & Sample Transport (couriers, cold chain) 55,308.97 54,283.33 56,250.00 38.49 37.78 39.14 Total HSC 3,334,806.09 3,079,073.76 3,558,647.50 2,320.68 2,142.82 2,476.44 Productivity Loss (PL) per case (morbidity) 9.PL_Inpatient_Days_Lost 37,002.43 7,144.03 60,724.28 24.89 4.81 40.85 10.PL_Recovery_Days 21,157.33 3,572.02 53,580.25 14.23 2.40 36.04 11.PL_Caregiver_Days 36,407.09 7,144.03 60,724.28 24.49 4.81 40.85 12.PL_Presenteeism_Days 36,223.91 7,144.03 53,580.25 24.37 4.81 36.04 Total PL ( MORBIDITY ) 130,790.76 25,004.11 228,609.06 87.98 16.83 153.78 Grand Total Economic burden per case (excluding death) 3,514,121.44 3,150,246.21 3,838,131.56 2,442.43 2,191.78 2,665.62 Note: - Converted to ₦ to USD 2024 as baseline; mean calculated using the PERT formula. -- Productivity Loss (PL) proxy = GDP per capita 2024; According to the International Monetary Fund, in the 2025 Article IV summary Nigeria’s nominal GDP per capita (US$) for 2024 is shown as US$806.9.-World Bank Open Data. - exchange rate = ₦1,437 / US$1. - Daily value for PL rounded to ₦3,176.95; displayed values rounded to whole ₦ and US$ to 2 decimals. - Confidence intervals for PL: derived from distribution of expert-reported days (2.5% and 97.5% percentiles) then monetised. Table 2 Monetised Productivity Loss per Lassa Fever Case (inpatient, recovery, caregiver, and presenteeism days) PL Component Mean Days (d) 95% LCI Days 95% UCI Days Daily Value (₦) Inpatient Days Lost 10.98 8.83 12.33 ₦3,176.95 Recovery Days 5.15 3.30 9.67 ₦3,176.95 Caregiver Days 10.96 8.83 12.33 ₦3,176.95 Presenteeism Days 10.98 3.83 14.83 ₦3,176.95 38.07 24.79 49.16 Notes: Daily value (₦3,176.95) is based on GDP per capita 2024; 95% LCI and UCI represent the 2.5th and 97.5th percentiles of expert-reported days; All values rounded to two decimal places where applicable. Productivity Loss Due to Death (Human Capital Approach) Each premature death leads to a loss of potential lifetime productivity, this approximated by GDP per capita multiply by the remaining life expectancy at time of death. Productivity loss due to death — Undiscounted human-capital Productivity loss per death (undiscounted) = GDP_per_capita × L (number of working years lost due to premature death) = 806.95 × 23.36 ≈ = US$18,850.25 per death Total productivity loss (all deaths) undiscounted = 18,850.25 × 214 (no of death: see appendix) = US$4,033,953.81 Per-case (spread total death loss across all cases) = 4,033,953.81 ÷ 1,309 ≈ US$3,081.71 per case In NGN (₦1,486.57/USD): ₦4,581,172.43 per case (undiscounted) *Discounted human-capital (3% annual discount) PL_death (discounted) = GDP per capita×PV factor for remaining PV factor for L = 23.36 years at r = 0.03 ⇒ PV = 1−(1 + 0.03) − 23.36 Productivity loss per death (discounted) = GDP_per_capita × PV ≈ US$13,413.36 per death Total productivity loss (discounted) = 13,413.36 × 214 ≈ US$2,870,458.94 Per-case (discounted) = 2,870,458.94 ÷ 1,309 ≈ US$2,192.86 per case In NGN: ₦3,259,845.80 per case (discounted) The analysis shows that premature mortality drives the bulk of Lassa fever’s economic burden: using a human‑capital approach, each death translates into roughly US $13,400 of discounted lifetime productivity loss (≈ ₦3.26 million) versus US $18,850 undiscounted (≈ ₦4.58 million), and with 214 deaths this amounts to a total loss of US $2.87 million (discounted) or US $4.03 million (undiscounted). When spread across the 1,309 reported cases, the per‑case death‑related loss is US $2,193 (discounted) or US $3,082 (undiscounted). Adding direct medical expenses—out‑of‑pocket costs (≈ US $34) and health‑system costs (≈ US $2,321)—and morbidity‑related productivity loss (≈ US $88) yields a subtotal (excluding death) of about US $2,442 per case. Consequently, the overall per‑case economic burden is US $5,525 (undiscounted) and US $4,557 (discounted), with the discounted figure representing the recommended 3% present‑value adjustment for long‑term losses. These results underscore that interventions that prevent deaths—or markedly reduce fatality rates—will produce the greatest cost savings, while variations in out‑of‑pocket or health‑system expenditures have comparatively minor impact on total burden. Table 3 Per-Case Cost Components of Lassa Fever Component Mean (₦) 95% LCI (₦) 95% UCI (₦) Mean (US$) 95% LCI (US$) 95% UCI (US$) OOP (Out-of-pocket) 48,524.59 46,168.34 50,875.00 33.77 32.13 35.40 HSC (Health system costs) 3,334,806.09 3,079,073.76 3,558,647.50 2,320.68 2,142.82 2,476.44 PL – Morbidity (Productivity Loss due to illness) 130,790.76 25,004.11 228,609.06 87.98 16.83 153.78 Subtotal (Economic burden excluding death) 3,514,121.44 3,150,246.21 3,838,131.56 2,442.43 2,191.78 2,665.62 PL – Death (Productivity Loss, undiscounted per case) 4,581,172.43 875,812.17 8,007,427.46 3,081.7 609.47 5,572.32 PL – Death (Productivity Loss, discounted per case) 3,259,845.80 623,205.67 5,697,881.75 2,192.86 433.69 3,965.12 Total (Overall Economic Burden, death inclusive – undiscounted) 8,095,293.87 4,026,058.38 11,845,559.02 5,524.13 2,801.71 8,243.26 Total (Overall Economic Burden, death inclusive – discounted) 6,773,967.24 3,773,451.88 9,536,013.31 4,556.78 2,625.92 6,636.06 • WHO‑CHOICE recommends a 3% discount rate for both costs and health outcomes. *Undiscounted totals for immediate, observable costs (useful for budgeting). * Discounted totals for the productivity loss component (especially YLL‑derived loss), because those losses extend far into the future. * Economic‑burden or cost‑effectiveness analyses that incorporate long‑term outcomes such as years of life lost (YLL) or disability‑adjusted life years (DALYs).• Any model that projects losses beyond the current fiscal year (e.g., lifetime productivity loss from premature death). Use undiscounted values for a straightforward, year‑specific COI snapshot (direct medical costs + short‑term work loss). Apply discounting (typically 3% per year) when you need to value future productivity losses (mortality, long‑term disability) as part of a comprehensive economic‑burden assessment. Annual Lassa Fever Cost Burden (2019–2024) Using national case counts from 2019–2024 and adjusting for inflation, the total economic burden per year shows significant variability. For example, in 2019, with 833 cases, the total cost burden was US$1,601,942.3, while in 2020, with 1,189 cases, it rose to US$2,042,412.9. By 2024, the baseline scenario with 1309 cases resulted in a total burden of US$5,964,825.0. The cumulative economic burden over the six-year period is approximately US$13,697,578.70. These estimates provide a clear indication of the fiscal impact of Lassa Fever on both households and the health system in Nigeria. Table 3 Lassa Fever: Cost burden (US$) per year Year PPP conversion factor ÷ Market rate (Proportion)* Cost-burden per case (US$) equivalent at that year’s purchasing power Number of cases Total Cost burden(US$) 2019 0.4224 1,923.1 833 1,601,942.3 2020 0.3772 1,718.1 1189 2,042,412.9 2021 0.3673 1,673.7 510 853,587.0 2022 0.3596 1,636.9 1067 1,746,977.3 2023 0.2572 1,170.2 1271 1,487,834.2 2024 – 4,556.78 (baseline) 1309 5,964,825.0 Total (anually − 2019–2024) 13,697,578.70 * https://data.un.org/Data.aspx?d=WDI&f=Indicator_Code%3APA.NUS.PPPC.RF%3BCountry_Code%3ANGA&q=nigeria+gdp DALY, CEA, and ICER: Health and Economic Burden Metrics of Lassa Fever These additional fields are crucial for interpreting cost-effectiveness and incremental cost-effectiveness analyses (CEA and ICER). The Cost per DALY reflects the overall efficiency of each strategy, quantifying how much is spent for every disability-adjusted life year incurred or averted. The Cost vs. Baseline captures the incremental financial burden, revealing how much extra investment an intervention demands relative to the current practice. The DALYs vs. Baseline measures incremental health benefits, indicating the extent of additional health gains or losses achieved by an intervention. Finally, the ICER (USD per DALY saved) serves as the key decision metric in health economics, showing the extra cost required to obtain one additional DALY relative to baseline; a lower ICER denotes a more cost-effective intervention within a given willingness-to-pay threshold. Human-capital valuation approach (framework): DALY Years of Life Lost (YLL)* due to death Productivity Loss from Premature Death Parameter Value Formula / Source Average age of death 33 years From NCDC data Life expectancy at birth 56.36 years Nigerian population Remaining life expectancy (L) 23.36 years L = 56.36 − 33 GDP per capita (2024) $806.95 World Bank Discount rate 3% WHO-CHOICE recommendation Present value factor (PV) 1−(1 + r) −L r = 0.03, L = 23.36 PL per death (discounted) $13,413.36 GDP×PV PL per death (undiscounted) $18,850.25 GDP×L Total PL for all deaths $2,870,458.94 (discounted) 214 deaths × $13,413.36 Per-case PL death $2,192.86 (discounted) Spread across 1,309 cases *Incidence-based DALYs count YLL from each death: YLL=Number of deaths × Remaining life expectancy at age of death Years Lived with Disability (YLD) due to illness YLD = total number of lost workdays for patients and caregivers x daily GDP-per-capita value to estimate lost productivity. YLD* – Productivity Loss from Morbidity Component Avg Days Lost Daily GDP Value Productivity Loss per case (US$) Inpatient days 10.98 $2.23 (₦3,176.95) $24.89 Recovery days 5.15 $2.23 $14.23 Caregiver days 10.96 $2.23 $24.49 Presenteeism 10.98 $2.23 $24.37 Total morbidity PL (YLD proxy) 38.07 days — $87.98 per case *Incidence-based YLD: YLD=Incident case × DW × Duration of illness (DW ~ 1.0) DW is NOT literally replaced, but approximated using human-capital productivity loss.These studies specifically validate productivity loss as a measure of morbidity severity, equivalent to a DW proxy ( sources : Krol, M., & Brouwer, W. B. F. (2014). How to estimate productivity costs in economic evaluations. PharmacoEconomics, 32(4), 335–344. https://doi.org/10.1007/s40273-014-0132-3;Shepard , D. S., Undurraga, E. A., & Halasa, Y. A. (2013). Economic and disease burden of dengue in Southeast Asia. PLoS Neglected Tropical Diseases, 7(2), e2055. https://doi.org/10.1371/journal.pntd.0002055 ; World Bank. (2018). The human capital project: Concept note. https://documents.worldbank.org/en/publication/documents-reports/documentdetail/793421540087227031/revised-human-capital-project-paper ). Morbidity-related productivity loss (YLD proxy) is monetized using GDP per capita, representing lost work capacity for both patients and caregivers. Combine YLL + YLD → Total DALY per case Component US$ per case PL death (YLL) 2,192.86 (discounted) PL morbidity (YLD proxy) 87.98 Total DALY monetized per case $2,280.84 These DALYs are then used to calculate Cost per DALY and ICER in CEA: ICER (USD per DALY saved)= [ΔCost vs. Baseline] / [ΔDALYs vs. Baseline] Example from Intervention B: ICER = 511,309/416.55≈$1,227 per DALY saved NOTE: Total DALYs (baseline, Table 6 ) = 3,616.55 years Number of cases = 1,309 Monetised DALY per case (YLL + YLD) = $2,280.84 per case Step 1 : DALYs per case DALYs_per_case = Total_DALYs / Cases = 3616.55 / 1309 = 2.7628342246 years per case Step 2 : Implied $ value per DALY $/DALY = Monetised_per_case / DALYs_per_case = 2,280.84 / 2.7628342246 ≈ $825.54 per DALY Explanation: • $825.54 per DALY ≈ the monetisation factor used (close to GDP per capita ≈ $806.95). • Thus: 2.7628 years × $825.54/year ≈ $2,280.84 per case (matches the monetised per-case figure). Conclusion (one line): Table 6 reports total DALYs (years lost) while the $2,280.84 is the monetary value of those lost years for a single case — you convert between them by multiplying DALYs (years) by the chosen value-per-DALY (here ≈ GDP per capita → ≈ $825/DALY). Figure 4 illustrates the distribution and relationships among key economic and health variables influencing the cost-effectiveness of Lassa fever interventions. The histogram of cost per DALY shows the spread of efficiency values across simulated scenarios, highlighting the variability in health gains per dollar spent. The relationship between discount rate and cost per DALY demonstrates how higher discounting of future health benefits increases the cost per DALY, reducing apparent cost-effectiveness. Meanwhile, the correlation between GDP per capita and productivity loss indicates that higher economic productivity amplifies the estimated monetary burden of illness and death. Together, these patterns underscore the sensitivity of DALY-based evaluations to economic and methodological assumptions, emphasizing the need for contextual calibration in cost-effectiveness analyses. This incremental cost-effectiveness analysis compares two potential Lassa fever control interventions against the baseline scenario. Intervention A raises total program expenditures by US $1.21 million but averts approximately 617 DALYs, yielding an ICER of US $1,965 per DALY saved. Intervention B adds only US $511 thousand while saving 417 DALYs, giving a more favorable ICER of US $1,228 per DALY saved. Both strategies improve health outcomes, but Intervention B is more cost-effective, delivering greater health gains per dollar spent. These incremental fields- Cost, DALYs, and ICER- are vital for CEA and ICER interpretation because they allow direct comparison between strategies, quantifying trade-offs between added cost and health benefit. By relating these ratios to a willingness-to-pay (WTP) threshold (often 1–3× GDP per capita, US $807–2,421 in Nigeria for 2024), policymakers can judge whether an intervention represents “good value for money.” Under this threshold, both interventions would be considered cost-effective, with Intervention B being the preferred option for optimal resource allocation. Table 6 Incremental Cost-Effectiveness Analysis (CEA) and ICER Results for Alternative Lassa Fever Intervention Scenarios Scenario Total Cost (USD) DALYs Cost / DALY (USD) Δ Cost vs. Baseline (USD) Δ DALYs vs. Baseline ICER (USD / DALY Saved) Baseline 5,988,691 3,616.55 1,655.91 0 0.00 — (Reference) Intervention A 7,200,000 3,000.00 2,400.00 + 1,211,309 –616.55 1,964.66 Intervention B 6,500,000 3,200.00 2,031.25 + 511,309 –416.55 1,227.49 Incremental Cost-Effectiveness and Net Monetary Benefit (NMB) at three WTP thresholds of Lassa Fever Interventions Compared with Baseline This table synthesizes the economic and health outcomes of Lassa fever control strategies in 2024 using both cost-effectiveness (CEA) and net monetary benefit (NMB) frameworks. The baseline represents current practice, with two intervention scenarios compared against it. Intervention A increases total costs by $1.21 million and averts 617 DALYs, yielding an ICER of $1,965 per DALY saved. Intervention B costs $511 k more and averts 417 DALYs, with a more favorable ICER of $1,227 per DALY saved. At a low WTP threshold of $807.2 per DALY (Nigeria’s GDP per capita), both interventions have negative NMB, indicating that health benefits do not offset added costs. As WTP rises to $2,000, both become cost-effective, but Intervention B yields far greater net value (+$322 k vs. +$22 k). At $2,421.6 (~ 3× GDP per capita), both generate substantial positive NMBs, with B remaining dominant (higher NMB at every threshold). In economic evaluation, Cost quantifies the budget impact, informing affordability; DALYs measures health gain, expressing clinical value; ICER links the two, describing efficiency; and NMB translates both into a single monetary metric, allowing direct ranking across competing health investments. These additional fields are vital for cost-effectiveness analysis (CEA) and incremental cost-effectiveness ratio (ICER) assessment because they provide a full decision-making picture - how much extra is paid, what health is gained, and whether that trade-off is worthwhile at the chosen WTP. In summary, Intervention B consistently outperforms A and the baseline in both ICER and NMB terms. At any realistic WTP above $1,200/DALY, it is the most cost-effective and economically attractive option, offering the greatest “health return on investment.” Probabilistic Sensitivity Analysis (PSA) Summary of the Economic and Health Burden of Lassa Fever Probabilistic Sensitivity Analysis (PSA) was conducted using Monte Carlo simulations to propagate uncertainty across the economic burden of Lassa fever. The probabilistic sensitivity analysis demonstrates that the mean per-case economic burden of Lassa fever was approximately ₦6.8 million (US$4,575), with aggregate national losses of about US$5.99 million. Productivity losses dominate the cost profile, particularly from fatal cases, where each death corresponds to a lifetime productivity loss of about US$13,500. The total discounted Years of Life Lost (YLL) and Years Lived with Disability (YLD) together yield 3,617 Disability-Adjusted Life Years (DALYs), representing the total health burden of the outbreak. With an estimated cost per DALY of US$1,668, Lassa fever imposes an economic cost exceedingly twice Nigeria’s GDP per capita (US$807), classifying it as a high-burden, economically catastrophic disease under WHO-CHOICE cost-effectiveness criteria. In cost-effectiveness and decision-analytic terms, the DALY metric enables translation of these losses into incremental cost-effectiveness ratios (ICERs) when comparing potential interventions—such as vaccination, rapid diagnostics, or improved surveillance—against the “no intervention” baseline. An ICER below one to three times GDP per capita (~ US$807–2,421) per DALY averted would indicate that a preventive measure is highly cost-effective relative to the economic burden presented here. Thus, the observed cost per DALY (US$1,668) provides both a benchmark for evaluating intervention value and a quantitative reflection of Lassa fever’s substantial societal cost in Nigeria. Table 5 Combined Descriptive and Probabilistic Multivariate Sensitivity Analysis (PSA) Summary of the Economic and Health Burden of Lassa Fever (Simulation Baseline- Input parameter assumptions used in uncertainty analysis and generated by Monte-carlo simulation). Metric / Variable Mean Median 95% LCI 95% UCI Min 1st Quartile 3rd Quartile Max GDP per capita (USD) 806.4 806.6 779.1 833.4 666.3 779.1 833.4 962.9 Discount rate (r) 0.0302 0.0303 0.0203 0.0401 0.0100 0.0203 0.0401 0.0500 Productivity loss per death (USD) 13,525 13,339 12,071 14,884 9,575 12,071 14,884 19,022 Total productivity loss due to death (USD) 2,894,330 2,854,641 2,583,177 3,185,201 2,048,980 2,583,177 3,185,201 4,070,641 Productivity loss per case (USD) 2,211 2,181 1,973 2,433 1,565 1,973 2,433 3,110 Combined per-case cost (₦) 6,801,076 6,756,003 6,110,000 7,640,000 5,841,051 6,447,713 7,131,404 8,136,957 Combined per-case cost (USD) 4,575 4,545 4,110 5,140 3,929 4,337 4,797 5,474 Total economic cost (USD) 5,988,691 5,949,002 5,380,000 6,730,000 5,143,341 5,677,537 6,279,562 7,165,002 Years of Life Lost (YLL, discounted) 16.77 16.57 14.98 18.46 13.60 14.98 18.46 20.74 Total YLL (all deaths) 3,589 3,546 3,207 3,951 2,911 3,207 3,951 4,438 Total YLD (Years Lived with Disability) 27.14 26.86 22.10 31.81 9.55 22.10 31.81 49.63 Total DALYs (YLL + YLD) 3,617 3,571 2,970 4,410 2,928 3,234 3,978 4,483 Cost per DALY (USD) 1,668 1,666 1,470 1,870 1,377 1,579 1,757 1,980 Notes: All monetary values are in 2024 U.S. dollars (USD) unless stated otherwise; NGN values converted using the IMF 2024 exchange rate of ₦1,486.57 / US$. -Estimates are derived from 10,000 Monte Carlo simulations of the probabilistic model incorporating uncertainty in costs, discount rate, and case outcomes. − 95% LCI and UCI correspond to the 2.5th and 97.5th percentiles of simulated distributions. - DALY = YLL + YLD (discounted at 3%), where YLL represents premature mortality and YLD represents morbidity burden. The descriptive statistics (Min, Quartiles, Max) summarize simulation outputs, while PSA metrics (Mean, 95% CI) provide policy-relevant uncertainty intervals Scenario Analysis The scenario analysis reveals that the economic burden of Lassa fever is overwhelmingly driven by mortality‑related productivity loss, with the baseline (214 deaths, full hospitalization) costing US $5,965 million and generating 3,616 DALYs at a cost‑per‑DALY of $1,668; reducing deaths to 120 cuts total costs by ~ US $1.26 billion (to US $4.70 million) and improves cost‑effectiveness dramatically to $1,520 per DALY, whereas a high‑mortality scenario (300 deaths) more than doubles the total cost to US $7.11 million and inflates the cost‑per‑DALY to $1,753. Partial hospitalization (60% admission) lowers health‑system expenditures (from ₦3.33 million to ₦2.00 million per case) and reduces the overall cost to US $3.65 million, achieving the most favorable cost‑per‑DALY of $1,168 despite a modest drop in deaths (129). Economic sensitivity to macro‑level inputs is evident: a lower GDP per capita (-30%) reduces productivity‑loss values, trimming total costs to US $5.37 million and raising the cost‑per‑DALY to $1,486, while a 20% increase in health‑system costs pushes total spending to US $6.70 million and worsens cost‑per‑DALY to $1,851. Finally, applying a lower discount rate (3% versus the base‑case) raises the present value of mortality losses, lifting total costs to US $6.36 million and the cost‑per‑DALY to $1,609. Across all scenarios, the per‑case out‑of‑pocket expense remains modest (₦48 k/US $34), confirming that policy levers targeting mortality reduction, hospital‑access expansion, and efficient health‑system financing deliver the greatest returns in terms of both total expenditure and cost‑effectiveness. Table 6 Scenario Analysis – Economic Burden of Lassa Fever(Per-case & Total Economic Burden) Scenario Deaths Per-case HSC (₦) Per-case PL morbidity (₦) Per-case PL death (₦) Total per-case (₦) Total per-case (USD) Total cost (USD) DALYs (YLL + YLD) Cost per DALY (USD) Baseline 214 3,334,805 130,791 3,259,846 6,773,966 4,556.78 5,964,825 3,616 1,668 Lower mortality 120 3,334,805 130,791 1,828,814 5,342,935 3,593.25 4,701,112 3,092 1,520 Higher mortality 300 3,334,805 130,791 4,567,280 8,081,401 5,436.12 7,110,113 4,056 1,753 Partial hospital (0.6) 129 2,000,883 130,791 1,967,991 4,148,190 2,789.55 3,645,675 3,119 1,168 Lower GDPpc 214 3,334,805 130,791 2,607,877 6,122,998 4,115.63 5,373,591 3,616 1,486 Higher HSC 214 4,168,506 130,791 3,259,846 7,607,668 5,118.12 6,696,400 3,616 1,851 Lower discount 214 3,334,805 130,791 3,712,377 7,226,498 4,860.82 6,362,585 3,951 1,609 Baseline case = 1309; Hospital rate = 1.00; Per-case OOP (₦) = 48,525 Spearman’s Rank Correlation Sensitivity key parameters The Spearman‑rank analysis makes clear that the total economic burden of Lassa fever is driven almost entirely by productivity loss per case (PL_per_case_usd), which alone accounts for roughly half of the uncertainty (ρ = 1.0, 51% contribution); consequently, policies that prevent deaths or shorten the period of incapacitation—such as rapid case detection, effective treatment protocols, and vaccination—will yield the greatest reductions in mean total cost. The discount rate (discount_r) and GDP per capita (gdp_pc_usd) are the only other parameters with substantive influence (ρ ≈ ‑0.70 and + 0.68, explaining about 25% and 24% of the variance respectively), reflecting the importance of how future productivity losses are valued and the underlying wage level; while these factors are largely exogenous, sensitivity to them underscores the need for consistent, transparent discounting practices in economic evaluations. All remaining cost components—out‑of‑pocket expenses, health‑system costs, morbidity‑related productivity loss, and hospitalization rate—have negligible correlations (|ρ| < 0.01) and contribute virtually nothing to uncertainty, indicating that fine‑tuning these elements will have minimal impact on the overall burden. In sum, to minimise both the mean and the variability of total cost, policymakers should concentrate resources on interventions that curb mortality and associated productivity loss, while ensuring that discounting assumptions are appropriately justified. Table 7 Spearman’s Rank Correlation Sensitivity Analysis of Input Parameters on Total Economic Burden of Lassa Fever Description Spearman’s ρ (Correlation with Total Cost) R² (Variance Explained) % Contribution to Overall Uncertainty Impact Strength on Total Cost Productivity loss per case (USD) + 1.000 1.000 51.1% Very strong positive GDP per capita (USD, proxy for productivity valuation) + 0.684 0.468 23.9% Strong positive Discount rate (for DALYs and costs) −0.700 0.490 25.0% Strong negative Productivity loss from morbidity (USD) + 0.007 0.000 0.0% Negligible Health system cost per case (USD) + 0.007 0.000 0.0% Negligible Out-of-pocket cost per case (USD) −0.008 0.000 0.0% Negligible Hospitalization rate among confirmed cases −0.004 0.000 0.0% Negligible Discussion In comparing the results with the recent studys on the economic burden of Lassa Fever in Nigeria and West Africa, a number of important consistencies and divergences emerge. On one hand, this study’s estimate of the per‑case economic burden — for example a mean overall cost of about US $ 2,442.43 (excluding death) and US $ 4,556.78 (death‑inclusive, discounted) per case — resonates with findings from recent modelling and empirical work. Critically, the temporal annual burden estimates (cumulative US $ 13.7 million from 2019–2024) highlight increasing cost with rising case counts (US $ 1.6 m in 2019 → US $ 5.96 m in 2024). This illustrates the policy imperative of scaling interventions, a point echoed in the literature. For example, Moore (2024) outlines research priorities, emphasising preventive vaccine development and integrated One Health approaches. In another research study, Smith et al. (2024) modelled West‑Africa‑wide Lassa burden at US $ 1.1 billion in productivity losses and US $ 506 million in direct healthcare costs over ten years. Likewise, Eneh et al. (2025) emphasised the “substantial economic costs” beyond health sectors in Nigeria. Thus, your quantification of OOP, health‑system, and productivity losses aligns with broader characterisations of Lassa’s economic footprint. The modelling study by Smith et al. (2024) also shows that preventive vaccination campaigns avert billions in productivity loss. Yet there are also notable differences. In this study’s health system cost per case (~ US $ 2,320.68 mean) appears higher in proportional terms than some regional aggregates which emphasise productivity losses as dominant. For example, your sensitivity analysis shows ~ 51% of variance coming from productivity loss per case, somewhat different to models where direct healthcare costs were large (Smith et al. 2024). Meanwhile, Eneh et al. (2025) highlighted that agriculture/trade disruption and economy‑wide impacts may extend far beyond the healthcare cost category. The breakdown, showing OOP (~ US $ 33.77 mean) far lower than system or productivity costs because most of the cost for diseases like Lassa fever and Ebola are taken over by the Health System to encourage immediate isolation. In some cases, the patients and family members would have suffered some indirect cost before getting to the treatment centres. This reinforces the pattern that indirect costs dominate, but it might under‑represent informal economic losses such as reduced labour participation or household coping strategies (such as asset sales, job loss and disability). This is consistent with qualitative findings that households incur catastrophic financial burdens despite “free treatment” claims. Thus your results support the call for investment in preparedness rather than only reactive care. However, one must contrast the methodological assumptions: the use of GDP‑per‑capita as proxy for daily productivity value (₦3,176.95 ≈ US $ 2.21 per day) and PERT formula for cost‑means is transparent and plausible, but other studies have used alternate valuation ( such as value of statistical life, DALYs monetised). For example, Smith et al. (2024) monetised DALYs at US $ 288 million over ten years in West Africa. In this study, the per‑case death‑inclusive cost (~ US $ 5,524 undiscounted) falls somewhat below potential lifetime productivity loss estimates in highly fatal cases reported elsewhere. For instance, meta-analysis shows case fatality rates in Nigeria around ~ 16% (vs Sierra Leone 48%) which may imply greater loss per death in other settings. Thus, this results are robust for your assumptions but may under‑state high‐fatality scenarios or long‐term disability costs (such as hearing loss sequelae) which Besson (2024) notes are often neglected. The Spearman sensitivity reveals that uncertainty in the estimated productivity loss per case—closely tied to mortality rates and GDP per capita—drives nearly all variability in total costs. These findings highlight the importance of accurate mortality data and income-based valuation in Lassa fever burden models. Conversely, variation in health-system or household-level costs contributes little to overall uncertainty, consistent with previous analyses of high-mortality diseases such as Ebola and Lassa fever (Asogun et al., 2019; Ilori et al.,, 2021). This findings offer strong empirical support to the emerging consensus that Lassa fever imposes substantial hidden costs- particularly productivity losses- and reinforce the urgency of policy responses (health‑financing protection, surveillance strengthening, vaccine readiness). At the same time, compared with recent research it is worth considering two refinements: (1) expand modelling of long‑term sequelae and quality‑of‑life losses beyond acute productivity days; and (2) explore heterogeneity in cost per case across states, fatality levels and care modalities to capture variation seen in other West African settings. Policy Implications The economic analysis of Lassa fever demonstrates a substantial financial burden on both households and the health system, with per-case costs exceeding US $ 2,400 and total national costs approaching US $ 6 million in 2024 alone. Out-of-pocket expenses, though smaller than health system costs, still impose significant hardship on affected families, while productivity losses from morbidity and premature death amplify the societal impact. The policy implication is that targeted investments in preventive measures—such as early detection, vaccination, public health education, and strengthened treatment infrastructure—would not only reduce morbidity and mortality but also yield substantial economic savings, justifying prioritization of Lassa fever control in national health planning and budgeting. Comparison with Other Infectious Disease Burdens Relative to cholera and meningitis cost-of-illness estimates in Nigeria (ranging US $ 500–1,200 per case), the Lassa fever burden is disproportionately higher—mainly due to its prolonged clinical course and need for specialized isolation (Adetunde & Olalubi, 2018 ; Muhammad et al., 2024 ). This emphasizes that hemorrhagic fevers, although lower in incidence, carry “high-cost, low-frequency” economic characteristics that justify substantial preventive investment. Study Strengths and Limitations The study’s strength lies in its integrated cost framework, incorporating household, system, and productivity perspectives across six years. However, the analysis excludes intangible costs such as social stigma, long-term sequelae, and caregiver psychological stress—factors which, if monetized, would further amplify total burden estimates. Additionally, the underreporting of cases may bias aggregate estimates downward, though sensitivity analysis using upper uncertainty bounds mitigates this concern. Conclusion The economic burden of Lassa fever in Nigeria is both severe and preventable. A mean per-patient cost exceeding ₦6.8 million ( $ 4,556.78), driven primarily by public health system costs, represents a critical inefficiency that demands both preventive and fiscal reform. Beyond emergency response, strategic investment in surveillance, health insurance, and community risk protection will yield exponential returns in both economic and epidemiological terms. Lassa fever thus serves as a sentinel disease for evaluating Nigeria’s epidemic preparedness and the resilience of its health financing architecture. Declarations Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on request. Competing interests The authors declare that they have no competing interests related to this work. Ethical approval Ethical approval for this study was obtained from National Health Research Ethics Committee of Nigeria (NHREC) NHREC/01/01/2007-21/05/2025). All procedures involving human participants were conducted in accordance with the ethical standards of the institutional and national research committees and with the 1964 Helsinki Declaration and its later amendments. Informed consent was obtained from all individual participants included in the study Consent to Participate. Informed consent was obtained from all individual participants included in the study and all data were anonymised prior to analysis. Consent for publication Consent for publication is not applicable. All data were anonymised prior to analysis. Funding This research received no external financial support. All authors read and approved the final manuscript. Acknowledgements The authors thank the library staff at Nasarawa State University and the Nigeria Centre for Disease Control for facilitating access to website, weekly situation report, database and journals. We also appreciate the constructive comments from anonymous peer reviewers that helped improve this manuscript. References Adetunde OT, Olalubi OA. Re-emerging Lassa fever outbreaks in Nigeria: Re-enforcing One Health community surveillance and emergency response practice. Infect Dis Poverty. 2018;7:37. https://doi.org/10.1186/s40249-018-0421-8 . ICIR. (2023). How underfunding, lack of preparedness impact surge in Lassa fever. International Centre for Investigative Reporting (ICIR). https://www.icirnigeria.org/how-underfunding-lack-of-preparedness-impact-surge-in-lassa-fever/ Muhammad IB, Abdullahi I, Tahiru AG, Musa A. Economic Impact of Lassa Fever on Biological, Environmental and Hygienic Factors in Bauchi State. Int J Sci Res Math Stat Sci. 2024;11(2):47–50. https://www.isroset.org/pub_paper/IJSRMSS/7-ISROSET-IJSRMSS-09513.pdf . Nyinoh IW, Utume LN, Bob-Echikwonye O. A review of Lassa fever cases in Nigeria for the year 2020. Int J Community Med Public Health. 2021;8(5):2572–5. (Note: full text appears on ijcmph.com) — [Link unavailable via web search, please check your institutional access or journal site]. Osakwe F. (2018). Lassa fever: Prohibitive cost of treatment engendering high mortality. The Guardian (Nigeria). https://guardian.ng/news/lassa-fever-prohibitive-cost-of-treatment-engendering-high-mortality/ Obiejisi K. (2018). Despite FG’s claims, Lassa Fever patients recount high treatment costs. ICIR Nigeria. https://www.icirnigeria.org/despite-fgs-claims-lassa-fever-patients-recount-high-treatment-costs/ Heckert J et al. (2022). Health and economic impacts of Lassa vaccination campaigns in West Africa. PLOS / PMC [Modeling study]. (I was not able to locate a *publicly available full text with a stable link in my search — please verify in PLoS or PMC repositories for the correct DOI / URL.). Science Nigeria. (2025). Lassa Fever Outbreak Exposes IPC Gaps, As NCDC Reveals Healthcare Costs. Science Nigeria. (Note: I did not find a stable article with that exact title in major archives; please verify the Science Nigeria website.). Woldetsadik MD, Lugala PC, Wondimagegnehu A, Ihekweazu C. (2019). Epidemiologic and Clinical Features of Lassa Fever Outbreak in Nigeria, January 1–May 6, 2018. Emerg Infect Dis, 25(6). https://wwwnc.cdc.gov/eid/article/25/6/18-1035_article Increase in Lassa Fever Cases in Nigeria. January–March 2018 Emerg Infect Dis, 25(5), May 2019. https://wwwnc.cdc.gov/eid/article/25/5/18-1247_article WHO. (2018, April 20). Lassa Fever – Nigeria. World Health Organization. https://www.who.int/emergencies/disease-outbreak-news/item/20-april-2018-lassa-fever-nigeria-en WHO African Region. (2018, March 26). WHO: Nigeria’s Lassa fever outbreak is slowing, but remains a concern. WHO Regional Office for Africa. https://www.afro.who.int/news/who-nigerias-lassa-fever-outbreak-is-slowing-remains-concern Olayinka JO et al. (2023). The resurgence of Lassa fever in Nigeria: economic impact, challenges, and strategic public health interventions. [Journal / article from PubMed]. PubMed. https://pubmed.ncbi.nlm.nih.gov/40740381/ Additional Declarations No competing interests reported. Supplementary Files APPENDIX.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 06 May, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviewers invited by journal 23 Apr, 2026 Editor invited by journal 07 Apr, 2026 Editor assigned by journal 04 Apr, 2026 Submission checks completed at journal 03 Apr, 2026 First submitted to journal 03 Apr, 2026 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-9093577","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633529955,"identity":"41eebc5d-39cd-4491-884b-34035d9056f7","order_by":0,"name":"Joseph Gbenga Solomom","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACCQjFw8AMJD8AMRs7KVoYZ4C0MBOpBQyYecAkAR3y0c3HHn6p2SYj38587LHNr23yfMwMjB8+5uDWYnjnWLqxzLHbPAaH2dKNc/tuG7YxMzBLztyGR8uMHDNpCTagFmYeM+ncntuMQC1szLx4teR/k5b4d5tHvhmoxbLntj1BLfISOWySH9tu8zAcBmph+HE7kaAWA5ljZtKMfWC/pEn2NtxObmNmbMbrF/nZzc8kf3y7bS/ff/iYxI8/t23ntzcf/PARny0HYNEBAoxtYLIBt3qQLUBpxh9w7h+8ikfBKBgFo2CEAgDxV0tRJKi1XwAAAABJRU5ErkJggg==","orcid":"","institution":"Nasarawa state University, Keffi, Nasarawa state.","correspondingAuthor":true,"prefix":"","firstName":"Joseph","middleName":"Gbenga","lastName":"Solomom","suffix":""},{"id":633529956,"identity":"7e7ed06d-4f65-4c17-85f8-f4ebdb3149d9","order_by":1,"name":"Adamu Ishaku Akyala","email":"","orcid":"","institution":"Nasarawa state University","correspondingAuthor":false,"prefix":"","firstName":"Adamu","middleName":"Ishaku","lastName":"Akyala","suffix":""},{"id":633529962,"identity":"66d62c90-38f7-4e12-adff-a54c45e45fdb","order_by":2,"name":"Joseph M. Ibbih","email":"","orcid":"","institution":"Nasarawa state University, Keffi, Nasarawa state.","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"M.","lastName":"Ibbih","suffix":""},{"id":633529969,"identity":"25c3a9c5-73aa-4660-a6b2-cb442342b34c","order_by":3,"name":"Jide Idris","email":"","orcid":"","institution":"Nigeria Centre for Disease Control and Prevention, Abuja, Nigeria.","correspondingAuthor":false,"prefix":"","firstName":"Jide","middleName":"","lastName":"Idris","suffix":""},{"id":633529970,"identity":"9cc9ae4e-d2c2-4d06-b65e-ff7e51d99556","order_by":4,"name":"Joseph Femi Emmanuel","email":"","orcid":"","institution":"Hospital management board","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"Femi","lastName":"Emmanuel","suffix":""}],"badges":[],"createdAt":"2026-03-11 11:08:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9093577/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9093577/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108412338,"identity":"97091e0f-89dd-414f-9801-cc95a73ee528","added_by":"auto","created_at":"2026-05-04 10:25:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":157190,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4. DALY and other parameters (variables)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9093577/v1/1417b087b4c74ebdb7193e6c.png"},{"id":108412318,"identity":"0a997b4c-082c-431b-9109-2dd352ab1c77","added_by":"auto","created_at":"2026-05-04 10:25:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16225,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 5. ICER (USD / DALY Saved)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9093577/v1/dd277141a3fc4c17a17523d9.png"},{"id":108412123,"identity":"4cd8a983-4ff4-4161-9282-3cf609a4d35c","added_by":"auto","created_at":"2026-05-04 10:25:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76240,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5. Incremental Cost-Effectiveness and Net Monetary Benefit (NMB) at three WTP thresholds of Lassa Fever Interventions Compared with Baseline (All values in USD).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9093577/v1/3bf5866912f9f9c1087b8c13.png"},{"id":108412224,"identity":"376b94b2-7222-4fb0-aa0a-16f15b180c2e","added_by":"auto","created_at":"2026-05-04 10:25:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":116426,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 2.Share of Per-case Cost Components of Lassa Fever\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9093577/v1/fdd6700853fbd8635a647e64.png"},{"id":108412077,"identity":"4b0b6f25-57a4-41c7-b081-527aa48d62cb","added_by":"auto","created_at":"2026-05-04 10:25:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45809,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigures 3. Boxplot for Distribution of Costs \u003c/strong\u003e\u003cbr\u003e\n The boxplots highlight that system-level costs dominate the upper range of expenditure, whereas OOP and PL morbidity showed relatively low spread.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9093577/v1/8c3b3ac6c02aae461da74f12.png"},{"id":108412074,"identity":"76fa6699-f33e-4732-af9e-5a157d347cca","added_by":"auto","created_at":"2026-05-04 10:25:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":33268,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3. Quick Plot Scenario Analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9093577/v1/acfaa56baba5d45f3401c408.png"},{"id":108412298,"identity":"242ba87e-37d3-46a1-a817-78be068fad78","added_by":"auto","created_at":"2026-05-04 10:25:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":47596,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 8. The Spearman sensitivity\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9093577/v1/4bc1cacab4c995209cb0287a.png"},{"id":108412459,"identity":"340d88ce-0625-4262-98a2-1322015023b0","added_by":"auto","created_at":"2026-05-04 10:26:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1101982,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9093577/v1/26e20d8f-f013-4c43-aa6c-9ebbeafbb8e0.pdf"},{"id":108412126,"identity":"7f5b1e52-f613-40fe-9daa-52c33acc87bb","added_by":"auto","created_at":"2026-05-04 10:25:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16055,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDIX.docx","url":"https://assets-eu.researchsquare.com/files/rs-9093577/v1/dbc8873d4a7b9d9020627dea.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the economic burden and financial protection gaps of Lassa fever in Nigeria using a multicomponent cost of illness analysis 2019 to 2024","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLassa fever is a severe viral hemorrhagic disease endemic in Nigeria, posing a recurrent threat to public health and economic stability (Adetunde \u0026amp; Olalubi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The annual outbreaks frequently lead to high case fatality rates, with recent data indicating mortality among confirmed cases in Nigeria ranging between 13.5% and 18.3% (Olayinka et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nyinoh, Utume \u0026amp; Bob-Echikwonye, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The disease exerts immense strain on the health system, particularly during peak seasons, and challenges infection prevention and control in hospitals (Adetunde \u0026amp; Olalubi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; NCDC, cited in Science Nigeria, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Economically, Lassa fever inflicts a heavy burden across households and the health infrastructure. Though ribavirin\u0026mdash;the mainstay of Lassa fever treatment\u0026mdash;is said to be subsidized in some centers, patients still report paying large sums for other care components such as bed space, blood transfusion, diagnostics, and ancillary medications (Obiejisi, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Osakwe, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Reports from treatment centers suggest that total patient expenses may reach ₦180,000 even when ribavirin is provided \"free\" (Osakwe, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These out-of-pocket (OOP) costs may drive catastrophic health expenditure, eroding the financial resilience of affected families.\u003c/p\u003e \u003cp\u003eOn the public side, the Nigerian health system shoulders substantial costs during Lassa fever outbreaks. The demands of isolation wards, intensive care, including dialysis, diagnostics, personal protective equipment, contact tracing, and case investigation place a heavy toll on already stretched health budgets (PMC, 2025; ICIR, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The infrastructure gaps, budget underfunding, and poor state-level fiscal ownership exacerbate the financial burden (ICIR, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite these substantial economic implications, there remains a lack of rigorous, country-specific cost-of-illness studies of Lassa fever in Nigeria that jointly quantify household OOP costs, public system expenditures, and productivity losses. Much of the existing literature focuses on epidemiology (Nyinoh, Utume \u0026amp; Bob-Echikwonye, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), health system challenges (Science Nigeria, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and burden projections (Heckert et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) but rarely integrates real-world cost data.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to address this gap by estimating the economic burden of Lassa fever in Nigeria using a cost-of-illness framework. We will quantify out-of-pocket costs borne by households, recurrent and capital costs to the public health system, and losses in productivity. Our findings will provide critical evidence to guide policy makers in designing efficient interventions, prioritizing resource allocation, and strengthening financial risk protection mechanisms for vulnerable populations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eResearch Design\u003c/h2\u003e\n\u003cp\u003eThis study used a quantitative multi-method design combining primary data from a Structured Expert Elicitation (SEE) survey and secondary macroeconomic and epidemiological datasets. The SEE component captured cost-of-illness (COI) information from health-system experts, caregivers, and recovered patients. Secondary data included exchange-rate, purchasing-power-parity, and dollar-index indicators from the International Monetary Fund (IMF), Central Bank of Nigeria (CBN), U.S. Federal Reserve, and lassa fever cases reported from 1019- 2024b from the Nigeria Centre for Disease Control and Prevention (NCDC) and World Health Organization (WHO). These datasets were used to generate inflation-adjusted, internationally comparable economic estimates. The used multiple quantitative datasets approach allows for robust triangulation, improves internal validity, and provides a more nuanced understanding of the economic dimensions of health‑system costs than a single‑source analysis would permit (Johnson \u0026amp; Onwuegbuzie, 2004; Creswell \u0026amp; Plano Clark, 2018).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStudy Setting and Population\u003c/h3\u003e\n\u003cp\u003eThe study considered nationwide pool of data in Nigeria, the most populous African country with an estimated 223\u0026nbsp;million residents in 2023 (National Population Commission, 2023; United Nations, 2022). Nigeria is divided into six geopolitical zones, 36 states, and the Federal Capital Territory, a structure that facilitates region‑specific health‑system analyses. Two distinct populations are addressed within the quantitative multi‑method design.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSurveillance cohort (secondary data)\u003c/em\u003e \u0026ndash; All Lassa fever cases reported to the Nigeria Centre for Disease Control (NCDC) from 2019 through 2024 are included. These data were extracted from the Integrated Disease Surveillance and Response (IDSR) and weekly situation reports uploaded on the NCDC website, providing complete national coverage. Using the full surveillance dataset enables robust assessment of temporal and spatial trends at the national level.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExpert cohort (primary data) \u0026ndash;\u003c/em\u003e Fifty-one (50) health‑system experts and frontline practitioners are recruited to complete a structured expert‑elicitation questionnaire with assistance of trained enumerators. The instrument gathers individual out‑of‑pocket expenditures, public‑health spending, and productivity‑loss estimates needed to calculate the Cost‑of‑Illness (COI).\u003c/p\u003e\n\u003ch3\u003eSample size, Sample technique and Methods of Data Collection\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eSample‑size determination\u003c/h2\u003e\n\u003cp\u003eA conventional power‑based formula for estimating a proportion was used because the primary outcome (cost‑of‑illness estimate) is derived from expert-elicited quantitative responses.\u003c/p\u003e\n\u003cp\u003e[ n={Z\u003csup\u003e2\u003c/sup\u003e p (1-p)}/{d\u003csup\u003e2\u003c/sup\u003e}\u003c/p\u003e\n(Z) \u0026ndash; Z‑value for the desired confidence level (1.96 for 95%).\n\u003cp\u003e(p) \u0026ndash; Expected proportion of \u0026ldquo;agreement\u0026rdquo; among experts; a conservative value of 0.5 maximises the required sample.\u003c/p\u003e\n\u003cp\u003e(d) \u0026ndash; Margin of error (precision) set at 0.14 (\u0026plusmn;\u0026thinsp;14%).\u003c/p\u003e\n\u003cp\u003e[n={(1.96)\u003csup\u003e2\u003c/sup\u003e x 0.5 x (1-0.5)}/{(0.14)\u003csup\u003e2\u003c/sup\u003e} =49]\u003c/p\u003e\n\u003cp\u003eTo accommodate possible non‑response and to obtain an even distribution across the six geopolitical zones, the target was marked up to 55 participants (a 10% increase).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eSampling technique\u003c/h3\u003e\n\u003cp\u003eThe study was able to secure a total of 50 participants valid for this study from treatment centre in each of Nigeria\u0026rsquo;s six geopolitical zones, yielding six centres. Six Lassa fever treatment centres\u0026mdash;one from each geopolitical zone\u0026mdash;were selected using simple balloting, introducing randomisation into the site-selection process. Within each centre, Health‑system experts\u0026mdash;including case‑managers, and members of the Lassa fever Technical Working Group with 50 individuals (approximately eight to nine per zone). These experts were identified through purposive sampling, targeting persons known to be directly involved in Lassa fever case management and surveillance. In addition, two caregivers were recruited via convenience sampling of the next‑of‑kin of recently recovered Lassa fever patients identified through facility registers. From these caregiver contacts, five recovered patients were enrolled using snowball sampling; all patients had completed treatment at the selected facilities.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eData‑collection procedures\u003c/h2\u003e\n\u003cp\u003ePrimary quantitative data (expert elicitation) \u0026ndash; A Structured Expert Elicitation (SEE) form hosted on KoboCollect was used to gather information on out‑of‑pocket expenses incurred by patients, public‑health system expenditures related to Lassa fever case management (including drugs, consumables, and staff time), and productivity losses measured as work‑days missed due to illness. The SEE form were administered face‑to‑face to the respondents (the two caregivers, five recovered patients, and two state officials). All responses were exported directly into R for subsequent analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eTechniques for Data Analysis and Model Specification\u003c/h3\u003e\n\u003cp\u003eQuantitative data from secondary surveillance records and primary expert surveys will be processed in R using a suite of advanced statistical tools. First, descriptive statistics will summarize the expert‑reported cost components. Next, a probabilistic multivariate sensitivity analysis will be performed via Monte Carlo simulation (10 000 draws), assigning probability distributions to all key parameters such as hospitalization rate, unit costs, work‑days lost. Correlation and tornado plots will identify the strongest cost drivers.\u003c/p\u003e\n\u003cp\u003eEconomic model specification follows a cost‑of‑illness (COI) structure that distinguishes household out‑of‑pocket (OOP) spending, health‑system costs (HSC), and productivity loss (PL).\u003c/p\u003e\n\u003cp\u003eThis mathematical framework quantifies the full economic burden of Lassa fever by integrating the following:\u003c/p\u003e\n\u003ch3\u003eMathematical Economic Model Specification for Lassa Fever\u003c/h3\u003e\n\u003cp\u003eLet:\u003c/p\u003e\n\u003cp\u003eC\u0026thinsp;=\u0026thinsp;number of cases\u003c/p\u003e\n\u003cp\u003eD\u0026thinsp;=\u0026thinsp;number of deaths\u003c/p\u003e\n\u003cp\u003eA\u003csub\u003edeath\u003c/sub\u003e = average age at death\u003c/p\u003e\n\u003cp\u003eLE\u0026thinsp;=\u0026thinsp;life expectancy at birth\u003c/p\u003e\n\u003cp\u003er\u0026thinsp;=\u0026thinsp;discount rate (per annum)\u003c/p\u003e\n\u003cp\u003eGDP\u003csub\u003e\u003cem\u003epc\u003c/em\u003e\u003c/sub\u003e = GDP per capita (USD)\u003c/p\u003e\n\u003cp\u003eDW\u0026thinsp;=\u0026thinsp;disability weight\u003c/p\u003e\n\u003cp\u003ed\u003csub\u003e\u003cem\u003einpatient\u003c/em\u003e\u003c/sub\u003e,d\u003csub\u003e\u003cem\u003erecovery\u003c/em\u003e\u003c/sub\u003e,d\u003csub\u003e\u003cem\u003ecaregiver\u003c/em\u003e\u003c/sub\u003e,d\u003csub\u003e\u003cem\u003epres\u003c/em\u003e\u003c/sub\u003e =days lost per case\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003ePer-case Cost Components\u003c/h2\u003e\n\u003cp\u003ePer-Case Cost\u0026thinsp;=\u0026thinsp;OOP+HSC+PLmorb+PLdeath\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eTotal Economic Burden\u003c/h2\u003e\n\u003cp\u003eTotal Cost\u0026thinsp;=\u0026thinsp;Per-Case Cost\u0026times;C\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eDALY Computation\u003c/h2\u003e\n\u003cp\u003eDALY\u0026thinsp;=\u0026thinsp;YLL (years of life lost, discounted) + Years Lived with Disability (YLD)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eCost per DALY\u003c/h2\u003e\n\u003cp\u003eCost Per DALY =(Total Cost/DALY)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003eProbabilistic Sensitivity Analysis (PSA)\u003c/h2\u003e\n\u003cp\u003eInputs GDP\u003csub\u003e\u003cem\u003epc\u003c/em\u003e\u003c/sub\u003e, r, d, DW, OOP, HSC are treated as random variables drawn from appropriate distributions (PERT), and Monte Carlo simulation propagates uncertainty to:\u003c/p\u003e\n\u003cp\u003ePer Case Cost, Total Cost, DALY, Cost Per DALY\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003eScenario Analysis\u003c/h2\u003e\n\u003cp\u003eVary parameters like D, C, r, GDP\u003csub\u003epc\u003c/sub\u003e, HSC, OOP, and hospitalization rate to quantify their impact on Total Cost and Cost Per DALY.\u003c/p\u003e\n\u003cp\u003eThis model captures direct medical costs, indirect costs, productivity losses, and health outcomes, while supporting probabilistic and scenario-based sensitivity analyses. When estimating the economic burden of Lassa fever, the hospitalisation‑rate (HR) is still included. but because virtually every confirmed case is admitted, HR is set to 1 (or to a distribution tightly centred on 1, e.g., Beta(9, 1) with a mean of \u0026asymp;\u0026thinsp;0.9) to reflect the very small chance of mild conditions possibly managed for very few days or in clinic outside the health facility as outpatient probably while preserving probabilistic uncertainty. Retaining HR\u0026mdash;even when it effectively equals 1\u0026mdash;ensures model consistency across diseases, allows severity‑weighting, supports \u0026ldquo;what‑if\u0026rdquo; policy scenarios (e.g., shifting care to community isolation units), and enables Monte Carlo sensitivity analysis to propagate any residual uncertainty into the final cost estimates.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eLassa Fever imposes a substantial economic burden on both households and the public health system in Nigeria. In addition, the mean costs per patient are considerably higher than other outbreaks like cholera, reflecting the intensive clinical care, specialized personnel, and resources required to treat and manage Lassa Fever cases. Out-of-pocket costs represent the direct financial burden borne by households when seeking treatment for Lassa Fever. These expenses include consultation fees, transportations, PPE, phone calls, feeding and other minor healthcare expenditures within and outside the treatment centres. The mean OOP cost per patient is ₦48,525 (US$33.77). This low per-patient figure reflects the fact that many Lassa Fever cases may seek care at public facilities where subsidized care is available or free medical cost, yet it still captures the variability in household expenditures depending on severity, care-seeking behavior, and accessibility to healthcare services.\u003c/p\u003e\n\u003cp\u003eHealth system costs for Lassa Fever are substantial due to the need for specialized personnel, medical supplies, diagnostics, inpatient operations, contact tracing, and logistics. The mean HSC per patient is ₦3,334,806.09 (US$2,320.68). These costs represent the public health system expenditure on Lassa Fever management and illustrate the intensive resource allocation required to provide adequate care, maintain isolation wards, ensure biosafety, and carry out community interventions such as contact tracing and logistical support. The high HSC emphasizes the fiscal pressure on Nigeria\u0026rsquo;s healthcare system during Lassa Fever outbreaks.\u003c/p\u003e\n\u003cp\u003eProductivity losses account for indirect costs due to morbidity and include patient illness, caregiver time, recovery periods, and reduced productivity while working sick (presenteeism). On average, patients lose 38.07 days (95% CI: 24.79\u0026ndash;49.16 days) per Lassa Fever episode. Using Nigeria\u0026rsquo;s GDP per capita per day (₦3,434/day for 2024), this translates to a mean indirect cost of ₦130,790.76 per patient (US$88). This metric captures the broader societal impact of illness, reflecting lost income and reduced productivity for both patients and caregivers during acute illness and recovery.\u003c/p\u003e\n\u003cp\u003eSumming the OOP, HSC, and PL components gives the total economic burden per Lassa Fever patient. The mean total cost is ₦3,514,121.44 (US$2,442.43), with a 95% confidence interval of ₦3,150,246.21\u0026ndash;₦3,838,131.56 (US$2,191.78\u0026ndash;$2,665.62). This figure represents the comprehensive per-patient cost, providing policymakers with a clear estimate of the financial implications of Lassa Fever at the individual level.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCost Components of Lassa Fever per Patient (₦ and US$)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComponent\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean (₦)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% LCI (₦)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% UCI (₦)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean (US$)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% LCI (US$)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% UCI (US$)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOut-of-Pocket (OOP) Costs\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 \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\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\u003e1.OOP Consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4,414.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,201.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5,508.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.OOP Feeding \u0026amp;Transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e44,110.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e42,966.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e45,366.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e30.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e29.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e31.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal OOP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e48,524.59\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e46,168.34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e50,875.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e33.77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e32.13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e35.40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth System Component (HSC)\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003e3.HSC Personnel (salaries, overtime, hazard pay)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e687,748.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e565,166.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e762,104.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e478.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e393.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e530.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.HSC Medical Supplies (PPE, Ribavirin, IVfluids, consumables., oxygen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2,063,759.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1,985,841.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2,141,760.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1,436.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1,381.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1,490.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.HSC Diagnostics \u0026amp; Laboratory (kits, reagents, biosafety)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e240,121.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e236,922.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e244,472.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e167.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e164.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e170.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.HSC Inpatient Operations (isolation ward, dialysis, WASH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e144,557.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e99,637.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e204,725.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e100.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e69.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e142.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.HSC Contact Tracing (staff time, transport, allowances)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e143,309.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e137,221.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e149,335.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e99.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e95.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e103.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.HSC Logistics \u0026amp; Sample Transport (couriers, cold chain)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e55,308.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e54,283.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e56,250.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e38.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e37.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e39.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal HSC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e3,334,806.09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e3,079,073.76\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e3,558,647.50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e2,320.68\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e2,142.82\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e2,476.44\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProductivity Loss (PL) per case (morbidity)\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003e9.PL_Inpatient_Days_Lost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e37,002.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e7,144.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e60,724.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e24.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e40.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.PL_Recovery_Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e21,157.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,572.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e53,580.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e14.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e36.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.PL_Caregiver_Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e36,407.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e7,144.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e60,724.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e24.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e40.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.PL_Presenteeism_Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e36,223.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e7,144.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e53,580.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e24.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e36.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal PL (\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eMORBIDITY\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e130,790.76\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e25,004.11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e228,609.06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e87.98\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.83\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e153.78\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrand Total Economic burden per case (excluding death)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e3,514,121.44\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e3,150,246.21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e3,838,131.56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e2,442.43\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e2,191.78\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e2,665.62\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote: - Converted to ₦ to USD 2024 as baseline; mean calculated using the PERT formula.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e-- Productivity Loss (PL) proxy\u0026thinsp;=\u0026thinsp;GDP per capita 2024; According to the International Monetary Fund, in the 2025 Article IV summary Nigeria\u0026rsquo;s nominal GDP per capita (US$) for 2024 is shown as US$806.9.-World Bank Open Data.\u003c/p\u003e\n\u003cp\u003e- exchange rate = ₦1,437 / US$1.\u003c/p\u003e\n\u003cp\u003e- Daily value for PL rounded to ₦3,176.95; displayed values rounded to whole ₦ and US$ to 2 decimals.\u003c/p\u003e\n\u003cp\u003e- Confidence intervals for PL: derived from distribution of expert-reported days (2.5% and 97.5% percentiles) then monetised.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMonetised Productivity Loss per Lassa Fever Case (inpatient, recovery, caregiver, and presenteeism days)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePL Component\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Days (d)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% LCI Days\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% UCI Days\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDaily Value (₦)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInpatient Days Lost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e10.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e12.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e₦3,176.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecovery Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e9.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e₦3,176.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaregiver Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e10.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e12.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e₦3,176.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresenteeism Days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e10.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e14.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e₦3,176.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e38.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e24.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e49.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNotes: Daily value (₦3,176.95) is based on GDP per capita 2024; 95% LCI and UCI represent the 2.5th and 97.5th percentiles of expert-reported days; All values rounded to two decimal places where applicable.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eProductivity Loss Due to Death (Human Capital Approach)\u003c/h2\u003e\n \u003cp\u003eEach premature death leads to a loss of potential lifetime productivity, this approximated by GDP per capita multiply by the remaining life expectancy at time of death.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eProductivity loss due to death \u0026mdash; Undiscounted human-capital\u003c/h2\u003e\n \u003cp\u003eProductivity loss per death (undiscounted) = GDP_per_capita \u0026times; L (number of working years lost due to premature death)\u003c/p\u003e\n \u003cp\u003e=\u0026thinsp;806.95 \u0026times; 23.36\u0026thinsp;\u0026asymp;\u0026thinsp;=\u0026thinsp;US$18,850.25 per death\u003c/p\u003e\n \u003cp\u003eTotal productivity loss (all deaths) undiscounted\u0026thinsp;=\u0026thinsp;18,850.25 \u0026times; 214 (no of death: see appendix)\u0026thinsp;=\u0026thinsp;US$4,033,953.81\u003c/p\u003e\n \u003cp\u003ePer-case (spread total death loss across all cases)\u0026thinsp;=\u0026thinsp;4,033,953.81\u0026thinsp;\u0026divide;\u0026thinsp;1,309\u0026thinsp;\u0026asymp;\u0026thinsp;US$3,081.71 per case\u003c/p\u003e\n \u003cp\u003eIn NGN (₦1,486.57/USD): ₦4,581,172.43 per case (undiscounted)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e*Discounted human-capital (3% annual discount)\u003c/h2\u003e\n \u003cp\u003ePL_death (discounted)\u0026thinsp;=\u0026thinsp;GDP per capita\u0026times;PV factor for remaining\u003c/p\u003e\n \u003cp\u003ePV factor for L\u0026thinsp;=\u0026thinsp;23.36 years at r\u0026thinsp;=\u0026thinsp;0.03 \u0026rArr; PV\u0026thinsp;=\u0026thinsp;1\u0026minus;(1\u0026thinsp;+\u0026thinsp;0.03)\u0026thinsp;\u0026minus;\u0026thinsp;23.36\u003c/p\u003e\n \u003cp\u003eProductivity loss per death (discounted) = GDP_per_capita \u0026times; PV\u0026thinsp;\u0026asymp;\u0026thinsp;US$13,413.36 per death\u003c/p\u003e\n \u003cp\u003eTotal productivity loss (discounted)\u0026thinsp;=\u0026thinsp;13,413.36 \u0026times; 214\u0026thinsp;\u0026asymp;\u0026thinsp;US$2,870,458.94\u003c/p\u003e\n \u003cp\u003ePer-case (discounted)\u0026thinsp;=\u0026thinsp;2,870,458.94\u0026thinsp;\u0026divide;\u0026thinsp;1,309\u0026thinsp;\u0026asymp;\u0026thinsp;US$2,192.86 per case\u003c/p\u003e\n \u003cp\u003eIn NGN: ₦3,259,845.80 per case (discounted)\u003c/p\u003e\n \u003cp\u003eThe analysis shows that premature mortality drives the bulk of Lassa fever\u0026rsquo;s economic burden: using a human‑capital approach, each death translates into roughly US $13,400 of discounted lifetime productivity loss (\u0026asymp; ₦3.26\u0026nbsp;million) versus US $18,850 undiscounted (\u0026asymp; ₦4.58\u0026nbsp;million), and with 214 deaths this amounts to a total loss of US $2.87\u0026nbsp;million (discounted) or US $4.03\u0026nbsp;million (undiscounted). When spread across the 1,309 reported cases, the per‑case death‑related loss is US $2,193 (discounted) or US $3,082 (undiscounted). Adding direct medical expenses\u0026mdash;out‑of‑pocket costs (\u0026asymp;\u0026thinsp;US $34) and health‑system costs (\u0026asymp;\u0026thinsp;US $2,321)\u0026mdash;and morbidity‑related productivity loss (\u0026asymp;\u0026thinsp;US $88) yields a subtotal (excluding death) of about US $2,442 per case. Consequently, the overall per‑case economic burden is US $5,525 (undiscounted) and US $4,557 (discounted), with the discounted figure representing the recommended 3% present‑value adjustment for long‑term losses. These results underscore that interventions that prevent deaths\u0026mdash;or markedly reduce fatality rates\u0026mdash;will produce the greatest cost savings, while variations in out‑of‑pocket or health‑system expenditures have comparatively minor impact on total burden.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePer-Case Cost Components of Lassa Fever\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComponent\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean (₦)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% LCI (₦)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% UCI (₦)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean (US$)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% LCI (US$)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% UCI (US$)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOOP (Out-of-pocket)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e48,524.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e46,168.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e50,875.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e33.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e32.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e35.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHSC (Health system costs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,334,806.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,079,073.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,558,647.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2,320.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2,142.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2,476.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL \u0026ndash; Morbidity (Productivity Loss due to illness)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e130,790.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e25,004.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e228,609.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e87.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e16.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e153.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubtotal (Economic burden excluding death)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,514,121.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,150,246.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,838,131.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2,442.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2,191.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2,665.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL \u0026ndash; Death (Productivity Loss, undiscounted per case)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4,581,172.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e875,812.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8,007,427.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,081.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e609.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5,572.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL \u0026ndash; Death (Productivity Loss, discounted per case)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,259,845.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e623,205.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5,697,881.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2,192.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e433.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,965.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal (Overall Economic Burden, death inclusive \u0026ndash; undiscounted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8,095,293.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4,026,058.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e11,845,559.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5,524.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2,801.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8,243.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (Overall Economic Burden, death inclusive \u0026ndash; discounted)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e6,773,967.24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e3,773,451.88\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e9,536,013.31\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e4,556.78\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e2,625.92\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e6,636.06\u003c/strong\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 \u003cp\u003e\u0026bull; WHO‑CHOICE recommends a 3% discount rate for both costs and health outcomes.\u003c/p\u003e\n \u003cp\u003e*Undiscounted totals for immediate, observable costs (useful for budgeting).\u003c/p\u003e\n \u003cp\u003e* Discounted totals for the productivity loss component (especially YLL‑derived loss), because those losses extend far into the future.\u003c/p\u003e\n \u003cp\u003e* Economic‑burden or cost‑effectiveness analyses that incorporate long‑term outcomes such as years of life lost (YLL) or disability‑adjusted life years (DALYs).\u0026bull; Any model that projects losses beyond the current fiscal year (e.g., lifetime productivity loss from premature death).\u003c/p\u003e\n \u003cp\u003eUse undiscounted values for a straightforward, year‑specific COI snapshot (direct medical costs\u0026thinsp;+\u0026thinsp;short‑term work loss).\u003c/p\u003e\n \u003cp\u003eApply discounting (typically 3% per year) when you need to value future productivity losses (mortality, long‑term disability) as part of a comprehensive economic‑burden assessment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eAnnual Lassa Fever Cost Burden (2019\u0026ndash;2024)\u003c/h2\u003e\n \u003cp\u003eUsing national case counts from 2019\u0026ndash;2024 and adjusting for inflation, the total economic burden per year shows significant variability. For example, in 2019, with 833 cases, the total cost burden was US$1,601,942.3, while in 2020, with 1,189 cases, it rose to US$2,042,412.9. By 2024, the baseline scenario with 1309 cases resulted in a total burden of US$5,964,825.0. The cumulative economic burden over the six-year period is approximately US$13,697,578.70. These estimates provide a clear indication of the fiscal impact of Lassa Fever on both households and the health system in Nigeria.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLassa Fever: Cost burden (US$) per year\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePPP conversion factor\u0026thinsp;\u0026divide;\u0026thinsp;Market rate (Proportion)*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCost-burden per case (US$) equivalent at that year\u0026rsquo;s purchasing power\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of cases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Cost burden(US$)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,923.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1,601,942.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,718.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2,042,412.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,673.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e853,587.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,636.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1,746,977.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,170.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1,487,834.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,556.78 (baseline)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5,964,825.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal (anually\u0026thinsp;\u0026minus;\u0026thinsp;2019\u0026ndash;2024)\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 \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e13,697,578.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e*\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.un.org/Data.aspx?d=WDI\u0026amp;f=Indicator_Code%3APA.NUS.PPPC.RF%3BCountry_Code%3ANGA\u0026amp;q=nigeria+gdp\u003c/span\u003e\u003c/span\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003eDALY, CEA, and ICER: Health and Economic Burden Metrics of Lassa Fever\u003c/h2\u003e\n \u003cp\u003eThese additional fields are crucial for interpreting cost-effectiveness and incremental cost-effectiveness analyses (CEA and ICER). The Cost per DALY reflects the overall efficiency of each strategy, quantifying how much is spent for every disability-adjusted life year incurred or averted. The Cost vs. Baseline captures the incremental financial burden, revealing how much extra investment an intervention demands relative to the current practice. The DALYs vs. Baseline measures incremental health benefits, indicating the extent of additional health gains or losses achieved by an intervention. Finally, the ICER (USD per DALY saved) serves as the key decision metric in health economics, showing the extra cost required to obtain one additional DALY relative to baseline; a lower ICER denotes a more cost-effective intervention within a given willingness-to-pay threshold.\u003c/p\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003eHuman-capital valuation approach (framework): DALY\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eYears of Life Lost (YLL)* due to death\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eProductivity Loss from Premature Death\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFormula / Source\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage age of death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrom NCDC data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLife expectancy at birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.36 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNigerian population\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRemaining life expectancy (L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.36 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL\u0026thinsp;=\u0026thinsp;56.36\u0026thinsp;\u0026minus;\u0026thinsp;33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$806.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiscount rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWHO-CHOICE recommendation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent value factor (PV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026minus;(1\u0026thinsp;+\u0026thinsp;r)\u003csup\u003e\u0026minus;L\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.03, L\u0026thinsp;=\u0026thinsp;23.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL per death (discounted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$13,413.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u0026times;PV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL per death (undiscounted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$18,850.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u0026times;L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal PL for all deaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$2,870,458.94 (discounted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214 deaths \u0026times; $13,413.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer-case PL death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$2,192.86 (discounted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpread across 1,309 cases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e*Incidence-based DALYs count YLL from each death:\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eYLL=Number of deaths \u0026times; Remaining life expectancy at age of death\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYears Lived with Disability (YLD) due to illness\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYLD\u0026thinsp;=\u0026thinsp;total number of lost workdays for patients and caregivers x daily GDP-per-capita value to estimate lost productivity.\u003c/p\u003e\n \u003cp\u003eYLD* \u0026ndash; Productivity Loss from Morbidity\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComponent\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAvg Days Lost\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDaily GDP Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProductivity Loss per case (US$)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInpatient days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$2.23 (₦3,176.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$24.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecovery days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$14.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaregiver days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$24.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresenteeism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$24.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal morbidity PL (YLD proxy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.07 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$87.98 per case\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e*Incidence-based YLD: YLD=Incident case \u0026times; DW \u0026times; Duration of illness\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e(DW\u0026thinsp;~\u0026thinsp;1.0) DW is NOT literally replaced, but approximated using human-capital productivity loss.These studies specifically validate productivity loss as a measure of morbidity severity, equivalent to a DW proxy\u003c/p\u003e\n \u003cp\u003e(\u003cstrong\u003esources\u003c/strong\u003e: Krol, M., \u0026amp; Brouwer, W. B. F. (2014). How to estimate productivity costs in economic evaluations. PharmacoEconomics, 32(4), 335\u0026ndash;344.\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u0026nbsp;\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40273-014-0132-3;Shepard\u003c/span\u003e\u0026nbsp;\u003c/span\u003e, D. S., Undurraga, E. A., \u0026amp; Halasa, Y. A. (2013). Economic and disease burden of dengue in Southeast Asia. PLoS Neglected Tropical Diseases, 7(2), e2055.\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u0026nbsp;\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pntd.0002055\u003c/span\u003e\u0026nbsp;\u003c/span\u003e; World Bank. (2018). The human capital project: Concept note.\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u0026nbsp;\u003cspan class=\"RefSource\"\u003ehttps://documents.worldbank.org/en/publication/documents-reports/documentdetail/793421540087227031/revised-human-capital-project-paper\u003c/span\u003e\u0026nbsp;\u003c/span\u003e ).\u003c/p\u003e\n \u003cp\u003eMorbidity-related productivity loss (YLD proxy) is monetized using GDP per capita, representing lost work capacity for both patients and caregivers.\u003c/p\u003e\n \u003cp\u003eCombine YLL\u0026thinsp;+\u0026thinsp;YLD \u0026rarr; Total DALY per case\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComponent\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUS$ per case\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL death (YLL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,192.86 (discounted)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePL morbidity (YLD proxy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal DALY monetized per case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e$2,280.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThese DALYs are then used to calculate Cost per DALY and ICER in CEA:\u003c/p\u003e\n \u003cp\u003eICER (USD per DALY saved)= [\u0026Delta;Cost vs. Baseline] / [\u0026Delta;DALYs vs. Baseline]\u003c/p\u003e\n \u003cp\u003eExample from Intervention B:\u003c/p\u003e\n \u003cp\u003eICER\u0026thinsp;=\u0026thinsp;511,309/416.55\u0026asymp;$1,227 per DALY saved\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabd\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNOTE: Total DALYs (baseline, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;3,616.55 years\u003c/p\u003e\n \u003cp\u003eNumber of cases\u0026thinsp;=\u0026thinsp;1,309\u003c/p\u003e\n \u003cp\u003eMonetised DALY per case (YLL\u0026thinsp;+\u0026thinsp;YLD) = $2,280.84 per case\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 1\u003c/strong\u003e: DALYs per case\u003c/p\u003e\n \u003cp\u003eDALYs_per_case\u0026thinsp;=\u0026thinsp;Total_DALYs / Cases\u003c/p\u003e\n \u003cp\u003e= 3616.55 / 1309\u003c/p\u003e\n \u003cp\u003e= 2.7628342246 years per case\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 2\u003c/strong\u003e: Implied $ value per DALY\u003c/p\u003e\n \u003cp\u003e$/DALY\u0026thinsp;=\u0026thinsp;Monetised_per_case / DALYs_per_case\u003c/p\u003e\n \u003cp\u003e= 2,280.84 / 2.7628342246\u003c/p\u003e\n \u003cp\u003e\u0026asymp; $825.54 per DALY\u003c/p\u003e\n \u003cp\u003eExplanation:\u003c/p\u003e\n \u003cp\u003e\u0026bull; $825.54 per DALY\u0026thinsp;\u0026asymp;\u0026thinsp;the monetisation factor used (close to GDP per capita \u0026asymp; $806.95).\u003c/p\u003e\n \u003cp\u003e\u0026bull; Thus: 2.7628 years \u0026times; $825.54/year \u0026asymp; $2,280.84 per case (matches the monetised per-case figure).\u003c/p\u003e\n \u003cp\u003eConclusion (one line): Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e reports total DALYs (years lost) while the $2,280.84 is the monetary value of those lost years for a single case \u0026mdash; you convert between them by multiplying DALYs (years) by the chosen value-per-DALY (here\u0026thinsp;\u0026asymp;\u0026thinsp;GDP per capita \u0026rarr; \u0026asymp; $825/DALY).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the distribution and relationships among key economic and health variables influencing the cost-effectiveness of Lassa fever interventions. The histogram of cost per DALY shows the spread of efficiency values across simulated scenarios, highlighting the variability in health gains per dollar spent. The relationship between discount rate and cost per DALY demonstrates how higher discounting of future health benefits increases the cost per DALY, reducing apparent cost-effectiveness. Meanwhile, the correlation between GDP per capita and productivity loss indicates that higher economic productivity amplifies the estimated monetary burden of illness and death. Together, these patterns underscore the sensitivity of DALY-based evaluations to economic and methodological assumptions, emphasizing the need for contextual calibration in cost-effectiveness analyses.\u003c/p\u003e\n \u003cp\u003eThis incremental cost-effectiveness analysis compares two potential Lassa fever control interventions against the baseline scenario. Intervention A raises total program expenditures by US $1.21\u0026nbsp;million but averts approximately 617 DALYs, yielding an ICER of US $1,965 per DALY saved. Intervention B adds only US $511 thousand while saving 417 DALYs, giving a more favorable ICER of US $1,228 per DALY saved. Both strategies improve health outcomes, but Intervention B is more cost-effective, delivering greater health gains per dollar spent.\u003c/p\u003e\n \u003cp\u003eThese incremental fields- Cost, DALYs, and ICER- are vital for CEA and ICER interpretation because they allow direct comparison between strategies, quantifying trade-offs between added cost and health benefit. By relating these ratios to a willingness-to-pay (WTP) threshold (often 1\u0026ndash;3\u0026times; GDP per capita, US $807\u0026ndash;2,421 in Nigeria for 2024), policymakers can judge whether an intervention represents \u0026ldquo;good value for money.\u0026rdquo; Under this threshold, both interventions would be considered cost-effective, with Intervention B being the preferred option for optimal resource allocation.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIncremental Cost-Effectiveness Analysis (CEA) and ICER Results for Alternative Lassa Fever Intervention Scenarios\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScenario\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Cost (USD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDALYs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCost / DALY (USD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta; Cost vs. Baseline (USD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta; DALYs vs. Baseline\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eICER (USD / DALY Saved)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5,988,691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,616.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1,655.91\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=\"char\" char=\".\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash; \u003cem\u003e(Reference)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e7,200,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2,400.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;1,211,309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026ndash;616.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,964.66\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e6,500,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,200.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2,031.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;511,309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026ndash;416.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,227.49\u003c/strong\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 \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIncremental Cost-Effectiveness and Net Monetary Benefit (NMB) at three WTP thresholds of Lassa Fever Interventions Compared with Baseline\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThis table synthesizes the economic and health outcomes of Lassa fever control strategies in 2024 using both cost-effectiveness (CEA) and net monetary benefit (NMB) frameworks. The baseline represents current practice, with two intervention scenarios compared against it. Intervention A increases total costs by $1.21\u0026nbsp;million and averts 617 DALYs, yielding an ICER of $1,965 per DALY saved. Intervention B costs $511 k more and averts 417 DALYs, with a more favorable ICER of $1,227 per DALY saved.\u003c/p\u003e\n \u003cp\u003eAt a low WTP threshold of $807.2 per DALY (Nigeria\u0026rsquo;s GDP per capita), both interventions have negative NMB, indicating that health benefits do not offset added costs. As WTP rises to $2,000, both become cost-effective, but Intervention B yields far greater net value (+$322 k vs. +$22 k). At $2,421.6 (~\u0026thinsp;3\u0026times; GDP per capita), both generate substantial positive NMBs, with B remaining dominant (higher NMB at every threshold). In economic evaluation, Cost quantifies the budget impact, informing affordability; DALYs measures health gain, expressing clinical value; ICER links the two, describing efficiency; and NMB translates both into a single monetary metric, allowing direct ranking across competing health investments. These additional fields are vital for cost-effectiveness analysis (CEA) and incremental cost-effectiveness ratio (ICER) assessment because they provide a full decision-making picture - how much extra is paid, what health is gained, and whether that trade-off is worthwhile at the chosen WTP. In summary, Intervention B consistently outperforms A and the baseline in both ICER and NMB terms. At any realistic WTP above $1,200/DALY, it is the most cost-effective and economically attractive option, offering the greatest \u0026ldquo;health return on investment.\u0026rdquo;\u003c/p\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003eProbabilistic Sensitivity Analysis (PSA) Summary of the Economic and Health Burden of Lassa Fever\u003c/h2\u003e\n \u003cp\u003eProbabilistic Sensitivity Analysis (PSA) was conducted using Monte Carlo simulations to propagate uncertainty across the economic burden of Lassa fever. The probabilistic sensitivity analysis demonstrates that the mean per-case economic burden of Lassa fever was approximately ₦6.8\u0026nbsp;million (US$4,575), with aggregate national losses of about US$5.99\u0026nbsp;million. Productivity losses dominate the cost profile, particularly from fatal cases, where each death corresponds to a lifetime productivity loss of about US$13,500. The total discounted Years of Life Lost (YLL) and Years Lived with Disability (YLD) together yield 3,617 Disability-Adjusted Life Years (DALYs), representing the total health burden of the outbreak. With an estimated cost per DALY of US$1,668, Lassa fever imposes an economic cost exceedingly twice Nigeria\u0026rsquo;s GDP per capita (US$807), classifying it as a high-burden, economically catastrophic disease under WHO-CHOICE cost-effectiveness criteria.\u003c/p\u003e\n \u003cp\u003eIn cost-effectiveness and decision-analytic terms, the DALY metric enables translation of these losses into incremental cost-effectiveness ratios (ICERs) when comparing potential interventions\u0026mdash;such as vaccination, rapid diagnostics, or improved surveillance\u0026mdash;against the \u0026ldquo;no intervention\u0026rdquo; baseline. An ICER below one to three times GDP per capita (~\u0026thinsp;US$807\u0026ndash;2,421) per DALY averted would indicate that a preventive measure is highly cost-effective relative to the economic burden presented here. Thus, the observed cost per DALY (US$1,668) provides both a benchmark for evaluating intervention value and a quantitative reflection of Lassa fever\u0026rsquo;s substantial societal cost in Nigeria.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCombined Descriptive and Probabilistic Multivariate Sensitivity Analysis (PSA) Summary of the Economic and Health Burden of Lassa Fever (Simulation Baseline- Input parameter assumptions used in uncertainty analysis and generated by Monte-carlo simulation).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetric / Variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% LCI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% UCI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1st Quartile\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3rd Quartile\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e806.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e806.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e779.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e833.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e666.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e779.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e833.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e962.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiscount rate (r)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProductivity loss per death (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19,022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal productivity loss due to death (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,894,330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,854,641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,583,177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,185,201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,048,980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,583,177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,185,201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,070,641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProductivity loss per case (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCombined per-case cost (₦)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,801,076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,756,003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,110,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,640,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,841,051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,447,713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,131,404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,136,957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCombined per-case cost (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal economic cost (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,988,691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,949,002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,380,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,730,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,143,341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,677,537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,279,562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,165,002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears of Life Lost (YLL, discounted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal YLL (all deaths)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,438\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal YLD (Years Lived with Disability)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal DALYs (YLL\u0026thinsp;+\u0026thinsp;YLD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,483\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost per DALY (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,980\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eNotes: All monetary values are in 2024 U.S. dollars (USD) unless stated otherwise; NGN values converted using the IMF 2024 exchange rate of ₦1,486.57 / US$.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e-Estimates are derived from 10,000 Monte Carlo simulations of the probabilistic model incorporating uncertainty in costs, discount rate, and case outcomes.\u003c/p\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;95% LCI and UCI correspond to the 2.5th and 97.5th percentiles of simulated distributions.\u003c/p\u003e\n \u003cp\u003e- DALY\u0026thinsp;=\u0026thinsp;YLL\u0026thinsp;+\u0026thinsp;YLD (discounted at 3%), where YLL represents premature mortality and YLD represents morbidity burden.\u003c/p\u003e\n \u003cp\u003eThe descriptive statistics (Min, Quartiles, Max) summarize simulation outputs, while PSA metrics (Mean, 95% CI) provide policy-relevant uncertainty intervals\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\n \u003ch2\u003eScenario Analysis\u003c/h2\u003e\n \u003cp\u003eThe scenario analysis reveals that the economic burden of Lassa fever is overwhelmingly driven by mortality‑related productivity loss, with the baseline (214 deaths, full hospitalization) costing US $5,965\u0026nbsp;million and generating 3,616 DALYs at a cost‑per‑DALY of $1,668; reducing deaths to 120 cuts total costs by ~\u0026thinsp;US $1.26\u0026nbsp;billion (to US $4.70\u0026nbsp;million) and improves cost‑effectiveness dramatically to $1,520 per DALY, whereas a high‑mortality scenario (300 deaths) more than doubles the total cost to US $7.11\u0026nbsp;million and inflates the cost‑per‑DALY to $1,753. Partial hospitalization (60% admission) lowers health‑system expenditures (from ₦3.33\u0026nbsp;million to ₦2.00\u0026nbsp;million per case) and reduces the overall cost to US $3.65\u0026nbsp;million, achieving the most favorable cost‑per‑DALY of $1,168 despite a modest drop in deaths (129). Economic sensitivity to macro‑level inputs is evident: a lower GDP per capita (-30%) reduces productivity‑loss values, trimming total costs to US $5.37\u0026nbsp;million and raising the cost‑per‑DALY to $1,486, while a 20% increase in health‑system costs pushes total spending to US $6.70\u0026nbsp;million and worsens cost‑per‑DALY to $1,851. Finally, applying a lower discount rate (3% versus the base‑case) raises the present value of mortality losses, lifting total costs to US $6.36\u0026nbsp;million and the cost‑per‑DALY to $1,609. Across all scenarios, the per‑case out‑of‑pocket expense remains modest (₦48 k/US $34), confirming that policy levers targeting mortality reduction, hospital‑access expansion, and efficient health‑system financing deliver the greatest returns in terms of both total expenditure and cost‑effectiveness.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eScenario Analysis \u0026ndash; Economic Burden of Lassa Fever(Per-case \u0026amp; Total Economic Burden)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr style=\"height: 48px;\"\u003e\n \u003cth style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eScenario\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eDeaths\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003ePer-case HSC (₦)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003ePer-case PL morbidity (₦)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003ePer-case PL death (₦)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eTotal per-case (₦)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eTotal per-case (USD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eTotal cost (USD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eDALYs (YLL\u0026thinsp;+\u0026thinsp;YLD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eCost per DALY (USD)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,334,805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e130,791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,259,846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e6,773,966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e4,556.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e5,964,825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e1,668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eLower mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,334,805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e130,791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e1,828,814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e5,342,935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,593.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e4,701,112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e1,520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eHigher mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,334,805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e130,791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e4,567,280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e8,081,401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e5,436.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e7,110,113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e4,056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e1,753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 48px;\"\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003ePartial hospital (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e2,000,883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e130,791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e1,967,991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e4,148,190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e2,789.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,645,675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e1,168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eLower GDPpc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,334,805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e130,791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e2,607,877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e6,122,998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e4,115.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e5,373,591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e1,486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eHigher HSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e4,168,506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e130,791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,259,846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e7,607,668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e5,118.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e6,696,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e1,851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eLower discount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,334,805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e130,791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,712,377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e7,226,498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e4,860.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e6,362,585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e3,951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e1,609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr style=\"height: 13.9173px;\"\u003e\n \u003ctd style=\"height: 13.9173px;\" colspan=\"10\"\u003e\u003cstrong\u003eBaseline case\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;1309; \u003cstrong\u003eHospital rate\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;1.00; \u003cstrong\u003ePer-case OOP (₦)\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;48,525\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\n \u003ch2\u003eSpearman\u0026rsquo;s Rank Correlation Sensitivity key parameters\u003c/h2\u003e\n \u003cp\u003eThe Spearman‑rank analysis makes clear that the total economic burden of Lassa fever is driven almost entirely by productivity loss per case (PL_per_case_usd), which alone accounts for roughly half of the uncertainty (\u0026rho;\u0026thinsp;=\u0026thinsp;1.0, 51% contribution); consequently, policies that prevent deaths or shorten the period of incapacitation\u0026mdash;such as rapid case detection, effective treatment protocols, and vaccination\u0026mdash;will yield the greatest reductions in mean total cost. The discount rate (discount_r) and GDP per capita (gdp_pc_usd) are the only other parameters with substantive influence (\u0026rho; \u0026asymp; ‑0.70 and +\u0026thinsp;0.68, explaining about 25% and 24% of the variance respectively), reflecting the importance of how future productivity losses are valued and the underlying wage level; while these factors are largely exogenous, sensitivity to them underscores the need for consistent, transparent discounting practices in economic evaluations. All remaining cost components\u0026mdash;out‑of‑pocket expenses, health‑system costs, morbidity‑related productivity loss, and hospitalization rate\u0026mdash;have negligible correlations (|\u0026rho;| \u0026lt; 0.01) and contribute virtually nothing to uncertainty, indicating that fine‑tuning these elements will have minimal impact on the overall burden. In sum, to minimise both the mean and the variability of total cost, policymakers should concentrate resources on interventions that curb mortality and associated productivity loss, while ensuring that discounting assumptions are appropriately justified.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSpearman\u0026rsquo;s Rank Correlation Sensitivity Analysis of Input Parameters on Total Economic Burden of Lassa Fever\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\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\u003eSpearman\u0026rsquo;s \u0026rho; (Correlation with Total Cost)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u0026sup2; (Variance Explained)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% Contribution to Overall Uncertainty\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eImpact Strength on Total Cost\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProductivity loss per case (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e+\u0026thinsp;1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e51.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery strong positive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita (USD, proxy for productivity valuation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e+\u0026thinsp;0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e23.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrong positive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiscount rate (for DALYs and costs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026minus;0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e25.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrong negative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProductivity loss from morbidity (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e+\u0026thinsp;0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegligible\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth system cost per case (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e+\u0026thinsp;0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegligible\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOut-of-pocket cost per case (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026minus;0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegligible\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospitalization rate among confirmed cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026minus;0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegligible\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn comparing the results with the recent studys on the economic burden of Lassa Fever in Nigeria and West Africa, a number of important consistencies and divergences emerge. On one hand, this study\u0026rsquo;s estimate of the per‑case economic burden \u0026mdash; for example a mean overall cost of about US\u003cspan\u003e$\u003c/span\u003e2,442.43 (excluding death) and US\u003cspan\u003e$\u003c/span\u003e4,556.78 (death‑inclusive, discounted) per case \u0026mdash; resonates with findings from recent modelling and empirical work. Critically, the temporal annual burden estimates (cumulative US\u003cspan\u003e$\u003c/span\u003e13.7\u0026nbsp;million from 2019\u0026ndash;2024) highlight increasing cost with rising case counts (US\u003cspan\u003e$\u003c/span\u003e1.6 m in 2019 \u0026rarr; US\u003cspan\u003e$\u003c/span\u003e5.96 m in 2024). This illustrates the policy imperative of scaling interventions, a point echoed in the literature. For example, Moore (2024) outlines research priorities, emphasising preventive vaccine development and integrated One Health approaches. In another research study, Smith et al. (2024) modelled West‑Africa‑wide Lassa burden at US\u003cspan\u003e$\u003c/span\u003e1.1\u0026nbsp;billion in productivity losses and US\u003cspan\u003e$\u003c/span\u003e506\u0026nbsp;million in direct healthcare costs over ten years. Likewise, Eneh et al. (2025) emphasised the \u0026ldquo;substantial economic costs\u0026rdquo; beyond health sectors in Nigeria. Thus, your quantification of OOP, health‑system, and productivity losses aligns with broader characterisations of Lassa\u0026rsquo;s economic footprint. The modelling study by Smith et al. (2024) also shows that preventive vaccination campaigns avert billions in productivity loss.\u003c/p\u003e \u003cp\u003eYet there are also notable differences. In this study\u0026rsquo;s health system cost per case (~\u0026thinsp;US\u003cspan\u003e$\u003c/span\u003e2,320.68 mean) appears higher in proportional terms than some regional aggregates which emphasise productivity losses as dominant. For example, your sensitivity analysis shows\u0026thinsp;~\u0026thinsp;51% of variance coming from productivity loss per case, somewhat different to models where direct healthcare costs were large (Smith et al. 2024). Meanwhile, Eneh et al. (2025) highlighted that agriculture/trade disruption and economy‑wide impacts may extend far beyond the healthcare cost category.\u003c/p\u003e \u003cp\u003eThe breakdown, showing OOP (~\u0026thinsp;US\u003cspan\u003e$\u003c/span\u003e33.77 mean) far lower than system or productivity costs because most of the cost for diseases like Lassa fever and Ebola are taken over by the Health System to encourage immediate isolation. In some cases, the patients and family members would have suffered some indirect cost before getting to the treatment centres. This reinforces the pattern that indirect costs dominate, but it might under‑represent informal economic losses such as reduced labour participation or household coping strategies (such as asset sales, job loss and disability). This is consistent with qualitative findings that households incur catastrophic financial burdens despite \u0026ldquo;free treatment\u0026rdquo; claims. Thus your results support the call for investment in preparedness rather than only reactive care. However, one must contrast the methodological assumptions: the use of GDP‑per‑capita as proxy for daily productivity value (₦3,176.95\u0026thinsp;\u0026asymp;\u0026thinsp;US\u003cspan\u003e$\u003c/span\u003e2.21 per day) and PERT formula for cost‑means is transparent and plausible, but other studies have used alternate valuation ( such as value of statistical life, DALYs monetised). For example, Smith et al. (2024) monetised DALYs at US\u003cspan\u003e$\u003c/span\u003e288\u0026nbsp;million over ten years in West Africa.\u003c/p\u003e \u003cp\u003eIn this study, the per‑case death‑inclusive cost (~\u0026thinsp;US\u003cspan\u003e$\u003c/span\u003e5,524 undiscounted) falls somewhat below potential lifetime productivity loss estimates in highly fatal cases reported elsewhere. For instance, meta-analysis shows case fatality rates in Nigeria around ~\u0026thinsp;16% (vs Sierra Leone 48%) which may imply greater loss per death in other settings. Thus, this results are robust for your assumptions but may under‑state high‐fatality scenarios or long‐term disability costs (such as hearing loss sequelae) which Besson (2024) notes are often neglected.\u003c/p\u003e \u003cp\u003eThe Spearman sensitivity reveals that uncertainty in the estimated productivity loss per case\u0026mdash;closely tied to mortality rates and GDP per capita\u0026mdash;drives nearly all variability in total costs. These findings highlight the importance of accurate mortality data and income-based valuation in Lassa fever burden models. Conversely, variation in health-system or household-level costs contributes little to overall uncertainty, consistent with previous analyses of high-mortality diseases such as Ebola and Lassa fever (Asogun et al., 2019; Ilori et al.,, 2021). This findings offer strong empirical support to the emerging consensus that Lassa fever imposes substantial hidden costs- particularly productivity losses- and reinforce the urgency of policy responses (health‑financing protection, surveillance strengthening, vaccine readiness). At the same time, compared with recent research it is worth considering two refinements: (1) expand modelling of long‑term sequelae and quality‑of‑life losses beyond acute productivity days; and (2) explore heterogeneity in cost per case across states, fatality levels and care modalities to capture variation seen in other West African settings.\u003c/p\u003e\n\u003ch3\u003ePolicy Implications\u003c/h3\u003e\n\u003cp\u003eThe economic analysis of Lassa fever demonstrates a substantial financial burden on both households and the health system, with per-case costs exceeding US\u003cspan\u003e$\u003c/span\u003e2,400 and total national costs approaching US\u003cspan\u003e$\u003c/span\u003e6\u0026nbsp;million in 2024 alone. Out-of-pocket expenses, though smaller than health system costs, still impose significant hardship on affected families, while productivity losses from morbidity and premature death amplify the societal impact. The policy implication is that targeted investments in preventive measures\u0026mdash;such as early detection, vaccination, public health education, and strengthened treatment infrastructure\u0026mdash;would not only reduce morbidity and mortality but also yield substantial economic savings, justifying prioritization of Lassa fever control in national health planning and budgeting.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eComparison with Other Infectious Disease Burdens\u003c/h2\u003e \u003cp\u003eRelative to cholera and meningitis cost-of-illness estimates in Nigeria (ranging US \u003cspan\u003e$\u003c/span\u003e500\u0026ndash;1,200 per case), the Lassa fever burden is disproportionately higher\u0026mdash;mainly due to its prolonged clinical course and need for specialized isolation (Adetunde \u0026amp; Olalubi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Muhammad et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This emphasizes that hemorrhagic fevers, although lower in incidence, carry \u0026ldquo;high-cost, low-frequency\u0026rdquo; economic characteristics that justify substantial preventive investment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eStudy Strengths and Limitations\u003c/h2\u003e \u003cp\u003eThe study\u0026rsquo;s strength lies in its integrated cost framework, incorporating household, system, and productivity perspectives across six years. However, the analysis excludes intangible costs such as social stigma, long-term sequelae, and caregiver psychological stress\u0026mdash;factors which, if monetized, would further amplify total burden estimates. Additionally, the underreporting of cases may bias aggregate estimates downward, though sensitivity analysis using upper uncertainty bounds mitigates this concern.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe economic burden of Lassa fever in Nigeria is both severe and preventable. A mean per-patient cost exceeding ₦6.8\u0026nbsp;million (\u003cspan\u003e$\u003c/span\u003e4,556.78), driven primarily by public health system costs, represents a critical inefficiency that demands both preventive and fiscal reform. Beyond emergency response, strategic investment in surveillance, health insurance, and community risk protection will yield exponential returns in both economic and epidemiological terms. Lassa fever thus serves as a sentinel disease for evaluating Nigeria\u0026rsquo;s epidemic preparedness and the resilience of its health financing architecture.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests related to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from National Health Research Ethics Committee of Nigeria (NHREC) NHREC/01/01/2007-21/05/2025).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll procedures involving human participants were conducted in accordance with the ethical standards of the institutional and national research committees and with the 1964 Helsinki Declaration and its later amendments. Informed consent was obtained from all individual participants included in the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study\u0026nbsp;and all data were anonymised prior to analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent for publication is not applicable. \u0026nbsp;All data were anonymised prior to analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external financial support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAll authors read and approved the final manuscript.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the library staff at Nasarawa State University and the Nigeria Centre for Disease Control for facilitating access to \u0026nbsp;website, weekly situation report, database and journals. We also appreciate the constructive comments from anonymous peer reviewers that helped improve this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdetunde OT, Olalubi OA. Re-emerging Lassa fever outbreaks in Nigeria: Re-enforcing One Health community surveillance and emergency response practice. Infect Dis Poverty. 2018;7:37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40249-018-0421-8\u003c/span\u003e\u003cspan address=\"10.1186/s40249-018-0421-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eICIR. (2023). How underfunding, lack of preparedness impact surge in Lassa fever. International Centre for Investigative Reporting (ICIR). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.icirnigeria.org/how-underfunding-lack-of-preparedness-impact-surge-in-lassa-fever/\u003c/span\u003e\u003cspan address=\"https://www.icirnigeria.org/how-underfunding-lack-of-preparedness-impact-surge-in-lassa-fever/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuhammad IB, Abdullahi I, Tahiru AG, Musa A. Economic Impact of Lassa Fever on Biological, Environmental and Hygienic Factors in Bauchi State. Int J Sci Res Math Stat Sci. 2024;11(2):47\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.isroset.org/pub_paper/IJSRMSS/7-ISROSET-IJSRMSS-09513.pdf\u003c/span\u003e\u003cspan address=\"https://www.isroset.org/pub_paper/IJSRMSS/7-ISROSET-IJSRMSS-09513.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyinoh IW, Utume LN, Bob-Echikwonye O. A review of Lassa fever cases in Nigeria for the year 2020. Int J Community Med Public Health. 2021;8(5):2572\u0026ndash;5. (Note: full text appears on ijcmph.com) \u0026mdash; [Link unavailable via web search, please check your institutional access or journal site].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsakwe F. (2018). Lassa fever: Prohibitive cost of treatment engendering high mortality. The Guardian (Nigeria). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://guardian.ng/news/lassa-fever-prohibitive-cost-of-treatment-engendering-high-mortality/\u003c/span\u003e\u003cspan address=\"https://guardian.ng/news/lassa-fever-prohibitive-cost-of-treatment-engendering-high-mortality/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObiejisi K. (2018). Despite FG\u0026rsquo;s claims, Lassa Fever patients recount high treatment costs. ICIR Nigeria. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.icirnigeria.org/despite-fgs-claims-lassa-fever-patients-recount-high-treatment-costs/\u003c/span\u003e\u003cspan address=\"https://www.icirnigeria.org/despite-fgs-claims-lassa-fever-patients-recount-high-treatment-costs/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeckert J et al. (2022). Health and economic impacts of Lassa vaccination campaigns in West Africa. PLOS / PMC [Modeling study]. (I was not able to locate a *publicly available full text with a stable link in my search \u0026mdash; please verify in PLoS or PMC repositories for the correct DOI / URL.).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScience Nigeria. (2025). Lassa Fever Outbreak Exposes IPC Gaps, As NCDC Reveals Healthcare Costs. Science Nigeria. (Note: I did not find a stable article with that exact title in major archives; please verify the Science Nigeria website.).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoldetsadik MD, Lugala PC, Wondimagegnehu A, Ihekweazu C. (2019). Epidemiologic and Clinical Features of Lassa Fever Outbreak in Nigeria, January 1\u0026ndash;May 6, 2018. Emerg Infect Dis, 25(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwnc.cdc.gov/eid/article/25/6/18-1035_article\u003c/span\u003e\u003cspan address=\"https://wwwnc.cdc.gov/eid/article/25/6/18-1035_article\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIncrease in Lassa Fever Cases in Nigeria. January\u0026ndash;March 2018 Emerg Infect Dis, 25(5), May 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwnc.cdc.gov/eid/article/25/5/18-1247_article\u003c/span\u003e\u003cspan address=\"https://wwwnc.cdc.gov/eid/article/25/5/18-1247_article\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. (2018, April 20). Lassa Fever \u0026ndash; Nigeria. World Health Organization. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/emergencies/disease-outbreak-news/item/20-april-2018-lassa-fever-nigeria-en\u003c/span\u003e\u003cspan address=\"https://www.who.int/emergencies/disease-outbreak-news/item/20-april-2018-lassa-fever-nigeria-en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO African Region. (2018, March 26). WHO: Nigeria\u0026rsquo;s Lassa fever outbreak is slowing, but remains a concern. WHO Regional Office for Africa. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.afro.who.int/news/who-nigerias-lassa-fever-outbreak-is-slowing-remains-concern\u003c/span\u003e\u003cspan address=\"https://www.afro.who.int/news/who-nigerias-lassa-fever-outbreak-is-slowing-remains-concern\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlayinka JO et al. (2023). The resurgence of Lassa fever in Nigeria: economic impact, challenges, and strategic public health interventions. [Journal / article from PubMed]. PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/40740381/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/40740381/\" targettype=\"URL\" 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":false,"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":"discover-health-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dihs","sideBox":"Learn more about [Discover Health Systems](https://www.springer.com/44250)","snPcode":"44250","submissionUrl":"https://submission.nature.com/new-submission/44250/3","title":"Discover Health Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lassa fever, cost-of-illness, economic burden, productivity loss, out-of-pocket expenditure, health system cost, Nigeria.","lastPublishedDoi":"10.21203/rs.3.rs-9093577/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9093577/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLassa fever remains one of the most persistent zoonotic and epidemic-prone diseases in West Africa, causing recurrent fiscal and health shocks to Nigeria\u0026rsquo;s public health system. Despite ongoing control efforts, the disease imposes substantial direct and indirect economic costs on households and government institutions. This study quantified the economic burden of Lassa fever in Nigeria, focusing on out-of-pocket expenditure (OOP), health system cost (HSC), and productivity loss (PL) from illness between 2019 and 2024.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo estimate the mean cost per patient and the aggregate national economic burden of Lassa fever, while identifying major cost drivers and policy gaps in financial protection.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA quantitative multi‑method approach was used, combining primary survey data with secondary macro‑economic analysis. An expert‑validated questionnaire (n\u0026thinsp;=\u0026thinsp;50) was administered. The instrument captured out‑of‑pocket (OOP) expenses, public‑health system expenditures, and productivity losses attributable to illness, enabling calculation of the Cost‑of‑Illness (COI). Furthermore, annual foreign‑exchange rates, purchasing‑power parity (PPP) figures, and dollar index values were obtained from the International Monetary Fund (IMF) and the Central Bank of Nigeria (CBN). These macro‑economic indicators were applied to convert COI estimates into Nigerian Naira (₦) and United States Dollars (US\u003cspan\u003e$\u003c/span\u003e). All analyses were performed in R. Descriptive statistics summarized the primary data, while probabilistic sensitivity analysis, Monte Carlo simulation, and scenario modeling examined the impact of exchange‑rate fluctuations driven in part by U.S. Federal Reserve monetary policy\u0026mdash;on the COI.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study quantified the economic burden of Lassa Fever in Nigeria (2019\u0026ndash;2024), encompassing out-of-pocket (OOP) costs, health system costs (HSC), and productivity losses. Mean per-case costs were ₦48,525 (US\u003cspan\u003e$\u003c/span\u003e33.77) for OOP, ₦3,334,806 (US\u003cspan\u003e$\u003c/span\u003e2,320.68) for HSC, and ₦130,791 (US\u003cspan\u003e$\u003c/span\u003e87.98) for morbidity-related productivity loss. Including mortality, per-case loss reached ₦3,259,846 (US\u003cspan\u003e$\u003c/span\u003e2,192.86, discounted). Annual total costs rose from US\u003cspan\u003e$\u003c/span\u003e1.6\u0026nbsp;million in 2019 to US\u003cspan\u003e$\u003c/span\u003e5.96\u0026nbsp;million in 2024, cumulating US\u003cspan\u003e$\u003c/span\u003e13.7\u0026nbsp;million. Sensitivity analysis highlighted productivity loss per case as the main driver. Findings underscore substantial fiscal impact, fragile financial protection, and urgent need for strengthened interventions and health system preparedness.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eLassa fever imposes a substantial and preventable economic burden on Nigeria\u0026rsquo;s health system and households. Strengthening insurance coverage is critical to mitigating future financial shocks.\u003c/p\u003e","manuscriptTitle":"Assessing the economic burden and financial protection gaps of Lassa fever in Nigeria using a multicomponent cost of illness analysis 2019 to 2024","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 10:23:59","doi":"10.21203/rs.3.rs-9093577/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-06T09:12:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216926889102001184421859808728929853486","date":"2026-04-27T01:10:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-23T06:35:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-07T11:36:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-04T12:35:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-03T19:05:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Health Systems","date":"2026-04-03T19:00:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-health-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dihs","sideBox":"Learn more about [Discover Health Systems](https://www.springer.com/44250)","snPcode":"44250","submissionUrl":"https://submission.nature.com/new-submission/44250/3","title":"Discover Health Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"def69cdf-c6b9-4574-aaa6-94e5a5d5f6d8","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-06T09:12:40+00:00","index":44,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T10:24:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 10:23:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9093577","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9093577","identity":"rs-9093577","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}