Cost and Return on Investment of the Mandla Malaria Elimination Demonstration Project: Health and Economic Modelling for the National Malaria Elimination Goal

Cost and Return on Investment of the Mandla Malaria Elimination Demonstration Project: Health and Economic Modelling for the National Malaria Elimination Goal | 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 Cost and Return on Investment of the Mandla Malaria Elimination Demonstration Project: Health and Economic Modelling for the National Malaria Elimination Goal Mrigendra P Singh, Harsh Rajvanshi, Praveen K Bharti, Himanshu Jayswar, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8778637/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background: Malaria elimination is among the most cost-effective public health strategies, with strong evidence for economic and societal benefits. India has achieved an ~80% reduction in malaria cases between 2015 and 2023. At this time, 37 districts continue to report an Annual Parasite Incidence (API) greater than one and remain high-priority for intensified interventions. Subnational analyses are needed to guide resource allocation and sustain progress. Methods : The Malaria Elimination Demonstration Project (MEDP) was implemented in Mandla, a high-endemic tribal district in Madhya Pradesh, through a public–private partnership. Intensified surveillance, case management, vector control, and community engagement were deployed. A difference-in-difference regression model using data from 2001 to 2023 compared Mandla with four control districts. An economic evaluation used a cost-of-illness framework to estimate avoided household costs, productivity losses, the value of lives saved, personal protection expenditures, and reduced health system costs. Sensitivity analyses were conducted using Monte Carlo simulations. Results: MEDP reduced malaria incidence to near zero, averting ~998 cases annually. The intervention prevented household and health system costs totalling INR 73.6 million (US$0.87 million) annually, with a cost–benefit ratio of 1.64 and an ROI of 64%. Benefits extend beyond health, with tourism in the Kanha Tiger Reserve increasing faster than the national average following the MEDP. Conclusion: Mandla’s experience demonstrates that elimination is feasible and economically advantageous. Scaling this model to India’s high-priority districts with an API greater than one could accelerate national elimination goals while delivering broad developmental benefits. The project was supported through a public–private partnership involving the Indian Council of Medical Research, the Government of Madhya Pradesh, and the Foundation for Disease Elimination and Control of India. MEDP Malaria Elimination ROI Investment case Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Malaria elimination is widely regarded as one of the most cost-effective public health strategies, delivering significant returns on investment. Malaria slows development by undermining education, tourism, agriculture, and industry, and disproportionately affects poor and remote communities, reinforcing cycles of poverty (1). In 2024, an estimated 282 million malaria cases occurred worldwide. The Asia-Pacific region, including countries from the WHO South-East Asia, Western Pacific, and Eastern Mediterranean regions, accounted for approximately 7.8 million cases, with Pakistan alone contributing over 2 million. The WHO South-East Asia Region, which includes India, reported 2.7 million cases. This highlights the substantial and concentrated malaria burden that continues to challenge regional elimination goals (2). Regional analyses highlight the potential economic gains of investing in malaria elimination. A 2019 study across 22 Asia‑Pacific countries estimated that malaria elimination between 2017 and 2030 would generate US $ 87.73 billion in benefits, avert 123 million cases, and save 386,167 lives, yielding a US $ 6 return for every US $ 1 invested (3). The WHO Global Technical Strategy for Malaria 2016–2030, Asia Pacific Leaders Malaria Elimination Roadmap, and Action and Investment to defeat Malaria 2016–2030, reinforce elimination as a high-value investment, projecting that accelerated action could avert billions of cases and generate trillions of dollars in economic impact (4–6). Experiences from Sri Lanka and China demonstrate that sustained investments can deliver both health security and economic growth; however, forested and cross-border regions remain vulnerable to resurgence (7,8). India has achieved substantial progress towards malaria elimination goals. Between 2015 and 2023, malaria cases declined by approximately 80% (from ~ 1.17 million to ~ 227,000), and deaths decreased from 384 to 83 (9). By 2023, 122 districts reported zero indigenous cases, and several states advanced to lower transmission categories (9,10). Surveillance has strengthened, as reflected in improved Annual Blood Examination Rates (ABER) (9). Out of 780 (approx.) districts in India, around 37 districts report an API of more than one, which are spread across 8 to 9 states and are considered high-priority districts (11). At present, sustaining gains in tribal, high-burden, and hard-to-reach areas remains challenging. Subnational economic evaluations are limited, leaving policymakers without evidence on the cost‑effectiveness of district‑level elimination models. As external support declines and domestic financing becomes central, localized analyses are essential to guide India’s elimination strategy. Against this backdrop, the Malaria Elimination Demonstration Project (MEDP) in Mandla district, Madhya Pradesh, was undertaken to assess the feasibility of eliminating indigenous malaria transmission using existing case management and vector control tools (12–14). This study evaluates MEDP’s impact to estimate the cost–benefit ratio (CBR) and return on investment (ROI). By situating Mandla’s experience within national frameworks, the goal of malaria elimination in India is within reach, and the investment yields measurable returns. MATERIALS AND METHODS Study Design and Setting The MEDP was implemented in a high-endemic, tribal dominated, hilly-forested district, Mandla, Madhya Pradesh, through a Public–Private Partnership (PPP) between the Indian Council of Medical Research (ICMR), the Government of Madhya Pradesh (GoMP), and the Foundation for Disease Elimination and Control of India (FDEC India). Covering a population of 1.2 million, the project aimed to eliminate indigenous malaria through intensified surveillance, case management, vector control, community engagement, and capacity building. Implementation adhered to national guidelines while introducing innovations in monitoring, evaluation, and data reporting (14). Epidemiological Analysis Mandla served as the MEDP intervention district, while three bordering districts (Balaghat, Dindori, and Kawardha) and one ecologically comparable district (Gadchiroli) were selected as controls. These districts were broadly homogeneous in terms of demography, terrain, geoclimatic conditions, and malaria endemicity (Table 1). Routine anti-malaria activities implemented by the national programme were identical across all selected districts. The pre-intervention period was defined as 2001–2016, and the intervention and post-intervention periods as 2017–2023 (with sub-centre-level data available only until 2023). Temporal trend of malaria cases in the control and MEDP (intervention) group during pre- and post-intervention periods was analysed using the Poisson Joinpoint regression model. To assess the impact of MEDP, a difference-in-difference (DiD) regression model was applied to interrupted time-series data from 2001 to 2023. The model estimated malaria cases under a counterfactual scenario in the absence of MEDP. The regression equation was specified as: $$\:{Y}_{it}={\beta\:}_{0}+{\beta\:}_{1}{Post}_{t}+{\beta\:}_{2}{Treatment}_{i}+{\beta\:}_{3}\left({Post}_{t}\:x\:{Treatment}_{i}\right)+{e}_{it}\:$$ Where, \(\:{Y}_{it}\) is malaria incidence in district i at time t ;\(\:\:{Post}_{t}\) is a binary dummy variable equal to 1 for the post-treatment period, zero for pre-treatment; \(\:{Treatment}_{i}\) is a binary dummy variable equal to 1 if the unit is in the treatment group (MEDP), zero otherwise (Control); \(\:{Post}_{t}\:x\:{Treatment}_{i}\) is the interaction term (DiD estimator\(\:);\:{\beta\:}_{3}\) is the DiD estimator — the estimate of the treatment effect\(\:;\:{e}_{it}\) is the error term. Economic analysis The economic impact of MEDP was estimated using the cost-of-illness (COI) method to quantify savings from reduced malaria burden in the district. This included direct treatment costs (medical and non-medical), productivity losses from lost workdays, the economic value of lives lost to malaria-attributable premature deaths, and expenditures on personal protection (such as mosquito repellents, window mesh screens, bed nets, smoke, etc.) incurred by the at-risk population. All costs were calculated in Indian Rupees and converted to US dollars using a constant exchange rate of INR 85 per US$. Cost of Illness (COI) Framework The Total Economic Burden of malaria can be expressed as Whe re each component represents: DC (Direct Cost) as the out-of-pocket expenses for treatment, including both medical (consultations, medicines, diagnostics, hospitalization) and non-medical (transport, food, lodging) costs; IDC (Indirect Cost) as the income lost due to workdays missed by patients and/or caregivers. This includes waiting time to obtain initial treatment, travel time to health facilities, time spent with healthcare providers, and the number of days of full or partial productivity loss; VLL (Value of Lives Lost) as the economic value of premature deaths attributable to malaria; OCE (Other Contingent Expenditure) as the household spending on disease prevention, such as personal protection measures to avoid mosquito bites; and ITC (Intangible Cost) as the non-monetary burden of pain, grief, suffering, loss of leisure time, and broader societal impacts such as lost opportunities for development, trade, and tourism. Although critical, these costs are difficult to quantify in monetary terms (15,16). Assumptions Considered The economic evaluation was based on several key assumptions. Direct medical treatment costs, non-medical expenses, and earnings foregone were derived from the most recent round of the National Sample Survey (17), stratified by treatment source, and then adjusted to 2023 values using the GDP deflator from the official GDP series (18). The VLL for the intervention and post-intervention periods (2017–2023) was estimated using a three-year moving average applied to pre-intervention mortality data (2001–2016). This counterfactual was then monetized as the product of potential productive years of life lost (age-stratified) (19) and per capita annual income (18). Household expenditure on personal protection measures, such as mosquito repellents, insecticides, smoke, and window mesh screens, was estimated assuming that approximately one-third of the district population resided in high-risk malaria areas and was willing to invest in some form of preventive measure (20). Finally, reductions in direct public health system costs for case management and vector control were incorporated, reflecting the district's transition to zero indigenous malaria cases. Cost-Benefit and ROI Estimation The Cost–Benefit Ratio (CBR) was calculated as the ratio of annualised economic and health system savings to the additional annual investment under MEDP, while the return on investment (ROI) was computed as [(Savings – Cost)/Cost] × 100. Sensitivity analysis To account for uncertainty, a probabilistic sensitivity analysis (PSA) was performed using Monte Carlo simulations with 10,000 iterations. Triangular distributions were assigned to key variables, including treatment costs, productivity losses, and mortality assumptions, to reflect variability in epidemiological and economic parameters. RESULTS Across the pre-intervention period, both the Control and MEDP groups initially exhibited substantial declines in malaria burden, followed by marked reversals. In the Control group, malaria cases decreased sharply before the 2008 breakpoint (Annual Percent Change (APC) = −11.5%; 95% CI: −11.6 to −11.4), but the trend shifted thereafter to a statistically significant annual increase of 12.6% (95% CI: 12.4 to 12.7), yielding a small but significant overall rise in cases across the whole period (Average Annual Percent Change (AAPC) = 1.1%; 95% CI: 1.0 to 1.2). A similar pattern was observed in the MEDP group before the intervention, with a significant annual decline through 2012 (APC = −9.1%; 95% CI: −9.4 to −8.9), followed by an annual increase of 11% (95% CI: 10 to 12). However, due to the more decisive influence of the earlier downward trend, the overall pre-intervention trajectory in MEDP remained significantly negative (AAPC = −6.1%; 95% CI: −6.2 to −5.9). In contrast, the post-intervention period showed divergent patterns between the two study arms. In the Control group, malaria cases declined sharply through 2019 (APC = −26.5%; 95% CI: −27.4 to −25.5), but this improvement was not sustained, as cases increased sharply thereafter (APC = 19.0%; 95% CI: 18.3 to 19.8). Consequently, the overall post-intervention trend in the Control group represented a significant net increase in malaria burden (AAPC = 3.7%; 95% CI: 3.3 to 4.1). In contrast, the MEDP group showed a consistently substantial, statistically significant decline throughout the post-intervention period. Malaria cases decreased by 37.5% per year (95% CI: −40.8 to −34.2) up to the 2020 breakpoint and continued to decline even more rapidly thereafter (APC = −45.1%; 95% CI: −51.0 to −38.6). This resulted in a large and highly significant overall reduction in malaria burden during the post-intervention period (AAPC = −39.7%; 95% CI: −41.8 to −37.6). Collectively, these findings indicate that while both groups experienced temporary improvements, the MEDP intervention was associated with sustained, substantially greater declines in malaria cases during the post-intervention period than in the Control group (Fig. 1). The DiD model predicted that, in the absence of MEDP, Mandla would have reported an average of 998 malaria cases annually during the post-intervention period (2017–2023) (Fig. 2; Table 2A). Following implementation of the elimination plan in the district, malaria incidence reduced to nearly zero, indicating that MEDP reduced malaria cases by 998 per year at an additional cost of INR 37 per capita annually (US$ 0.44) during 2017–2020 (Table 2B), with these gains sustained over the subsequent three years (2021–2023) (Fig 3). This epidemiological impact translated into significant economic benefits. The annual household-level total cost of illness (direct and indirect costs) avoided due to MEDP was estimated at INR 1.55 million (US$0.018 million) (Table 3A). At the same time, the value of lives saved, based on the assumption of three deaths averted stratified by age and YPPLL and per capita Net National Income (NNI) at current price for the year 2023 of INR 172276, amounted to INR 12.09 million (US$0.14 million) annually (Table 3B). Additionally, personal protection costs avoided, assuming one third of the 1.2 million population at high risk of malaria and willingness-to-pay of US$0.58 per person (Mills et al., 2008) adjusted with a 2.82 inflation rate at current price for the year 2023, totalled INR 55.05 million (US$0.65 million) annually (Table 3C). At the health system level, savings were estimated using the National Strategic Plan (NSP) 2023–2027 budget projections, which indicated a 41% reduction in intervention costs between 2023 and 2027 as districts transitioned to the elimination phase. The per capita cost to achieve the malaria elimination goal was estimated at INR 10.05 in the year 2023 (Table 4) (21). Applying this proportion to Mandla, where the estimated annual health system expenditure for malaria elimination was INR 12.06 million (approximately US$0.14 million), the projected annual savings were INR 4.94 million (approximately US$0.06 million). Overall, the combined economic savings - including reductions in household costs of illness, value of lives saved, personal protection costs, and health system expenditures - amounted to INR 73.63 million (US$0.87 million) annually, compared with an average annual investment in MEDP of INR 45 million (US$0.53 million) during 2017–2020. This yielded a cost–benefit ratio of 1.64, indicating that every rupee invested in MEDP generated INR 1.64 in economic savings. The annual ROI was 64%, and probabilistic sensitivity analysis confirmed the robustness of this estimate (mean ROI: 64%, 95% CI: 42–88%). Beyond quantifiable economic returns, the intervention would have yielded intangible benefits, including reduced school absenteeism, thereby positively impacting human resource development, trade stability, and tourism. Notably, Kanha Tiger Reserve - a key tourist attraction in Mandla, recorded a rise in tourist arrivals from an annual average of 0.15 million in 2010–2015 to 0.18 million in 2021–2024, reflecting a 20.6% yearly growth rate in the post-MEDP and post-COVID period, substantially higher than the national tourism growth rate of 7.1% (22) With tourism contributing 1.77% to India’s GDP and 12.57% to employment, the sustained malaria-free status of Mandla has likely supported accelerated regional economic development. DISCUSSION Analyses of temporal trends in malaria burden showed clear differences between the intervention (MEDP) and control districts across the pre- and post-intervention periods. In the control districts, malaria incidence remained relatively stable, with a modest APC during both periods (pre and post) and no evidence of a significant inflection in trend. In contrast, the MEDP district demonstrated a pronounced decline in malaria cases following implementation of the intervention. While the pre-intervention trend showed little year-to-year change, the post-intervention period was characterized by a substantially steeper downward trajectory. The Poisson Joinpoint regression model confirmed this pattern, identifying a slope shift consistent with accelerated reductions following the initiation of MEDP interventions. The estimated AAPC in the MEDP district was substantially larger than in the control district, indicating that the intervention was associated with a significantly faster decline in malaria incidence over time. These findings support the effectiveness of MEDP interventions in intensifying malaria reduction beyond background trends observed in the non-intervention comparison districts. The findings from Mandla district demonstrate that malaria elimination is both epidemiologically achievable and economically advantageous when interventions are implemented rigorously, with adequate resources, through systematic collaboration among the government, research institutions, civil society partners, and management and technical controls, and with independent reviews. MEDP achieved a rapid and sustained decline to zero indigenous cases in less than 4 years, enabled by adherence to national strategies and innovations, including enhanced monitoring, electronic reporting, and structured stakeholder engagement. The DiD analysis confirmed that MEDP prevented nearly 1,000 cases annually at a modest per capita cost. These epidemiological gains translated into economic benefits across multiple domains, including reduced treatment expenditures, avoided productivity losses, fewer premature deaths, and lower household spending on personal protection. Health system savings further underscored the value of shifting from control to elimination phases. A cost–benefit ratio of 1.64 and an ROI of 64% indicate that elimination yields strong financial returns on additional investment. Comparable analyses across Asia-Pacific reinforce these findings. Thailand’s comprehensive cost–benefit study demonstrated returns of US $ 2–15 per US $ 1 invested (23). In the Philippines, a 2017 analysis estimated a 13:1 ROI, likely higher because the study considered indirect benefits such as educational outcomes and tourism (24). Sri Lanka offers a cautionary perspective, suggesting that resurgence would cost 13 times as much as prevention (25). These data highlight both the dividends of elimination and the steep costs of inaction. Global evidence echoes this economic rationale. The Lancet Commission on Malaria Eradication (2019) argued that the Asia-Pacific is the proving ground for eradication, with success here essential to achieving global goals. Eliminating malaria permanently reduces the need for intensive, costly surveillance systems, yielding lasting economic dividends (26). Other reports, including the WHO’s World Malaria Reports and regional investment case reports, consistently reaffirm elimination as one of the most compelling global health investments (2,27). Mandla’s experience also highlights broader development spillovers. The growth of tourism around Kanha Tiger Reserve illustrates how malaria-free status can enhance destination appeal, stimulate local economies, and support employment. Although causality is multifactorial, malaria elimination likely contributed to creating a safer and more attractive environment. The 37 high-priority districts identified by the National Programme, spread across nine states, constitute a combined population of 33.5 million. Despite comprising only 2.36% of the national population, these districts account for 70% of malaria cases (9). Therefore, intensified surveillance, case management, and vector control efforts in this smaller population, with requisite funding, have become an urgent priority towards eliminating malaria. At a programmatic level, five lessons stand out: (i) combining national guidelines with innovation in delivery and accountability accelerates progress; (ii) integration of surveillance, vector control, and community engagement under strong political and administrative oversight ensures sustainability; (iii) structured PPPs can effectively leverage private resources and technical expertise within a public health framework, (iv) utilisation of time-tested operational, management, and technical controls throughout the project activities, and (v) the returns on investment (ROI) for optimal funding to eliminate malaria would be significant. Looking ahead, sustaining the gains in elimination will require appropriate financing with built-in accountability frameworks, stronger domestic resource mobilisation, and streamlined partnerships. Without such measures, hard-won gains risk reversal. At the same time, the lessons learned can also be utilised to eliminate other vector-borne diseases. In summary, the evidence from Mandla, reinforced by regional and global analyses, presents a compelling case: malaria elimination yields extraordinary health and economic benefits, protects the most vulnerable populations, and catalyses sustainable development. Yet realizing these benefits requires not just technical excellence but also durable financing and governance structures capable of safeguarding progress against emerging threats. CONCLUSION The AAPC estimates indicate a sustained impact of MEDP interventions relative to control districts. These findings demonstrate that with adequate investment, rigorous implementation, and multisectoral collaboration, malaria elimination is achievable within a relatively short timeframe and can generate substantial epidemiological, economic, and societal benefits. By preventing nearly 1,000 cases annually, saving households and the health system close to INR 74 million (USD 0.87 million) each year, and yielding a cost–benefit ratio of 1.64 with a return on investment exceeding 60%, MEDP not only validated the technical feasibility of malaria elimination but also demonstrated its economic rationale. Importantly, the findings have direct implications for the 37 high-priority districts identified by the Government of India, which have an API greater than one. These districts, which continue to bear a disproportionate share of the country’s malaria burden, can particularly benefit from Mandla’s model of integrated surveillance, strong community engagement, and evidence-based vector control. Scaling and adapting the Mandla approach in such high-risk settings could accelerate progress toward national malaria elimination targets while simultaneously reducing health system costs and generating economic returns for households and local economies. The lessons from Mandla underscore that malaria elimination should be regarded not solely as a health objective but as a strategic investment in national development, capable of delivering long-term dividends for health systems, households, and the broader economy. Abbreviations AAPC – Average Annual Percent Change ABER – Annual Blood Examination Rate ACT – Artemisinin-based Combination Therapy API – Annual Parasite Incidence APC – Annual Percent Change APLMA – Asia Pacific Leaders Malaria Alliance BSTS – Bayesian Structural Time Series CBR – Cost–Benefit Ratio CI – Confidence Interval COI – Cost of Illness COVID – Coronavirus Disease DC – Direct Cost DiD – Difference-in-Difference FDEC – Foundation for Disease Elimination and Control GDP – Gross Domestic Product GIS – Geographic Information System GoMP – Government of Madhya Pradesh ICMR – Indian Council of Medical Research IDC – Indirect Cost INR – Indian Rupee ITC – Intangible Cost LLIN – Long-Lasting Insecticidal Net MEDP – Malaria Elimination Demonstration Project MoSPI – Ministry of Statistics and Programme Implementation NNI – Net National Income NGO – Non-Governmental Organization NSP – National Strategic Plan NSS – National Sample Survey NSSO – National Sample Survey Office OCE – Other Contingent Expenditure PPP – Public–Private Partnership PSA – Probabilistic Sensitivity Analysis ROI – Return on Investment SOP – Standard Operating Procedure USD – United States Dollar VLL – Value of Lives Lost WHO – World Health Organization XGBoost – Extreme Gradient Boosting YPPLL – Years of Potential Productive Life Lost Declarations Ethics approval and consent to participate: Not applicable Consent for publication: All authors have approved and consented the manuscript for publication. Availability of data and materials: All data and materials available upon request from the corresponding author. Competing interests: None Funding: Study funded by the Foundation for Disease Elimination and Control of India Authors' contributions: MPS and HR contributed equally and share first authorship. Both were responsible for Conceptualisation, Methodology, Formal analysis, Data curation, Validation, Software, Visualisation, and Writing – original draft; PKB: Investigation, Data curation, Validation, Writing – review & editing; HJ: Writing – review & editing; AK: Writing – review & editing; NKG: Writing – review & editing; YKG: Writing – review & editing; SKG: Investigation, Resources, Writing – review & editing; AAL: Conceptualisation, Supervision, Funding acquisition, Project administration, Writing – review & editing. References Mezieobi KC, Alum EU, Ugwu OPC, Uti DE, Alum BN, Egba SI, et al. Economic burden of malaria on developing countries: a mini review. 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The Lancet Commission on malaria eradication. The Lancet. 2018;391(10130):1556–8. An Investment Case for Eliminating Malaria in the Asia Pacific Region [Internet]. The Global Health Group, University of California, San Francisco; Available from: https://shrinkingthemalariamap.org/sites/default/fi Tables Table 1 – Background characteristics of the control and intervention districts Variable Control (average) Intervention Population (census 2011) (000) 1075 1055 Schedule Tribe Population (%) 37 58 Literacy (%) 59 57 Average Family Size 4.3 4.2 Average Annual Per Capita Income (000)* 57 45 Average Rainfall (mm) 1383 1425 Average Temperature 26.84 24.73 Forest Cover (% of Total Geographic Area)** 49.57 48.88 Source: * http://data.icrisat.org/dld/src/gdp.html ** https://www.data.gov.in/catalog/district-wise-forest-cover?page=1 Table 2A – Diff-in-diff model statistics of pre-intervention and post-intervention periods across MEDP district vs. control districts. Group Pre-Intervention (2008-2016) Post-Intervention (2017-2023) Difference MEDP district 2303.64 192.85 2110.79 Control districts 6322.70 3214.37 3108.33 Difference 4019.06 3021.52 997.54 Table 2B – Additional investment of MEDP during the four-year intervention period towards malaria elimination in Mandla district along with calculation of per capita and per annum additional expenditures. Target population Additional Expenditure during 4-year MEDP period Per capita Additional Expenditure (INR) Yearly Per Capita Additional Expenditure (INR) Predicted Annual Cases in absence of MEDP (A) (B) (C) = (B/A) D = (C/4) (diff-in-diff) 1.2 million INR 180 million (USD 2.17 million) 150 37.5 998 Table 3A: Direct Cost (Treatment Cost: medical+non-medical) and Indirect Cost (Earning Forgone) of the counterfactual malaria cases (988) in Mandla *Source of Treatment Sought *Percent *Unit Cost Number of Cases (N=988) COI (unit cost x number of cases) with Inflation @1.32** COI (million INR) Govt/Public Health Facilities 29.35 334.5 290 128046.6 0.13 Pvt Hospitals 24.05 1060 238 333009.6 0.33 Charitable/NGO Health Facilities 1.1 744 11 10802.88 0.01 Pvt Doctor Clinics 42.85 640 423 357350.4 0.36 Informal Health Providers 2.65 761 26 26117.52 0.03 Total Medical Cost 855327 0.86 Non-Medical Cost 83 988 108245.28 0.11 Total Treatment Cost (Direct cost) 963572.28 0.96 ***Earning Foregone (Indirect cost) 455 988 593392.8 0.59 Total COI (Direct + Indirect cost) 1.55 *Source: 75 th NSS, 2018; **GDP deflator from GDP series available at mospi.gov.in was used to arrive at expenditure figures for 2023; ***The average work days lost was assumed 10 days as per 75 th National Sample Survey (NSS), 2018. Work day loss in case of children illness was considered for caregivers; COI: Cost of Illness Table 3B: Value of lives loss Age Group of Deaths *Percentage Estimated Number of Deaths (N=3) YPPLL VLL VLL (million INR) Adults 58 1.74 15 4496403.6 4.50 5-15 Years 27 0.81 35 4884024.6 4.88 <5 years 15 0.45 35 2713347 2.71 Total VLL 12093775.2 12.09 *Source: Dhingra et al. 2010; YPPLL: Estimated Year of Potential Productive Lives Lost; VLL: Value of Lives Loss (Estimated Number of Deaths x YPPLL x Per capita Net National Income at current price for year 2023: INR 172276); An average of 15 years of YPPLL for adults was assumed at assuming 45 years of average age at malarial death among adults, an average age of work potency of 60 years or age of retirement from active work, and an average of 35 years of YPPLL was assumed for children death cases. ** mospi.gov.in Table 3C: Personal protection cost District Population Estimated Population at high risk (@33% of the total district population) *Unit cost Personal Protection Cost (PPC) PPC (million INR) 1.2 million 396000 139.026 55054296 55.05 *Source: Mills et al. 2008 (USD 0.58 x Inflation 2.82 at current price) x 85 (US$ to INR Conversion rate) Table 4 - Component wise cost (INR million) estimates for malaria elimination in India as per the National Strategic Plan for Malaria Elimination 2023-2027 Types of Expenditure 2023-2024 (2023) 2024-2025 (2024) 2025-2026 (2025) 2026-2027 (2026) 2027-2028 (2027) % Difference between 2023 and 2027 Intervention costs 10,063.5 8,364.3 5,468.7 6,392.7 5,971.0 -40.67 Program costs 3,846.9 6,231.3 5,126.6 5,315.6 5,373.7 39.69 Governance and others 40.7 105.7 105.8 55.8 35.9 -11.79 Total costs 13,951.1 14,701.3 10,701.1 11,764.1 11,380.6 -18.43 *India: Projected population (million) 1388.16 1400.74 1413.32 1425.91 1436.48 Per capita cost for malaria elimination (INR) 10.05 Mandla: Projected population (million) 1.20 Mandla: Estimated cost for malaria elimination (million INR) 12.06 Estimated saving @41% for early achieving elimination goal (million INR) 4.94 Source: National Strategic Plan for Malaria Elimination 2023-2027; * Census of India: Projected population of India Additional Declarations No competing interests reported. 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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-8778637","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594995371,"identity":"44db94d2-aaea-4be4-bb1d-a204c508792c","order_by":0,"name":"Mrigendra P Singh","email":"","orcid":"","institution":"Foundation for Disease Elimination and Control of India","correspondingAuthor":false,"prefix":"","firstName":"Mrigendra","middleName":"P","lastName":"Singh","suffix":""},{"id":594995372,"identity":"cd133ca7-42eb-44c7-a920-559aac008bd5","order_by":1,"name":"Harsh Rajvanshi","email":"","orcid":"","institution":"Foundation for Disease Elimination and Control of India","correspondingAuthor":false,"prefix":"","firstName":"Harsh","middleName":"","lastName":"Rajvanshi","suffix":""},{"id":594995373,"identity":"4ad0dd78-4777-4a96-8e6a-b6ecc1de3c5e","order_by":2,"name":"Praveen K Bharti","email":"","orcid":"","institution":"National Institute of Malaria Research","correspondingAuthor":false,"prefix":"","firstName":"Praveen","middleName":"K","lastName":"Bharti","suffix":""},{"id":594995374,"identity":"4329dc8d-3888-4b66-b979-a42fc9c3f54c","order_by":3,"name":"Himanshu Jayswar","email":"","orcid":"","institution":"Government of Madhya Pradesh","correspondingAuthor":false,"prefix":"","firstName":"Himanshu","middleName":"","lastName":"Jayswar","suffix":""},{"id":594995375,"identity":"a0d7e48d-e6f9-4f9d-a3b8-ca0b53815194","order_by":4,"name":"Azadar Khan","email":"","orcid":"","institution":"Foundation for Disease Elimination and Control of 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16:55:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8778637/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8778637/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103345599,"identity":"a64f1858-c4be-4d97-9855-d2f1797a569c","added_by":"auto","created_at":"2026-02-24 16:13:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":201342,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalyses of temporal trends of malaria cases in control and MEDP district during pre and post intervention period using Poisson Joinpoint regression model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8778637/v1/78aaf589107addf633d5337b.png"},{"id":103345598,"identity":"32c1c8b8-60c3-4a8b-9e82-ce818a8f5dac","added_by":"auto","created_at":"2026-02-24 16:13:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":139659,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8778637/v1/34e36423d0afb0d222f772f8.png"},{"id":103506652,"identity":"d45e6b22-03a0-487d-8609-56e2289e2772","added_by":"auto","created_at":"2026-02-26 13:38:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":782723,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProgression of malaria-free units in Mandla district from 2016 (baseline), 2017 to March 2021 (intervention), and 2022 to 2023 (post-MEDP).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8778637/v1/f981f622ffbc392bda44cb73.png"},{"id":104835114,"identity":"9b3edee7-4101-436b-af10-154f907118ef","added_by":"auto","created_at":"2026-03-17 17:40:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2523975,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8778637/v1/c48e5b7a-af83-4702-be11-8958409dfe1f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCost and Return on Investment of the Mandla Malaria Elimination Demonstration Project: Health and Economic Modelling for the National Malaria Elimination Goal\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMalaria elimination is widely regarded as one of the most cost-effective public health strategies, delivering significant returns on investment. Malaria slows development by undermining education, tourism, agriculture, and industry, and disproportionately affects poor and remote communities, reinforcing cycles of poverty (1). In 2024, an estimated 282\u0026nbsp;million malaria cases occurred worldwide. The Asia-Pacific region, including countries from the WHO South-East Asia, Western Pacific, and Eastern Mediterranean regions, accounted for approximately 7.8\u0026nbsp;million cases, with Pakistan alone contributing over 2\u0026nbsp;million. The WHO South-East Asia Region, which includes India, reported 2.7\u0026nbsp;million cases. This highlights the substantial and concentrated malaria burden that continues to challenge regional elimination goals (2).\u003c/p\u003e \u003cp\u003eRegional analyses highlight the potential economic gains of investing in malaria elimination. A 2019 study across 22 Asia‑Pacific countries estimated that malaria elimination between 2017 and 2030 would generate US\u003cspan\u003e$\u003c/span\u003e87.73\u0026nbsp;billion in benefits, avert 123\u0026nbsp;million cases, and save 386,167 lives, yielding a US\u003cspan\u003e$\u003c/span\u003e6 return for every US\u003cspan\u003e$\u003c/span\u003e1 invested (3).\u003c/p\u003e \u003cp\u003eThe WHO Global Technical Strategy for Malaria 2016\u0026ndash;2030, Asia Pacific Leaders Malaria Elimination Roadmap, and Action and Investment to defeat Malaria 2016\u0026ndash;2030, reinforce elimination as a high-value investment, projecting that accelerated action could avert billions of cases and generate trillions of dollars in economic impact (4\u0026ndash;6). Experiences from Sri Lanka and China demonstrate that sustained investments can deliver both health security and economic growth; however, forested and cross-border regions remain vulnerable to resurgence (7,8).\u003c/p\u003e \u003cp\u003eIndia has achieved substantial progress towards malaria elimination goals. Between 2015 and 2023, malaria cases declined by approximately 80% (from ~\u0026thinsp;1.17\u0026nbsp;million to ~\u0026thinsp;227,000), and deaths decreased from 384 to 83 (9). By 2023, 122 districts reported zero indigenous cases, and several states advanced to lower transmission categories (9,10). Surveillance has strengthened, as reflected in improved Annual Blood Examination Rates (ABER) (9). Out of 780 (approx.) districts in India, around 37 districts report an API of more than one, which are spread across 8 to 9 states and are considered high-priority districts (11).\u003c/p\u003e \u003cp\u003eAt present, sustaining gains in tribal, high-burden, and hard-to-reach areas remains challenging. Subnational economic evaluations are limited, leaving policymakers without evidence on the cost‑effectiveness of district‑level elimination models. As external support declines and domestic financing becomes central, localized analyses are essential to guide India\u0026rsquo;s elimination strategy.\u003c/p\u003e \u003cp\u003eAgainst this backdrop, the Malaria Elimination Demonstration Project (MEDP) in Mandla district, Madhya Pradesh, was undertaken to assess the feasibility of eliminating indigenous malaria transmission using existing case management and vector control tools (12\u0026ndash;14). This study evaluates MEDP\u0026rsquo;s impact to estimate the cost\u0026ndash;benefit ratio (CBR) and return on investment (ROI). By situating Mandla\u0026rsquo;s experience within national frameworks, the goal of malaria elimination in India is within reach, and the investment yields measurable returns.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MEDP was implemented in a high-endemic, tribal dominated, hilly-forested district, Mandla, Madhya Pradesh, through a Public–Private Partnership (PPP) between the Indian Council of Medical Research (ICMR), the Government of Madhya Pradesh (GoMP), and the Foundation for Disease Elimination and Control of India (FDEC India). Covering a population of 1.2 million, the project aimed to eliminate indigenous malaria through intensified surveillance, case management, vector control, community engagement, and capacity building. Implementation adhered to national guidelines while introducing innovations in monitoring, evaluation, and data reporting (14).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEpidemiological Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMandla served as the MEDP intervention district, while three bordering districts (Balaghat, Dindori, and Kawardha) and one ecologically comparable district (Gadchiroli) were selected as controls. These districts were broadly homogeneous in terms of demography, terrain, geoclimatic conditions, and malaria endemicity (Table 1). Routine anti-malaria activities implemented by the national programme were identical across all selected districts. The pre-intervention period was defined as 2001–2016, and the intervention and post-intervention periods as 2017–2023 (with sub-centre-level data available only until 2023). Temporal trend of malaria cases in the control and MEDP (intervention) group during pre- and post-intervention periods was analysed using the Poisson Joinpoint regression model. To assess the impact of MEDP, a difference-in-difference (DiD) regression model was applied to interrupted time-series data from 2001 to 2023. The model estimated malaria cases under a counterfactual scenario in the absence of MEDP.\u003c/p\u003e\n\u003cp\u003eThe regression equation was specified as:\u003c/p\u003e\n\u003cdiv id=\"Equa\"\u003e\n \u003cdiv id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:{Y}_{it}={\\beta\\:}_{0}+{\\beta\\:}_{1}{Post}_{t}+{\\beta\\:}_{2}{Treatment}_{i}+{\\beta\\:}_{3}\\left({Post}_{t}\\:x\\:{Treatment}_{i}\\right)+{e}_{it}\\:$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere, \\(\\:{Y}_{it}\\) is malaria incidence in district \u003cem\u003ei\u003c/em\u003e at time \u003cem\u003et\u003c/em\u003e;\\(\\:\\:{Post}_{t}\\) is a binary dummy variable equal to 1 for the post-treatment period, zero for pre-treatment; \\(\\:{Treatment}_{i}\\) is a binary dummy variable equal to 1 if the unit is in the treatment group (MEDP), zero otherwise (Control); \\(\\:{Post}_{t}\\:x\\:{Treatment}_{i}\\) is the interaction term (DiD estimator\\(\\:);\\:{\\beta\\:}_{3}\\) is the DiD estimator — the estimate of the treatment effect\\(\\:;\\:{e}_{it}\\) is the error term.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEconomic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe economic impact of MEDP was estimated using the cost-of-illness (COI) method to quantify savings from reduced malaria burden in the district. This included direct treatment costs (medical and non-medical), productivity losses from lost workdays, the economic value of lives lost to malaria-attributable premature deaths, and expenditures on personal protection (such as mosquito repellents, window mesh screens, bed nets, smoke, etc.) incurred by the at-risk population. All costs were calculated in Indian Rupees and converted to US dollars using a constant exchange rate of INR 85 per US$.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCost of Illness (COI) Framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Total Economic Burden of malaria can be expressed as\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhe\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ere each component represents: \u003cem\u003eDC (Direct Cost)\u003c/em\u003e as the out-of-pocket expenses for treatment, including both medical (consultations, medicines, diagnostics, hospitalization) and non-medical (transport, food, lodging) costs; \u003cem\u003eIDC (Indirect Cost)\u003c/em\u003e as the income lost due to workdays missed by patients and/or caregivers. This includes waiting time to obtain initial treatment, travel time to health facilities, time spent with healthcare providers, and the number of days of full or partial productivity loss; \u003cem\u003eVLL (Value of Lives Lost)\u003c/em\u003e as the economic value of premature deaths attributable to malaria; \u003cem\u003eOCE (Other Contingent Expenditure)\u003c/em\u003e as the household spending on disease prevention, such as personal protection measures to avoid mosquito bites; and \u003cem\u003eITC (Intangible Cost)\u003c/em\u003e as the non-monetary burden of pain, grief, suffering, loss of leisure time, and broader societal impacts such as lost opportunities for development, trade, and tourism. Although critical, these costs are difficult to quantify in monetary terms (15,16).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssumptions Considered\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe economic evaluation was based on several key assumptions. Direct medical treatment costs, non-medical expenses, and earnings foregone were derived from the most recent round of the National Sample Survey (17), stratified by treatment source, and then adjusted to 2023 values using the GDP deflator from the official GDP series (18). The VLL for the intervention and post-intervention periods (2017–2023) was estimated using a three-year moving average applied to pre-intervention mortality data (2001–2016). This counterfactual was then monetized as the product of potential productive years of life lost (age-stratified) (19) and per capita annual income (18). Household expenditure on personal protection measures, such as mosquito repellents, insecticides, smoke, and window mesh screens, was estimated assuming that approximately one-third of the district population resided in high-risk malaria areas and was willing to invest in some form of preventive measure (20). Finally, reductions in direct public health system costs for case management and vector control were incorporated, reflecting the district's transition to zero indigenous malaria cases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCost-Benefit and ROI Estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Cost–Benefit Ratio (CBR) was calculated as the ratio of annualised economic and health system savings to the additional annual investment under MEDP, while the return on investment (ROI) was computed as [(Savings – Cost)/Cost] × 100.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo account for uncertainty, a probabilistic sensitivity analysis (PSA) was performed using Monte Carlo simulations with 10,000 iterations. Triangular distributions were assigned to key variables, including treatment costs, productivity losses, and mortality assumptions, to reflect variability in epidemiological and economic parameters.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eAcross the pre-intervention period, both the Control and MEDP groups initially exhibited substantial declines in malaria burden, followed by marked reversals. In the Control group, malaria cases decreased sharply before the 2008 breakpoint (Annual Percent Change (APC) = −11.5%; 95% CI: −11.6 to −11.4), but the trend shifted thereafter to a statistically significant annual increase of 12.6% (95% CI: 12.4 to 12.7), yielding a small but significant overall rise in cases across the whole period (Average Annual Percent Change (AAPC) = 1.1%; 95% CI: 1.0 to 1.2). A similar pattern was observed in the MEDP group before the intervention, with a significant annual decline through 2012 (APC = −9.1%; 95% CI: −9.4 to −8.9), followed by an annual increase of 11% (95% CI: 10 to 12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, due to the more decisive influence of the earlier downward trend, the overall pre-intervention trajectory in MEDP remained significantly negative (AAPC = −6.1%; 95% CI: −6.2 to −5.9). \u0026nbsp;In contrast, the post-intervention period showed divergent patterns between the two study arms. In the Control group, malaria cases declined sharply through 2019 (APC = −26.5%; 95% CI: −27.4 to −25.5), but this improvement was not sustained, as cases increased sharply thereafter (APC = 19.0%; 95% CI: 18.3 to 19.8). Consequently, the overall post-intervention trend in the Control group represented a significant net increase in malaria burden (AAPC = 3.7%; 95% CI: 3.3 to 4.1). In contrast, the MEDP group showed a consistently substantial, statistically significant decline throughout the post-intervention period. Malaria cases decreased by 37.5% per year (95% CI: −40.8 to −34.2) up to the 2020 breakpoint and continued to decline even more rapidly thereafter (APC = −45.1%; 95% CI: −51.0 to −38.6). This resulted in a large and highly significant overall reduction in malaria burden during the post-intervention period (AAPC = −39.7%; 95% CI: −41.8 to −37.6). Collectively, these findings indicate that while both groups experienced temporary improvements, the MEDP intervention was associated with sustained, substantially greater declines in malaria cases during the post-intervention period than in the Control group (Fig. 1).\u003c/p\u003e\n\u003cp\u003eThe DiD model predicted that, in the absence of MEDP, Mandla would have reported an average of 998 malaria cases annually during the post-intervention period (2017–2023) (Fig. 2; Table 2A). Following implementation of the elimination plan in the district, malaria incidence reduced to nearly zero, indicating that MEDP reduced malaria cases by 998 per year at an additional cost of INR 37 per capita annually (US$ 0.44) during 2017–2020 (Table 2B), with these gains sustained over the subsequent three years (2021–2023) (Fig 3). This epidemiological impact translated into significant economic benefits.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe annual household-level total cost of illness (direct and indirect costs) avoided due to MEDP was estimated at INR 1.55 million (US$0.018 million) (Table 3A). At the same time, the value of lives saved, based on the assumption of three deaths averted stratified by age and YPPLL and per capita Net National Income (NNI) at current price for the year 2023 of\u0026nbsp;INR 172276, amounted to INR 12.09 million (US$0.14 million) annually (Table 3B). Additionally, personal protection costs avoided, assuming one third of the 1.2 million population at high risk of malaria and willingness-to-pay of US$0.58 per person (Mills et al., 2008) adjusted with a 2.82 inflation rate at current price for the year 2023, totalled INR 55.05 million (US$0.65 million) annually (Table 3C).\u003c/p\u003e\n\u003cp\u003eAt the health system level, savings were estimated using the National Strategic Plan (NSP) 2023–2027 budget projections, which indicated a 41% reduction in intervention costs between 2023 and 2027 as districts transitioned to the elimination phase. The per capita cost to achieve the malaria elimination goal was estimated at INR 10.05 in the year 2023 (Table 4) (21). Applying this proportion to Mandla, where the estimated annual health system expenditure for malaria elimination was INR 12.06 million (approximately US$0.14 million), the projected annual savings were INR 4.94 million (approximately US$0.06 million).\u003c/p\u003e\n\u003cp\u003eOverall, the combined economic savings - including reductions in household costs of illness, value of lives saved, personal protection costs, and health system expenditures - amounted to INR 73.63 million (US$0.87 million) annually, compared with an average annual investment in MEDP of INR 45 million (US$0.53 million) during 2017–2020. This yielded a cost–benefit ratio of 1.64, indicating that every rupee invested in MEDP generated INR 1.64 in economic savings. The annual ROI was 64%, and probabilistic sensitivity analysis confirmed the robustness of this estimate (mean ROI: 64%, 95% CI: 42–88%).\u003c/p\u003e\n\u003cp\u003eBeyond quantifiable economic returns, the intervention would have yielded intangible benefits, including reduced school absenteeism, thereby positively impacting human resource development, trade stability, and tourism. Notably, Kanha Tiger Reserve - a key tourist attraction in Mandla, recorded a rise in tourist arrivals from an annual average of 0.15 million in 2010–2015 to 0.18 million in 2021–2024, reflecting a 20.6% yearly growth rate in the post-MEDP and post-COVID period, substantially higher than the national tourism growth rate of 7.1% (22) With tourism contributing 1.77% to India’s GDP and 12.57% to employment, the sustained malaria-free status of Mandla has likely supported accelerated regional economic development.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAnalyses of temporal trends in malaria burden showed clear differences between the intervention (MEDP) and control districts across the pre- and post-intervention periods. In the control districts, malaria incidence remained relatively stable, with a modest APC during both periods (pre and post) and no evidence of a significant inflection in trend.\u003c/p\u003e \u003cp\u003eIn contrast, the MEDP district demonstrated a pronounced decline in malaria cases following implementation of the intervention. While the pre-intervention trend showed little year-to-year change, the post-intervention period was characterized by a substantially steeper downward trajectory. The Poisson Joinpoint regression model confirmed this pattern, identifying a slope shift consistent with accelerated reductions following the initiation of MEDP interventions. The estimated AAPC in the MEDP district was substantially larger than in the control district, indicating that the intervention was associated with a significantly faster decline in malaria incidence over time. These findings support the effectiveness of MEDP interventions in intensifying malaria reduction beyond background trends observed in the non-intervention comparison districts.\u003c/p\u003e \u003cp\u003eThe findings from Mandla district demonstrate that malaria elimination is both epidemiologically achievable and economically advantageous when interventions are implemented rigorously, with adequate resources, through systematic collaboration among the government, research institutions, civil society partners, and management and technical controls, and with independent reviews. MEDP achieved a rapid and sustained decline to zero indigenous cases in less than 4 years, enabled by adherence to national strategies and innovations, including enhanced monitoring, electronic reporting, and structured stakeholder engagement.\u003c/p\u003e \u003cp\u003eThe DiD analysis confirmed that MEDP prevented nearly 1,000 cases annually at a modest per capita cost. These epidemiological gains translated into economic benefits across multiple domains, including reduced treatment expenditures, avoided productivity losses, fewer premature deaths, and lower household spending on personal protection. Health system savings further underscored the value of shifting from control to elimination phases. A cost\u0026ndash;benefit ratio of 1.64 and an ROI of 64% indicate that elimination yields strong financial returns on additional investment.\u003c/p\u003e \u003cp\u003eComparable analyses across Asia-Pacific reinforce these findings. Thailand\u0026rsquo;s comprehensive cost\u0026ndash;benefit study demonstrated returns of US\u003cspan\u003e$\u003c/span\u003e2\u0026ndash;15 per US\u003cspan\u003e$\u003c/span\u003e1 invested (23). In the Philippines, a 2017 analysis estimated a 13:1 ROI, likely higher because the study considered indirect benefits such as educational outcomes and tourism (24). Sri Lanka offers a cautionary perspective, suggesting that resurgence would cost 13 times as much as prevention (25). These data highlight both the dividends of elimination and the steep costs of inaction.\u003c/p\u003e \u003cp\u003eGlobal evidence echoes this economic rationale. The Lancet Commission on Malaria Eradication (2019) argued that the Asia-Pacific is the proving ground for eradication, with success here essential to achieving global goals. Eliminating malaria permanently reduces the need for intensive, costly surveillance systems, yielding lasting economic dividends (26). Other reports, including the WHO\u0026rsquo;s World Malaria Reports and regional investment case reports, consistently reaffirm elimination as one of the most compelling global health investments (2,27).\u003c/p\u003e \u003cp\u003eMandla\u0026rsquo;s experience also highlights broader development spillovers. The growth of tourism around Kanha Tiger Reserve illustrates how malaria-free status can enhance destination appeal, stimulate local economies, and support employment. Although causality is multifactorial, malaria elimination likely contributed to creating a safer and more attractive environment.\u003c/p\u003e \u003cp\u003eThe 37 high-priority districts identified by the National Programme, spread across nine states, constitute a combined population of 33.5\u0026nbsp;million. Despite comprising only 2.36% of the national population, these districts account for 70% of malaria cases (9). Therefore, intensified surveillance, case management, and vector control efforts in this smaller population, with requisite funding, have become an urgent priority towards eliminating malaria.\u003c/p\u003e \u003cp\u003eAt a programmatic level, five lessons stand out: (i) combining national guidelines with innovation in delivery and accountability accelerates progress; (ii) integration of surveillance, vector control, and community engagement under strong political and administrative oversight ensures sustainability; (iii) structured PPPs can effectively leverage private resources and technical expertise within a public health framework, (iv) utilisation of time-tested operational, management, and technical controls throughout the project activities, and (v) the returns on investment (ROI) for optimal funding to eliminate malaria would be significant.\u003c/p\u003e \u003cp\u003eLooking ahead, sustaining the gains in elimination will require appropriate financing with built-in accountability frameworks, stronger domestic resource mobilisation, and streamlined partnerships. Without such measures, hard-won gains risk reversal. At the same time, the lessons learned can also be utilised to eliminate other vector-borne diseases.\u003c/p\u003e \u003cp\u003eIn summary, the evidence from Mandla, reinforced by regional and global analyses, presents a compelling case: malaria elimination yields extraordinary health and economic benefits, protects the most vulnerable populations, and catalyses sustainable development. Yet realizing these benefits requires not just technical excellence but also durable financing and governance structures capable of safeguarding progress against emerging threats.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe AAPC estimates indicate a sustained impact of MEDP interventions relative to control districts. These findings demonstrate that with adequate investment, rigorous implementation, and multisectoral collaboration, malaria elimination is achievable within a relatively short timeframe and can generate substantial epidemiological, economic, and societal benefits. By preventing nearly 1,000 cases annually, saving households and the health system close to INR 74\u0026nbsp;million (USD 0.87\u0026nbsp;million) each year, and yielding a cost\u0026ndash;benefit ratio of 1.64 with a return on investment exceeding 60%, MEDP not only validated the technical feasibility of malaria elimination but also demonstrated its economic rationale.\u003c/p\u003e \u003cp\u003eImportantly, the findings have direct implications for the 37 high-priority districts identified by the Government of India, which have an API greater than one. These districts, which continue to bear a disproportionate share of the country\u0026rsquo;s malaria burden, can particularly benefit from Mandla\u0026rsquo;s model of integrated surveillance, strong community engagement, and evidence-based vector control. Scaling and adapting the Mandla approach in such high-risk settings could accelerate progress toward national malaria elimination targets while simultaneously reducing health system costs and generating economic returns for households and local economies. The lessons from Mandla underscore that malaria elimination should be regarded not solely as a health objective but as a strategic investment in national development, capable of delivering long-term dividends for health systems, households, and the broader economy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAAPC\u003c/strong\u003e \u0026ndash; Average Annual Percent Change\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eABER\u003c/strong\u003e \u0026ndash; Annual Blood Examination Rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACT\u003c/strong\u003e \u0026ndash; Artemisinin-based Combination Therapy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAPI\u003c/strong\u003e \u0026ndash; Annual Parasite Incidence\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAPC\u003c/strong\u003e \u0026ndash; Annual Percent Change\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAPLMA\u003c/strong\u003e \u0026ndash; Asia Pacific Leaders Malaria Alliance\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBSTS\u003c/strong\u003e \u0026ndash; Bayesian Structural Time Series\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCBR\u003c/strong\u003e \u0026ndash; Cost\u0026ndash;Benefit Ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e \u0026ndash; Confidence Interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOI\u003c/strong\u003e \u0026ndash; Cost of Illness\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOVID\u003c/strong\u003e \u0026ndash; Coronavirus Disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDC\u003c/strong\u003e \u0026ndash; Direct Cost\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiD\u003c/strong\u003e \u0026ndash; Difference-in-Difference\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFDEC\u003c/strong\u003e \u0026ndash; Foundation for Disease Elimination and Control\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGDP\u003c/strong\u003e \u0026ndash; Gross Domestic Product\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGIS\u003c/strong\u003e \u0026ndash; Geographic Information System\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGoMP\u003c/strong\u003e \u0026ndash; Government of Madhya Pradesh\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eICMR\u003c/strong\u003e \u0026ndash; Indian Council of Medical Research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIDC\u003c/strong\u003e \u0026ndash; Indirect Cost\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eINR\u003c/strong\u003e \u0026ndash; Indian Rupee\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eITC\u003c/strong\u003e \u0026ndash; Intangible Cost\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLLIN\u003c/strong\u003e \u0026ndash; Long-Lasting Insecticidal Net\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMEDP\u003c/strong\u003e \u0026ndash; Malaria Elimination Demonstration Project\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMoSPI\u003c/strong\u003e \u0026ndash; Ministry of Statistics and Programme Implementation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNNI\u003c/strong\u003e \u0026ndash; Net National Income\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNGO\u003c/strong\u003e \u0026ndash; Non-Governmental Organization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNSP\u003c/strong\u003e \u0026ndash; National Strategic Plan\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNSS\u003c/strong\u003e \u0026ndash; National Sample Survey\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNSSO\u003c/strong\u003e \u0026ndash; National Sample Survey Office\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOCE\u003c/strong\u003e \u0026ndash; Other Contingent Expenditure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPP\u003c/strong\u003e \u0026ndash; Public\u0026ndash;Private Partnership\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePSA\u003c/strong\u003e \u0026ndash; Probabilistic Sensitivity Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROI\u003c/strong\u003e \u0026ndash; Return on Investment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSOP\u003c/strong\u003e \u0026ndash; Standard Operating Procedure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUSD\u003c/strong\u003e \u0026ndash; United States Dollar\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVLL\u003c/strong\u003e \u0026ndash; Value of Lives Lost\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWHO\u003c/strong\u003e \u0026ndash; World Health Organization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXGBoost\u003c/strong\u003e \u0026ndash; Extreme Gradient Boosting\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYPPLL\u003c/strong\u003e \u0026ndash; Years of Potential Productive Life Lost\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate:\u003c/em\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication:\u003c/em\u003e All authors have approved and consented the manuscript for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials:\u003c/em\u003e All data and materials available upon request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests:\u003c/em\u003e None\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding:\u003c/em\u003e Study funded by the Foundation for Disease Elimination and Control of India\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors' contributions:\u003c/em\u003e MPS and HR contributed equally and share first authorship. Both were responsible for Conceptualisation, Methodology, Formal analysis, Data curation, Validation, Software, Visualisation, and Writing – original draft; PKB: Investigation, Data curation, Validation, Writing – review \u0026amp; editing; HJ: Writing – review \u0026amp; editing; AK: \u0026nbsp;Writing – review \u0026amp; editing; NKG: Writing – review \u0026amp; editing; YKG: Writing – review \u0026amp; editing; SKG: Investigation, Resources, Writing – review \u0026amp; editing; AAL: Conceptualisation, Supervision, Funding acquisition, Project administration, Writing – review \u0026amp; editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMezieobi KC, Alum EU, Ugwu OPC, Uti DE, Alum BN, Egba SI, et al. Economic burden of malaria on developing countries: a mini review. Parasite Epidemiol Control. 2025;30:e00435. \u003c/li\u003e\n\u003cli\u003eWorld malaria report 2025. Geneva: Global Malaria Programme; Report No.: 978-92-4-011782\u0026ndash;2. \u003c/li\u003e\n\u003cli\u003eShretta R, Silal SP, Celhay OJ, Mercado CEG, Kyaw SS, Avancena A, et al. Malaria elimination transmission and costing in the Asia-Pacific: Developing an investment case. Wellcome Open Res. 2020;4:60. \u003c/li\u003e\n\u003cli\u003eAsia Pacific Leaders Malaria Elimination Roadmap [Internet]. Singapore: APLMA; Available from: https://aplma.s3.ap-southeast-1.amazonaws.com/aplma/assets/KULDDbkg/aplma-roadmap.pdf\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Global technical strategy for malaria 2016-2030. World Health Organization; 2015. \u003c/li\u003e\n\u003cli\u003eAction and Investment to defeat Malaria 2016-2030. Geneva: Roll Back Malaria Partnership; \u003c/li\u003e\n\u003cli\u003eFeng J, Xia ZG, Vong S, Yang WZ, Zhou SS, Xiao N. Preparedness for malaria resurgence in China: case study on imported cases in 2000\u0026ndash;2012. Adv Parasitol. 2014;86:231\u0026ndash;65. \u003c/li\u003e\n\u003cli\u003eShretta R, Baral R, Avance\u0026ntilde;a AL, Fox K, Dannoruwa AP, Jayanetti R, et al. An investment case to prevent the reintroduction of malaria in Sri Lanka. Am J Trop Med Hyg. 2017;96(3):602. \u003c/li\u003e\n\u003cli\u003eMalaria situation in India from 2021 [Internet]. National Center for Vector Borne Diseases Control, Ministry of Health and Family Welfare, Government of India; Available from: https://ncvbdc.mohfw.gov.in/WriteReadData/l892s/47323504061758001088.pdf\u003c/li\u003e\n\u003cli\u003eAPLMA Leaders\u0026rsquo; Dashboard. Singapore: Asia Pacific Leaders Malaria Alliance; \u003c/li\u003e\n\u003cli\u003eSingh MP, Bharti PK, Rajvanshi H, Sahu RS, Jayswar H, Anvikar AR, et al. Malaria Elimination: situation analysis of cases in India, the state of Madhya Pradesh in central India, and district Mandla of Madhya Pradesh. Front Public Health. 2024;12:1363736. \u003c/li\u003e\n\u003cli\u003eBharti PK, Rajvanshi H, Nisar S, Jayswar H, Saha KB, Shukla MM, et al. Demonstration of indigenous malaria elimination through Track-Test-Treat-Track (T4) strategy in a malaria elimination demonstration project in Mandla, Madhya Pradesh. Malar J. 2020;19(1):339. \u003c/li\u003e\n\u003cli\u003eRajvanshi H, Mishra K, Bharti PK, Sandhibigraha D, Nisar S, Jayswar H, et al. Learnings from two independent malaria elimination demonstration projects in India. Trans R Soc Trop Med Hyg. 2021;115(11):1229\u0026ndash;33. \u003c/li\u003e\n\u003cli\u003eRajvanshi H, Bharti PK, Nisar S, Jain Y, Jayswar H, Mishra AK, et al. Study design and operational framework for a community-based Malaria Elimination Demonstration Project (MEDP) in 1233 villages of district Mandla, Madhya Pradesh. Malar J. 2020;19(1):410. \u003c/li\u003e\n\u003cli\u003eMia MS, Begum R, Er A, Abidin R, Pereira J. Burden of malaria at household level: A baseline review in the advent of climate change. J Env Sci Technol. 2012;5(1):1\u0026ndash;15. \u003c/li\u003e\n\u003cli\u003eTodd EC. Costs of acute bacterial foodborne disease in Canada and the United States. Int J Food Microbiol. 1989;9(4):313\u0026ndash;26. \u003c/li\u003e\n\u003cli\u003eNational Sample Survey Office (NSSO). Key Indicators of Social Consumption in India: Health (NSS 75th Round, July 2017\u0026ndash;June 2018). New Delhi: Ministry of Statistics and Programme Implementation (MoSPI), Government of India; 2019. \u003c/li\u003e\n\u003cli\u003eMinistry of Statistics and Programme Implementation (MoSPI). National Accounts Statistics: GDP Series (Base Year 2011\u0026ndash;12) [Internet]. New Delhi: Government of India; 2023. Available from: https://mospi.gov.in\u003c/li\u003e\n\u003cli\u003eDhingra N, Jha P, Sharma VP, Cohen AA, Jotkar RM, Rodriguez PS, et al. Adult and child malaria mortality in India: a nationally representative mortality survey. The Lancet. 2010;376(9754):1768\u0026ndash;74. \u003c/li\u003e\n\u003cli\u003eMills A, Lubell Y, Hanson K. Malaria eradication: the economic, financial and institutional challenge. Malar J. 2008;7(Suppl 1):S11. \u003c/li\u003e\n\u003cli\u003eCensus of India 2011, National Commission on Population, Ministry of Health \u0026amp; Family Welfare. Population Projections for India and States 2011-2036: Report of the Technical Group on Population Projections [Internet]. New Delhi: Ministry of Health \u0026amp; Family Welfare, Government of India; 2020. Available from: https://main.mohfw.gov.in/sites/default/files/Population%20Projection%20Report%202011-2036%20-%20upload_compressed_0.pdf\u003c/li\u003e\n\u003cli\u003eIndia Brand Equity Foundation (IBEF). Economic Survey 2024-25 [Internet]. IBEF; 2024. Available from: https://ibef.org/economy/economic-survey-2024-25\u003c/li\u003e\n\u003cli\u003eSudathip P, Kongkasuriyachai D, Stelmach R, Bisanzio D, Sine J, Sawang S, et al. The investment case for malaria elimination in Thailand: a cost\u0026ndash;benefit analysis. Am J Trop Med Hyg. 2019;100(6):1445. \u003c/li\u003e\n\u003cli\u003eAn Investment Case for Malaria Elimination in the Philippines [Internet]. The Global Health Group, University of California, San Francisco.; Available from: https://shrinkingthemalariamap.org/sites/default/files/resources/MEI_PHL-investment-case-report_May-2017.pdf\u003c/li\u003e\n\u003cli\u003eShretta R, Baral R, Avance\u0026ntilde;a AL, Fox K, Dannoruwa AP, Jayanetti R, et al. An investment case to prevent the reintroduction of malaria in Sri Lanka. Am J Trop Med Hyg. 2017;96(3):602. \u003c/li\u003e\n\u003cli\u003eChen I, Cooney R, Feachem RG, Lal A, Mpanju-Shumbusho W. The Lancet Commission on malaria eradication. The Lancet. 2018;391(10130):1556\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eAn Investment Case for Eliminating Malaria in the Asia Pacific Region [Internet]. The Global Health Group, University of California, San Francisco; Available from: https://shrinkingthemalariamap.org/sites/default/fi\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 \u0026ndash; Background characteristics of the control and intervention districts\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"578\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.1762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.1071%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl (average)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7168%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.1762%;\"\u003e\n \u003cp\u003ePopulation (census 2011) (000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.1071%;\"\u003e\n \u003cp\u003e1075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7168%;\"\u003e\n \u003cp\u003e1055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.1762%;\"\u003e\n \u003cp\u003eSchedule Tribe Population (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.1071%;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7168%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.1762%;\"\u003e\n \u003cp\u003eLiteracy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.1071%;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7168%;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.1762%;\"\u003e\n \u003cp\u003eAverage Family Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.1071%;\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7168%;\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.1762%;\"\u003e\n \u003cp\u003eAverage Annual Per Capita Income (000)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.1071%;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7168%;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.1762%;\"\u003e\n \u003cp\u003eAverage Rainfall (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.1071%;\"\u003e\n \u003cp\u003e1383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7168%;\"\u003e\n \u003cp\u003e1425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.1762%;\"\u003e\n \u003cp\u003eAverage Temperature\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.1071%;\"\u003e\n \u003cp\u003e26.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7168%;\"\u003e\n \u003cp\u003e24.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.1762%;\"\u003e\n \u003cp\u003eForest Cover (% of Total Geographic Area)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.1071%;\"\u003e\n \u003cp\u003e49.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7168%;\"\u003e\n \u003cp\u003e48.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: * http://data.icrisat.org/dld/src/gdp.html\u003c/p\u003e\n\u003cp\u003e** https://www.data.gov.in/catalog/district-wise-forest-cover?page=1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2A \u0026ndash; Diff-in-diff model statistics of pre-intervention and post-intervention periods across MEDP district vs. control districts.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"591\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-Intervention\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(2008-2016)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-Intervention\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(2017-2023)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMEDP district\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2303.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e192.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2110.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl districts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6322.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3214.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3108.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4019.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3021.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e997.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2B \u0026ndash; Additional investment of MEDP during the four-year intervention period towards malaria elimination in Mandla district along with calculation of per capita and per annum additional expenditures.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"591\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget population\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdditional Expenditure during 4-year MEDP period\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer capita Additional Expenditure (INR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYearly Per Capita Additional Expenditure (INR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted Annual Cases in absence of MEDP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(C) = (B/A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD = (C/4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(diff-in-diff)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.2 million\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;INR 180 million\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(USD 2.17 million)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3A: Direct Cost (Treatment Cost: medical+non-medical) and Indirect Cost (Earning Forgone) of the counterfactual malaria cases (988) in Mandla\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"637\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e*Source of Treatment Sought\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e*Percent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e*Unit Cost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Cases\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=988)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOI (unit cost x number of cases) with Inflation @1.32**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(million INR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGovt/Public Health Facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e29.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e334.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e128046.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePvt Hospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e24.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e333009.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCharitable/NGO Health Facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10802.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePvt Doctor Clinics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e42.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e357350.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eInformal Health Providers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e26117.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Medical Cost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e855327\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Medical Cost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e83\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e988\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e108245.28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Treatment Cost (Direct cost)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e963572.28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e***Earning Foregone (Indirect cost)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e455\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e988\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e593392.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal COI (Direct + Indirect cost)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Source: 75\u003csup\u003eth\u003c/sup\u003e NSS, 2018; **GDP deflator from GDP series available at mospi.gov.in was used to arrive at expenditure figures for 2023; ***The average work days lost was assumed 10 days as per 75\u003csup\u003eth\u003c/sup\u003e National Sample Survey (NSS), 2018. Work day loss in case of children illness was considered for caregivers; COI: Cost of Illness\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3B: Value of lives loss\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group of Deaths\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e*Percentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimated\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDeaths (N=3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYPPLL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVLL\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVLL\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(million INR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003eAdults\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4496403.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e5-15 Years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4884024.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026lt;5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2713347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal VLL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12093775.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e12.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Source: Dhingra et al. 2010; YPPLL: Estimated Year of Potential Productive Lives Lost; VLL: Value of Lives Loss (Estimated Number of Deaths x YPPLL x Per capita Net National Income at current price for year 2023: INR 172276); An average of 15 years of YPPLL for adults was assumed at assuming 45 years of average age at malarial death among adults, an average age of work potency of 60 years or age of retirement from active work, and an average of 35 years of YPPLL was assumed for children death cases. ** mospi.gov.in\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3C: Personal protection cost\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistrict Population\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimated Population\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eat high risk\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(@33% of the total\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003edistrict population)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e*Unit cost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePersonal\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eProtection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCost (PPC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(million INR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.2 million\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e396000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e139.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e55054296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e55.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Source: Mills et al. 2008 (USD 0.58 x Inflation 2.82 at current price) x 85 (US$ to INR Conversion rate)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 - Component wise cost (INR million) estimates for malaria elimination in India as per the National Strategic Plan for Malaria Elimination 2023-2027\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"617\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTypes of Expenditure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2023-2024\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(2023)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2024-2025\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(2024)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2025-2026\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(2025)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2026-2027\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(2026)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2027-2028\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(2027)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Difference\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ebetween\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2023 and 2027\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eIntervention costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10,063.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8,364.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5,468.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6,392.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5,971.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-40.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eProgram costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3,846.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6,231.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5,126.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5,315.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5,373.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGovernance and others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e105.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e105.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e55.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-11.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal costs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e13,951.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e14,701.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e10,701.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e11,764.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e11,380.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-18.43\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e*India: Projected population (million)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1388.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1400.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1413.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1425.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1436.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePer capita cost for malaria elimination (INR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMandla: Projected population (million)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMandla: Estimated cost for malaria elimination (million INR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEstimated saving @41% for early achieving elimination goal (million INR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u0026nbsp;\u003c/strong\u003eNational Strategic Plan for Malaria Elimination 2023-2027; * Census of India: Projected population of India\u003c/p\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":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MEDP, Malaria Elimination, ROI, Investment case","lastPublishedDoi":"10.21203/rs.3.rs-8778637/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8778637/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eMalaria elimination is among the most cost-effective public health strategies, with strong evidence for economic and societal benefits. India has achieved an ~80% reduction in malaria cases between 2015 and 2023. At this time, 37 districts continue to report an Annual Parasite Incidence (API) greater than one and remain high-priority for intensified interventions. Subnational analyses are needed to guide resource allocation and sustain progress.\u003cbr\u003e\n\u003cstrong\u003eMethods\u003c/strong\u003e: The Malaria Elimination Demonstration Project (MEDP) was implemented in Mandla, a high-endemic tribal district in Madhya Pradesh, through a public–private partnership. Intensified surveillance, case management, vector control, and community engagement were deployed. A difference-in-difference regression model using data from 2001 to 2023 compared Mandla with four control districts. An economic evaluation used a cost-of-illness framework to estimate avoided household costs, productivity losses, the value of lives saved, personal protection expenditures, and reduced health system costs. Sensitivity analyses were conducted using Monte Carlo simulations.\u003cbr\u003e\n\u003cstrong\u003eResults:\u003c/strong\u003e \u0026nbsp;MEDP reduced malaria incidence to near zero, averting ~998 cases annually. The intervention prevented household and health system costs totalling INR 73.6 million (US$0.87 million) annually, with a cost–benefit ratio of 1.64 and an ROI of 64%. Benefits extend beyond health, with tourism in the Kanha Tiger Reserve increasing faster than the national average following the MEDP.\u003cbr\u003e\n\u003cstrong\u003eConclusion:\u003c/strong\u003e \u0026nbsp;Mandla’s experience demonstrates that elimination is feasible and economically advantageous. Scaling this model to India’s high-priority districts with an API greater than one could accelerate national elimination goals while delivering broad developmental benefits. The project was supported through a public–private partnership involving the Indian Council of Medical Research, the Government of Madhya Pradesh, and the Foundation for Disease Elimination and Control of India.\u003c/p\u003e","manuscriptTitle":"Cost and Return on Investment of the Mandla Malaria Elimination Demonstration Project: Health and Economic Modelling for the National Malaria Elimination Goal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 16:13:14","doi":"10.21203/rs.3.rs-8778637/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-03T05:20:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-01T12:50:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-18T15:13:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265182582735821288218288582004338975637","date":"2026-02-26T08:25:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"320074556051006149418822377718723070157","date":"2026-02-24T09:19:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166959301726559470766917122482602542962","date":"2026-02-22T23:33:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-19T04:09:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-06T16:03:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-04T18:05:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Malaria Journal","date":"2026-02-03T16:31:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"80f18c98-8e52-46e4-be21-59ac080e42dd","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-03T05:20:11+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-03T05:24:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 16:13:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8778637","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8778637","identity":"rs-8778637","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}