Cost-Effectiveness Analysis of Deep Brain Stimulation for Parkinson's Disease in Türkiye

Cost-Effectiveness Analysis of Deep Brain Stimulation for Parkinson's Disease in Türkiye | 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-Effectiveness Analysis of Deep Brain Stimulation for Parkinson's Disease in Türkiye Elif Giral, Vahit Yiğit This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9528728/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Deep brain stimulation (DBS) represents a transformative neurosurgical intervention for advanced Parkinson’s disease (PD), providing profound clinical relief where standard pharmacotherapy fails. However, the formidable upfront procedural and device investments demand rigorous economic justification to ensure sustainable patient access. To empower evidence-based neurosurgical practice, this study comprehensively evaluates the long-term cost-effectiveness of DBS combined with medical therapy (MT) versus MT alone from the Turkish healthcare payer perspective. Methods A Markov cohort simulation modeled the clinical trajectory of advanced PD over a lifetime horizon. Direct medical costs and quality-adjusted life years (QALYs) were discounted at 3% annually. Parameter uncertainty was evaluated through deterministic one-way and 10,000-iteration probabilistic sensitivity analyses. Results Discounted lifetime costs were $ 18,520 for DBS and $ 3,303 for MT, generating 3.61 and 1.93 QALYs, respectively. This yielded an incremental cost of $ 15,217 and a clinical benefit of 1.68 QALYs. The resulting incremental cost-effectiveness ratio (ICER) of $ 9,058 per QALY gained falls securely below national willingness-to-pay thresholds. Sensitivity analyses confirmed robust stability, identifying upfront device and surgical investments as the principal ICER drivers. Conclusions Despite formidable upfront surgical and device investments, the profound neuromodulatory efficacy and sustained reduction in pharmacological reliance establish DBS as a highly cost-effective, high-value intervention over a lifetime horizon. These robust health-economic findings conclusively justify initial neurosurgical expenditures, endorsing the broader and sustainable integration of DBS for advanced PD in Türkiye. Parkinson's disease Deep brain stimulation Cost-effectiveness Quality-adjusted life years Markov model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Parkinson’s disease (PD) is a chronic, progressive neurodegenerative disorder characterized by profound functional decline and a substantial deterioration in health-related quality of life [1,2]. Its epidemiological burden escalates significantly with advancing age, affecting approximately 1–2% of the global population over 65 years [3,4]. Clinically, PD is defined by a complex constellation of cardinal motor deficits including rigidity, bradykinesia, and postural instability alongside a broad spectrum of debilitating non-motor symptoms [5,6]. The primary therapeutic objective is the comprehensive management of these symptoms to maintain optimal functional capacity, given the absence of definitive disease-modifying therapies that can halt the underlying progression [7]. Within this paradigm, pharmacotherapy constitutes the cornerstone of clinical management, heavily reliant on levodopa and dopamine agonists [8]. Although levodopa remains the gold standard for symptomatic relief and yields significant clinical benefits during the early stages of the disease [9], its long-term efficacy invariably diminishes. As the disease advances, a majority of patients develop severe, treatment-refractory motor complications, including unpredictable 'on-off' fluctuations, end-of-dose deterioration, and levodopa-induced dyskinesias, which cumulatively exert a devastating impact on patients' quality of life and render ongoing medical management exceedingly complex [7,10,11]. DBS has fundamentally transformed the neurosurgical landscape for advanced PD. Compared to traditional ablative neurosurgical procedures, DBS is overwhelmingly preferred due to its established safety profile, unparalleled therapeutic adjustability, and inherent reversibility, firmly establishing it as the surgical gold standard for mitigating severe motor fluctuations and levodopa-induced dyskinesias [8,12,13]. By significantly reducing daily dopaminergic medication requirements, this advanced neuromodulatory approach profoundly restores functional independence and enhances health-related quality of life [15,16]. However, despite its robust clinical success and the rapid evolution of neurosurgical technologies, the broader global expansion of DBS particularly in middle-income countries remains constrained by formidable financial barriers [21]. The procedure necessitates highly specialized multidisciplinary teams, meticulous perioperative care, and substantial upfront capital investments in implantable DBS device (e.g., pulse generators and microelectrodes) [17,22]. Consequently, neurosurgeons and clinical stakeholders frequently face intense scrutiny from hospital administrators and national reimbursement institutions regarding these initial surgical expenditures. Therefore, rigorous and comprehensive health economic evaluations are critically imperative to accurately weigh these profound long-term clinical benefits against the substantial initial DBS device costs. By arming neurosurgeons with quantifiable evidence, such evaluations ultimately guide the sustainable integration of DBS into national healthcare reimbursement frameworks [14,25,40]. Methods Study Design We constructed a comprehensive cohort-based decision-analytic model to compare the long-term clinical and economic outcomes of two distinct therapeutic strategies for advanced PD: standard MT alone versus MT combined with DBS. To arm clinical and policy decision-makers with real-world financial evidence, the economic evaluation was conducted from the perspective of the Turkish healthcare payer. Because the primary measure of clinical efficacy in our decision-analytic framework is the QALY. which rigorously incorporates both survival duration and health-related quality of life, this evaluation is formally classified as a cost-utility analysis. Furthermore, the health-economic analysis plan was formulated prior to data extraction and model execution, strictly aligning with standard pharmacoeconomic modeling practices. The primary outcome was ICER per quality-adjusted life year (QALY) gained. To ensure the highest methodological rigor, the model integrated clinical efficacy, epidemiological, and health-economic parameters systematically derived from published randomized controlled trials (RCTs), established economic evaluations, national hospital databases, and expert neurosurgical consultation. The study was conducted and reported in strict adherence to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS 2022). Study Population and Data Sources To accurately reflect country-specific resource utilization and direct medical expenditures, baseline clinical and financial data were retrospectively collected from three major public hospitals in Türkiye (one tertiary university hospital and two training and research hospitals). Following the identification of an initial cohort of 128 patients diagnosed with advanced PD (ICD-10 code G20) between January 2019 and June 2023, 88 patients were excluded due to incomplete longitudinal follow-up data. The final local clinical cohort comprised 40 patients (31 receiving MT alone and 9 undergoing DBS implantation). The mean age of this real-world cohort was 74.6 years (62.1% male) for the DBS group and 70.7 years (62.3% male) for the MT group. Because primary data collection coincided with the peak of the COVID-19 pandemic, the administration of prospective health-related quality-of-life (HRQoL) questionnaires was severely restricted. Consequently, this local hospital cohort was strictly utilized to inform country-specific real-world resource utilization, direct medical costs, and baseline descriptive characteristics. To model long-term clinical efficacy (e.g., transition probabilities and utility values) with robust internal validity, the Markov model's starting age was deliberately set to 60 years. This methodological adjustment was strictly necessitated to align with the baseline age of the international landmark RCTs from which our primary efficacy data were systematically derived. All age-dependent variables, including background mortality rates, were subsequently adjusted to reflect this parameter, thereby eliminating systematic bias and ensuring structural consistency throughout the simulation. Clinical Evidence Identification Given the stringent clinical restrictions during the COVID-19 pandemic, conducting prospective primary data collection and administering health-related quality of life (HRQoL) instruments within the local Turkish clinical cohort was not feasible. To systematically bridge this critical methodological gap, the clinical efficacy, health state utilities, and transition probabilities essential for populating the decision-analytic model were rigorously derived from a recent comprehensive systematic review. This approach ensured that the model was built upon a robust, evidence-based foundation, seamlessly integrating international clinical efficacy data with real-world local cost parameters to maximize internal validity. To determine these precise model inputs, the foundational systematic literature review was executed across the Medline, PubMed, and Scopus databases. The search strategy incorporated predefined Medical Subject Headings (MeSH) and keywords, including "Parkinson’s disease and cost-effectiveness analysis," "device-assisted therapy methods in Parkinson’s disease," and "deep brain stimulation." Peer-reviewed, full-text articles published from 2010 onwards were meticulously screened adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Supplementary Table 1). The study selection was strictly governed by the PICOS framework: P opulation: Patients with advanced PD I ntervention: Deep brain stimulation (DBS) C omparator: Standard medical therapy (MT) or DBS combined with MT O utcomes: Clinical and cost-effectiveness outcomes (including QALYs, ICERs, and cumulative costs) S tudy Design: Cost-effectiveness analyses and model-based evaluations. The rigorous screening process ultimately yielded 12 high-quality cost-effectiveness studies that supplied the foundational clinical transition probabilities and utility values for our Markov simulation (Supplementary Fig. 1). To further guarantee the highest level of methodological rigor and parameter stability, these derived inputs were cross-validated against a comprehensive 2024 systematic meta-analysis, which evaluated 2,190 records and synthesized data from 14 landmark evaluations. Ultimately, this systematic identification process firmly anchors our Turkish payer-perspective cost analysis within internationally validated clinical and HRQoL parameters [25]. In alignment with established gold standards in the neurosurgical and health-economic modeling of PD, clinical progression was simulated using a combination of Hoehn and Yahr (H&Y) stages and the percentage of daily "off" time. Health utility values were predominantly informed by high-quality RCTs to ensure methodological consistency. Specifically, utility weights were systematically extracted from Liou et al. [36] and Palmer et al. [29], as these remain the most widely validated and cited sources for utility scores stratified by H&Y stages and motor fluctuations. Furthermore, to accurately contextualize our parameter selection within a developing healthcare economy, a recent cost-effectiveness evaluation by Guo et al. [21] from China was utilized as a comparative methodological framework. Ultimately, this systematic approach ensures that all model inputs are deeply grounded in robust clinical evidence and accompanied by strict methodological justification. Model Structure A cohort-based Markov decision-analytic model was developed using TreeAge Pro to evaluate the cost-effectiveness of the DBS surgical strategy compared with MT alone in advanced PD. Two simulated cohorts were analyzed using an identical model structure with 6-month cycles over a lifetime horizon. To ensure rigorous comparability with established neurosurgical health-economic literature, the model cohort was initiated at age 60 [17, 22, 26, 27]. As previously justified, the real-world clinical cohort (mean age 70.7–74.6 years) served exclusively to inform baseline resource utilization; transition probabilities were derived from the published literature and were not age-specific. Because age-related mortality was dynamically accounted for through background mortality rates applied continuously throughout the simulation, the divergence between the model's starting age and the clinical cohort's age does not introduce systematic bias into the results. Health states were defined using a dual-criteria approach incorporating H&Y stages (1–5) and the percentage of daily "off" time (0–25%, 26–50%, 51–75%, 76–100%), alongside death as the absorbing state [28, 29]. For advanced PD, capturing levodopa-induced "on-off" phenomena is paramount; thus, the percentage of daily "off" time was integrated to directly reflect the functional burden of motor fluctuations and periods of inadequate symptom control [30, 31]. Utilizing this composite framework of H&Y stages and "off" time categories allows for a highly accurate representation of the treatment's capacity to mitigate motor complications and its subsequent impact on health-related quality of life. The model structure and transitions between these clinical health states are depicted in Fig. 1 . The structural selection of this specific Markov model was explicitly guided by a recent systematic review of DBS health-economic models by Sasidharan et al. [40], ensuring close methodological alignment with key international neurosurgical frameworks established by Dams et al. [17], Fann et al. [19], Guo et al. [21], Pietzsch et al. [22], and Kawamoto et al. [26]. We modeled disease progression as a strictly unidirectional process. During each 6-month cycle, patients could remain in their current state, progress to a more advanced H&Y stage, transition to a higher OFF-time category, or die. Refined backward transitions (i.e., improvements to a less severe H&Y stage or a lower OFF-time category) were not permitted, reflecting the chronic and irreversible neurodegenerative nature of PD. In this structural design, the clinical efficacy of DBS does not manifest as backward stage transitions, but rather as a significant improvement in health state utilities (QALYs) and a reduction in long-term medication costs within those existing states. A lifetime horizon was deliberately selected for the base-case analysis to functionally approximate a lifetime perspective. Given the model's starting age of 60 years and the escalating background mortality associated with advanced PD, over 99% of the simulated cohort naturally reaches the absorbing state (death) within this period. Crucially from a neurosurgical health-economics perspective, this specific timeframe perfectly accommodates exactly three complete 5-year lifespans of a standard non-rechargeable implantable pulse generator (IPG). This structural alignment ensures a precise and exhaustive accounting of the formidable periodic DBS device replacement costs, which are principal drivers of long-term economic outcomes. Furthermore, as previously established, while the local Turkish clinical cohort (> 70 years) was strictly utilized to inform real-world resource utilization and direct medical expenditures, the model’s starting age was anchored at 60 years. This methodological adjustment was imperative to perfectly align with the baseline demographics of the international randomized clinical trials from which our primary transition probabilities and utility values were systematically derived, thereby guaranteeing rigorous internal validity throughout the simulation. Parameter Estimation To ensure the highest methodological rigor, all model parameters were systematically derived from established long-term clinical trials and comprehensive health-economic evaluations. Transition probabilities dictating disease progression across H&Y stages and dynamic shifts between daily "off"-time categories were extracted from robust longitudinal cohorts [29, 33]. Within the simulation, baseline disease progression was strictly modeled to mirror the irreversible natural history of advanced PD. To accurately capture survival trajectories, age-specific background mortality was integrated with PD-specific relative mortality risks as reported by Liou et al. [36]. Furthermore, the incidence of procedural and clinical adverse events specifically including surgical site infections, DBS device-related complications, and disease-related falls was rigorously parameterized using established neurosurgical literature [16, 32, 34, 35]. Health-related quality of life (HRQoL) was quantified using composite utility values that meticulously cross-reference both H&Y stages and the percentage of daily "off" time, thereby accurately capturing the profound functional impairment of the cohort [29, 36]. A comprehensive summary of all model input parameters, their deterministic base-case values, and corresponding data sources is systematically detailed in Table 1 . Table 1 Summary of Decision-Analytic Model Input Parameters, Deterministic Base-Case Values, and Data Sources Parameter Description Value Source Transition Probabilities According to H&Y Status H&Y 1 to 2 0.188 (33) H&Y 2 to 3 0.04 H&Y 3 to 4 0.159 H&Y 4 to 5 0.148 Transition Probabilities Based on Percentage of Time %0–25 to %26–50 0.127 (29) %26–50 to %51–75 0.074 %51–75 to %76–100 0.043 Relative Risk of Mortality Relative Risk of H&Y 2 compared to H&Y 1 2.03 (36) Relative Risk of H&Y 3 compared to H&Y 1 2.16 Relative Risk of H&Y 4 compared to H&Y 1 4.99 Relative Risk of H&Y 5 compared to H&Y 1 4.99 Procedural and DBS device-Related Complications Possibility of infection associated with DBS (first cycle) 0.132 (35) Possibility of DBS-related infection (next cycle) 0.026 (16) Possibility of withdrawal from DBS (withdrawal of the Neurostimulator) in the first cycle 0.100 Possibility of withdrawal from DBS in each subsequent cycle 0.020 (32) Relative Risk of Falling Number of falls per cycle in H&Y 3 patients 1 (34) Relative risk of falling in H&Y 4 (compared to H&Y 3) 1.72 Relative risk of falling in H&Y 5 (compared to H&Y 3) 2.96 Health-Related Quality of Life (HRQoL) Composite Utilities H&Y 1 %0–25 ‘OFF’ Time 0.74 (29) %26–50 ‘OFF’ Time 0.68 %51–75 ‘OFF’ Time 0.64 %76–100 ‘OFF’ Time 0.52 H&Y 2 %0–25 ‘OFF’ Time 0.72 (29) %26–50 ‘OFF’ Time 0.72 %51–75 ‘OFF’ Time 0.66 %76–100 ‘OFF’ Time 0.49 H&Y 3 %0–25 ‘OFF’ Time 0.643 (36) %26–50 ‘OFF’ Time 0.555 %51–75 ‘OFF’ Time 0.467 %76–100 ‘OFF’ Time 0.379 H&Y 4 %0–25 ‘OFF’ Time 0.387 (36) %26–50 ‘OFF’ Time 0.299 %51–75 ‘OFF’ Time 0.211 %76–100 ‘OFF’ Time 0.123 H&Y 5 %0–25 ‘OFF’ Time 0.131 (36) %26–50 ‘OFF’ Time 0.043 %51–75 ‘OFF’ Time -0.045* %76–100 ‘OFF’ Time -0.133* * Negative utility values rigorously reflect severe health states (worse than death) as reported in the source literature. Transition probabilities were applied identically to both the MT and DBS arms. ** PSA parameter distributions: Beta distribution was systematically utilized for probabilities and utility values; Gamma distribution was applied for cost parameters. Standard errors were either directly derived from the published literature or conservatively assumed to be 10% of the mean deterministic value. Cost Estimation To accurately reflect real-world financial burdens, direct medical expenditures were estimated strictly from the perspective of the Turkish healthcare payer. Cost parameters were systematically derived from three major public hospitals, longitudinal administrative records, and the official national reimbursement tariffs outlined in the Turkish Health Implementation Communiqué (Sağlık Uygulama Tebliği - SUT) valid as of February 2024. By deliberately utilizing official tariffs rather than micro-costing or unadjusted list prices, this methodology ensures that unit costs authentically represent actual payer expenditures in Türkiye. In accordance with this payer perspective, indirect costs (e.g., productivity losses) were excluded. Within the simulation, patient-level costs and quality-of-life values were dynamically calculated based on H&Y stages and the percentage of daily "off" time. For the DBS surgical cohort, cost estimations exhaustively captured the substantial upfront acquisition of implantable DBS hardware (i.e., IPG, leads, extension cables, and microelectrodes) (Supplementary Table 2), neurosurgical procedures and anesthesia, inpatient hospital stays, routine outpatient IPG programming, and the clinical management of potential surgical or DBS device-related complications. In strict alignment with established neurosurgical literature, IPG battery replacement costs for the DBS cohort were applied at 5-year intervals [19, 32, 37]. Pharmacological costs were meticulously stratified by H&Y stage to reflect differences in drug consumption and treatment intensity across disease severities. Consistent with robust clinical evidence, the DBS intervention was modeled to significantly reduce long-term PD medication costs across all H&Y stages (Supplementary Table 3). All 2024 cost data were calculated in Turkish Lira (TRY) and converted to US dollars using the exchange rate valid as of February 5, 2024 (1 USD = 30.45 TRY) to guarantee international comparability. Due to the absence of a defined national discount rate in Türkiye, future costs and health outcomes (QALYs) were discounted at an annual rate of 3%, adhering to standard health-economic evaluation guidelines. Sensitivity Analysis To rigorously evaluate parameter uncertainty and test the structural robustness of the model, both deterministic one-way sensitivity analysis (OWSA) and PSA were systematically conducted. In the OWSA, key model parameters were independently varied utilizing their established distributional data (e.g., published 95% confidence intervals or derived standard errors), with the resulting variance on the ICER presented as a Tornado diagram. To comprehensively account for second-order uncertainty, appropriate probability distributions were explicitly assigned to all key parameters in the PSA. Specifically, Beta distributions were applied to clinical transition probabilities and health-state utility values (strictly bounded between 0 and 1), whereas Gamma distributions were assigned to all medical and surgical cost parameters (bounded from 0 to positive infinity). The standard errors required to define the shape and scale of these distributions were directly derived from the 95% confidence intervals or standard deviations reported in the respective source literature. In instances lacking precise variance data, the standard error was conservatively assumed to be 10% of the mean deterministic value. To guarantee complete mathematical convergence and the robust stability of the simulated ICERs and acceptability curves, the PSA was executed via a second-order Monte Carlo simulation utilizing 10,000 iterations. Outcomes were summarized using average cumulative costs, average QALYs gained, expected ICERs, and the net monetary benefit (NMB), calculated as: NMB = (QALY × WTP) – Cost. Recognizing that Türkiye currently lacks an officially WTP threshold for health technology assessments, we adopted the World Health Organization (WHO) recommendation of three times the per capita gross domestic product (GDP) (13,243 USD) as our primary metric (38). However, acknowledging that the rigid 3× GDP criterion has been increasingly debated in contemporary health-economic discourse, this threshold was utilized strictly as a pragmatic benchmark. To support local health policy and clinical decision-makers with a comprehensive financial perspective, outcomes were deliberately interpreted across a continuous spectrum of more conservative WTP thresholds (e.g., 1× and 2× GDP per capita). These robust interpretations are visually detailed in the cost-effectiveness acceptability curve (CEAC) and the corresponding NMB threshold analysis. Results Base Case Analysis Table 2 delineates the comprehensive costs associated with each therapeutic strategy, stratified by PD H&Y stages and specific resource utilization categories. In the MT alone cohort, economic expenditures are predominantly driven by routine outpatient care and escalating pharmacological regimens. As disease severity advances, the annual financial burden within this arm escalates markedly, from $ 359.34 in H&Y Stage 1 to $ 1,870.03 in H&Y Stage 5, reflecting a progressive reliance on intensive pharmacotherapy. In contrast, the DBS surgical strategy necessitates a substantial, fixed upfront investment for bilateral DBS device implantation ( $ 4,845.26), supplemented by routine IPG programming visits and ongoing pharmacological management. Importantly, postoperative medication expenditures in the surgical arm are substantially attenuated compared to the MT alone cohort across all corresponding disease stages. Importantly, postoperative medication expenditures in the surgical arm are substantially attenuated compared to the MT alone cohort across all corresponding disease stages, yielding total annual maintenance costs that range from $ 4,981.57 to 5,458.15. Furthermore, the analysis explicitly accounts for the anticipated battery replacement expenditure incurred at five-year intervals, which constitutes a paramount determinant of the long-term cumulative economic burden associated with the DBS intervention, Table 2 Comprehensive Cost Components of Treatment Strategies Stratified by H&Y Stages (Annual/USD) Cost Categories H&Y 1 H&Y 2 H&Y 3 H&Y 4 H&Y 5 MT Outpatient Treatment Cost 16.55 32.90 34.40 39.16 37.9 Drug Cost 342.79 932.97 1,056.24 1,490.56 1,832.09 Total MT Costs ($) 359.34 965.84 1,090.70 1,529.70 1,870.03 DBS Therapy (Bilateral Non-Rechargeable Neurostimulator) DBS Surgery & DBS device 4,845.26 4,845.26 4,845.26 4,845.26 4,845.26 Outpatient IPG Programming 26.62 26.62 26.62 26.62 26.62 Drug Cost 109.69 298.55 338.00 476.98 586.27 Total ($) 4,981.57 5,170.43 5,209.88 5,348.86 5,458.15 Battery Replacement Cost (Every 5 Years) 6,342.10 6,342.10 6,342.10 6,342.10 6,342.10 Time Horizon Scenarios Over the lifetime horizon, the DBS surgical strategy yielded superior health gains albeit at a higher cumulative expenditure compared with MT alone. Specifically, total discounted costs were estimated at 563,924.94 TRY ( $ 18,519.70) for the DBS cohort and 100,573.96 TRY( $ 3,302.92) for the MT cohort, resulting in an incremental cost of 463,350.98 TRY ( $ 15,216.78). Concurrently, patients under going DBS achieved 3.61 QALYs compared to 1.93 QALYs in the MT group, clinical benefit of 1.68 QALYs. Consequently, the base−case ICE) fort he DBS strategy was securely established at 275,804.15 TRY ( $ 9,057.61) per QALY gained. In the base-case analysis, the DBS strategy provided greater health gains at a higher cost compared with MT alone. A lifetime horizon, the total discounted cost was estimated at 563,924.94 TRY ( $ 18,519.70 USD) for patients receiving DBS and 100,573.96 TRY ( $ 3,302.92 USD) for those receiving MT, resulting in an incremental cost of 463,350.98 TRY ( $ 15,216.78 USD). Patients in the DBS cohort achieved 3.61 QALYs, whereas the MT cohort achieved 1.93 QALYs, yielding an incremental health benefit of 1.68 QALYs. Consequently, ICER of DBS versus MT was calculated to be 275,804.15 TRY ( $ 9,057.61 USD) per QALY. guarantees sustained clinical independence and cost-efficiency for advanced PD patients. Table 3 Cost-Effectiveness Analysis Results Across Various Time Horizon Scenarios Time Horizon Strategy Cost (TRY/USD) Effect (QALY) Incremental Costs (TRY/USD) Incremental Effect (QALY) ICER (TRY/USD per QALY) 1 Year MT 74,992.51 (2,462.81) 0,67 - - - DBS 495.960,54 (16.287,70) 1,38 420.968,03 (13.824,89) 0,71 596.764,53 (19.598,18) 5 year MT 93.928,28 (3.084,67) 1,59 - - - DBS 547.285,60 (17.973,25) 3 453.357,32 (14.888,58) 1,41 321.042,68 (10.543,27) 10 year MT 98.159,06 (3.223,61) 1,81 - - - DBS 561.085,25 (18.426,44) 3,51 462.926,19 (15.202,83) 1,7 131.976,74 (4.334,21) Lifetime MT 100.573,96 (3.302,92) 1,93 - - - DBS 563.924,94 (18.519,70) 3,61 463.350,97 (15.216,78) 1,68 275.804,15 (9.057,61) Sensitivity Analyses The deterministic one-way sensitivity analysis (OWSA) comparing the DBS surgical strategy to MT alone yielded a robust base-case ICER of 275,804.15 TRY ( $ 9,057.61) per QALY gained, identifying the upfront acquisition costs of the DBS device and surgical implantation as the principal drivers of the economic outcomes. As illustrated in the Tornado diagram (Fig. 2 ), which explicitly highlights the most influential parameters to ensure clinical clarity, the ICER variance exhibited the highest sensitivity to the DBS device cost during H&Y Stage 1. Notably, the proportional impact of these hardware cost variations on the overall economic output progressively diminished as the disease advanced from Stage 2 through Stage 5. Conversely, clinical effectiveness parameters, stage-specific pharmacological expenditures, and discount rates exerted only a marginal impact on the ICER relative to the sheer magnitude of the initial surgical and hardware investments. Furthermore, the PSA, conducted via a comprehensive 10,000-iteration second-order Monte Carlo simulation to rigorously account for parameter uncertainty and guarantee mathematical convergence, robustly corroborated the base-case findings. Figures 3 and 4 illustrate the resulting cost-effectiveness plane. The scatterplot demonstrates that the simulated point estimates are exclusively clustered within the northeast quadrant; thereby conclusively confirming that the DBS strategy consistently yields significantly greater health utility (QALYs) albeit at higher cumulative costs compared to MT alone. The dense convergence of these iterations around the deterministic base-case estimate—with all simulated ICER iterations falling securely below the pragmatic national WTP threshold of 923,856 TRY ( $ 30,340.09)—underscores the model's robust stability. Ultimately, this probabilistic distribution definitively substantiates that the substantial long-term clinical gains achieved through DBS categorically justify the initial procedural expenditures, despite broad parameter uncertainties. The cost-effectiveness plane derived from the PSA, utilizing a 10,000-iteration second-order Monte Carlo simulation to account for parameter uncertainty. The scatterplot demonstrates that the simulated point estimates are exclusively clustered within the northeast quadrant, confirming that the DBS strategy consistently yields significantly greater health utility (QALYs) albeit at higher cumulative costs compared to MT alone. Furthermore, the dense convergence of these iterations around the deterministic base-case estimate underscores the model's robust stability. Ultimately, this probabilistic distribution conclusively substantiates that the substantial clinical gains achieved through DBS justify the additional expenditures within the pragmatic national WTP (Fig. 3 ). The CEAC derived from the probabilistic sensitivity analysis delineates the probability of each treatment strategy being cost-effective across a continuous spectrum of WTP thresholds (Fig. 4 ). The analysis demonstrates a distinct, threshold-dependent economic transition. At a conservative WTP threshold of 200,000 TRY ( $ 6,568.14),the probability of the DBS surgical strategy being cost effective is approximately 509,852.22), this probability rises sharply to nearly 90%, ultimately plateauing at over 95% for thresholds exceeding 400,000 TRY ( $ 13,136.29). Conversely, the MT alone strategy maintains a high probability of cost-effectiveness exclusively at highly restrictive thresholds (< 200,000 TRY), with its economic viability diminishing to near zero as the WTP surpasses 400,000 TRY. Importantly, at the pragmatic national WTP threshold of 3× GDP per capita (923,856 TRY / 30,340.09),the probability of DBS being cost−effective remains exceptionally robust. Ultimately, for any threshold exceeding 300,000TRY ( $ 9,852.22), the DBS strategy possesses a markedly superior probability of being the most cost-effective therapeutic intervention. The NMB outcomes for both the MT alone and the DBS surgical strategies, evaluated at the pragmatic national WTP threshold of 923,856 TRY ( $ 30,340.10). The analysis demonstrates that the DBS strategy yields a substantially higher overall economic value, generating an estimated NMB of 3,049,833.37 TRY ( $ 100,158.73), compared to 1,828,819.93 TRY ( $ 60,059.77) for the MT cohort. Furthermore, the INMB associated with the neurosurgical interventionis robustly positive, calculated at 1,221,013.44TRY( $ 40,098.96). This definitively positive INMB mathematically substantiates the economic viability of the procedure, confirming that the long-term clinical utility (QALYs) achieved through DBS conclusively justifies the additional cumulative surgical and hardware expenditures, thereby establishing it as a highly cost-effective therapeutic alternative to standard medical management (Table 4 ). Table 4 INMB outcomes of treatment options Treatment Options Total Cost (TRY) Total QALY WTP NMB INMB MT 100,573.96 1.93 923,856.00 1,828,819.93 DBS 563,924.94 3.61 3,049,833.37 1,221,013.44 The NMB trajectories for both the DBS surgical strategy and the MT alone cohort were evaluated across a continuous spectrum of WTP thresholds (Fig. 5 ). The intersection of these two curves identifies the precise WTP threshold at which the incremental net benefit equals zero, mathematically corresponding to the deterministic ICER and representing the point of absolute economic equivalence between the two therapeutic interventions. At lower, highly restrictive WTP thresholds (to the left of this inflection point), the MT strategy yields a comparatively higher NMB, rendering it the optimal choice under stringent financial constraints. Conversely, as the WTP valuation escalates (to the right of the intersection), the DBS surgical strategy demonstrates a markedly superior NMB trajectory. Consequently, at moderate-to-high valuation thresholds, the substantial upfront surgical and hardware investments are conclusively offset by profound long-term clinical utility (QALYs), unequivocally establishing DBS as the highly cost-effective and preferred therapeutic strategy. The EVPI/EVPPI values peak at approximately 34,000 TRY ( $ 1,116.58) at a conservative WTP threshold of 200,000 TRY ( $ 6,568.14). This indicates that for WTP thresholds between 200,000 TRY ( $ 6,568.14) and 400,000 TRY ( $ 13,136.29), parameter uncertainty exerts a substantial impact on policy decision-making and warrants rigorous consideration. Beyond this apex, the EVPI/EVPPI values decrease precipitously as the WTP threshold escalates. This definitive trend suggests that at higher WTP valuations, structural and parameter uncertainties exert significantly less influence on the decision-making process, implying that clinical and reimbursement decisions can be confidently executed based on currently available evidence. Consequently, if the prevailing WTP threshold is strictly anchored near 200,000 TRY ( $ 6,568.14), further investigational research is justified to mitigate uncertainty. Conversely, as the WTP threshold exceeds 400,000 TRY ( $ 13,136.29) towards the pragmatic national threshold, the value of additional research and data acquisition becomes highly limited, as the logistical costs of such research would likely eclipse its potential economic benefits (Fig. 6 ). In addition to the primary ICER outcomes, supplementary probabilistic evaluations further corroborated the robust economic viability of the DBS surgical strategy. The NMB remained decidedly positive across all pragmatic WTP thresholds, confirming the definitive monetary value of the clinical utility (QALYs) gained. Concurrently, the EVPI analysis successfully highlighted specific decision-uncertainty thresholds, further guiding evidence-based resource allocation within the Turkish healthcare setting. Discussion This study provides a comprehensive decision-analytic evaluation of the long-term cost-effectiveness of the DBS surgical strategy combined with standard MT versus MT alone for the management of advanced Parkinson's disease, strictly from the perspective of the Turkish healthcare payer. The analytical findings robustly indicate that while the DBS intervention inherently necessitates a formidable upfront investment for surgical implantation and hardware acquisition, it systematically generates profound and sustained clinical utility over the extended lifetime (15-year) horizon. Specifically, the neurosurgical strategy yields an impressive incremental clinical benefit of 1.68 QALYs, ultimately culminating in a highly favorable ICER of 275,804.15 TRY ( $ 9,057.61) per QALY gained. Consequently, this study provides compelling health-economic evidence to directly inform national reimbursement frameworks, strongly advocating for the integration of DBS into standard care policies. To bridge the critical gap between long-term economic value and short-term budget constraints, Turkish policymakers and the reimbursing agency must critically consider innovative, value-based pricing paradigms. Implementing staggered payment schemes over the lifespan of the IPG or establishing outcome-based risk-sharing agreements with device manufacturers could significantly mitigate the immediate financial impact and ensure broader, equitable access to this highly cost-effective therapy. A critical observation derived from our deterministic sensitivity analysis is the predominant influence of the initial DBS device and surgical costs on the overall ICER, whereas clinical effectiveness parameters demonstrated a mathematically limited impact. This phenomenon is structurally plausible and strictly expected within the context of high-investment neurosurgical interventions. The sheer magnitude of the upfront surgical and hardware costs dictates that substantial and sustained quality-of-life improvements over many years are fundamentally necessary to offset the initial procedural investment. Therefore, marginal variations in clinical efficacy exert significantly less leverage on the overall ICER variance than the principal acquisition costs of the device itself. This mathematically confirms that upfront hardware acquisition expenditures, rather than minor fluctuations in treatment efficacy, constitute the primary barrier to achieving cost-effectiveness in developing healthcare systems. The analytical findings of this decision-analytic evaluation robustly align with the broader international health-economic literature evaluating the cost-effectiveness of the DBS surgical strategy for advanced Parkinson’s disease. Evidence from high-income European nations, including Germany and the United Kingdom, consistently demonstrates that the formidable upfront surgical and hardware investments required for DBS are effectively offset in the long term by profound improvements in health-related quality of life and sustained reductions in pharmacological expenditures [17, 20, 22, 32]. Furthermore, comprehensive evaluations from the United States underscore that DBS remains highly cost-effective across pragmatic WTP thresholds for both early and advanced disease stages, emphasizing that the neurosurgical intervention yields augmented QALYs and mitigates long-term healthcare resource utilization [20, 22]. Similarly, longitudinal economic evaluations from East Asian and developing regions, including Japan, Taiwan, and China, categorically corroborate the ultimate cost-effectiveness of the therapy [21, 26, 37]. However, a critical economic divergence is evident in the global literature: whereas ICERs securely fall well below conservative thresholds in high-income nations, the ratios in developing economies frequently approach the upper limits of national WTP thresholds. This structural discrepancy is primarily driven by the disproportionately high acquisition costs of imported IPG and specialized surgical equipment relative to local economic capacities. Our results meticulously mirror this global paradigm, confirming that within the Turkish healthcare context, DBS functions as a high-investment, high-yield intervention. The formidable short-term financial burden is systematically neutralized by sustained clinical utility, ultimately achieving a highly favorable cost-effectiveness profile over an extended time horizon. Rigorous sensitivity analyses revealed that the upfront cost of DBS implantation and hardware acquisition is the predominant driver of the ICER variance. As depicted in the Tornado diagram, clinical effectiveness parameters appear to exert a mathematically limited impact relative to hardware costs and discount rates. As previously established, this phenomenon is structurally plausible and strictly expected within high-investment neurosurgical interventions: the sheer magnitude of the upfront surgical and hardware costs dictates that substantial and sustained quality-of-life improvements over many years are fundamentally necessary simply to offset the initial procedural investment. Consequently, marginal variations in clinical efficacy possess significantly less leverage on the overall ICER than the principal acquisition costs of the IPG itself. Corroborating our findings, established literature further indicates that cost-effectiveness outcomes are profoundly influenced by patient demographics, particularly age and the optimal timing of the neurosurgical intervention [18]. Additionally, IPG battery longevity has been repeatedly identified as a paramount determinant of cost-effectiveness, as more frequent hardware replacements substantially inflate cumulative long-term expenditures [22]. A pivotal finding of our decision-analytic evaluation is that while the DBS surgical strategy may not achieve cost-effectiveness in the short term (1–5 years) due to the formidable upfront surgical and hardware investments, it systematically emerges as highly cost-effective over an extended 10-to-15-year horizon. However, evaluating this longitudinal dynamic strictly from a healthcare payer perspective inherently yields a highly conservative estimation of the intervention's true economic impact. While the upfront hardware acquisition constitutes a significant immediate barrier from a strict budget impact perspective, transitioning to a broader societal perspective reveals that the DBS surgical strategy generates profound indirect benefits entirely uncaptured in our payer-centric model. These include substantial productivity gains, accelerated return-to-work potential for appropriately selected younger surgical candidates, and a drastically mitigated caregiver burden. Consequently, the comprehensive societal value of DBS within Türkiye is categorically underestimated in our current deterministic projections. This critical discrepancy between rigid payer constraints and profound societal gains further underscores the absolute necessity for health policymakers to actively bridge this financial dichotomy. As explicitly demonstrated in the OWSA Tornado diagram, clinical effectiveness parameters exert a mathematically limited impact on the ICER variance when juxtaposed with hardware costs and discount rates. This structural dynamic is strictly expected within high-investment neurosurgical interventions; the sheer magnitude of the upfront DBS device cost heavily outweighs marginal variations in clinical efficacy and health state utilities (QALYs). While the EVPI analysis highlights specific decision-uncertainty thresholds warranting further research, our deterministic sensitivity analyses unequivocally isolate the upfront acquisition cost of the DBS device as the singular most dominant driver of the ICER. Given the substantial immediate budgetary impact this imposes on the Turkish healthcare payer, the active exploration of alternative pricing paradigms is paramount for long-term therapeutic sustainability. The strategic implementation of outcome-based risk-sharing agreements and staggered payment schemes between device manufacturers and the national reimbursement institution would effectively neutralize short-term financial uncertainty. Furthermore, negotiating targeted institutional procurement discounts would substantially alleviate the initial procedural burden. Our model mathematically corroborates this strategic approach: a hypothetical 20% reduction in DBS device costs profoundly suppresses the ICER, rendering the neurosurgical intervention highly favorable and securing it well below standard WTP thresholds. Adopting such innovative, value-based payment models would systematically dismantle the acute financial barrier, thereby guaranteeing broader and highly equitable access to this transformative therapy for advanced PD patients across Türkiye. From a generalizability perspective, a notable limitation of this study is the requisite reliance on international randomized controlled trial (RCT) data to derive key clinical transition probabilities and health state utilities, necessitated by the relatively constrained size of our local Turkish clinical cohort. While incorporating established RCT data guarantees robust internal validity, it inherently raises considerations regarding external validity. Latent demographic and clinical heterogeneities between the Turkish PD population and the foundational RCT cohorts—including variations in baseline disease severity, age, comorbidity profiles, and disparities in access to specialized postoperative neurorehabilitation—may influence both real-world clinical effectiveness and long-term cost trajectories. Currently, a paucity of large-scale, prospective Turkish observational registries precludes definitive confirmation that DBS efficacy in local clinical practice perfectly mirrors the rigorous outcomes achieved in international trials. Consequently, our assumption of comparable clinical utility serves as a necessary caveat; should real-world effectiveness in Türkiye be attenuated by these localized factors, the true ICER could prove less favorable than our current deterministic projections. Furthermore, a pronounced methodological limitation of strictly adopting a healthcare payer perspective is the deliberate exclusion of the profound indirect costs inextricably linked to advanced PD, particularly during the highly debilitating H&Y stages 4 and 5. Established empirical evidence demonstrates that as the neurodegenerative disease progresses, the multifaceted societal burden escalates exponentially, manifesting through severe productivity losses, profound caregiver burnout, and the continuous necessity for specialized institutional or home care [1, 3]. Were a broader societal perspective integrated into our decision-analytic framework, the substantial mitigation of these extensive indirect costs—directly facilitated by sustained functional restoration and reduced pharmacological dependence following DBS—would likely offset the formidable upfront surgical and DBS device investments. Under such a comprehensive paradigm, the DBS surgical strategy could transition from being highly cost-effective to emerging as an economically dominant (cost-saving) intervention over an extended time horizon. Consequently, evaluating this transformative neurosurgical intervention exclusively through the restrictive paradigm of direct medical expenditures inevitably compels health policymakers and reimbursing agencies to significantly undervalue the comprehensive societal and long-term economic utility of DBS. While the preponderance of existing health-economic evaluations is intrinsically limited to short-term horizons (typically 1–5 years), this decision-analytic framework deliberately employs an extended lifetime horizon, thereby enabling a definitive and comprehensive assessment of long-term neurosurgical outcomes. To our knowledge, this constitutes the first rigorous cost-effectiveness analysis conducted within Türkiye utilizing a Markov cohort model to evaluate advanced PD therapeutic strategies. Consequently, it delivers a foundational, model-based evidence corpus for national health technology assessments, while offering highly translatable strategic insights for other developing economies navigating analogous macroeconomic constraints. Although our decision-analytic model unequivocally demonstrates the profound long-term economic value of the DBS surgical strategy, the successful translation of this theoretical viability into routine clinical practice remains inextricably linked to meticulous patient selection paradigms and the proactive management of postoperative clinical challenges. Recent comprehensive evaluations within the contemporary neurosurgical literature emphasize that optimizing surgical outcomes necessitates the rigorous stratification of preoperative cognitive and non-motor risk profiles to effectively preempt and mitigate acute complications, notably postoperative delirium [41]. Furthermore, maximizing the long-term therapeutic yield relies heavily on a sophisticated understanding of the rapidly evolving neuromodulation landscape. This paradigm is increasingly defined by personalized stimulation parameters, closed-loop adaptive systems, emerging non-invasive deep brain stimulation techniques, and the continuous systematic synthesis of global clinical trends via advanced bibliometric tracking [42, 43]. By firmly establishing the definitive cost-effectiveness of DBS, our analysis directly complements this expanding corpus of neurosurgical evidence, mathematically substantiating that the formidable upfront surgical and device investments are conclusively justified by sustained clinical utility (QALYs) and profoundly optimized patient trajectories. Limitations Despite these robust findings, several methodological limitations must be acknowledged when interpreting the results. First, by adopting a strictly payer-centric perspective, our analysis inherently excludes broader societal benefits, such as significant improvements in patient productivity and profound reductions in caregiver burden. Because these substantial indirect benefits are not captured, the 'true' economic and societal value of DBS is likely underestimated in our findings. The second critical limitation of this study pertains to external validity and the generalizability of our clinical inputs. Because our local clinical cohort was relatively small and lacked prospective quality-of-life data, we relied predominantly on international randomized controlled trials (RCTs) to derive key transition probabilities and health state utilities. While this approach ensures methodological rigor and internal validity, there may be inherent clinical and demographic discrepancies between our real-world Turkish PD population and the highly selected cohorts within these international RCTs. Factors such as differences in baseline disease severity, age, underlying comorbidities, and unequal access to structured postoperative rehabilitation could significantly influence both the magnitude of actual clinical effectiveness and the precise realization of cost estimates in the local setting. Consequently, the real-world cost-effectiveness of DBS in Türkiye might exhibit slight variances from our model’s predictions, underscoring the need for future large-scale, prospective local observational registries to fully corroborate these findings. Due to the constrained size of the local clinical cohort and the strict adherence to a healthcare payer perspective, specific subgroup analyses characterizing patient heterogeneity and formal evaluations of distributional effects across different socioeconomic demographics were not performed." Finally, the Markov model structurally does not allow for backward transitions (i.e., improvements) between H&Y states. While this conservative assumption appropriately reflects the progressive, irreversible nature of PD and strictly aligns with established economic models [22, 33], it prevents patients from moving to a better H&Y stage post-surgery. Consequently, the model may slightly undervalue the immediate and dramatic functional gains frequently observed early after DBS implantation, suggesting that our ICER estimates conservative. Conclusıon In conclusion, this comprehensive decision-analytic evaluation definitively demonstrates that the DBS surgical strategy is a highly cost-effective intervention for advanced Parkinson's disease within the Turkish healthcare system over a lifetime horizon. Despite the formidable upfront surgical and DBS device investments, the profound long-term clinical utility (QALYs) and sustained reductions in cumulative pharmacological expenditures categorically justify the initial procedural costs compared to medical therapy alone. As the first country-specific, model-based health economic evaluation in Türkiye, this study provides compelling evidence to directly inform national reimbursement frameworks, strongly advocating for the systematic integration of DBS into the standard continuum of care for eligible surgical candidates. To further refine these projections, future research incorporating prospective, Türkiye-specific real-world clinical registries and a broader societal cost perspective—capturing productivity gains and reduced caregiver burden—is highly warranted to elucidate the exhaustive economic trajectory of this transformative neurosurgical therapy. Abbreviations CEAC Cost-Effectiveness Acceptability Curve CHEERS Consolidated Health Economic Evaluation Reporting Standards DBS Deep Brain Stimulation EVPI Expected Value of Perfect Information EVPPI Expected Value of Partial Perfect Information GDP Gross Domestic Product H&Y Hoehn and Yahr Scale ICER Incremental Cost-Effectiveness Ratio INMB Incremental Net Monetary Benefit IPG Implantable Pulse Generator MT Medical Therapy NMB Net Monetary Benefit OWSA One-Way Sensitivity Analysis PD Parkinson's Disease PSA Probabilistic Sensitivity Analysis QALY Quality-Adjusted Life Year RCT Randomized Controlled Trial SUT Sağlık Uygulama Tebliği (Health Implementation Communiqué) TRY Turkish Lira USD United States Dollar WHO World Health Organization WTP Willingness-To-Pay Declarations Supplementary Information The online version contains supplementary material available at Author Contributions All authors contributed to the study conception and design. Material preparation, data collection, and data analysis were performed by Elif Giral and Vahit Yiğit. The first draft of the manuscript was written by Elif Giral, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding This study was conducted as part of a doctoral dissertation at a public university in Türkiye. Author Elif Giral was supported by the 100/2000 Ph.D. Scholarship Program of the Council of Higher Education (YÖK) of Türkiye. No additional specific funding or financial support was received for the design, execution, or publication of this study. Data availability Data from the Health Implementation Communiqué is publicly available and usable. However, data from hospitals is not publicly available due to data privacy protection concerns. The decision-analytic model generated and analyzed during the current study is available from the corresponding author upon reasonable request. Ethics declarations consent to participate This retrospective economic evaluation study was approved by the Institutional Ethics Board of a public university. Due to the retrospective design, informed consent was not obtained. Clinical data were obtained from published literature, hospital databases, and expert opinions. Ethics committee approval was obtained for the study from a public university ethics committee (Registration No: 45/1; Registration Date: September 30, 2020). Research permission was also obtained from the three public hospitals from which the data were obtained. No human participants were directly involved in the study. Data were obtained from hospital system records. Clinical trial number: Not applicable Consent for publication Not applicable. The manuscript does not contain any individual person’s data, identifiable images, or videos. 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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-9528728","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":632223945,"identity":"30f3c288-189f-49a8-9625-7c915d6c72d4","order_by":0,"name":"Elif Giral","email":"","orcid":"","institution":"Ministry of Environment, Urbanization and Climate Change","correspondingAuthor":false,"prefix":"","firstName":"Elif","middleName":"","lastName":"Giral","suffix":""},{"id":632223946,"identity":"d44d0936-3f2b-430f-951a-9bd5e11eb9c0","order_by":1,"name":"Vahit Yiğit","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3OsQrCMBCA4SvCuVS7Jgj2FSqCOEh9lYjgVBzFSVuETKKzoO/g5BxxcKnOrtK1QsXVwVTtmnYUzD8lcF8uADrdT2aCACCAUBLyLBNFiEgJIsuI4eeRz7toOsWIczztD3fedhf2/JHcONSrF1aOxioSDpnYc9LnWNnRDYcmvTAjCBWE+p7zJihJqcKht02J6md0GWfEjB6STHOJRT5bXEmgJglz8oncEp4JQxy06PpMGqvwGqxUBC2vmYxHk649O0RJPOrY1WNf3FXknYHQ+w6R9JoLZE/oFpjS6XS6f+0FZzFQhEOvE/4AAAAASUVORK5CYII=","orcid":"","institution":"Süleyman Demirel University","correspondingAuthor":true,"prefix":"","firstName":"Vahit","middleName":"","lastName":"Yiğit","suffix":""}],"badges":[],"createdAt":"2026-04-26 01:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9528728/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9528728/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108840415,"identity":"8e8d814b-1b1e-447f-b76c-f981bec3ad5f","added_by":"auto","created_at":"2026-05-09 00:54:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":213495,"visible":true,"origin":"","legend":"\u003cp\u003eStructure of the Markov model. Markov states are defined by H\u0026amp;Y stages crossed with \"off\" time categories, with death as the absorbing state. During each cycle, a patient could either remain stable or progress by one level in the H\u0026amp;Y stage, and remain stable or progress by one stage in the percentage of \"off\" time. Transitions to death are possible from any state. Backward transitions (recovery) are not permitted. MT: Medical treatment, DBS: Deep brain stimulation, H\u0026amp;Y: Hoehn and Yahr.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9528728/v1/979f797ede5d311e17c57a49.png"},{"id":108840397,"identity":"99de0358-d4ae-4594-9a79-893fc6072186","added_by":"auto","created_at":"2026-05-09 00:54:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":97038,"visible":true,"origin":"","legend":"\u003cp\u003eTornado diagram of the one-way sensitivity analysis (OWSA)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9528728/v1/3afc68134c519c74626ad80e.png"},{"id":108840419,"identity":"cbcf1ffa-c1a4-4741-8a11-a2f98f363a55","added_by":"auto","created_at":"2026-05-09 00:54:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":311457,"visible":true,"origin":"","legend":"\u003cp\u003eCost-effectiveness plane derived from the 10,000-iteration PSA. The exclusive clustering of simulated point estimates in the northeast quadrant visually confirms the sustained clinical superiority and stable incremental cost of the DBS strategy.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9528728/v1/8a98be6f127acb6b2a7d1ec7.png"},{"id":108840423,"identity":"6bb0a9aa-5807-4633-b9c5-2421a62ec7eb","added_by":"auto","created_at":"2026-05-09 00:54:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":144511,"visible":true,"origin":"","legend":"\u003cp\u003eProbability of cost-effectiveness at various WTP threshold\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9528728/v1/e30870e1c3fd921543f80bee.png"},{"id":108840451,"identity":"edd4b355-16ad-4b03-9e79-6675310f71cd","added_by":"auto","created_at":"2026-05-09 00:54:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":91451,"visible":true,"origin":"","legend":"\u003cp\u003eNMB analysis across different WTP thresholds\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9528728/v1/76869ae96eca8e27bf78ef15.png"},{"id":108840421,"identity":"e335cb0c-aab9-4fe9-83cc-53ad341b0414","added_by":"auto","created_at":"2026-05-09 00:54:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":105973,"visible":true,"origin":"","legend":"\u003cp\u003eExpected Value of Perfect Information (EVPI) and Partial Perfect Information (EVPPI) decision summary across varying WTP thresholds.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9528728/v1/25a3580c12d810da9c1826ba.png"},{"id":108840482,"identity":"10d9bb69-fc61-4e56-9663-51b54b033884","added_by":"auto","created_at":"2026-05-09 00:55:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1265670,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9528728/v1/a93a3423-cdf7-439a-8403-65931915ef96.pdf"},{"id":108840420,"identity":"17c11ae9-f267-4345-8508-4ac6dc8c89f0","added_by":"auto","created_at":"2026-05-09 00:54:46","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":67048,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsNRR.docx","url":"https://assets-eu.researchsquare.com/files/rs-9528728/v1/1c5c9d49add5929e6303493c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cost-Effectiveness Analysis of Deep Brain Stimulation for Parkinson's Disease in Türkiye","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) is a chronic, progressive neurodegenerative disorder characterized by profound functional decline and a substantial deterioration in health-related quality of life [1,2]. Its epidemiological burden escalates significantly with advancing age, affecting approximately 1\u0026ndash;2% of the global population over 65 years [3,4]. Clinically, PD is defined by a complex constellation of cardinal motor deficits including rigidity, bradykinesia, and postural instability alongside a broad spectrum of debilitating non-motor symptoms [5,6].\u003c/p\u003e \u003cp\u003eThe primary therapeutic objective is the comprehensive management of these symptoms to maintain optimal functional capacity, given the absence of definitive disease-modifying therapies that can halt the underlying progression [7]. Within this paradigm, pharmacotherapy constitutes the cornerstone of clinical management, heavily reliant on levodopa and dopamine agonists [8]. Although levodopa remains the gold standard for symptomatic relief and yields significant clinical benefits during the early stages of the disease [9], its long-term efficacy invariably diminishes. As the disease advances, a majority of patients develop severe, treatment-refractory motor complications, including unpredictable 'on-off' fluctuations, end-of-dose deterioration, and levodopa-induced dyskinesias, which cumulatively exert a devastating impact on patients' quality of life and render ongoing medical management exceedingly complex [7,10,11].\u003c/p\u003e \u003cp\u003eDBS has fundamentally transformed the neurosurgical landscape for advanced PD. Compared to traditional ablative neurosurgical procedures, DBS is overwhelmingly preferred due to its established safety profile, unparalleled therapeutic adjustability, and inherent reversibility, firmly establishing it as the surgical gold standard for mitigating severe motor fluctuations and levodopa-induced dyskinesias [8,12,13]. By significantly reducing daily dopaminergic medication requirements, this advanced neuromodulatory approach profoundly restores functional independence and enhances health-related quality of life [15,16].\u003c/p\u003e \u003cp\u003eHowever, despite its robust clinical success and the rapid evolution of neurosurgical technologies, the broader global expansion of DBS particularly in middle-income countries remains constrained by formidable financial barriers [21]. The procedure necessitates highly specialized multidisciplinary teams, meticulous perioperative care, and substantial upfront capital investments in implantable DBS device (e.g., pulse generators and microelectrodes) [17,22]. Consequently, neurosurgeons and clinical stakeholders frequently face intense scrutiny from hospital administrators and national reimbursement institutions regarding these initial surgical expenditures. Therefore, rigorous and comprehensive health economic evaluations are critically imperative to accurately weigh these profound long-term clinical benefits against the substantial initial DBS device costs. By arming neurosurgeons with quantifiable evidence, such evaluations ultimately guide the sustainable integration of DBS into national healthcare reimbursement frameworks [14,25,40].\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eWe constructed a comprehensive cohort-based decision-analytic model to compare the long-term clinical and economic outcomes of two distinct therapeutic strategies for advanced PD: standard MT alone versus MT combined with DBS. To arm clinical and policy decision-makers with real-world financial evidence, the economic evaluation was conducted from the perspective of the Turkish healthcare payer. Because the primary measure of clinical efficacy in our decision-analytic framework is the QALY. which rigorously incorporates both survival duration and health-related quality of life, this evaluation is formally classified as a cost-utility analysis. Furthermore, the health-economic analysis plan was formulated prior to data extraction and model execution, strictly aligning with standard pharmacoeconomic modeling practices.\u003c/p\u003e \u003cp\u003eThe primary outcome was ICER per quality-adjusted life year (QALY) gained. To ensure the highest methodological rigor, the model integrated clinical efficacy, epidemiological, and health-economic parameters systematically derived from published randomized controlled trials (RCTs), established economic evaluations, national hospital databases, and expert neurosurgical consultation. The study was conducted and reported in strict adherence to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS 2022).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population and Data Sources\u003c/h3\u003e\n\u003cp\u003eTo accurately reflect country-specific resource utilization and direct medical expenditures, baseline clinical and financial data were retrospectively collected from three major public hospitals in T\u0026uuml;rkiye (one tertiary university hospital and two training and research hospitals). Following the identification of an initial cohort of 128 patients diagnosed with advanced PD (ICD-10 code G20) between January 2019 and June 2023, 88 patients were excluded due to incomplete longitudinal follow-up data. The final local clinical cohort comprised 40 patients (31 receiving MT alone and 9 undergoing DBS implantation). The mean age of this real-world cohort was 74.6 years (62.1% male) for the DBS group and 70.7 years (62.3% male) for the MT group.\u003c/p\u003e \u003cp\u003eBecause primary data collection coincided with the peak of the COVID-19 pandemic, the administration of prospective health-related quality-of-life (HRQoL) questionnaires was severely restricted. Consequently, this local hospital cohort was strictly utilized to inform country-specific real-world resource utilization, direct medical costs, and baseline descriptive characteristics. To model long-term clinical efficacy (e.g., transition probabilities and utility values) with robust internal validity, the Markov model's starting age was deliberately set to 60 years. This methodological adjustment was strictly necessitated to align with the baseline age of the international landmark RCTs from which our primary efficacy data were systematically derived. All age-dependent variables, including background mortality rates, were subsequently adjusted to reflect this parameter, thereby eliminating systematic bias and ensuring structural consistency throughout the simulation.\u003c/p\u003e\n\u003ch3\u003eClinical Evidence Identification\u003c/h3\u003e\n\u003cp\u003eGiven the stringent clinical restrictions during the COVID-19 pandemic, conducting prospective primary data collection and administering health-related quality of life (HRQoL) instruments within the local Turkish clinical cohort was not feasible. To systematically bridge this critical methodological gap, the clinical efficacy, health state utilities, and transition probabilities essential for populating the decision-analytic model were rigorously derived from a recent comprehensive systematic review. This approach ensured that the model was built upon a robust, evidence-based foundation, seamlessly integrating international clinical efficacy data with real-world local cost parameters to maximize internal validity.\u003c/p\u003e \u003cp\u003eTo determine these precise model inputs, the foundational systematic literature review was executed across the Medline, PubMed, and Scopus databases. The search strategy incorporated predefined Medical Subject Headings (MeSH) and keywords, including \"Parkinson\u0026rsquo;s disease and cost-effectiveness analysis,\" \"device-assisted therapy methods in Parkinson\u0026rsquo;s disease,\" and \"deep brain stimulation.\" Peer-reviewed, full-text articles published from 2010 onwards were meticulously screened adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Supplementary Table\u0026nbsp;1). The study selection was strictly governed by the PICOS framework:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eP\u003c/b\u003eopulation: Patients with advanced PD\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eI\u003c/b\u003entervention: Deep brain stimulation (DBS)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eC\u003c/b\u003eomparator: Standard medical therapy (MT) or DBS combined with MT\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eO\u003c/b\u003eutcomes: Clinical and cost-effectiveness outcomes (including QALYs, ICERs, and cumulative costs)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eS\u003c/b\u003etudy Design: Cost-effectiveness analyses and model-based evaluations.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe rigorous screening process ultimately yielded 12 high-quality cost-effectiveness studies that supplied the foundational clinical transition probabilities and utility values for our Markov simulation (Supplementary Fig.\u0026nbsp;1). To further guarantee the highest level of methodological rigor and parameter stability, these derived inputs were cross-validated against a comprehensive 2024 systematic meta-analysis, which evaluated 2,190 records and synthesized data from 14 landmark evaluations. Ultimately, this systematic identification process firmly anchors our Turkish payer-perspective cost analysis within internationally validated clinical and HRQoL parameters [25].\u003c/p\u003e \u003cp\u003eIn alignment with established gold standards in the neurosurgical and health-economic modeling of PD, clinical progression was simulated using a combination of Hoehn and Yahr (H\u0026amp;Y) stages and the percentage of daily \"off\" time. Health utility values were predominantly informed by high-quality RCTs to ensure methodological consistency. Specifically, utility weights were systematically extracted from Liou et al. [36] and Palmer et al. [29], as these remain the most widely validated and cited sources for utility scores stratified by H\u0026amp;Y stages and motor fluctuations. Furthermore, to accurately contextualize our parameter selection within a developing healthcare economy, a recent cost-effectiveness evaluation by Guo et al. [21] from China was utilized as a comparative methodological framework. Ultimately, this systematic approach ensures that all model inputs are deeply grounded in robust clinical evidence and accompanied by strict methodological justification.\u003c/p\u003e\n\u003ch3\u003eModel Structure\u003c/h3\u003e\n\u003cp\u003eA cohort-based Markov decision-analytic model was developed using TreeAge Pro to evaluate the cost-effectiveness of the DBS surgical strategy compared with MT alone in advanced PD. Two simulated cohorts were analyzed using an identical model structure with 6-month cycles over a lifetime horizon. To ensure rigorous comparability with established neurosurgical health-economic literature, the model cohort was initiated at age 60 [17, 22, 26, 27]. As previously justified, the real-world clinical cohort (mean age 70.7\u0026ndash;74.6 years) served exclusively to inform baseline resource utilization; transition probabilities were derived from the published literature and were not age-specific. Because age-related mortality was dynamically accounted for through background mortality rates applied continuously throughout the simulation, the divergence between the model's starting age and the clinical cohort's age does not introduce systematic bias into the results.\u003c/p\u003e \u003cp\u003eHealth states were defined using a dual-criteria approach incorporating H\u0026amp;Y stages (1\u0026ndash;5) and the percentage of daily \"off\" time (0\u0026ndash;25%, 26\u0026ndash;50%, 51\u0026ndash;75%, 76\u0026ndash;100%), alongside death as the absorbing state [28, 29]. For advanced PD, capturing levodopa-induced \"on-off\" phenomena is paramount; thus, the percentage of daily \"off\" time was integrated to directly reflect the functional burden of motor fluctuations and periods of inadequate symptom control [30, 31]. Utilizing this composite framework of H\u0026amp;Y stages and \"off\" time categories allows for a highly accurate representation of the treatment's capacity to mitigate motor complications and its subsequent impact on health-related quality of life. The model structure and transitions between these clinical health states are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe structural selection of this specific Markov model was explicitly guided by a recent systematic review of DBS health-economic models by Sasidharan et al. [40], ensuring close methodological alignment with key international neurosurgical frameworks established by Dams et al. [17], Fann et al. [19], Guo et al. [21], Pietzsch et al. [22], and Kawamoto et al. [26]. We modeled disease progression as a strictly unidirectional process. During each 6-month cycle, patients could remain in their current state, progress to a more advanced H\u0026amp;Y stage, transition to a higher OFF-time category, or die. Refined backward transitions (i.e., improvements to a less severe H\u0026amp;Y stage or a lower OFF-time category) were not permitted, reflecting the chronic and irreversible neurodegenerative nature of PD. In this structural design, the clinical efficacy of DBS does not manifest as backward stage transitions, but rather as a significant improvement in health state utilities (QALYs) and a reduction in long-term medication costs within those existing states.\u003c/p\u003e \u003cp\u003eA lifetime horizon was deliberately selected for the base-case analysis to functionally approximate a lifetime perspective. Given the model's starting age of 60 years and the escalating background mortality associated with advanced PD, over 99% of the simulated cohort naturally reaches the absorbing state (death) within this period. Crucially from a neurosurgical health-economics perspective, this specific timeframe perfectly accommodates exactly three complete 5-year lifespans of a standard non-rechargeable implantable pulse generator (IPG). This structural alignment ensures a precise and exhaustive accounting of the formidable periodic DBS device replacement costs, which are principal drivers of long-term economic outcomes. Furthermore, as previously established, while the local Turkish clinical cohort (\u0026gt;\u0026thinsp;70 years) was strictly utilized to inform real-world resource utilization and direct medical expenditures, the model\u0026rsquo;s starting age was anchored at 60 years. This methodological adjustment was imperative to perfectly align with the baseline demographics of the international randomized clinical trials from which our primary transition probabilities and utility values were systematically derived, thereby guaranteeing rigorous internal validity throughout the simulation.\u003c/p\u003e\n\u003ch3\u003eParameter Estimation\u003c/h3\u003e\n\u003cp\u003eTo ensure the highest methodological rigor, all model parameters were systematically derived from established long-term clinical trials and comprehensive health-economic evaluations. Transition probabilities dictating disease progression across H\u0026amp;Y stages and dynamic shifts between daily \"off\"-time categories were extracted from robust longitudinal cohorts [29, 33]. Within the simulation, baseline disease progression was strictly modeled to mirror the irreversible natural history of advanced PD.\u003c/p\u003e \u003cp\u003eTo accurately capture survival trajectories, age-specific background mortality was integrated with PD-specific relative mortality risks as reported by Liou et al. [36]. Furthermore, the incidence of procedural and clinical adverse events specifically including surgical site infections, DBS device-related complications, and disease-related falls was rigorously parameterized using established neurosurgical literature [16, 32, 34, 35]. Health-related quality of life (HRQoL) was quantified using composite utility values that meticulously cross-reference both H\u0026amp;Y stages and the percentage of daily \"off\" time, thereby accurately capturing the profound functional impairment of the cohort [29, 36]. A comprehensive summary of all model input parameters, their deterministic base-case values, and corresponding data sources is systematically detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Decision-Analytic Model Input Parameters, Deterministic Base-Case Values, and Data Sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eTransition Probabilities According to H\u0026amp;Y Status\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u0026amp;Y 1 to 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u0026amp;Y 2 to 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u0026amp;Y 3 to 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u0026amp;Y 4 to 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTransition Probabilities Based on Percentage of Time\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%0\u0026ndash;25 to %26\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e(29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%26\u0026ndash;50 to %51\u0026ndash;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%51\u0026ndash;75 to %76\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRelative Risk of Mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative Risk of H\u0026amp;Y 2 compared to H\u0026amp;Y 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative Risk of H\u0026amp;Y 3 compared to H\u0026amp;Y 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative Risk of H\u0026amp;Y 4 compared to H\u0026amp;Y 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative Risk of H\u0026amp;Y 5 compared to H\u0026amp;Y 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProcedural and DBS device-Related Complications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePossibility of infection associated with DBS (first cycle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePossibility of DBS-related infection (next cycle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePossibility of withdrawal from DBS (withdrawal of the Neurostimulator) in the first cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePossibility of withdrawal from DBS in each subsequent cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRelative Risk of Falling\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of falls per cycle in H\u0026amp;Y 3 patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e(34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative risk of falling in H\u0026amp;Y 4 (compared to H\u0026amp;Y 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative risk of falling in H\u0026amp;Y 5 (compared to H\u0026amp;Y 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth-Related Quality of Life (HRQoL) Composite Utilities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH\u0026amp;Y 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e%0\u0026ndash;25 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e%26\u0026ndash;50 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e%51\u0026ndash;75 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e%76\u0026ndash;100 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH\u0026amp;Y 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%0\u0026ndash;25 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%26\u0026ndash;50 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%51\u0026ndash;75 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%76\u0026ndash;100 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH\u0026amp;Y 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%0\u0026ndash;25 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%26\u0026ndash;50 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%51\u0026ndash;75 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%76\u0026ndash;100 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH\u0026amp;Y 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%0\u0026ndash;25 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%26\u0026ndash;50 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%51\u0026ndash;75 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%76\u0026ndash;100 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH\u0026amp;Y 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%0\u0026ndash;25 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%26\u0026ndash;50 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%51\u0026ndash;75 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.045*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%76\u0026ndash;100 \u0026lsquo;OFF\u0026rsquo; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.133*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e* Negative utility values rigorously reflect severe health states (worse than death) as reported in the source literature. Transition probabilities were applied identically to both the MT and DBS arms.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e** PSA parameter distributions: Beta distribution was systematically utilized for probabilities and utility values; Gamma distribution was applied for cost parameters. Standard errors were either directly derived from the published literature or conservatively assumed to be 10% of the mean deterministic value.\u003c/em\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCost Estimation\u003c/h2\u003e \u003cp\u003eTo accurately reflect real-world financial burdens, direct medical expenditures were estimated strictly from the perspective of the Turkish healthcare payer. Cost parameters were systematically derived from three major public hospitals, longitudinal administrative records, and the official national reimbursement tariffs outlined in the Turkish Health Implementation Communiqu\u0026eacute; (Sağlık Uygulama Tebliği - SUT) valid as of February 2024. By deliberately utilizing official tariffs rather than micro-costing or unadjusted list prices, this methodology ensures that unit costs authentically represent actual payer expenditures in T\u0026uuml;rkiye. In accordance with this payer perspective, indirect costs (e.g., productivity losses) were excluded. Within the simulation, patient-level costs and quality-of-life values were dynamically calculated based on H\u0026amp;Y stages and the percentage of daily \"off\" time.\u003c/p\u003e \u003cp\u003eFor the DBS surgical cohort, cost estimations exhaustively captured the substantial upfront acquisition of implantable DBS hardware (i.e., IPG, leads, extension cables, and microelectrodes) (Supplementary Table\u0026nbsp;2), neurosurgical procedures and anesthesia, inpatient hospital stays, routine outpatient IPG programming, and the clinical management of potential surgical or DBS device-related complications. In strict alignment with established neurosurgical literature, IPG battery replacement costs for the DBS cohort were applied at 5-year intervals [19, 32, 37]. Pharmacological costs were meticulously stratified by H\u0026amp;Y stage to reflect differences in drug consumption and treatment intensity across disease severities.\u003c/p\u003e \u003cp\u003eConsistent with robust clinical evidence, the DBS intervention was modeled to significantly reduce long-term PD medication costs across all H\u0026amp;Y stages (Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eAll 2024 cost data were calculated in Turkish Lira (TRY) and converted to US dollars using the exchange rate valid as of February 5, 2024 (1 USD\u0026thinsp;=\u0026thinsp;30.45 TRY) to guarantee international comparability. Due to the absence of a defined national discount rate in T\u0026uuml;rkiye, future costs and health outcomes (QALYs) were discounted at an annual rate of 3%, adhering to standard health-economic evaluation guidelines.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSensitivity Analysis\u003c/h3\u003e\n\u003cp\u003eTo rigorously evaluate parameter uncertainty and test the structural robustness of the model, both deterministic one-way sensitivity analysis (OWSA) and PSA were systematically conducted. In the OWSA, key model parameters were independently varied utilizing their established distributional data (e.g., published 95% confidence intervals or derived standard errors), with the resulting variance on the ICER presented as a Tornado diagram.\u003c/p\u003e \u003cp\u003eTo comprehensively account for second-order uncertainty, appropriate probability distributions were explicitly assigned to all key parameters in the PSA. Specifically, Beta distributions were applied to clinical transition probabilities and health-state utility values (strictly bounded between 0 and 1), whereas Gamma distributions were assigned to all medical and surgical cost parameters (bounded from 0 to positive infinity). The standard errors required to define the shape and scale of these distributions were directly derived from the 95% confidence intervals or standard deviations reported in the respective source literature. In instances lacking precise variance data, the standard error was conservatively assumed to be 10% of the mean deterministic value. To guarantee complete mathematical convergence and the robust stability of the simulated ICERs and acceptability curves, the PSA was executed via a second-order Monte Carlo simulation utilizing 10,000 iterations. Outcomes were summarized using average cumulative costs, average QALYs gained, expected ICERs, and the net monetary benefit (NMB), calculated as: NMB = (QALY \u0026times; WTP) \u0026ndash; Cost.\u003c/p\u003e \u003cp\u003eRecognizing that T\u0026uuml;rkiye currently lacks an officially WTP threshold for health technology assessments, we adopted the World Health Organization (WHO) recommendation of three times the per capita gross domestic product (GDP) (13,243 USD) as our primary metric (38). However, acknowledging that the rigid 3\u0026times; GDP criterion has been increasingly debated in contemporary health-economic discourse, this threshold was utilized strictly as a pragmatic benchmark. To support local health policy and clinical decision-makers with a comprehensive financial perspective, outcomes were deliberately interpreted across a continuous spectrum of more conservative WTP thresholds (e.g., 1\u0026times; and 2\u0026times; GDP per capita). These robust interpretations are visually detailed in the cost-effectiveness acceptability curve (CEAC) and the corresponding NMB threshold analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBase Case Analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e delineates the comprehensive costs associated with each therapeutic strategy, stratified by PD H\u0026amp;Y stages and specific resource utilization categories. In the MT alone cohort, economic expenditures are predominantly driven by routine outpatient care and escalating pharmacological regimens. As disease severity advances, the annual financial burden within this arm escalates markedly, from \u003cspan\u003e$\u003c/span\u003e359.34 in H\u0026amp;Y Stage 1 to \u003cspan\u003e$\u003c/span\u003e1,870.03 in H\u0026amp;Y Stage 5, reflecting a progressive reliance on intensive pharmacotherapy.\u003c/p\u003e \u003cp\u003eIn contrast, the DBS surgical strategy necessitates a substantial, fixed upfront investment for bilateral DBS device implantation (\u003cspan\u003e$\u003c/span\u003e4,845.26), supplemented by routine IPG programming visits and ongoing pharmacological management. Importantly, postoperative medication expenditures in the surgical arm are substantially attenuated compared to the MT alone cohort across all corresponding disease stages. Importantly, postoperative medication expenditures in the surgical arm are substantially attenuated compared to the MT alone cohort across all corresponding disease stages, yielding total annual maintenance costs that range from \u003cspan\u003e$\u003c/span\u003e4,981.57 to 5,458.15. Furthermore, the analysis explicitly accounts for the anticipated battery replacement expenditure incurred at five-year intervals, which constitutes a paramount determinant of the long-term cumulative economic burden associated with the DBS intervention,\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComprehensive Cost Components of Treatment Strategies Stratified by H\u0026amp;Y Stages (Annual/USD)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost Categories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH\u0026amp;Y 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH\u0026amp;Y 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026amp;Y 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH\u0026amp;Y 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u0026amp;Y 5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eMT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient Treatment Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e342.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e932.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,056.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,490.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,832.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal MT Costs ($)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e359.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e965.84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1,090.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1,529.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1,870.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDBS Therapy (Bilateral Non-Rechargeable Neurostimulator)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBS Surgery \u0026amp; DBS device\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,845.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,845.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,845.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,845.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,845.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient IPG Programming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e338.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e476.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e586.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal ($)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4,981.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5,170.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5,209.88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5,348.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e5,458.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBattery Replacement Cost (Every 5 Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,342.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,342.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,342.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,342.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,342.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTime Horizon Scenarios\u003c/h2\u003e \u003cp\u003eOver the lifetime horizon, the DBS surgical strategy yielded superior health gains albeit at a higher cumulative expenditure compared with MT alone. Specifically, total discounted costs were estimated at 563,924.94 TRY (\u003cspan\u003e$\u003c/span\u003e18,519.70) for the DBS cohort and 100,573.96 TRY(\u003cspan\u003e$\u003c/span\u003e3,302.92) for the MT cohort, resulting in an incremental cost of 463,350.98 TRY (\u003cspan\u003e$\u003c/span\u003e15,216.78). Concurrently, patients under going DBS achieved 3.61 QALYs compared to 1.93 QALYs in the MT group, clinical benefit of 1.68 QALYs. Consequently, the base\u0026minus;case ICE) fort he DBS strategy was securely established at 275,804.15 TRY (\u003cspan\u003e$\u003c/span\u003e9,057.61) per QALY gained.\u003c/p\u003e \u003cp\u003eIn the base-case analysis, the DBS strategy provided greater health gains at a higher cost compared with MT alone. A lifetime horizon, the total discounted cost was estimated at 563,924.94 TRY (\u003cspan\u003e$\u003c/span\u003e18,519.70 USD) for patients receiving DBS and 100,573.96 TRY (\u003cspan\u003e$\u003c/span\u003e3,302.92 USD) for those receiving MT, resulting in an incremental cost of 463,350.98 TRY (\u003cspan\u003e$\u003c/span\u003e15,216.78 USD). Patients in the DBS cohort achieved 3.61 QALYs, whereas the MT cohort achieved 1.93 QALYs, yielding an incremental health benefit of 1.68 QALYs. Consequently, ICER of DBS versus MT was calculated to be 275,804.15 TRY (\u003cspan\u003e$\u003c/span\u003e9,057.61 USD) per QALY.\u003c/p\u003e \u003cp\u003eguarantees sustained clinical independence and cost-efficiency for advanced PD patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCost-Effectiveness Analysis Results Across Various Time Horizon Scenarios\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime Horizon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrategy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCost (TRY/USD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEffect (QALY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncremental Costs (TRY/USD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncremental Effect (QALY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eICER (TRY/USD per QALY)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1 Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74,992.51 (2,462.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e495.960,54 (16.287,70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e420.968,03 (13.824,89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e596.764,53 (19.598,18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.928,28 (3.084,67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e547.285,60 (17.973,25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e453.357,32 (14.888,58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e321.042,68 (10.543,27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.159,06 (3.223,61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e561.085,25 (18.426,44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e462.926,19 (15.202,83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e131.976,74 (4.334,21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLifetime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.573,96 (3.302,92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e563.924,94 (18.519,70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e463.350,97 (15.216,78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e275.804,15 (9.057,61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analyses\u003c/h2\u003e \u003cp\u003eThe deterministic one-way sensitivity analysis (OWSA) comparing the DBS surgical strategy to MT alone yielded a robust base-case ICER of 275,804.15 TRY (\u003cspan\u003e$\u003c/span\u003e9,057.61) per QALY gained, identifying the upfront acquisition costs of the DBS device and surgical implantation as the principal drivers of the economic outcomes. As illustrated in the Tornado diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which explicitly highlights the most influential parameters to ensure clinical clarity, the ICER variance exhibited the highest sensitivity to the DBS device cost during H\u0026amp;Y Stage 1. Notably, the proportional impact of these hardware cost variations on the overall economic output progressively diminished as the disease advanced from Stage 2 through Stage 5. Conversely, clinical effectiveness parameters, stage-specific pharmacological expenditures, and discount rates exerted only a marginal impact on the ICER relative to the sheer magnitude of the initial surgical and hardware investments.\u003c/p\u003e \u003cp\u003eFurthermore, the PSA, conducted via a comprehensive 10,000-iteration second-order Monte Carlo simulation to rigorously account for parameter uncertainty and guarantee mathematical convergence, robustly corroborated the base-case findings. Figures\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrate the resulting cost-effectiveness plane. The scatterplot demonstrates that the simulated point estimates are exclusively clustered within the northeast quadrant; thereby conclusively confirming that the DBS strategy consistently yields significantly greater health utility (QALYs) albeit at higher cumulative costs compared to MT alone. The dense convergence of these iterations around the deterministic base-case estimate\u0026mdash;with all simulated ICER iterations falling securely below the pragmatic national WTP threshold of 923,856 TRY (\u003cspan\u003e$\u003c/span\u003e30,340.09)\u0026mdash;underscores the model's robust stability. Ultimately, this probabilistic distribution definitively substantiates that the substantial long-term clinical gains achieved through DBS categorically justify the initial procedural expenditures, despite broad parameter uncertainties.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe cost-effectiveness plane derived from the PSA, utilizing a 10,000-iteration second-order Monte Carlo simulation to account for parameter uncertainty. The scatterplot demonstrates that the simulated point estimates are exclusively clustered within the northeast quadrant, confirming that the DBS strategy consistently yields significantly greater health utility (QALYs) albeit at higher cumulative costs compared to MT alone. Furthermore, the dense convergence of these iterations around the deterministic base-case estimate underscores the model's robust stability. Ultimately, this probabilistic distribution conclusively substantiates that the substantial clinical gains achieved through DBS justify the additional expenditures within the pragmatic national WTP (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe CEAC derived from the probabilistic sensitivity analysis delineates the probability of each treatment strategy being cost-effective across a continuous spectrum of WTP thresholds (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The analysis demonstrates a distinct, threshold-dependent economic transition. At a conservative WTP threshold of 200,000 TRY (\u003cspan\u003e$\u003c/span\u003e6,568.14),the probability of the DBS surgical strategy being cost effective is approximately 509,852.22), this probability rises sharply to nearly 90%, ultimately plateauing at over 95% for thresholds exceeding 400,000 TRY (\u003cspan\u003e$\u003c/span\u003e13,136.29). Conversely, the MT alone strategy maintains a high probability of cost-effectiveness exclusively at highly restrictive thresholds (\u0026lt;\u0026thinsp;200,000 TRY), with its economic viability diminishing to near zero as the WTP surpasses 400,000 TRY. Importantly, at the pragmatic national WTP threshold of 3\u0026times; GDP per capita (923,856 TRY / 30,340.09),the probability of DBS being cost\u0026minus;effective remains exceptionally robust. Ultimately, for any threshold exceeding 300,000TRY (\u003cspan\u003e$\u003c/span\u003e9,852.22), the DBS strategy possesses a markedly superior probability of being the most cost-effective therapeutic intervention.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe NMB outcomes for both the MT alone and the DBS surgical strategies, evaluated at the pragmatic national WTP threshold of 923,856 TRY (\u003cspan\u003e$\u003c/span\u003e30,340.10). The analysis demonstrates that the DBS strategy yields a substantially higher overall economic value, generating an estimated NMB of 3,049,833.37 TRY (\u003cspan\u003e$\u003c/span\u003e100,158.73), compared to 1,828,819.93 TRY (\u003cspan\u003e$\u003c/span\u003e60,059.77) for the MT cohort. Furthermore, the INMB associated with the neurosurgical interventionis robustly positive, calculated at 1,221,013.44TRY(\u003cspan\u003e$\u003c/span\u003e40,098.96). This definitively positive INMB mathematically substantiates the economic viability of the procedure, confirming that the long-term clinical utility (QALYs) achieved through DBS conclusively justifies the additional cumulative surgical and hardware expenditures, thereby establishing it as a highly cost-effective therapeutic alternative to standard medical management (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eINMB outcomes of treatment options\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment Options\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Cost (TRY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal QALY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWTP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNMB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eINMB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100,573.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e923,856.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,828,819.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e563,924.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,049,833.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,221,013.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe NMB trajectories for both the DBS surgical strategy and the MT alone cohort were evaluated across a continuous spectrum of WTP thresholds (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The intersection of these two curves identifies the precise WTP threshold at which the incremental net benefit equals zero, mathematically corresponding to the deterministic ICER and representing the point of absolute economic equivalence between the two therapeutic interventions. At lower, highly restrictive WTP thresholds (to the left of this inflection point), the MT strategy yields a comparatively higher NMB, rendering it the optimal choice under stringent financial constraints. Conversely, as the WTP valuation escalates (to the right of the intersection), the DBS surgical strategy demonstrates a markedly superior NMB trajectory. Consequently, at moderate-to-high valuation thresholds, the substantial upfront surgical and hardware investments are conclusively offset by profound long-term clinical utility (QALYs), unequivocally establishing DBS as the highly cost-effective and preferred therapeutic strategy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe EVPI/EVPPI values peak at approximately 34,000 TRY (\u003cspan\u003e$\u003c/span\u003e1,116.58) at a conservative WTP threshold of 200,000 TRY (\u003cspan\u003e$\u003c/span\u003e6,568.14). This indicates that for WTP thresholds between 200,000 TRY (\u003cspan\u003e$\u003c/span\u003e6,568.14) and 400,000 TRY (\u003cspan\u003e$\u003c/span\u003e13,136.29), parameter uncertainty exerts a substantial impact on policy decision-making and warrants rigorous consideration. Beyond this apex, the EVPI/EVPPI values decrease precipitously as the WTP threshold escalates. This definitive trend suggests that at higher WTP valuations, structural and parameter uncertainties exert significantly less influence on the decision-making process, implying that clinical and reimbursement decisions can be confidently executed based on currently available evidence. Consequently, if the prevailing WTP threshold is strictly anchored near 200,000 TRY (\u003cspan\u003e$\u003c/span\u003e6,568.14), further investigational research is justified to mitigate uncertainty. Conversely, as the WTP threshold exceeds 400,000 TRY (\u003cspan\u003e$\u003c/span\u003e13,136.29) towards the pragmatic national threshold, the value of additional research and data acquisition becomes highly limited, as the logistical costs of such research would likely eclipse its potential economic benefits (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition to the primary ICER outcomes, supplementary probabilistic evaluations further corroborated the robust economic viability of the DBS surgical strategy. The NMB remained decidedly positive across all pragmatic WTP thresholds, confirming the definitive monetary value of the clinical utility (QALYs) gained. Concurrently, the EVPI analysis successfully highlighted specific decision-uncertainty thresholds, further guiding evidence-based resource allocation within the Turkish healthcare setting.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides a comprehensive decision-analytic evaluation of the long-term cost-effectiveness of the DBS surgical strategy combined with standard MT versus MT alone for the management of advanced Parkinson's disease, strictly from the perspective of the Turkish healthcare payer. The analytical findings robustly indicate that while the DBS intervention inherently necessitates a formidable upfront investment for surgical implantation and hardware acquisition, it systematically generates profound and sustained clinical utility over the extended lifetime (15-year) horizon. Specifically, the neurosurgical strategy yields an impressive incremental clinical benefit of 1.68 QALYs, ultimately culminating in a highly favorable ICER of 275,804.15 TRY (\u003cspan\u003e$\u003c/span\u003e9,057.61) per QALY gained. Consequently, this study provides compelling health-economic evidence to directly inform national reimbursement frameworks, strongly advocating for the integration of DBS into standard care policies. To bridge the critical gap between long-term economic value and short-term budget constraints, Turkish policymakers and the reimbursing agency must critically consider innovative, value-based pricing paradigms. Implementing staggered payment schemes over the lifespan of the IPG or establishing outcome-based risk-sharing agreements with device manufacturers could significantly mitigate the immediate financial impact and ensure broader, equitable access to this highly cost-effective therapy.\u003c/p\u003e \u003cp\u003eA critical observation derived from our deterministic sensitivity analysis is the predominant influence of the initial DBS device and surgical costs on the overall ICER, whereas clinical effectiveness parameters demonstrated a mathematically limited impact. This phenomenon is structurally plausible and strictly expected within the context of high-investment neurosurgical interventions. The sheer magnitude of the upfront surgical and hardware costs dictates that substantial and sustained quality-of-life improvements over many years are fundamentally necessary to offset the initial procedural investment. Therefore, marginal variations in clinical efficacy exert significantly less leverage on the overall ICER variance than the principal acquisition costs of the device itself. This mathematically confirms that upfront hardware acquisition expenditures, rather than minor fluctuations in treatment efficacy, constitute the primary barrier to achieving cost-effectiveness in developing healthcare systems.\u003c/p\u003e \u003cp\u003eThe analytical findings of this decision-analytic evaluation robustly align with the broader international health-economic literature evaluating the cost-effectiveness of the DBS surgical strategy for advanced Parkinson\u0026rsquo;s disease. Evidence from high-income European nations, including Germany and the United Kingdom, consistently demonstrates that the formidable upfront surgical and hardware investments required for DBS are effectively offset in the long term by profound improvements in health-related quality of life and sustained reductions in pharmacological expenditures [17, 20, 22, 32]. Furthermore, comprehensive evaluations from the United States underscore that DBS remains highly cost-effective across pragmatic WTP thresholds for both early and advanced disease stages, emphasizing that the neurosurgical intervention yields augmented QALYs and mitigates long-term healthcare resource utilization [20, 22]. Similarly, longitudinal economic evaluations from East Asian and developing regions, including Japan, Taiwan, and China, categorically corroborate the ultimate cost-effectiveness of the therapy [21, 26, 37]. However, a critical economic divergence is evident in the global literature: whereas ICERs securely fall well below conservative thresholds in high-income nations, the ratios in developing economies frequently approach the upper limits of national WTP thresholds. This structural discrepancy is primarily driven by the disproportionately high acquisition costs of imported IPG and specialized surgical equipment relative to local economic capacities. Our results meticulously mirror this global paradigm, confirming that within the Turkish healthcare context, DBS functions as a high-investment, high-yield intervention. The formidable short-term financial burden is systematically neutralized by sustained clinical utility, ultimately achieving a highly favorable cost-effectiveness profile over an extended time horizon.\u003c/p\u003e \u003cp\u003eRigorous sensitivity analyses revealed that the upfront cost of DBS implantation and hardware acquisition is the predominant driver of the ICER variance. As depicted in the Tornado diagram, clinical effectiveness parameters appear to exert a mathematically limited impact relative to hardware costs and discount rates. As previously established, this phenomenon is structurally plausible and strictly expected within high-investment neurosurgical interventions: the sheer magnitude of the upfront surgical and hardware costs dictates that substantial and sustained quality-of-life improvements over many years are fundamentally necessary simply to offset the initial procedural investment. Consequently, marginal variations in clinical efficacy possess significantly less leverage on the overall ICER than the principal acquisition costs of the IPG itself. Corroborating our findings, established literature further indicates that cost-effectiveness outcomes are profoundly influenced by patient demographics, particularly age and the optimal timing of the neurosurgical intervention [18]. Additionally, IPG battery longevity has been repeatedly identified as a paramount determinant of cost-effectiveness, as more frequent hardware replacements substantially inflate cumulative long-term expenditures [22].\u003c/p\u003e \u003cp\u003eA pivotal finding of our decision-analytic evaluation is that while the DBS surgical strategy may not achieve cost-effectiveness in the short term (1\u0026ndash;5 years) due to the formidable upfront surgical and hardware investments, it systematically emerges as highly cost-effective over an extended 10-to-15-year horizon. However, evaluating this longitudinal dynamic strictly from a healthcare payer perspective inherently yields a highly conservative estimation of the intervention's true economic impact. While the upfront hardware acquisition constitutes a significant immediate barrier from a strict budget impact perspective, transitioning to a broader societal perspective reveals that the DBS surgical strategy generates profound indirect benefits entirely uncaptured in our payer-centric model. These include substantial productivity gains, accelerated return-to-work potential for appropriately selected younger surgical candidates, and a drastically mitigated caregiver burden. Consequently, the comprehensive societal value of DBS within T\u0026uuml;rkiye is categorically underestimated in our current deterministic projections. This critical discrepancy between rigid payer constraints and profound societal gains further underscores the absolute necessity for health policymakers to actively bridge this financial dichotomy.\u003c/p\u003e \u003cp\u003eAs explicitly demonstrated in the OWSA Tornado diagram, clinical effectiveness parameters exert a mathematically limited impact on the ICER variance when juxtaposed with hardware costs and discount rates. This structural dynamic is strictly expected within high-investment neurosurgical interventions; the sheer magnitude of the upfront DBS device cost heavily outweighs marginal variations in clinical efficacy and health state utilities (QALYs). While the EVPI analysis highlights specific decision-uncertainty thresholds warranting further research, our deterministic sensitivity analyses unequivocally isolate the upfront acquisition cost of the DBS device as the singular most dominant driver of the ICER. Given the substantial immediate budgetary impact this imposes on the Turkish healthcare payer, the active exploration of alternative pricing paradigms is paramount for long-term therapeutic sustainability. The strategic implementation of outcome-based risk-sharing agreements and staggered payment schemes between device manufacturers and the national reimbursement institution would effectively neutralize short-term financial uncertainty. Furthermore, negotiating targeted institutional procurement discounts would substantially alleviate the initial procedural burden. Our model mathematically corroborates this strategic approach: a hypothetical 20% reduction in DBS device costs profoundly suppresses the ICER, rendering the neurosurgical intervention highly favorable and securing it well below standard WTP thresholds. Adopting such innovative, value-based payment models would systematically dismantle the acute financial barrier, thereby guaranteeing broader and highly equitable access to this transformative therapy for advanced PD patients across T\u0026uuml;rkiye.\u003c/p\u003e \u003cp\u003eFrom a generalizability perspective, a notable limitation of this study is the requisite reliance on international randomized controlled trial (RCT) data to derive key clinical transition probabilities and health state utilities, necessitated by the relatively constrained size of our local Turkish clinical cohort. While incorporating established RCT data guarantees robust internal validity, it inherently raises considerations regarding external validity. Latent demographic and clinical heterogeneities between the Turkish PD population and the foundational RCT cohorts\u0026mdash;including variations in baseline disease severity, age, comorbidity profiles, and disparities in access to specialized postoperative neurorehabilitation\u0026mdash;may influence both real-world clinical effectiveness and long-term cost trajectories. Currently, a paucity of large-scale, prospective Turkish observational registries precludes definitive confirmation that DBS efficacy in local clinical practice perfectly mirrors the rigorous outcomes achieved in international trials. Consequently, our assumption of comparable clinical utility serves as a necessary caveat; should real-world effectiveness in T\u0026uuml;rkiye be attenuated by these localized factors, the true ICER could prove less favorable than our current deterministic projections.\u003c/p\u003e \u003cp\u003eFurthermore, a pronounced methodological limitation of strictly adopting a healthcare payer perspective is the deliberate exclusion of the profound indirect costs inextricably linked to advanced PD, particularly during the highly debilitating H\u0026amp;Y stages 4 and 5. Established empirical evidence demonstrates that as the neurodegenerative disease progresses, the multifaceted societal burden escalates exponentially, manifesting through severe productivity losses, profound caregiver burnout, and the continuous necessity for specialized institutional or home care [1, 3]. Were a broader societal perspective integrated into our decision-analytic framework, the substantial mitigation of these extensive indirect costs\u0026mdash;directly facilitated by sustained functional restoration and reduced pharmacological dependence following DBS\u0026mdash;would likely offset the formidable upfront surgical and DBS device investments. Under such a comprehensive paradigm, the DBS surgical strategy could transition from being highly cost-effective to emerging as an economically dominant (cost-saving) intervention over an extended time horizon. Consequently, evaluating this transformative neurosurgical intervention exclusively through the restrictive paradigm of direct medical expenditures inevitably compels health policymakers and reimbursing agencies to significantly undervalue the comprehensive societal and long-term economic utility of DBS.\u003c/p\u003e \u003cp\u003eWhile the preponderance of existing health-economic evaluations is intrinsically limited to short-term horizons (typically 1\u0026ndash;5 years), this decision-analytic framework deliberately employs an extended lifetime horizon, thereby enabling a definitive and comprehensive assessment of long-term neurosurgical outcomes. To our knowledge, this constitutes the first rigorous cost-effectiveness analysis conducted within T\u0026uuml;rkiye utilizing a Markov cohort model to evaluate advanced PD therapeutic strategies. Consequently, it delivers a foundational, model-based evidence corpus for national health technology assessments, while offering highly translatable strategic insights for other developing economies navigating analogous macroeconomic constraints.\u003c/p\u003e \u003cp\u003eAlthough our decision-analytic model unequivocally demonstrates the profound long-term economic value of the DBS surgical strategy, the successful translation of this theoretical viability into routine clinical practice remains inextricably linked to meticulous patient selection paradigms and the proactive management of postoperative clinical challenges. Recent comprehensive evaluations within the contemporary neurosurgical literature emphasize that optimizing surgical outcomes necessitates the rigorous stratification of preoperative cognitive and non-motor risk profiles to effectively preempt and mitigate acute complications, notably postoperative delirium [41]. Furthermore, maximizing the long-term therapeutic yield relies heavily on a sophisticated understanding of the rapidly evolving neuromodulation landscape. This paradigm is increasingly defined by personalized stimulation parameters, closed-loop adaptive systems, emerging non-invasive deep brain stimulation techniques, and the continuous systematic synthesis of global clinical trends via advanced bibliometric tracking [42, 43]. By firmly establishing the definitive cost-effectiveness of DBS, our analysis directly complements this expanding corpus of neurosurgical evidence, mathematically substantiating that the formidable upfront surgical and device investments are conclusively justified by sustained clinical utility (QALYs) and profoundly optimized patient trajectories.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eDespite these robust findings, several methodological limitations must be acknowledged when interpreting the results. First, by adopting a strictly payer-centric perspective, our analysis inherently excludes broader societal benefits, such as significant improvements in patient productivity and profound reductions in caregiver burden. Because these substantial indirect benefits are not captured, the 'true' economic and societal value of DBS is likely underestimated in our findings.\u003c/p\u003e \u003cp\u003eThe second critical limitation of this study pertains to external validity and the generalizability of our clinical inputs. Because our local clinical cohort was relatively small and lacked prospective quality-of-life data, we relied predominantly on international randomized controlled trials (RCTs) to derive key transition probabilities and health state utilities. While this approach ensures methodological rigor and internal validity, there may be inherent clinical and demographic discrepancies between our real-world Turkish PD population and the highly selected cohorts within these international RCTs. Factors such as differences in baseline disease severity, age, underlying comorbidities, and unequal access to structured postoperative rehabilitation could significantly influence both the magnitude of actual clinical effectiveness and the precise realization of cost estimates in the local setting. Consequently, the real-world cost-effectiveness of DBS in T\u0026uuml;rkiye might exhibit slight variances from our model\u0026rsquo;s predictions, underscoring the need for future large-scale, prospective local observational registries to fully corroborate these findings. Due to the constrained size of the local clinical cohort and the strict adherence to a healthcare payer perspective, specific subgroup analyses characterizing patient heterogeneity and formal evaluations of distributional effects across different socioeconomic demographics were not performed.\"\u003c/p\u003e \u003cp\u003eFinally, the Markov model structurally does not allow for backward transitions (i.e., improvements) between H\u0026amp;Y states. While this conservative assumption appropriately reflects the progressive, irreversible nature of PD and strictly aligns with established economic models [22, 33], it prevents patients from moving to a better H\u0026amp;Y stage post-surgery. Consequently, the model may slightly undervalue the immediate and dramatic functional gains frequently observed early after DBS implantation, suggesting that our ICER estimates conservative.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusıon","content":"\u003cp\u003eIn conclusion, this comprehensive decision-analytic evaluation definitively demonstrates that the DBS surgical strategy is a highly cost-effective intervention for advanced Parkinson's disease within the Turkish healthcare system over a lifetime horizon. Despite the formidable upfront surgical and DBS device investments, the profound long-term clinical utility (QALYs) and sustained reductions in cumulative pharmacological expenditures categorically justify the initial procedural costs compared to medical therapy alone. As the first country-specific, model-based health economic evaluation in T\u0026uuml;rkiye, this study provides compelling evidence to directly inform national reimbursement frameworks, strongly advocating for the systematic integration of DBS into the standard continuum of care for eligible surgical candidates. To further refine these projections, future research incorporating prospective, T\u0026uuml;rkiye-specific real-world clinical registries and a broader societal cost perspective\u0026mdash;capturing productivity gains and reduced caregiver burden\u0026mdash;is highly warranted to elucidate the exhaustive economic trajectory of this transformative neurosurgical therapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCEAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCost-Effectiveness Acceptability Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHEERS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConsolidated Health Economic Evaluation Reporting Standards\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDeep Brain Stimulation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEVPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExpected Value of Perfect Information\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEVPPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExpected Value of Partial Perfect Information\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGDP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGross Domestic Product\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eH\u0026amp;Y\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHoehn and Yahr Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIncremental Cost-Effectiveness Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eINMB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIncremental Net Monetary Benefit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIPG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImplantable Pulse Generator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedical Therapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNMB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNet Monetary Benefit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOWSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOne-Way Sensitivity Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eParkinson's Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProbabilistic Sensitivity Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQALY\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuality-Adjusted Life Year\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandomized Controlled Trial\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSUT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSağlık Uygulama Tebliği (Health Implementation Communiqu\u0026eacute;)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTRY\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTurkish Lira\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited States Dollar\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWTP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWillingness-To-Pay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e The online version contains supplementary material available at\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and data analysis were performed by Elif Giral and Vahit Yiğit. The first draft of the manuscript was written by Elif Giral, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted as part of a doctoral dissertation at a public university in T\u0026uuml;rkiye. Author Elif Giral was supported by the 100/2000 Ph.D. Scholarship Program of the Council of Higher Education (Y\u0026Ouml;K) of T\u0026uuml;rkiye. No additional specific funding or financial support was received for the design, execution, or publication of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from the Health Implementation Communiqu\u0026eacute; is publicly available and usable. However, data from hospitals is not publicly available due to data privacy protection concerns. The decision-analytic model generated and analyzed during the current study is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective economic evaluation study was approved by the Institutional Ethics Board of a public university. Due to the retrospective design, informed consent was not obtained. Clinical data were obtained from published literature, hospital databases, and expert opinions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics committee approval was obtained for the study from a public university ethics committee (Registration No: 45/1; Registration Date: September 30, 2020). Research permission was also obtained from the three public hospitals from which the data were obtained. No human participants were directly involved in the study. Data were obtained from hospital system records.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. The manuscript does not contain any individual person\u0026rsquo;s data, identifiable images, or videos.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven the retrospective and model-based design of this study, obtaining informed consent to participate was waived by the institutional ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests. Furthermore, the authors have no personal, financial, or institutional affiliation with any drug, material, or device discussed in this article.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBoland DF, Stacy M (2014) The Economic and Quality of Life Burden Associated With Parkinson\u0026rsquo;s Disease: A Focus on Symptoms. Am J Manag Care 18(7):168\u0026ndash;175\u003c/li\u003e\n \u003cli\u003eBecerra JE, Zorro O, Ruiz-Gaviria R, et al (2016) Economic Analysis of Deep Brain Stimulation in Parkinson\u0026apos;s Disease: Systematic Review of the Literature. World Neurosurg 93:44\u0026ndash;49. https://doi.org/10.1016/j.wneu.2016.05.070\u003c/li\u003e\n \u003cli\u003ePringsheim T, Jette N, Frolkis A, Steeves TDL (2014) The Prevalence of Parkinson\u0026rsquo;s Disease: A Systematic Review And Meta-Analysis. 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JAMA 323(6):548\u0026ndash;560. https://doi.org/10.1001/jama.2019.22360\u003c/li\u003e\n \u003cli\u003eKalia SK, Sankar T, Lozano AM (2013) Deep Brain Stimulation for Parkinson\u0026apos;s Disease and Other Movement Disorders. Curr Opin Neurol 26(4):374\u0026ndash;380. https://doi.org/10.1097/WCO.0b013e3283632d08\u003c/li\u003e\n \u003cli\u003eHauser RA, Zesiewicz T (2002) Parkinson\u0026rsquo;s Disease: Questions and Answers, 2nd edn. Merit Publishing International, Ankara\u003c/li\u003e\n \u003cli\u003eJankovic J (2005) Motor Fluctuations and Dyskinesias in Parkinson\u0026rsquo;s Disease: Clinical Manifestations. Mov Disord 20(11):S11\u0026ndash;S16. https://doi.org/10.1002/mds.20458\u003c/li\u003e\n \u003cli\u003eKurihara K, Nakagawa R, Ishido M, et al (2020) Impact of Motor and Nonmotor Symptoms in Parkinson Disease on the Quality of Life: The Japanese Quality-of-Life Survey of Parkinson Disease (JAQPAD) study. 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Value Health 18(7):A352. https://doi.org/10.1016/j.jval.2015.09.645\u003c/li\u003e\n \u003cli\u003eZhao YJ, Wee HL, Chan YH, et al (2010) Progression of Parkinson\u0026rsquo;s Disease as Evaluated by Hoehn and Yahr Stage Transition Times. Mov Disord 25(6):710\u0026ndash;716. https://doi.org/10.1002/mds.22874\u003c/li\u003e\n \u003cli\u003ePickering RM, Grimbergen YAM, Rigney U, et al (2007) A Meta-Analysis of Six Prospective Studies of Falling in Parkinson\u0026rsquo;s Disease. Mov Disord 22(13):1892\u0026ndash;1900. https://doi.org/10.1002/mds.21598\u003c/li\u003e\n \u003cli\u003eWeaver FM, Folett K, Stern M, et al (2010) Bilateral Deep Brain Stimulation vs Best Medical Therapy for Patients With Advanced Parkinson Disease: A Randomized Controlled Trial. JAMA 301(1):63\u0026ndash;73. https://doi.org/10.1001/jama.2008.929\u003c/li\u003e\n \u003cli\u003eLiou HH, Wu CY, Chiu YH, et al (2009) Mortality of Parkinson\u0026rsquo;s Disease by Hoehn-Yahr Stage From Community-Based and Clinic Series. J Eval Clin Pract 15:587\u0026ndash;591. https://doi.org/10.1111/j.1365-2753.2008.01053.x\u003c/li\u003e\n \u003cli\u003eZhu XL, Chan DTM, Lau CKY, et al (2014) Cost-Effectiveness of Subthalamic Nucleus Deep Brain Stimulation for the Treatment of Advanced Parkinson Disease in Hong Kong: A Prospective Study. World Neurosurg 82(6):987\u0026ndash;993. https://doi.org/10.1016/j.wneu.2014.08.012\u003c/li\u003e\n \u003cli\u003eWorld Health Organization (2003) Making Choices in Health: WHO Guide to Cost-Effectiveness Analysis. WHO, Geneva\u003c/li\u003e\n \u003cli\u003eHusereau D, Drummond M, Augustovski F, et al (2022) Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. MDM Policy Pract 7(1):23814683211061097. https://doi.org/10.1177/23814683211061097\u003c/li\u003e\n \u003cli\u003eSasidharan A, Bagepally BS, Kumar SS (2024) Cost Effectiveness of Deep Brain Stimulation for Parkinson\u0026apos;s Disease: A Systematic Review. Appl Health Econ Health Policy 22:181\u0026ndash;192. https://doi.org/10.1007/s40258-023-00851-x\u003c/li\u003e\n \u003cli\u003eYousef O, Abouelmagd ME, Abbas A, et al (2025) Delirium after deep brain stimulation for Parkinson\u0026apos;s disease: a meta-analysis of incidence and risk factors. Neurosurg Rev 48(1):73. https://doi.org/10.1007/s10143-025-03206-9\u003c/li\u003e\n \u003cli\u003eRektorov\u0026aacute;, I., Pup\u0026iacute;kov\u0026aacute;, M., Fleury, L. et al. Non-invasive brain stimulation: current and future applications in neurology. Nat Rev Neurol 21, 669\u0026ndash;686 (2025). https://doi.org/10.1038/s41582-025-01137-z\u003c/li\u003e\n \u003cli\u003eAbdallat M, Abumurad SK, Tarazi A, et al (2025) Deep brain stimulation and Parkinson disease: a bibliometric and visual analysis (1993\u0026ndash;2023). Neurosurg Rev 48(1):24. https://doi.org/10.1007/s10143-025-03178-w\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Parkinson's disease, Deep brain stimulation, Cost-effectiveness, Quality-adjusted life years, Markov model","lastPublishedDoi":"10.21203/rs.3.rs-9528728/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9528728/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDeep brain stimulation (DBS) represents a transformative neurosurgical intervention for advanced Parkinson\u0026rsquo;s disease (PD), providing profound clinical relief where standard pharmacotherapy fails. However, the formidable upfront procedural and device investments demand rigorous economic justification to ensure sustainable patient access. To empower evidence-based neurosurgical practice, this study comprehensively evaluates the long-term cost-effectiveness of DBS combined with medical therapy (MT) versus MT alone from the Turkish healthcare payer perspective.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA Markov cohort simulation modeled the clinical trajectory of advanced PD over a lifetime horizon. Direct medical costs and quality-adjusted life years (QALYs) were discounted at 3% annually. Parameter uncertainty was evaluated through deterministic one-way and 10,000-iteration probabilistic sensitivity analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDiscounted lifetime costs were \u003cspan\u003e$\u003c/span\u003e18,520 for DBS and \u003cspan\u003e$\u003c/span\u003e3,303 for MT, generating 3.61 and 1.93 QALYs, respectively. This yielded an incremental cost of \u003cspan\u003e$\u003c/span\u003e15,217 and a clinical benefit of 1.68 QALYs. The resulting incremental cost-effectiveness ratio (ICER) of \u003cspan\u003e$\u003c/span\u003e9,058 per QALY gained falls securely below national willingness-to-pay thresholds. Sensitivity analyses confirmed robust stability, identifying upfront device and surgical investments as the principal ICER drivers.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDespite formidable upfront surgical and device investments, the profound neuromodulatory efficacy and sustained reduction in pharmacological reliance establish DBS as a highly cost-effective, high-value intervention over a lifetime horizon. These robust health-economic findings conclusively justify initial neurosurgical expenditures, endorsing the broader and sustainable integration of DBS for advanced PD in T\u0026uuml;rkiye.\u003c/p\u003e","manuscriptTitle":"Cost-Effectiveness Analysis of Deep Brain Stimulation for Parkinson's Disease in Türkiye","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-09 00:53:32","doi":"10.21203/rs.3.rs-9528728/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d93aacf5-f604-4dfe-bb8d-d873c407031c","owner":[],"postedDate":"May 9th, 2026","published":true,"recentEditorialEvents":[{"type":"checksComplete","content":"","date":"2026-04-30T07:00:50+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T00:53:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-09 00:53:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9528728","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9528728","identity":"rs-9528728","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}