Global Multi-Cancer Environmental Causal Engine (GMCE) Simulation Study in Hodeidah, Yemen

Global Multi-Cancer Environmental Causal Engine (GMCE) Simulation Study in Hodeidah, Yemen | 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 Method Article Global Multi-Cancer Environmental Causal Engine (GMCE) Simulation Study in Hodeidah, Yemen Hussein Bakery Hussein Dedy, Ali Bannawi ALZubaidy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9044297/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: Air pollution, particularly PM2.5, is a known risk factor for multiple types of cancer. Yemen has limited air quality monitoring and cancer registry data, particularly in Hodeidah Governorate. Objective: To estimate cumulative multi-cancer risk due to environmental exposure using a mechanistic, Bayesian hierarchical, and causally identifiable framework (GMCE). Methods: PM2.5 exposure data were obtained from public monitoring sources (Weather.com) and averaged for Hodeidah (~17 µg/m³). GMCE was applied for 10 cancer types (Lung, Bladder, Breast, Colon, Leukemia, Kidney, Liver, Pancreas, Stomach, Esophagus), using tissue-specific parameters from published literature. Bayesian hierarchical modeling provided 95% Credible Intervals for cumulative risk estimates. Scenario analyses simulated changes in PM2.5 levels. Results: Estimated cumulative risk was highest for lung cancer (R ≈ 0.71, 95% CI: 0.65–0.77) and bladder cancer (R ≈ 0.52, 95% CI: 0.45–0.59). Scenario analysis showed a 20% reduction in PM2.5 decreased lung cancer risk to R ≈ 0.57 (0.51– 0.63). Conclusion: GMCE demonstrates feasibility for estimating multi-cancer risk in data-limited regions. Findings highlight the importance of PM2.5 reduction strategies in Hodeidah to lower population cancer risk. 1. Introduction Air pollution has emerged as a major environmental health challenge worldwide. Fine particulate matter (PM2.5) has been increasingly associated with chronic diseases and cancer development [1-5].Fine particulate matter (PM2.5) is particularly harmful due to its ability to penetrate deep into lung tissue and enter systemic circulation, causing oxidative stress and DNA damage. Multiple studies have demonstrated associations between PM2.5 exposure and cancers of the lung, bladder, breast, colon, and hematopoietic system (Pope et al., 2019; IARC, 2016; Turner et al., 2020). Yemen has experienced increased air pollution due to urbanization, traffic, and dust storms. Hodeidah Governorate, with its port and industrial activities, is susceptible to elevated PM2.5 exposure. However, cancer registry data are limited, and systematic studies on the environmental contribution to multi-cancer risk are scarce. This study applies a Global Multi-Cancer Environmental Causal Engine (GMCE) to Hodeidah, leveraging available PM2.5 data, published tissue-specific parameters, and Bayesian hierarchical modeling. GMCE provides a mechanistic and causally identifiable framework to estimate cumulative risk for multiple cancer types, supporting policy decisions for air quality management and public health interventions. 2. Methods Quantitative modeling approaches have become essential in environmental health research [6-10] 2.1 Study Area Hodeidah Governorate, Yemen; population ~2.7 million PM2.5 levels from Weather.com: daily averages 14–20 µg/m³ 2.2 GMCE Multi-Cancer Model Ten cancer types: Lung, Bladder, Breast, Colon, Leukemia, Kidney, Liver, Pancreas, Stomach, Esophagus Tissue-specific damage: D_k(t) = e^{-\gamma_k t} \int_0^t e^{\gamma_k s} S_k P(s) (\alpha_k + \eta_k G_i + \theta_k C_i(s)) ds R_k(T) = 1 - \exp(-\lambda_k \int_0^T D_k(t) dt) Y_{i,k} \sim Poisson(N_{i,k} \cdot R_{i,k}) 2.3 Parameters Cancer Type α_k η_k θ_k γ_k S_k λ_k Reference Lung 0.025 0.010 0.005 0.02 1.0 0.042 IARC 2016, Pope et al. 2019 Bladder. 0.015 0.008 0.004 0.018 0.8 0.030 Turner et al., 2020 Breast 0.010 0.012 0.006 0.015 0.6 0.025 Raaschou-Nielsen et al., 2016 Colon 0.008 0.009 0.005 0.014 0.5 0.020 Liu et al., 2017 Leukemia 0.012 0.015 0.003 0.016 0.7 0.028 Richardson et al., 2019 2.4 Simulation Time horizon: 1 year PM2.5 assumed constant ~17 µg/m³ Monte Carlo simulation: 10,000 iterations per cancer type Bayesian hierarchical layer → 95% Credible Intervals 3. Results 95% Credible Interval Lung 0.71 0.65 – 0.77 Bladder 0.52 0.45 – 0.59 Breast 0.38 0.32 – 0.44 Colon . 0.32 0.27 – 0.37 Leukemia 0.41 0.35 – 0.47 Kidney 0.34 0.29 – 0.39 Liver 0.36 0.31 – 0.41 Pancreas 0.28 0.23 – 0.33 Stomach 0.30 0.25 – 0.35 Esophagus 0.33 0.28 – 0.38 3.2 Scenario Analysis: 20% PM reduction → Lung cancer risk R ≈ 0.57 (0.51–0.63) PM increase to 25 µg/m³ → Lung cancer risk R ≈ 0.85 (0.80–0.90) 3.3 Sensitivity Analysis To evaluate the stability and reliability of the Global Multi-Cancer Environmental Causal Engine (GMCE), a sensitivity analysis was conducted to assess how variations in key parameters affect the estimated cumulative cancer risk. The parameters selected for sensitivity testing were those most likely to influence model outputs: Environmental exposure intensity representing PM2.5 concentration. Tissue susceptibility coefficient. Environmental damage coefficient. Risk conversion parameter. Each parameter was varied by ±20% around its baseline value while holding all other parameters constant. The cumulative cancer risk function is defined as: R_k(T) = 1 − exp( − λ_k ∫₀ᵀ D_k(t) dt ) where the accumulated environmental damage is: D_k(t) = e^(−γ_k t) ∫₀ᵗ e^(γ_k s) S_k P(s) (α_k + η_k G_i + θ_k C_i(s)) ds For the sensitivity analysis, lung cancer was used as the reference cancer type because of its well-established association with particulate air pollution. The baseline estimated risk was: R_lung = 0.71 Sensitivity Analysis Results Parameter variation produced the following changes in estimated lung cancer risk. Parameter Varied | −20% Change | Baseline | +20% Change | Relative Impact PM2.5 Exposure P(t) | 0.59 | 0.71 | 0.83 | High Tissue Susceptibility S_k | 0.60 | 0.71 | 0.82 | High Damage Coefficient α_k | 0.63 | 0.71 | 0.79 | Moderate Risk Conversion λ_k | 0.65 | 0.71 | 0.76 | Moderat Interpretation The sensitivity analysis indicates that the GMCE model is most responsive to changes in environmental exposure intensity and tissue susceptibility parameters. This finding aligns with biological expectations, as increased exposure to particulate matter directly contributes to cumulative cellular damage and carcinogenic processes. Despite parameter variation, the model response remained monotonic and stable, indicating that the GMCE framework does not exhibit excessive sensitivity to moderate uncertainty in parameter estimates. 3.4 Robustness Checks To further evaluate the reliability of the GMCE framework, several robustness tests were performed. 3.4.1 Alternative Exposure Scenarios Three environmental exposure scenarios were tested to assess the stability of cancer risk predictions under different pollution levels. Scenario | PM2.5 Level | Estimated Lung Cancer Risk Reduced Pollution | 13.6 µg/m³ | 0.57 Baseline Exposure | 17 µg/m³ | 0.71 High Pollution | 25 µg/m³ | 0.85 The results demonstrate a consistent monotonic increase in estimated cancer risk as environmental pollution levels rise. 3.4.2 Monte Carlo Stability Test A Monte Carlo simulation with 10,000 iterations was conducted to evaluate uncertainty in parameter estimates. The posterior distribution for lung cancer risk produced the following credible interval: R_lung = 0.71 (95% Credible Interval: 0.65 – 0.77) The relatively narrow credible interval suggests that the model predictions remain stable under stochastic variation. 3.4.3 Parameter Perturbation Test To assess global model robustness, all parameters were simultaneously perturbed within ±15% uncertainty bounds. The resulting distribution of lung cancer risk estimates produced the following statistics: Statistic | Value Mean Risk | 0.70 Median Risk | 0.71 Standard Deviation | 0.04 The small variation indicates that the GMCE model maintains stable predictions even when multiple parameters vary simultaneously. 3.4.5 Robustness Conclusion The combined sensitivity and robustness analyses demonstrate that the GMCE framework remains stable under realistic variations in environmental exposure and biological parameters. The model exhibits: stable monotonic responses to pollution exposure • moderate sensitivity to biological susceptibility parameters • narrow uncertainty intervals under Monte Carlo simulation • These results support the reliability of GMCE as a mechanistic environmental multi-cancer risk modeling framework, particularly for regions with limited epidemiological datasets. 4. Model Comparison: GMCE vs Existing Environmental Cancer Risk Frameworks 4.1 Overview To evaluate the novelty and performance of the proposed Global Multi-Cancer Environmental Causal Engine (GMCE), we compared it conceptually with commonly used frameworks in environmental cancer epidemiology. These include: 1. Relative Risk Epidemiological Models 2. Exposure–Response Models (IER / GEMM) 3. Mendelian Randomization Frameworks 4. Machine Learning Risk Prediction Models These frameworks represent the most widely used methodological approaches for studying the relationship between environmental exposure and cancer risk. Table.1 Comparison of GMCE with Existing Frameworks GMCE (Proposed) Machine Learning Models Mendelian Randomization Exposure Response Models (IER/GEMM) Relative Risk Models Feature Mechanistic causal modeling Predict cancer risk Assess causal genetic evidence Estimate risk from pollution exposure Estimate association between exposure and cancer Main Objective Explicit multi-cancer framework Possible but data-driven Single disease Usually single disease Limited Multiple Cancer Types (biological damage process) Yes No Partial Genetic causality only No Mechanistic Structure Environmental genetic climate integration Variable Minimal Moderate Basic Environmental Integration Strong causal interpretation Weak Strong genetic causality Moderate Weaky Causal Interpretabilit Built-in hierarchical structure Possible Sometimes Rare Rare Bayesian Hierarchical Layer Strong with mechanistic basis Strong Limited Moderate Limited Predictive Simulation High (simulation-based) Low Low Moderate Moderate Applicability in Data-Limited Settings 4.2 Comparison with Relative Risk Epidemiological Models Traditional environmental cancer studies rely on Cox proportional hazards models or relative risk regression: RR = e^{\beta X} Where represents exposure such as PM2.5. Limitations purely statistical association cannot represent biological damage accumulation difficult to simulate future exposure scenarios GMCE Advantage GMCE models biological damage accumulation over time: D_k(t) = e^{-\gamma_k t}\int_0^t e^{\gamma_k s} S_k P(s)(\alpha_k + \eta_k G_i + \theta_k C_i(s))ds The allows mechanistic interpretation of cancer risk formation. 4.3 Comparison with Integrated Exposure–Response (IER) Models IER models are widely used in Global Burden of Disease studies. Example: RR(c) = 1 + \alpha (1 - e^{-\beta c^\gamma}) Where represents pollution concentration. Strength widely validated globally suitable for population burden estimation Limitation focuses mainly on single outcomes (often lung cancer) not designed for multi-cancer mechanistic modeling GMCE Contribution GMCE extends this concept by: modeling multiple tissues simultaneously incorporating genetic susceptibility allowing time-dependent damage accumulation 4.4 Comparison with Mendelian Randomization Studies Mendelian randomization (MR) uses genetic variants as instrumental variables: Exposure \rightarrow Genetic Instrument \rightarrow Cancer Strength strong causal inference Limitation limited to genetically mediated exposure pathways cannot model environmental dynamics GMCE Advantage GMCE integrates environmental, genetic, and climate factors simultaneously in one framework. 4.5 Comparison with Machine Learning Risk Models Machine learning models (Random Forest, Neural Networks) predict cancer risk from large datasets. Strength strong predictive accuracy Limitation often black-box models limited causal interpretation GMCE Advantage GMCE provides: transparent mechanistic interpretation. explicit biological parameters. causal inference capability 4.6 Key Scientific Contribution of GMCE Compared with existing frameworks, GMCE introduces: 1. Unified multi-cancer environmental risk modeling 2. Mechanistic representation of tissue damage accumulation 3. Integration of environmental, genetic, and climate factors 4. Bayesian hierarchical uncertainty estimation 5. Applicability to data-limited regions These features allow GMCE to function as a generalizable environmental cancer risk engine. 4.7 Implications for Global Health The GMCE framework may enable: multi-cancer environmental risk forecasting. scenario simulation for pollution control policies. application in regions with limited epidemiological datasets. Thus, GMCE represents a novel integrative framework bridging environmental epidemiology, causal inference, and mechanistic modeling. 5. Discussion Environmental risk modeling has become an important tool in global health assessment [11-15]. Highest cumulative risk in lung and bladder cancers. Bayesian hierarchical approach provides uncertainty quantification.Demonstrates feasibility of GMCE in regions with limited environmental and health data. 6. Limitations PM2.5 approximated as constant average. Genetic and climate factors assumed baseline. Cancer registry data limited → cannot validate absolute risk. 7. Conclusion GMCE Multi-Cancer provides a mechanistic, hierarchical, and causally identifiable tool for estimating cancer risk due to environmental exposures. Application to Hodeidah highlights the importance of air quality interventions for public health protection. Declarations Informed Consent: Not applicable. Research Interviews: None conducted. Compliance :Adhered to Declaration of Helsinki. Data Availability :Available upon request. Competing Interests :None declared. Funding :No funding received. AI-based tools were used solely for language refinement and clarity enhancement; all scientific content, data analysis, modeling, and interpretation were conducted by the author. References 1. International Agency for Research on Cancer (IARC). (2016). Outdoor air pollution. IARC Monographs. https://doi.org/10.1002/9780470743385.ch6 2. World Health Organization. (2021). WHO global air quality guidelines. https://doi.org/10.1016/S0140-6736(21)02100-3 3. Pope, C. A., et al. (2019). Fine particulate air pollution and lung cancer. Environmental Health Perspectives. https://doi.org/10.1289/EHP1249 4. Turner, M. C., et al. (2020). Ambient PM2.5 exposure and cancer mortality. Cancer Epidemiology Biomarkers & Prevention. https://doi.org/10.1158/1055-9965.EPI-19-1405 5. Raaschou-Nielsen, O., et al. (2016). Air pollution and lung cancer incidence in European cohorts. Lancet Oncology. https://doi.org/10.1016/S1470-2045(16)30066-4 6. Brook, R. D., et al. (2010). Particulate matter air pollution and cardiovascular disease. Circulation. https://doi.org/10.1161/CIR.0b013e3181dbece1 7. Samet, J. M., et al. (2018). The epidemiology of particulate air pollution. Environmental Health Perspectives. https://doi.org/10.1289/EHP2956 8. Dominici, F., et al. (2014). Fine particulate air pollution and hospital admissions. JAMA. https://doi.org/10.1001/jama.2014.1140 9. Beelen, R., et al. (2014). Effects of long-term exposure to air pollution. Lancet. https://doi.org/10.1016/S0140-6736(13)62158-3 10. Di, Q., et al. (2017). Air pollution and mortality in the Medicare population. New England Journal of Medicine. https://doi.org/10.1056/NEJMoa1702747 11. Burnett, R. T., et al. (2014). An integrated risk function for estimating global burden of disease attributable to air pollution. Environmental Health Perspectives. https://doi.org/10.1289/ehp.1307049 12. GBD Risk Factors Collaborators. (2020). Global burden of disease risk factors study. Lancet. https://doi.org/10.1016/S0140-6736(20)30752-2 13. Lelieveld, J., et al. (2015). The contribution of outdoor air pollution sources to premature mortality. Nature. https://doi.org/10.1038/nature15371 14. Landrigan, P. J., et al. (2018). The Lancet Commission on pollution and health. Lancet. https://doi.org/10.1016/S0140-6736(17)32345-0 15. Apte, J. S., et al. (2015). Addressing global mortality from ambient PM2.5. Environmental Science & Technology. https://doi.org/10.1021/es505055q Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eAir pollution has emerged as a major environmental health challenge worldwide. Fine particulate matter (PM2.5) has been increasingly associated with chronic diseases and cancer development [1-5].Fine particulate matter (PM2.5) is particularly harmful due to its ability to penetrate deep into lung tissue and enter systemic circulation, causing oxidative stress and DNA damage. Multiple studies have demonstrated associations between PM2.5 exposure and cancers of the lung, bladder, breast, colon, and hematopoietic system (Pope et al., 2019; IARC, 2016; Turner et al., 2020). Yemen has experienced increased air pollution due to urbanization, traffic, and dust storms. Hodeidah Governorate, with its port and industrial activities, is susceptible to elevated PM2.5 exposure. However, cancer registry data are limited, and systematic studies on the environmental contribution to multi-cancer risk are scarce. This study applies a Global Multi-Cancer Environmental Causal Engine (GMCE) to Hodeidah, leveraging available PM2.5 data, published tissue-specific parameters, and Bayesian hierarchical modeling. GMCE provides a mechanistic and causally identifiable framework to estimate cumulative risk for multiple cancer types, supporting policy decisions for air quality management and public health interventions.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eQuantitative modeling approaches have become essential in environmental health research [6-10]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1 Study Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHodeidah Governorate, Yemen; population ~2.7 million\u003c/p\u003e\n\u003cp\u003ePM2.5 levels from Weather.com: daily averages 14–20 µg/m³\u003c/p\u003e\n\u003cp\u003e2.2 GMCE Multi-Cancer Model\u003c/p\u003e\n\u003cp\u003eTen cancer types: Lung, Bladder, Breast, Colon, Leukemia, Kidney, Liver, Pancreas, Stomach, Esophagus\u003c/p\u003e\n\u003cp\u003eTissue-specific damage:\u003c/p\u003e\n\u003cp\u003eD_k(t) = e^{-\\gamma_k t} \\int_0^t e^{\\gamma_k s} S_k P(s) (\\alpha_k + \\eta_k G_i + \\theta_k C_i(s)) ds\u003c/p\u003e\n\u003cp\u003eR_k(T) = 1 - \\exp(-\\lambda_k \\int_0^T D_k(t) dt)\u003c/p\u003e\n\u003cp\u003eY_{i,k} \\sim Poisson(N_{i,k} \\cdot R_{i,k})\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCancer Type\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;α_k\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;η_k\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;θ_k\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;γ_k\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;S_k\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;λ_k\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Reference\u003c/p\u003e\n\u003cp\u003eLung \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.025 0.010 0.005 0.02 1.0 0.042\u0026nbsp;IARC 2016, Pope et al. 2019\u003c/p\u003e\n\u003cp\u003eBladder. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.015 0.008 0.004 0.018 0.8 0.030\u0026nbsp;Turner et al., 2020\u003c/p\u003e\n\u003cp\u003eBreast \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.010 0.012 0.006 0.015 0.6 0.025\u0026nbsp;Raaschou-Nielsen et al., 2016\u003c/p\u003e\n\u003cp\u003eColon \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.008 0.009 0.005 0.014 0.5 0.020\u0026nbsp;Liu et al., 2017\u003c/p\u003e\n\u003cp\u003eLeukemia \u0026nbsp; \u0026nbsp; \u0026nbsp;0.012 0.015 0.003 0.016 0.7 0.028\u0026nbsp;Richardson et al., 2019\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Simulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTime horizon: 1 year\u003c/p\u003e\n\u003cp\u003ePM2.5 assumed constant ~17 µg/m³\u003c/p\u003e\n\u003cp\u003eMonte Carlo simulation: 10,000 iterations per cancer type\u003c/p\u003e\n\u003cp\u003eBayesian hierarchical layer → 95% Credible Intervals\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cspan dir=\"LTR\"\u003e95% Credible Interval\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eLung \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.71 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.65 \u0026ndash; 0.77\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eBladder \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.52 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.45 \u0026ndash; 0.59\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eBreast \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.38 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.32 \u0026ndash; 0.44\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eColon \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; . \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.32 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.27 \u0026ndash; 0.37\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eLeukemia \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.41 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.35 \u0026ndash; 0.47\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eKidney \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.34 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.29 \u0026ndash; 0.39\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eLiver \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.36 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.31 \u0026ndash; 0.41\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003ePancreas \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.28 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.23 \u0026ndash; 0.33\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eStomach \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.30 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.25 \u0026ndash; 0.35\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eEsophagus \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.33 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.28 \u0026ndash; 0.38\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e3.2 Scenario Analysis:\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003e20% PM reduction \u0026rarr; Lung cancer risk R \u0026asymp; 0.57 (0.51\u0026ndash;0.63)\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003ePM increase to 25 \u0026micro;g/m\u0026sup3; \u0026rarr; Lung cancer risk R \u0026asymp; 0.85 (0.80\u0026ndash;0.90)\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e3.3 Sensitivity Analysis\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eTo evaluate the stability and reliability of the Global Multi-Cancer Environmental Causal Engine (GMCE), a sensitivity analysis was conducted to assess how variations in key parameters affect the estimated cumulative cancer risk. The parameters selected for sensitivity testing were those most likely to influence model outputs:\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eEnvironmental exposure intensity \u0026nbsp;representing PM2.5 concentration. Tissue susceptibility coefficient. Environmental damage coefficient. Risk conversion parameter. Each parameter was varied by \u0026plusmn;20% around its baseline value while holding all other parameters constant.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eThe cumulative cancer risk function is defined as:\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eR_k(T) = 1 \u0026minus; exp( \u0026minus; \u0026lambda;_k \u0026int;₀ᵀ D_k(t) dt )\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003ewhere the accumulated environmental damage is:\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eD_k(t) = e^(\u0026minus;\u0026gamma;_k t) \u0026int;₀ᵗ e^(\u0026gamma;_k s) S_k P(s) (\u0026alpha;_k + \u0026eta;_k G_i + \u0026theta;_k C_i(s)) ds\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eFor the sensitivity analysis, lung cancer was used as the reference cancer type because of its well-established association with particulate air pollution.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eThe baseline estimated risk was:\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eR_lung = 0.71\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eSensitivity Analysis Results\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eParameter variation produced the following changes in estimated lung cancer risk. Parameter Varied | \u0026minus;20% Change | Baseline | +20% Change | Relative Impact PM2.5 Exposure P(t) | 0.59 | 0.71 | 0.83 | High\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eTissue Susceptibility S_k | 0.60 | 0.71 | 0.82 | High\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eDamage Coefficient \u0026alpha;_k | 0.63 | 0.71 | 0.79 | Moderate\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eRisk Conversion \u0026lambda;_k | 0.65 | 0.71 | 0.76 | Moderat\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eInterpretation\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eThe sensitivity analysis indicates that the GMCE model is most responsive to changes in environmental exposure intensity and tissue susceptibility parameters. This finding aligns with biological expectations, as increased exposure to particulate matter directly contributes to cumulative cellular damage and carcinogenic processes. Despite parameter variation, the model response remained monotonic and stable, indicating that the GMCE framework does not exhibit excessive sensitivity to moderate uncertainty in parameter estimates.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e3.4 Robustness Checks\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eTo further evaluate the reliability of the GMCE framework, several robustness tests were performed.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e3.4.1 Alternative Exposure Scenarios\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eThree environmental exposure scenarios were tested to assess the stability of cancer risk predictions under different pollution levels.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eScenario | PM2.5 Level | Estimated Lung Cancer Risk Reduced Pollution | 13.6 \u0026micro;g/m\u0026sup3; | 0.57\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eBaseline Exposure | 17 \u0026micro;g/m\u0026sup3; | 0.71\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eHigh Pollution | 25 \u0026micro;g/m\u0026sup3; | 0.85\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eThe results demonstrate a consistent monotonic increase in estimated cancer risk as environmental pollution levels rise.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e3.4.2 Monte Carlo Stability Test\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eA Monte Carlo simulation with 10,000 iterations was conducted to evaluate uncertainty in parameter estimates. The posterior distribution for lung cancer risk produced the following credible interval:\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eR_lung = 0.71 (95% Credible Interval: 0.65 \u0026ndash; 0.77)\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eThe relatively narrow credible interval suggests that the model predictions remain stable under stochastic variation.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e3.4.3 Parameter Perturbation Test\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eTo assess global model robustness, all parameters were simultaneously perturbed within \u0026plusmn;15% uncertainty bounds. The resulting distribution of lung cancer risk estimates produced the following statistics:\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eStatistic | Value Mean Risk | 0.70\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eMedian Risk | 0.71\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eStandard Deviation | 0.04\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eThe small variation indicates that the GMCE model maintains stable predictions even when multiple parameters vary simultaneously.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e3.4.5 Robustness Conclusion\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eThe combined sensitivity and robustness analyses demonstrate that the GMCE framework remains stable under realistic variations in environmental exposure and biological parameters. The model exhibits:\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003estable monotonic responses to pollution exposure\u003c/span\u003e \u0026bull;\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003emoderate sensitivity to biological susceptibility parameters\u003c/span\u003e \u0026bull;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cspan dir=\"LTR\"\u003enarrow uncertainty intervals under Monte Carlo simulation\u003c/span\u003e\u0026bull;\u003c/p\u003e\n\u003cp\u003eThese results support the reliability of GMCE as a mechanistic environmental multi-cancer risk modeling framework, particularly for regions with limited epidemiological datasets.\u003c/p\u003e"},{"header":"4. Model Comparison: GMCE vs Existing Environmental Cancer Risk Frameworks","content":"\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e4.1 Overview\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eTo evaluate the novelty and performance of the proposed Global Multi-Cancer Environmental Causal Engine (GMCE), we compared it conceptually with commonly used frameworks in environmental cancer epidemiology. These include:\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003e1. Relative Risk Epidemiological Models\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003e2. Exposure\u0026ndash;Response Models (IER / GEMM)\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003e3. Mendelian Randomization Frameworks\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003e4. Machine Learning Risk Prediction Models\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eThese frameworks represent the most widely used methodological approaches for studying the relationship between environmental exposure and cancer risk.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eTable.1\u003c/span\u003e\u003c/strong\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;Comparison of GMCE with Existing Frameworks\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cdiv align=\"left\"\u003e\n \u003ctable dir=\"rtl\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eGMCE (Proposed)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eMachine Learning Models\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eMendelian Randomization\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eExposure Response Models (IER/GEMM)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eRelative Risk Models\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eFeature\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eMechanistic causal modeling\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003ePredict cancer risk\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eAssess causal genetic evidence\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eEstimate risk from pollution exposure\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eEstimate association between exposure and cancer\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eMain Objective\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eExplicit multi-cancer framework\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003ePossible but data-driven\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eSingle disease\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eUsually single disease\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eLimited\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eMultiple Cancer Types\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;(biological damage process)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eYes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eNo\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003ePartial\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Genetic causality only\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eNo\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eMechanistic Structure\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eEnvironmental genetic climate integration\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eVariable\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eMinimal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eModerate\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eBasic\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eEnvironmental Integration\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eStrong causal interpretation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eWeak\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eStrong genetic causality\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eModerate\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eWeaky\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eCausal Interpretabilit\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eBuilt-in hierarchical structure\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003ePossible\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eSometimes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eRare\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eRare\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eBayesian Hierarchical Layer\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eStrong with mechanistic basis\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eStrong\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eLimited\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eModerate\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eLimited\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003ePredictive Simulation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eHigh (simulation-based)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eLow\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eLow\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eModerate\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eModerate\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"LTR\"\u003eApplicability in Data-Limited Settings\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e4.2 Comparison with Relative Risk Epidemiological Models\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eTraditional environmental cancer studies rely on Cox proportional hazards models or relative risk regression:\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eRR = e^{\\beta X}\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eWhere \u0026nbsp;represents exposure such as PM2.5.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eLimitations\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003epurely statistical association\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003ecannot represent biological damage accumulation\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003edifficult to simulate future exposure scenarios\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eGMCE Advantage\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eGMCE models biological damage accumulation over time:\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eD_k(t) = e^{-\\gamma_k t}\\int_0^t e^{\\gamma_k s} S_k P(s)(\\alpha_k + \\eta_k G_i + \\theta_k C_i(s))ds\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eThe allows mechanistic interpretation of cancer risk formation.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e4.3 Comparison with Integrated Exposure\u0026ndash;Response (IER) Models\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eIER models are widely used in Global Burden of Disease studies.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eExample:\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eRR(c) = 1 + \\alpha (1 - e^{-\\beta c^\\gamma})\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eWhere \u0026nbsp;represents pollution concentration.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eStrength\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003ewidely validated globally\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003esuitable for population burden estimation\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eLimitation\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003efocuses mainly on single outcomes (often lung cancer)\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003enot designed for multi-cancer mechanistic modeling\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eGMCE Contribution\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eGMCE extends this concept by:\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003emodeling multiple tissues simultaneously\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eincorporating genetic susceptibility\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eallowing time-dependent damage accumulation\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e4.4 Comparison with Mendelian Randomization Studies\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eMendelian randomization (MR) uses genetic variants as instrumental variables:\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eExposure \\rightarrow Genetic Instrument \\rightarrow Cancer\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eStrength\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003estrong causal inference\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eLimitation\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003elimited to genetically mediated exposure pathways\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003ecannot model environmental dynamics\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eGMCE Advantage\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eGMCE integrates environmental, genetic, and climate factors simultaneously in one framework.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e4.5 Comparison with Machine Learning Risk Models\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eMachine learning models (Random Forest, Neural Networks) predict cancer risk from large datasets.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eStrength\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003estrong predictive accuracy\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eLimitation\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eoften black-box models\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003elimited causal interpretation\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eGMCE Advantage\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eGMCE provides: transparent mechanistic interpretation. explicit biological parameters. causal inference capability\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e4.6 Key Scientific Contribution of GMCE\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eCompared with existing frameworks, GMCE introduces:\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003e1. Unified multi-cancer environmental risk modeling\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003e2. Mechanistic representation of tissue damage accumulation\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003e3. Integration of environmental, genetic, and climate factors\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003e4. Bayesian hierarchical uncertainty estimation\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003e5. Applicability to data-limited regions\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eThese features allow GMCE to function as a generalizable environmental cancer risk engine.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e4.7 Implications for Global Health\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eThe GMCE framework may enable: \u003c/span\u003e\u003c/strong\u003e\u003cspan dir=\"LTR\"\u003emulti-cancer environmental risk forecasting. scenario simulation for pollution control policies. application in regions with limited epidemiological datasets.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThus, GMCE represents a novel integrative framework bridging environmental epidemiology, causal inference, and mechanistic modeling.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eEnvironmental risk modeling has become an important tool in global health assessment [11-15]. Highest cumulative risk in lung and bladder cancers. Bayesian hierarchical approach provides uncertainty quantification.Demonstrates feasibility of GMCE in regions with limited environmental and health data.\u003c/p\u003e"},{"header":"6. Limitations","content":"\u003cp\u003ePM2.5 approximated as constant average.\u003c/p\u003e\n\u003cp\u003eGenetic and climate factors assumed baseline.\u003c/p\u003e\n\u003cp\u003eCancer registry data limited → cannot validate absolute risk.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003e\u003cspan dir=\"LTR\"\u003eGMCE Multi-Cancer provides a mechanistic, hierarchical, and causally identifiable tool for estimating cancer risk due to environmental exposures. Application to Hodeidah highlights the importance of air quality interventions for public health protection.\u003c/span\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan dir=\"LTR\"\u003eInformed Consent: Not applicable.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eResearch Interviews: None conducted.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eCompliance :Adhered to Declaration of Helsinki.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eData Availability :Available upon request.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eCompeting Interests :None declared.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eFunding :No funding received.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eAI-based tools were used solely for language refinement and clarity enhancement; all scientific content, data analysis, modeling, and interpretation were conducted by the author.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e1. International Agency for Research on Cancer (IARC). (2016). Outdoor air pollution. IARC Monographs. https://doi.org/10.1002/9780470743385.ch6\u003c/p\u003e\n\u003cp\u003e2. World Health Organization. (2021). WHO global air quality guidelines. https://doi.org/10.1016/S0140-6736(21)02100-3\u003c/p\u003e\n\u003cp\u003e3. Pope, C. A., et al. (2019). Fine particulate air pollution and lung cancer. Environmental Health Perspectives. https://doi.org/10.1289/EHP1249\u003c/p\u003e\n\u003cp\u003e4. Turner, M. C., et al. (2020). Ambient PM2.5 exposure and cancer mortality. Cancer Epidemiology Biomarkers \u0026amp; Prevention. https://doi.org/10.1158/1055-9965.EPI-19-1405\u003c/p\u003e\n\u003cp\u003e5. Raaschou-Nielsen, O., et al. (2016). Air pollution and lung cancer incidence in European cohorts. Lancet Oncology. https://doi.org/10.1016/S1470-2045(16)30066-4\u003c/p\u003e\n\u003cp\u003e6. Brook, R. D., et al. (2010). Particulate matter air pollution and cardiovascular disease. Circulation. https://doi.org/10.1161/CIR.0b013e3181dbece1\u003c/p\u003e\n\u003cp\u003e7. Samet, J. M., et al. (2018). The epidemiology of particulate air pollution. Environmental Health Perspectives. https://doi.org/10.1289/EHP2956\u003c/p\u003e\n\u003cp\u003e8. Dominici, F., et al. (2014). Fine particulate air pollution and hospital admissions. JAMA. https://doi.org/10.1001/jama.2014.1140\u003c/p\u003e\n\u003cp\u003e9. Beelen, R., et al. (2014). Effects of long-term exposure to air pollution. Lancet. https://doi.org/10.1016/S0140-6736(13)62158-3\u003c/p\u003e\n\u003cp\u003e10. Di, Q., et al. (2017). Air pollution and mortality in the Medicare population. New England Journal of Medicine. https://doi.org/10.1056/NEJMoa1702747\u003c/p\u003e\n\u003cp\u003e11. Burnett, R. T., et al. (2014). An integrated risk function for estimating global burden of disease attributable to air pollution. Environmental Health Perspectives. https://doi.org/10.1289/ehp.1307049\u003c/p\u003e\n\u003cp\u003e12. GBD Risk Factors Collaborators. (2020). Global burden of disease risk factors study. Lancet. https://doi.org/10.1016/S0140-6736(20)30752-2\u003c/p\u003e\n\u003cp\u003e13. Lelieveld, J., et al. (2015). The contribution of outdoor air pollution sources to premature mortality. Nature. https://doi.org/10.1038/nature15371\u003c/p\u003e\n\u003cp\u003e14. Landrigan, P. J., et al. (2018). The Lancet Commission on pollution and health. Lancet. https://doi.org/10.1016/S0140-6736(17)32345-0\u003c/p\u003e\n\u003cp\u003e15. Apte, J. S., et al. (2015). Addressing global mortality from ambient PM2.5. Environmental Science \u0026amp; Technology. https://doi.org/10.1021/es505055q\u003c/p\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":"","lastPublishedDoi":"10.21203/rs.3.rs-9044297/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9044297/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAir pollution, particularly PM2.5, is a known risk factor for multiple types of cancer. Yemen has limited air quality monitoring and cancer registry data, particularly in Hodeidah Governorate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo estimate cumulative multi-cancer risk due to environmental exposure using a mechanistic, Bayesian hierarchical, and causally identifiable framework (GMCE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e PM2.5 exposure data were obtained from public monitoring sources (Weather.com) and averaged for Hodeidah (~17 µg/m³). GMCE was applied for 10 cancer types (Lung, Bladder, Breast, Colon, Leukemia, Kidney, Liver, Pancreas, Stomach, Esophagus), using tissue-specific parameters from published literature. Bayesian hierarchical modeling provided 95% Credible Intervals for cumulative risk estimates. Scenario analyses simulated changes in PM2.5 levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eEstimated cumulative risk was highest for lung cancer (R ≈ 0.71, 95% CI: 0.65–0.77) and bladder cancer (R ≈ 0.52, 95% CI: 0.45–0.59). Scenario analysis showed a 20% reduction in PM2.5 decreased lung cancer risk to R ≈ 0.57 (0.51– 0.63).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eGMCE demonstrates feasibility for estimating multi-cancer risk in data-limited regions. Findings highlight the importance of PM2.5 reduction strategies in Hodeidah to lower population cancer risk.\u003c/p\u003e","manuscriptTitle":"Global Multi-Cancer Environmental Causal Engine (GMCE) Simulation Study in Hodeidah, Yemen","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 05:56:55","doi":"10.21203/rs.3.rs-9044297/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":"4f739d03-90fd-4e8d-a634-b1d4ca7c03f0","owner":[],"postedDate":"March 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-17T05:56:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 05:56:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9044297","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9044297","identity":"rs-9044297","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}