Global Correlation Between Cancer Incidence and Dementia Incidence Based on Cross National Regression Analyses

Global Correlation Between Cancer Incidence and Dementia Incidence Based on Cross National Regression Analyses | 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 Global Correlation Between Cancer Incidence and Dementia Incidence Based on Cross National Regression Analyses Wenpeng You, Brendon J Coventry, Maciej Henneberg This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7533062/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: Cancer and dementia are two major global health challenges shaped by population ageing and socioeconomic transitions. Both impose substantial burdens, yet their interrelationship at the population level remains underexplored. This study examined the global relationship between cancer incidence and dementia incidence, taking into account developmental, demographic, and healthcare-related covariates. Methods: Data on cancer incidence and dementia incidence were obtained from the Institute for Health Metrics and Evaluation. Covariates included economic affluence, urbanisation, selection opportunity, and life expectancy at age 60. Analyses across 204 countries employed Pearson and Spearman correlations, partial correlations, principal component analysis, and multiple linear regression (enter and stepwise). Subgroup analyses were stratified by World Bank income level, UN development status, WHO regions, and additional geopolitical groupings. Results: Cancer incidence was strongly correlated with dementia incidence globally (r = 0.873; ρ = 0.938, p < 0.001). Associations remained robust across regions and income groups, particularly in upper-middle-income and developing countries. Partial correlations confirmed the relationship persisted after adjusting for covariates, with cancer explaining 59.8% of dementia variance. In regression models, socioeconomic and demographic factors explained 51.7% of variance; adding cancer increased explanatory power to 80.1%. Cancer uniquely accounted for 28.3% in the enter model and 28.8% in the stepwise model, confirming its role as the dominant independent predictor. Conclusion: Cancer incidence is strongly and independently associated with dementia incidence worldwide, surpassing traditional predictors. Findings highlight shared determinants and underscore the importance of integrated chronic disease strategies, particularly in low-resource settings. Cancer epidemiology Dementia epidemiology Alzheimer Disease Global Health Socioeconomic Factors Life Expectancy Ecological Studies Figures Figure 1 Background Cancer and dementia are two of the most pressing global health challenges of the 21 st century, each imposing profound social, economic, and healthcare burdens [1, 2]. Both conditions are shaped by population ageing, socioeconomic development, and demographic transitions [1, 3, 4]. Dementia, encompassing Alzheimer’s disease and related disorders, has become a leading cause of disability and dependency among older adults, with incidence rising in parallel with life expectancy [5]. Cancer remains a major cause of morbidity and mortality worldwide, its incidence amplified by demographic shifts, lifestyle transitions, and improvements in diagnostic capacity [6]. While often studied in isolation, the coexistence of cancer and dementia at the population level suggests overlapping determinants and potential shared pathways that merit systematic investigation [7, 8]. Epidemiological findings on the cancer–dementia relationship are mixed [9]. At the individual level, several studies report an inverse association, with cancer survivors showing reduced dementia risk, possibly due to selective survival, competing risks, or protective oncological/ immunological mechanisms [10, 11]. At the population level, however, patterns may differ. Ecological studies capture the influence of demographic and structural drivers such as longevity, affluence, urbanisation, and healthcare infrastructure that shape disease distributions [1]. Countries with high cancer incidence often also report elevated dementia incidence, underscoring the role of shared contextual determinants [12]. Examining these associations at a global scale can provide critical insight into how chronic diseases interrelate within ageing societies. Socioeconomic and demographic factors are central to the epidemiology of both diseases [1, 13, 14]. Economic affluence is positively linked to measured cancer incidence through greater healthcare access and lifestyle risks tied to economic transition [15]. Dementia prevalence is likewise higher in affluent settings, where longer survival and stronger diagnostic capacity increase case detection [16]. Urbanisation introduces environmental exposures and lifestyle changes that increase the risk of both diseases [1, 16]. Moreover, declining global birth rates have been postulated as additional risk factors for these conditions [17, 18]. Complementary measures, such as extended life expectancy [19], and “relaxation of selection opportunity,” an index of natural selection strength [20-22], offer further insight into how population structure shapes vulnerability to chronic disease. Despite these overlaps, the direct global relationship between cancer incidence and dementia incidence remains insufficiently studied [23]. Few analyses have tested whether cancer is independently predictive of dementia after accounting for socioeconomic and demographic covariates [24, 25]. Nor is it well established whether this association is consistent across diverse economic, developmental, and regional contexts. Addressing this gap is essential for clarifying whether cancer incidence can serve as a robust indicator of dementia burden and for guiding integrated strategies in chronic disease prevention and resource allocation [14]. This study investigates the global association between cancer and dementia incidence using ecological data from 204 countries. We specifically examined: (i) the strength and consistency of correlations worldwide and across socioeconomic and regional classifications; (ii) whether the relationship persists after adjusting for economic affluence, urbanisation, selection opportunity, and life expectancy; and (iii) the relative contribution of cancer compared with these covariates in explaining dementia incidence. By applying correlation, partial correlation, principal component, and multiple regression analyses, this study provides the most comprehensive ecological assessment to date of the cancer–dementia link. The findings not only investigate the independent coincidence of cancer incidence and dementia burden but also situate both conditions within the broader demographic and socioeconomic transformations reshaping global health. Material and Method Data sources Data on cancer and dementia incidence were obtained from the Global Burden of Disease (GBD) database, maintained by the Institute for Health Metrics and Evaluation (IHME), University of Washington, USA. Cancer incidence rates (per 100,000 population) were obtained from the 2017 dataset, while dementia incidence, encompassing Alzheimer’s disease and other dementias, was sourced from the 2021 dataset. Both sets of estimates are grounded in systematic reviews, cancer registries, health surveys, and statistical modelling conducted within the GBD framework. Socioeconomic and demographic indicators were drawn from widely recognised international sources. Economic affluence measured by gross domestic product (GDP) per capita (purchasing power parity, constant 2018 USA dollars,) and urbanisation (percentage of the population residing in urban areas, 2018) were retrieved from the World Bank’s World Development Indicators. Life expectancy at age 60 (Life e₆₀, 2018) was sourced from the World Health Organization’s (WHO) Global Health Observatory. Selection Opportunity, an index reflecting the strength of natural selection operating in human populations, was derived from previously published estimates [21]. Countries were categorised using multiple international systems to enable subgroup comparisons. These included the World Bank income classifications (low, lower-middle, upper-middle, and high income), the United Nations development categories (developed vs. developing), and the six WHO regions (AFRO, AMRO, EMRO, EURO, SEARO, WPRO). Additional political, cultural, and regional groupings (e.g., ACD, APEC, Arab World, EEA, EU, Latin America and the Caribbean, OECD, SADC, SCO, English-speaking countries) were applied to capture wider diversity in socioeconomic and geopolitical contexts. Analyses were based on data from 204 countries, with only those cases containing complete information across all variables retained in the final models. In this ecological study, cancer incidence was treated as the predictor variable and dementia incidence as the outcome. This direction reflects biological and epidemiological reasoning: dementia is primarily a late-life condition, cancer is more often diagnosed earlier, and the pathway from cancer to dementia, including possible treatment-related effects, has been advanced in prior research. This study’s ecological design minimises concerns of reverse causality, as cancer and dementia incidence were analysed at the population level rather than the individual level. Moreover, the use of population-level incidence data from independent time points (2017 for cancer and 2021 for dementia) further reduces the possibility that dementia occurrence influenced cancer reporting. This design strengthens confidence that the observed associations represent population-level determinants rather than reverse causal effects. To evaluate the generalisability of the cancer–dementia relationship, stratified analyses were undertaken across the major classification systems. These encompassed income level (World Bank), development status (UN), and regional groupings (WHO), alongside additional subgroup analyses reflecting geopolitical or cultural contexts (e.g., APEC, Arab World, English-speaking countries, EEA, EU, Latin America and the Caribbean, OECD, SADC). Only populations with complete data and an unambiguous classification were included in these stratified models. Data Preparation Data were collated from multiple international repositories to construct a comprehensive country-level dataset. Cancer incidence (2017) and dementia incidence (2021) were matched with socioeconomic and demographic indicators, including gross domestic product per capita adjusted for purchasing power parity (constant US dollars), degree of urbanisation (percentage of population living in urban areas), life expectancy at age 60 (Life e₆₀, in years), and selection opportunity, an index reflecting the strength of natural selection in human populations. To ensure comparability across sources, all variables were expressed in standardised population-based units: incidence rates per 100,000 population, percentages for urbanisation, years for life expectancy, and constant US dollars for economic affluence. Cases with missing data on any study variable were excluded using listwise deletion, resulting in a final analytic sample of 204 countries. This ensured that correlations, principal component analyses, and regression models were based on identical datasets. No data transformations, such as log conversion or rescaling, were applied, as the distributions of the main variables demonstrated acceptable ranges for both parametric and non-parametric analyses. Outliers, including very high cancer incidence values (>1,000 per 100,000), were retained to preserve global variability, with their influence addressed analytically. Regression diagnostics assessed multicollinearity among predictors. Tolerance values (≥0.10) and Variance Inflation Factor values (≤10) confirmed acceptable thresholds, reinforcing the robustness and validity of the analytical framework [26]. Statistical Analysis Descriptive statistics were used to summarize cancer incidence, dementia incidence, and selected socioeconomic and demographic indicators across countries. Associations between cancer incidence, dementia incidence, and covariates were assessed using Pearson’s correlation coefficients for parametric relationships and Spearman’s rho for non-parametric tests. Differences in correlation strength across income levels, development classifications, and regional groupings were evaluated using Fisher’s r-to-z transformations. To examine whether cancer incidence clustered with other developmental indicators, a principal component analysis (PCA) was conducted including economic affluence, urbanization, selection opportunity, and Life e₆₀. Sampling adequacy was assessed using the Kaiser–Meyer–Olkin (KMO) statistic and Bartlett’s test of sphericity. The independence of the cancer–dementia association was further tested using partial correlations, sequentially adjusting for economic affluence, urbanization, selection opportunity, Life e₆₀. Finally, multiple linear regression models were employed to predict dementia incidence. An enter method model compared explanatory power with and without cancer incidence, while a stepwise model identified the strongest predictors. For all models, standardized beta coefficients (β), adjusted R², and F-statistics were reported. Statistical significance was set at p < 0.05 (two-tailed). Analyses were conducted using IBM SPSS Statistics (version 30). Ethical Considerations and Compliance This quantitative investigation employed six variables sourced entirely from publicly accessible datasets, including open-access publications and official websites of the United Nations (UN) and its partner agencies. Approval for the secondary use and analysis of these data was obtained from the Office of Research Ethics, Compliance and Integrity (ORECI) at the University of Adelaide (Approval No. 36289). Results A strong non-linear association was observed between cancer incidence in 2017 and dementia incidence in 2021 (Figure 1). The quadratic regression model demonstrated an excellent fit (R 2 =0.884), indicating that approximately 88.4% of the variance in dementia incidence was explained by cancer incidence. Dementia incidence increased steeply with rising cancer incidence at lower to moderate levels, but the trend plateaued and slightly declined at very high cancer incidence values (>1,000 per 100,000). This pattern suggests a positive but curvilinear relationship, where higher cancer incidence is generally associated with greater dementia incidence, although the effect appears to diminish at extreme cancer incidence levels. Bivariate correlations were conducted to examine the associations between cancer incidence, dementia incidence, and selected socioeconomic and demographic indicators. Both Pearson’s parametric and Spearman’s nonparametric tests demonstrated consistent and significant patterns. Pearson’s correlations revealed a very strong positive relationship between cancer incidence and dementia incidence (r = 0.873, p < 0.001). Cancer incidence was also significantly associated with economic affluence (r = 0.647, p < 0.001), urbanization (r = 0.482, p < 0.001), relaxed selection opportunity (r = 0.528, p < 0.001), and Life e₆₀ (r = 0.637, p < 0.001). Dementia incidence similarly correlated with these developmental indicators, most strongly with life expectancy (r = 0.717, p < 0.001) and relaxed selection opportunity (r = 0.604, p < 0.001). Spearman’s rho correlations further confirmed these associations, often with stronger coefficients. Cancer incidence and dementia incidence demonstrated an exceptionally strong monotonic relationship (ρ = 0.938, p < 0.001). Cancer incidence also showed robust associations with economic affluence (ρ = 0.782), selection opportunity (ρ = 0.838), and Life e₆₀ (ρ = 0.737), all p < 0.001. Dementia incidence followed a similar pattern, with particularly high correlations with relaxed selection opportunity (ρ = 0.846) and life expectancy (ρ = 0.785). Overall, these findings indicate that at the population level cancer incidence is tightly linked with dementia incidence and that both conditions cluster within broader socioeconomic and demographic contexts. Higher affluence, longevity, and demographic opportunity are consistently associated with increased population-level vulnerability to dementia. Table 1. Correlation Matrix of Cancer Incidence, Dementia, and Key Demographic, Economic, and Healthcare Indicators Variables Cancers Incidence Dementia Incidence Economic Affluence Urbanization, Relaxed Selection Opportunity Life e₆₀ Cancers Incidence 1 0.873** 0.647** 0.482** 0.528** 0.637** Dementia Incidence 0.938** 1 0.597** 0.498** 0.604** 0.717** Economic Affluence 0.782** 0.761** 1 0.647** 0.571** 0.714** Urbanization 0.561** 0.527** 0.718** 1 0.533** 0.664** Selection Opportunity 0.838** 0.846** 0.896** 0.631** 1 0.729** Life e₆₀ 0.737** 0.785** 0.811** 0.676** 0.834** 1 Significance level: **p < 0.01 Note: Pearson’s r values are presented above the diagonal, and Spearman’s ρ values are presented below the diagonal. Data on cancer incidence and dementia incidence were extracted from the Global Burden of Disease (GBD) study curated by the Institute for Health Metrics and Evaluation (IHME). Life expectancy at age 60 (Life e₆₀) was sourced from the World Health Organization (WHO) Global Health Observatory. Economic affluence, measured as GDP per capita (PPP, constant 2018 US dollars), and urbanization, defined as the percentage of the population living in urban areas, were obtained from the World Bank’s World Development Indicators database. Selection Opportunity, measured using the Biological State Index (I bs ), was obtained from previously published estimates (You & Henneberg, 2018). The principal component analysis (PCA) demonstrated strong sampling adequacy (KMO = 0.850) and a significant Bartlett’s test of sphericity (χ² = 481.972, df = 10, p < .001), confirming the suitability of the data for factor extraction. A single component with an eigenvalue of 3.47 emerged, accounting for 69.3% of the total variance. All five indicators loaded strongly on this component, with the highest loadings for Life e₆₀ (0.903), economic affluence (0.864), and relaxed selection opportunity (0.814), followed by urbanization (0.796) and cancer incidence (0.779). Communalities ranged from 0.607 (cancer incidence) to 0.815 (Life e₆₀), indicating substantial shared variance with the extracted factor. These results suggest that cancer incidence is embedded within a broader latent construct of socioeconomic development and population health, reinforcing its interdependence with longevity, affluence, and demographic opportunity in shaping dementia vulnerability at the population level. Table 3. Principal Component Analysis of Cancer Incidence, Demographic, and Economic Variables Measure Results KMO and Bartlett’s Test Kaiser-Meyer-Olkin (KMO) 0.850 Bartlett’s Test of Sphericity χ² = 481.972, df = 10, p < 0.001 Component (Eigenvalues, % variance) 1 Eigenvalue = 3.465; Variance = 69.30% 2 Eigenvalue = 0.533; Variance = 10.67% 3 Eigenvalue = 0.489; Variance = 9.78% 4 Eigenvalue = 0.292; Variance = 5.84% 5 Eigenvalue = 0.221; Variance = 4.41% Communalities (Extraction) Cancer Incidence 2017 0.607 Selection Opportunity 0.663 Economic affluence 0.746 Urbanization 0.634 Life e(60) 0.815 Component Matrix (Component 1) Cancer Incidence 2017 0.779 Selection Opportunity 0.814 Economic affluence 0.864 Urbanization 0.796 Life e(60) 0.903 Total Variance Explained by Component 1 69.30% Globally, cancer incidence demonstrated a very strong positive correlation with dementia incidence (Pearson’s r = 0.873, Spearman’s ρ = 0.938, p < 0.001, n = 204). When stratified by World Bank income classifications, correlations remained consistently positive but varied in magnitude. Strongest associations were observed in upper-middle-income countries (r = 0.917, ρ = 0.927, n = 54), while the weakest emerged in low-income countries (r = 0.661, ρ = 0.458, n = 28). Comparisons indicated significantly stronger Pearson correlations in LMICs compared to high-income countries (z = 4.00, p < 0.001), although the non-parametric test showed no difference (z = 0.97, p = 0.332). Using the UN’s developed/developing classification, developing countries displayed a much stronger association (r = 0.902, ρ = 0.879, n = 150) compared with developed countries (r = 0.434, ρ = 0.594, n = 49). Fisher’s z-tests confirmed that these differences were highly significant for both Pearson (z = 6.03, p < 0.001) and non-parametric correlations (z = 4.07, p < 0.001). Across WHO regions, the correlation was strongest in South-East Asia (r = 0.948, ρ = 0.755, n = 11) and weakest in the Americas (r = 0.686, ρ = 0.862, n = 38), although all regions demonstrated statistically significant positive associations. Analyses of additional country groupings showed heterogeneous patterns. The correlation was highest in the ACD group (r = 0.958, ρ = 0.778, n = 29) and Latin America (r = 0.905, ρ = 0.891, n = 24), whereas weaker associations were found in the OECD (r = 0.425, ρ = 0.549, n = 37) and EU (r = 0.553, ρ = 0.452, n = 27). Nevertheless, significant positive correlations were evident across all groupings. Table 3: Comparative Correlations Between Cancer and Dementia Incidence Across Income Levels, Development Status, and WHO Regions Cancer incidence correlated to dementia incidence Country groupings Pearson Non-parametric n Worldwide 0.873** 0.938** 204 World Bank income classifications Low income .661** .458* 28 Lower middle income .849** .713** 49 Upper middle income .917** .927** 54 High income .739** .842** 68 LMIC .915** .880** 131 Fisher A-to-Z: LMICs vs high income in Pearson’s r (z= 4.00, p< 0.01) and in non-parametric (z= 0.97, p =0.332) UN common practice Developed .434** .594** 49 Developing .902** .879** 150 Fisher A-to-Z: developing vs developed in Pearson’s r (z= 6.03, p< 0.01) and in non-parametric (z= 4.07, p< 0.01). WHO Regions AFRO .766** .709** 47 AMRO .686** .862** 38 EMRO .836** .704** 21 EURO .843** .825** 53 SEARO .948** .755** 11 WPRO .856** .872** 29 Countries grouped based on various factors ACD .958** .778** 29 APEC .624** .917** 20 Arab World .806** .622** 21 EEA .564** .508** 29 EOL .810** .933** 54 EU .553** .452* 27 LA .905** .891** 24 LAC .845** .827** 35 OECD .425** .549** 37 SADC .780** .721** 16 SCO . 899** .795** 25 Notes: ** indicates correlation significant at 0.01 level; * at 0.05 level. Partial correlation analyses were conducted to assess the relationship between cancer incidence and dementia incidence after adjusting for socioeconomic and demographic covariates. The zero-order Pearson correlation between cancer and dementia incidence was very strong (r = 0.873, p < 0.001). When economic affluence was controlled, the partial correlation remained highly significant (r = 0.796, p < 0.001). Controlling for both economic affluence and urbanization produced a nearly identical result (r = 0.794, p < 0.001). The inclusion of selection opportunity slightly attenuated the association (r = 0.783, p < 0.001), and further adjustment for Life e₆₀ resulted in a modest reduction (r = 0.773, p < 0.001). Even after controlling for all four confounders, cancer incidence still explained approximately 59.8% of the variance in dementia incidence at the population level. Despite the incremental controls, the association between cancer incidence and dementia incidence consistently remained strong and significant across all models. These findings indicate that while socioeconomic development, demographic opportunity, and longevity explain part of the variance, cancer incidence retains an independent and robust relationship with dementia incidence at the population level. Table 4. Zero-Order and Partial Correlations of Cancer and Dementia Incidence Controlling for Socioeconomic and Demographic Factors Model Control Variables Partial Correlation (r) Variance Explained (r²) Zero-order None 0.873** 0.762 (76.2%) Model 1 Economic Affluence 0.796** 0.634 (63.4%) Model 2 Economic Affluence; Urbanization 0.794** 0.631 (63.1%) Model 3 Economic Affluence; Urbanization; Selection Opportunity 0.783** 0.613 (61.3%) Model 4 Economic Affluence; Urbanization; Selection Opportunity; Life e₆₀ 0.773** 0.598 (59.8%) Note. All correlations are statistically significant at ** p < 0.001 . A multiple regression analysis was conducted to examine the predictors of dementia incidence across socioeconomic, demographic, and cancer-related indicators. Model 1 (without cancer incidence). When cancer incidence was excluded, Life e₆₀ emerged as the strongest predictor (β = 0.483, p < 0.001), followed by selection opportunity (β = 0.184, p = 0.021). Economic affluence (β = 0.148, p = 0.071) and urbanization (β = –0.025, p = 0.739) were not statistically significant. The overall model accounted for 51.7% of the variance in dementia incidence (Adjusted R² = 0.517, F(4,168) = 46.98, p < 0.001). Model 2 (with cancer incidence). When cancer incidence was included, it became the dominant predictor (β = 0.725, p < 0.001), while life expectancy (β = 0.258, p < 0.001) and selection opportunity (β = 0.104, p = 0.044) retained smaller but significant effects. Economic affluence became a significant negative predictor (β = –0.121, p = 0.030), whereas urbanization remained non-significant (β = 0.003, p = 0.947). The inclusion of cancer incidence increased explanatory power to 80.0% of the variance (Adjusted R² = 0.800, F(5,167) = 138.80, p < 0.001). Comparison across models. Excluding cancer incidence, socioeconomic and demographic factors explained just over half of dementia variability (Adjusted R² = 0.517). Including cancer incidence improved explanatory capacity by 28.3 percentage points (Δ Adjusted R² = +0.283), raising explained variance to 80.0%. This confirms cancer incidence as the strongest independent predictor of dementia burden, above and beyond affluence, demographic opportunity, and longevity. Table 5 Multiple Linear Regression (Enter Method) Predicting Dementia Incidence: Comparison of Models With and Without Cancer Incidence Predictor Model 1 β Sig. Model 2 β Sig. Economic Affluence 0.148 .071 –0.121* 0.030 Urbanization –0.025 .739 0.003 .947 Relaxed Selection Opportunity 0.184 .021 0.104* 0.044 Life e₆₀ 0.483*** < 0.001 0.258*** < 0.001 Cancer Incidence Excl. – 0.725*** < 0.001 Adjusted R² 0.517 0.800 F-statistic 46.98*** 138.80*** Change in Adjusted R² – +0.283 Significance level: ***p < .001, *p < 0.05. A stepwise regression analysis was performed to identify the strongest predictors of dementia incidence among socioeconomic, demographic, and cancer-related indicators. Model 1 (without cancer incidence). When cancer incidence was excluded, stepwise regression selected life expectancy at age 60 as the first predictor (β = 0.708, p < 0.001), accounting for 50.1% of the variance in dementia incidence (Adjusted R² = 0.498). The addition of relaxed selection opportunity improved the model modestly (β = 0.197, p = 0.013), raising the explained variance to 51.8% (Adjusted R² = 0.513). economic affluence and urbanization were excluded as non-significant. Model 2 (with cancer incidence). When cancer incidence was included in the stepwise analysis, it entered first and emerged as the dominant predictor (β = 0.868, p < 0.001), explaining 75.3% of the variance alone (Adjusted R² = 0.752). The sequential addition of Life e₆₀ (β = 0.265, p < 0.001), GDP (β = –0.113, p = 0.031), and selection opportunity (β = 0.104, p = 0.041) further improved the model, with the final model explaining 80.6% of the variance (Adjusted R² = 0.801). Urbanization was consistently excluded across all steps. Comparison across models. Without cancer, developmental and longevity factors explained just over half of dementia variability. With cancer, explanatory power increased sharply, adding 28.3% percentage points of variance explained (from 51.8% to 80.6%). This confirms cancer incidence as the strongest independent predictor of dementia burden, above and beyond socioeconomic and demographic variables. Table 6. Multiple Linear Regression (stepwise) Predicting Dementia Incidence Rate: Comparison of Models With and Without Cancers Incidence (n = 173) Predictor Model 1 (Excl. Cancer Incidence) β p-value Adj. R² Model 2 (Incl. Cancer Incidence) β p-value Adj. R² Cancer Incidence Excl. – – 0.725*** < 0.001 0.752 Life e(60) 0.562 < 0.001 0.498 0.259*** < 0.001 0.793 Economic Affluence – – – –0.120* 0.021 0.798 Selection Opportunity 0.197 0.013 0.513 0.104* 0.041 0.801 Urbanization – – – 0.003 0.947 ns (no increase) F-statistic (df) 91.49*** (2,170) 174.54*** (4,168) Δ Adjusted R² – – +0.288 (≈ +29%) *Note. β = standardized regression coefficient. Significance level: ***p < .001, *p < 0.05. “–” indicates the predictor was not entered into the respective model under the stepwise selection procedure (typically due to insignificance or redundancy). Adjusted R² values show that cancer incidence alone explained 75.2% of variance, increasing to 80.1% when combined with other predictors, compared with 51.3% explained by non-cancer predictors. Discussion This ecological study provides robust evidence that cancer incidence is strongly and independently associated with dementia incidence across 204 countries. The global correlation between the two conditions was exceptionally high, persisting even after adjustment for economic affluence, urbanization, selection opportunity, and Life e₆₀. Importantly, regression analyses demonstrated that cancer incidence was the dominant predictor of dementia incidence, explaining nearly 30% more variance than traditional socioeconomic indicators. These findings highlight the interconnectedness of chronic diseases at the population level and extend the literature on shared determinants of diseases usually considered as non-communicable [14]. Our results align with previous work showing that dementia and cancer share common population-level drivers. Prince et al. (2016) and Li et al. (2022) have highlighted how ageing, affluence, and demographic transition fuel rising dementia prevalence globally [3, 16]. Similarly, Ferlay et al. (2018) demonstrated that cancer incidence follows the same trajectory, increasing steeply in middle- and high-income countries with expanding longevity and improved diagnostic systems [27]. The strong correlations observed in this study reinforce the notion that dementia and cancer are both products of broader epidemiological transitions associated with development. Partial correlation analyses confirmed that the cancer–dementia relationship was not fully explained by the developmental factors used. Even after controlling for affluence, urbanisation, relaxed selection opportunity, and longevity, cancer incidence accounted for nearly 60% of dementia variance. This independence suggests overlapping but distinct etiological processes. Prior studies have identified shared risk factors such as tobacco use, obesity, diet, physical inactivity, and vascular disease [1, 28]. In addition, systemic biological processes, including inflammation, oxidative stress, and immune dysfunction, are implicated in both oncogenesis and neurodegeneration [29, 30]. Our ecological findings are consistent with these individual-level observations, suggesting that population-level increases in cancer and dementia reflect converging risk environments [1]. The results of our regression models are particularly noteworthy. Without cancer incidence, life expectancy and selection opportunity emerged as the strongest predictors of dementia incidence, consistent with earlier studies linking longevity and demographic structure to dementia burden [13, 31]. However, once cancer incidence was introduced, it overshadowed these factors, raising explained variance from 51% to 80%. This finding resonates with recent ecological studies demonstrating that chronic disease indicators can serve as proxies for broader health system and demographic transitions [14, 32]. Cancer incidence, in particular, may capture latent dimensions of development that economic affluence or life expectancy alone cannot, making it a powerful predictor of dementia burden [13]. Our results also shed light on the long-standing debate regarding the cancer–dementia relationship [24]. At the individual level, some cohort and case-control studies report an inverse association between cancer and dementia, suggesting that biological antagonism or selective survival may protect cancer patients from developing dementia [24, 33]. For example, inverse links have been proposed between tumor suppressor genes and amyloid processing pathways [34, 35]. However, more recent longitudinal studies have questioned the robustness of this inverse association, noting potential survival bias and competing mortality [25, 36]. By contrast, our ecological findings demonstrate a strong positive relationship at the population level. These seemingly contradictory results can be reconciled: at the micro level, cancer and dementia may sometimes diverge due to selective survival, but at the macro level, both conditions rise together in contexts of demographic ageing and socioeconomic development [24, 25]. The observed heterogeneity across regions and income groups also warrants discussion. The strongest associations were found in upper-middle-income and developing countries, whereas weaker relationships emerged in high-income or developed contexts. This pattern parallels global health transition theory, whereby low- and middle-income countries face steep increases in chronic disease burden as infectious diseases recede and populations age [14, 37]. Developed countries, in contrast, may show attenuated correlations due to plateauing incidence rates, greater heterogeneity in survival, or the influence of advanced medical technologies that alter detection and reporting [1, 38]. These contextual differences highlight the value of stratified analyses in understanding the cancer–dementia link [37]. A key methodological insight from this study is that the estimated contribution of cancer incidence to dementia burden varies substantially by statistical approach. Partial correlations indicated that cancer explained 59.8% of dementia variance even after adjusting for economic affluence, urbanisation, selection opportunity, and Life e₆₀; however, this method may inflate independent effects when predictors share variance [39, 40]. In the enter regression model, cancer’s contribution was estimated at 28.3% within a total R² of 80.0%, reflecting the combined influence of all predictors [40]. In the stepwise regression model, cancer entered first and explained 28.8% of the variance, with additional predictors only modestly increasing the total explained variance to 80.1% [39]. The close alignment between the enter (28.3%) and stepwise (28.8%) results indicates that cancer’s independent effect lies in this range, while the higher partial correlation estimate likely reflects inflation due to shared variance among predictors. Emphasising this 28–29% range provides the most transparent and credible interpretation. Importantly, only populations with complete data and unambiguous classification were included in these stratified models, which further strengthens interpretative confidence. Future research implications are evident. First, the strong and independent association between cancer and dementia incidence highlights the need for studies exploring shared biological pathways, including mechanisms of inflammation, immune regulation, and cellular ageing that may link the two conditions [29, 30]. Second, longitudinal and cohort-based investigations are necessary to clarify the temporal sequencing and mitigate residual concerns of reverse causality, thereby moving beyond ecological analyses [24, 41]. Third, comparative studies across regions and income groups could illuminate why the strength of association varies by socioeconomic context, particularly in rapidly developing countries experiencing demographic transitions [42, 43]. Fourth, research integrating cancer and dementia epidemiology with genetics, lifestyle exposures, and healthcare access would help disentangle the relative contributions of biological and structural determinants [44]. Finally, interdisciplinary approaches bridging oncology, neurology, and public health are needed to build a comprehensive understanding of how population-level and individual-level factors converge in shaping the joint burden of cancer and dementia [31]. This offers assistance with health resource allocation for optimising public benefit. Strengths and limitations This study benefits from several strengths. It draws on comprehensive and internationally comparable datasets from the Global Burden of Disease (GBD), World Bank, and WHO, covering 204 countries across all world regions. Multiple analytical approaches, including correlations, partial correlations, principal component analysis, and both enter and stepwise regression models, were applied to provide convergent evidence and reinforce the robustness of the findings. Subgroup analyses across income groups, development classifications, and WHO regions allowed nuanced comparisons, while variance-explained metrics quantified the independent contribution of cancer incidence relative to socioeconomic and demographic predictors. Limitations should also be acknowledged. The ecological design precludes individual-level inference and may be affected by ecological fallacy. Variation in diagnostic capacity, cancer registry completeness, and dementia ascertainment across countries could introduce bias, particularly in low-resource settings. Unmeasured confounders such as genetic predisposition, healthcare access, cultural practices, and lifestyle factors may also contribute to unexplained variance. Reliance on secondary data created temporal mismatches (cancer incidence, dementia incidence, and socioeconomic indicators), which may affect associations. Finally, incidence estimates may partly reflect detection capacity rather than true disease burden. Despite these caveats, the consistency of results across methods, classifications, and regions strengthens confidence in the conclusion that cancer incidence is a strong and independent predictor of dementia incidence at the global level. Public Health Implications This study highlights key implications for public health and global health planning. The strong association between cancer and dementia supports integrated prevention strategies targeting shared modifiable risk factors such as smoking, obesity, poor diet, and physical inactivity. Cancer incidence may also serve as a sentinel indicator for anticipating dementia burden, especially in countries undergoing rapid demographic transitions, enabling earlier health system preparedness. Consistent associations across regions further emphasise the need for cross-sectoral collaboration between oncology, neurology, geriatrics, and primary care to address multimorbidity and cognitive health. Finally, embedding dementia within the broader (assumed) non-communicable disease agenda alongside cancer, cardiovascular disease, and diabetes can strengthen prevention and resource allocation, particularly in LMICs facing a rising chronic disease burden. As alluded to earlier, it is also possible that cancer treatment may be associated with dementia risk through the neurological effects of chemotherapy, radiation therapy and perhaps anaesthesia and surgery – effects that have been reported previously [45]. Additionally, there is some emerging evidence that Herpes simplex (HSV) and zoster (HSV/VSV) viruses (also immunosuppressive) may be associated with the development of dementia [46-49] and some (perhaps more) cancers, indicating a possible, but currently unclear, latent association with infection [49-51]. Shin et. al. 2024 [46], found that HSV, VSV, and co-infection were associated with an increased risk of all dementia types. Interestingly, the longer the time of follow-up after HZV infection identification the greater the incidence of cancer with the curves between Herpes zoster infected and non-infected controls continuing to widen after 1-2 years post-infection [52]. Of note, the malignancy overall was positively associated with zoster risk (adjusted OR = 1.29) and HZV (above) associated with Dementia [53]. Importantly, our and these cited studies show the considerable power and value of longitudinal population studies enabling detailed analysis of large data collections over long time periods. Conclusion This study demonstrates that cancer incidence is a robust and independent predictor of dementia incidence worldwide. The strong correlations observed across most regions and income levels indicate that both diseases are closely tied to demographic and socioeconomic transitions. Importantly, cancer incidence explained substantially more variance in dementia burden than traditional developmental indicators, positioning it as a potential proxy for anticipating future dementia trends. Integrating cancer and dementia prevention into unified public health strategies may improve efficiency and population outcomes in the face of rising global chronic disease prevalence. Abbreviations ACD – African, Caribbean, and Pacific Group of States AFRO – WHO African Region AMRO – WHO Region of the Americas APEC – Asia-Pacific Economic Cooperation EEA – European Economic Area EMRO – WHO Eastern Mediterranean Region EOL – Economies of Late development EU – European Union EURO – WHO European Region GBD – Global Burden of Disease GDP PPP – Gross Domestic Product, Purchasing Power Parity IHME – Institute for Health Metrics and Evaluation KMO – Kaiser–Meyer–Olkin LA – Latin America LAC – Latin America and the Caribbean LMIC – Low- and Middle-Income Countries OECD – Organisation for Economic Co-operation and Development PCA – Principal Component Analysis SADC – Southern African Development Community SCO – Shanghai Cooperation Organisation SEARO – WHO South-East Asia Region UN – United Nations WHO – World Health Organization WPRO – WHO Western Pacific Region Declarations Ethics and Consent to Publish declarations Ethical approval was obtained from the Office of Research Ethics, Compliance and Integrity (ORECI), University of Adelaide (Approval No. 36289). Consent to publish: Not applicable. Consent to Participate declaration: not applicable Consent for publication Not applicable. Availability of data and materials The data sources for this study are described in the "Materials and Methods" section. All data used are freely available from the official websites of international organisations. Formal permission to use the data for non-commercial research purposes was not required, as their use complies with the public access permissions outlined in the respective agencies' terms and conditions. Competing interest The authors declared that there is no conflict of interest. GEN AI Use Statement During initial preparation of this manuscript, the lead author used ChatGPT to enhance readability and language, without replacing key authoring tasks. After utilising this tool, all authors edited the text, taking full responsibility for the integrity and authenticity of the publication's content. Author Contribution WY conceived the hypothesis and study design, and WY and MH collected the data and conducted the analyses. WY, BJC and MH interpreted the data. BJC and MH provided inputs for WY to draft and revise the manuscript. All authors reviewed, edited and approved the final manuscript. Acknowledgement Not applicable References Bray, F., et al., Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 2024. 74 (3): p. 229-263. Collaborators, G.D.F., Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. The Lancet. Public Health, 2022. 7 (2): p. e105. Prince, M., et al., World Alzheimer report 2016: improving healthcare for people living with dementia: coverage, quality and costs now and in the future. 2016. 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Ospina-Romero, M., et al., Association between Alzheimer disease and cancer with evaluation of study biases: a systematic review and meta-analysis. JAMA network open, 2020. 3 (11): p. e2025515-e2025515. Sun, M., et al., The association between cancer and dementia: A national cohort study in Sweden. Frontiers in oncology, 2020. 10 : p. 73. Zheng, G., et al., Meta-analysis reveals an inverse relationship between Alzheimer’s disease and cancer. Behavioural Brain Research, 2025. 478 : p. 115327. Karlsson, E.K., D.P. Kwiatkowski, and P.C. Sabeti, Natural selection and infectious disease in human populations. Nature Reviews Genetics, 2014. 15 (6): p. 379-393. Prince, M., et al., World Alzheimer Report 2016. Improving healthcare for people living with dementia: Coverage, Quality and costs now and in the future . 2016, Alzheimer's Disease International. Vos, T., et al., Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 2020. 396 (10258): p. 1204-1222. You, W. and M. Henneberg, Cancer incidence increasing globally: The role of relaxed natural selection. Evol Appl., 2017. 00:1–13 . Li, X., et al., Global, regional, and national burden of Alzheimer’s disease and other dementias. The alzheimer’s disease challenge, volume II, 2023. 16648714 : p. 194. You, W. and M. Henneberg, Large household reduces dementia mortality: A cross-sectional data analysis of 183 populations. PloS one, 2022. 17 (3): p. e0263309. https://doi.org/10.1371/journal.pone.0263309. You, W., et al., Greater family size is associated with less cancer risk: an ecological analysis of 178 countries. BMC cancer, 2018. 18 (1): p. 1-14. https://doi.org/10.1186/s12885-018-4837-0. 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Musicco, M., et al., Inverse occurrence of cancer and Alzheimer disease: a population-based incidence study. Neurology, 2013. 81 (4): p. 322-328. Frain, L., et al., Association of cancer and Alzheimer's disease risk in a national cohort of veterans. Alzheimer's & Dementia, 2017. 13 (12): p. 1364-1370. O’brien, R.M., A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 2007. 41 (5): p. 673-690. https://doi.org/10.1007/s11135-006-9018-6S. Ferlay J, et al. Cancer Today (powered by GLOBOCAN 2018) IARC CancerBase No. 15 . 2018 February 12 2022]; Available from: https://publications.iarc.fr/577. Livingston, G., et al., Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. The Lancet, 2020. 396 (10248): p. 413-446. https://doi.org/10.1016/S0140-6736(20)30367-6. Hou, Y., et al., Ageing as a risk factor for neurodegenerative disease. Nature reviews neurology, 2019. 15 (10): p. 565-581. Mantovani, A., et al., Cancer-related inflammation. nature, 2008. 454 (7203): p. 436-444. Winblad, B., et al., Defeating Alzheimer's disease and other dementias: a priority for European science and society. The Lancet Neurology, 2016. 15 (5): p. 455-532. Omram, A.R., The epidemiologic transition: a theory of the epidemiology of population change. Bulletin of the World Health Organization, 2001. 79 (2): p. 161-170. Driver, J.A., et al., Inverse association between cancer and Alzheimer’s disease: results from the Framingham Heart Study. Bmj, 2012. 344 . Zhu, X., et al., Alzheimer's disease: the two-hit hypothesis. The Lancet Neurology, 2004. 3 (4): p. 219-226. Rojas, N.G., et al., Neurodegenerative diseases and cancer: sharing common mechanisms in complex interactions. Journal of integrative neuroscience, 2020. 19 (1): p. 187-199. Bowles, E.J.A., et al., Risk of Alzheimer's disease or dementia following a cancer diagnosis. PloS one, 2017. 12 (6): p. e0179857. Omran, A.R., The Epidemiologic Transition: A Theory of the Epidemiology of Population Change. Milbank Quarterly, 2005. 83 (4): p. 731-757. Satizabal, C.L., et al., Incidence of dementia over three decades in the Framingham Heart Study. New England journal of medicine, 2016. 374 (6): p. 523-532. Field, A., Discovering statistics using IBM SPSS statistics 5th ed , in UK: University of Sussex . 2018, Sage. Tabachnick, B., L. Fidell, and J. Ullman, Using multivariate statistics (Vol. 6, pp. 497-516) . 2019, Boston, MA: Pearson: London. Sedgwick, P., Ecological studies: advantages and disadvantages. Bmj, 2014. 348 . Kontodimopoulos, N., The association between social development and population health: a cross-sectional study across countries of different economic growth. Research in Health Services & Regions, 2022. 1 (1): p. 2. Stringhini, S., et al., The social patterning of risk factors for noncommunicable diseases in five countries: evidence from the modeling the epidemiologic transition study (METS). BMC public health, 2016. 16 (1): p. 956. Consortium, A.D.G., C.f. Heart, and A.R.i.G.E. Consortium, Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nature genetics, 2019. 51 (3): p. 414-430. Kao, Y.-S., C.-C. Yeh, and Y.-F. Chen, The relationship between cancer and dementia: an updated review. Cancers, 2023. 15 (3): p. 640. Shin, E., et al., The associations of herpes simplex virus and varicella zoster virus infection with dementia: a nationwide retrospective cohort study. Alzheimer's Research & Therapy, 2024. 16 (1): p. 57. Vestin, E., et al., Herpes simplex viral infection doubles the risk of dementia in a contemporary cohort of older adults: a prospective study. Journal of Alzheimer’s Disease, 2024. 97 (4): p. 1841-1850. Araya, K., et al., Increased risk of dementia associated with herpes simplex virus infections: Evidence from a retrospective cohort study using US electronic health records. Journal of Alzheimer’s Disease, 2025. 104 (2): p. 393-402. Eyting, M., et al., A natural experiment on the effect of herpes zoster vaccination on dementia. Nature, 2025: p. 1-9. Cisneros IV, F., B. Martin, and S. Mito, Correlation between varicella-zoster virus infection and cancer development: A comprehensive analysis. Microbes & Immunity, 2025: p. 8320. Cotton, S., et al., The risk of a subsequent cancer diagnosis after herpes zoster infection: primary care database study. British journal of cancer, 2013. 108 (3): p. 721-726. Sim, J.-H., et al., The association between herpes zoster and increased cancer risk: a nationwide population-based matched control study. Current Oncology, 2021. 28 (4): p. 2720-2730. Hansson, E., et al., Herpes zoster risk after 21 specific cancers: population-based case–control study. British journal of cancer, 2017. 116 (12): p. 1643-1651. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7533062","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533803727,"identity":"51311c3c-2343-4a05-b5e7-ff20a464374e","order_by":0,"name":"Wenpeng 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1","display":"","copyAsset":false,"role":"figure","size":164414,"visible":true,"origin":"","legend":"\u003cp\u003eCurvilinear Relationship Between Cancer and Dementia Incidence\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sources:\u003c/strong\u003e \u0026nbsp;Cancer incidence rate (2017) and dementia incidence rate (2021) (including Alzheimer’s disease and other dementias), for both sexes, were obtained from the Institute for Health Metrics and Evaluation (IHME), University of Washington. Incidence rates are reported as the number of new cases per 100,000 population.\u003c/p\u003e","description":"","filename":"Figure1CancernDementia.png","url":"https://assets-eu.researchsquare.com/files/rs-7533062/v1/31b265e07b287825ba6ad5f7.png"},{"id":99679652,"identity":"ffb9e5fe-07a7-4c26-af34-86fed41aeab0","added_by":"auto","created_at":"2026-01-07 08:40:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1177559,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7533062/v1/b0cc4e74-b8a8-4d90-b54f-0a3e67112377.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global Correlation Between Cancer Incidence and Dementia Incidence Based on Cross National Regression Analyses","fulltext":[{"header":"Background","content":"\u003cp\u003eCancer and dementia are two of the most pressing global health challenges of the 21\u003csup\u003est\u003c/sup\u003e century, each imposing profound social, economic, and healthcare burdens [1, 2]. Both conditions are shaped by population ageing, socioeconomic development, and demographic transitions [1, 3, 4]. Dementia, encompassing Alzheimer\u0026rsquo;s disease and related disorders, has become a leading cause of disability and dependency among older adults, with incidence rising in parallel with life expectancy [5]. Cancer remains a major cause of morbidity and mortality worldwide, its incidence amplified by demographic shifts, lifestyle transitions, and improvements in diagnostic capacity [6]. While often studied in isolation, the coexistence of cancer and dementia at the population level suggests overlapping determinants and potential shared pathways that merit systematic investigation [7, 8].\u003c/p\u003e\n\u003cp\u003eEpidemiological findings on the cancer\u0026ndash;dementia relationship are mixed [9]. At the individual level, several studies report an inverse association, with cancer survivors showing reduced dementia risk, possibly due to selective survival, competing risks, or protective oncological/ immunological mechanisms [10, 11]. At the population level, however, patterns may differ. Ecological studies capture the influence of demographic and structural drivers such as longevity, affluence, urbanisation, and healthcare infrastructure that shape disease distributions [1]. Countries with high cancer incidence often also report elevated dementia incidence, underscoring the role of shared contextual determinants [12]. Examining these associations at a global scale can provide critical insight into how chronic diseases interrelate within ageing societies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSocioeconomic and demographic factors are central to the epidemiology of both diseases [1, 13, 14]. Economic affluence is positively linked to measured cancer incidence through greater healthcare access and lifestyle risks tied to economic transition [15]. Dementia prevalence is likewise higher in affluent settings, where longer survival and stronger diagnostic capacity increase case detection [16]. Urbanisation introduces environmental exposures and lifestyle changes that increase the risk of both diseases [1, 16]. Moreover, declining global birth rates have been postulated as additional risk factors for these conditions [17, 18]. Complementary measures, such as extended life expectancy [19], and \u0026ldquo;relaxation of selection opportunity,\u0026rdquo; an index of natural selection strength [20-22], offer further insight into how population structure shapes vulnerability to chronic disease.\u003c/p\u003e\n\u003cp\u003eDespite these overlaps, the direct global relationship between cancer incidence and dementia incidence remains insufficiently studied [23]. Few analyses have tested whether cancer is independently predictive of dementia after accounting for socioeconomic and demographic covariates [24, 25]. Nor is it well established whether this association is consistent across diverse economic, developmental, and regional contexts. Addressing this gap is essential for clarifying whether cancer incidence can serve as a robust indicator of dementia burden and for guiding integrated strategies in chronic disease prevention and resource allocation [14].\u003c/p\u003e\n\u003cp\u003eThis study investigates the global association between cancer and dementia incidence using ecological data from 204 countries. We specifically examined: (i) the strength and consistency of correlations worldwide and across socioeconomic and regional classifications; (ii) whether the relationship persists after adjusting for economic affluence, urbanisation, selection opportunity, and life expectancy; and (iii) the relative contribution of cancer compared with these covariates in explaining dementia incidence. By applying correlation, partial correlation, principal component, and multiple regression analyses, this study provides the most comprehensive ecological assessment to date of the cancer\u0026ndash;dementia link. The findings not only investigate the independent coincidence of cancer incidence and dementia burden but also situate both conditions within the broader demographic and socioeconomic transformations reshaping global health.\u0026nbsp;\u003c/p\u003e"},{"header":"Material and Method","content":"\u003ch2\u003eData sources\u003c/h2\u003e\n\u003cp\u003eData on cancer and dementia incidence were obtained from the Global Burden of Disease (GBD) database, maintained by the Institute for Health Metrics and Evaluation (IHME), University of Washington, USA. Cancer incidence rates (per 100,000 population) were obtained from the 2017 dataset, while dementia incidence, encompassing Alzheimer\u0026rsquo;s disease and other dementias, was sourced from the 2021 dataset. Both sets of estimates are grounded in systematic reviews, cancer registries, health surveys, and statistical modelling conducted within the GBD framework.\u003c/p\u003e\n\u003cp\u003eSocioeconomic and demographic indicators were drawn from widely recognised international sources. Economic affluence measured by gross domestic product (GDP) per capita (purchasing power parity, constant 2018 USA dollars,) and urbanisation (percentage of the population residing in urban areas, 2018) were retrieved from the World Bank\u0026rsquo;s World Development Indicators. Life expectancy at age 60 (Life e₆₀, 2018) was sourced from the World Health Organization\u0026rsquo;s (WHO) Global Health Observatory. Selection Opportunity, an index reflecting the strength of natural selection operating in human populations, was derived from previously published estimates [21].\u003c/p\u003e\n\u003cp\u003eCountries were categorised using multiple international systems to enable subgroup comparisons. These included the World Bank income classifications (low, lower-middle, upper-middle, and high income), the United Nations development categories (developed vs. developing), and the six WHO regions (AFRO, AMRO, EMRO, EURO, SEARO, WPRO). Additional political, cultural, and regional groupings (e.g., ACD, APEC, Arab World, EEA, EU, Latin America and the Caribbean, OECD, SADC, SCO, English-speaking countries) were applied to capture wider diversity in socioeconomic and geopolitical contexts. Analyses were based on data from 204 countries, with only those cases containing complete information across all variables retained in the final models.\u003c/p\u003e\n\u003cp\u003eIn this ecological study, cancer incidence was treated as the predictor variable and dementia incidence as the outcome. This direction reflects biological and epidemiological reasoning: dementia is primarily a late-life condition, cancer is more often diagnosed earlier, and the pathway from cancer to dementia, including possible treatment-related effects, has been advanced in prior research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study\u0026rsquo;s ecological design minimises concerns of reverse causality, as cancer and dementia incidence were analysed at the population level rather than the individual level. Moreover, the use of population-level incidence data from independent time points (2017 for cancer and 2021 for dementia) further reduces the possibility that dementia occurrence influenced cancer reporting. This design strengthens confidence that the observed associations represent population-level determinants rather than reverse causal effects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo evaluate the generalisability of the cancer\u0026ndash;dementia relationship, stratified analyses were undertaken across the major classification systems. These encompassed income level (World Bank), development status (UN), and regional groupings (WHO), alongside additional subgroup analyses reflecting geopolitical or cultural contexts (e.g., APEC, Arab World, English-speaking countries, EEA, EU, Latin America and the Caribbean, OECD, SADC). Only populations with complete data and an unambiguous classification were included in these stratified models.\u003c/p\u003e\n\u003ch2\u003eData Preparation\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eData were collated from multiple international repositories to construct a comprehensive country-level dataset. Cancer incidence (2017) and dementia incidence (2021) were matched with socioeconomic and demographic indicators, including gross domestic product per capita adjusted for purchasing power parity (constant US dollars), degree of urbanisation (percentage of population living in urban areas), life expectancy at age 60 (Life e₆₀, in years), and selection opportunity, an index reflecting the strength of natural selection in human populations. To ensure comparability across sources, all variables were expressed in standardised population-based units: incidence rates per 100,000 population, percentages for urbanisation, years for life expectancy, and constant US dollars for\u0026nbsp;economic affluence.\u003c/p\u003e\n\u003cp\u003eCases with missing data on any study variable were excluded using listwise deletion, resulting in a final analytic sample of 204 countries. This ensured that correlations, principal component analyses, and regression models were based on identical datasets. No data transformations, such as log conversion or rescaling, were applied, as the distributions of the main variables demonstrated acceptable ranges for both parametric and non-parametric analyses. Outliers, including very high cancer incidence values (\u0026gt;1,000 per 100,000), were retained to preserve global variability, with their influence addressed analytically.\u003c/p\u003e\n\u003cp\u003eRegression diagnostics assessed multicollinearity among predictors. Tolerance values (\u0026ge;0.10) and Variance Inflation Factor values (\u0026le;10) confirmed acceptable thresholds, reinforcing the robustness and validity of the analytical framework\u0026nbsp;[26].\u003c/p\u003e\n\u003ch2\u003eStatistical Analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eDescriptive statistics were used to summarize cancer incidence, dementia incidence, and selected socioeconomic and demographic indicators across countries.\u003c/p\u003e\n\u003cp\u003eAssociations between cancer incidence, dementia incidence, and covariates were assessed using Pearson\u0026rsquo;s correlation coefficients for parametric relationships and Spearman\u0026rsquo;s rho for non-parametric tests. Differences in correlation strength across income levels, development classifications, and regional groupings were evaluated using Fisher\u0026rsquo;s r-to-z transformations.\u003c/p\u003e\n\u003cp\u003eTo examine whether cancer incidence clustered with other developmental indicators, a principal component analysis (PCA) was conducted including economic affluence, urbanization, selection opportunity, and\u0026nbsp;Life e₆₀. Sampling adequacy was assessed using the Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) statistic and Bartlett\u0026rsquo;s test of sphericity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe independence of the cancer\u0026ndash;dementia association was further tested using partial correlations, sequentially adjusting for economic affluence, urbanization, selection opportunity,\u0026nbsp;Life e₆₀.\u003c/p\u003e\n\u003cp\u003eFinally, multiple linear regression models were employed to predict dementia incidence. An enter method model compared explanatory power with and without cancer incidence, while a stepwise model identified the strongest predictors. For all models, standardized beta coefficients (\u0026beta;), adjusted R\u0026sup2;, and F-statistics were reported. Statistical significance was set at p \u0026lt; 0.05 (two-tailed). Analyses were conducted using IBM SPSS Statistics (version 30).\u003c/p\u003e\n\u003cp\u003eEthical Considerations and Compliance\u003cbr\u003e\u0026nbsp;This quantitative investigation employed six variables sourced entirely from publicly accessible datasets, including open-access publications and official websites of the United Nations (UN) and its partner agencies. Approval for the secondary use and analysis of these data was obtained from the Office of Research Ethics, Compliance and Integrity (ORECI) at the University of Adelaide (Approval No. 36289). \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA strong non-linear association was observed between cancer incidence in 2017 and dementia incidence in 2021 (Figure 1). The quadratic regression model demonstrated an excellent fit (R\u003csup\u003e2\u003c/sup\u003e=0.884), indicating that approximately 88.4% of the variance in dementia incidence was explained by cancer incidence. Dementia incidence increased steeply with rising cancer incidence at lower to moderate levels, but the trend plateaued and slightly declined at very high cancer incidence values (\u0026gt;1,000 per 100,000). This pattern suggests a positive but curvilinear relationship, where higher cancer incidence is generally associated with greater dementia incidence, although the effect appears to diminish at extreme cancer incidence levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBivariate correlations were conducted to examine the associations between cancer incidence, dementia incidence, and selected socioeconomic and demographic indicators. Both Pearson\u0026rsquo;s parametric and Spearman\u0026rsquo;s nonparametric tests demonstrated consistent and significant patterns.\u003c/p\u003e\n\u003cp\u003ePearson\u0026rsquo;s correlations revealed a very strong positive relationship between cancer incidence and dementia incidence (r = 0.873, p \u0026lt; 0.001). Cancer incidence was also significantly associated with economic affluence (r = 0.647, p \u0026lt; 0.001), urbanization (r = 0.482, p \u0026lt; 0.001), relaxed selection opportunity (r = 0.528, p \u0026lt; 0.001), and Life e₆₀\u0026nbsp;(r = 0.637, p \u0026lt; 0.001). Dementia incidence similarly correlated with these developmental indicators, most strongly with life expectancy (r = 0.717, p \u0026lt; 0.001) and relaxed selection opportunity (r = 0.604, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eSpearman\u0026rsquo;s rho correlations further confirmed these associations, often with stronger coefficients. Cancer incidence and dementia incidence demonstrated an exceptionally strong monotonic relationship (\u0026rho; = 0.938, p \u0026lt; 0.001). Cancer incidence also showed robust associations with economic affluence (\u0026rho; = 0.782), selection opportunity (\u0026rho; = 0.838), and Life e₆₀\u0026nbsp;(\u0026rho; = 0.737), all p \u0026lt; 0.001. Dementia incidence followed a similar pattern, with particularly high correlations with relaxed selection opportunity (\u0026rho; = 0.846) and life expectancy (\u0026rho; = 0.785).\u003c/p\u003e\n\u003cp\u003eOverall, these findings indicate that at the population level cancer incidence is tightly linked with dementia incidence and that both conditions\u0026nbsp;cluster within broader socioeconomic and demographic contexts. Higher affluence, longevity, and demographic opportunity are consistently associated with increased population-level vulnerability to dementia.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1. Correlation Matrix of Cancer Incidence, Dementia, and Key Demographic, Economic, and Healthcare Indicators \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCancers Incidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDementia Incidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEconomic Affluence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eUrbanization,\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRelaxed Selection Opportunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLife e₆₀\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCancers Incidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.873**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.647**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.482**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.528**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.637**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDementia Incidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.938**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.597**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.498**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.604**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.717**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic Affluence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.782**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.761**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.647**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.571**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.714**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrbanization\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.561**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.527**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.718**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.533**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.664**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSelection Opportunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.838**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.846**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.896**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.631**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.729**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLife e₆₀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.737**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.785**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.811**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.676**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.834**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSignificance level: **p \u0026lt; 0.01\u003cbr\u003eNote: Pearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e values are presented above the diagonal, and Spearman\u0026rsquo;s \u0026rho; values are presented below the diagonal.\u003c/p\u003e\n\u003cp\u003eData on cancer incidence and dementia incidence were extracted from the Global Burden of Disease (GBD) study curated by the Institute for Health Metrics and Evaluation (IHME). Life expectancy at age 60 (Life e₆₀) was sourced from the World Health Organization (WHO) Global Health Observatory. Economic affluence, measured as GDP per capita (PPP, constant 2018 US dollars), and urbanization, defined as the percentage of the population living in urban areas, were obtained from the World Bank\u0026rsquo;s World Development Indicators database. Selection Opportunity, measured using the Biological State Index (I\u003csub\u003ebs\u003c/sub\u003e), was obtained from previously published estimates (You \u0026amp; Henneberg, 2018). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe principal component analysis (PCA) demonstrated strong sampling adequacy (KMO = 0.850) and a significant Bartlett\u0026rsquo;s test of sphericity (\u0026chi;\u0026sup2; = 481.972, df = 10, p \u0026lt; .001), confirming the suitability of the data for factor extraction. A single component with an eigenvalue of 3.47 emerged, accounting for 69.3% of the total variance. All five indicators loaded strongly on this component, with the highest loadings for\u0026nbsp;Life e₆₀\u0026nbsp;(0.903), economic affluence (0.864), and relaxed selection opportunity (0.814), followed by urbanization (0.796) and cancer incidence (0.779). Communalities ranged from 0.607 (cancer incidence) to 0.815 (Life e₆₀), indicating substantial shared variance with the extracted factor. These results suggest that cancer incidence is embedded within a broader latent construct of socioeconomic development and population health, reinforcing its interdependence with longevity, affluence, and demographic opportunity in shaping dementia vulnerability at the population level.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 3. Principal Component Analysis of Cancer Incidence, Demographic, and Economic Variables \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKMO and Bartlett\u0026rsquo;s Test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKaiser-Meyer-Olkin (KMO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBartlett\u0026rsquo;s Test of Sphericity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 481.972, df = 10, p \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponent (Eigenvalues, % variance)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEigenvalue = 3.465; Variance = 69.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEigenvalue = 0.533; Variance = 10.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEigenvalue = 0.489; Variance = 9.78%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEigenvalue = 0.292; Variance = 5.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEigenvalue = 0.221; Variance = 4.41%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunalities (Extraction)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCancer Incidence 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSelection Opportunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic affluence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrbanization\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLife e(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponent Matrix (Component 1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCancer Incidence 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSelection Opportunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic affluence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrbanization\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLife e(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Variance Explained by Component 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGlobally, cancer incidence demonstrated a very strong positive correlation with dementia incidence (Pearson\u0026rsquo;s r = 0.873, Spearman\u0026rsquo;s \u0026rho; = 0.938, p \u0026lt; 0.001, n = 204).\u003c/p\u003e\n\u003cp\u003eWhen stratified by World Bank income classifications, correlations remained consistently positive but varied in magnitude. Strongest associations were observed in upper-middle-income countries (r = 0.917, \u0026rho; = 0.927, n = 54), while the weakest emerged in low-income countries (r = 0.661, \u0026rho; = 0.458, n = 28). Comparisons indicated significantly stronger Pearson correlations in LMICs compared to high-income countries (z = 4.00, p \u0026lt; 0.001), although the non-parametric test showed no difference (z = 0.97, p = 0.332).\u003c/p\u003e\n\u003cp\u003eUsing the UN\u0026rsquo;s developed/developing classification, developing countries displayed a much stronger association (r = 0.902, \u0026rho; = 0.879, n = 150) compared with developed countries (r = 0.434, \u0026rho; = 0.594, n = 49). Fisher\u0026rsquo;s z-tests confirmed that these differences were highly significant for both Pearson (z = 6.03, p \u0026lt; 0.001) and non-parametric correlations (z = 4.07, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eAcross WHO regions, the correlation was strongest in South-East Asia (r = 0.948, \u0026rho; = 0.755, n = 11) and weakest in the Americas (r = 0.686, \u0026rho; = 0.862, n = 38), although all regions demonstrated statistically significant positive associations.\u003c/p\u003e\n\u003cp\u003eAnalyses of additional country groupings showed heterogeneous patterns. The correlation was highest in the ACD group (r = 0.958, \u0026rho; = 0.778, n = 29) and Latin America (r = 0.905, \u0026rho; = 0.891, n = 24), whereas weaker associations were found in the OECD (r = 0.425, \u0026rho; = 0.549, n = 37) and EU (r = 0.553, \u0026rho; = 0.452, n = 27). Nevertheless, significant positive correlations were evident across all groupings.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 3: Comparative Correlations Between Cancer and Dementia Incidence Across Income Levels, Development Status, and WHO Regions\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eCancer incidence correlated to dementia incidence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry groupings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePearson\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-parametric\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorldwide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.873**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.938**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorld Bank income classifications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.661**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.458*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLower middle income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.849**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.713**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUpper middle income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.917**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.927**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.739**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.842**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.915**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.880**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 624px;\"\u003e\n \u003cp\u003eFisher A-to-Z: LMICs vs high income in Pearson\u0026rsquo;s r (z= 4.00, p\u0026lt; 0.01) and in non-parametric (z= 0.97, p =0.332) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUN common practice\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDeveloped\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.434**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.594**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDeveloping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.902**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.879**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 567px;\"\u003e\n \u003cp\u003eFisher A-to-Z: developing vs developed in Pearson\u0026rsquo;s r (z= 6.03, p\u0026lt; 0.01) and in non-parametric (z= 4.07, p\u0026lt; 0.01).\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWHO Regions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAFRO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.766**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.709**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAMRO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.686**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.862**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEMRO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.836**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.704**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEURO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.843**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.825**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSEARO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.948**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.755**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWPRO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.856**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.872**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountries grouped based on various factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eACD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.958**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.778**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAPEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.624**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.917**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArab World\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.806**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.622**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.564**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.508**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEOL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.810**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.933**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.553**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.452*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.905**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.891**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.845**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.827**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOECD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.425**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.549**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.780**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.721**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e. 899**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.795**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u0026nbsp;\u003c/strong\u003e** indicates correlation significant at 0.01 level; * at 0.05 level.\u003c/p\u003e\n\u003cp\u003ePartial correlation analyses were conducted to assess the relationship between cancer incidence and dementia incidence after adjusting for socioeconomic and demographic covariates. The zero-order Pearson correlation between cancer and dementia incidence was very strong (r = 0.873, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eWhen economic affluence was controlled, the partial correlation remained highly significant (r = 0.796, p \u0026lt; 0.001). Controlling for both economic affluence and urbanization produced a nearly identical result (r = 0.794, p \u0026lt; 0.001). The inclusion of selection opportunity slightly attenuated the association (r = 0.783, p \u0026lt; 0.001), and further adjustment for Life e₆₀ resulted in a modest reduction (r = 0.773, p \u0026lt; 0.001). Even after controlling for all four confounders, cancer incidence still explained approximately 59.8% of the variance in dementia incidence at the population level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite the incremental controls, the association between cancer incidence and dementia incidence consistently remained strong and significant across all models. These findings indicate that while socioeconomic development, demographic opportunity, and longevity explain part of the variance, cancer incidence retains an independent and robust relationship with dementia incidence at the population level.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 4. Zero-Order and Partial Correlations of Cancer and Dementia Incidence Controlling for Socioeconomic and Demographic Factors\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePartial Correlation (r)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariance Explained (r\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZero-order\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.873**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.762 (76.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic Affluence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.796**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.634 (63.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic Affluence; Urbanization\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.794**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.631 (63.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic Affluence; Urbanization; Selection Opportunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.783**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.613 (61.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic Affluence; Urbanization; Selection Opportunity; Life e₆₀\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.773**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.598 (59.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote.\u003c/strong\u003e All correlations are statistically significant at **\u003cem\u003ep \u0026lt; 0.001\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eA multiple regression analysis was conducted to examine the predictors of dementia incidence across socioeconomic, demographic, and cancer-related indicators.\u003c/p\u003e\n\u003cp\u003eModel 1 (without cancer incidence). When cancer incidence was excluded, Life e₆₀\u0026nbsp;emerged as the strongest predictor (\u0026beta; = 0.483, p \u0026lt; 0.001), followed by selection opportunity (\u0026beta; = 0.184, p = 0.021). Economic affluence (\u0026beta; = 0.148, p = 0.071) and urbanization (\u0026beta; = \u0026ndash;0.025, p = 0.739) were not statistically significant. The overall model accounted for 51.7% of the variance in dementia incidence (Adjusted R\u0026sup2; = 0.517, F(4,168) = 46.98, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eModel 2 (with cancer incidence). When cancer incidence was included, it became the dominant predictor (\u0026beta; = 0.725, p \u0026lt; 0.001), while life expectancy (\u0026beta; = 0.258, p \u0026lt; 0.001) and selection opportunity (\u0026beta; = 0.104, p = 0.044) retained smaller but significant effects. Economic affluence became a significant negative predictor (\u0026beta; = \u0026ndash;0.121, p = 0.030), whereas urbanization remained non-significant (\u0026beta; = 0.003, p = 0.947). The inclusion of cancer incidence increased explanatory power to 80.0% of the variance (Adjusted R\u0026sup2; = 0.800, F(5,167) = 138.80, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eComparison across models. Excluding cancer incidence, socioeconomic and demographic factors explained just over half of dementia variability (Adjusted R\u0026sup2; = 0.517). Including cancer incidence improved explanatory capacity by 28.3 percentage points (\u0026Delta; Adjusted R\u0026sup2; = +0.283), raising explained variance to 80.0%. This confirms cancer incidence as the strongest independent predictor of dementia burden, above and beyond affluence, demographic opportunity, and longevity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 5\u003c/em\u003e \u003cem\u003eMultiple Linear Regression (Enter Method) Predicting Dementia Incidence: Comparison of Models With and Without Cancer Incidence\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModel 1 \u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModel 2 \u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic Affluence\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;0.121*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrbanization\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.947\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRelaxed Selection Opportunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.104*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLife e₆₀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.483***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.258***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCancer Incidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExcl.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.725***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.98***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e138.80***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChange in Adjusted R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSignificance level: ***p \u0026lt; .001, *p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eA stepwise regression analysis was performed to identify the strongest predictors of dementia incidence among socioeconomic, demographic, and cancer-related indicators.\u003c/p\u003e\n\u003cp\u003eModel 1 (without cancer incidence). When cancer incidence was excluded, stepwise regression selected life expectancy at age 60 as the first predictor (\u0026beta; = 0.708, p \u0026lt; 0.001), accounting for 50.1% of the variance in dementia incidence (Adjusted R\u0026sup2; = 0.498). The addition of relaxed selection opportunity improved the model modestly (\u0026beta; = 0.197, p = 0.013), raising the explained variance to 51.8% (Adjusted R\u0026sup2; = 0.513). economic affluence and urbanization were excluded as non-significant.\u003c/p\u003e\n\u003cp\u003eModel 2 (with cancer incidence). When cancer incidence was included in the stepwise analysis, it entered first and emerged as the dominant predictor (\u0026beta; = 0.868, p \u0026lt; 0.001), explaining 75.3% of the variance alone (Adjusted R\u0026sup2; = 0.752). The sequential addition of Life e₆₀\u0026nbsp;(\u0026beta; = 0.265, p \u0026lt; 0.001), GDP (\u0026beta; = \u0026ndash;0.113, p = 0.031), and selection opportunity (\u0026beta; = 0.104, p = 0.041) further improved the model, with the final model explaining 80.6% of the variance (Adjusted R\u0026sup2; = 0.801). Urbanization was consistently excluded across all steps.\u003c/p\u003e\n\u003cp\u003eComparison across models. Without cancer, developmental and longevity factors explained just over half of dementia variability. With cancer, explanatory power increased sharply, adding 28.3% percentage points of variance explained (from 51.8% to 80.6%). This confirms cancer incidence as the strongest independent predictor of dementia burden, above and beyond socioeconomic and demographic variables.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 6. Multiple Linear Regression (stepwise) Predicting Dementia Incidence Rate: Comparison of Models With and Without Cancers Incidence (n = 173)\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eModel 1 (Excl. Cancer Incidence) \u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAdj. R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eModel 2 (Incl. Cancer Incidence) \u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAdj. R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCancer Incidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExcl.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.725***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.752 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLife e(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.498 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.259***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic Affluence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;0.120*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSelection Opportunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.104*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrbanization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens (no increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF-statistic (df)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91.49*** (2,170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e174.54*** (4,168)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026Delta; Adjusted R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+0.288 (\u0026asymp; +29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Note. \u0026beta; = standardized regression coefficient. Significance level: ***p \u0026lt; .001, *p \u0026lt; 0.05. \u0026ldquo;\u0026ndash;\u0026rdquo; indicates the predictor was not entered into the respective model under the stepwise selection procedure (typically due to insignificance or redundancy). Adjusted R\u0026sup2; values show that cancer incidence alone explained 75.2% of variance, increasing to 80.1% when combined with other predictors, compared with 51.3% explained by non-cancer predictors.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis ecological study provides robust evidence that cancer incidence is strongly and independently associated with dementia incidence across 204 countries. The global correlation between the two conditions was exceptionally high, persisting even after adjustment for economic affluence, urbanization, selection opportunity, and Life e₆₀. Importantly, regression analyses demonstrated that cancer incidence was the dominant predictor of dementia incidence, explaining nearly 30% more variance than traditional socioeconomic indicators. These findings highlight the interconnectedness of chronic diseases at the population level and extend the literature on shared determinants of diseases usually considered as non-communicable [14].\u003c/p\u003e\n\u003cp\u003eOur results align with previous work showing that dementia and cancer share common population-level drivers. Prince et al. (2016) and Li et al. (2022) have highlighted how ageing, affluence, and demographic transition fuel rising dementia prevalence globally [3, 16]. Similarly, Ferlay et al. (2018) demonstrated that cancer incidence follows the same trajectory, increasing steeply in middle- and high-income countries with expanding longevity and improved diagnostic systems [27]. The strong correlations observed in this study reinforce the notion that dementia and cancer are both products of broader epidemiological transitions associated with development.\u003c/p\u003e\n\u003cp\u003ePartial correlation analyses confirmed that the cancer\u0026ndash;dementia relationship was not fully explained by the developmental factors used. Even after controlling for affluence, urbanisation, relaxed selection opportunity, and longevity, cancer incidence accounted for nearly 60% of dementia variance. This independence suggests overlapping but distinct etiological processes. Prior studies have identified shared risk factors such as tobacco use, obesity, diet, physical inactivity, and vascular disease [1, 28]. In addition, systemic biological processes, including inflammation, oxidative stress, and immune dysfunction, are implicated in both oncogenesis and neurodegeneration [29, 30]. Our ecological findings are consistent with these individual-level observations, suggesting that population-level increases in cancer and dementia reflect converging risk environments [1].\u003c/p\u003e\n\u003cp\u003eThe results of our regression models are particularly noteworthy. Without cancer incidence, life expectancy and selection opportunity emerged as the strongest predictors of dementia incidence, consistent with earlier studies linking longevity and demographic structure to dementia burden [13, 31]. However, once cancer incidence was introduced, it overshadowed these factors, raising explained variance from 51% to 80%. This finding resonates with recent ecological studies demonstrating that chronic disease indicators can serve as proxies for broader health system and demographic transitions [14, 32]. Cancer incidence, in particular, may capture latent dimensions of development that economic affluence or life expectancy alone cannot, making it a powerful predictor of dementia burden [13].\u003c/p\u003e\n\u003cp\u003eOur results also shed light on the long-standing debate regarding the cancer\u0026ndash;dementia relationship [24]. At the individual level, some cohort and case-control studies report an inverse association between cancer and dementia, suggesting that biological antagonism or selective survival may protect cancer patients from developing dementia [24, 33]. For example, inverse links have been proposed between tumor suppressor genes and amyloid processing pathways [34, 35]. However, more recent longitudinal studies have questioned the robustness of this inverse association, noting potential survival bias and competing mortality [25, 36]. By contrast, our ecological findings demonstrate a strong positive relationship at the population level. These seemingly contradictory results can be reconciled: at the micro level, cancer and dementia may sometimes diverge due to selective survival, but at the macro level, both conditions rise together in contexts of demographic ageing and socioeconomic development [24, 25].\u003c/p\u003e\n\u003cp\u003eThe observed heterogeneity across regions and income groups also warrants discussion. The strongest associations were found in upper-middle-income and developing countries, whereas weaker relationships emerged in high-income or developed contexts. This pattern parallels global health transition theory, whereby low- and middle-income countries face steep increases in chronic disease burden as infectious diseases recede and populations age [14, 37]. Developed countries, in contrast, may show attenuated correlations due to plateauing incidence rates, greater heterogeneity in survival, or the influence of advanced medical technologies that alter detection and reporting [1, 38]. These contextual differences highlight the value of stratified analyses in understanding the cancer\u0026ndash;dementia link [37].\u003c/p\u003e\n\u003cp\u003eA key methodological insight from this study is that the estimated contribution of cancer incidence to dementia burden varies substantially by statistical approach. Partial correlations indicated that cancer explained 59.8% of dementia variance even after adjusting for economic affluence, urbanisation, selection opportunity, and Life e₆₀; however, this method may inflate independent effects when predictors share variance [39, 40].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the enter regression model, cancer\u0026rsquo;s contribution was estimated at 28.3% within a total R\u0026sup2; of 80.0%, reflecting the combined influence of all predictors \u0026nbsp;[40]. In the stepwise regression model, cancer entered first and explained 28.8% of the variance, with additional predictors only modestly increasing the total explained variance to 80.1% [39]. The close alignment between the enter (28.3%) and stepwise (28.8%) results indicates that cancer\u0026rsquo;s independent effect lies in this range, while the higher partial correlation estimate likely reflects inflation due to shared variance among predictors. Emphasising this 28\u0026ndash;29% range provides the most transparent and credible interpretation. Importantly, only populations with complete data and unambiguous classification were included in these stratified models, which further strengthens interpretative confidence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFuture research implications are evident. First, the strong and independent association between cancer and dementia incidence highlights the need for studies exploring shared biological pathways, including mechanisms of inflammation, immune regulation, and cellular ageing that may link the two conditions [29, 30]. Second, longitudinal and cohort-based investigations are necessary to clarify the temporal sequencing and mitigate residual concerns of reverse causality, thereby moving beyond ecological analyses [24, 41]. Third, comparative studies across regions and income groups could illuminate why the strength of association varies by socioeconomic context, particularly in rapidly developing countries experiencing demographic transitions [42, 43]. Fourth, research integrating cancer and dementia epidemiology with genetics, lifestyle exposures, and healthcare access would help disentangle the relative contributions of biological and structural determinants [44]. Finally, interdisciplinary approaches bridging oncology, neurology, and public health are needed to build a comprehensive understanding of how population-level and individual-level factors converge in shaping the joint burden of cancer and dementia [31]. This offers assistance with health resource allocation for optimising public benefit.\u003c/p\u003e\n\u003ch2\u003eStrengths and limitations\u003c/h2\u003e\n\u003cp\u003eThis study benefits from several strengths. It draws on comprehensive and internationally comparable datasets from the Global Burden of Disease (GBD), World Bank, and WHO, covering 204 countries across all world regions. Multiple analytical approaches, including correlations, partial correlations, principal component analysis, and both enter and stepwise regression models, were applied to provide convergent evidence and reinforce the robustness of the findings. Subgroup analyses across income groups, development classifications, and WHO regions allowed nuanced comparisons, while variance-explained metrics quantified the independent contribution of cancer incidence relative to socioeconomic and demographic predictors.\u003c/p\u003e\n\u003cp\u003eLimitations should also be acknowledged. The ecological design precludes individual-level inference and may be affected by ecological fallacy. Variation in diagnostic capacity, cancer registry completeness, and dementia ascertainment across countries could introduce bias, particularly in low-resource settings. Unmeasured confounders such as genetic predisposition, healthcare access, cultural practices, and lifestyle factors may also contribute to unexplained variance. Reliance on secondary data created temporal mismatches (cancer incidence, dementia incidence, and socioeconomic indicators), which may affect associations. Finally, incidence estimates may partly reflect detection capacity rather than true disease burden.\u003c/p\u003e\n\u003cp\u003eDespite these caveats, the consistency of results across methods, classifications, and regions strengthens confidence in the conclusion that cancer incidence is a strong and independent predictor of dementia incidence at the global level.\u003c/p\u003e\n\u003ch2\u003ePublic Health Implications\u003c/h2\u003e\n\u003cp\u003eThis study highlights key implications for public health and global health planning. The strong association between cancer and dementia supports integrated prevention strategies targeting shared modifiable risk factors such as smoking, obesity, poor diet, and physical inactivity. Cancer incidence may also serve as a sentinel indicator for anticipating dementia burden, especially in countries undergoing rapid demographic transitions, enabling earlier health system preparedness. Consistent associations across regions further emphasise the need for cross-sectoral collaboration between oncology, neurology, geriatrics, and primary care to address multimorbidity and cognitive health. Finally, embedding dementia within the broader (assumed) non-communicable disease agenda alongside cancer, cardiovascular disease, and diabetes can strengthen prevention and resource allocation, particularly in LMICs facing a rising chronic disease burden.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs alluded to earlier, it is also possible that cancer treatment may be associated with dementia risk through the neurological effects of chemotherapy, radiation therapy and perhaps anaesthesia and surgery \u0026ndash; effects that have been reported previously [45]. Additionally, there is some emerging evidence that \u003cem\u003eHerpes simplex\u003c/em\u003e (HSV) and zoster (HSV/VSV) viruses (also immunosuppressive) may be associated with the development of dementia [46-49] and some (perhaps more) cancers, indicating a possible, but currently unclear, latent association with infection [49-51]. Shin et. al. 2024 [46], found that HSV, VSV, and co-infection were associated with an increased risk of all dementia types. Interestingly, the longer the time of follow-up after HZV infection identification the greater the incidence of cancer with the curves between \u003cem\u003eHerpes zoster\u003c/em\u003e infected and non-infected controls continuing to widen after 1-2 years post-infection [52]. Of note, the malignancy overall was positively associated with zoster risk (adjusted OR = 1.29) and HZV (above) associated with Dementia [53].\u003cem\u003e\u0026nbsp;\u003c/em\u003eImportantly, our and these cited studies show the considerable power and value of longitudinal population studies enabling detailed analysis of large data collections over long time periods.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that cancer incidence is a robust and independent predictor of dementia incidence worldwide. The strong correlations observed across most regions and income levels indicate that both diseases are closely tied to demographic and socioeconomic transitions. Importantly, cancer incidence explained substantially more variance in dementia burden than traditional developmental indicators, positioning it as a potential proxy for anticipating future dementia trends. Integrating cancer and dementia prevention into unified public health strategies may improve efficiency and population outcomes in the face of rising global chronic disease prevalence.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eACD\u003c/strong\u003e \u0026ndash; African, Caribbean, and Pacific Group of States\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAFRO\u003c/strong\u003e \u0026ndash; WHO African Region\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAMRO\u003c/strong\u003e \u0026ndash; WHO Region of the Americas\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAPEC\u003c/strong\u003e \u0026ndash; Asia-Pacific Economic Cooperation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEEA\u003c/strong\u003e \u0026ndash; European Economic Area\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEMRO\u003c/strong\u003e \u0026ndash; WHO Eastern Mediterranean Region\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEOL\u003c/strong\u003e \u0026ndash; Economies of Late development\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEU\u003c/strong\u003e \u0026ndash; European Union\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEURO\u003c/strong\u003e \u0026ndash; WHO European Region\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGBD\u003c/strong\u003e \u0026ndash; Global Burden of Disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGDP PPP\u003c/strong\u003e \u0026ndash; Gross Domestic Product, Purchasing Power Parity\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIHME\u003c/strong\u003e \u0026ndash; Institute for Health Metrics and Evaluation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKMO\u003c/strong\u003e \u0026ndash; Kaiser\u0026ndash;Meyer\u0026ndash;Olkin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLA\u003c/strong\u003e \u0026ndash; Latin America\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLAC\u003c/strong\u003e \u0026ndash; Latin America and the Caribbean\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLMIC\u003c/strong\u003e \u0026ndash; Low- and Middle-Income Countries\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOECD\u003c/strong\u003e \u0026ndash; Organisation for Economic Co-operation and Development\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCA\u003c/strong\u003e \u0026ndash; Principal Component Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSADC\u003c/strong\u003e \u0026ndash; Southern African Development Community\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCO\u003c/strong\u003e \u0026ndash; Shanghai Cooperation Organisation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSEARO\u003c/strong\u003e \u0026ndash; WHO South-East Asia Region\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUN\u003c/strong\u003e \u0026ndash; United Nations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWHO\u003c/strong\u003e \u0026ndash; World Health Organization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWPRO\u003c/strong\u003e \u0026ndash; WHO Western Pacific Region\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics and Consent to Publish declarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Office of Research Ethics, Compliance and Integrity (ORECI), University of Adelaide (Approval No. 36289). Consent to publish: Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sources for this study are described in the \u0026quot;Materials and Methods\u0026quot; section. All data used are freely available from the official websites of international organisations. Formal permission to use the data for non-commercial research purposes was not required, as their use complies with the public access permissions outlined in the respective agencies\u0026apos; terms and conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that there is no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGEN AI Use Statement\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eDuring initial preparation of this manuscript, the lead author used ChatGPT to enhance readability and language, without replacing key authoring tasks. After utilising this tool, all authors edited the text, taking full responsibility for the integrity and authenticity of the publication\u0026apos;s content. \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWY conceived the hypothesis and study design, and WY and MH collected the data and conducted the analyses. WY, BJC and MH interpreted the data. BJC and MH provided inputs for WY to draft and revise the manuscript. All authors reviewed, edited and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray, F., et al., \u003cem\u003eGlobal cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.\u003c/em\u003e CA: a cancer journal for clinicians, 2024. \u003cstrong\u003e74\u003c/strong\u003e(3): p. 229-263.\u003c/li\u003e\n\u003cli\u003eCollaborators, G.D.F., \u003cem\u003eEstimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019.\u003c/em\u003e The Lancet. 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Mito, \u003cem\u003eCorrelation between varicella-zoster virus infection and cancer development: A comprehensive analysis.\u003c/em\u003e Microbes \u0026amp; Immunity, 2025: p. 8320.\u003c/li\u003e\n\u003cli\u003eCotton, S., et al., \u003cem\u003eThe risk of a subsequent cancer diagnosis after herpes zoster infection: primary care database study.\u003c/em\u003e British journal of cancer, 2013. \u003cstrong\u003e108\u003c/strong\u003e(3): p. 721-726.\u003c/li\u003e\n\u003cli\u003eSim, J.-H., et al., \u003cem\u003eThe association between herpes zoster and increased cancer risk: a nationwide population-based matched control study.\u003c/em\u003e Current Oncology, 2021. \u003cstrong\u003e28\u003c/strong\u003e(4): p. 2720-2730.\u003c/li\u003e\n\u003cli\u003eHansson, E., et al., \u003cem\u003eHerpes zoster risk after 21 specific cancers: population-based case\u0026ndash;control study.\u003c/em\u003e British journal of cancer, 2017. \u003cstrong\u003e116\u003c/strong\u003e(12): p. 1643-1651.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cancer epidemiology, Dementia epidemiology, Alzheimer Disease, Global Health, Socioeconomic Factors, Life Expectancy, Ecological Studies","lastPublishedDoi":"10.21203/rs.3.rs-7533062/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7533062/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Cancer and dementia are two major global health challenges shaped by population ageing and socioeconomic transitions. Both impose substantial burdens, yet their interrelationship at the population level remains underexplored. This study examined the global relationship between cancer incidence and dementia incidence, taking into account developmental, demographic, and healthcare-related covariates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Data on cancer incidence and dementia incidence were obtained from the Institute for Health Metrics and Evaluation. Covariates included economic affluence, urbanisation, selection opportunity, and life expectancy at age 60. Analyses across 204 countries employed Pearson and Spearman correlations, partial correlations, principal component analysis, and multiple linear regression (enter and stepwise). Subgroup analyses were stratified by World Bank income level, UN development status, WHO regions, and additional geopolitical groupings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Cancer incidence was strongly correlated with dementia incidence globally (r = 0.873; ρ = 0.938, p \u0026lt; 0.001). Associations remained robust across regions and income groups, particularly in upper-middle-income and developing countries. Partial correlations confirmed the relationship persisted after adjusting for covariates, with cancer explaining 59.8% of dementia variance. In regression models, socioeconomic and demographic factors explained 51.7% of variance; adding cancer increased explanatory power to 80.1%. Cancer uniquely accounted for 28.3% in the enter model and 28.8% in the stepwise model, confirming its role as the dominant independent predictor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Cancer incidence is strongly and independently associated with dementia incidence worldwide, surpassing traditional predictors. Findings highlight shared determinants and underscore the importance of integrated chronic disease strategies, particularly in low-resource settings.\u003c/p\u003e","manuscriptTitle":"Global Correlation Between Cancer Incidence and Dementia Incidence Based on Cross National Regression Analyses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 13:44:31","doi":"10.21203/rs.3.rs-7533062/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":"197c3a0e-753a-4d25-98d4-d2485027e21b","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T08:40:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 13:44:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7533062","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7533062","identity":"rs-7533062","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}