From Survival to Susceptibility: How Relaxed Natural Selection Shapes Global Neurological Disease Patterns

From Survival to Susceptibility: How Relaxed Natural Selection Shapes Global Neurological Disease Patterns | 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 From Survival to Susceptibility: How Relaxed Natural Selection Shapes Global Neurological Disease Patterns Wenpeng You, Maciej Henneberg This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8182547/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: The global burden of dementia and other neurological disorders is increasing rapidly, particularly in low and middle income countries (LMICs). While ageing, economic development, and urbanization are well established contributors, the potential influence of evolutionary processes such as relaxed natural selection, captured through the concept of selection opportunity, has not been fully explored. Methods: This ecological study analysed data from 204 countries to examine the relationship between selection opportunity, measured by the Biological State Index (I bs ), and the incidence of nine neurological disorder indicators. These included grouped conditions such as total neurological disorders and individual conditions such as Alzheimer’s disease, Parkinson’s disease, and headache disorders. Data were obtained from the Institute for Health Metrics and Evaluation, the World Bank, and the World Health Organization. Statistical analyses included Pearson and Spearman correlations, partial correlations, multivariate linear regressions (enter and stepwise methods), and Fisher’s z-tests to assess global associations and subgroup differences by income level and geographic region. Results: Selection opportunity was significantly and positively associated with global dementia incidence (r = 0.651, p < .001), with stronger relationships observed in Lower-and-Middle-Income Countries settings. After controlling for ageing, national wealth, and urbanization, selection opportunity remained an independent predictor of dementia (β = 0.304, p < .001), Parkinson’s disease (β = 0.290, p < .001), and total neurological disorder burden (β = 0.181, p < .05). In contrast, idiopathic epilepsy showed a strong negative association (β = − 0.623, p < .001), and no significant associations were found for migraine or multiple sclerosis. Conclusions: This study provides new evidence that relaxed natural selection may independently contribute to the global incidence of neurological disorders. Integrating evolutionary perspectives into public health strategies may improve the understanding and management of these conditions, particularly in countries undergoing demographic and health transitions. Selection Opportunity Biological State Index Neurological Disorders Evolutionary Epidemiology Global Health Figures Figure 1 Background Neurological diseases represent an escalating public health challenge worldwide. Together, conditions such as dementia, Parkinson’s disease, motor neuron disease, multiple sclerosis, idiopathic epilepsy, and headache disorders account for a substantial and rising share of global disability, morbidity, and mortality [ 1 ]. According to recent estimates, neurological disorders are the second leading cause of death and the leading cause of disability-adjusted life years (DALYs) globally [ 1 , 2 ]. As populations age and healthcare systems expand, the burden of neurological disorders is projected to rise sharply, particularly in low- and middle-income countries (LMICs), where demographic transitions are occurring rapidly, and health systems often struggle to meet the complex needs of ageing populations [ 3 ]. Traditionally, the increasing incidence of neurological diseases has been attributed to well-recognized factors, including longer life expectancy, urbanization, improvements in diagnostic capabilities, and shifts in lifestyle and environmental exposures [ 4 , 5 ]. However, emerging research suggests that broader biological and evolutionary processes may also contribute to these trends, beyond conventional socioeconomic and demographic explanations [ 6 ]. One such process is the relaxation of natural selection, operationalized through the Biological State Index (I bs ) [ 7 ]. The I bs provides a measure of the extent to which individuals survive and reproduce, reflecting the impact of healthcare, public health improvements, and technological advances in reducing mortality and fertility differentials [ 8 ]. While the relationship between selection opportunity and chronic conditions such as cancer [ 9 ], obesity [ 10 ], diabetes [ 11 ], and cardiovascular diseases [ 12 ] has been previously established, its role in shaping the epidemiology of neurological diseases remains largely underexplored. Many neurological disorders, particularly Alzheimer’s disease, Parkinson’s disease, and motor neuron disease, are strongly associated with ageing and shaped by complex genetic factors [ 13 – 15 ]. Under ancestral conditions characterized by stronger natural selection, individuals carrying genetic predispositions for such late-onset disorders would often not have survived long enough to express or transmit these traits [ 16 ]. In contrast, in modern societies characterized by decreased early-life mortality, increased survival into older age, and widespread reproductive success, these genetic vulnerabilities are allowed to accumulate and become epidemiologically visible [ 17 ]. From an evolutionary perspective, the rising global burden of late-onset neurological diseases may be the visible manifestation of genetic load, referring to harmful mutations that were historically constrained by natural selection but are now increasingly expressed at the population level [ 18 , 19 ]. Previous ecological research has linked selection opportunity specifically to dementia incidence [ 20 ], demonstrating that healthcare services relaxing natural selection may partly contribute to the global rise in dementia. However, the broader question of whether relaxed natural selection similarly influences the incidence of other neurological conditions, such as Parkinson’s disease, motor neuron disease, multiple sclerosis, idiopathic epilepsy, and headache disorders, remains largely unexplored. Given the diverse genetic, neurodevelopmental, and environmental underpinnings of these disorders, investigating whether a shared evolutionary-demographic mechanism contributes to their incidence is crucial for understanding emerging global health patterns [ 20 , 21 ]. Additionally, variations in selection opportunity are likely to intersect with differences in economic affluence, ageing trajectories, urban living environments, and health system capacities [ 21 ]. These contextual factors could moderate or amplify the relationship between evolutionary relaxation and disease burden. For example, LMICs experiencing rapid demographic transitions may face disproportionate neurological disease burdens due to both rising life expectancy and limited public health resources [ 22 ]. Conversely, high-income countries may buffer the effects of selection relaxation through advanced medical interventions, early diagnosis, and prevention programs [ 2 ]. Understanding these dynamics across diverse settings is important for informing targeted global health strategies. This study examines the global relationship between selection opportunity and the incidence of nine major neurological diseases across 204 countries. It also evaluates whether this association remains independent of key confounders, including ageing, economic affluence, and urban living, and explores how the relationship varies across different economic and regional contexts. By investigating these associations, this study aims to advance the understanding of how evolutionary processes and demographic transitions interact to shape the global distribution of neurological diseases. It contributes to a growing interdisciplinary field that integrates evolutionary biology, epidemiology, and public health to explain contemporary patterns of non-communicable diseases. Findings from this research may offer valuable insights for anticipating future neurological disease trends and designing more resilient, evolutionarily informed health systems capable of managing the cognitive and neurological health needs of populations worldwide. Material and Method Data sources This ecological study used publicly available, country-level data from reputable international databases, with all variables log-transformed to improve distribution normality and cross-national comparability. Selection opportunity was measured using the Biological State Index (I bs ), a validated indicator of relaxed natural selection. Higher I bs values reflect greater survival and reproduction opportunity of individuals with potentially deleterious traits. I bs data were drawn from prior studies by Budnik and Henneberg [ 8 ]. Total neurological disease incidence, including dementia (Alzheimer’s and other dementias), Parkinson’s disease, motor neuron disease, multiple sclerosis, idiopathic epilepsy, and headache disorders (migraine and tension-type), was obtained from the IHME Global Burden of Disease (GBD) 2021 dataset [ 23 ]. Age-standardized incidence rates per 100,000 population were used. Ageing was represented by life expectancy at age 60 (e (60) ), sourced from the WHO Global Health Observatory [ 24 ]. Economic affluence was assessed via GDP per capita (PPP) from the World Bank [ 25 ], and urban living by the percentage of the population residing in urban areas, also from the World Bank [ 26 ]. Subgroup analyses were based on classifications from the World Bank (income level), United Nations (development status), WHO regions, and major geopolitical/economic blocs (e.g., APEC, SCO, EU, OECD, LAC, Arab World, EOL). These sources enabled robust cross-national analyses of the relationship between selection opportunity and neurological disease incidence. Study Design and Statistical Analysis This study employed a cross-sectional ecological design to examine the relationship between selection opportunity and the incidence of neurological diseases, with a focus on dementia. Country-level data from 204 nations were analysed using both bivariate and multivariate approaches to assess global and regional patterns. The unit of analysis was the nation-state, and the study included subgroup comparisons based on established international classifications (e.g., World Bank income groups, WHO regions). The ecological design allowed for the exploration of macro-level associations between evolutionary indicators and health outcomes across diverse population contexts. While this approach limits individual-level inference, it provides valuable insights into population-level trends that can inform global health policy and future hypothesis-driven research. All continuous variables were log-transformed to improve distribution normality and reduce skewness/kurtosis in cross-national data distributions. The statistical procedures included: Pearson’s correlation coefficients (r) and Spearman’s rank-order correlations (ρ) were computed to assess associations between selection opportunity and disease incidence across all countries and within specific subgroups (e.g., income levels, development status, WHO regions, and economic blocs). Partial correlation analyses were performed to examine the unique association between selection opportunity and each neurological outcome while controlling for potential confounders: ageing ( e (60) ), economic affluence (GDP PPP), and urban living (% urban population). Multivariate linear regression models were conducted using both the enter method and stepwise method to assess the independent predictive value of selection opportunity after adjusting for covariates. Standardized regression coefficients (β), p-values, and adjusted R² values were reported for each model. Subgroup comparisons were made using Fisher’s r-to-z transformation to statistically test differences in correlation strength between country groupings (e.g., high-income vs. LMICs, developed vs. developing nations). All analyses were conducted using IBM SPSS Statistics (Version 30). Significance levels were set at p < .05 for all two-tailed tests, with p < .01 and p < .001 used to denote stronger significance thresholds. Results were interpreted with a focus on both statistical significance and practical relevance, especially in the context of global health disparities. Results Figure 1 illustrates a non-linear association between selection opportunity, as measured by the Biological State Index (I bs ), and the incidence of total neurological disorders. Notably, the incidence remains relatively stable at lower and mid-range I bs values but rises sharply at higher levels, particularly beyond 0.95. This trend, captured by a fifth-degree polynomial curve (R² = 0.6149), suggests that as natural selection becomes increasingly relaxed which is often in more developed populations, the burden of neurological disorders significantly escalates. These findings support the hypothesis that diminished selection pressure may contribute to the increased prevalence of late-onset (50 years or older) and genetically influenced neurological conditions. Table 1 summarises the correlation coefficients among selection opportunity, ageing, economic affluence, urban living, and the prevalence of major neurological disorders, with all variables log-transformed. I bs values showed strong positive correlations with ageing (r = 0.737), economic affluence (r = 0.789), and urban living (r = 0.506), all at p < .001. It was also significantly associated with total neurological disorder incidence (r = 0.651), dementia (r = 0.746), Parkinson’s disease (r = 0.775), and motor neuron disease (r = 0.533). Ageing was similarly correlated with dementia (r = 0.769), Parkinson’s (r = 0.798), and motor neuron disease (r = 0.701). Economic affluence showed a comparable pattern, particularly for Parkinson’s disease (r = 0.802), Alzheimer’s disease (r = 0.758), and multiple sclerosis (r = 0.659). Urban living had moderate correlations, strongest for Parkinson’s (r = 0.549) and Alzheimer’s (r = 0.509). In contrast, migraine (r = − 0.277) and idiopathic epilepsy (r = − 0.262) showed negative associations with selection opportunity, suggesting distinct etiological, diagnostic or demographic influences. Overall, age-related and neurodegenerative disorders were positively linked to indicators of socioeconomic development, longevity, and relaxed natural selection, while migraine and epilepsy followed a different pattern. Table 1 Correlation Matrix of Variables: Selection Opportunity, Ageing, Economic Affluence, Urban Living, and the Prevalence of Major Neurological Disorders Selection Opportunity Ageing e(60) Economic Affluence Urban Living Neurological disorders, Total Alzheimer's disease and other dementias Headache disorders, Total Headache disorders, Migraine Headache disorders, Tension-type headache Idiopathic epilepsy Motor neuron disease Multiple sclerosis Parkinson's disease Selection Opportunity 1 0.737 *** 0.789 *** 0.506 *** 0.651 *** 0.746 *** 0.643 *** -0.055 0.655 *** -0.324 *** 0.533 *** 0.490 ** 0.775 *** Ageing e(60) 0.834 *** 1 0.801 *** 0.598 *** 0.661 *** 0.769 *** 0.644 *** -0.100 0.663 *** -0.113 0.701 *** 0.601 *** 0.798 *** Economic Affluence 0.896 *** 0.811 *** 1 0.679 *** 0.753 *** 0.758 *** 0.742 *** -0.141 0.762 *** -0.128 0.683 *** 0.659 *** 0.802 *** Urban Living 0.631 *** 0.676 *** 0.718 *** 1 0.527 *** 0.509 *** 0.518 *** -0.034 0.527 *** -0.005 0.467 *** 0.485 *** 0.549 *** Neurological disorders, Total 0.799 *** 0.704 *** 0.762 *** 0.483 *** 1 0.750 *** 0.999 *** 0.086 0.995 *** -0.265 *** 0.680 *** 0.718 *** 0.758 *** Alzheimer's disease and other dementias 0.846 *** 0.785 *** 0.761 *** 0.527 *** 0.762 *** 1 0.724 *** − .260 *** 0.759 *** -0.347 *** 0.786 *** 0.546 *** 0.952 *** Headache disorders, Total 0.788 *** 0.689 *** 0.750 *** 0.467 *** 0.998 *** 0.737 *** 1 0.111 0.994 *** -0.266 *** 0.657 *** 0.714 *** 0.734 *** Headache disorders, Migraine -0.277 *** -0.181 * -0.288 *** -0.157 * -0.077 -0.364 *** -0.053 1 -0.002 0.013 -0.201 ** 0.003 -0.181 ** Headache disorders, Tension-type headache 0.809 *** 0.709 *** 0.778 *** 0.495 *** 0.996 *** 0.766 *** 0.994 *** -0.122 1 -0.264 *** 0.686 *** 0.720 *** 0.761 *** Idiopathic epilepsy -0.262 *** -0.126 -0.132 -0.001 -0.280 *** -0.354 *** -0.283 *** -0.007 -0.269 ** 1 -0.064 -0.045 -0.328 *** Motor neuron disease 0.817 *** 0.750 *** 0.734 *** 0.527 *** 0.687 *** 0.858 *** 0.669 *** − .347 *** 0.696 ** -0.160 * 1 0.662 ** 0.803 *** Multiple sclerosis 0.644 *** 0.628 *** 0.664 *** 0.480 *** 0.723 *** 0.538 *** 0.725 *** -0.087 0.720 ** -0.026 0.651 *** 1.000 0.573 *** Parkinson's disease 0.880 *** 0.810 *** 0.797 *** 0.562 *** 0.751 *** 0.954 *** 0.730 *** − .317 *** 0.757 ** -0.334 *** 0.862 *** 0.554 *** 1 Sample sizes for the correlation analyses ranged from n = 179 to 204. Significance levels: *** p < .001, ** p < .01, * p < .05. All data were log-transformed prior to analysis to improve normality and comparability across variables. Selection Opportunity was measured using the Biological State Index values from ( Ibs ; Budnik & Henneberg, 2017). Neurological disease incidence rates (total of nine conditions) were obtained from the Institute for Health Metrics and Evaluation (IHME), University of Washington, and are reported as new cases per 100,000 population in 2021. Age was operationalized as life expectancy at age 60 (World Health Organization, 2018). Economic affluence was measured by gross domestic product (GDP) per capita in purchasing power parity (PPP; World Bank, 2018). Urban living was defined as the percentage of the population residing in urban areas (World Bank, 2018). Table 2 presents the bivariate and partial correlations between selection opportunity and the incidence of nine major neurological disorders, with all variables log-transformed. In bivariate analyses, selection opportunity showed significant positive correlations with most neurological conditions. The strongest associations were observed for Parkinson’s disease (r = 0.737, ρ = 0.880), Alzheimer’s disease and other dementias (r = 0.746, ρ = 0.846), and total neurological disorder burden (r = 0.651, ρ = 0.799), all at p < .001. Similarly strong correlations were found for tension-type headaches (r = 0.655, ρ = 0.809) and total headache disorders (r = 0.643, ρ = 0.788). By contrast, migraine (r = − 0.055, ρ = − 0.277) and idiopathic epilepsy (r = − 0.324, ρ = − 0.262) showed significant negative correlations (p < .001). After adjusting for ageing, economic affluence, and urban living, partial correlations remained significant for Parkinson’s disease (r = 0.311), Alzheimer’s disease and dementias (r = 0.293), total neurological disorders (r = 0.164), total headache disorders (r = 0.169), and tension-type headache (r = 0.163), all at p < .05. Idiopathic epilepsy maintained a negative association (r = − 0.371, p < .001), while correlations with motor neuron disease and multiple sclerosis became non-significant. These findings indicate that selection opportunity is independently associated with several age-related and neurodegenerative conditions, particularly dementia and Parkinson’s disease, beyond the effects of ageing and socioeconomic status. Table 2 Associations Between Alzheimer's Disease and Cardiovascular/Cerebrovascular Conditions: Pearson, Spearman, and Partial Correlations (Controlling for Ageing, Economic Affluence, Genetic Predisposition, and Urban Living) Bivariate Correlation with Selection Opportunity Partial Correlation with Selection Opportunity keeping other variables statistically constant Pearson r Spearman ρ n Correlation Coefficient n Neurological disorders, Total 0.651 *** 0.799 *** 186 0.164 * 168 Alzheimer's disease and other dementias 0.746 *** 0.846 *** 186 0.293 *** 168 Headache disorders, Total 0.643 *** 0.788 *** 186 0.169 * 168 Headache disorders, Migraine -0.055 -0.277 *** 186 0.085 168 Headache disorders, Tension-type headache 0.655 *** 0.809 *** 186 0.163 * 168 Idiopathic epilepsy -0.324 *** -0.262 *** 186 -0.371 *** 168 Motor neuron disease 0.533 *** 0.817 *** 186 -0.123 168 Multiple sclerosis 0.490 *** 0.644 *** 186 -0.075 168 Parkinson's disease 0.737 *** 0.880 *** 179 0.311 *** 168 Ageing e(60) 0.789 *** 0.834 *** 179 Control Variable Economic Affluence 0.506 *** 0.896 *** 186 Control Variable Urban Living 0.737 *** 0.631 *** 179 Control Variable Significance levels: *** p < .001, ** p < .01, * p < .05. All data were log-transformed prior to analysis to improve normality and comparability across variables. Selection Opportunity was measured using the Biological State Index values from ( Ibs ; Budnik & Henneberg, 2017). Neurological disease incidence rates (total of nine conditions) were obtained from the Institute for Health Metrics and Evaluation (IHME), University of Washington, and are reported as new cases per 100,000 population in 2021. Age was operationalized as life expectancy at age 60 (World Health Organization, 2018). Economic affluence was measured by gross domestic product (GDP) per capita in purchasing power parity (PPP; World Bank, 2018). Urban living was defined as the percentage of the population residing in urban areas (World Bank, 2018). Table 3 presents multivariate linear regression results assessing the independent effect of selection opportunity on the incidence of total and specific neurological disorders, using both enter and stepwise methods. All models controlled for ageing (life expectancy at age 60), economic affluence, and urban living, with all variables log-transformed. Biological State Index was a significant independent predictor of total neurological disorder incidence (β = 0.181, p < .05), alongside economic affluence (β = 0.515, p < .001). In the stepwise model, its inclusion modestly increased the model’s explanatory power (Adjusted R² = 0.594). For Alzheimer’s disease and other dementias, selection opportunity showed a stronger effect (β = 0.304, p < .001), contributing substantially to a high model fit (Adjusted R² = 0.666). Positive associations were also observed for headache disorders (β = 0.190, p < .05) and tension-type headaches, while migraine remained non-significant. Notably, idiopathic epilepsy was negatively associated with selection opportunity (β = − 0.623, p < .001), with urban living also contributing (Adjusted R² = 0.162). In contrast, motor neuron disease and multiple sclerosis were primarily explained by ageing and economic affluence, with no significant contribution from selection opportunity. For Parkinson’s disease, selection opportunity was a strong and consistent predictor (β = 0.290, p < .001), with the stepwise model showing high explanatory value (Adjusted R² = 0.733). Overall, selection opportunity emerged as a robust factor associated with several age-related neurological conditions, particularly dementia, Parkinson’s disease, and headache disorders, independent of key socioeconomic and demographic variables. Table 3 Multivariate Linear Regression Results Examining the Effects of Selection Opportunity on Grouped and Specific Neurological Disease Incidence Enter Method Stepwise Methods Model Variable Standardized (β) Significance (p) Model Variable Adjusted R 2 Significance (p) Dependent Variable: Neurological disorders, Total 1 Selection Opportunity 0.181 < 0.050 1 Economic Affluence 0.582 < 0.001 Ageing e(60) 0.076 0.376 2 Selection Opportunity 0.594 < 0.05 Economic Affluence 0.515 < 0.001 Economic Affluence Insignificance Urban Living 0.066 0.335 Urban Living Insignificance Dependent Variable: Alzheimer's disease and other dementias 1 Selection Opportunity 0.304 < 0.001 1 Ageing e(60) 0.578 < 0.001 Ageing e(60) 0.349 < 0.001 2 Selection Opportunity 0.653 < 0.001 Economic Affluence 0.273 < 0.010 3 Economic Affluence 0.666 < 0.050 Urban Living -0.054 0.377 Urban Living Insignificance Dependent Variable: Headache disorders, Total 1 Selection Opportunity 0.190 < 0.050 1 Economic Affluence 0.567 < 0.001 Ageing e(60) 0.051 0.556 2 Selection Opportunity 0.579 < 0.050 Economic Affluence 0.515 < 0.001 Ageing e(60) Insignificance Urban Living 0.069 0.322 Urban Living Insignificance Dependent Variable: Headache disorders, Migraine 1 Selection Opportunity 0.146 0.270 Selection Opportunity Insignificance Ageing e(60) -0.122 0.364 Ageing e(60) Insignificance Economic Affluence -0.188 0.252 Economic Affluence Insignificance Urban Living 0.076 0.473 Urban Living Insignificance Dependent Variable: Headache disorders, Tension-type headache 1 Selection Opportunity 0.177 0.177 1 Economic Affluence 0.596 < 0.001 Ageing e(60) 0.065 0.065 2 Selection Opportunity 0.607 < 0.050 Economic Affluence 0.540 0.540 Ageing e(60) Insignificance Urban Living 0.060 0.060 Urban Living Insignificance Dependent Variable: Idiopathic epilepsy 1 Selection Opportunity -0.623 < 0.001 Selection Opportunity 0.114 < 0.001 Ageing e(60) 0.184 0.133 Urban Living 0.162 < 0.001 Economic Affluence 0.051 0.731 Economic Affluence Insignificance Urban Living 0.195 < 0.050 Ageing e(60) Insignificance Dependent Variable: Motor neuron disease 1 Selection Opportunity -0.147 0.111 Ageing e(60) 0.481 < 0.001 Ageing e(60) 0.481 < 0.001 Economic Affluence 0.517 < 0.001 Economic Affluence 0.419 < 0.001 Selection Opportunity Insignificance Urban Living -0.017 0.816 Urban Living Insignificance Dependent Variable: Multiple sclerosis 1 Selection Opportunity -0.093 0.334 1 Economic Affluence 0.443 < 0.001 Ageing e(60) 0.206 < 0.050 2 Urban Living 0.459 < 0.050 Economic Affluence 0.455 < 0.001 Ageing e(60) Insignificance Urban Living 0.176 < 0.050 Selection Opportunity Insignificance Dependent Variable: Parkinson's disease 1 Selection Opportunity 0.290 < 0.001 1 Economic Affluence 0.638 < 0.001 Ageing e(60) 0.348 < 0.001 2 Ageing e(60) 0.705 < 0.001 Economic Affluence 0.321 < 0.001 3 Selection Opportunity 0.733 < 0.001 Urban Living -0.042 0.452 Urban Living Insignificance Significance levels: *** p < .001, ** p < .01, * p < .05. All data were log-transformed prior to analysis to improve normality and comparability across variables. Selection Opportunity was measured using the Biological State Index values from ( I bs ; Budnik & Henneberg, 2017). Neurological disease incidence rates (total of nine conditions) were obtained from the Institute for Health Metrics and Evaluation (IHME), University of Washington, and are reported as new cases per 100,000 population in 2021. Age was operationalized as life expectancy at age 60 (World Health Organization, 2018). Economic affluence was measured by gross domestic product (GDP) per capita in purchasing power parity (PPP; World Bank, 2018). Urban living was defined as the percentage of the population residing in urban areas (World Bank, 2018). Table 4 summarises the correlation between selection opportunity and dementia incidence across global country groupings, based on both Pearson’s and Spearman’s correlation coefficients (all variables log-transformed). At the global level, the association was strong and significant (r = 0.651, ρ = 0.799, p < .001; n = 186). Across World Bank income classifications, selection opportunity was significantly correlated with dementia incidence in all income groups. The strength of association increased from low-income (r = 0.524) to high-income countries (r = 0.597), with the most pronounced correlation observed when combining all LMICs (r = 0.759, ρ = 0.790, p < .001; n = 122). Statistical comparison showed a significantly stronger association in LMICs compared to high-income countries (z = − 2.00 for Pearson, p < .05; z = − 3.13 for Spearman, p < .001). Using UN development classifications, developing countries showed a stronger association (r = 0.759, ρ = 0.841, p < .001) than developed ones (r = 0.559, ρ = 0.743, p < .01), with Pearson’s z test reaching significance (z = − 2.03, p < .05), and Spearman’s test approaching significance (z = − 1.5, p = 0.067). By WHO region, the strongest correlations were observed in EMRO (r = 0.835, ρ = 0.914), WPRO (r = 0.764, ρ = 0.875), and EURO (r = 0.705, ρ = 0.867). Other regions, including AFRO, AMRO, and SEARO, also showed moderate-to-strong, mostly significant associations. Across geopolitical and economic blocs, consistent positive correlations emerged. Notably high correlations were found in APEC (r = 0.860, ρ = 0.874), the Arab World (r = 0.826, ρ = 0.888), and Economies of Longevity (r = 0.793, ρ = 0.898). Other blocs, including SCO, OECD, EU, and LAC, also demonstrated significant associations (r values between 0.582 and 0.709). Overall, these findings suggest that the relationship between selection opportunity and total neurological disease incidence is robust across diverse contexts, with stronger correlations often seen in LMICs and developing regions, supporting the hypothesis of an emerging evolutionary burden. Table 4 Correlations Between Selection Opportunity and Dementia Incidence Across Global Country Groupings Dementia Incidence Country groupings Pearson Non-parametric n Worldwide 0.651 *** 0.799 *** 186 World Bank income classifications Low income 0.524** 0.426* 24 Low middle income 0.509*** 0.620*** 48 Upper middle income 0.422** 0.503*** 50 High income 0.597*** 0.532*** 70 LMIC 0.759*** 0.790*** 122 Fisher A-to-Z: high income vs LMICs in Pearson’s r (z = − 2.00, p < 0.05) and in non-parametric (z = − 3.13, p < 0.001) UN common practice Developed 0.559 ** 0.743 ** 44 Developing 0.759 *** 0.841 *** 135 Fisher A-to-Z: developed vs developing in Pearson’s r (z= -2.03, p < 0.05) and in non-parametric (z = − 1.5, p = 0.067) WHO Regions AFRO 0.586 *** 0.531 *** 45 AMRO 0.671 *** 0.631 *** 33 EMRO 0.835 *** 0.914 *** 19 EURO 0.705 *** 0.867 *** 49 SEARO 0.617 * 0.527 10 WPRO 0.764 *** 0.875 *** 23 Countries grouped based on various factors ACD 0.765 *** 0.874 *** 29 APEC 0.860 *** 0.874 *** 19 Arab World 0.826 *** 0.888 *** 19 EEA 0.596 *** 0.665 *** 29 EOL 0.793 *** 0.898 *** 52 EU 0.582 *** 0.667 *** 27 LA 0.709 *** 0.713 *** 21 LAC 0.659 *** 0.557 *** 31 OECD 0.672 *** 0.589 *** 37 SADC 0.670 ** 0.653 ** 16 SCO 0.704 *** 0.849 *** 25 Significance levels: *** p < .001, ** p < .01, * p < .05. All data were log-transformed prior to analysis to improve normality and comparability across variables. Selection Opportunity was measured using the Biological State Index values from ( Ibs ; Budnik & Henneberg, 2017). Neurological disease incidence rates (total of nine conditions) were obtained from the Institute for Health Metrics and Evaluation (IHME), University of Washington, and are reported as new cases per 100,000 population in 2021. Discussion This ecological analysis offers novel evidence supporting a robust global association between relaxed natural selection, quantified using the Biological State Index (I bs ), and the incidence of neurological and neurodegenerative disorders. The findings demonstrate that countries with higher I bs values, indicating greater survival and reproduction opportunity among individuals with potentially deleterious genetic traits, tend to report higher rates of total neurological disorder, and specific neurological disorders, such as dementia, Parkinson’s disease, multiple sclerosis, and other neurological conditions. This relationship was particularly prominent in LMICs and developing countries. Our results are consistent with previous studies showing that increased survival into older age, driven by medical advancements and improved living conditions, elevates the likelihood of developing conditions that typically appear later in life, such as dementia, Parkinson’s disease, and other neurodegenerative disorders [ 27 , 28 ]. As natural selection weakens, particularly in populations with declining mortality and fertility rates, genetic variants linked to age-related diseases are less likely to be removed from the population. Instead, they persist and gradually accumulate in the gene pool across generations [ 18 , 19 ]. The genetic nature of neurological diseases supports this hypothesis [ 29 ]. Most of these disorders are polygenic, involving the interplay of numerous small-effect variants that individually may not cause disease but collectively increase susceptibility [ 30 ]. In the absence of strong selective pressures, these risk alleles are retained across generations, potentially elevating population-level disease vulnerability [ 21 ]. This mechanism echoes evolutionary theories put forth by Henneberg and colleagues, who argue that modern healthcare systems, while improving survival, inadvertently weaken natural selection and permit the accumulation of deleterious genetic traits related to non-communicable diseases [ 21 , 31 ]. Importantly, higher I bs values reflect reduced natural selection, and our analyses show a strong, positive association between I bs and neurological disease incidence, especially in regions with falling mortality and fertility rates [ 20 ]. The implication is that evolutionary pressures, which previously acted to eliminate harmful genetic variants, are now diminished, allowing these variants to become more prevalent [ 32 ]. Evidence for the cumulative genetic load associated with relaxed natural selection can be observed in other partially heritable conditions [ 11 ]. For example, studies on phenylketonuria (PKU) have shown that the disorder became more prevalent only after several generations of allelic accumulation, with trait frequencies increasing by approximately 0.2 to 2 percent per generation, depending on population structure and the strength of selection pressure [ 21 , 33 ]. These observations offer empirical support for the hypothesis that weakened selection pressure contributes to the rising incidence of chronic diseases, including those affecting the nervous system [ 34 ]. This concept aligns with the theoretical framework advanced by Henneberg and colleagues, who argued that modern reductions in natural selection pressure allow for the gradual accumulation of deleterious mutations associated with non-communicable diseases such as obesity, type 2 diabetes, and cancer [ 11 , 20 , 35 ]. It also complements recent ecological research by You and colleagues [ 35 ], which found that selection opportunity significantly explained variance across multiple non-communicable diseases at the population level, suggesting a common evolutionary mechanism underlying many modern chronic health conditions. One important observation from our study is that scatter plots revealed a curvilinear relationship between selection opportunity, measured by the I bs , and the total incidence of neurological diseases. These findings support the hypothesis that reduced selection pressure may allow the persistence of genetic vulnerabilities that contribute to neurological conditions typically emerging in older adulthood, such as Alzheimer's and Parkinson's diseases [ 36 ]. Furthermore, demographic transitions such as declining fertility and increased life expectancy may reshape the genetic architecture of populations, enabling the persistence of late onset conditions like dementia and Parkinson’s disease [ 8 , 37 ]. These findings highlight the importance of considering both evolutionary and demographic processes when interpreting global disparities in neurological disease incidence. The rising global burden of neurological diseases, particularly in LMICs undergoing rapid demographic and epidemiological transitions [ 38 ], indicates that evolutionary factors may be interacting with environmental, social, and health system determinants to shape disease patterns [ 39 ]. In many LMICs, improvements in child and maternal survival, access to basic healthcare, and vaccination coverage have extended life expectancy across all age groups. However, these advances have not been matched by commensurate growth in specialist services for neurological disease prevention, diagnosis, and management [ 38 , 40 ]. As a result, these populations are increasingly exposed to the phenotypic expression of inherited vulnerabilities, including genetic predispositions to dementia, epilepsy, and other neurodegenerative or neurodevelopmental conditions [ 41 ]. This infrastructure lag, in which survival improves faster than system capacity, creates a vulnerability gap, leaving health systems unprepared to respond to conditions that manifest later in life [ 42 ]. This contributes to the so-called "double burden" of disease, where LMICs must still manage high rates of communicable diseases and undernutrition while simultaneously facing a rising wave of non-communicable and genetically influenced chronic illnesses, including neurological disorders [ 3 , 43 ]. This dual pressure places significant strain on already limited health resources, deepens health inequities, and underscores the urgent need for integrated health system planning [ 44 ]. Such planning must anticipate the genetic consequences of demographic change and prioritise neurological care capacity-building as a central component of sustainable health development strategies [ 45 ]. The consistent inverse relationship between selection opportunity and idiopathic epilepsy incidence across all statistical models, including bivariate, partial, and multivariate regressions is a distinct finding that contrasts with the positive associations observed for other neurological conditions. Global studies support this pattern; for instance, regions with higher Sociodemographic Index (SDI) and I bs report higher age-standardized incidence rates (ASIRs), while lower-SDI regions tend to show lower ASIRs but higher mortality and disability burdens from epilepsy [ 46 , 47 ]. This likely reflects underdiagnosis or misclassification in settings with limited diagnostic capacity [ 47 ], whereas high-I bs countries with advanced healthcare systems may detect and reclassify idiopathic cases more accurately, reducing their reported incidence. From an evolutionary standpoint, idiopathic epilepsy may be under stronger purifying selection, especially if it affects individuals early in life or reduces reproductive fitness, thereby limiting the accumulation of associated alleles even in high-I bs populations [ 48 ]. Furthermore, the genetic basis of idiopathic epilepsy is complex and heterogeneous, with risk variants varying across populations and shaped by access to genomic tools and diagnostic practices [ 49 ]. These factors, combined with differing health system capabilities, may explain the global variability in idiopathic epilepsy data [ 47 ]. Overall, this inverse association underscores the importance of considering both evolutionary and socioeconomic influences when interpreting neurological disease patterns and highlights the need for further investigation into the unique etiological and diagnostic features of idiopathic epilepsy, particularly in low-resource settings. The regional variation observed across WHO regions and international blocs, with the strongest associations identified in EMRO, WPRO, APEC, and the Arab World, suggests that sociocultural factors and healthcare system capacity may influence the manifestation of underlying biological risk. In high-income countries, elevated selection opportunity may be counterbalanced by more advanced diagnostic, preventive, and therapeutic services [ 50 ]. In contrast, many emerging economies may lack such protective systems, making the evolutionary burden associated with relaxed natural selection more apparent through higher rates of neurological disease [ 51 ]. Taken together, these findings build on earlier theoretical work suggesting that the relaxation of natural selection in modern human populations is reshaping the global epidemiology of diseases that typically emerge after the age of 50, such as dementia [ 20 ]. They highlight the importance of considering evolutionary forces, which are often overlooked in public health discourse, alongside economic, demographic, and health system factors when modelling the global burden of disease. Strengths and limitations This study has several notable strengths. It is the first global analysis to systematically examine the association between selection opportunity and neurological disease incidence, using a comprehensive ecological approach. By including 204 countries across diverse economic, developmental, and geopolitical contexts, it enables robust cross-national comparisons. The use of bivariate, partial correlation, and multivariate regression analyses strengthens internal validity by adjusting for key confounders such as ageing, economic status, and urbanization. Additionally, data were sourced from credible global institutions (IHME, WHO, World Bank), enhancing reliability and comparability. A key contribution of this study is the integration of evolutionary theory with global health, offering a novel lens on the rising burden of dementia and other neurological disorders. It identifies selection opportunity as a potentially overlooked factor in shaping late-life disease risk, especially in the context of demographic transitions and global inequality. However, several limitations must be acknowledged. The ecological design precludes individual-level causal inference. The I bs , while theoretically sound, may not fully capture the long-term complexity of selection pressures or their interaction with environmental factors as insufficient data for I bs calculation. Despite adjusting for major confounders, residual confounding from unmeasured variables such as healthcare quality, diagnostic capacity, genetic diversity, and cultural attitudes may still remain. Furthermore, dementia incidence data are largely modelled estimates, especially in low-resource settings where surveillance systems are limited, possibly introducing bias. Despite these limitations, the study provides novel, population-level evidence linking relaxed natural selection to neurological disease patterns. It highlights the need for further interdisciplinary research that bridges evolutionary biology and public health, particularly to inform strategies for dementia prevention and management in diverse global settings. Public Health Implications This study carries important implications for global health planning. The strong and consistent association between selection opportunity and the rising incidence of dementia and other neurological disorders suggests that evolutionary forces, particularly the relaxation of natural selection, may play a role in shaping disease risk, especially in countries undergoing rapid demographic transitions [ 5 ]. The effect appears most pronounced in LMICs, where increasing life expectancy is not yet supported by adequate diagnostic capacity, long-term care infrastructure, or a trained workforce equipped to manage cognitive decline [ 52 ]. As a result, these regions may face a growing burden of neurological disorders without the systems in place to effectively respond. To address this, public health systems, particularly in low- and middle-income countries, should focus on early detection and intervention, invest in developing a dementia-specific workforce, establish culturally appropriate and accessible long-term care models, and incorporate evolutionary and demographic indicators into disease surveillance and health planning [ 53 ]. By integrating evolutionary perspectives into public health strategies, countries can better anticipate future neurological disease trends and implement more equitable, sustainable responses to the global dementia challenge. Conclusion This study demonstrates that selection opportunity, a proxy for relaxed natural selection, is significantly associated with the global incidence of total neurological disorders, particularly dementia and Parkinson’s disease, independent of ageing, affluence, and urbanization. The association was consistent across countries and notably stronger in LMICs and developing nations. These findings highlight the need to strengthen neurological health systems, especially where infrastructure is limited. Incorporating evolutionary perspectives into public health planning may improve forecasting, and future research should explore causal pathways to support equitable, adaptable cognitive care worldwide. Abbreviations I bs – Biological State Index LMICs – Low- and Middle-Income Countries GDP PPP – Gross Domestic Product per capita in Purchasing Power Parity WHO – World Health Organization IHME – Institute for Health Metrics and Evaluation UN – United Nations OECD – Organisation for Economic Co-operation and Development EMRO – Eastern Mediterranean Region (WHO) EURO – European Region (WHO) AFRO – African Region (WHO) AMRO – Region of the Americas (WHO) SEARO – South-East Asia Region (WHO) WPRO – Western Pacific Region (WHO) EOL – English as Official Language SCO – Shanghai Cooperation Organisation APEC – Asia-Pacific Economic Cooperation LAC – Latin America and the Caribbean EEA – European Economic Area EU – European Union SADC – Southern African Development Community ACD – Asia Cooperation Dialogue r / ρ – Pearson’s correlation coefficient / Spearman’s rank-order correlation coefficient β – Standardized regression coefficient R² / Adjusted R² – Coefficient of determination / Adjusted coefficient of determination Declarations Ethics Statement This study did not involve research with individual human participants or animals. All data used were publicly available and obtained from Institute for Health Metrics and Evaluation and official websites of United Nations (UN) agencies. According to the National Statement on Ethical Conduct in Human Research (2007, updated 2018), the XXX University classifies research as exempt from ethical review when it involves only negligible risk and uses existing, non-identifiable data about human beings (Reference No. XXXX, Details to be provided once this manuscript is accepted.). Funding There is no specific funding to support this study. Clinical trial number 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 organizations. 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 declare 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 publication's content. Author Contribution Wenpeng You: Conceptualization; Data Curation; Formal Analysis; Funding Acquisition; Investigation; Methodology; Project Administration; Resources; Software; Validation; Visualization; Writing – Original Draft Preparation; Writing – Review & Editing Maciej Henneberg: Conceptualization; Data Curation; Formal Analysis; Funding Acquisition; Investigation; Methodology; Resources; Software; Validation; Visualization; Writing – Review & Editing Acknowledgement The authors appreciate Ms Turi Christensen from the Institute for Health Metrics and Evaluation of the University of Washington for her assistance in locating and defining the data on cardiovascular disease incidence rate. References Guo X, et al. Global, regional, and national burden of four major neurological diseases in women from 1990 to 2021. Front Public Health. 2025;13:1561216. Feigin V et al. Soriano award lecture: The epidemiology and burden of neurological disorders. J Neurol Sci, 2023. 455. Steinmetz JD, et al. Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Neurol. 2024;23(4):344–81. Alzheimer's Disease International. World Alzheimer Report 2015: The Global Impact of Dementia (An AnAlysIs of prevAlence, IncIDence, cosT AnD TrenDs) . 2025 20 April 2024 ]; Available from: https://www.alzint.org/u/WorldAlzheimerReport2015.pdf?utm_source=chatgpt.com Livingston G, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396(10248):413–46. https://doi.org/10.1016/S0140-6736(20)30367-6 . Saniotis A, Henneberg M. Medicine could be constructing human bodies in the future. Med Hypotheses. 2011;77(4):560–64. Henneberg M, Piontek J. Biological state index of human groups. Przeglad Anthropologiczny. 1975;XLI:191–201. Budnik A, Henneberg M. Worldwide increase of obesity is related to the reduced opportunity for natural selection. PLoS ONE. 2017;12(1):e0170098. You W, Henneberg M. Cancer incidence increasing globally: The role of relaxed natural selection. Evol Appl. 2018;11(2):140–52. You W, Henneberg M. Relaxed natural selection contributes to global obesity increase more in males than in females due to more environmental modifications in female body mass. PLoS ONE. 2018;13(7):e0199594. https://doi.org/10.1371/journal.pone.0199594 . You W-P, Henneberg M. Type 1 diabetes prevalence increasing globally and regionally: the role of natural selection and life expectancy at birth. BMJ open diabetes Res care. 2016;4(1):e000161. You W, Henneberg M. Modern medical services, a double-edged sword manages symptoms, but accumulates genetic background of cardiovascular diseases: A cross populational analysis of 217 countries. 2024. Williamson J, Goldman J, Marder KS. Genetic aspects of Alzheimer disease. Neurologist. 2009;15(2):80–6. Loy CT, et al. Genetics of dementia. Lancet. 2014;383(9919):828–40. Deol K. Unravelling the Genetic and Environmental Connection in Parkinson’s Disease: Implications for Pathogenesis and Therapeutic Strategies. 2024. Saniotis A, Henneberg M, Mohammadi K. Genetic load and biological changes to extant humans. J Biosoc Sci. 2021;53(4):639–42. Cui R, et al. Relaxed selection limits lifespan by increasing mutation load. Cell. 2020;180(6):1272–9. Stearns SC, et al. Measuring selection in contemporary human populations. Nat Rev Genet. 2010;11(9):611–22. Milot E et al. Evidence for evolution in response to natural selection in a contemporary human population. Proceedings of the National Academy of Sciences, 2011. 108(41): pp. 17040–17045. You W, Henneberg R, Henneberg M. Healthcare services relaxing natural selection may contribute to increase of dementia incidence. Sci Rep. 2022;12(1):1–10. Stephan CN, Henneberg M. Medicine may be reducing the human capacity to survive. Med Hypotheses. 2001;57(5):633–37. Winkler AS. The growing burden of neurological disorders in low-income and middle-income countries: priorities for policy making. Lancet Neurol. 2020;19(3):200–2. IHME. Global Burden of Disease Study 2019 (GBD 2019) Covariates 1980–2019. 2023 March 12 2023]; Available from: https://ghdx.healthdata.org/record/global-burden-disease-study-2019-gbd-2019-covariates-1980-2019 WHO. Global Health Observatory: Life expectancy at age 60 (years). 2025 22 March 2025]; Available from: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/life-expectancy-at-age-60-(years ). World Bank. GDP per capita, PPP (current international $ ). 2019 December 28 2021]; Available from: https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD World Bank. Urban population (% of total) 2014. 2022; Available from: http://data.worldbank.org Hou Y, et al. Ageing as a risk factor for neurodegenerative disease. Nat reviews Neurol. 2019;15(10):565–81. Licher S, et al. Lifetime risk of common neurological diseases in the elderly population. J Neurol Neurosurg Psychiatry. 2019;90(2):148–56. Quintana-Murci L. Understanding rare and common diseases in the context of human evolution. Genome Biol. 2016;17:1–14. O'Connor LJ, et al. Extreme polygenicity of complex traits is explained by negative selection. Am J Hum Genet. 2019;105(3):456–76. Saniotis A, Henneberg M. Evolutionary Medicine and Future of Humanity: Will Evolution Have the Final Word? Humanities. 2013;2(2):278–91. Henneberg M. Natural selection through differential fertility in human populations: Quantitative evaluation of selection intensity. Przeglad Antropologiczny. 1980;46:21–60. Medawar PB. Do advances in medicine lead to genetic deterioration? Natural Selection in Human Populations. New York: Robert E. Krieger Publishing Co.; 1971. pp. 300–08. Bajema C. J., Editor. Elhawary NA, et al. Genetic etiology and clinical challenges of phenylketonuria. Hum Genomics. 2022;16(1):22. You W, Henneberg M. Modern medical services, a double-edged sword manages symptoms, but accumulates genetic background of cardiovascular diseases: A cross populational analysis of 217 countries. Health Sci Rep. 2024;7(1):e1828. Hunt BG et al. Relaxed selection is a precursor to the evolution of phenotypic plasticity. Proceedings of the National Academy of Sciences, 2011. 108(38): pp. 15936–15941. Wells JCK. The thrifty phenotype as an adaptive maternal effect. Biol Rev. 2007;82(1):143–72. Feigin VL, et al. Global, regional, and national burden of neurological disorders, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18(5):459–80. Ding C, et al. Global, regional, and national burden and attributable risk factors of neurological disorders: The Global Burden of Disease study 1990–2019. Front public health. 2022;10:952161. Carroll WM. The global burden of neurological disorders. Lancet Neurol. 2019;18(5):418–9. Feigin VL, et al. The global burden of neurological disorders: translating evidence into policy. Lancet Neurol. 2020;19(3):255–65. Béjot Y. Neurological disorders and age: The demographic transition. J Neurol Sci, 2021. 429. Vaidya A, et al. Implementing a package of essential non-communicable diseases interventions in low-and middle-income countries: a realist review protocol. BMJ open. 2023;13(9):e074336. Neurology TL. Sustainable development demands brain health . 2023. p. 871. Winter SF, et al. National plans and awareness campaigns as priorities for achieving global brain health. Lancet Global Health. 2024;12(4):e697–706. Tu F, et al. Evolving trends and burden of idiopathic epilepsy among children (0–14 years), 1990–2021: a systematic analysis for the Global Burden of Disease study 2021. Front Neurol. 2025;16:1548477. Xu L, et al. Global trends and burden of idiopathic epilepsy: regional and gender differences from 1990 to 2021 and future outlook. J Health Popul Nutr. 2025;44(1):45. Ellis CA, Petrovski S, Berkovic SF. Epilepsy genetics: clinical impacts and biological insights. Lancet Neurol. 2020;19(1):93–100. Zhang M-W, et al. Epilepsy-associated genes: an update. Seizure: Eur J Epilepsy. 2024;116:4–13. Yang L-z, et al. A population-based analysis of the global burden of epilepsy across all age groups (1990–2021): utilizing the Global Burden of Disease 2021 data. Front Neurol. 2024;15:1448596. Bender N, Rühli F, Henneberg M. Ongoing Human Evolution? The Evolutionary Roots of Human Brain Diseases, 2024: p. 472. Feigin VL, et al. Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021;20(10):795–820. 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. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":104514,"visible":true,"origin":"","legend":"\u003cp\u003eCurvilinear relationship between Selection Opportunity and total neurological disease incidence.\u003c/p\u003e\n\u003cp\u003eData sources: Selection Opportunity, measured by Biological State Index (\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003ebs\u003c/em\u003e\u003c/sub\u003e), downloaded from previous publication (Budnik and Henneberg, 2017); Neurological disease (total) incidence rate 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 in 2021. \u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure1IbsnNeuro.png","url":"https://assets-eu.researchsquare.com/files/rs-8182547/v1/ee73f3f3d6f43f9ee394a71f.png"},{"id":102415527,"identity":"3d7fc39c-352e-475d-8e38-b6c2981c49c0","added_by":"auto","created_at":"2026-02-11 12:44:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1382643,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8182547/v1/7d2cf368-0524-4d1a-b964-fde10feb98df.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Survival to Susceptibility: How Relaxed Natural Selection Shapes Global Neurological Disease Patterns","fulltext":[{"header":"Background","content":"\u003cp\u003eNeurological diseases represent an escalating public health challenge worldwide. Together, conditions such as dementia, Parkinson’s disease, motor neuron disease, multiple sclerosis, idiopathic epilepsy, and headache disorders account for a substantial and rising share of global disability, morbidity, and mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to recent estimates, neurological disorders are the second leading cause of death and the leading cause of disability-adjusted life years (DALYs) globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As populations age and healthcare systems expand, the burden of neurological disorders is projected to rise sharply, particularly in low- and middle-income countries (LMICs), where demographic transitions are occurring rapidly, and health systems often struggle to meet the complex needs of ageing populations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTraditionally, the increasing incidence of neurological diseases has been attributed to well-recognized factors, including longer life expectancy, urbanization, improvements in diagnostic capabilities, and shifts in lifestyle and environmental exposures [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, emerging research suggests that broader biological and evolutionary processes may also contribute to these trends, beyond conventional socioeconomic and demographic explanations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. One such process is the relaxation of natural selection, operationalized through the Biological State Index (I\u003csub\u003ebs\u003c/sub\u003e) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The I\u003csub\u003ebs\u003c/sub\u003e provides a measure of the extent to which individuals survive and reproduce, reflecting the impact of healthcare, public health improvements, and technological advances in reducing mortality and fertility differentials [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile the relationship between selection opportunity and chronic conditions such as cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], obesity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], diabetes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and cardiovascular diseases [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] has been previously established, its role in shaping the epidemiology of neurological diseases remains largely underexplored. Many neurological disorders, particularly Alzheimer’s disease, Parkinson’s disease, and motor neuron disease, are strongly associated with ageing and shaped by complex genetic factors [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Under ancestral conditions characterized by stronger natural selection, individuals carrying genetic predispositions for such late-onset disorders would often not have survived long enough to express or transmit these traits [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In contrast, in modern societies characterized by decreased early-life mortality, increased survival into older age, and widespread reproductive success, these genetic vulnerabilities are allowed to accumulate and become epidemiologically visible [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. From an evolutionary perspective, the rising global burden of late-onset neurological diseases may be the visible manifestation of genetic load, referring to harmful mutations that were historically constrained by natural selection but are now increasingly expressed at the population level [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious ecological research has linked selection opportunity specifically to dementia incidence [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], demonstrating that healthcare services relaxing natural selection may partly contribute to the global rise in dementia. However, the broader question of whether relaxed natural selection similarly influences the incidence of other neurological conditions, such as Parkinson’s disease, motor neuron disease, multiple sclerosis, idiopathic epilepsy, and headache disorders, remains largely unexplored. Given the diverse genetic, neurodevelopmental, and environmental underpinnings of these disorders, investigating whether a shared evolutionary-demographic mechanism contributes to their incidence is crucial for understanding emerging global health patterns [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, variations in selection opportunity are likely to intersect with differences in economic affluence, ageing trajectories, urban living environments, and health system capacities [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These contextual factors could moderate or amplify the relationship between evolutionary relaxation and disease burden. For example, LMICs experiencing rapid demographic transitions may face disproportionate neurological disease burdens due to both rising life expectancy and limited public health resources [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Conversely, high-income countries may buffer the effects of selection relaxation through advanced medical interventions, early diagnosis, and prevention programs [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Understanding these dynamics across diverse settings is important for informing targeted global health strategies.\u003c/p\u003e \u003cp\u003eThis study examines the global relationship between selection opportunity and the incidence of nine major neurological diseases across 204 countries. It also evaluates whether this association remains independent of key confounders, including ageing, economic affluence, and urban living, and explores how the relationship varies across different economic and regional contexts.\u003c/p\u003e \u003cp\u003eBy investigating these associations, this study aims to advance the understanding of how evolutionary processes and demographic transitions interact to shape the global distribution of neurological diseases. It contributes to a growing interdisciplinary field that integrates evolutionary biology, epidemiology, and public health to explain contemporary patterns of non-communicable diseases. Findings from this research may offer valuable insights for anticipating future neurological disease trends and designing more resilient, evolutionarily informed health systems capable of managing the cognitive and neurological health needs of populations worldwide.\u003c/p\u003e"},{"header":"Material and Method","content":"\u003cp\u003eData sources\u003c/p\u003e\u003cp\u003eThis ecological study used publicly available, country-level data from reputable international databases, with all variables log-transformed to improve distribution normality and cross-national comparability.\u003c/p\u003e\u003cp\u003eSelection opportunity was measured using the Biological State Index (I\u003csub\u003ebs\u003c/sub\u003e), a validated indicator of relaxed natural selection. Higher I\u003csub\u003ebs\u003c/sub\u003e values reflect greater survival and reproduction opportunity of individuals with potentially deleterious traits. I\u003csub\u003ebs\u003c/sub\u003e data were drawn from prior studies by Budnik and Henneberg [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTotal neurological disease incidence, including dementia (Alzheimer’s and other dementias), Parkinson’s disease, motor neuron disease, multiple sclerosis, idiopathic epilepsy, and headache disorders (migraine and tension-type), was obtained from the IHME Global Burden of Disease (GBD) 2021 dataset [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Age-standardized incidence rates per 100,000 population were used.\u003c/p\u003e\u003cp\u003eAgeing was represented by life expectancy at age 60 (e\u003csub\u003e(60)\u003c/sub\u003e), sourced from the WHO Global Health Observatory [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Economic affluence was assessed via GDP per capita (PPP) from the World Bank [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and urban living by the percentage of the population residing in urban areas, also from the World Bank [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSubgroup analyses were based on classifications from the World Bank (income level), United Nations (development status), WHO regions, and major geopolitical/economic blocs (e.g., APEC, SCO, EU, OECD, LAC, Arab World, EOL).\u003c/p\u003e\u003cp\u003eThese sources enabled robust cross-national analyses of the relationship between selection opportunity and neurological disease incidence.\u003c/p\u003e\u003cp\u003eStudy Design and Statistical Analysis\u003c/p\u003e\u003cp\u003eThis study employed a cross-sectional ecological design to examine the relationship between selection opportunity and the incidence of neurological diseases, with a focus on dementia. Country-level data from 204 nations were analysed using both bivariate and multivariate approaches to assess global and regional patterns. The unit of analysis was the nation-state, and the study included subgroup comparisons based on established international classifications (e.g., World Bank income groups, WHO regions). The ecological design allowed for the exploration of macro-level associations between evolutionary indicators and health outcomes across diverse population contexts. While this approach limits individual-level inference, it provides valuable insights into population-level trends that can inform global health policy and future hypothesis-driven research.\u003c/p\u003e\u003cp\u003eAll continuous variables were log-transformed to improve distribution normality and reduce skewness/kurtosis in cross-national data distributions.\u003c/p\u003e\u003cp\u003eThe statistical procedures included:\u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePearson’s correlation coefficients (r)\u003c/b\u003e and \u003cb\u003eSpearman’s rank-order correlations (ρ)\u003c/b\u003e were computed to assess associations between selection opportunity and disease incidence across all countries and within specific subgroups (e.g., income levels, development status, WHO regions, and economic blocs).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePartial correlation analyses\u003c/b\u003e were performed to examine the unique association between selection opportunity and each neurological outcome while controlling for potential confounders: ageing (\u003cem\u003ee\u003c/em\u003e\u003csub\u003e\u003cem\u003e(60)\u003c/em\u003e\u003c/sub\u003e), economic affluence (GDP PPP), and urban living (% urban population).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMultivariate linear regression models\u003c/b\u003e were conducted using both the enter method and stepwise method to assess the independent predictive value of selection opportunity after adjusting for covariates. Standardized regression coefficients (β), p-values, and adjusted R² values were reported for each model.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSubgroup comparisons\u003c/b\u003e were made using Fisher’s r-to-z transformation to statistically test differences in correlation strength between country groupings (e.g., high-income vs. LMICs, developed vs. developing nations).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e\u003cp\u003eAll analyses were conducted using IBM SPSS Statistics (Version 30). Significance levels were set at \u003cem\u003ep\u003c/em\u003e \u0026lt; .05 for all two-tailed tests, with \u003cem\u003ep\u003c/em\u003e \u0026lt; .01 and \u003cem\u003ep\u003c/em\u003e \u0026lt; .001 used to denote stronger significance thresholds. Results were interpreted with a focus on both statistical significance and practical relevance, especially in the context of global health disparities.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates a non-linear association between selection opportunity, as measured by the Biological State Index (I\u003csub\u003ebs\u003c/sub\u003e), and the incidence of total neurological disorders. Notably, the incidence remains relatively stable at lower and mid-range I\u003csub\u003ebs\u003c/sub\u003e values but rises sharply at higher levels, particularly beyond 0.95. This trend, captured by a fifth-degree polynomial curve (R\u0026sup2; = 0.6149), suggests that as natural selection becomes increasingly relaxed which is often in more developed populations, the burden of neurological disorders significantly escalates. These findings support the hypothesis that diminished selection pressure may contribute to the increased prevalence of late-onset (50 years or older) and genetically influenced neurological conditions.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarises the correlation coefficients among selection opportunity, ageing, economic affluence, urban living, and the prevalence of major neurological disorders, with all variables log-transformed. I\u003csub\u003ebs\u003c/sub\u003e values showed strong positive correlations with ageing (r\u0026thinsp;=\u0026thinsp;0.737), economic affluence (r\u0026thinsp;=\u0026thinsp;0.789), and urban living (r\u0026thinsp;=\u0026thinsp;0.506), all at p\u0026thinsp;\u0026lt;\u0026thinsp;.001. It was also significantly associated with total neurological disorder incidence (r\u0026thinsp;=\u0026thinsp;0.651), dementia (r\u0026thinsp;=\u0026thinsp;0.746), Parkinson\u0026rsquo;s disease (r\u0026thinsp;=\u0026thinsp;0.775), and motor neuron disease (r\u0026thinsp;=\u0026thinsp;0.533).\u003c/p\u003e \u003cp\u003eAgeing was similarly correlated with dementia (r\u0026thinsp;=\u0026thinsp;0.769), Parkinson\u0026rsquo;s (r\u0026thinsp;=\u0026thinsp;0.798), and motor neuron disease (r\u0026thinsp;=\u0026thinsp;0.701). Economic affluence showed a comparable pattern, particularly for Parkinson\u0026rsquo;s disease (r\u0026thinsp;=\u0026thinsp;0.802), Alzheimer\u0026rsquo;s disease (r\u0026thinsp;=\u0026thinsp;0.758), and multiple sclerosis (r\u0026thinsp;=\u0026thinsp;0.659). Urban living had moderate correlations, strongest for Parkinson\u0026rsquo;s (r\u0026thinsp;=\u0026thinsp;0.549) and Alzheimer\u0026rsquo;s (r\u0026thinsp;=\u0026thinsp;0.509).\u003c/p\u003e \u003cp\u003eIn contrast, migraine (r = \u0026minus;\u0026thinsp;0.277) and idiopathic epilepsy (r = \u0026minus;\u0026thinsp;0.262) showed negative associations with selection opportunity, suggesting distinct etiological, diagnostic or demographic influences. Overall, age-related and neurodegenerative disorders were positively linked to indicators of socioeconomic development, longevity, and relaxed natural selection, while migraine and epilepsy followed a different pattern.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Matrix of Variables: Selection Opportunity, Ageing, Economic Affluence, Urban Living, and the Prevalence of Major Neurological Disorders\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNeurological disorders, Total\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAlzheimer's disease and other dementias\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHeadache disorders, Total\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHeadache disorders, Migraine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHeadache disorders, Tension-type headache\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIdiopathic epilepsy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMotor neuron disease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eMultiple sclerosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eParkinson's disease\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.737\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.789\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.506\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.651\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.746\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.643\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.655\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.324\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.533\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.490\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.775\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.834\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.801\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.598\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.661\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.769\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.644\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.663\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.701\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.601\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.798\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.896\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.811\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.679\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.753\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.758\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.742\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.762\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.683\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.659\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.802\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.631\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.676\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.718\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.527\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.509\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.518\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.527\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.467\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.485\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.549\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurological disorders, Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.799\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.704\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.762\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.483\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.750\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.999\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.995\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.265\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.680\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.718\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.758\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlzheimer's disease and other dementias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.846\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.785\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.761\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.527\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.762\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.724\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.260\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.759\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.347\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.786\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.546\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.952\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache disorders, Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.788\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.689\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.750\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.467\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.998\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.737\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.994\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.266\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.657\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.714\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.734\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache disorders, Migraine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.277\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.181\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.288\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.157\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.364\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.201\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.181\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache disorders, Tension-type headache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.809\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.709\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.778\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.495\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.996\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.766\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.994\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.264\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.686\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.720\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.761\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdiopathic epilepsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.262\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.280\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.354\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.283\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.269\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.328\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotor neuron disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.817\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.750\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.734\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.527\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.687\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.858\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.669\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.347\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.696\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.160\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.662\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.803\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple sclerosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.644\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.628\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.664\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.480\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.723\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.538\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.725\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.720\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.651\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.573\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParkinson's disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.880\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.810\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.797\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.562\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.751\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.954\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.730\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.317\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.757\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.334\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.862\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.554\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSample sizes for the correlation analyses ranged from \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;179 to 204.\u003c/p\u003e \u003cp\u003eSignificance levels: *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e \u003cp\u003eAll data were log-transformed prior to analysis to improve normality and comparability across variables.\u003c/p\u003e \u003cp\u003eSelection Opportunity was measured using the Biological State Index values from (\u003cem\u003eIbs\u003c/em\u003e; Budnik \u0026amp; Henneberg, 2017). Neurological disease incidence rates (total of nine conditions) were obtained from the Institute for Health Metrics and Evaluation (IHME), University of Washington, and are reported as new cases per 100,000 population in 2021.\u003c/p\u003e \u003cp\u003eAge was operationalized as life expectancy at age 60 (World Health Organization, 2018).\u003c/p\u003e \u003cp\u003eEconomic affluence was measured by gross domestic product (GDP) per capita in purchasing power parity (PPP; World Bank, 2018).\u003c/p\u003e \u003cp\u003eUrban living was defined as the percentage of the population residing in urban areas (World Bank, 2018).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the bivariate and partial correlations between selection opportunity and the incidence of nine major neurological disorders, with all variables log-transformed. In bivariate analyses, selection opportunity showed significant positive correlations with most neurological conditions. The strongest associations were observed for Parkinson\u0026rsquo;s disease (r\u0026thinsp;=\u0026thinsp;0.737, ρ\u0026thinsp;=\u0026thinsp;0.880), Alzheimer\u0026rsquo;s disease and other dementias (r\u0026thinsp;=\u0026thinsp;0.746, ρ\u0026thinsp;=\u0026thinsp;0.846), and total neurological disorder burden (r\u0026thinsp;=\u0026thinsp;0.651, ρ\u0026thinsp;=\u0026thinsp;0.799), all at p\u0026thinsp;\u0026lt;\u0026thinsp;.001. Similarly strong correlations were found for tension-type headaches (r\u0026thinsp;=\u0026thinsp;0.655, ρ\u0026thinsp;=\u0026thinsp;0.809) and total headache disorders (r\u0026thinsp;=\u0026thinsp;0.643, ρ\u0026thinsp;=\u0026thinsp;0.788).\u003c/p\u003e \u003cp\u003eBy contrast, migraine (r = \u0026minus;\u0026thinsp;0.055, ρ = \u0026minus;\u0026thinsp;0.277) and idiopathic epilepsy (r = \u0026minus;\u0026thinsp;0.324, ρ = \u0026minus;\u0026thinsp;0.262) showed significant negative correlations (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). After adjusting for ageing, economic affluence, and urban living, partial correlations remained significant for Parkinson\u0026rsquo;s disease (r\u0026thinsp;=\u0026thinsp;0.311), Alzheimer\u0026rsquo;s disease and dementias (r\u0026thinsp;=\u0026thinsp;0.293), total neurological disorders (r\u0026thinsp;=\u0026thinsp;0.164), total headache disorders (r\u0026thinsp;=\u0026thinsp;0.169), and tension-type headache (r\u0026thinsp;=\u0026thinsp;0.163), all at p\u0026thinsp;\u0026lt;\u0026thinsp;.05. Idiopathic epilepsy maintained a negative association (r = \u0026minus;\u0026thinsp;0.371, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), while correlations with motor neuron disease and multiple sclerosis became non-significant.\u003c/p\u003e \u003cp\u003eThese findings indicate that selection opportunity is independently associated with several age-related and neurodegenerative conditions, particularly dementia and Parkinson\u0026rsquo;s disease, beyond the effects of ageing and socioeconomic status.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations Between Alzheimer's Disease and Cardiovascular/Cerebrovascular Conditions: Pearson, Spearman, and Partial Correlations (Controlling for Ageing, Economic Affluence, Genetic Predisposition, and Urban Living)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBivariate Correlation with Selection Opportunity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePartial Correlation with Selection Opportunity keeping other variables statistically constant\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpearman ρ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCorrelation Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurological disorders, Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.651\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.799\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.164\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlzheimer's disease and other dementias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.746\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.846\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.293\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache disorders, Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.643\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.788\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.169\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache disorders, Migraine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.277\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache disorders, Tension-type headache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.655\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.809\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.163\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdiopathic epilepsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.324\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.262\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.371\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotor neuron disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.533\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.817\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple sclerosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.490\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.644\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParkinson's disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.737\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.880\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.311\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.789\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.834\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eControl Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.506\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.896\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eControl Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.737\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.631\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eControl Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSignificance levels: *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e \u003cp\u003eAll data were log-transformed prior to analysis to improve normality and comparability across variables.\u003c/p\u003e \u003cp\u003eSelection Opportunity was measured using the Biological State Index values from (\u003cem\u003eIbs\u003c/em\u003e; Budnik \u0026amp; Henneberg, 2017). Neurological disease incidence rates (total of nine conditions) were obtained from the Institute for Health Metrics and Evaluation (IHME), University of Washington, and are reported as new cases per 100,000 population in 2021.\u003c/p\u003e \u003cp\u003eAge was operationalized as life expectancy at age 60 (World Health Organization, 2018).\u003c/p\u003e \u003cp\u003eEconomic affluence was measured by gross domestic product (GDP) per capita in purchasing power parity (PPP; World Bank, 2018).\u003c/p\u003e \u003cp\u003eUrban living was defined as the percentage of the population residing in urban areas (World Bank, 2018).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents multivariate linear regression results assessing the independent effect of selection opportunity on the incidence of total and specific neurological disorders, using both enter and stepwise methods. All models controlled for ageing (life expectancy at age 60), economic affluence, and urban living, with all variables log-transformed.\u003c/p\u003e \u003cp\u003eBiological State Index was a significant independent predictor of total neurological disorder incidence (β\u0026thinsp;=\u0026thinsp;0.181, p\u0026thinsp;\u0026lt;\u0026thinsp;.05), alongside economic affluence (β\u0026thinsp;=\u0026thinsp;0.515, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). In the stepwise model, its inclusion modestly increased the model\u0026rsquo;s explanatory power (Adjusted R\u0026sup2; = 0.594). For Alzheimer\u0026rsquo;s disease and other dementias, selection opportunity showed a stronger effect (β\u0026thinsp;=\u0026thinsp;0.304, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), contributing substantially to a high model fit (Adjusted R\u0026sup2; = 0.666).\u003c/p\u003e \u003cp\u003ePositive associations were also observed for headache disorders (β\u0026thinsp;=\u0026thinsp;0.190, p\u0026thinsp;\u0026lt;\u0026thinsp;.05) and tension-type headaches, while migraine remained non-significant. Notably, idiopathic epilepsy was negatively associated with selection opportunity (β = \u0026minus;\u0026thinsp;0.623, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), with urban living also contributing (Adjusted R\u0026sup2; = 0.162). In contrast, motor neuron disease and multiple sclerosis were primarily explained by ageing and economic affluence, with no significant contribution from selection opportunity.\u003c/p\u003e \u003cp\u003eFor Parkinson\u0026rsquo;s disease, selection opportunity was a strong and consistent predictor (β\u0026thinsp;=\u0026thinsp;0.290, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), with the stepwise model showing high explanatory value (Adjusted R\u0026sup2; = 0.733). Overall, selection opportunity emerged as a robust factor associated with several age-related neurological conditions, particularly dementia, Parkinson\u0026rsquo;s disease, and headache disorders, independent of key socioeconomic and demographic variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Linear Regression Results Examining the Effects of Selection Opportunity on Grouped and Specific Neurological Disease Incidence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEnter Method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eStepwise Methods\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardized (β)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificance (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSignificance (p)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDependent Variable: Neurological disorders, Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDependent Variable: Alzheimer's disease and other dementias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDependent Variable: Headache disorders, Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDependent Variable: Headache disorders, Migraine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDependent Variable: Headache disorders, Tension-type headache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDependent Variable: Idiopathic epilepsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDependent Variable: Motor neuron disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDependent Variable: Multiple sclerosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDependent Variable: Parkinson's disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAgeing e(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic Affluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelection Opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUrban Living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsignificance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSignificance levels: *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e \u003cp\u003eAll data were log-transformed prior to analysis to improve normality and comparability across variables.\u003c/p\u003e \u003cp\u003eSelection Opportunity was measured using the Biological State Index values from (\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003ebs\u003c/em\u003e\u003c/sub\u003e; Budnik \u0026amp; Henneberg, 2017). Neurological disease incidence rates (total of nine conditions) were obtained from the Institute for Health Metrics and Evaluation (IHME), University of Washington, and are reported as new cases per 100,000 population in 2021.\u003c/p\u003e \u003cp\u003eAge was operationalized as life expectancy at age 60 (World Health Organization, 2018).\u003c/p\u003e \u003cp\u003eEconomic affluence was measured by gross domestic product (GDP) per capita in purchasing power parity (PPP; World Bank, 2018).\u003c/p\u003e \u003cp\u003eUrban living was defined as the percentage of the population residing in urban areas (World Bank, 2018).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarises the correlation between selection opportunity and dementia incidence across global country groupings, based on both Pearson\u0026rsquo;s and Spearman\u0026rsquo;s correlation coefficients (all variables log-transformed). At the global level, the association was strong and significant (r\u0026thinsp;=\u0026thinsp;0.651, ρ\u0026thinsp;=\u0026thinsp;0.799, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; n\u0026thinsp;=\u0026thinsp;186).\u003c/p\u003e \u003cp\u003eAcross World Bank income classifications, selection opportunity was significantly correlated with dementia incidence in all income groups. The strength of association increased from low-income (r\u0026thinsp;=\u0026thinsp;0.524) to high-income countries (r\u0026thinsp;=\u0026thinsp;0.597), with the most pronounced correlation observed when combining all LMICs (r\u0026thinsp;=\u0026thinsp;0.759, ρ\u0026thinsp;=\u0026thinsp;0.790, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; n\u0026thinsp;=\u0026thinsp;122). Statistical comparison showed a significantly stronger association in LMICs compared to high-income countries (z = \u0026minus;\u0026thinsp;2.00 for Pearson, p\u0026thinsp;\u0026lt;\u0026thinsp;.05; z = \u0026minus;\u0026thinsp;3.13 for Spearman, p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003eUsing UN development classifications, developing countries showed a stronger association (r\u0026thinsp;=\u0026thinsp;0.759, ρ\u0026thinsp;=\u0026thinsp;0.841, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) than developed ones (r\u0026thinsp;=\u0026thinsp;0.559, ρ\u0026thinsp;=\u0026thinsp;0.743, p\u0026thinsp;\u0026lt;\u0026thinsp;.01), with Pearson\u0026rsquo;s z test reaching significance (z = \u0026minus;\u0026thinsp;2.03, p\u0026thinsp;\u0026lt;\u0026thinsp;.05), and Spearman\u0026rsquo;s test approaching significance (z = \u0026minus;\u0026thinsp;1.5, p\u0026thinsp;=\u0026thinsp;0.067).\u003c/p\u003e \u003cp\u003eBy WHO region, the strongest correlations were observed in EMRO (r\u0026thinsp;=\u0026thinsp;0.835, ρ\u0026thinsp;=\u0026thinsp;0.914), WPRO (r\u0026thinsp;=\u0026thinsp;0.764, ρ\u0026thinsp;=\u0026thinsp;0.875), and EURO (r\u0026thinsp;=\u0026thinsp;0.705, ρ\u0026thinsp;=\u0026thinsp;0.867). Other regions, including AFRO, AMRO, and SEARO, also showed moderate-to-strong, mostly significant associations.\u003c/p\u003e \u003cp\u003eAcross geopolitical and economic blocs, consistent positive correlations emerged. Notably high correlations were found in APEC (r\u0026thinsp;=\u0026thinsp;0.860, ρ\u0026thinsp;=\u0026thinsp;0.874), the Arab World (r\u0026thinsp;=\u0026thinsp;0.826, ρ\u0026thinsp;=\u0026thinsp;0.888), and Economies of Longevity (r\u0026thinsp;=\u0026thinsp;0.793, ρ\u0026thinsp;=\u0026thinsp;0.898). Other blocs, including SCO, OECD, EU, and LAC, also demonstrated significant associations (r values between 0.582 and 0.709).\u003c/p\u003e \u003cp\u003eOverall, these findings suggest that the relationship between selection opportunity and total neurological disease incidence is robust across diverse contexts, with stronger correlations often seen in LMICs and developing regions, supporting the hypothesis of an emerging evolutionary burden.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations Between Selection Opportunity and Dementia Incidence Across Global Country Groupings\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eDementia Incidence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry groupings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-parametric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorldwide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.651\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.799\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eWorld Bank income classifications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.524**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.426*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow middle income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.509***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.620***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper middle income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.422**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.503***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.597***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.532***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.759***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.790***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eFisher A-to-Z: high income vs LMICs in Pearson\u0026rsquo;s r (z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.00, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and in non-parametric (z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.13, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eUN common practice\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeveloped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.559\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.743\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeveloping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.759\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.841\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eFisher A-to-Z: developed vs developing in Pearson\u0026rsquo;s r (z= -2.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and in non-parametric (z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.5, p\u0026thinsp;=\u0026thinsp;0.067)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eWHO Regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.586\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.531\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.671\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.631\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEMRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.835\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.914\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEURO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.705\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.867\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEARO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.617\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWPRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.764\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.875\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCountries grouped based on various factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.765\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.874\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.860\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.874\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArab World\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.826\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.888\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.596\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.665\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.793\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.898\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.582\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.667\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.709\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.713\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.659\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.557\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOECD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.672\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.589\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSADC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.670\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.653\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.704\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.849\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSignificance levels: *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e \u003cp\u003eAll data were log-transformed prior to analysis to improve normality and comparability across variables.\u003c/p\u003e \u003cp\u003eSelection Opportunity was measured using the Biological State Index values from (\u003cem\u003eIbs\u003c/em\u003e; Budnik \u0026amp; Henneberg, 2017). Neurological disease incidence rates (total of nine conditions) were obtained from the Institute for Health Metrics and Evaluation (IHME), University of Washington, and are reported as new cases per 100,000 population in 2021.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis ecological analysis offers novel evidence supporting a robust global association between relaxed natural selection, quantified using the Biological State Index (I\u003csub\u003ebs\u003c/sub\u003e), and the incidence of neurological and neurodegenerative disorders. The findings demonstrate that countries with higher I\u003csub\u003ebs\u003c/sub\u003e values, indicating greater survival and reproduction opportunity among individuals with potentially deleterious genetic traits, tend to report higher rates of total neurological disorder, and specific neurological disorders, such as dementia, Parkinson\u0026rsquo;s disease, multiple sclerosis, and other neurological conditions. This relationship was particularly prominent in LMICs and developing countries.\u003c/p\u003e \u003cp\u003eOur results are consistent with previous studies showing that increased survival into older age, driven by medical advancements and improved living conditions, elevates the likelihood of developing conditions that typically appear later in life, such as dementia, Parkinson\u0026rsquo;s disease, and other neurodegenerative disorders [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. As natural selection weakens, particularly in populations with declining mortality and fertility rates, genetic variants linked to age-related diseases are less likely to be removed from the population. Instead, they persist and gradually accumulate in the gene pool across generations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe genetic nature of neurological diseases supports this hypothesis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Most of these disorders are polygenic, involving the interplay of numerous small-effect variants that individually may not cause disease but collectively increase susceptibility [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In the absence of strong selective pressures, these risk alleles are retained across generations, potentially elevating population-level disease vulnerability [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This mechanism echoes evolutionary theories put forth by Henneberg and colleagues, who argue that modern healthcare systems, while improving survival, inadvertently weaken natural selection and permit the accumulation of deleterious genetic traits related to non-communicable diseases [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImportantly, higher I\u003csub\u003ebs\u003c/sub\u003e values reflect reduced natural selection, and our analyses show a strong, positive association between I\u003csub\u003ebs\u003c/sub\u003e and neurological disease incidence, especially in regions with falling mortality and fertility rates [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The implication is that evolutionary pressures, which previously acted to eliminate harmful genetic variants, are now diminished, allowing these variants to become more prevalent [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Evidence for the cumulative genetic load associated with relaxed natural selection can be observed in other partially heritable conditions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For example, studies on phenylketonuria (PKU) have shown that the disorder became more prevalent only after several generations of allelic accumulation, with trait frequencies increasing by approximately 0.2 to 2 percent per generation, depending on population structure and the strength of selection pressure [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These observations offer empirical support for the hypothesis that weakened selection pressure contributes to the rising incidence of chronic diseases, including those affecting the nervous system [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This concept aligns with the theoretical framework advanced by Henneberg and colleagues, who argued that modern reductions in natural selection pressure allow for the gradual accumulation of deleterious mutations associated with non-communicable diseases such as obesity, type 2 diabetes, and cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. It also complements recent ecological research by You and colleagues [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], which found that selection opportunity significantly explained variance across multiple non-communicable diseases at the population level, suggesting a common evolutionary mechanism underlying many modern chronic health conditions.\u003c/p\u003e \u003cp\u003eOne important observation from our study is that scatter plots revealed a curvilinear relationship between selection opportunity, measured by the I\u003csub\u003ebs\u003c/sub\u003e, and the total incidence of neurological diseases. These findings support the hypothesis that reduced selection pressure may allow the persistence of genetic vulnerabilities that contribute to neurological conditions typically emerging in older adulthood, such as Alzheimer's and Parkinson's diseases [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, demographic transitions such as declining fertility and increased life expectancy may reshape the genetic architecture of populations, enabling the persistence of late onset conditions like dementia and Parkinson\u0026rsquo;s disease [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These findings highlight the importance of considering both evolutionary and demographic processes when interpreting global disparities in neurological disease incidence.\u003c/p\u003e \u003cp\u003eThe rising global burden of neurological diseases, particularly in LMICs undergoing rapid demographic and epidemiological transitions [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], indicates that evolutionary factors may be interacting with environmental, social, and health system determinants to shape disease patterns [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In many LMICs, improvements in child and maternal survival, access to basic healthcare, and vaccination coverage have extended life expectancy across all age groups. However, these advances have not been matched by commensurate growth in specialist services for neurological disease prevention, diagnosis, and management [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs a result, these populations are increasingly exposed to the phenotypic expression of inherited vulnerabilities, including genetic predispositions to dementia, epilepsy, and other neurodegenerative or neurodevelopmental conditions [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This infrastructure lag, in which survival improves faster than system capacity, creates a vulnerability gap, leaving health systems unprepared to respond to conditions that manifest later in life [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis contributes to the so-called \"double burden\" of disease, where LMICs must still manage high rates of communicable diseases and undernutrition while simultaneously facing a rising wave of non-communicable and genetically influenced chronic illnesses, including neurological disorders [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This dual pressure places significant strain on already limited health resources, deepens health inequities, and underscores the urgent need for integrated health system planning [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Such planning must anticipate the genetic consequences of demographic change and prioritise neurological care capacity-building as a central component of sustainable health development strategies [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe consistent inverse relationship between selection opportunity and idiopathic epilepsy incidence across all statistical models, including bivariate, partial, and multivariate regressions is a distinct finding that contrasts with the positive associations observed for other neurological conditions. Global studies support this pattern; for instance, regions with higher Sociodemographic Index (SDI) and I\u003csub\u003ebs\u003c/sub\u003e report higher age-standardized incidence rates (ASIRs), while lower-SDI regions tend to show lower ASIRs but higher mortality and disability burdens from epilepsy [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This likely reflects underdiagnosis or misclassification in settings with limited diagnostic capacity [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], whereas high-I\u003csub\u003ebs\u003c/sub\u003e countries with advanced healthcare systems may detect and reclassify idiopathic cases more accurately, reducing their reported incidence. From an evolutionary standpoint, idiopathic epilepsy may be under stronger purifying selection, especially if it affects individuals early in life or reduces reproductive fitness, thereby limiting the accumulation of associated alleles even in high-I\u003csub\u003ebs\u003c/sub\u003e populations [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Furthermore, the genetic basis of idiopathic epilepsy is complex and heterogeneous, with risk variants varying across populations and shaped by access to genomic tools and diagnostic practices [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. These factors, combined with differing health system capabilities, may explain the global variability in idiopathic epilepsy data [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Overall, this inverse association underscores the importance of considering both evolutionary and socioeconomic influences when interpreting neurological disease patterns and highlights the need for further investigation into the unique etiological and diagnostic features of idiopathic epilepsy, particularly in low-resource settings.\u003c/p\u003e \u003cp\u003eThe regional variation observed across WHO regions and international blocs, with the strongest associations identified in EMRO, WPRO, APEC, and the Arab World, suggests that sociocultural factors and healthcare system capacity may influence the manifestation of underlying biological risk. In high-income countries, elevated selection opportunity may be counterbalanced by more advanced diagnostic, preventive, and therapeutic services [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In contrast, many emerging economies may lack such protective systems, making the evolutionary burden associated with relaxed natural selection more apparent through higher rates of neurological disease [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTaken together, these findings build on earlier theoretical work suggesting that the relaxation of natural selection in modern human populations is reshaping the global epidemiology of diseases that typically emerge after the age of 50, such as dementia [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. They highlight the importance of considering evolutionary forces, which are often overlooked in public health discourse, alongside economic, demographic, and health system factors when modelling the global burden of disease.\u003c/p\u003e \u003cp\u003eStrengths and limitations\u003c/p\u003e \u003cp\u003eThis study has several notable strengths. It is the first global analysis to systematically examine the association between selection opportunity and neurological disease incidence, using a comprehensive ecological approach. By including 204 countries across diverse economic, developmental, and geopolitical contexts, it enables robust cross-national comparisons. The use of bivariate, partial correlation, and multivariate regression analyses strengthens internal validity by adjusting for key confounders such as ageing, economic status, and urbanization. Additionally, data were sourced from credible global institutions (IHME, WHO, World Bank), enhancing reliability and comparability.\u003c/p\u003e \u003cp\u003eA key contribution of this study is the integration of evolutionary theory with global health, offering a novel lens on the rising burden of dementia and other neurological disorders. It identifies selection opportunity as a potentially overlooked factor in shaping late-life disease risk, especially in the context of demographic transitions and global inequality.\u003c/p\u003e \u003cp\u003eHowever, several limitations must be acknowledged. The ecological design precludes individual-level causal inference. The I\u003csub\u003ebs\u003c/sub\u003e, while theoretically sound, may not fully capture the long-term complexity of selection pressures or their interaction with environmental factors as insufficient data for I\u003csub\u003ebs\u003c/sub\u003e calculation. Despite adjusting for major confounders, residual confounding from unmeasured variables such as healthcare quality, diagnostic capacity, genetic diversity, and cultural attitudes may still remain. Furthermore, dementia incidence data are largely modelled estimates, especially in low-resource settings where surveillance systems are limited, possibly introducing bias.\u003c/p\u003e \u003cp\u003eDespite these limitations, the study provides novel, population-level evidence linking relaxed natural selection to neurological disease patterns. It highlights the need for further interdisciplinary research that bridges evolutionary biology and public health, particularly to inform strategies for dementia prevention and management in diverse global settings.\u003c/p\u003e \u003cp\u003ePublic Health Implications\u003c/p\u003e \u003cp\u003eThis study carries important implications for global health planning. The strong and consistent association between selection opportunity and the rising incidence of dementia and other neurological disorders suggests that evolutionary forces, particularly the relaxation of natural selection, may play a role in shaping disease risk, especially in countries undergoing rapid demographic transitions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The effect appears most pronounced in LMICs, where increasing life expectancy is not yet supported by adequate diagnostic capacity, long-term care infrastructure, or a trained workforce equipped to manage cognitive decline [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs a result, these regions may face a growing burden of neurological disorders without the systems in place to effectively respond. To address this, public health systems, particularly in low- and middle-income countries, should focus on early detection and intervention, invest in developing a dementia-specific workforce, establish culturally appropriate and accessible long-term care models, and incorporate evolutionary and demographic indicators into disease surveillance and health planning [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBy integrating evolutionary perspectives into public health strategies, countries can better anticipate future neurological disease trends and implement more equitable, sustainable responses to the global dementia challenge.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that selection opportunity, a proxy for relaxed natural selection, is significantly associated with the global incidence of total neurological disorders, particularly dementia and Parkinson\u0026rsquo;s disease, independent of ageing, affluence, and urbanization. The association was consistent across countries and notably stronger in LMICs and developing nations. These findings highlight the need to strengthen neurological health systems, especially where infrastructure is limited. Incorporating evolutionary perspectives into public health planning may improve forecasting, and future research should explore causal pathways to support equitable, adaptable cognitive care worldwide.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul\u003e\n \u003cli\u003eI\u003csub\u003ebs\u003c/sub\u003e \u0026ndash; Biological State Index\u003c/li\u003e\n \u003cli\u003eLMICs \u0026ndash; Low- and Middle-Income Countries\u003c/li\u003e\n \u003cli\u003eGDP PPP \u0026ndash; Gross Domestic Product per capita in Purchasing Power Parity\u003c/li\u003e\n \u003cli\u003eWHO \u0026ndash; World Health Organization\u003c/li\u003e\n \u003cli\u003eIHME \u0026ndash; Institute for Health Metrics and Evaluation\u003c/li\u003e\n \u003cli\u003eUN \u0026ndash; United Nations\u003c/li\u003e\n \u003cli\u003eOECD \u0026ndash; Organisation for Economic Co-operation and Development\u003c/li\u003e\n \u003cli\u003eEMRO \u0026ndash; Eastern Mediterranean Region (WHO)\u003c/li\u003e\n \u003cli\u003eEURO \u0026ndash; European Region (WHO)\u003c/li\u003e\n \u003cli\u003eAFRO \u0026ndash; African Region (WHO)\u003c/li\u003e\n \u003cli\u003eAMRO \u0026ndash; Region of the Americas (WHO)\u003c/li\u003e\n \u003cli\u003eSEARO \u0026ndash; South-East Asia Region (WHO)\u003c/li\u003e\n \u003cli\u003eWPRO \u0026ndash; Western Pacific Region (WHO)\u003c/li\u003e\n \u003cli\u003eEOL \u0026ndash; English as Official Language\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSCO \u0026ndash; Shanghai Cooperation Organisation\u003c/li\u003e\n \u003cli\u003eAPEC \u0026ndash; Asia-Pacific Economic Cooperation\u003c/li\u003e\n \u003cli\u003eLAC \u0026ndash; Latin America and the Caribbean\u003c/li\u003e\n \u003cli\u003eEEA \u0026ndash; European Economic Area\u003c/li\u003e\n \u003cli\u003eEU \u0026ndash; European Union\u003c/li\u003e\n \u003cli\u003eSADC \u0026ndash; Southern African Development Community\u003c/li\u003e\n \u003cli\u003eACD \u0026ndash; Asia Cooperation Dialogue\u003c/li\u003e\n \u003cli\u003er / \u0026rho; \u0026ndash; Pearson\u0026rsquo;s correlation coefficient / Spearman\u0026rsquo;s rank-order correlation coefficient\u003c/li\u003e\n \u003cli\u003e\u0026beta; \u0026ndash; Standardized regression coefficient\u003c/li\u003e\n \u003cli\u003eR\u0026sup2; / Adjusted R\u0026sup2; \u0026ndash; Coefficient of determination / Adjusted coefficient of determination\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":" \u003ch2\u003eEthics Statement\u003c/h2\u003e \u003cp\u003eThis study did not involve research with individual human participants or animals. All data used were publicly available and obtained from Institute for Health Metrics and Evaluation and official websites of United Nations (UN) agencies. According to the \u003cem\u003eNational Statement on Ethical Conduct in Human Research\u003c/em\u003e (2007, updated 2018), the XXX University classifies research as exempt from ethical review when it involves only negligible risk and uses existing, non-identifiable data about human beings (Reference No. XXXX, Details to be provided once this manuscript is accepted.).\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThere is no specific funding to support this study.\u003c/p\u003e \u003cp\u003eClinical trial number\u003c/p\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003cp\u003eConsent to Participate declaration:\u003c/p\u003e \u003cp\u003enot applicable\u003c/p\u003e \u003cp\u003eConsent for publication\u003c/p\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003cp\u003eAvailability of data and materials\u003c/p\u003e \u003cp\u003eThe 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 organizations. 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.\u003c/p\u003e \u003cp\u003eCompeting interest\u003c/p\u003e \u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e \u003cp\u003eGEN AI Use Statement\u003c/p\u003e \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 publication's content.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWenpeng You: Conceptualization; Data Curation; Formal Analysis; Funding Acquisition; Investigation; Methodology; Project Administration; Resources; Software; Validation; Visualization; Writing \u0026ndash; Original Draft Preparation; Writing \u0026ndash; Review \u0026amp; Editing Maciej Henneberg: Conceptualization; Data Curation; Formal Analysis; Funding Acquisition; Investigation; Methodology; Resources; Software; Validation; Visualization; Writing \u0026ndash; Review \u0026amp; Editing\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors appreciate Ms Turi Christensen from the Institute for Health Metrics and Evaluation of the University of Washington for her assistance in locating and defining the data on cardiovascular disease incidence rate.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGuo X, et al. Global, regional, and national burden of four major neurological diseases in women from 1990 to 2021. Front Public Health. 2025;13:1561216.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeigin V et al. Soriano award lecture: The epidemiology and burden of neurological disorders. J Neurol Sci, 2023. 455.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteinmetz JD, et al. Global, regional, and national burden of disorders affecting the nervous system, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Neurol. 2024;23(4):344\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlzheimer's Disease International. \u003cem\u003eWorld Alzheimer Report 2015: The Global Impact of Dementia (An AnAlysIs of prevAlence, IncIDence, cosT AnD TrenDs)\u003c/em\u003e. 2025 20 April 2024 ]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.alzint.org/u/WorldAlzheimerReport2015.pdf?utm_source=chatgpt.com\u003c/span\u003e\u003cspan address=\"https://www.alzint.org/u/WorldAlzheimerReport2015.pdf?utm_source=chatgpt.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLivingston G, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396(10248):413\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(20)30367-6\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(20)30367-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaniotis A, Henneberg M. Medicine could be constructing human bodies in the future. Med Hypotheses. 2011;77(4):560\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenneberg M, Piontek J. Biological state index of human groups. Przeglad Anthropologiczny. 1975;XLI:191\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBudnik A, Henneberg M. Worldwide increase of obesity is related to the reduced opportunity for natural selection. PLoS ONE. 2017;12(1):e0170098.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou W, Henneberg M. Cancer incidence increasing globally: The role of relaxed natural selection. Evol Appl. 2018;11(2):140\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou W, Henneberg M. Relaxed natural selection contributes to global obesity increase more in males than in females due to more environmental modifications in female body mass. PLoS ONE. 2018;13(7):e0199594. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0199594\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0199594\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou W-P, Henneberg M. Type 1 diabetes prevalence increasing globally and regionally: the role of natural selection and life expectancy at birth. BMJ open diabetes Res care. 2016;4(1):e000161.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou W, Henneberg M. \u003cem\u003eModern medical services, a double-edged sword manages symptoms, but accumulates genetic background of cardiovascular diseases: A cross populational analysis of 217 countries.\u003c/em\u003e 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliamson J, Goldman J, Marder KS. Genetic aspects of Alzheimer disease. Neurologist. 2009;15(2):80\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoy CT, et al. Genetics of dementia. Lancet. 2014;383(9919):828\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeol K. \u003cem\u003eUnravelling the Genetic and Environmental Connection in Parkinson\u0026rsquo;s Disease: Implications for Pathogenesis and Therapeutic Strategies.\u003c/em\u003e 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaniotis A, Henneberg M, Mohammadi K. Genetic load and biological changes to extant humans. J Biosoc Sci. 2021;53(4):639\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui R, et al. Relaxed selection limits lifespan by increasing mutation load. Cell. 2020;180(6):1272\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStearns SC, et al. Measuring selection in contemporary human populations. Nat Rev Genet. 2010;11(9):611\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilot E et al. \u003cem\u003eEvidence for evolution in response to natural selection in a contemporary human population.\u003c/em\u003e Proceedings of the National Academy of Sciences, 2011. 108(41): pp. 17040\u0026ndash;17045.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou W, Henneberg R, Henneberg M. Healthcare services relaxing natural selection may contribute to increase of dementia incidence. Sci Rep. 2022;12(1):1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStephan CN, Henneberg M. Medicine may be reducing the human capacity to survive. Med Hypotheses. 2001;57(5):633\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinkler AS. The growing burden of neurological disorders in low-income and middle-income countries: priorities for policy making. Lancet Neurol. 2020;19(3):200\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIHME. Global Burden of Disease Study 2019 (GBD 2019) Covariates 1980\u0026ndash;2019. 2023 March 12 2023]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ghdx.healthdata.org/record/global-burden-disease-study-2019-gbd-2019-covariates-1980-2019\u003c/span\u003e\u003cspan address=\"https://ghdx.healthdata.org/record/global-burden-disease-study-2019-gbd-2019-covariates-1980-2019\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. Global Health Observatory: Life expectancy at age 60 (years). 2025 22 March 2025]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/data/gho/data/indicators/indicator-details/GHO/life-expectancy-at-age-60-(years\u003c/span\u003e\u003cspan address=\"https://www.who.int/data/gho/data/indicators/indicator-details/GHO/life-expectancy-at-age-60-(years\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Bank. GDP per capita, PPP (current international \u003cspan\u003e$\u003c/span\u003e). 2019 December 28 2021]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD\u003c/span\u003e\u003cspan address=\"https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Bank. Urban population (% of total) 2014. 2022; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://data.worldbank.org\u003c/span\u003e\u003cspan address=\"http://data.worldbank.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou Y, et al. Ageing as a risk factor for neurodegenerative disease. Nat reviews Neurol. 2019;15(10):565\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLicher S, et al. Lifetime risk of common neurological diseases in the elderly population. J Neurol Neurosurg Psychiatry. 2019;90(2):148\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuintana-Murci L. Understanding rare and common diseases in the context of human evolution. Genome Biol. 2016;17:1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Connor LJ, et al. Extreme polygenicity of complex traits is explained by negative selection. Am J Hum Genet. 2019;105(3):456\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaniotis A, Henneberg M. Evolutionary Medicine and Future of Humanity: Will Evolution Have the Final Word? Humanities. 2013;2(2):278\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenneberg M. Natural selection through differential fertility in human populations: Quantitative evaluation of selection intensity. Przeglad Antropologiczny. 1980;46:21\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMedawar PB. Do advances in medicine lead to genetic deterioration? Natural Selection in Human Populations. New York: Robert E. Krieger Publishing Co.; 1971. pp. 300\u0026ndash;08. Bajema C. J., Editor.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElhawary NA, et al. Genetic etiology and clinical challenges of phenylketonuria. Hum Genomics. 2022;16(1):22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou W, Henneberg M. Modern medical services, a double-edged sword manages symptoms, but accumulates genetic background of cardiovascular diseases: A cross populational analysis of 217 countries. Health Sci Rep. 2024;7(1):e1828.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunt BG et al. \u003cem\u003eRelaxed selection is a precursor to the evolution of phenotypic plasticity.\u003c/em\u003e Proceedings of the National Academy of Sciences, 2011. 108(38): pp. 15936\u0026ndash;15941.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWells JCK. The thrifty phenotype as an adaptive maternal effect. Biol Rev. 2007;82(1):143\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeigin VL, et al. Global, regional, and national burden of neurological disorders, 1990\u0026ndash;2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18(5):459\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing C, et al. Global, regional, and national burden and attributable risk factors of neurological disorders: The Global Burden of Disease study 1990\u0026ndash;2019. Front public health. 2022;10:952161.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarroll WM. The global burden of neurological disorders. Lancet Neurol. 2019;18(5):418\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeigin VL, et al. The global burden of neurological disorders: translating evidence into policy. Lancet Neurol. 2020;19(3):255\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB\u0026eacute;jot Y. Neurological disorders and age: The demographic transition. J Neurol Sci, 2021. 429.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaidya A, et al. Implementing a package of essential non-communicable diseases interventions in low-and middle-income countries: a realist review protocol. BMJ open. 2023;13(9):e074336.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeurology TL. \u003cem\u003eSustainable development demands brain health\u003c/em\u003e. 2023. p. 871.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinter SF, et al. National plans and awareness campaigns as priorities for achieving global brain health. Lancet Global Health. 2024;12(4):e697\u0026ndash;706.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTu F, et al. Evolving trends and burden of idiopathic epilepsy among children (0\u0026ndash;14 years), 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease study 2021. Front Neurol. 2025;16:1548477.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu L, et al. Global trends and burden of idiopathic epilepsy: regional and gender differences from 1990 to 2021 and future outlook. J Health Popul Nutr. 2025;44(1):45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllis CA, Petrovski S, Berkovic SF. Epilepsy genetics: clinical impacts and biological insights. Lancet Neurol. 2020;19(1):93\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang M-W, et al. Epilepsy-associated genes: an update. Seizure: Eur J Epilepsy. 2024;116:4\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L-z, et al. A population-based analysis of the global burden of epilepsy across all age groups (1990\u0026ndash;2021): utilizing the Global Burden of Disease 2021 data. Front Neurol. 2024;15:1448596.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBender N, R\u0026uuml;hli F, Henneberg M. \u003cem\u003eOngoing Human Evolution?\u003c/em\u003e The Evolutionary Roots of Human Brain Diseases, 2024: p. 472.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeigin VL, et al. Global, regional, and national burden of stroke and its risk factors, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021;20(10):795\u0026ndash;820.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrince M et al. \u003cem\u003eWorld Alzheimer report 2016: improving healthcare for people living with dementia: coverage, quality and costs now and in the future.\u003c/em\u003e 2016.\u003c/span\u003e\u003c/li\u003e\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":"Selection Opportunity, Biological State Index, Neurological Disorders, Evolutionary Epidemiology, Global Health","lastPublishedDoi":"10.21203/rs.3.rs-8182547/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8182547/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eThe global burden of dementia and other neurological disorders is increasing rapidly, particularly in low and middle income countries (LMICs). While ageing, economic development, and urbanization are well established contributors, the potential influence of evolutionary processes such as relaxed natural selection, captured through the concept of selection opportunity, has not been fully explored.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThis ecological study analysed data from 204 countries to examine the relationship between selection opportunity, measured by the Biological State Index (I\u003csub\u003ebs\u003c/sub\u003e), and the incidence of nine neurological disorder indicators. These included grouped conditions such as total neurological disorders and individual conditions such as Alzheimer\u0026rsquo;s disease, Parkinson\u0026rsquo;s disease, and headache disorders. Data were obtained from the Institute for Health Metrics and Evaluation, the World Bank, and the World Health Organization. Statistical analyses included Pearson and Spearman correlations, partial correlations, multivariate linear regressions (enter and stepwise methods), and Fisher\u0026rsquo;s z-tests to assess global associations and subgroup differences by income level and geographic region.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eSelection opportunity was significantly and positively associated with global dementia incidence (r\u0026thinsp;=\u0026thinsp;0.651, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), with stronger relationships observed in Lower-and-Middle-Income Countries settings. After controlling for ageing, national wealth, and urbanization, selection opportunity remained an independent predictor of dementia (β\u0026thinsp;=\u0026thinsp;0.304, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), Parkinson\u0026rsquo;s disease (β\u0026thinsp;=\u0026thinsp;0.290, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and total neurological disorder burden (β\u0026thinsp;=\u0026thinsp;0.181, p\u0026thinsp;\u0026lt;\u0026thinsp;.05). In contrast, idiopathic epilepsy showed a strong negative association (β = \u0026minus;\u0026thinsp;0.623, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and no significant associations were found for migraine or multiple sclerosis.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThis study provides new evidence that relaxed natural selection may independently contribute to the global incidence of neurological disorders. Integrating evolutionary perspectives into public health strategies may improve the understanding and management of these conditions, particularly in countries undergoing demographic and health transitions.\u003c/p\u003e","manuscriptTitle":"From Survival to Susceptibility: How Relaxed Natural Selection Shapes Global Neurological Disease Patterns","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 10:43:56","doi":"10.21203/rs.3.rs-8182547/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":"859cea1b-d5ac-439b-b60c-e14d1b1ba24c","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T12:42:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-30 10:43:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8182547","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8182547","identity":"rs-8182547","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}