搜索结果：Nonlinear dynamics, psychology, and life sciences

For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. Gates Foundation and Bloomberg Philanthropies.

Comprehensive, comparable, and timely estimates of demographic metrics-including life expectancy and age-specific mortality-are essential for evaluating, understanding, and addressing trends in population health. The COVID-19 pandemic highlighted the importance of timely and all-cause mortality estimates for being able to respond to changing trends in health outcomes, showing a strong need for demographic analysis tools that can produce all-cause mortality estimates more rapidly with more readily available all-age vital registration (VR) data. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) is an ongoing research effort that quantifies human health by estimating a range of epidemiological quantities of interest across time, age, sex, location, cause, and risk. This study-part of the latest GBD release, GBD 2023-aims to provide new and updated estimates of all-cause mortality and life expectancy for 1950 to 2023 using a novel statistical model that accounts for complex correlation structures in demographic data across age and time. We used 24 025 data sources from VR, sample registration, surveys, censuses, and other sources to estimate all-cause mortality for males, females, and all sexes combined across 25 age groups in 204 countries and territories as well as 660 subnational units in 20 countries and territories, for the years 1950-2023. For the first time, we used complete birth history data for ages 5-14 years, age-specific sibling history data for ages 15-49 years, and age-specific mortality data from Health and Demographic Surveillance Systems. We developed a single statistical model that incorporates both parametric and non-parametric methods, referred to as OneMod, to produce estimates of all-cause mortality for each age-sex-location group. OneMod includes two main steps: a detailed regression analysis with a generalised linear modelling tool that accounts for age-specific covariate effects such as the Socio-demographic Index (SDI) and a population attributable fraction (PAF) for all risk factors combined; and a non-parametric analysis of residuals using a multivariate kernel regression model that smooths across age and time to adaptably follow trends in the data without overfitting. We calibrated asymptotic uncertainty estimates using Pearson residuals to produce 95% uncertainty intervals (UIs) and corresponding 1000 draws. Life expectancy was calculated from age-specific mortality rates with standard demographic methods. For each measure, 95% UIs were calculated with the 25th and 975th ordered values from a 1000-draw posterior distribution. In 2023, 60·1 million (95% UI 59·0-61·1) deaths occurred globally, of which 4·67 million (4·59-4·75) were in children younger than 5 years. Due to considerable population growth and ageing since 1950, the number of annual deaths globally increased by 35·2% (32·2-38·4) over the 1950-2023 study period, during which the global age-standardised all-cause mortality rate declined by 66·6% (65·8-67·3). Trends in age-specific mortality rates between 2011 and 2023 varied by age group and location, with the largest decline in under-5 mortality occurring in east Asia (67·7% decrease); the largest increases in mortality for those aged 5-14 years, 25-29 years, and 30-39 years occurring in high-income North America (11·5%, 31·7%, and 49·9%, respectively); and the largest increases in mortality for those aged 15-19 years and 20-24 years occurring in Eastern Europe (53·9% and 40·1%, respectively). We also identified higher than previously estimated mortality rates in sub-Saharan Africa for all sexes combined aged 5-14 years (87·3% higher in GBD 2023 than GBD 2021 on average across countries and territories over the 1950-2021 period) and for females aged 15-29 years (61·2% higher), as well as lower than previously estimated mortality rates in sub-Saharan Africa for all sexes combined aged 50 years and older (13·2% lower), reflecting advances in our modelling approach. Global life expectancy followed three distinct trends over the study period. First, between 1950 and 2019, there were considerable improvements, from 51·2 (50·6-51·7) years for females and 47·9 (47·4-48·4) years for males in 1950 to 76·3 (76·2-76·4) years for females and 71·4 (71·3-71·5) years for males in 2019. Second, this period was followed by a decrease in life expectancy during the COVID-19 pandemic, to 74·7 (74·6-74·8) years for females and 69·3 (69·2-69·4) years for males in 2021. Finally, the world experienced a period of post-pandemic recovery in 2022 and 2023, wherein life expectancy generally returned to pre-pandemic (2019) levels in 2023 (76·3 [76·0-76·6] years for females and 71·5 [71·2-71·8] years for males). 194 (95·1%) of 204 countries and territories experienced at least partial post-pandemic recovery in age-standardised mortality rates by 2023, with 61·8% (126 of 204) recovering to or falling below pre-pandemic levels. There were several mortality trajectories during and following the pandemic across countries and territories. Long-term mortality trends also varied considerably between age groups and locations, demonstrating the diverse landscape of health outcomes globally. This analysis identified several key differences in mortality trends from previous estimates, including higher rates of adolescent mortality, higher rates of young adult mortality in females, and lower rates of mortality in older age groups in much of sub-Saharan Africa. The findings also highlight stark differences across countries and territories in the timing and scale of changes in all-cause mortality trends during and following the COVID-19 pandemic (2020-23). Our estimates of evolving trends in mortality and life expectancy across locations, ages, sexes, and SDI levels in recent years as well as over the entire 1950-2023 study period provide crucial information for governments, policy makers, and the public to ensure that health-care systems, economies, and societies are prepared to address the world's health needs, particularly in populations with higher rates of mortality than previously known. The estimates from this study provide a robust framework for GBD and a valuable foundation for policy development, implementation, and evaluation around the world. Gates Foundation.

Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6-47·0) in 1990 to 63·4 years (63·1-63·7) in 2023. For males, mean age increased from 45·4 years (45·1-45·7) to 61·2 years (60·7-61·6), and for females it increased from 48·5 years (48·1-48·8) to 65·9 years (65·5-66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9-81·0) and for males 74·8 years (74·8-74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5-38·4) for females and 35·6 years (35·2-35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. We examined global mortality patterns over the past three decades, highlighting-with enhanced estimation methods-the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. Gates Foundation.

Cancer is a leading cause of death globally. Accurate cancer burden information is crucial for policy planning, but many countries do not have up-to-date cancer surveillance data. To inform global cancer-control efforts, we used the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 framework to generate and analyse estimates of cancer burden for 47 cancer types or groupings by age, sex, and 204 countries and territories from 1990 to 2023, cancer burden attributable to selected risk factors from 1990 to 2023, and forecasted cancer burden up to 2050. Cancer estimation in GBD 2023 used data from population-based cancer registration systems, vital registration systems, and verbal autopsies. Cancer mortality was estimated using ensemble models, with incidence informed by mortality estimates and mortality-to-incidence ratios (MIRs). Prevalence estimates were generated from modelled survival estimates, then multiplied by disability weights to estimate years lived with disability (YLDs). Years of life lost (YLLs) were estimated by multiplying age-specific cancer deaths by the GBD standard life expectancy at the age of death. Disability-adjusted life-years (DALYs) were calculated as the sum of YLLs and YLDs. We used the GBD 2023 comparative risk assessment framework to estimate cancer burden attributable to 44 behavioural, environmental and occupational, and metabolic risk factors. To forecast cancer burden from 2024 to 2050, we used the GBD 2023 forecasting framework, which included forecasts of relevant risk factor exposures and used Socio-demographic Index as a covariate for forecasting the proportion of each cancer not affected by these risk factors. Progress towards the UN Sustainable Development Goal (SDG) target 3.4 aim to reduce non-communicable disease mortality by a third between 2015 and 2030 was estimated for cancer. In 2023, excluding non-melanoma skin cancers, there were 18·5 million (95% uncertainty interval 16·4 to 20·7) incident cases of cancer and 10·4 million (9·65 to 10·9) deaths, contributing to 271 million (255 to 285) DALYs globally. Of these, 57·9% (56·1 to 59·8) of incident cases and 65·8% (64·3 to 67·6) of cancer deaths occurred in low-income to upper-middle-income countries based on World Bank income group classifications. Cancer was the second leading cause of deaths globally in 2023 after cardiovascular diseases. There were 4·33 million (3·85 to 4·78) risk-attributable cancer deaths globally in 2023, comprising 41·7% (37·8 to 45·4) of all cancer deaths. Risk-attributable cancer deaths increased by 72·3% (57·1 to 86·8) from 1990 to 2023, whereas overall global cancer deaths increased by 74·3% (62·2 to 86·2) over the same period. The reference forecasts (the most likely future) estimate that in 2050 there will be 30·5 million (22·9 to 38·9) cases and 18·6 million (15·6 to 21·5) deaths from cancer globally, 60·7% (41·9 to 80·6) and 74·5% (50·1 to 104·2) increases from 2024, respectively. These forecasted increases in deaths are greater in low-income and middle-income countries (90·6% [61·0 to 127·0]) compared with high-income countries (42·8% [28·3 to 58·6]). Most of these increases are likely due to demographic changes, as age-standardised death rates are forecast to change by -5·6% (-12·8 to 4·6) between 2024 and 2050 globally. Between 2015 and 2030, the probability of dying due to cancer between the ages of 30 years and 70 years was forecasted to have a relative decrease of 6·5% (3·2 to 10·3). Cancer is a major contributor to global disease burden, with increasing numbers of cases and deaths forecasted up to 2050 and a disproportionate growth in burden in countries with scarce resources. The decline in age-standardised mortality rates from cancer is encouraging but insufficient to meet the SDG target set for 2030. Effectively and sustainably addressing cancer burden globally will require comprehensive national and international efforts that consider health systems and context in the development and implementation of cancer-control strategies across the continuum of prevention, diagnosis, and treatment. Gates Foundation, St Jude Children's Research Hospital, and St Baldrick's Foundation.

Traditionally, applied mathematics and nonlinear analysis have focused on creating models of real-world systems using first principles. However, in many contemporary scientific domains, first-principles approaches face increasing limitations, including but not limited to the challenges posed by complex, high-dimensional, or poorly characterized systems. In response, data science leverages the availability of large datasets and computational power to study problems where empirical understanding is incomplete or the underlying mechanisms are only partially known. A combination of both paradigms is crucial when some data is available, but our understanding of the phenomenon remains limited. In this context, methods such as sparse identification of nonlinear dynamics (SINDy), a data-driven technique designed to discover nonlinear dynamical systems from empirical data using regularization methods, have proved to be successful in the study of multivariate time series and nonlinear dynamics. Sparse identification capitalizes on the observation that many natural phenomena can be described by systems with only a few nonlinear terms, yielding interpretable models. In this paper, we will discuss the effectiveness of sparse identification in accurately determining the nonlinear dynamics of systems such as the van der Pol equation and a coupled system of van der Pol oscillators, two systems often used as a major benchmark examples for testing new data-driven methods on systems with rich dynamics, also when chaotic behavior of solutions and synchronization are possible.

Smoking is the leading behavioural risk factor for mortality globally, accounting for more than 175 million deaths and nearly 4·30 billion years of life lost (YLLs) from 1990 to 2021. The pace of decline in smoking prevalence has slowed in recent years for many countries, and although strategies have recently been proposed to achieve tobacco-free generations, none have been implemented to date. Assessing what could happen if current trends in smoking prevalence persist, and what could happen if additional smoking prevalence reductions occur, is important for communicating the effect of potential smoking policies. In this analysis, we use the Institute for Health Metrics and Evaluation's Future Health Scenarios platform to forecast the effects of three smoking prevalence scenarios on all-cause and cause-specific YLLs and life expectancy at birth until 2050. YLLs were computed for each scenario using the Global Burden of Disease Study 2021 reference life table and forecasts of cause-specific mortality under each scenario. The reference scenario forecasts what could occur if past smoking prevalence and other risk factor trends continue, the Tobacco Smoking Elimination as of 2023 (Elimination-2023) scenario quantifies the maximum potential future health benefits from assuming zero percent smoking prevalence from 2023 onwards, whereas the Tobacco Smoking Elimination by 2050 (Elimination-2050) scenario provides estimates for countries considering policies to steadily reduce smoking prevalence to 5%. Together, these scenarios underscore the magnitude of health benefits that could be reached by 2050 if countries take decisive action to eliminate smoking. The 95% uncertainty interval (UI) of estimates is based on the 2·5th and 97·5th percentile of draws that were carried through the multistage computational framework. Global age-standardised smoking prevalence was estimated to be 28·5% (95% UI 27·9-29·1) among males and 5·96% (5·76-6·21) among females in 2022. In the reference scenario, smoking prevalence declined by 25·9% (25·2-26·6) among males, and 30·0% (26·1-32·1) among females from 2022 to 2050. Under this scenario, we forecast a cumulative 29·3 billion (95% UI 26·8-32·4) overall YLLs among males and 22·2 billion (20·1-24·6) YLLs among females over this period. Life expectancy at birth under this scenario would increase from 73·6 years (95% UI 72·8-74·4) in 2022 to 78·3 years (75·9-80·3) in 2050. Under our Elimination-2023 scenario, we forecast 2·04 billion (95% UI 1·90-2·21) fewer cumulative YLLs by 2050 compared with the reference scenario, and life expectancy at birth would increase to 77·6 years (95% UI 75·1-79·6) among males and 81·0 years (78·5-83·1) among females. Under our Elimination-2050 scenario, we forecast 735 million (675-808) and 141 million (131-154) cumulative YLLs would be avoided among males and females, respectively. Life expectancy in 2050 would increase to 77·1 years (95% UI 74·6-79·0) among males and 80·8 years (78·3-82·9) among females. Existing tobacco policies must be maintained if smoking prevalence is to continue to decline as forecast by the reference scenario. In addition, substantial smoking-attributable burden can be avoided by accelerating the pace of smoking elimination. Implementation of new tobacco control policies are crucial in avoiding additional smoking-attributable burden in the coming decades and to ensure that the gains won over the past three decades are not lost. Bloomberg Philanthropies and the Bill & Melinda Gates Foundation.

BACKGROUND: Discrete choice experiment (DCE) methods are an increasingly popular valuation method, particularly for the EQ-5D-5L. While EQ-5D-5L value sets developed using DCE have traditionally assumed linear time preferences, this assumption has been challenged. This has led to the development of DCE modelling methods that allow for nonlinear time preferences. The aim of this study was to explore the impact of a model that accounts for nonlinear time preferences with DCE choice set formats and design construction methods for EQ-5D-5L value sets. METHODS: This study used a four-arm (2 × 2) between-subjects design to investigate the impact of two commonly used DCE choice set formats (i.e. a third option of either immediate death or full health) and two commonly used DCE design construction methods (i.e. generator-developed and efficient designs) on EQ-5D-5L value sets. Mixed logit models that used exponential discounting to account for nonlinear time preferences were estimated in OpenBUGS. This was tested in a sample of respondents from Peru (n = 942) and Denmark (n = 988). RESULTS: Across all arms and for both countries, discounting was found to be present when modelling explicitly for nonlinear time preferences. Although estimated discount rates varied widely from 1 to 117%, both type of choice set format and type of design construction method influenced the utilities for more severe health states. Choice sets with full health tended to produce a wider range of utility weights, while choice sets with immediate death tended to produce higher estimated discount rates. Generator-developed designs tended to produce the highest and lowest utility weights for health states compared to the efficient designs. CONCLUSIONS: This study provides a comparison of DCE choice set format and design construction method when nonlinear time preferences were explicitly modelled. Limitations to this study are discussed including data quality issues with the Peruvian dataset and small sample sizes. Further investigation is needed to confirm the suitability of models that account for nonlinear time preferences in EQ-5D-5L valuation studies.

Obesity and aging share biological processes, but their relationship remains unclear, especially in late life. Understanding how body mass index (BMI) and biological aging influence each other can guide strategies to reduce age- and obesity-related health risks. We examined the bidirectional, longitudinal association between changes in BMI and biological aging, measured by frailty index (FI) and functional aging index (FAI), across late life. This longitudinal cohort study used data from the Swedish Twin Registry substudies, GENDER, OCTO-Twin and SATSA, collected via in-person assessments from 1986 to 2014 at 2- to 4-year intervals. We analysed 6216-6512 evaluations from 1902 to 1976 Swedish twins. Dual change score models were applied to assess the bidirectional, longitudinal association between BMI and FI or FAI from ages 60.0-91.9. FI measured physiological aging, while FAI assessed functional aging through a composite score of functional abilities. At first measurement, mean age was 74 ± 8, and 41% were males. BMI-FI relationship was bidirectional (p value ≤ 0.001): Higher BMI predicted a greater increase in FI over time (coupling effect [γ] = 0.86, 95% confidence interval [CI] = 0.65-1.06, p value ≤ 0.001), and higher FI predicted steeper decline in BMI (γ = -0.04, 95% CI = -0.05 to -0.03, p value ≤ 0.001). When including coupling from FI, BMI showed a nonlinear trajectory with a mean intercept of 26.32 kg/m2 (95% CI = 25.76-26.88), declining more rapidly after age 75. When including BMI coupling, FI increased from a mean intercept of 7.91% (95% CI = 6.41-9.42), with steeper growth from ages 60-75. BMI-FAI relationship was unidirectional (p value ≤ 0.001): Higher FAI predicted a steeper BMI decline (γ = -0.02, 95% CI = -0.02 to -0.01, p value ≤ 0.001). By including FAI coupling, BMI had a mean intercept of 26.10 kg/m2 (95% CI = 25.47-26.74), declining rapidly after age 75. FAI increased exponentially from a mean intercept of 36.49 (95% CI = 34.54-38.43). Higher BMI predicted a steeper increase in FI, substantiating the hypothesis that obesity accelerates biological aging. Higher biological aging, measured as FI and FAI, drove a steeper BMI decline in late life, signalling that late-life weight loss may result from accelerated aging. Higher BMI may accelerate aspects of the aging process, and the aging process, in turn, accelerates late-life BMI decline, necessitating an integrated approach to manage both obesity and unintentional weight loss among older adults.

The unique demands for analyzing nonlinear time series produced by complex systems have generated a paradigm shift in statistical theory and application in much the same way as nonlinear dynamics have augmented the understanding of specific phenomena in the life and social sciences. Topics covered include: the statistical computation of the fractal dimensions, ergodicity, strategic use of nonlinear model libraries, identifying oscillators, state space analysis and entropy, time delays and the production of emergents, and quantum computing of fractal images. Substantive applications include US unemployment, political affiliation in the Netherlands, bipolar disorder, biomechanics, heart rate complexity, Bitcoin and other market prices, temperature anomalies and climate change, and economic growth.

Effective motor coordination is essential for adaptive athletic performance, including musculoskeletal injury prevention, particularly in high impact activities. Understanding how the lower extremities adapt to added constraints, such as increased load, can provide valuable insights into the resilience of movement patterns. This study examined the influence of added load on intralimb coordination during a drop-vertical jump (DVJ). Twenty-six participants (14 female, age = 23.10 +/- 3.97 years, 76.81 +/- 18.73kg) performed 5 body weight DVJs and 5 with an additional 25% body weight using a weighted vest. 3D joint kinematics were recorded using OpenCap markerless motion capture (OpenCap, Menlo Park, CA). Linear measures were calculated for the knee and hip, while nonlinear cross recurrence quantification analysis indexed intralimb coordination between the knee and hip joints. Alpha level was set a priori at α=.05. Paired-sample t-tests revealed smaller peak knee flexion (p=.013) and decreased total range of motion in both the hip and knee (p=.014 and .013, respectively) in the +25% body weight condition. Additionally, recurrence rate (p=.036), determinism (p=.046), adjusted mean line (p=.023), and adjusted trapping time (p=.011) were all lower in the +25% body weight condition. These results indicate that weight-based constraints lead to stiffer landing mechanics and noisier, less tightly coupled intralimb coordination. These findings highlight the need to consider both the landing mechanics and coordination dynamics when considering the implementation of movement assessments for athletic performance and injury prevention under increased load conditions.

This introduction to a special issue of Nonlinear Dynamics, Psychology and Life Sciences on the topic of resilience discusses the contributing articles in terms of their flexibility in methods, models, scale, and contexts combined with their integrity in shared theoretical understanding and generative knowledge. The ubiquity of resilience is discussed, a feature of potentially any living or non-living system and substance. This breadth calls for a flexible set of models and methods, along with the quest for integrative theory to make resilience science more resilient. Since resilience involves the ability of a substance or system to persist, to repair or recover, and to evolve, any common theory would consider structural integrity (the ability to hold together), flexibility (the ability to adjust and return), time and timing. Nonlinear dynamical systems theory is proposed as the only scientific perspective capable of building this sort of common knowledge of a ubiquitous process involving these specific features. The synopsis of each article's contribution to the issue includes an analysis of the flexibility the article adds in terms of models, methods, scale, and applied context, along with the theoretical integrity produced with respect to these common features of resilient processes: flexibility, integrity, time, and timing.

BackgroundMultiscale dispersion entropy (MDEnt) is a nonlinear EEG measure that quantifies brain complexity across time scales, reflecting both local and global brain dynamics. Previous research indicates lower complexity at short time scales in Alzheimer's disease (AD) compared to mild cognitive impairment (MCI) and healthy controls (HCs), with MCI also showing lower values than HCs. Major depressive disorder (MDD) has also been preliminarily linked to reduced complexity during acute episodes.ObjectiveTo assess whether MDEnt at short time scales can distinguish AD from MCI and HCs, and to examine complexity differences across additional groups, remitted MDD (rMDD) and rMDD + MCI, while exploring associations with cognitive performance.MethodsThe study included 316 older adults: 44 HCs, 46 with rMDD, 114 with MCI, 71 with rMDD + MCI, and 41 with AD. Resting-state, eyes-closed EEGs were analyzed using MDEnt at 24 ms (short) and 60 ms (long) time scales. Cognitive function was measured with the Montreal Cognitive Assessment and a composite cognitive score.ResultsShort time scale complexity was lowest in AD, followed by MCI, and highest in HCs; rMDD presence had no impact. Only AD showed reduced complexity at long time scales. Complexity at both time scales was significantly correlated with cognitive performance.ConclusionsThis study highlights the value of MDEnt to assess complexity at short time scale and differentiate individuals with AD, MCI, or HCs. Reduced complexity in these individuals may underlie their cognitive impairment. In contrast, our study suggests that any MDD impact on complexity is likely related to active depressive symptoms.

This study evaluated autonomic synchrony and variability in synchrony as control variables in cusp catastrophe models of workload and fatigue for teams making dynamics decisions. In this experiment, 136 undergraduates were organized into 32 groups of three, four, and five members playing an online computer game while wearing electrodermal sensors. They also completed cognitive measures of elasticity-rigidity and situation awareness during the games. Synchrony was calculated using the SE coefficient from the driver-empath model. Analyses were constructed to determine whether SE or SE variability added value to the cusp catastrophe models for cognitive workload and fatigue that were determined previously in Guastello and McGuigan (2024). Results indicated that SE made a strong impact on changes in performance as a bifurcation variable in both the workload and fatigue models. The positive and negative impact on performance shown by SE variability suggested that team members were grappling with the best ways to coordinate with each other or that teams found an advantage to turning synchrony on and off. SE variability made a strong impact as a bifurcation variable in the workload load model, and a strong impact as a compensatory ability (asymmetry parameter) in the fatigue model. Practical implications are that synchrony could be functional or dysfunctional, depending on situational demands that could be momentary. The study opened new questions regarding the qualitative relationship between elasticity-rigidity variables and synchrony and the possible roles of strong vs. weak ties in a closed network of this type.

Two Worlds, Two Urns is a turnkey classroom experiment that empirically demonstrates and teaches ergodicity, while also serving as a compact research protocol for history-dependent dynamics in behavioral economics. By contrasting an ergodic i.i.d. process (Urn B) with a non-ergodic, self-reinforcing Pólya process (Urn A), students can experience instances where time and ensemble averages align, as well as instances where they diverge. The design highlights microeconomic methodological limitations that arise from 'freezing' time in static models, such as additive and symmetry assumptions, reliance on process averages, and neglecting path dependence. In the ergodic world, early shocks alter transition probabilities, resulting in stochastic symmetry breaking, cumulative advantage and increasing dispersion from identical initial conditions. We provide a replicable protocol comprising instructions, tracking sheets and analysis templates, and recommend reporting time-averaged growth, ensemble means, variance growth, rank mobility and inequality metrics, as well as process diagnostics (e.g. estimated reinforcement). The experiment supports theory-informed pedagogy and offers an entry point for integrating ergodic reasoning into behavioural research on risk, learning and inequality, where lived trajectories, rather than just cross-sectional snapshots, determine outcomes.

The estimation of fractal patterns in time series data is a small but important specialty in the expansion of research methods that are specifically attuned to dealing with nonlinear dynamics and complex processes. This paper provides a brief methodological overview of the detection and confirmation of fractal patterns in time series data, focusing on two approaches, fractional differencing, a regression-based approach that estimates the relative contribution of a fractal parameter to the overall variability in the series, and spectral density analysis, which decides whether a Fourier-transformed series yields a linear relationship between the log relative frequencies and the log amplitude in the power spectrum. It is demonstrated how finding such a relationship points to a fractal pattern in the data (self-affinity). Three existing datasets are analyzed for illustrative purposes: annual recordings of the flow of the River Nile between 622 and 1285AD, monthly recordings of US unemployment figures from 1948 to 2020, and weekly survey responses concerning self-reported left-right political orientation in the Netherlands. It is shown how the two methods are able to detect fractals in the political orientation and River Nile data, but not in the unemployment data.

The DARPA OP TEMPO program seeks to accelerate warfighter readiness by supplying instructors with objective, automatic assessments of team performance during simulation training. To that end, we created the Bio-behavioral Team Dynamics Measurement System (BioTDMS), a multimodal sensing and analytics pipeline that discovers bio-behavioral 'signatures' emanating from within the human body and through team-member interactions that predict team performance. BioTDMS employs a layered symbolic dynamics model that converts time-aligned neural, cardio-respiratory, eye tracking, and verbal data, collected using a multimodal sensor suite. Moving-window entropy and mutual information computed across the symbolic sensor space yield real-time metrics that quantify team adaptability following perturbation (e.g., 'training injects') and distribution of team members' influence across biological and behavioral subsystems. These features feed a multitask, multi-kernel learning engine that refines performance prediction while preserving explainability through team construct mapping and a command-line user interface. We present preliminary results from field testing a full physical and computational implementation of BioTDMS during Fire Support Team (FiST) training exercises at the U.S. Marine Corps Air-Ground Combat Center, Twentynine Palms, CA. An onsite team instrumented five-person FiST crews with multimodal sensor suites. Sensor data were processed by BioTDMS for real-time and post hoc analytics. BioTDMS currently accounts for 90â¯% of variance in a subjective team perform-ance assessment made by instructors, with improvements expected upon further refinements of BioTDMS modeling components. These findings demonstrate BioTDMS's potential as an operational tool for automatic, objective team assessments. Future assessments within air combat teams, including configura-tions with human-autonomy teaming, will evaluate the generalizability of BioTDMS.

Traditional psychological models often treat motivation as a predictable, linear process, failing to capture how religious drive shifts erratically between fluid detachment and intense fervor. To resolve this limitation, a generative nonlinear dynamical systems framework is developed wherein desire is formalized as a vector field defined by two macroscopic order parameters: motivational intensity and semantic orientation. A coupled system of stochastic differential equations models how religious practices manipulate control parameters, specifically systemic gain and landscape curvature. Numerical simulations utilizing Euler-Maruyama integration reveal that these parameter adjustments induce critical bifurcations between two distinct topological regimes. The first, characteristic of monotheistic strategies, generates a stable fixed point exhibiting cusp-like hysteresis, which protects belief systems against environmental noise. The second, characteristic of Daoist strategies, generates a metastable regime of flexible wandering, maximizing cognitive adaptability. These findings suggest that religious traditions function as dynamic control mechanisms for managing the stability-plasticity dilemma. Ultimately, the model bridges abstract phenomenology with empirical psychology, providing testable blueprints based on the cusp catastrophe model to evaluate sudden phase transitions and cognitive hysteresis in motivational regulation.

This paper is a study of applications of the Lambert W function to solve delay differential equation models that arise in economics and finance. The Lambert W function, one of the mathematical special functions, is a basis of an effective technique to examine the stability and the dynamical regimes, especially cyclical one. The introduction to the Lambert W function is presented, showing its analytical properties. To demonstrate the method at work two economic models are chosen. First, the model of economic growth with a production delay and second, the market price model with supply dependent on a delayed price. In the Solow model of economic growth with a delay, the conditions are derived to determine how the interaction of the delay and the population growth rate can lead to the appearance of cyclical behavior in the economy and possibly the switch in stability near the steady state, and emergence of constant fluctuations and growth cycles, in consequence. Second, in the model of market price, similarly to the Solow model with a delay, exact conditions for the emergence of fluctuations and possible stability switch are constructed.

Previous research has demonstrated that interpersonal dynamics are fractal, and that conflict is a key control parameter that drives fractal complexity. The present study aimed to extend this line of research to examine the putative fractal structure of conflict dynamics over time, and the role that this self-organizing fractal structure may play in the resilience of romantic relationships. An experience sampling methodology was used to assess levels of conflict, satisfaction, and commitment in the dating relationships of undergraduate students, three times per day for 30 days. Hypothesis 1 was supported, with conflict ratings over time generally conforming to an inverse power-law distribution (IPL) distribution. Hypothesis 2 was supported as well, with better IPL fits measured as variance accounted for (R2), predicting higher levels of satisfaction and commitment over the 30 days. Hypothesis 3 showed mixed support, with moderate network linkages (i.e., soft assembly) between conflict and satisfaction and commitment predicting higher IPL fits (the linkage of satisfaction and commitment did not predict IPL fit as predicted). Hypothesis 4 predicted that IPL fit would interact with mean conflict, buffering the impacts of conflict on mean satisfaction and commitment across the 30 days. This hypothesis was not supported; however, several statistical factors may have obscured the buffering effects of higher IPL fit and so results may be inconclusive. These methodological factors, and others, are discussed along with the potential theoretical and practical implications of the current results.

This article discusses the spreading model of addicted smoking involving five compartments, namely susceptible, addicted, temporary quitters, permanent quitters, and not interested in smoking. This model is expressed as a system of nonlinear ordinary differential equations. Parental guidance and anti-nicotine therapy are considered in the model as strategies to control and prevent the spread of addicted smoking. Addicted and non-addicted fixed points of the model are analyzed using linearization, eigenvalues, the Routh-Hurwitz test, and the basic reproduction number. Sensitivity analysis of the model parameters to the basic reproduction number was carried out to determine the influence of the parameters, and it was found that the transmission rate has a significant contribution to the spread of addicted smoking. The model with control is then related to the problem of minimizing the number of individuals addicted to smoking. By using the Pontryagin minimum principle, an optimal path is obtained that minimizes the number of individuals addicted to smoking in a specific time interval. The simulation used several assumptions and model parameter values estimated from actual data. From the optimal path with and without controls, it was found that both controls significantly reduced the number of individuals addicted to smoking.