Machine Learning for Prediction of Cognitive Health in Adults Using Sociodemographic, Neighbourhood Environmental, and Lifestyle Factors

The environment we live in, and our lifestyle within this environment, can shape our cognitive health. We investigated whether sociodemographic, neighbourhood environment, and lifestyle variables can be used to predict cognitive health status in adults. Cross-sectional data from the AusDiab3 study, an Australian cohort study of adults (34–97 years) (n = 4141) was used. Cognitive function was measured using processing speed and memory tests, which were categorized into distinct classes using latent profile analysis. Sociodemographic variables, measures of the built and natural environment estimated using geographic information system data, and physical activity and sedentary behaviours were used as predictors. Machine learning was performed using gradient boosting machine, support vector machine, artificial neural network, and linear models. Sociodemographic variables predicted processing speed (r2 = 0.43) and memory (r2 = 0.20) with good accuracy. Lifestyle factors also accurately predicted processing speed (r2 = 0.29) but weakly predicted memory (r2 = 0.10). Neighbourhood and built environment factors were weak predictors of cognitive function. Sociodemographic (AUC = 0.84) and lifestyle (AUC = 0.78) factors also accurately classified cognitive classes. Sociodemographic and lifestyle variables can predict cognitive function in adults. Machine learning tools are useful for population-level assessment of cognitive health status via readily available and easy-to-collect data

Urban Neighbourhood Environments, Cardiometabolic Health and Cognitive Function: A National Cross-Sectional Study of Middle-Aged and Older Adults in Australia

Population ageing and urbanisation are global phenomena that call for an understanding of the impacts of features of the urban environment on older adults’ cognitive function. Because neighbourhood characteristics that can potentially have opposite effects on cognitive function are interdependent, they need to be considered in conjunction. Using data from an Australian national sample of 4141 adult urban dwellers, we examined the extent to which the associations of interre-lated built and natural environment features and ambient air pollution with cognitive function are explained by cardiometabolic risk factors relevant to cognitive health. All examined environmental features were directly and/or indirectly related to cognitive function via other environmental features and/or cardiometabolic risk factors. Findings suggest that dense, interconnected urban environments with access to parks, blue spaces and low levels of air pollution may benefit cognitive health through cardiometabolic risk factors and other mechanisms not captured in this study. This study also high-lights the need for a particularly fine-grained characterisation of the built environment in research on cognitive function, which would enable the differentiation of the positive effects of destination-rich neighbourhoods on cognition via participation in cognition-enhancing activities from the negative effects of air pollutants typically present in dense, destination-rich urban areas

Do neighbourhood traffic-related air pollution and socio-economic status moderate the associations of the neighbourhood physical environment with cognitive function? Findings from the AusDiab study

Characteristics of the neighbourhood environment, including the built and natural environment, area-level socio-economic status (SES) and air pollution, have been linked to cognitive health. However, most studies have focused on single neighbourhood characteristics and have not considered the extent to which the effects of environmental factors may interact. We examined the associations of measures of the neighbourhood built and natural environment, area-level SES and traffic-related air pollution (TRAP) with two cognitive function domains (memory and processing speed), and the extent to which area-level SES and TRAP moderated the associations. We used cross-sectional data from the AusDiab3 study, an Australian cohort study of adults (mean age: 61 years) in 2011–12 (N = 4141) for which geocoded residential addresses were available. Spatial data were used to create composite indices of built environment complexity (population density, intersection density, non-commercial land use mix, commercial land use) and natural environment (parkland and blue spaces). Area-level SES was obtained from national census indices and TRAP was based on estimates of annual average levels of nitrogen dioxide (NO2). Confounder-adjusted generalised additive mixed models were used to estimate the independent associations of the environmental measures with cognitive function and the moderating effects of area-level SES and TRAP. The positive associations between built environment complexity and memory were stronger in those living in areas with higher SES and lower NO2 concentrations. A positive association between the natural environment and memory was found only in those living in areas with lower NO2 concentrations and average or below-average SES. Built environment complexity and the natural environment were positively related to processing speed. Complex urban environments and access to nature may benefit cognitive health in ageing populations. For higher-order cognitive abilities, such as memory, these positive effects may be stronger in areas with lower levels of TRAP

Neighbourhood environments and cognitive health in the longitudinal Personality and Total Health (PATH) through life study: A 12-year follow-up of older Australians

Background: Urban neighbourhood environments may impact older adults’ cognitive health. However, longitudinal studies examining key environmental correlates of cognitive health are lacking. We estimated cross-sectional and longitudinal associations of neighbourhood built and natural environments and ambient air pollution with multiple cognitive health outcomes in Australian urban dwellers aged 60+ years. Methods: The study included 1160 participants of the PATH Through Life study (60+ cohort) who were followed up for 12 years (four assessments; 2001/02 to 2013/15) and with data on socio-demographics, health, cognitive functions and diagnoses, and full residential address. Neighbourhood environmental features encompassed population and street-intersection densities, non-commercial land use mix, transit points, presence of blue space, percentages of commercial land, parkland and tree cover, and annual average PM2.5 and NO2 concentrations. All exposures except for tree cover were assessed at two time points. Generalised additive mixed models estimated associations of person-level average, and within-person changes in, exposures with cognitive functions. Multi-state hidden Markov models estimated the associations of neighbourhood attributes with transitions to/from mild cognitive impairment (MCI). Results: Dense, destination-rich neighbourhoods were associated with a lower likelihood of transition to MCI and reversal to no MCI. Positive cross-sectional and longitudinal associations of non-commercial land use mix, street intersection density and percentage of commercial land were observed especially with global cognition and processing speed. While access to parkland and blue spaces were associated with a lower risk of transition to MCI, the findings related to cognitive functions were mixed and supportive of an effect of parkland on verbal memory only. Higher levels of PM2.5 and NO2 were consistently associated with steeper declines and/or decreases in cognitive functions and worse cognitive states across time. Conclusion: To support cognitive health in ageing populations, neighbourhoods need to provide an optimal mix of environmental complexity, destinations and access to the natural environment and, at the same time, minimise ambient air pollution

Protein levels, air pollution and vitamin D deficiency: links with allergy

This study provides novel insights into mechanisms of traffic-related air pollution-induced allergy by down-regulation via complement regulators (CFI, PROS1 and PLG) and its interaction with vitamin D deficiency via the complement inhibitor PLG https://bit.ly/3x0jYOw

Avoidable mortality attributable to anthropogenic fine particulate matter (Pm2.5) in Australia

Ambient fine particulate matter 2.5) air pollution increases premature mortalityglobally. Some PM2.5 is natural, but anthropogenic PM2.5 is comparatively avoidable. We determinedthe impact of long-term exposures to the anthropogenic PM component on mortality in Australia.PM2.5-attributable deaths were calculated for all Australian Statistical Area 2 (SA2; n = 2310) regions.All-cause death rates from Australian mortality and population databases were combined withannual anthropogenic PM2.5 exposures for the years 2006–2016. Relative risk estimates were derivedfrom the literature. Population-weighted average PM2.5 concentrations were estimated in eachSA2 using a satellite and land use regression model for Australia. PM2.5-attributable mortality wascalculated using a health-impact assessment methodology with life tables and all-cause death rates.The changes in life expectancy (LE) from birth, years of life lost (YLL), and economic cost of lostlife years were calculated using the 2019 value of a statistical life. Nationally, long-term populationweighted average total and anthropogenic PM2.5 concentrations were 6.5 µg/m3(min 1.2–max 14.2)and 3.2 µg/m3(min 0–max 9.5), respectively. Annually, anthropogenic PM2.5-pollution is associatedwith 2616 (95% confidence intervals 1712, 3455) deaths, corresponding to a 0.2-year (95% CI 0.14, 0.28)reduction in LE for children aged 0–4 years, 38,962 (95%CI 25,391, 51,669) YLL and an average annualeconomic burden of $6.2 billion (95$4.0 billion, $8.1 billion). We conclude that the anthropogenicPM2.5-related costs of mortality in Australia are higher than community standards should allow,and reductions in emissions are recommended to achieve avoidable mortality

Neighborhood environments and transition to cognitive states: Sydney Memory and Ageing Study

\ua9 2025 The Author(s). Alzheimer\u27s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer\u27s Association.INTRODUCTION: Features of the neighborhood environment and ambient air pollution have been associated with onset and progression of neurocognitive disorders, but data from longitudinal population-based studies are limited. METHODS: One thousand thirty-six participants (78.3 \ub1 4.8 years) of the Sydney Memory and Ageing Study were followed for up to 13.7 years with biennial cognitive assessments. Neighborhood environmental features were assessed around the participants’ homes. Associations between environmental features and transitions to cognitive states were estimated. RESULTS: Population density, street connectivity, access to commercial services, public transport, water bodies, and tree canopy were associated with a lower likelihood of worsening cognitive state. The opposite was observed for annual average concentrations of PM2.5. Access to parkland, blue spaces, and public transport were associated with a higher likelihood of reversal from mild cognitive impairment to normal cognition. DISCUSSION: Healthy neighborhood environments may delay cognitive decline and the onset of dementia in older individuals. Highlights: This is the first published study on neighborhood built and natural environmental correlates of transition to dementia. This study was conducted in socially advantaged areas with relatively low ambient air pollution. Walkable neighborhoods are associated with a lower likelihood of worsening cognitive state. Neighborhood tree canopy is consistently predictive of better cognitive outcomes. Access to public transport, and blue and green spaces is associated with higher probability of improved cognitive state

Mortality, morbidity, and hospitalisations due to influenza lower respiratory tract infections, 2017: an analysis for the Global Burden of Disease Study 2017

Background Although the burden of influenza is often discussed in the context of historical pandemics and the threat of future pandemics, every year a substantial burden of lower respiratory tract infections (LRTIs) and other respiratory conditions (like chronic obstructive pulmonary disease) are attributable to seasonal influenza. The Global Burden of Disease Study (GBD) 2017 is a systematic scientific effort to quantify the health loss associated with a comprehensive set of diseases and disabilities. In this Article, we focus on LRTIs that can be attributed to influenza.Methods We modelled the LRTI incidence, hospitalisations, and mortality attributable to influenza for every country and selected subnational locations by age and year from 1990 to 2017 as part of GBD 2017. We used a counterfactual approach that first estimated the LRTI incidence, hospitalisations, and mortality and then attributed a fraction of those outcomes to influenza.Findings Influenza LRTI was responsible for an estimated 145 000 (95% uncertainty interval [UI] 99 000–200 000) deaths among all ages in 2017. The influenza LRTI mortality rate was highest among adults older than 70 years (16·4 deaths per 100 000 [95% UI 11·6–21·9]), and the highest rate among all ages was in eastern Europe (5·2 per 100 000 population [95% UI 3·5–7·2]). We estimated that influenza LRTIs accounted for 9 459 000 (95% UI 3 709 000–22 935 000) hospitalisations due to LRTIs and 81 536 000 hospital days (24 330 000–259 851 000). We estimated that 11·5% (95% UI 10·0–12·9) of LRTI episodes were attributable to influenza, corresponding to 54 481 000 (38 465 000–73 864 000) episodes and 8 172 000 severe episodes (5 000 000–13 296 000).Interpretation This comprehensive assessment of the burden of influenza LRTIs shows the substantial annual effect of influenza on global health. Although preparedness planning will be important for potential pandemics, health loss due to seasonal influenza LRTIs should not be overlooked, and vaccine use should be considered. Efforts to improve influenza prevention measures are neede