Advancing Urban Healthcare Equity Analysis: Integrating Public Participation GIS with Fuzzy Best–Worst Decision-Making

This study provides an innovative collaborative spatial decision support system (SDSS) that aims to ensure an equitable spatial distribution of healthcare services. Evaluating the equality of access to health services across different geographical areas is important, as it requires the analysis of various criteria such as the proximity of health centres and hospitals (HCHs), the quality of services offered, connectivity to primary roads, the availability of public transportation hubs, and the density and distribution patterns of HCHs. This purpose is accomplished via the use of geographic information systems (GIS) and multi-criteria decision analysis (MCDA) methods. The proposed model includes the weights of the criteria, which are determined through the ordered weighted average (OWA) and evaluated based on their ORness, which ranges from 0 to 1. Furthermore, this model is improved by the best–worst fuzzy method (F-BWM). This approach produces a spatial map that clearly shows the equity of healthcare systems in urban environments. The findings show that the maximum score observed in this study was 0.38% (with an ORness value of 1), whilst the minimum score recorded was 0.28%. In the most severe scenario (ORness = 0), over 70% of the region shows different degrees of fairness, ranging from moderate to suitable and very suitable conditions. Governments and health authorities can use this information strategically to allocate resources and address inequities in access to healthcare facilities

GPS driving: A digital biomarker for preclinical Alzheimer disease

BACKGROUND: Alzheimer disease (AD) is the most common cause of dementia. Preclinical AD is the period during which early AD brain changes are present but cognitive symptoms have not yet manifest. The presence of AD brain changes can be ascertained by molecular biomarkers obtained via imaging and lumbar puncture. However, the use of these methods is limited by cost, acceptability, and availability. The preclinical stage of AD may have a subtle functional signature, which can impact complex behaviours such as driving. The objective of the present study was to evaluate the ability of in-vehicle GPS data loggers to distinguish cognitively normal older drivers with preclinical AD from those without preclinical AD using machine learning methods. METHODS: We followed naturalistic driving in cognitively normal older drivers for 1 year with a commercial in-vehicle GPS data logger. The cohort included n = 64 individuals with and n = 75 without preclinical AD, as determined by cerebrospinal fluid biomarkers. Four Random Forest (RF) models were trained to detect preclinical AD. RF Gini index was used to identify the strongest predictors of preclinical AD. RESULTS: The F1 score of the RF models for identifying preclinical AD was 0.85 using APOE ε4 status and age only, 0.82 using GPS-based driving indicators only, 0.88 using age and driving indicators, and 0.91 using age, APOE ε4 status, and driving. The area under the receiver operating curve for the final model was 0.96. CONCLUSION: The findings suggest that GPS driving may serve as an effective and accurate digital biomarker for identifying preclinical AD among older adults

Cognitive and brain reserve predict decline in adverse driving behaviors among cognitively normal older adults

Daily driving is a multi-faceted, real-world, behavioral measure of cognitive functioning requiring multiple cognitive domains working synergistically to complete this instrumental activity of daily living. As the global population of older adult continues to grow, motor vehicle crashes become more frequent among this demographic. Cognitive reserve (CR) is the brain\u27s adaptability or functional robustness despite damage, while brain reserve (BR) refers the structural, neuroanatomical resources. This study examined whether CR and BR predicted changes in adverse driving behaviors in cognitively normal older adults. Cognitively normal older adults (Clinical Dementia Rating 0) were enrolled from longitudinal studies at the Knight Alzheimer\u27s Disease Research Center at Washington University. Participants

Driving assessment in preclinical Alzheimer’s disease: progress to date and the path forward

Abstract Background Changes in driving behaviour may start at the preclinical stage of Alzheimer’s disease (AD), where the underlying AD biological process has begun in the presence of cognitive normality. Here, we summarize the emerging evidence suggesting that preclinical AD may impact everyday driving behaviour. Main Increasing evidence links driving performance and behaviour with AD biomarkers in cognitively intact older adults. These studies have found subtle yet detectable differences in driving associated with AD biomarker status among cognitively intact older adults. Conclusion Recent studies suggest that changes in driving, a highly complex activity, are linked to, and can indicate the presence of, neuropathological AD. Future research must now examine the internal and external validity of driving for widespread use in identifying biological AD

Bringing the "Place" to Life-Space in Gerontology Research

Understanding older adults' relationships with their environments and the way this relationship evolves over time have been increasingly acknowledged in gerontological research. This relationship is often measured in terms of life-space, defined as the spatial area through which a person moves within a specific period of time. Life-space is traditionally reported using questionnaires or travel diaries and is, thus, subject to inaccuracies. More recently, studies are using a global positioning system to accurately measure life-space. Although life-space provides useful insights into older adults' relationships with their environment, it does not capture the inherent complexities of environmental exposures. In the fields of travel behaviour and health geography, a substantial amount of research has looked at people's spatial behaviour using the notion of "Activity Space," allowing for increasing sophistication in understanding older adults' experience of their environment. This manuscript discusses developments and directions for extending the life-space framework in environmental gerontology by drawing on the advancements in the activity space framework

A GPS-Based Framework for Understanding Outdoor Mobility Patterns of Older Adults with Dementia: An Exploratory Study

Introduction: An active lifestyle may protect older adults from cognitive decline. Yet, due to the complex nature of outdoor environments, many people living with dementia experience decreased access to outdoor activities. In this context, conceptualizing and measuring outdoor mobility is of great significance. Using the global positioning system (GPS) provides an avenue for capturing the multi-dimensional nature of outdoor mobility. The objective of this study is to develop a comprehensive framework for comparing outdoor mobility patterns of cognitively intact older adults and older adults with dementia using passively collected GPS data. Methods: A total of 7 people with dementia (PwD) and 8 cognitively intact controls (CTLs), aged 65 years or older, carried a GPS device when travelling outside their homes for 4 weeks. We applied a framework incorporating 12 GPS-based indicators to capture spatial, temporal, and semantic dimensions of outdoor mobility. Results: Despite a small sample size, the application of our mobility framework identified several significant differences between the 2 groups. We found that PwD participated in more medical-related (Cliff’s Delta = 0.71, 95% CI: 0.34–1) and fewer sport-related (Cliff’s Delta = −0.78, 95% CI: −1 to −0.32) activities compared to the cognitively intact CTLs. Our results also suggested that longer duration of daily walking time (Cliff’s Delta = 0.71, 95% CI: 0.148–1) and longer outdoor activities at night, after 8 p.m. (Hedges’ g = 1.42, 95% CI: 0.85–1.09), are associated with cognitively intact individuals. Conclusion: Based on the proposed framework incorporating 12 GPS-based indicators, we were able to identify several differences in outdoor mobility in PwD compared with cognitively intact CTLs

Socioeconomic and environmental determinants of foot and mouth disease incidence: an ecological, cross-sectional study across Iran using spatial modeling

Abstract Foot-and-mouth disease (FMD) is a highly contagious animal disease caused by a ribonucleic acid (RNA) virus, with significant economic costs and uneven distribution across Asia, Africa, and South America. While spatial analysis and modeling of FMD are still in their early stages, this research aimed to identify socio-environmental determinants of FMD incidence in Iran at the provincial level by studying 135 outbreaks reported between March 21, 2017, and March 21, 2018. We obtained 46 potential socio-environmental determinants and selected four variables, including percentage of population, precipitation in January, percentage of sheep, and percentage of goats, to be used in spatial regression models to estimate variation in spatial heterogeneity. In our analysis, we employed global models, namely ordinary least squares (OLS), spatial error model (SEM), and spatial lag model (SLM), as well as local models, including geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The MGWR model yielded the highest adjusted $${R}^{2}$$ R 2 of 90%, outperforming the other local and global models. Using local models to map the effects of environmental determinants (such as the percentage of sheep and precipitation) on the spatial variability of FMD incidence provides decision-makers with helpful information for targeted interventions. Our findings advocate for multiscale and multidisciplinary policies to reduce FMD incidence

GPS driving: a digital biomarker for preclinical Alzheimer disease

Abstract Background Alzheimer disease (AD) is the most common cause of dementia. Preclinical AD is the period during which early AD brain changes are present but cognitive symptoms have not yet manifest. The presence of AD brain changes can be ascertained by molecular biomarkers obtained via imaging and lumbar puncture. However, the use of these methods is limited by cost, acceptability, and availability. The preclinical stage of AD may have a subtle functional signature, which can impact complex behaviours such as driving. The objective of the present study was to evaluate the ability of in-vehicle GPS data loggers to distinguish cognitively normal older drivers with preclinical AD from those without preclinical AD using machine learning methods. Methods We followed naturalistic driving in cognitively normal older drivers for 1 year with a commercial in-vehicle GPS data logger. The cohort included n = 64 individuals with and n = 75 without preclinical AD, as determined by cerebrospinal fluid biomarkers. Four Random Forest (RF) models were trained to detect preclinical AD. RF Gini index was used to identify the strongest predictors of preclinical AD. Results The F1 score of the RF models for identifying preclinical AD was 0.85 using APOE ε4 status and age only, 0.82 using GPS-based driving indicators only, 0.88 using age and driving indicators, and 0.91 using age, APOE ε4 status, and driving. The area under the receiver operating curve for the final model was 0.96. Conclusion The findings suggest that GPS driving may serve as an effective and accurate digital biomarker for identifying preclinical AD among older adults

Neuropsychological correlates of changes in driving behavior among clinically healthy older adults

OBJECTIVES: To determine the extent to which cognitive domain scores moderate change in driving behavior in cognitively healthy older adults using naturalistic (GPS based) driving outcomes and to compare against self-reported outcomes using an established driving questionnaire. METHOD: We analyzed longitudinal naturalistic driving behavior from a sample (N = 161, 45% female, Mean age = 74.7 years, Mean education = 16.5 years) of cognitively healthy, non-demented older adults. Composite driving variables were formed that indexed driving space and driving performance . All participants completed a baseline comprehensive cognitive assessment that measured multiple domains as well as an annual self-reported driving outcomes questionnaire. RESULTS: Across an average of 24 months of naturalistic driving, our results showed that attentional control, broadly defined as the ability to focus on relevant aspects of the environment and ignore distracting or competing information as measured behaviorally with tasks such as the Stroop color naming test, moderated change in driving space scores over time. Specifically, individuals with lower attentional control scores drove fewer trips per month, drove less at night, visited fewer unique locations, and drove in smaller spaces than those with higher attentional control scores. No cognitive domain predicted driving performance such as hard braking or sudden acceleration. DISCUSSION: Attentional control is a key moderator of change over time in driving space but not driving performance in older adults. We speculate on mechanisms that may relate attentional control ability to modifications of driving behaviors

Everyday Driving and Plasma Biomarkers in Alzheimer\u27s Disease: Leveraging Artificial Intelligence to Expand Our Diagnostic Toolkit

BACKGROUND: Driving behavior as a digital behavior marker and recent developments in blood-based biomarkers show promise as a widespread solution for the early identification of Alzheimer\u27s disease (AD). OBJECTIVE: This study used artificial intelligence methods to evaluate the association between naturalistic driving behavior and blood-based biomarkers of AD. METHODS: We employed an artificial neural network (ANN) to examine the relationship between everyday driving behavior and plasma biomarker of AD. The primary outcome was plasma Aβ 42/Aβ 40, and Aβ 42/Aβ 40 \u3c  0.1013 was used to define amyloid positivity. Two ANN models were trained and tested for predicting the outcome. The first model architecture only includes driving variables as input, whereas the second architecture includes the combination of age, APOE ɛ4 status, and driving variables. RESULTS: All 142 participants (mean [SD] age 73.9 [5.2] years; 76 [53.5%] men; 80 participants [56.3% ] with amyloid positivity based on plasma Aβ 42/Aβ 40) were cognitively normal. The six driving features, included in the ANN models, were the number of trips during rush hour, the median and standard deviation of jerk, the number of hard braking incidents and night trips, and the standard deviation of speed. The F1 score of the model with driving variables alone was 0.75 [0.023] for predicting plasma Aβ 42/Aβ 40. Incorporating age and APOE ɛ4 carrier status improved the diagnostic performance of the model to 0.80 [\u3e0.051]. CONCLUSION: Blood-based AD biomarkers offer a novel opportunity to establish the efficacy of naturalistic driving as an accessible digital marker for AD pathology in driving research