Ultrafine particles and late-life cognitive function: Influence of stationary mobile monitoring design on health inferences.

On-road mobile monitoring is increasingly used to assess air pollution, but the implications of monitoring and analytic decisions on exposure prediction and health inferences remain unclear. This study evaluated the influence of on-road monitoring approaches in environmental epidemiology, specifically ultrafine particle (UFP) exposures and late-life cognitive function. We used data from a Seattle-based mobile monitoring campaign to develop a reference roadside UFP exposure model based on repeated measurements at 309 roadside locations and examine associations with cognitive function (Cognitive Abilities Screening Instrument-Item Response Theory [CASI-IRT]), in the Adult Changes in Thought cohort (N = 5283). To evaluate alternative designs, we subsampled on-road UFP measurements collected along 600 km of roadways, varying location visit frequencies, spatial balancing, and sampling times. New UFP models, some incorporating temporal and plume adjustments, were developed using universal kriging with partial least squares and used to estimate associations between UFP and CASI-IRT, after adjusting for age, year, sex, education, race, and socioeconomic status. Using the reference exposure model in the primary health model, the mean baseline CASI-IRT score increased by 0.007 (95% CI: -0.013, 0.027) per 1900 pt/cm³ increment in PNC. Associations were similar but relatively attenuated for all on-road sampling designs. Route-based sampling (which accounted for logistical field constraints and spatiotemporal correlation in the data) and very short (4- vs 12-visit) campaigns produced more variable health estimates. Applying temporal and plume adjustments had a minimal impact on the inferential results. In analyses where no association was observed between UFP and cognitive function, the on-road monitoring design produced similar but slightly attenuated point estimates. Secondary analyses with a reduced health model, which indicated a statistically significant but potentially confounded association, suggest that on-road design-particularly monitoring beyond weekday business hours-may have greater implications in other contexts. How does mobile monitoring design impact health studies? We assess UFP data and health measures to evaluate visit number, spatial balance, timing, and adjustments to assess their influence on exposure and health models. Mobile monitoring is increasingly used to develop air pollution exposure models, yet the influence of monitoring design on health inferences remains unclear. Using extensive ultrafine particle (UFP) data from a monitoring campaign and health measures from a long-standing cohort study, we assess how on-road campaigns can be designed for epidemiologic research. We evaluate the effects of visit number, spatial balance, time selection, temporal adjustment methods, and plume adjustments on exposure and health models, providing guidance for mobile monitoring design in air pollution health research.

Epidemiological studies often make use of exposure data that is collected in opportunistic and logistically convenient ways. And, while exposure assessment is fundamental to environmental epidemiology, little is known about what exposure assessment study designs are optimal for health inference. The objective of this project was to advance our understanding of the design of exposure assessment measurement campaigns and evaluate their impact on estimating the associations between long-term average air pollution exposure and cognitive function. This feeds into the broader goal of advancing understanding of air pollution exposure assessment design for application to epidemiological inference. We leveraged data from the Adult Changes in Thought (ACT3) Air Pollution study (ACT-AP) to characterize exposures for over 5,000 participants from the ongoing ACT cohort. This is a population-based cohort of urban and suburban elderly individuals in the greater Puget Sound region drawn from Group Health Cooperative, now Kaiser Permanente, starting in 1994. Participants were routinely followed with routine biennial visits until dementia incidence, drop-out, or death. Extensive health, lifestyle, biological, and demographic data were also collected. The outcome measure used in this report is cognitive function at baseline based on the Cognitive Abilities Screening Instrument derived using Item Response Theory (CASI-IRT). The IRT transformation of the CASI score improves score accuracy, measures cognitive change with less bias, and accounts for missing test items. Health association analyses were based on 5,409 participants with both a valid CASI score and who had lived in the mobile monitoring region during at least 95% of the 5 years prior to baseline. We used 5-year average exposures that accounted for residential history. We found that air pollution exposure assessment design is critical for exposure prediction, and also impacts health inference. We showed that a mobile monitoring study with stationary roadside sampling that has at least 12 visits per location in a balanced and temporally unrestricted design optimizes exposure model performance while also limiting costs. Relative to weaker alternatives, a balanced and temporally unrestricted design has improved accuracy and reduced variability of health inferences, particularly for confounder model 1. To address temporal balance, it is important that the exposure sampling in mobile monitoring campaigns cover all days of the week, most hours of the day, and at least two seasons. The popular temporally restricted business-hours sampling design had the poorest performance, which was not improved by adjusting for the temporally unbalanced sampling approach. We found similar patterns using on-road data, though the findings were weaker overall. This project has shown that there should be greater attention to the design of the exposure data collection campaigns used in epidemiological inference. Based on the multiple investigations conducted, many of which focused on UFPs, we found that exposure predictions with better performance statistics resulted in health association estimates that were generally more consistent with those obtained using the "best" exposure model predictions (the model with all data included), although the pattern of health estimates was often less conclusive than the pattern of prediction model performances. Furthermore, we found that it is possible to design air pollution exposure assessment studies that achieve good exposure prediction model performance while controlling their relative cost.

BACKGROUND AND AIM: Mobile monitoring has recently made it possible to measure the long-term trends of less commonly measured pollutants. While many different monitoring approaches have been taken, few studies have looked at the importance of study design when the goal is application to epidemiologic cohort studies. We carried out a simulation study to better understand the role of short-term mobile monitoring design on the prediction of long-term air pollution exposure surfaces. Since air pollution concentrations include random and systematic variability, we hypothesized that mobile campaigns will benefit from balanced designs that randomly sample from all seasons of the year, days of the week and hours of the day. METHODS: We simulated various short-term sampling designs using oxides of nitrogen (NOx) monitoring data from California air quality system (AQS) sites. Designs studied included a year-around, Balanced Design and two more common designs from the literature: a Rush Hours and a Business Hours Design. We evaluated the resulting annual average exposure predictions against the observations from each design and against the measured true concentrations. RESULTS:We found that the Balanced Design consistently produced accurate annual averages, while the Rush Hours and Business Hours Designs generally resulted in more biased estimates and model predictions. The superior performance of the Balanced Design was evident when predictions were evaluated against true concentrations; importantly, this superior performance was less detectable when predictions were evaluated against the measurements from the same sampling campaign since these measurements were themselves biased. CONCLUSIONS:Balanced design campaigns are expected to produce generally unbiased, long-term averages. Differential exposure misclassification could result from unbalanced designs, which may result in misleading health effect estimates in epidemiologic investigations. Appropriate study design is crucial for mobile monitoring campaigns aiming to assess accurate long-term exposure in epidemiologic cohorts. KEYWORDS: air pollution, study design, exposure assessment, oxides of nitrogen, environmental epidemiology, long-term exposure

ObjectiveTo better understand life course influences affecting cognitive function and decline in later life, we explored sex and race/ethnicity differentials in the relationship between a history of psychiatric, emotional, or nervous problems and cognitive functioning in later life, while accounting for early life disadvantage and relevant covariates.MethodsMulti-level growth curve models examined associations between psychiatric history and cognitive functioning, and differences by sex and race/ethnicity (SRE), in 20,155 Health and Retirement Study (1995-2014) participants aged 65 or older, by estimating cognition scores and plotting trajectories of change with age by SRE.ResultsA history of psychiatric, emotional, or nervous problems was significantly related to cognition scores and rates of decline. Hispanic and Black participants had significantly lower cognition scores at age 75 and steeper rates of decline than White females, and Black race and the Hispanic race-sex interaction erased the protective effects of being female.ConclusionsOur findings indicate that members of minority groups with a history of psychiatric problems evidence lower cognitive function in later life, and as a result, have a greater need for community-based long-term care than their peers without this history. Future research should include longitudinal analyses of different components of cognitive function, specific psychiatric diagnoses, and life history data that capture socioeconomic and psychosocial experiences throughout the life course. Population level findings as reported here, along with aggregate findings from similar studies, can inform interventions and policies regarding support for populations that are vulnerable to mental illness and to subsequent cognitive decline.

BackgroundSocioeconomic position (SEP) across the lifecourse is positively associated with cognitive function in later life in studies of Western populations, with later SEP likely mediating the effect of early life...

Mobile and short-term monitoring campaigns are increasingly used to develop land use regression (LUR) models for ultrafine particles (UFP) and black carbon (BC). It is not yet established whether LUR models based on mobile or short-term stationary measurements result in comparable models and concentration predictions. The goal of this paper is to compare LUR models based on stationary (30 minutes) and mobile UFP and BC measurements from a single campaign. An electric car collected both repeated stationary and mobile measurements in Amsterdam and Rotterdam, The Netherlands. A total of 2,964 road segments and 161 stationary sites were sampled over two seasons. Our main comparison was based on predicted concentrations of the mobile and stationary monitoring LUR models at 12,682 residential addresses in Amsterdam. Predictor variables in the mobile and stationary LUR model were comparable resulting in highly correlated predictions at external residential addresses (R2 of 0.89 for UFP and 0.88 for BC). Mobile model predictions were on average 1.41 and 1.91 times higher than stationary model predictions for UFP and BC respectively. LUR models based upon mobile and stationary monitoring predicted highly correlated UFP and BC concentration surfaces, but predicted concentrations based on mobile measurements were systematically higher.

Many epidemiological studies have traditionally leveraged European maps derived from fixed-site measurements to investigate health effects, primarily emphasizing inter-city variations. Recently, mobile monitoring has been demonstrated to refine the spatial resolution focusing on intra-city variations. Nevertheless, efficiently scaling mobile monitoring campaigns to cover a large spatial area remains challenging.Tackling this challenge, we explored the transferability of mobile measurements across three European cities. We propose to adapt the traditional land use regression (LUR) models with unsupervised transfer learning algorithms. These models, named CORrelation ALignment (Coral) and its adapted form, inverse distance-weighted Coral (IDW_Coral), aim to estimate air pollution levels in Amsterdam. They rely solely on data from mobile monitoring campaigns in Copenhagen and Rotterdam, bypassing the need for local Amsterdam data itself. The first 30 collection days of mobile campaigns in Copenhagen and Rotterdam were used as the source data (training inputs). By harmonizing the feature space, Coral is designed to minimize the domain difference between the source and target areas. IDW_Coral integrates single Coral models following general geographic principles. Their performance was validated against external routine measurements and compared with a reference LUR model (AMS_SLR), fitted by sequentially increasing amounts of mobile measurements collected in Amsterdam for nitrogen dioxide (NO2). The proposed models were also compared with our previously published mixed-effect models using all Amsterdam mobile measurements for NO2 and Ultra Fine Particles (UFP).For nitrogen dioxide (NO2), IDW_Coral achieved a balanced performance with an R2 of 0.35.  This accounts for 67% of the accuracy of a locally fitted Amsterdam model (AMS_SLR, R2 = 0.52), developed using comprehensive mobile monitoring over 160 days in Amsterdam. The difference in absolute errors between the two models was marginal (0.75 for MAE and 0.66 µg/m3 for RMSE). The R2 of IDW_Coral matches that of AMS_SLR based on 25 days of data collection, implying that a minimum of 25 days is required to gather city-specific insights through mobile monitoring. If this condition isn't met, IDW_Coral presents a more cost-effective alternative. IDW_Coral correlated strongly (Spearman correlation of 0.72 for NO2 and 0.76 for UFP) with mixed-effect models fitted with all Amsterdam mobile measurements.Leveraging Tobler's first law of Geography, our IDW_Coral method proficiently delineates hyperlocal air pollution in areas not directly observed. Further improvements in accuracy and applicability can be achieved by expanding mobile-monitored areas. Requiring no direct measurements in the target area, IDW_Coral has the potential for application across Europe, promising substantial savings in collection efforts.

Background:There is some evidence to suggest that social isolation may be associated with poor cognitive function in later life. However, findings are inconsistent and there is wide variation in the measures used to assess social isolation.Objective:We conducted a systematic review and meta-analysis to investigate the association between social isolation and cognitive function in later life.Methods:A search for longitudinal studies assessing the relationship between aspects of social isolation (including social activity and social networks) and cognitive function (including global measures of cognition, memory, and executive function) was conducted in PsycInfo, CINAHL, PubMed, and AgeLine. A random effects meta-analysis was conducted to assess the overall association between measures of social isolation and cognitive function. Sub-analyses investigated the association between different aspects of social isolation and each of the measures of cognitive function.Results:Sixty-five articles were identified by the systematic review and 51 articles were included in the meta-analysis. Low levels of social isolation characterized by high engagement in social activity and large social networks were associated with better late-life cognitive function (r = 0.054, 95% CI: 0.043, 0.065). Sub-analyses suggested that the association between social isolation and measures of global cognitive function, memory, and executive function were similar and there was no difference according to gender or number of years follow-up.Conclusions:Aspects of social isolation are associated with cognitive function in later life. There is wide variation in approaches to measuring social activity and social networks across studies which may contribute to inconsistencies in reported findings.

High-resolution spatial and spatiotemporal modelling of air pollution using fixed site and mobile monitoring in a Canadian city

Assessment of long-term exposure to outdoor air pollution remains a major challenge for epidemiological studies. One of these challenges is characterizing fine-scale spatial variation of the ambient concentrations of key traffic-related air pollutants - including ultrafine particles (UFPs), black carbon (BC), and nitrogen dioxide (NO2). Epidemiological studies have used widely different approaches to address these challenges, including empirical land use regression (LUR) models based on fixed-site routine or targeted monitoring, low-cost sensor networks, mobile monitoring, and deterministic dispersion models. Little information is available about the relative performance of these different approaches for assessing long-term exposure to traffic-related air pollution. Different methods may result in heterogeneity in health effect estimates from epidemiological studies applying different exposure-assessment approaches. We evaluated annual average air pollution concentrations across the Netherlands using a suite of different exposure models, which differed in modeling approach (empirical LUR, deterministic dispersion models) and monitoring data used (low-cost sensors, mobile monitoring, nationwide and Europewide routine monitoring, and study-specific targeted monitoring). For empirical models, we tested three model development algorithms: supervised linear regression (SLR), Random Forest, and least absolute shrinkage and selection operator (LASSO). The predictions of the models were compared at 20,000 addresses across the Netherlands. The performance was also tested on external validation data, which were obtained from a new campaign (2021-2023) and existing data from different years, allowing assessment of how well recent models predict past air pollution exposure. Epidemiological analyses in three cohort studies were conducted to compare health effect estimates of the different exposure models. We assessed associations of air pollution in a national administrative cohort with natural-cause and cause-specific mortality, in a cohort study that had detailed lifestyle data with natural-cause mortality and incidence of stroke and coronary events, and in a mature birth cohort with lung function and asthma incidence. Exposure predictions at residential sites from the dispersion model and the Europewide hybrid LUR models were available for multiple years in the period 2010-2019. For these models, exposure predictions of different years in the period 2010-2019 were highly correlated for BC, NO2, and PM2.5 (Correlation coefficient R > 0.9). Consistently, the year of the exposure model did not affect the presence of an association with mortality and morbidity. Small differences in hazard ratios (HR) were related to exposure contrast for different years. The HR for the association of NO2 with natural-cause mortality was 1.026 (95% confidence interval [CI]: 1.022-1.031) for the 2010 exposure estimate and 1.030 (1.024-1.035) for the 2019 exposure estimate of the Europewide LUR model, expressed per 10 µg/m3. The main conclusions of the project.

BackgroundMeasures of cardiac ventricular electrophysiology have been associated with cognitive performance in cross-sectional studies. We sought to evaluate the association of worsening ventricular repolarization in midlife, as measured by incident prolonged QT interval, with cognitive decline in late life.MethodsMidlife QT interval was assessed by electrocardiography during three study visits from 1965/68 to 1971/74 in a cohort of Japanese American men aged 46–68 at Exam 1 from the Honolulu Heart Study. We defined incident prolonged QT as the QT interval in the upper quartile at Exam 2 or 3 after QT interval in lower three quartiles at Exam 1. Cognitive performance was assessed at least once using the Cognitive Abilities Screening Instrument (CASI), scored using item response theory (CASI-IRT), during four subsequent visits from 1991/93 to 1999/2000 among 2,511 of the 4,737 men in the Honolulu-Asia Aging Study otherwise eligible for inclusion in analyses. We used marginal structural modeling to determine the association of incident prolonged QT with cognitive decline, using weighting to account for confounding and attrition.ResultsIncident prolonged QT interval in midlife was not associated with late-life CASI-IRT at cognitive baseline (estimated difference in CASI-IRT: 0.04; 95% CI: -0.28, 0.35; p = 0.81), or change in CASI-IRT over time (estimated difference in annual change in CASI-IRT: -0.002; 95%CI: -0.013, 0.010; p = 0.79). Findings were consistent across sensitivity analyses.ConclusionsAlthough many midlife cardiovascular risk factors and cardiac structure and function measures are associated with late-life cognitive decline, incident prolonged QT interval in midlife was not associated with late-life cognitive performance or cognitive decline.

Measures of cardiac ventricular electrophysiology have been associated with cognitive performance in cross-sectional studies. We sought to evaluate the association of worsening ventricular repolarization in midlife, as measured by incident prolonged QT interval, with cognitive decline in late life. Midlife QT interval was assessed by electrocardiography during three study visits from 1965/68 to 1971/74 in a cohort of Japanese American men aged 46-68 at Exam 1 from the Honolulu Heart Study. We defined incident prolonged QT as the QT interval in the upper quartile at Exam 2 or 3 after QT interval in lower three quartiles at Exam 1. Cognitive performance was assessed at least once using the Cognitive Abilities Screening Instrument (CASI), scored using item response theory (CASI-IRT), during four subsequent visits from 1991/93 to 1999/2000 among 2,511 of the 4,737 men in the Honolulu-Asia Aging Study otherwise eligible for inclusion in analyses. We used marginal structural modeling to determine the association of incident prolonged QT with cognitive decline, using weighting to account for confounding and attrition. Incident prolonged QT interval in midlife was not associated with late-life CASI-IRT at cognitive baseline (estimated difference in CASI-IRT: 0.04; 95% CI: -0.28, 0.35; p = 0.81), or change in CASI-IRT over time (estimated difference in annual change in CASI-IRT: -0.002; 95%CI: -0.013, 0.010; p = 0.79). Findings were consistent across sensitivity analyses. Although many midlife cardiovascular risk factors and cardiac structure and function measures are associated with late-life cognitive decline, incident prolonged QT interval in midlife was not associated with late-life cognitive performance or cognitive decline.

Green space, life-course socioeconomic disparities and cognitive function in later life: A longitudinal analysis.

Although the role of the neighborhood environment in health is well documented, little is known about how the neighborhood affects cognitive function. This study aimed to examine how physical and social neighborhood environments affect cognitive function in later life. Furthermore, we examined the interaction effect of physical function and economic status on the relationship between the neighborhood environment and cognition. Data were obtained from a locally representative three-wave longitudinal study of older Japanese aged 74–86 years (N = 1064). We used subjective measures of two physical environmental factors (public facilities, such as community centers, and accessibility) and two social environmental factors (availability of social participation programs and social inclusion). MMSE was used to assess cognitive functioning. The results of multilevel analyses showed that the availability of social participation programs was positively associated with cognitive function and that the degree of social inclusion was negatively associated with cognitive function. Moreover, physical functioning had an interaction effect on the relationship between social inclusion and cognitive functioning. Among older adults with lower physical function, cognitive function was lower in those living in more socially inclusive neighborhoods than in those living in less socially inclusive neighborhoods. These results suggest that creating opportunities for social participation in neighborhoods contributes to maintaining cognitive function, whereas fostering social inclusiveness in neighborhoods contributes to older adults’ continued aging in place, even after their cognitive and physical function declines. These results reinforce the need for neighborhood-level interventions to maintain cognition in later life and facilitate aging in place despite cognitive decline.

Robustness of intra urban land-use regression models for ultrafine particles and black carbon based on mobile monitoring