Selecting a Scale for Spatial Confounding Adjustment

spconfShiny: An R Shiny application for calculating the spatial scale of smoothing splines for point data

Epidemiological analyses of environmental exposures often benefit from including spatial splines in models to account for confounding by spatial location. Understanding how the number of splines relates to physical spatial differences is not always intuitive and can be context-dependent. To address this, we developed a R Shiny application, spconfShiny, that provides a user-friendly platform to calculate an effective bandwidth metric that quantifies the relationship between spatial splines and the range of implied spatial smoothing. spconfShiny can be accessed at https://g2aging.shinyapps.io/spconfShiny/. We illustrate the procedure to compute the effective bandwidth and demonstrate its use for different numbers of spatial splines across England, India, Ireland, Northern Ireland, and the United States. Using spconfShiny, we show the effective bandwidth increases with the size of the region and decreases with the number of splines. Including 10 splines on a 10km grid corresponds to effective bandwidths of 92.2km in Ireland and 927.7km in the United States.

Long-term air pollution exposure and incident physical disability in older US adults: a cohort study

BACKGROUND

Disability is a key marker of overall physical health in older adults and is often preceded by chronic disease. Although air pollution is a well recognised risk factor for multiple chronic diseases, its association with physical disability has not been well characterised. We investigated the associations of air pollutants with physical disability in a large cohort representative of older adults in the USA.

METHODS

We used biennial data on incident activities of daily living (ADL) disability collected from respondents of the Health and Retirement Survey between 2000 and 2016. As part of the Environmental Predictors of Cognitive Health and Aging study, we estimated 10-year average PM2·5, PM10-2·5, nitrogen dioxide (NO2), and ozone (O3) concentrations at participant residences before each survey using spatiotemporal prediction models. We used a time-varying, weighted Cox model to estimate hazard ratios (HRs) for incident physical disability per interquartile increase of air pollution with detailed adjustments for confounders.

FINDINGS

Among 15 411 respondents aged 65 years and older (mean age 70·2 [SD 6·5] years; 55% female, 45% male), 48% of respondents reported newly having ADL disability during a mean follow-up of 7·9 years (SD 4·7). In fully adjusted models, we found greater risks of ADL disability associated with higher concentrations of PM2·5 (HR 1·03 per 3·7 μg/m³ [95% CI 0·99-1·08], p=0·16), PM10-2·5 (1·05 per 4·9 μg/m³ [1·00-1·11], p=0·022), and NO2 (1·03 per 7·5 ppb [0·99-1·08]. p=0·064), although not all these associations were statistically significant. In contrast, O3 was associated with a lower risk of ADL disability (0·95 per 3·7 ppb [0·91-1·00], p=0·030). In a multi-pollutant model, associations were similar to the single-pollutant models for PM10-2·5 (1·05 per 4·9 μg/m³ [1·00-1·11], p=0·041) and O3 (0·94 per 3·7 ppb [0·88-1·01], p=0·083).

INTERPRETATION

Our findings suggest that air pollution might be an underappreciated risk factor for physical disability in later life, although additional research is needed.

FUNDING

National Institutes of Environmental Health Sciences and National Institute on Aging.

Different types of greenspace within urban parks and depressive symptoms among older U.S. adults living in urban areas

Access to greenspace in the form of urban parks is frequently used to study the mental health benefits of nature and may alleviate depression. However, there is a lack of research that considers the different types of vegetated and non-vegetated spaces that parks can provide. Our aim was to investigate whether different types of accessible park area, grassy; tree covered; and non-vegetated, were associated with depressive symptoms among older (≥50 years) urban US adults. We used interviews from the Health and Retirement Study spanning 2010 through 2016 as our primary data source. We calculated total grassy, tree covered, and non-vegetated park space accessible to participants using a comprehensive national database of US parks and a high resolution (10 m) landcover dataset. To measure depressive symptoms, we used the CESD-8 analyzed as a continuous scale. We used Poisson regression to estimate the percent difference in CESD-8 scores comparing quartiles of accessible park space. To control for confounding, we adjusted for sociodemographic characteristics, geography, and climate. Aggregated accessible park area was not substantively associated with depressive symptoms. However, having grassy park area near the home was associated with as much as 27 % fewer depressive symptoms. In contrast, non-vegetated park area was associated with up to 54 % more depressive symptoms. Our findings were robust to adjustment for air pollution, environmental noise, and artificial light at night. Different types of accessible park space may have disparate effects on mental health among older urban US adults.

Source-Specific Air Pollution and Loss of Independence in Older Adults Across the US

Importance

Air pollution is a recognized risk factor associated with chronic diseases, including respiratory and cardiovascular conditions, which can lead to physical and cognitive impairments in later life. Although these losses of function, individually or in combination, reduce individuals' likelihood of living independently, little is known about the association of air pollution with this critical outcome.

Objective

To investigate associations between air pollution and loss of independence in later life.

Design, Setting, and Participants

This cohort study was conducted as part of the Environmental Predictors Of Cognitive Health and Aging study and used 1998 to 2016 data from the Health and Retirement Study. Participants included respondents from this nationally representative, population-based cohort who were older than 50 years and had not previously reported a loss of independence. Analyses were performed from August 31 to October 15, 2023.

Exposures

Mean 10-year pollutant concentrations (particulate matter less than 2.5 μm in diameter [PM2.5] or ranging from 2.5 μm to 10 μm in diameter [PM10-2.5], nitrogen dioxide [NO2], and ozone [O3]) were estimated at respondent addresses using spatiotemporal models along with PM2.5 levels from 9 emission sources.

Main Outcomes and Measures

Loss of independence was defined as newly receiving care for at least 1 activity of daily living or instrumental activity of daily living due to health and memory problems or moving to a nursing home. Associations were estimated with generalized estimating equation regression adjusting for potential confounders.

Results

Among 25 314 respondents older than 50 years (mean [SD] baseline age, 61.1 [9.4] years; 11 208 male [44.3%]), 9985 individuals (39.4%) experienced lost independence during a mean (SD) follow-up of 10.2 (5.5) years. Higher exposure levels of mean concentration were associated with increased risks of lost independence for total PM2.5 levels (risk ratio [RR] per 1-IQR of 10-year mean, 1.05; 95% CI, 1.01-1.10), PM2.5 levels from road traffic (RR per 1-IQR of 10-year mean, 1.09; 95% CI, 1.03-1.16) and nonroad traffic (RR per 1-IQR of 10-year mean, 1.13; 95% CI, 1.03-1.24), and NO2 levels (RR per 1-IQR of 10-year mean, 1.05; 95% CI, 1.01-1.08). Compared with other sources, traffic-generated pollutants were most consistently and robustly associated with loss of independence; only road traffic-related PM2.5 levels remained associated with increased risk after adjustment for PM2.5 from other sources (RR per 1-IQR increase in 10-year mean concentration, 1.10; 95% CI, 1.00-1.21). Other pollutant-outcome associations were null, except for O3 levels, which were associated with lower risks of lost independence (RR per 1-IQR increase in 10-year mean concentration, 0.94; 95% CI, 0.92-0.97).

Conclusions and Relevance

This study found that long-term exposure to air pollution was associated with the need for help for lost independence in later life, with especially large and consistent increases in risk for pollution generated by traffic-related sources. These findings suggest that controlling air pollution could be associated with diversion or delay of the need for care and prolonged ability to live independently.

Residential greenspace and major depression among older adults living in urban and suburban areas with different climates across the United States

BACKGROUND AND AIM

Residential greenspace could alleviate depression - a leading cause of disability. Fewer studies of depression and greenspace have considered major depression, and, to our knowledge, none have considered how climate, which determines vegetation abundance and type, may change the impacts of greenspace. Our aim was to investigate whether residential greenspace is associated with major depression among older adults and explore effect modification by climate.

METHODS

We used biennial interviews between 2008 and 2016 from the Health and Retirement Study. We calculated greenness within walking distance of home addresses as the maximum NDVI for the year of each participant interview averaged within a 1 km buffer. Reflecting clinical criteria, a score of ≥5 on the CIDI-SF indicated major depression in the preceding 12-months. We characterized climate using Köppen-Geiger classifications. To estimate prevalence ratios, we used Poisson regression. Our models adjusted for sociodemographic characteristics, geography, annual sunshine, and bluespace.

RESULTS

The 21,611 eligible participants were 65 ± 10 years old on average, 55% female, 81% White, 12% Black, 10% Hispanic/Latino, and 31% had at least a 4-year college degree. The 12-month prevalence of a major depression was 8%. In adjusted models, more residential greenspace was associated with a lower prevalence of major depression (prevalence ratio per IQR, 0.91; 95% CI, 0.84 to 0.98). There was evidence of effect modification by climate (P forinteraction, 0.062). We observed stronger associations in tropical (prevalence ratio per IQR 0.69; 95% CI, 0.47 to 1.01) and cold (prevalence ratio per IQR, 0.83; 95% CI, 0.74 to 0.93) climates compared to arid (prevalence ratio per IQR 0.99; 95% CI, 0.90 to 1.09) and temperate (prevalence ratio per IQR 0.98; 95% CI, 0.86 to 1.11) climates.

CONCLUSIONS

Residential greenspace may help reduce major depression. However, climate may influence how people benefit from greenspace.

Comparison of PM2.5 Air Pollution Exposures and Health Effects Associations Using 11 Different Modeling Approaches in the Women's Health Initiative Memory Study (WHIMS)

BACKGROUND

Many approaches to quantifying air pollution exposures have been developed. However, the impact of choice of approach on air pollution estimates and health-effects associations remains unclear.

OBJECTIVES

Our objective is to compare particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) concentrations and resulting health effects associations using multiple estimation approaches previously used in epidemiologic analyses.

METHODS

We assigned annual PM 2.5 exposure estimates from 1999 to 2004 derived from 11 different approaches to Women's Health Initiative Memory Study (WHIMS) participant addresses within the contiguous US. Approaches included geostatistical interpolation approaches, land-use regression or spatiotemporal models, satellite-derived approaches, air dispersion and chemical transport models, and hybrid models. We used descriptive statistics and plots to assess relative and absolute agreement among exposure estimates and examined the impact of approach on associations between PM 2.5 and death due to natural causes, cardiovascular disease (CVD) mortality, and incident CVD events, adjusting for individual-level covariates and climate-based region.

RESULTS

With a few exceptions, relative agreement of approach-specific PM 2.5 exposure estimates was high for PM 2.5 concentrations across the contiguous US. Agreement among approach-specific exposure estimates was stronger near PM 2.5 monitors, in certain regions of the country, and in 2004 vs. 1999. Collectively, our results suggest but do not quantify lower agreement at local spatial scales for PM 2.5 . There was no evidence of large differences in health effects associations with PM 2.5 among estimation approaches in analyses adjusted for climate region.

CONCLUSIONS

Different estimation approaches produced similar spatial patterns of PM 2.5 concentrations across the contiguous US and in areas with dense monitoring data, and PM 2.5 -health effects associations were similar among estimation approaches. PM 2.5 estimates and PM 2.5 -health effects associations may differ more in samples drawn from smaller areas or areas without substantial monitoring data, or in analyses with finer adjustment for participant location. Our results can inform decisions about PM 2.5 estimation approach in epidemiologic studies, as investigators balance concerns about bias, efficiency, and resource allocation. Future work is needed to understand whether these conclusions also apply in the context of other air pollutants of interest. https://doi.org/10.1289/EHP12995.

Residential Structural Racism and Prevalence of Chronic Health Conditions

Importance

Studies elucidating determinants of residential neighborhood-level health inequities are needed.

Objective

To quantify associations of structural racism indicators with neighborhood prevalence of chronic kidney disease (CKD), diabetes, and hypertension.

Design, Setting, and Participants

This cross-sectional study used public data (2012-2018) and deidentified electronic health records (2017-2018) to describe the burden of structural racism and the prevalence of CKD, diabetes, and hypertension in 150 residential neighborhoods in Durham County, North Carolina, from US census block groups and quantified their associations using bayesian models accounting for spatial correlations and residents' age. Data were analyzed from January 2021 to May 2023.

Exposures

Global (neighborhood percentage of White residents, economic-racial segregation, and area deprivation) and discrete (neighborhood child care centers, bus stops, tree cover, reported violent crime, impervious areas, evictions, election participation, income, poverty, education, unemployment, health insurance coverage, and police shootings) indicators of structural racism.

Main Outcomes and Measures

Outcomes of interest were neighborhood prevalence of CKD, diabetes, and hypertension.

Results

A total of 150 neighborhoods with a median (IQR) of 1708 (1109-2489) residents; median (IQR) of 2% (0%-6%) Asian residents, 30% (16%-56%) Black residents, 10% (4%-20%) Hispanic or Latino residents, 0% (0%-1%) Indigenous residents, and 44% (18%-70%) White residents; and median (IQR) residential income of $54 531 ($37 729.25-$78 895.25) were included in analyses. In models evaluating global indicators, greater burden of structural racism was associated with greater prevalence of CKD, diabetes, and hypertension (eg, per 1-SD decrease in neighborhood White population percentage: CKD prevalence ratio [PR], 1.27; 95% highest density interval [HDI], 1.18-1.35; diabetes PR, 1.43; 95% HDI, 1.37-1.52; hypertension PR, 1.19; 95% HDI, 1.14-1.25). Similarly in models evaluating discrete indicators, greater burden of structural racism was associated with greater neighborhood prevalence of CKD, diabetes, and hypertension (eg, per 1-SD increase in reported violent crime: CKD PR, 1.15; 95% HDI, 1.07-1.23; diabetes PR, 1.20; 95% HDI, 1.13-1.28; hypertension PR, 1.08; 95% HDI, 1.02-1.14).

Conclusions and Relevance

This cross-sectional study found several global and discrete structural racism indicators associated with increased prevalence of health conditions in residential neighborhoods. Although inferences from this cross-sectional and ecological study warrant caution, they may help guide the development of future community health interventions.

Comparison of Particulate Air Pollution From Different Emission Sources and Incident Dementia in the US

Importance

Emerging evidence indicates that exposure to fine particulate matter (PM2.5) air pollution may increase dementia risk in older adults. Although this evidence suggests opportunities for intervention, little is known about the relative importance of PM2.5 from different emission sources.

Objective

To examine associations of long-term exposure of total and source-specific PM2.5 with incident dementia in older adults.

Design, Setting, and Participants

The Environmental Predictors of Cognitive Health and Aging study used biennial survey data from January 1, 1998, to December 31, 2016, for participants in the Health and Retirement Study, which is a nationally representative, population-based cohort study in the US. The present cohort study included all participants older than 50 years who were without dementia at baseline and had available exposure, outcome, and demographic data between 1998 and 2016 (N = 27 857). Analyses were performed from January 31 to May 1, 2022.

Exposures

The 10-year mean total PM2.5 and PM2.5 from 9 emission sources at participant residences for each month during follow-up using spatiotemporal and chemical transport models.

Main Outcomes and Measures

The main outcome was incident dementia as classified by a validated algorithm incorporating respondent-based cognitive testing and proxy respondent reports. Adjusted hazard ratios (HRs) were estimated for incident dementia per IQR of residential PM2.5 concentrations using time-varying, weighted Cox proportional hazards regression models with adjustment for the individual- and area-level risk factors.

Results

Among 27 857 participants (mean [SD] age, 61 [10] years; 15 747 [56.5%] female), 4105 (15%) developed dementia during a mean (SD) follow-up of 10.2 [5.6] years. Higher concentrations of total PM2.5 were associated with greater rates of incident dementia (HR, 1.08 per IQR; 95% CI, 1.01-1.17). In single pollutant models, PM2.5 from all sources, except dust, were associated with increased rates of dementia, with the strongest associations for agriculture, traffic, coal combustion, and wildfires. After control for PM2.5 from all other sources and copollutants, only PM2.5 from agriculture (HR, 1.13; 95% CI, 1.01-1.27) and wildfires (HR, 1.05; 95% CI, 1.02-1.08) were robustly associated with greater rates of dementia.

Conclusion and Relevance

In this cohort study, higher residential PM2.5 levels, especially from agriculture and wildfires, were associated with higher rates of incident dementia, providing further evidence supporting PM2.5 reduction as a population-based approach to promote healthy cognitive aging. These findings also indicate that intervening on key emission sources might have value, although more research is needed to confirm these findings.

Spectral adjustment for spatial confounding

Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. We derive necessary conditions on the coherence between the exposure and the unmeasured confounder that ensure the effect of exposure is estimable. We specify our model and assumptions in the spectral domain to allow for different degrees of confounding at different spatial resolutions. One assumption that ensures identifiability is that confounding present at global scales dissipates at local scales. We show that this assumption in the spectral domain is equivalent to adjusting for global-scale confounding in the spatial domain by adding a spatially smoothed version of the exposure to the mean of the response variable. Within this general framework, we propose a sequence of confounder adjustment methods that range from parametric adjustments based on the Matérn coherence function to more robust semiparametric methods that use smoothing splines. These ideas are applied to areal and geostatistical data for both simulated and real datasets.

Discussion on "Spatial+: a novel approach to spatial confounding" by Emiko Dupont, Simon N. Wood, and Nicole H. Augustin

I congratulate Dupont, Wood, and Augustin (DWA hereon) for providing an easy-to-implement method for estimation in the presence of spatial confounding, and for addressing some of the complicated aspects on the topic. I discuss conceptual and operational issues that are fundamental to inference in spatial settings: (i) the target quantity and its interpretability, (ii) the nonspatial aspect of covariates and their relative spatial scales, and (iii) the impact of spatial smoothing. While DWA provide some insights on these issues, I believe that the audience might benefit from a deeper discussion.

Tracking the transmission dynamics of COVID-19 with a time-varying coefficient state-space model

The spread of COVID-19 has been greatly impacted by regulatory policies and behavior patterns that vary across counties, states, and countries. Population-level dynamics of COVID-19 can generally be described using a set of ordinary differential equations, but these deterministic equations are insufficient for modeling the observed case rates, which can vary due to local testing and case reporting policies and nonhomogeneous behavior among individuals. To assess the impact of population mobility on the spread of COVID-19, we have developed a novel Bayesian time-varying coefficient state-space model for infectious disease transmission. The foundation of this model is a time-varying coefficient compartment model to recapitulate the dynamics among susceptible, exposed, undetected infectious, detected infectious, undetected removed, hospitalized, detected recovered, and detected deceased individuals. The infectiousness and detection parameters are modeled to vary by time, and the infectiousness component in the model incorporates information on multiple sources of population mobility. Along with this compartment model, a multiplicative process model is introduced to allow for deviation from the deterministic dynamics. We apply this model to observed COVID-19 cases and deaths in several U.S. states and Colorado counties. We find that population mobility measures are highly correlated with transmission rates and can explain complicated temporal variation in infectiousness in these regions. Additionally, the inferred connections between mobility and epidemiological parameters, varying across locations, have revealed the heterogeneous effects of different policies on the dynamics of COVID-19.

Assessing the health estimation capacity of air pollution exposure prediction models

BACKGROUND

The era of big data has enabled sophisticated models to predict air pollution concentrations over space and time. Historically these models have been evaluated using overall metrics that measure how close predictions are to monitoring data. However, overall methods are not designed to distinguish error at timescales most relevant for epidemiologic studies, such as day-to-day errors that impact studies of short-term health associations.

METHODS

We introduce frequency band model performance, which quantifies health estimation capacity of air quality prediction models for time series studies of air pollution and health. Frequency band model performance uses a discrete Fourier transform to evaluate prediction models at timescales of interest. We simulated fine particulate matter (PM_2.5), with errors at timescales varying from acute to seasonal, and health time series data. To compare evaluation approaches, we use correlations and root mean squared error (RMSE). Additionally, we assess health estimation capacity through bias and RMSE in estimated health associations. We apply frequency band model performance to PM_2.5 predictions at 17 monitors in 8 US cities.

RESULTS

In simulations, frequency band model performance rates predictions better (lower RMSE, higher correlation) when there is no error at a particular timescale (e.g., acute) and worse when error is added to that timescale, compared to overall approaches. Further, frequency band model performance is more strongly associated (R² = 0.95) with health association bias compared to overall approaches (R² = 0.57). For PM_2.5 predictions in Salt Lake City, UT, frequency band model performance better identifies acute error that may impact estimated short-term health associations.

CONCLUSIONS

For epidemiologic studies, frequency band model performance provides an improvement over existing approaches because it evaluates models at the timescale of interest and is more strongly associated with bias in estimated health associations. Evaluating prediction models at timescales relevant for health studies is critical to determining whether model error will impact estimated health associations.

A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines but have only recently begun to take hold in spatial applications. Spatial causal inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided.