Ambient exposure to fine particulate matter with oxidative potential affects oxidative stress biomarkers in pregnancy.

Prenatal exposures to ambient particulate matter (PM2.5) from traffic may generate oxidative stress, and thus contribute to adverse birth outcomes. We investigated whether PM2.5 constituents from brake and tire wear affect levels of oxidative stress biomarkers (malondialdehyde (MDA), 8-hydroxy-2'-deoxyguanosine (8-OHdG)) using urine samples collected up to three times during pregnancy in 156 women recruited from antenatal clinics at the University of California Los Angeles. Land use regression models with co-kriging were employed to estimate average residential outdoor concentrations of black carbon (BC), PM2.5 mass, PM2.5 metal components, and three PM2.5 oxidative potential metrics during the 4-weeks prior to urine sample collection. 8-OHdG concentrations in mid-pregnancy increased by 24.8% (95% CI: 9.0, 42.8) and 14.3% (95% CI: 0.4%, 30.0%) per interquartile range (IQR) increase in PM2.5 mass and BC, respectively. The brake wear marker (barium) and the oxidative potential metrics were associated with increased MDA concentration in the 1st sample collected (10-17 gestational week), but 95% CIs included the null. Traffic-related air pollution contributed in early to mid-pregnancy to oxidative stress generation previously linked to adverse birth outcomes.

Spatial Difference Boundary Detection for Multiple Outcomes Using Bayesian Disease Mapping.

Regional aggregates of health outcomes over delineated administrative units (e.g., states, counties, and zip codes), or areal units, are widely used by epidemiologists to map mortality or incidence rates and capture geographic variation. To capture health disparities over regions, we seek "difference boundaries" that separate neighboring regions with significantly different spatial effects. Matters are more challenging with multiple outcomes over each unit, where we capture dependence among diseases as well as across the areal units. Here, we address multivariate difference boundary detection for correlated diseases. We formulate the problem in terms of Bayesian pairwise multiple comparisons and seek the posterior probabilities of neighboring spatial effects being different. To achieve this, we endow the spatial random effects with a discrete probability law using a class of multivariate areally referenced Dirichlet process models that accommodate spatial and interdisease dependence. We evaluate our method through simulation studies and detect difference boundaries for multiple cancers using data from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute.

Multivariate spatiotemporal functional principal component analysis for modeling hospitalization and mortality rates in the dialysis population.

Dialysis patients experience frequent hospitalizations and a higher mortality rate compared to other Medicare populations, in whom hospitalizations are a major contributor to morbidity, mortality, and healthcare costs. Patients also typically remain on dialysis for the duration of their lives or until kidney transplantation. Hence, there is growing interest in studying the spatiotemporal trends in the correlated outcomes of hospitalization and mortality among dialysis patients as a function of time starting from transition to dialysis across the United States Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multivariate spatiotemporal functional principal component analysis model to study the joint spatiotemporal patterns of hospitalization and mortality rates among dialysis patients. The proposal is based on a multivariate Karhunen-Loéve expansion that describes leading directions of variation across time and induces spatial correlations among region-specific scores. An efficient estimation procedure is proposed using only univariate principal components decompositions and a Markov Chain Monte Carlo framework for targeting the spatial correlations. The finite sample performance of the proposed method is studied through simulations. Novel applications to the USRDS data highlight hot spots across the United States with higher hospitalization and/or mortality rates and time periods of elevated risk.

Fine particulate matter and incident coronary heart disease events up to 10 years of follow-up among Deepwater Horizon oil spill workers.

BACKGROUND: During the 2010 Deepwater Horizon (DWH) disaster, in-situ burning and flaring were conducted to remove oil from the water. Workers near combustion sites were potentially exposed to burning-related fine particulate matter (PM2.5). Exposure to PM2.5 has been linked to increased risk of coronary heart disease (CHD), but no study has examined the relationship among oil spill workers. OBJECTIVES: To investigate the association between estimated PM2.5 from burning/flaring of oil/gas and CHD risk among the DWH oil spill workers. METHODS: We included workers who participated in response and cleanup activities on the water during the DWH disaster (N = 9091). PM2.5 exposures were estimated using a job-exposure matrix that linked modelled PM2.5 concentrations to detailed DWH spill work histories provided by participants. We ascertained CHD events as the first self-reported physician-diagnosed CHD or a fatal CHD event that occurred after each worker's last day of burning exposure. We estimated hazard ratios (HR) and 95% confidence intervals (95%CI) for the associations between categories of average or cumulative daily maximum PM2.5 exposure (versus a referent category of water workers not near controlled burning) and subsequent CHD. We assessed exposure-response trends by examining continuous exposure parameters in models. RESULTS: We observed increased CHD hazard among workers with higher levels of average daily maximum exposure (low vs. referent: HR = 1.26, 95% CI: 0.93, 1.70; high vs. referent: HR = 2.11, 95% CI: 1.08, 4.12; per 10 µg/m3 increase: HR = 1.10, 95% CI: 1.02, 1.19). We also observed suggestively elevated HRs among workers with higher cumulative daily maximum exposure (low vs. referent: HR = 1.19, 95% CI: 0.68, 2.08; medium vs. referent: HR = 1.38, 95% CI: 0.88, 2.16; high vs. referent: HR = 1.44, 95% CI: 0.96, 2.14; per 100 µg/m3-d increase: HR = 1.03, 95% CI: 1.00, 1.05). CONCLUSIONS: Among oil spill workers, exposure to PM2.5 from flaring/burning of oil/gas was associated with increased risk of CHD.

Exposure to volatile hydrocarbons and neurologic function among oil spill workers up to 6 years after the Deepwater Horizon disaster.

BACKGROUND: During the 2010 Deepwater Horizon (DWH) disaster, oil spill response and cleanup (OSRC) workers were exposed to toxic volatile components of crude oil. Few studies have examined exposure to individual volatile hydrocarbon chemicals below occupational exposure limits in relation to neurologic function among OSRC workers. OBJECTIVES: To investigate the association of several spill-related chemicals (benzene, toluene, ethylbenzene, xylene, n-hexane, i.e., BTEX-H) and total petroleum hydrocarbons (THC) with neurologic function among DWH spill workers enrolled in the Gulf Long-term Follow-up Study. METHODS: Cumulative exposure to THC and BTEX-H across the oil spill cleanup period were estimated using a job-exposure matrix that linked air measurement data to detailed self-reported DWH OSRC work histories. We ascertained quantitative neurologic function data via a comprehensive test battery at a clinical examination that occurred 4-6 years after the DWH disaster. We used multivariable linear regression and modified Poisson regression to evaluate relationships of exposures (quartiles (Q)) with 4 neurologic function measures. We examined modification of the associations by age at enrollment (<50 vs. ≥50 years). RESULTS: We did not find evidence of adverse neurologic effects from crude oil exposures among the overall study population. However, among workers ≥50 years of age, several individual chemical exposures were associated with poorer vibrotactile acuity of the great toe, with statistically significant effects observed in Q3 or Q4 of exposures (range of log mean difference in Q4 across exposures: 0.13-0.26 µm). We also observed suggestive adverse associations among those ≥ age 50 years for tests of postural stability and single-leg stance, although most effect estimates did not reach thresholds of statistical significance (p < 0.05). CONCLUSIONS: Higher exposures to volatile components of crude oil were associated with modest deficits in neurologic function among OSRC workers who were age 50 years or older at study enrollment.

Spatial factor modeling: A Bayesian matrix-normal approach for misaligned data.

Multivariate spatially oriented data sets are prevalent in the environmental and physical sciences. Scientists seek to jointly model multiple variables, each indexed by a spatial location, to capture any underlying spatial association for each variable and associations among the different dependent variables. Multivariate latent spatial process models have proved effective in driving statistical inference and rendering better predictive inference at arbitrary locations for the spatial process. High-dimensional multivariate spatial data, which are the theme of this article, refer to data sets where the number of spatial locations and the number of spatially dependent variables is very large. The field has witnessed substantial developments in scalable models for univariate spatial processes, but such methods for multivariate spatial processes, especially when the number of outcomes are moderately large, are limited in comparison. Here, we extend scalable modeling strategies for a single process to multivariate processes. We pursue Bayesian inference, which is attractive for full uncertainty quantification of the latent spatial process. Our approach exploits distribution theory for the matrix-normal distribution, which we use to construct scalable versions of a hierarchical linear model of coregionalization (LMC) and spatial factor models that deliver inference over a high-dimensional parameter space including the latent spatial process. We illustrate the computational and inferential benefits of our algorithms over competing methods using simulation studies and an analysis of a massive vegetation index data set.

Hierarchical multivariate directed acyclic graph autoregressive models for spatial diseases mapping.

Disease mapping is an important statistical tool used by epidemiologists to assess geographic variation in disease rates and identify lurking environmental risk factors from spatial patterns. Such maps rely upon spatial models for regionally aggregated data, where neighboring regions tend to exhibit similar outcomes than those farther apart. We contribute to the literature on multivariate disease mapping, which deals with measurements on multiple (two or more) diseases in each region. We aim to disentangle associations among the multiple diseases from spatial autocorrelation in each disease. We develop multivariate directed acyclic graphical autoregression models to accommodate spatial and inter-disease dependence. The hierarchical construction imparts flexibility and richness, interpretability of spatial autocorrelation and inter-disease relationships, and computational ease, but depends upon the order in which the cancers are modeled. To obviate this, we demonstrate how Bayesian model selection and averaging across orders are easily achieved using bridge sampling. We compare our method with a competitor using simulation studies and present an application to multiple cancer mapping using data from the Surveillance, Epidemiology, and End Results program.

A Bayesian multilevel time-varying framework for joint modeling of hospitalization and survival in patients on dialysis.

Over 782 000 individuals in the United States have end-stage kidney disease with about 72% of patients on dialysis, a life-sustaining treatment. Dialysis patients experience high mortality and frequent hospitalizations, at about twice per year. These poor outcomes are exacerbated at key time periods, such as the fragile period after transition to dialysis. In order to study the time-varying effects of modifiable patient and dialysis facility risk factors on hospitalization and mortality, we propose a novel Bayesian multilevel time-varying joint model. Efficient estimation and inference is achieved within the Bayesian framework using Markov chain Monte Carlo, where multilevel (patient- and dialysis facility-level) varying coefficient functions are targeted via Bayesian P-splines. Applications to the United States Renal Data System, a national database which contains data on nearly all patients on dialysis in the United States, highlight significant time-varying effects of patient- and facility-level risk factors on hospitalization risk and mortality. Finite sample performance of the proposed methodology is studied through simulations.

High-flow Nasal Oxygen Therapy in COVID-19 Critically Ill Patients with Acute Hypoxemic Respiratory Failure: A Prospective Observational Cohort Study.

Background: Coronavirus disease-2019 (COVID-19) is prone to acute hypoxemic respiratory failure (AHRF). Because tracheal intubation is associated with a higher risk of death in these patients, AHRF employs high-flow nasal oxygen therapy (HFNOT). The goal of this study was to assess the effect of HFNOT on oxygenation status as well as different predictors of HFNOT failure. Methods: A prospective observational cohort study was conducted in COVID-positive critically ill adult patients (age >18 years) with AHRF, who were unable to maintain SpO2 >90% on a non-rebreathing face mask at an oxygen flow ≥15 L/minute. Respiratory variables (PaO2/FiO2, SpO2, and RR) before HFNOT (baseline) and then at 1 hour, 6 hours, 7th day, and 14th day after HFNOT application were recorded. Borg CR10 scale and visual analogue scale were used to evaluate the subjective sensation of dyspnea and comfort level, respectively. As needed, Student's t, Mann-Whitney U, or Wilcoxon signed-rank tests were performed. To find parameters linked to HFNOT failure, multivariate logistic regression and receiver operating characteristic (ROC) analysis were employed. Results: A total of 114 patients were enrolled in the study, with an HFNOT failure rate of 29%. The median PaO2/FiO2 ratio at baseline (before the initiation of HFNOT) was 99.5 (80-110) which significantly increased at various time points (1 hour, 6 hours, 7th day, and 14th day) after HFNOT initiation in the successful group. Patients reported significant improvement in sensation of breathlessness [9 (8-10), 3 (2-4); p <0.001] as well as in comfort level [2 (1-2), 8 (4-9); p <0.001]. Multivariate logistic regression analysis, sequential organ failure assessment (SOFA) score >7, acute physiology and chronic health evaluation (APACHE) II score >20, admission P/F ratio <100, D-dimer >2 mg/L, IL-6 >40 pg/mL, random blood sugar (RBS) >250 mg/dL, and 6 hours ROX Index <3.5 were independent prognostic factors of HFNOT failure. Conclusion: The use of HFNOT significantly increased the oxygenation levels in COVID-19 patients with AHRF at various time periods after HFNOT beginning. Age, SOFA score, APACHE II score, ROX score, admission P/F ratio, IL-6, D-dimer, and RBS were independent prognostic factors of HFNOT failure in this cohort. How to cite this article: Khan MS, Prakash J, Banerjee S, Bhattacharya PK, Kumar R, Nirala DK. High-flow Nasal Oxygen Therapy in COVID-19 Critically Ill Patients with Acute Hypoxemic Respiratory Failure: A Prospective Observational Cohort Study. Indian J Crit Care Med 2022;26(5):596-603.

Multilevel modeling of spatially nested functional data: Spatiotemporal patterns of hospitalization rates in the US dialysis population.

End-stage renal disease patients on dialysis experience frequent hospitalizations. In addition to known temporal patterns of hospitalizations over the life span on dialysis, where poor outcomes are typically exacerbated during the first year on dialysis, variations in hospitalizations among dialysis facilities across the US contribute to spatial variation. Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multilevel spatiotemporal functional model to study spatiotemporal patterns of hospitalization rates among dialysis facilities. Hospitalization rates of dialysis facilities are considered as spatially nested functional data (FD) with longitudinal hospitalizations nested in dialysis facilities and dialysis facilities nested in geographic regions. A multilevel Karhunen-Loéve expansion is utilized to model the two-level (facility and region) FD, where spatial correlations are induced among region-specific principal component scores accounting for regional variation. A new efficient algorithm based on functional principal component analysis and Markov Chain Monte Carlo is proposed for estimation and inference. We report a novel application using USRDS data to characterize spatiotemporal patterns of hospitalization rates for over 400 health service areas across the US and over the posttransition time on dialysis. Finite sample performance of the proposed method is studied through simulations.

Spatial Joint Species Distribution Modeling using Dirichlet Processes.

Species distribution models usually attempt to explain presence-absence or abundance of a species at a site in terms of the environmental features (so-called abiotic features) present at the site. Historically, such models have considered species individually. However, it is well-established that species interact to influence presence-absence and abundance (envisioned as biotic factors). As a result, there has been substantial recent interest in joint species distribution models with various types of response, e.g., presence-absence, continuous and ordinal data. Such models incorporate dependence between species response as a surrogate for interaction. The challenge we address here is how to accommodate such modeling in the context of a large number of species (e.g., order 102) across sites numbering on the order of 102 or 103 when, in practice, only a few species are found at any observed site. Again, there is some recent literature to address this; we adopt a dimension reduction approach. The novel wrinkle we add here is spatial dependence. That is, we have a collection of sites over a relatively small spatial region so it is anticipated that species distribution at a given site would be similar to that at a nearby site. Specifically, we handle dimension reduction through Dirichlet processes, enabling clustering of species, joined with spatial dependence across sites through Gaussian processes. We use both simulated data and a plant communities dataset for the Cape Floristic Region (CFR) of South Africa to demonstrate our approach. The latter consists of presence-absence measurements for 639 tree species at 662 locations. Through both data examples we are able to demonstrate improved predictive performance using the foregoing specification.

Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping.

Gathering information about forest variables is an expensive and arduous activity. As such, directly collecting the data required to produce high-resolution maps over large spatial domains is infeasible. Next generation collection initiatives of remotely sensed Light Detection and Ranging (LiDAR) data are specifically aimed at producing complete-coverage maps over large spatial domains. Given that LiDAR data and forest characteristics are often strongly correlated, it is possible to make use of the former to model, predict, and map forest variables over regions of interest. This entails dealing with the high-dimensional (~102) spatially dependent LiDAR outcomes over a large number of locations (~105-106). With this in mind, we develop the Spatial Factor Nearest Neighbor Gaussian Process (SF-NNGP) model, and embed it in a two-stage approach that connects the spatial structure found in LiDAR signals with forest variables. We provide a simulation experiment that demonstrates inferential and predictive performance of the SF-NNGP, and use the two-stage modeling strategy to generate complete-coverage maps of forest variables with associated uncertainty over a large region of boreal forests in interior Alaska.

Multivariate spatial meta kriging.

This work extends earlier work on spatial meta kriging for the analysis of large multivariate spatial datasets as commonly encountered in environmental and climate sciences. Spatial meta-kriging partitions the data into subsets, analyzes each subset using a Bayesian spatial process model and then obtains approximate posterior inference for the entire dataset by optimally combining the individual posterior distributions from each subset. Importantly, as is often desired in spatial analysis, spatial meta kriging offers posterior predictive inference at arbitrary locations for the outcome as well as the residual spatial surface after accounting for spatially oriented predictors. Our current work explores spatial meta kriging idea to enhance scalability of multivariate spatial Gaussian process model that uses linear model co-regionalization (LMC) to account for the correlation between multiple components. The approach is simple, intuitive and scales multivariate spatial process models to big data effortlessly. A simulation study reveals inferential and predictive accuracy offered by spatial meta kriging on multivariate observations.

Toward a diagnostic toolkit for linear models with Gaussian-process distributed random effects.

Gaussian processes (GPs) are widely used as distributions of random effects in linear mixed models, which are fit using the restricted likelihood or the closely related Bayesian analysis. This article addresses two problems. First, we propose tools for understanding how data determine estimates in these models, using a spectral basis approximation to the GP under which the restricted likelihood is formally identical to the likelihood for a gamma-errors GLM with identity link. Second, to examine the data's support for a covariate and to understand how adding that covariate moves variation in the outcome y out of the GP and error parts of the fit, we apply a linear-model diagnostic, the added variable plot (AVP), both to the original observations and to projections of the data onto the spectral basis functions. The spectral- and observation-domain AVPs estimate the same coefficient for a covariate but emphasize low- and high-frequency data features respectively and thus highlight the covariate's effect on the GP and error parts of the fit, respectively. The spectral approximation applies to data observed on a regular grid; for data observed at irregular locations, we propose smoothing the data to a grid before applying our methods. The methods are illustrated using the forest-biomass data of Finley et al. (2008).

Multivariate left-censored Bayesian model for predicting exposure using multiple chemical predictors.

Environmental health exposures to airborne chemicals often originate from chemical mixtures. Environmental health professionals may be interested in assessing exposure to one or more of the chemicals in these mixtures, but often exposure measurement data are not available, either because measurements were not collected/assessed for all exposure scenarios of interest or because some of the measurements were below the analytical methods' limits of detection (i.e. censored). In some cases, based on chemical laws, two or more components may have linear relationships with one another, whether in a single or in multiple mixtures. Although bivariate analyses can be used if the correlation is high, often correlations are low. To serve this need, this paper develops a multivariate framework for assessing exposure using relationships of the chemicals present in these mixtures. This framework accounts for censored measurements in all chemicals, allowing us to develop unbiased exposure estimates. We assessed our model's performance against simpler models at a variety of censoring levels and assessed our model's 95% coverage. We applied our model to assess vapor exposure from measurements of three chemicals in crude oil taken on the Ocean Intervention III during the Deepwater Horizon oil spill response and clean-up.

Spatial Data Analysis.

With increasing accessibility to geographic information systems (GIS) software, statisticians and data analysts routinely encounter scientific data sets with geocoded locations. This has generated considerable interest in statistical modeling for location-referenced spatial data. In public health, spatial data routinely arise as aggregates over regions, such as counts or rates over counties, census tracts, or some other administrative delineation. Such data are often referred to as areal data. This review article provides a brief overview of statistical models that account for spatial dependence in areal data. It does so in the context of two applications: disease mapping and spatial survival analysis. Disease maps are used to highlight geographic areas with high and low prevalence, incidence, or mortality rates of a specific disease and the variability of such rates over a spatial domain. They can also be used to detect hot spots or spatial clusters that may arise owing to common environmental, demographic, or cultural effects shared by neighboring regions. Spatial survival analysis refers to the modeling and analysis for geographically referenced time-to-event data, where a subject is followed up to an event (e.g., death or onset of a disease) or is censored, whichever comes first. Spatial survival analysis is used to analyze clustered survival data when the clustering arises from geographical regions or strata. Illustrations are provided in these application domains.

Predicting tree biomass growth in the temperate-boreal ecotone: Is tree size, age, competition, or climate response most important?

As global temperatures rise, variation in annual climate is also changing, with unknown consequences for forest biomes. Growing forests have the ability to capture atmospheric CO2 and thereby slow rising CO2 concentrations. Forests' ongoing ability to sequester C depends on how tree communities respond to changes in climate variation. Much of what we know about tree and forest response to climate variation comes from tree-ring records. Yet typical tree-ring datasets and models do not capture the diversity of climate responses that exist within and among trees and species. We address this issue using a model that estimates individual tree response to climate variables while accounting for variation in individuals' size, age, competitive status, and spatially structured latent covariates. Our model allows for inference about variance within and among species. We quantify how variables influence aboveground biomass growth of individual trees from a representative sample of 15 northern or southern tree species growing in a transition zone between boreal and temperate biomes. Individual trees varied in their growth response to fluctuating mean annual temperature and summer moisture stress. The variation among individuals within a species was wider than mean differences among species. The effects of mean temperature and summer moisture stress interacted, such that warm years produced positive responses to summer moisture availability and cool years produced negative responses. As climate models project significant increases in annual temperatures, growth of species like Acer saccharum, Quercus rubra, and Picea glauca will vary more in response to summer moisture stress than in the past. The magnitude of biomass growth variation in response to annual climate was 92-95% smaller than responses to tree size and age. This means that measuring or predicting the physical structure of current and future forests could tell us more about future C dynamics than growth responses related to climate change alone.

A Comparison of the ß-Substitution Method and a Bayesian Method for Analyzing Left-Censored Data.

Classical statistical methods for analyzing exposure data with values below the detection limits are well described in the occupational hygiene literature, but an evaluation of a Bayesian approach for handling such data is currently lacking. Here, we first describe a Bayesian framework for analyzing censored data. We then present the results of a simulation study conducted to compare the ß-substitution method with a Bayesian method for exposure datasets drawn from lognormal distributions and mixed lognormal distributions with varying sample sizes, geometric standard deviations (GSDs), and censoring for single and multiple limits of detection. For each set of factors, estimates for the arithmetic mean (AM), geometric mean, GSD, and the 95th percentile (X0.95) of the exposure distribution were obtained. We evaluated the performance of each method using relative bias, the root mean squared error (rMSE), and coverage (the proportion of the computed 95% uncertainty intervals containing the true value). The Bayesian method using non-informative priors and the ß-substitution method were generally comparable in bias and rMSE when estimating the AM and GM. For the GSD and the 95th percentile, the Bayesian method with non-informative priors was more biased and had a higher rMSE than the ß-substitution method, but use of more informative priors generally improved the Bayesian method's performance, making both the bias and the rMSE more comparable to the ß-substitution method. An advantage of the Bayesian method is that it provided estimates of uncertainty for these parameters of interest and good coverage, whereas the ß-substitution method only provided estimates of uncertainty for the AM, and coverage was not as consistent. Selection of one or the other method depends on the needs of the practitioner, the availability of prior information, and the distribution characteristics of the measurement data. We suggest the use of Bayesian methods if the practitioner has the computational resources and prior information, as the method would generally provide accurate estimates and also provides the distributions of all of the parameters, which could be useful for making decisions in some applications.