Long-term exposure to traffic-related air pollution and non-accidental mortality: A systematic review and meta-analysis.

BACKGROUND: The health effects of traffic-related air pollution (TRAP) continue to be of important public health interest across the globe. Following its 2010 review, the Health Effects Institute appointed a new expert Panel to systematically evaluate the epidemiological evidence regarding the associations between long-term exposure to TRAP and selected health outcomes. This paper describes the main findings of the systematic review on non-accidental mortality. METHODS: The Panel used a systematic approach to conduct the review. An extensive search was conducted of literature published between 1980 and 2019. A new exposure framework was developed to determine whether a study was sufficiently specific to TRAP, which included studies beyond the near-roadway environment. We performed random-effects meta-analysis when at least three estimates were available of an association between a specific exposure and outcome. We evaluated confidence in the evidence using a modified Office of Health Assessment and Translation (OHAT) approach, supplemented with a broader narrative synthesis. RESULTS: Thirty-six cohort studies were included. Virtually all studies adjusted for a large number of individual and area-level covariates-including smoking, body mass index, and individual and area-level socioeconomic status-and were judged at a low or moderate risk for bias. Most studies were conducted in North America and Europe, and a few were based in Asia and Australia. The meta-analytic summary estimates for nitrogen dioxide, elemental carbon and fine particulate matter-pollutants with more than 10 studies-were 1.04 (95% CI 1.01, 1.06), 1.02 (1.00, 1.04) and 1.03 (1.01, 1.05) per 10, 1 and 5 µg/m3, respectively. Effect estimates are interpreted as the relative risk of mortality when the exposure differs with the selected increment. The confidence in the evidence for these pollutants was judged as high, because of upgrades for monotonic exposure-response and consistency across populations. The consistent findings across geographical regions, exposure assessment methods and confounder adjustment resulted in a high confidence rating using a narrative approach as well. CONCLUSIONS: The overall confidence in the evidence for a positive association between long-term exposure to TRAP and non-accidental mortality was high.

Long-term exposure to traffic-related air pollution and stroke: A systematic review and meta-analysis.

BACKGROUND: Stroke remains the second cause of death worldwide. The mechanisms underlying the adverse association of exposure to traffic-related air pollution (TRAP) with overall cardiovascular disease may also apply to stroke. Our objective was to systematically evaluate the epidemiological evidence regarding the associations of long-term exposure to TRAP with stroke. METHODS: PubMed and LUDOK electronic databases were searched systematically for observational epidemiological studies from 1980 through 2019 on long-term exposure to TRAP and stroke with an update in January 2022. TRAP was defined according to a comprehensive protocol based on pollutant and exposure assessment methods or proximity metrics. Study selection, data extraction, risk of bias (RoB) and confidence assessments were conducted according to standardized protocols. We performed meta-analyses using random effects models; sensitivity analyses were assessed by geographic area, RoB, fatality, traffic specificity and new studies. RESULTS: Nineteen studies were included. The meta-analytic relative risks (and 95% confidence intervals) were: 1.03 (0.98-1.09) per 1 µg/m3 EC, 1.09 (0.96-1.23) per 10 µg/m3 PM10, 1.08 (0.89-1.32) per 5 µg/m3 PM2.5, 0.98 (0.92; 1.05) per 10 µg/m3 NO2 and 0.99 (0.94; 1.04) per 20 µg/m3 NOx with little to moderate heterogeneity based on 6, 5, 4, 7 and 8 studies, respectively. The confidence assessments regarding the quality of the body of evidence and separately regarding the presence of an association of TRAP with stroke considering all available evidence were rated low and moderate, respectively. CONCLUSION: The available literature provides low to moderate evidence for an association of TRAP with stroke.

Long-term exposure to traffic-related air pollution and selected health outcomes: A systematic review and meta-analysis.

The health effects of traffic-related air pollution (TRAP) continue to be of important public health interest. Following its well-cited 2010 critical review, the Health Effects Institute (HEI) appointed a new expert Panel to systematically evaluate the epidemiological evidence regarding the associations between long-term exposure to TRAP and selected adverse health outcomes. Health outcomes were selected based on evidence of causality for general air pollution (broader than TRAP) cited in authoritative reviews, relevance for public health and policy, and resources available. The Panel used a systematic approach to search the literature, select studies for inclusion in the review, assess study quality, summarize results, and reach conclusions about the confidence in the evidence. An extensive search was conducted of literature published between January 1980 and July 2019 on selected health outcomes. A new exposure framework was developed to determine whether a study was sufficiently specific to TRAP. In total, 353 studies were included in the review. Respiratory effects in children (118 studies) and birth outcomes (86 studies) were the most commonly studied outcomes. Fewer studies investigated cardiometabolic effects (57 studies), respiratory effects in adults (50 studies), and mortality (48 studies). The findings from the systematic review, meta-analyses, and evaluation of the quality of the studies and potential biases provided an overall high or moderate-to-high level of confidence in an association between long-term exposure to TRAP and the adverse health outcomes all-cause, circulatory, ischemic heart disease and lung cancer mortality, asthma onsetin chilldren and adults, and acute lower respiratory infections in children. The evidence was considered moderate, low or very low for the other selected outcomes. In light of the large number of people exposed to TRAP - both in and beyond the near-road environment - the Panel concluded that the overall high or moderate-to-high confidence in the evidence for an association between long-term exposure to TRAP and several adverse health outcomes indicates that exposures to TRAP remain an important public health concern and deserve greater attention from the public and from policymakers.

Interventions to reduce ambient air pollution and their effects on health: An abridged Cochrane systematic review.

BACKGROUND: A broad range of interventions have been implemented to improve ambient air quality, and many of these have been evaluated. Yet to date no systematic review has been conducted to identify and synthesize these studies. In this systematic review, we assess the effectiveness of interventions in reducing ambient particulate matter air pollution and improving adverse health outcomes. METHODS: We searched a range of electronic databases across multiple disciplines, as well as grey literature databases, trial registries, reference lists of included studies and the contents of relevant journals, through August 2016. Eligible for inclusion were randomized and cluster randomized controlled trials, as well as several non-randomized study designs often used for evaluating air quality interventions. We included studies that evaluated interventions targeting industrial, residential, vehicular and multiple sources, with respect to their effect on mortality, morbidity and the concentrations of particulate matter (PM - including PM10, PM2.5, coarse particulate matter and combustion-related PM), as well as several criteria pollutants, including ozone, carbon monoxide, nitrogen oxides, nitrogen dioxide, nitric oxide and sulphur dioxide. We did not restrict studies based on the population, setting or comparison. Two authors independently assessed studies for inclusion, extracted data and assessed risk of bias. We assessed risk of bias using the Graphic Appraisal Tool for Epidemiological studies (GATE) for correlation studies, as modified and employed by the UK National Institute for Health and Care Excellence. We synthesized evidence narratively, as well as graphically using harvest plots. We assessed the certainty of evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system. RESULTS: We included 42 studies assessing 38 unique interventions. These comprised a heterogeneous mix of interventions, including those aiming to address industrial sources (n = 5; e.g. the closure of a factory), residential sources (n = 7; e.g. coal ban), vehicular sources (n = 22; e.g. low emission zones), and multiple sources (n = 4; e.g. tailored measures that target both local traffic and industrial polluters). Evidence for effectiveness was mixed. Most included studies observed either no significant association or an association favoring the intervention, with little evidence that the assessed interventions might be harmful. CONCLUSIONS: Given the heterogeneity across interventions, outcomes, and methods, it was difficult to derive overall conclusions regarding the effectiveness of interventions in terms of improved air quality or health. Some evidence suggests that interventions are associated with improvements in air quality and human health, with very little evidence suggesting interventions were harmful. The evidence base highlights the challenges related to establishing the effectiveness of specific air pollution interventions on outcomes. It also points to the need for improved study design and analysis methods, as well as more uniform evaluations. The prospective planning of evaluations and an evaluation component built into the design and implementation of interventions may also be particularly beneficial.

Parameter estimation for eutrophication models in reservoirs.

Excessive eutrophication is a major water quality issue in lakes and reservoirs worldwide. This complex biological process can lead to serious water quality problems. Although it can be adequately addressed by applying sophisticated mathematical models, the application of these tools in a reservoir management context requires significant amounts of data and large computation times. This work presents a simple primary production model and a calibration procedure that can efficiently be used in operational reservoir management frameworks. It considers four state variables: herbivorous zooplankton, algae (measured as chlorophyll-a pigment), phosphorous and nitrogen. The model was applied to a set of Portuguese reservoirs. We apply the model to 23 Portuguese reservoirs in two different calibration settings. This research work presents the results of the estimation of model parameters.

Maximum likelihood estimations in a nonlinear self-exciting point process model.

A nonlinear model generating a self-exciting point process is formulated and identification of the model using maximum likelihood estimations is discussed. Simulations have been performed and parameters are estimated numerically. (Dis)advantages with respect to identification approaches based on correlation densities are outlined. This method may function as a basis to interpret simultaneous recordings of single units in terms of underlying wiring diagrams.

The neurochrome. An identity preserving representation of activity patterns from neural populations.

Recording of simultaneous but separated activity of neural populations overwhelms the experimenter with a large amount of information. A clearly structured display technique the "Neurochrome" is introduced, usable on-line and real-time. It shows neural activity patterns while preserving neural identity by employing a color code. The Neurochrome assists the experimenter in generating and verifying hypotheses about neural correlations and stimulus-event relations already during the experiment. In auditory research single neurons are characterized by their spectro-temporal sensitivity to auditory stimuli. A straightforward generalization of this concept, applicable to neural populations, is proposed leading to a global indication of a populations' activity to stimuli: the Multi-Unit Spectro-Temporal Sensitivity. This approach is inversely related to the Neurochrome, the latter however containing more information. The combination of both approaches seems quite powerful in the investigation of neural assemblies. The procedures are illustrated with examples of extracellular multiunit recordings from the auditory midbrain of the grassfrog (Rana temporaria L.).

The master equation for neural interaction.

Based on neural interaction equations a random walk model for the stochastic dynamics of a single neuron is introduced. In this model the somatic potential corresponds to a state in the state space and action potentials provide the mechanism causing transitions. Time is made discrete, consisting of small finite increments delta t; assumptions are made about the transitions within such an increment and the associated probabilities are formulated. These quantities depend on delta t and on parameters derived from neural interaction equations. Moreover the model is chosen so that the sequence of somatic potentials is a Markov chain. By appropriately scaling the parameters, in the limit as delta t----0, a master equation for the probability in continuous time is obtained. Depending on the parameters, the master equation describes the evolution of a deterministic, a diffusion, or a discrete process. An interpretation for the diffusion and discrete processes is outlined. The conclusion is that the stochastic equations for neural interaction lead to a master equation representing a diffusion or a discrete process depending on the number, size of synaptic connectivity coefficients, and probability distribution of neural activity. An example is included describing how a master equation may be used to derive properties of the single neuron's output process.