Identifying the contribution of physical and chemical stressors to the daily number of hospital admissions implementing an artificial neural network model

Kassomenos, Pavlos; Petrakis, M.; Sarigiannis, D; Gotti, Alberto; Karakitsios, S

doi:10.1007/s11869-011-0139-2

Cited by 30 publications

(28 citation statements)

References 43 publications

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“…The relationship between temperature and mortality and hospitalization has been shown to be non-linear (Nordio et al, 2015) and spline methods as well as Artificial Neural Networks (Kassomenos et al, 2011) methods to study the effect of non-linear parameters such as the temperature on cardiovascular admissions have been used.…”

Section: Introductionmentioning

confidence: 99%

Fine-scale spatial and temporal variation in temperature and arrhythmia episodes in the VA Normative Aging Study

Zanobetti

Coull

Kloog

et al. 2016

Journal of the Air & Waste Management Association

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Many studies have demonstrated that cold and hot temperatures are associated with increased deaths and hospitalization rates; new findings indicate also an association with more specific cardiac risk factors. Most of these existing studies have relied on few weather stations to characterize exposures; few have used residence-specific estimates of temperature, or examined the exposure-response function. We investigated the association of arrhythmia episodes with spatial and temporal variation in temperature. We also evaluated the association between monitored ambient temperature (central) and the same outcome. This longitudinal analysis included 701 older men participating in the VA Normative Aging Study. Arrhythmia episodes were measured as ventricular ectopy (VE) (bigeminy, trigemini or couplets episodes) by 4min electrocardiogram (ECG) monitoring in repeated visits during 2000–2010. The outcome was defined as having or not VE episodes during a study visit. We applied a mixed effect logistic regression model with a random intercept for subject, controlling for seasonality, weekday, medication use, smoking, diabetes status, body mass index and age. We also examined effect modification by personal characteristics, confounding by air pollution, and the exposure-response function. For 1° C increase in the same day residence-specific temperature, the odds of having VE episodes was 1.10 (95%CI: 1.04–1.17). The odds associated with 1° C increase in central temperature was 1.05 (95%CI: 1.02–1.09). The exposure-response function was non-linear for averages of temperature, presenting a J-shaped pattern, suggesting greater risk at lower and higher temperatures. Increased warm temperature and decreased cold temperature may increase the risk of ventricular arrhythmias.

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Section: Introductionmentioning

confidence: 99%

Fine-scale spatial and temporal variation in temperature and arrhythmia episodes in the VA Normative Aging Study

Zanobetti

Coull

Kloog

et al. 2016

Journal of the Air & Waste Management Association

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“…PM long-term exposure was also associated with diabetes and cardiovascular and respiratory diseases (Martinelli et al, 2013), including atherosclerosis (Hoffmann et al, 2007), hypertensive episodes (Brook et al, 2007), arrhythmia (Rich et al, 2005) and asthma (Gehring et al, 2010). Kassomenos et al (2011) developed a neural network approach to estimate the significance of PM exposure in hospital admission for cardiovascular and respiratory diseases. The results for Athens showed that a 10 µg m −3 increase in PM 10 concentration led to an 8.6 % increase in hospitalizations .…”

Section: Particulate Matter and Healthmentioning

confidence: 99%

“…The analysis of national databases in the United States revealed higher mortality RRs for PM 2.5 exposure (Zanobetti and Schwartz, 2009). The APHEA project (Air Pollution and Health: a European Approach) investigated daily mortality data from 32 European cities and observed that mortality was associated with PM exposure: the daily mortality counts associated with 10 µg m −3 of PM 10 increased by 0.52 %, and it increased by 0.76 % and 0.71 % for cardiovascular and respiratory mortality, respectively (Analitis et al, 2006;Katsouyanni and Grp, 2006). The effects were more pronounced during the first and second day for total mortality and cardiovascular mortality, while respiratory mortality showed more prolonged lagged effects.…”

Section: Particulate Matter and Healthmentioning

confidence: 99%

Particulate matter, air quality and climate: lessons learned and future needs

et al. 2015

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Abstract. The literature on atmospheric particulate matter (PM), or atmospheric aerosol, has increased enormously over the last 2 decades and amounts now to some 1500-2000 papers per year in the refereed literature. This is in part due to the enormous advances in measurement technologies, which have allowed for an increasingly accurate understanding of the chemical composition and of the physical properties of atmospheric particles and of their processes in the atmosphere. The growing scientific interest in atmospheric aerosol particles is due to their high importance for environmental policy. In fact, particulate matter constitutes one of the most challenging problems both for air quality and for climate change policies. In this context, this paper reviews the most recent results within the atmospheric aerosol sciences and the policy needs, which have driven much of the increase in monitoring and mechanistic research over the last 2 decades.The synthesis reveals many new processes and developments in the science underpinning climate-aerosol interactions and effects of PM on human health and the environment. However, while airborne particulate matter is responsible for globally important influences on premature human mortality, we still do not know the relative importance of the different chemical components of PM for these effects. Likewise, the magnitude of the overall effects of PM on climate remains highly uncertain. Despite the uncertainty there are many things that could be done to mitigate local and global problems of atmospheric PM. Recent analyses have shown that reducing black carbon (BC) emissions, using known control measures, would reduce global warming and delay the time when anthropogenic effects on global temperature would exceed 2 • C. Likewise, cost-effective control measures on ammonia, an important agricultural precursor gas for secondary inorganic aerosols (SIA), would reduce regional eutrophication and PM concentrations in large areas of Europe, China and the USA. Thus, there is much that could be done to reduce the effects of atmospheric PM on the climate and the health of the environment and the human population.A prioritized list of actions to mitigate the full range of effects of PM is currently undeliverable due to shortcomings in the knowledge of aerosol science; among the shortcomings, the roles of PM in global climate and the relative roles of Published by Copernicus Publications on behalf of the European Geosciences Union. 8218S. Fuzzi et al.: Particulate matter, air quality and climate different PM precursor sources and their response to climate and land use change over the remaining decades of this century are prominent. In any case, the evidence from this paper strongly advocates for an integrated approach to air quality and climate policies.

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“…ANN models are capable of fast processing with several input and output variables (Lal and Tripathy 2012;Kakosimos et al 2011;Bose and Liang 1998;Anderson 1995). ANN models have been very accurate in many environmental health application areas, especially indoor environment (Skön et al 2012;Kassomenos et al 2011), air quality forecasting Goyal and Kumar;Alekseev and Seixas Citation details: Patra, A., Gautam, S., Majumdar, S., . Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model.…”

Section: Introductionmentioning

confidence: 99%

Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model

Patra

Gautam

Majumdar

et al. 2015

Air Qual Atmos Health

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Springer Science+Business Media Dordrecht Particulate matter (PM) is a major pollutant in and around opencast mine areas. The problem of degradation of air quality due to opencast mine is more severe than those in underground mine. Prediction of dust concentration must be known to implement control strategies and techniques to control air quality degradation in the workplace environment. Limited studies have reported the dispersion profile and travel time of PM between the benches inside the mine. In this paper, PM concentration has been measured and modeled in Malanjkhand Copper Project (MCP), which is one of the deepest opencast copper mines in India. Meteorological parameters (wind speed, temperature, relative humidity) and PM concentration in seven size ranges (i.e., PM0.23–0.3, PM0.3–0.4, PM0.4–0.5, PM0.5–0.65, PM0.65–0.8, PM0.8–1, and PM1–1.6) have been measured for 8 days. The results of the field study provide an understanding of the dispersion of the PM generated due to mining activities. This research work presents an approach to assess the exposure of enhanced level of PM concentration on mine workers and its variation with depth. The correlations study shows that concentration of PM during its travel from source to surface is associated with depth. Empirical equations are developed to represent relationships between concentrations of PM and depth. Artificial neural network (ANN) model showing the relationship between PM concentration and meteorological parameters has been developed. The performance of the ANN model is evaluated in terms of the correlation coefficient between the real and the forecasted data. The results show strong agreement between the experimental data and the modeled output. The findings of this work are important in understanding fine PM variation inside the mine at the workplace and the associated exposure of mine workers

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Identifying the contribution of physical and chemical stressors to the daily number of hospital admissions implementing an artificial neural network model

Cited by 30 publications

References 43 publications

Fine-scale spatial and temporal variation in temperature and arrhythmia episodes in the VA Normative Aging Study

Fine-scale spatial and temporal variation in temperature and arrhythmia episodes in the VA Normative Aging Study

Particulate matter, air quality and climate: lessons learned and future needs

Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model

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