Negative Binomial regression model for analysis of the relationship between hospitalization and air pollution

Ardiles, Leda; Tadano, Yara S.; Costa, Silvano César da; Urbina, Viviana; Capucim, Maurício; Silva, Iara da; Braga, Alfésio Luís Ferreira; Martins, Jorge Alberto; Martins, Leila Droprinchinski

doi:10.1016/j.apr.2017.10.010

Cited by 25 publications

(24 citation statements)

References 22 publications

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“…Even so, HIV infection was associated with higher mortality, which endorses our findings . In that study, air pollution was also associated with greater mortality in influenza patients and has been described as a risk factor for hospitalisation due to respiratory diseases . Therefore, the impact of air pollution on mortality should be evaluated in patients with SARI.…”

Section: Discussionsupporting

confidence: 85%

Charlson comorbidity index scores and in‐hospital prognosis of patients with severe acute respiratory infections

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Peres

Almeida

et al. 2020

Internal Medicine Journal

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Background Respiratory infections are one of the leading causes of mortality, and comorbid conditions play a significant role in the severity and fatality of these infections. Aims We evaluated the Charlson Comorbidity Index (CCI) score and possible predictors of mortality in hospitalised patients with severe acute respiratory infection (SARI), aiming to test if the CCI is a valid in‐hospital prognostic indicator. Methods Patients older than 14 years, hospitalised from 2010 to 2016 due to SARI by viral infection and who were submitted to respiratory virus testing were included. We assessed comorbidity retrospectively through chart review and calculated four variants of the CCI. Results Of the 291 patients assessed, 72.8% (n = 212) presented comorbidities, and 24% died (n = 70). The most recurrent comorbidities were chronic pulmonary disease (n = 76/212, 36%) and HIV (n = 50/212, 23.6%). The 1994 age‐adjusted CCI predicted in‐hospital mortality in SARI patients (P = 0.04), and HIV was associated with in‐hospital mortality (P = 0.032). Conclusions The comorbidity scores used to assess mortality risk in hospitalised patients with SARI displayed poor results, but HIV infection was considered a marker of severity. However, other factors should be considered in order to compose a score system that allows us to specifically assess the risk of mortality in patients with SARI.

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Section: Discussionsupporting

confidence: 85%

Charlson comorbidity index scores and in‐hospital prognosis of patients with severe acute respiratory infections

Setter

Peres

Almeida

et al. 2020

Internal Medicine Journal

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“…The coldest season is concentrated between the months of June and August, when polar atmospheric systems are more present and the solar incidence is lower, with temperatures average around 13°C. Rain occurs throughout the year, with less intensity in winter, which makes this season unfavorable for the dispersion of atmospheric pollutants [33][34][35].…”

Section: Locationmentioning

confidence: 99%

Risk assessment of temperature and air pollutants on hospitalizations for mental and behavioral disorders in Curitiba, Brazil

et al. 2020

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Background: Extreme ambient temperatures and air quality have been directly associated with various human diseases from several studies around the world. However, few analyses involving the association of these environmental circumstances with mental and behavioral disorders (MBD) have been carried out, especially in developing countries such as Brazil. Methods: A time series study was carried out to explore the associations between daily air pollutants (SO 2 , NO 2 , O 3 , and PM 10) concentrations and meteorological variables (temperature and relative humidity) on hospital admissions for mental and behavioral disorders for Curitiba, Brazil. Daily hospital admissions from 2010 to 2016 were analyzed by a semi-parametric generalized additive model (GAM) combined with a distributed lag non-linear model (DLNM). Results: Significant associations between environmental conditions (10 μg/m 3 increase in air pollutants and temperature°C) and hospitalizations by MBD were found. Air temperature was the environmental variable with the highest relative risk (RR) at 0-day lag for all ages and sexes analyzed, with RR values of 1.0182 (95% CI: 1.0009-1.0357) for men, and 1.0407 (95% CI: 1.0230-1.0587) for women. Ozone exposure was a risk for all women groups, being higher for the young group, with a RR of 1.0319 (95% CI: 1.0165-1.0483). Elderly from both sexes were more susceptible to temperature variability, with a RR of 1.0651 (95% CI: 1.0213-1.1117) for women, and 1.0215 (95% CI: 1.0195-1.0716) for men. Conclusions: This study suggests that temperatures above and below the thermal comfort threshold, in addition to high concentrations of air pollutants, present significant risks on hospitalizations by MBD; besides, there are physiological and age differences resulting from the effect of this exposure.

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“…Unfortunately, there is no consensus regarding what is the best approach to estimate air pollution health risks [16,20,25,26,41], as it depends on the database behavior. Even for conventional regressions, such as the Generalized Linear Model, the best distribution may depend on the dataset, as reported by Ardiles et al [58]. Therefore, the best results for mortality and morbidity being achieved by different models was expected.…”

Section: Case Studymentioning

confidence: 95%

Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America

et al. 2020

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The emission of pollutants from vehicles is presented as a prime factor deteriorating air quality. Thus, seeking public policies encouraging the use and the development of more sustainable vehicles is paramount to preserve populations’ health. To better understand the health risks caused by air pollution and exclusively by mobile sources urges the question of which input variables should be considered. Therefore, this research aims to estimate the impacts on populations’ health related to road transport variables for São Paulo, Brazil, the largest metropolis in South America. We used three Artificial Neural Networks (ANN) (Multilayer Perceptron—MLP, Extreme Learning Machines—ELM, and Echo State Neural Networks—ESN) to estimate the impacts of carbon monoxide, nitrogen oxides, ozone, sulfur dioxide, and particulate matter on outcomes for respiratory diseases (morbidity—hospital admissions and mortality). We also used unusual inputs, such as road vehicles fleet, distributed and sold fuels amount, and vehicle average mileage. We also used deseasonalization and the Variable Selection Methods (VSM) (Mutual Information Filter and Wrapper). The results showed that the VSM excluded some variables, but the best performances were reached considering all of them. The ELM achieved the best overall results to morbidity, and the ESN to mortality, both using deseasonalization. Our study makes an important contribution to the following United Nations Sustainable Development Goals: 3—good health and well-being, 7—affordable and clean energy, and 11—sustainable cities and communities. These research findings will guide government about future legislations, public policies aiming to warranty and improve the health system.

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Negative Binomial regression model for analysis of the relationship between hospitalization and air pollution

Cited by 25 publications

References 22 publications

Charlson comorbidity index scores and in‐hospital prognosis of patients with severe acute respiratory infections

Charlson comorbidity index scores and in‐hospital prognosis of patients with severe acute respiratory infections

Risk assessment of temperature and air pollutants on hospitalizations for mental and behavioral disorders in Curitiba, Brazil

Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America

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