scholarly journals Principal components and generalized linear modeling in the correlation between hospital admissions and air pollution

2014 ◽  
Vol 48 (3) ◽  
pp. 451-458 ◽  
Author(s):  
Juliana Bottoni de Souza ◽  
Valdério Anselmo Reisen ◽  
Jane Méri Santos ◽  
Glaura Conceição Franco
Keyword(s):  
Relative Risk ◽  
Air Pollutants ◽  
Generalized Additive Model ◽  
Hospital Admissions ◽  
Additive Model ◽  
Principal Component ◽  
Component Analysis ◽  
Additive Models ◽  
Southeastern Brazil ◽  
Linear Modeling

OBJECTIVE To analyze the association between concentrations of air pollutants and admissions for respiratory causes in children. METHODS Ecological time series study. Daily figures for hospital admissions of children aged < 6, and daily concentrations of air pollutants (PM10, SO2, NO2, O3 and CO) were analyzed in the Região da Grande Vitória, ES, Southeastern Brazil, from January 2005 to December 2010. For statistical analysis, two techniques were combined: Poisson regression with generalized additive models and principal model component analysis. Those analysis techniques complemented each other and provided more significant estimates in the estimation of relative risk. The models were adjusted for temporal trend, seasonality, day of the week, meteorological factors and autocorrelation. In the final adjustment of the model, it was necessary to include models of the Autoregressive Moving Average Models (p, q) type in the residuals in order to eliminate the autocorrelation structures present in the components. RESULTS For every 10:49 μg/m3 increase (interquartile range) in levels of the pollutant PM10 there was a 3.0% increase in the relative risk estimated using the generalized additive model analysis of main components-seasonal autoregressive – while in the usual generalized additive model, the estimate was 2.0%. CONCLUSIONS Compared to the usual generalized additive model, in general, the proposed aspect of generalized additive model − principal component analysis, showed better results in estimating relative risk and quality of fit.

scholarly journals Air pollutants and hospitalization due to pneumonia among children. An ecological time series study

2015 ◽  
Vol 133 (5) ◽  
pp. 408-413 ◽  
Author(s):  
Tassia Soldi Tuan ◽  
Taís Siqueira Venâncio ◽  
Luiz Fernando Costa Nascimento
Keyword(s):  
Time Series ◽  
Air Pollutants ◽  
Hospital Admissions ◽  
Population Attributable Fraction ◽  
Additive Model ◽  
Cost Savings ◽  
Time Series Study ◽  
Avoidable Hospitalizations ◽  
Relative Risks ◽  
Series Study

ABSTRACT CONTEXT AND OBJECTIVE: Exposure to air pollutants is one of the factors responsible for hospitalizations due to pneumonia among children. This has considerable financial cost, along with social cost. A study to identify the role of this exposure in relation to hospital admissions due to pneumonia among children up to 10 years of age was conducted. DESIGN AND SETTING: Ecological time series study using data from São José dos Campos, Brazil. METHODS: Daily data on hospitalizations due to pneumonia and on the pollutants CO, O3, PM10 and SO2, temperature and humidity in São José dos Campos, in 2012, were analyzed. A generalized additive model of Poisson's regression was used. Relative risks for hospitalizations due to pneumonia, according to lags of 0-5 days, were estimated. The population-attributable fraction, number of avoidable hospitalizations and cost savings from avoidable hospitalizations were calculated. RESULTS: There were 539 admissions. Exposure to CO and O3 was seen to be associated with hospitalizations, with risks of 1.10 and 1.15 on the third day after exposure to increased CO concentration of 200 ppb and ozone concentration of 20 µg/m3. Exposure to the pollutants of particulate matter and sulfur dioxide were not shown to be associated with hospitalizations. Decreases in CO and ozone concentrations could lead to 49 fewer hospitalizations and cost reductions of R$ 39,000.00. CONCLUSION: Exposure to certain air pollutants produces harmful effects on children's health, even in a medium-sized city. Public policies to reduce emissions of these pollutants need to be implemented.

scholarly journals Hierarchical generalized additive models: an introduction with mgcv

2018 ◽  
Author(s):  
Eric J Pedersen ◽  
David L. Miller ◽  
Gavin L. Simpson ◽  
Noam Ross
Keyword(s):  
Hierarchical Model ◽  
Generalized Additive Model ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Additive Models ◽  
Complex Structures ◽  
Ecological Data ◽  
Nonlinear Functional ◽  
Functional Relationships ◽  
Statistical Issues

In this paper, we discuss an extension to two popular approaches to modelling complex structures in ecological data: the generalized additive model (GAM) and the hierarchical model (HGLM). The hierarchical GAM (HGAM), allows modelling of nonlinear functional relationships between covariates and outcomes where the shape of the function itself varies between different grouping levels. We describe the theoretical connection between these models, HGLMs and GAMs, explain how to model different assumptions about the degree of inter-group variability in functional response, and show how HGAMs can be readily fitted using existing GAM software, the mgcv package in R. We also discuss computational and statistical issues with fitting these models, and demonstrate how to fit HGAMs on example data.

scholarly journals Temporal Cross-Correlations between Ambient Air Pollutants and Seasonality of Tuberculosis: A Time-Series Analysis

2019 ◽  
Vol 16 (9) ◽  
pp. 1585 ◽  
Author(s):  
Hua Wang ◽  
Changwei Tian ◽  
Wenming Wang ◽  
Xiaoming Luo
Keyword(s):  
Time Series ◽  
Air Pollutants ◽  
Quality Index ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Ambient Air ◽  
Delayed Effect ◽  
Incidence Data ◽  
Ambient Air Pollutants ◽  
Cross Correlations

The associations between ambient air pollutants and tuberculosis seasonality are unclear. We assessed the temporal cross-correlations between ambient air pollutants and tuberculosis seasonality. Monthly tuberculosis incidence data and ambient air pollutants (PM2.5, PM10, carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2)) and air quality index (AQI) from 2013 to 2017 in Shanghai were included. A cross-correlogram and generalized additive model were used. A 4-month delayed effect of PM2.5 (0.55), PM10 (0.52), SO2 (0.47), NO2 (0.40), CO (0.39), and AQI (0.45), and a 6-month delayed effect of O3 (−0.38) on the incidence of tuberculosis were found. The number of tuberculosis cases increased by 8%, 4%, 18%, and 14% for a 10 μg/m3 increment in PM2.5, PM10, SO2, and NO2; 4% for a 10 unit increment in AQI; 8% for a 0.1 mg/m3 increment in CO; and decreased by 4% for a 10 μg/m3 increment in O3. PM2.5 concentrations above 50 μg/m3, 70 μg/m3 for PM10, 16 μg/m3 for SO2, 47 μg/m3 for NO2, 0.85 mg/m3 for CO, and 85 for AQI, and O3 concentrations lower than 95 μg/m3 were positively associated with the incidence of tuberculosis. Ambient air pollutants were correlated with tuberculosis seasonality. However, this sort of study cannot prove causality.

scholarly journals The effects of air pollution on cardiovascular diseases: lag structures

2006 ◽  
Vol 40 (4) ◽  
pp. 677-683 ◽  
Author(s):  
Lourdes Conceição Martins ◽  
Luiz A A Pereira ◽  
Chin A Lin ◽  
Ubiratan P Santos ◽  
Gildeoni Prioli ◽  
...  
Keyword(s):  
Air Pollution ◽  
Cardiovascular Diseases ◽  
Air Pollutants ◽  
Hospital Admissions ◽  
Sao Paulo ◽  
Southeastern Brazil ◽  
Female Group ◽  
São Paulo ◽  
Distributed Lag ◽  
Gender Groups

OBJECTIVE: To assess the lag structure between air pollution exposure and elderly cardiovascular diseases hospital admissions, by gender. METHODS: Health data of people aged 64 years or older was stratified by gender in São Paulo city, Southeastern Brazil, from 1996 to 2001. Daily levels of air pollutants (CO, PM10, O3, NO2, and SO2) , minimum temperature, and relative humidity were also analyzed. It were fitted generalized additive Poisson regressions and used constrained distributed lag models adjusted for long time trend, weekdays, weather and holidays to assess the lagged effects of air pollutants on hospital admissions up to 20 days after exposure. RESULTS: Interquartile range increases in PM10 (26.21 mug/m³) and SO2 (10.73 mug/m³) were associated with 3.17% (95% CI: 2.09-4.25) increase in congestive heart failure and 0.89% (95% CI: 0.18-1.61) increase in total cardiovascular diseases at lag 0, respectively. Effects were higher among female group for most of the analyzed outcomes. Effects of air pollutants for different outcomes and gender groups were predominately acute and some "harvesting" were found. CONLUSIONS: The results show that cardiovascular diseases in São Paulo are strongly affected by air pollution.

scholarly journals Implementation of a Generalized Additive Model (GAM) for Soybean Maturity Prediction in African Environments

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1043
Author(s):  
Guillermo S. Marcillo ◽  
Nicolas F. Martin ◽  
Brian W. Diers ◽  
Michelle Da Fonseca Santos ◽  
Erica Pontes Leles ◽  
...  
Keyword(s):  
Minimum Temperature ◽  
Generalized Additive Model ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Additive Models ◽  
Unbalanced Data ◽  
Genotypic Differences ◽  
Breeding Programs ◽  
Important Trait ◽  
New Crop

Time to maturity (TTM) is an important trait in soybean breeding programs. However, soybeans are a relatively new crop in Africa. As such, TTM information for soybeans is not yet as well defined as in other major producing areas. Multi-environment trials (METs) allow breeders to analyze crop performance across diverse conditions, but also pose statistical challenges (e.g., unbalanced data). Modern statistical methods, e.g., generalized additive models (GAMs), can flexibly smooth a range of responses while retaining observations that could be lost under other approaches. We leveraged 5 years of data from an MET breeding program in Africa to identify the best geographical and seasonal variables to explain site and genotypic differences in soybean TTM. Using soybean cycle features (e.g., minimum temperature, daylength) along with trial geolocation (longitude, latitude), a GAM predicted soybean TTM within 10 days of the average observed TTM (RMSE = 10.3; x = 109 days post-planting). Furthermore, we found significant differences between cultivars (p < 0.05) in TTM sensitivity to minimum temperature and daylength. Our results show potential to advance the design of maturity systems that enhance soybean planting and breeding decisions in Africa.

scholarly journals Hierarchical generalized additive models: an introduction with mgcv

2018 ◽  
Author(s):  
Eric J Pedersen ◽  
David L. Miller ◽  
Gavin L. Simpson ◽  
Noam Ross
Keyword(s):  
Hierarchical Model ◽  
Generalized Additive Model ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Additive Models ◽  
Complex Structures ◽  
Ecological Data ◽  
Nonlinear Functional ◽  
Functional Relationships ◽  
Statistical Issues

In this paper, we discuss an extension to two popular approaches to modelling complex structures in ecological data: the generalized additive model (GAM) and the hierarchical model (HGLM). The hierarchical GAM (HGAM), allows modelling of nonlinear functional relationships between covariates and outcomes where the shape of the function itself varies between different grouping levels. We describe the theoretical connection between these models, HGLMs and GAMs, explain how to model different assumptions about the degree of inter-group variability in functional response, and show how HGAMs can be readily fitted using existing GAM software, the mgcv package in R. We also discuss computational and statistical issues with fitting these models, and demonstrate how to fit HGAMs on example data.

scholarly journals Hierarchical generalized additive models in ecology: an introduction with mgcv

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6876 ◽  
Author(s):  
Eric J. Pedersen ◽  
David L. Miller ◽  
Gavin L. Simpson ◽  
Noam Ross
Keyword(s):  
Generalized Additive Model ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Additive Models ◽  
Complex Structures ◽  
Ecological Data ◽  
Mixed Effect ◽  
Nonlinear Functional ◽  
Functional Relationships ◽  
Statistical Issues

In this paper, we discuss an extension to two popular approaches to modeling complex structures in ecological data: the generalized additive model (GAM) and the hierarchical model (HGLM). The hierarchical GAM (HGAM), allows modeling of nonlinear functional relationships between covariates and outcomes where the shape of the function itself varies between different grouping levels. We describe the theoretical connection between HGAMs, HGLMs, and GAMs, explain how to model different assumptions about the degree of intergroup variability in functional response, and show how HGAMs can be readily fitted using existing GAM software, themgcvpackage in R. We also discuss computational and statistical issues with fitting these models, and demonstrate how to fit HGAMs on example data. All code and data used to generate this paper are available at:github.com/eric-pedersen/mixed-effect-gams.

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