scholarly journals Isolated and synergistic effects of PM10 and average temperature on cardiovascular and respiratory mortality

2014 ◽  
Vol 48 (6) ◽  
pp. 881-888 ◽  
Author(s):  
Samya de Lara Lins de Araujo Pinheiro ◽  
Paulo Hilário Nascimento Saldiva ◽  
Joel Schwartz ◽  
Antonella Zanobetti
Keyword(s):  
Time Series ◽  
Relative Risk ◽  
Regression Model ◽  
Poisson Regression ◽  
Synergistic Effects ◽  
Poisson Regression Model ◽  
Respiratory Mortality ◽  
Average Temperature ◽  
Case Crossover ◽  
Crossover Analysis

OBJECTIVE To analyze the effect of air pollution and temperature on mortality due to cardiovascular and respiratory diseases. METHODS We evaluated the isolated and synergistic effects of temperature and particulate matter with aerodynamic diameter < 10 µm (PM10) on the mortality of individuals > 40 years old due to cardiovascular disease and that of individuals > 60 years old due to respiratory diseases in Sao Paulo, SP, Southeastern Brazil, between 1998 and 2008. Three methodologies were used to evaluate the isolated association: time-series analysis using Poisson regression model, bidirectional case-crossover analysis matched by period, and case-crossover analysis matched by the confounding factor, i.e., average temperature or pollutant concentration. The graphical representation of the response surface, generated by the interaction term between these factors added to the Poisson regression model, was interpreted to evaluate the synergistic effect of the risk factors. RESULTS No differences were observed between the results of the case-crossover and time-series analyses. The percentage change in the relative risk of cardiovascular and respiratory mortality was 0.85% (0.45;1.25) and 1.60% (0.74;2.46), respectively, due to an increase of 10 μg/m3 in the PM10 concentration. The pattern of correlation of the temperature with cardiovascular mortality was U-shaped and that with respiratory mortality was J-shaped, indicating an increased relative risk at high temperatures. The values for the interaction term indicated a higher relative risk for cardiovascular and respiratory mortalities at low temperatures and high temperatures, respectively, when the pollution levels reached approximately 60 μg/m3. CONCLUSIONS The positive association standardized in the Poisson regression model for pollutant concentration is not confounded by temperature, and the effect of temperature is not confounded by the pollutant levels in the time-series analysis. The simultaneous exposure to different levels of environmental factors can create synergistic effects that are as disturbing as those caused by extreme concentrations.

Risk Associated with Transportation and Lairage on Hide Contamination with Salmonella enterica in Finished Beef Cattle at Slaughter

2008 ◽  
Vol 71 (11) ◽  
pp. 2228-2232 ◽  
Author(s):  
G. A. DEWELL ◽  
C. A. SIMPSON ◽  
R. D. DEWELL ◽  
D. R. HYATT ◽  
K. E. BELK ◽  
...  
Keyword(s):  
Risk Factors ◽  
Relative Risk ◽  
Beef Cattle ◽  
Regression Model ◽  
Potential Risk ◽  
Poisson Regression ◽  
Salmonella Enterica ◽  
Poisson Regression Model ◽  
Potential Risk Factors ◽  
Culture Negative

Transportation of cattle to the slaughter plant could influence hide contamination with Salmonella enterica. Fecal and hide samples were obtained from 40 lots of cattle at the feedlot and again at the slaughter plant. Potential risk factors for hide contamination were evaluated. A multilevel Poisson regression model was used to determine whether transportation and lairage were associated with hide contamination by Salmonella. Cattle with hide samples positive for Salmonella at the feedlot had twice the risk of having positive slaughter hide samples compared with cattle without positive feedlot hide samples (relative risk [RR], 1.9). Cattle transported in trailers from which samples positive for Salmonella were collected had twice the risk of having positive slaughter hide samples compared with cattle transported in culture-negative trailers (RR, 2.3). Cattle transported for long distances had twice the risk of having positive hide samples at slaughter compared with cattle transported shorter distances (RR, 2.3). Cattle held in lairage pens contaminated with feces had twice the risk of having positive slaughter hide samples compared with cattle held in clean pens (RR, 1.8). Cattle held off feed longer than 18 h before loading had twice the risk of having positive slaughter hide samples compared with cattle held off feed for shorter times (RR, 1.7). Cattle that were agitated during loading had twice the risk of having positive slaughter hide samples compared with cattle that were calm (RR, 2.2). These findings suggest that variables associated with transportation and lairage can impact the presence of Salmonella on the hides of cattle at slaughter.

scholarly journals The effect of sociodemographic factors on COVID-19 incidence of 342 cities in China: a geographically weighted regression model analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Huihui Zhang ◽  
Yini Liu ◽  
Fangyao Chen ◽  
Baibing Mi ◽  
Lingxia Zeng ◽  
...  
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Linear Models ◽  
Intervention Strategy ◽  
Health Resources ◽  
Sociodemographic Factors ◽  
Information Criteria ◽  
Poisson Regression Model ◽  
Geographically Weighted Regression Model ◽  
Auto Correlation

Abstract Background Since December 2019, the coronavirus disease 2019 (COVID-19) has spread quickly among the population and brought a severe global impact. However, considerable geographical disparities in the distribution of COVID-19 incidence existed among different cities. In this study, we aimed to explore the effect of sociodemographic factors on COVID-19 incidence of 342 cities in China from a geographic perspective. Methods Official surveillance data about the COVID-19 and sociodemographic information in China’s 342 cities were collected. Local geographically weighted Poisson regression (GWPR) model and traditional generalized linear models (GLM) Poisson regression model were compared for optimal analysis. Results Compared to that of the GLM Poisson regression model, a significantly lower corrected Akaike Information Criteria (AICc) was reported in the GWPR model (61953.0 in GLM vs. 43218.9 in GWPR). Spatial auto-correlation of residuals was not found in the GWPR model (global Moran’s I = − 0.005, p = 0.468), inferring the capture of the spatial auto-correlation by the GWPR model. Cities with a higher gross domestic product (GDP), limited health resources, and shorter distance to Wuhan, were at a higher risk for COVID-19. Furthermore, with the exception of some southeastern cities, as population density increased, the incidence of COVID-19 decreased. Conclusions There are potential effects of the sociodemographic factors on the COVID-19 incidence. Moreover, our findings and methodology could guide other countries by helping them understand the local transmission of COVID-19 and developing a tailored country-specific intervention strategy.

A multivariate Poisson regression model for count data

2021 ◽  
pp. 1-17
Author(s):  
J. M. Muñoz-Pichardo ◽  
R. Pino-Mejías ◽  
J. García-Heras ◽  
F. Ruiz-Muñoz ◽  
M. Luz González-Regalado
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Poisson Regression Model

Monitoring social networks based on Zero-inflated Poisson regression model

2021 ◽  
pp. 1-17
Author(s):  
Narges Motalebi ◽  
Mohammad Saleh Owlia ◽  
Amirhossein Amiri ◽  
Mohammad Saber Fallahnezhad
Keyword(s):  
Social Networks ◽  
Regression Model ◽  
Poisson Regression ◽  
Poisson Regression Model

scholarly journals Age and Sex Specific Trends in Incidence of Juvenile Idiopathic Arthritis in Danish Birth Cohorts from 1992 to 2002: A Nationwide Register Linkage Study

2021 ◽  
Vol 18 (16) ◽  
pp. 8331
Author(s):  
Isabel Cardoso ◽  
Peder Frederiksen ◽  
Ina Olmer Specht ◽  
Mina Nicole Händel ◽  
Fanney Thorsteinsdottir ◽  
...  
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Regression Model ◽  
Incidence Rate ◽  
Poisson Regression ◽  
Linkage Study ◽  
Incidence Rates ◽  
Age Group ◽  
Poisson Regression Model ◽  
Birth Cohorts ◽  
Age And Sex

This study reports age- and sex-specific incidence rates of juvenile idiopathic arthritis (JIA) in complete Danish birth cohorts from 1992 through 2002. Data were obtained from the Danish registries. All persons born in Denmark, from 1992–2002, were followed from birth and until either the date of first diagnosis recording, death, emigration, 16th birthday or administrative censoring (17 May 2017), whichever came first. The number of incident JIA cases and its incidence rate (per 100,000 person-years) were calculated within sex and age group for each of the birth cohorts. A multiplicative Poisson regression model was used to analyze the variation in the incidence rates by age and year of birth for boys and girls separately. The overall incidence of JIA was 24.1 (23.6–24.5) per 100,000 person-years. The rate per 100,000 person-years was higher among girls (29.9 (29.2–30.7)) than among boys (18.5 (18.0–19.1)). There were no evident peaks for any age group at diagnosis for boys but for girls two small peaks appeared at ages 0–5 years and 12–15 years. This study showed that the incidence rates of JIA in Denmark were higher for girls than for boys and remained stable over the observed period for both sexes.

PARAMETER ESTIMATION ON HURDLE POISSON REGRESSION MODEL WITH CENSORED DATA

2012 ◽  
Vol 57 (1) ◽  
Author(s):  
SEYED EHSAN SAFFAR ◽  
ROBIAH ADNAN ◽  
WILLIAM GREENE
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Goodness Of Fit ◽  
Poisson Model ◽  
Likelihood Method ◽  
Poisson Regression Model ◽  
Response Variable ◽  
The Mean ◽  
Over Dispersion

A Poisson model typically is assumed for count data. In many cases, there are many zeros in the dependent variable and because of these many zeros, the mean and the variance values of the dependent variable are not the same as before. In fact, the variance value of the dependent variable will be much more than the mean value of the dependent variable and this is called over–dispersion. Therefore, Poisson model is not suitable anymore for this kind of data because of too many zeros. Thus, it is suggested to use a hurdle Poisson regression model to overcome over–dispersion problem. Furthermore, the response variable in such cases is censored for some values. In this paper, a censored hurdle Poisson regression model is introduced on count data with many zeros. In this model, we consider a response variable and one or more than one explanatory variables. The estimation of regression parameters using the maximum likelihood method is discussed and the goodness–of–fit for the regression model is examined. We study the effects of right censoring on estimated parameters and their standard errors via an example.

Using Poisson Regression Model to Examine Student Travel Frequency Patterns in Beijing

2014 ◽  
Vol 1030-1032 ◽  
pp. 2738-2741
Author(s):  
Guang Jun Zhan
Keyword(s):  
University Students ◽  
Regression Model ◽  
Poisson Regression ◽  
Family Income ◽  
Influence Factors ◽  
Poisson Regression Model ◽  
Web Based ◽  
Study Results ◽  
Online Travel ◽  
Student Travel

This paper applies Poisson regression model to examine university students' travel frequencies and relevant influence factors, using the data collected from four universities in Beijing by a web-based online travel survey. It finds that student grade, family income and school attended have significant effects on students' travel frequency. The study results reveal students travel frequency characteristics at a disaggregate level and provide information to well understand student travel frequency patterns.

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