Detecting signals of opioid analgesic abuse: application of a spatial mixed effect poisson regression model using data from a network of poison control centers

2008 ◽  
Vol 17 (11) ◽  
pp. 1050-1059 ◽  
Author(s):  
Meredith Y. Smith ◽  
William Irish ◽  
Jianmin Wang ◽  
J. David Haddox ◽  
Richard C. Dart
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Opioid Analgesic ◽  
Poison Control ◽  
Poisson Regression Model ◽  
Mixed Effect ◽  
Poison Control Centers ◽  
Using Data

scholarly journals Factors Influencing the Frequency of Consumers’ Purchases of Locally-Produced Rice in Indonesia: A Poisson Regression Analysis

Agriculture ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 117 ◽  
Author(s):  
Apri Wahyudi ◽  
John K. M. Kuwornu ◽  
Endro Gunawan ◽  
Avishek Datta ◽  
Loc T. Nguyen
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Marketing Strategy ◽  
Poisson Regression ◽  
Poisson Regression Model ◽  
Poisson Regression Analysis ◽  
Empirical Results ◽  
Factors Influencing ◽  
Using Data

This study assessed the factors influencing the frequency of purchases of locally-produced rice using data collected from a sample of 400 consumers in Jakarta Province in Indonesia. The empirical results of a Poisson regression model revealed that socio-economic characteristics of the consumers (i.e., gender, age, occupation, education, and income), characteristics of the product (i.e., label and color), and the product’s price and promotion significantly influenced consumers’ frequency of purchasing locally-produced rice. The implication is that increasing the quality of locally-produced rice, applying an appropriate marketing strategy such as offering a relatively lower-priced product compared to the price of imported rice, and product promotion are necessary for increasing the frequency of consumers’ purchases of locally-produced rice.

scholarly journals Prevalence and Associated Factors for Frailty among Elder Patients in China: A multicenter cross-sectional study

2019 ◽  
Author(s):  
Jing Jiao ◽  
Yu Wang ◽  
Chen Zhu ◽  
Fangfang Li ◽  
Minglei Zhu ◽  
...  
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Associated Factors ◽  
Cross Sectional Study ◽  
Small Sample ◽  
Poisson Regression Model ◽  
Sectional Study ◽  
Cross Sectional ◽  
Mixed Effect ◽  
Elder Patients

Abstract Background: Up to date, most of previous studies of frailty among hospitalized elder Chinese patients were conducted based on a small sample, which could not represent the elder patient population. The aim of this study is to identify the prevalence and the risk factors for frailty among elder patients in China. Study Design and Setting: This is a cross-sectional study, 9996 elder patients from 6 tertiary level hospitals in China were surveyed. The prevalence of frailty among patients from selected wards was surveyed by trained investigators. Mixed-effect Poisson regression model were used to analyze the associated factors of frailty among elder patients. Results: The mean age of all subjects was72.47±5.77years. The prevalence rate of frailty in this study was 18.02%. After controlling the confounding effect of hospital wards clustering effect, Mixed-effect Poisson regression model showed that the associated factors of frailty included: age(OR:1.016, 95% CI:1.012 -1.020), patients with BMI < 18.5(OR: 1.248, 95% CI:1.171-1.330), female(OR:1.058, 95% CI:1.004 -1.115), ethnic minorities(OR: 1.152, 95% CI: 1.073-1.236), admission to hospital by the emergency department(OR: 1.104, 95% CI:1.030 -1.184),the former drinker(OR: 1.094, 95% CI:1.022 -1.171),fall history in past 12 month(OR:1.257, 95%CI:1.194-1.323),vision dysfunction(OR:1.144 , 95% CI:1.080 -1.211),cognition impairment(OR:1.182 , 95% CI:1.130 -1.237),sleeping dysfunction(OR:1.215, 95% CI:1.215 -1.318),urinary dysfunction(OR: 1.175, 95% CI:1.104 -1.251),defecation dysfunction(OR:1.286 , 95% CI:1.217 -1.358). Conclusion: We identified a relatively high prevalence of frailty among elder patients, and there are several associate factors among the population derived from an investigation of a large-scale, multicenter, nationwide representative Chinese elderly inpatient population. Trial registration: Chinese clinical Trial registry, ChiCTR1800017682, Registered 09 August 2018 Keywords: Frailty; Elder patients; Prevalence; Associate factors

scholarly journals Prevalence and Associated Factors for Frailty among Elder Patients in China: A multicenter cross-sectional study

2020 ◽  
Author(s):  
Jing Jiao ◽  
Yu Wang ◽  
Chen Zhu ◽  
Fangfang Li ◽  
Minglei Zhu ◽  
...  
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Associated Factors ◽  
Cross Sectional Study ◽  
Small Sample ◽  
Poisson Regression Model ◽  
Sectional Study ◽  
Cross Sectional ◽  
Mixed Effect ◽  
Elder Patients

Abstract Background: Up to date, most of previous studies of frailty among hospitalized elder Chinese patients were conducted based on a small sample, which could not represent the elder patient population. The aim of this study is to identify the prevalence and the risk factors for frailty among elder patients in China. Study Design and Setting: This is a cross-sectional study, 9996 elder patients from 6 tertiary level hospitals in China were surveyed. The prevalence of frailty among patients from selected wards was surveyed by trained investigators. Mixed-effect Poisson regression model were used to analyze the associated factors of frailty among elder patients. Results: The mean age of all subjects was72.47±5.77years. The prevalence rate of frailty in this study was 18.02%. After controlling the confounding effect of hospital wards clustering effect, Mixed-effect Poisson regression model showed that the associated factors of frailty included: age(OR:1.016, 95% CI:1.012 -1.020), patients with BMI < 18.5(OR: 1.248, 95% CI:1.171-1.330), female(OR:1.058, 95% CI:1.004 -1.115), ethnic minorities(OR: 1.152, 95% CI: 1.073-1.236), admission to hospital by the emergency department(OR: 1.104, 95% CI:1.030 -1.184),the former drinker(OR: 1.094, 95% CI:1.022 -1.171),fall history in past 12 month(OR:1.257, 95%CI:1.194-1.323),vision dysfunction(OR:1.144 , 95% CI:1.080 -1.211),cognition impairment(OR:1.182 , 95% CI:1.130 -1.237),sleeping dysfunction(OR:1.215, 95% CI:1.215 -1.318),urinary dysfunction(OR: 1.175, 95% CI:1.104 -1.251),defecation dysfunction(OR:1.286 , 95% CI:1.217 -1.358). Conclusion: We identified a relatively high prevalence of frailty among elder patients, and there are several associate factors among the population derived from an investigation of a large-scale, multicenter, nationwide representative Chinese elderly inpatient population. Trial registration: Chinese clinical Trial registry, ChiCTR1800017682, Registered 09 August 2018 Keywords: Frailty; Elder patients; Prevalence; Associate factors

scholarly journals Prevalence and Associated Factors for Frailty among Elder Patients in China: A multicenter cross-sectional study

2020 ◽  
Author(s):  
Jing Jiao ◽  
Yu Wang ◽  
Chen Zhu ◽  
Fangfang Li ◽  
Minglei Zhu ◽  
...  
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Associated Factors ◽  
Cross Sectional Study ◽  
Small Sample ◽  
Poisson Regression Model ◽  
Sectional Study ◽  
Cross Sectional ◽  
Mixed Effect ◽  
Elder Patients

Abstract Background: Up to date, most of previous studies of frailty among hospitalized elder Chinese patients were conducted based on a small sample, which could not represent the elder patient population. The aim of this study is to identify the prevalence and the risk factors for frailty among elder patients in China. Study Design and Setting: This is a cross-sectional study, 9996 elder patients from 6 tertiary level hospitals in China were surveyed. The prevalence of frailty among patients from selected wards was surveyed by trained investigators. Mixed-effect Poisson regression model were used to analyze the associated factors of frailty among elder patients. Results: The mean age of all subjects was72.47±5.77years. The prevalence rate of frailty in this study was 18.02%. After controlling the confounding effect of hospital wards clustering effect, Mixed-effect Poisson regression model showed that the associated factors of frailty included: age(OR:1.016, 95% CI:1.012 -1.020), patients with BMI < 18.5(OR: 1.248, 95% CI:1.171-1.330), female(OR:1.058, 95% CI:1.004 -1.115), ethnic minorities(OR: 1.152, 95% CI: 1.073-1.236), admission to hospital by the emergency department(OR: 1.104, 95% CI:1.030 -1.184),the former drinker(OR: 1.094, 95% CI:1.022 -1.171),fall history in past 12 month(OR:1.257, 95%CI:1.194-1.323),vision dysfunction(OR:1.144 , 95% CI:1.080 -1.211),cognition impairment(OR:1.182 , 95% CI:1.130 -1.237),sleeping dysfunction(OR:1.215, 95% CI:1.215 -1.318),urinary dysfunction(OR: 1.175, 95% CI:1.104 -1.251),defecation dysfunction(OR:1.286 , 95% CI:1.217 -1.358). Conclusion: We identified a relatively high prevalence of frailty among elder patients, and there are several associate factors among the population derived from an investigation of a large-scale, multicenter, nationwide representative Chinese elderly inpatient population. Trial registration: Chinese clinical Trial registry, ChiCTR1800017682, Registered 09 August 2018 Keywords: Frailty; Elder patients; Prevalence; Associate factors

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.

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