Hurdle Poisson Regression Model for Identifying Factors Related to Noncompliance and Waiting Time for Confirmatory Diagnosis in Colorectal Cancer Screening

2019 ◽  
Vol 35 (2) ◽  
pp. 85-91 ◽  
Author(s):  
Hsiao-Hsuan Jen ◽  
Tsung-Hsi Wang ◽  
Han-Mo Chiu ◽  
Szu-Min Peng ◽  
Chen-Yang Hsu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Regression Model ◽  
Waiting Time ◽  
Poisson Regression ◽  
Contextual Factors ◽  
Individual Characteristics ◽  
Assessment Model ◽  
Poisson Regression Model ◽  
Screening Programs ◽  
Factors Associated

AbstractObjectivesPopulation-based colorectal cancer (CRC) screening programs that use a fecal immunochemical test (FIT) are often faced with a noncompliance issue and its subsequent waiting time (WT) for those FIT positives complying with confirmatory diagnosis. We aimed to identify factors associated with both of the correlated problems in the same model.MethodsA total of 294,469 subjects, either with positive FIT test results or having a family history, collected from 2004 to 2013 were enrolled for analysis. We applied a hurdle Poisson regression model to accommodate the hurdle of compliance and also its related WT for undergoing colonoscopy while assessing factors responsible for the mixture of the two outcomes.ResultsThe effect on compliance and WT varied with contextual factors, such as geographic areas, type of screening units, and level of urbanization. The hurdle score, representing the risk score in association with noncompliance, and the WT score, reflecting the rate of taking colonoscopy, were used to classify subjects into each of three groups representing the degree of compliance and the level of health awareness.ConclusionOur model was not only successfully applied to evaluating factors associated with the compliance and the WT distribution, but also developed into a useful assessment model for stratifying the risk and predicting whether and when screenees comply with the procedure of receiving confirmatory diagnosis given contextual factors and individual characteristics.

Risk of post-colonoscopy colorectal cancer in Denmark: time trends and comparison with Sweden and the English National Health Service

Endoscopy ◽  
2019 ◽  
Vol 51 (08) ◽  
pp. 733-741 ◽  
Author(s):  
Lasse Pedersen ◽  
Roland Valori ◽  
Inge Bernstein ◽  
Karen Lindorff-Larsen ◽  
Charlotte Green ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Poisson Regression ◽  
Audit And Feedback ◽  
Consensus Method ◽  
Poisson Regression Model ◽  
Contributing Factors ◽  
Poisson Regression Analysis ◽  
Factors Associated ◽  
Training Background ◽  
English National Health

Abstract Background The post-colonoscopy colorectal cancer (PCCRC) rate is a key quality indicator for colonoscopy. Previously published PCCRC rates have been difficult to compare owing to differences in methodology. The primary aim of this study was to compare Danish PCCRC rates internationally and to calculate Danish PCCRC rates using the World Endoscopy Organization (WEO) consensus method for future comparison. The secondary aim was to identify factors associated with PCCRC. Methods National registries were used to examine the risk of PCCRC. The Danish 3-year rate of PCCRC (PCCRC-3yr) was calculated using previously published methods from England, Sweden, and the WEO. Poisson regression analysis was performed to identify factors associated with PCCRC. Results The Danish PCCRC-3yr was significantly higher than the rate in the English NHS (relative risk [RR] 1.12, 95 % confidence interval [CI] 1.05 – 1.19) and Sweden (RR 1.15, 95 %CI 1.06 – 1.24). The Danish PCCRC-3yr based on the WEO consensus method fell from 22.5 % in 2001 to 7.9 % in 2012. The multivariable Poisson regression model found PCCRC to be significantly associated with diverticulitis (RR 3.25, 95 %CI 2.88 – 3.66), ulcerative colitis (RR 3.44, 95 %CI 2.79 – 4.23), hereditary cancer (age < 60 years: RR 7.39, 95 %CI 5.77 – 9.47; age ≥ 60 years: RR 3.81, 95 %CI 2.74 – 5.31), and location in the transverse (RR 1.57, 95 %CI 1.28 – 1.94) and ascending colon (RR 1.85, 95 %CI 1.64 – 2.08). Conclusions The PCCRC-3yr was higher in Denmark than in comparable countries. Differences in colonoscopist training, background, and certification are possible contributing factors. A review of colonoscopist training and certification in Denmark, and continuous audit and feedback of colonoscopist performance may reduce PCCRC-3yr.

scholarly journals Approximation of the Cox survival regression model by MCMC Bayesian Hierarchical Poisson modelling of factors associated with childhood mortality in Nigeria

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. F. Fagbamigbe ◽  
M. M. Salawu ◽  
S. M. Abatan ◽  
O. Ajumobi
Keyword(s):  
Regression Model ◽  
Survival Data ◽  
Poisson Regression ◽  
Maternal Education ◽  
Contextual Factors ◽  
Childhood Mortality ◽  
Bayesian Hierarchical ◽  
Under Five ◽  
Factors Associated ◽  
Survival Regression

AbstractThe need for more pragmatic approaches to achieve sustainable development goal on childhood mortality reduction necessitated this study. Simultaneous study of the influence of where the children live and the censoring nature of children survival data is scarce. We identified the compositional and contextual factors associated with under-five (U5M) and infant (INM) mortality in Nigeria from 5 MCMC Bayesian hierarchical Poisson regression models as approximations of the Cox survival regression model. The 2018 DHS data of 33,924 under-five children were used. Life table techniques and the Mlwin 3.05 module for the analysis of hierarchical data were implemented in Stata Version 16. The overall INM rate (INMR) was 70 per 1000 livebirths compared with U5M rate (U5MR) of 131 per 1000 livebirth. The INMR was lowest in Ogun (17 per 1000 live births) and highest in Kaduna (106), Gombe (112) and Kebbi (116) while the lowest U5MR was found in Ogun (29) and highest in Jigawa (212) and Kebbi (248). The risks of INM and U5M were highest among children with none/low maternal education, multiple births, low birthweight, short birth interval, poorer households, when spouses decide on healthcare access, having a big problem getting to a healthcare facility, high community illiteracy level, and from states with a high proportion of the rural population in the fully adjusted model. Compared with the null model, 81% vs 13% and 59% vs 35% of the total variation in INM and U5M were explained by the state- and neighbourhood-level factors respectively. Infant- and under-five mortality in Nigeria is influenced by compositional and contextual factors. The Bayesian hierarchical Poisson regression model used in estimating the factors associated with childhood deaths in Nigeria fitted the survival data.

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.

scholarly journals Geographical Variation and Factors Associated with Colorectal Cancer Mortality in a Universal Health Care System

2014 ◽  
Vol 28 (4) ◽  
pp. 191-197 ◽  
Author(s):  
Mahmoud Torabi ◽  
Christopher Green ◽  
Zoann Nugent ◽  
Salaheddin M Mahmud ◽  
Alain A Demers ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Health Care ◽  
Geographical Variation ◽  
Geographical Area ◽  
Capital City ◽  
Universal Health Care ◽  
Screening Programs ◽  
Crc Screening ◽  
Factors Associated ◽  
Universal Health

OBJECTIVE: To investigate the geographical variation and small geographical area level factors associated with colorectal cancer (CRC) mortality.METHODS: Information regarding CRC mortality was obtained from the population-based Manitoba Cancer Registry, population counts were obtained from Manitoba’s universal health care plan Registry and characteristics of the area of residence were obtained from the 2001 Canadian census. Bayesian spatial Poisson mixed models were used to evaluate the geographical variation of CRC mortality and Poisson regression models for determining associations with CRC mortality. Time trends of CRC mortality according to income group were plotted using joinpoint regression.RESULTS: The southeast (mortality rate ratio [MRR] 1.31 [95% CI 1.12 to 1.54) and southcentral (MRR 1.62 [95% CI 1.35 to 1.92]) regions of Manitoba had higher CRC mortality rates than suburban Winnipeg (Manitoba’s capital city). Between 1985 and 1996, CRC mortality did not vary according to household income; however, between 1997 and 2009, individuals residing in the highest-income areas were less likely to die from CRC (MRR 0.77 [95% CI 0.65 to 0.89]). Divergence in CRC mortality among individuals residing in different income areas increased over time, with rising CRC mortality observed in the lowest income areas and declining CRC mortality observed in the higher income areas.CONCLUSIONS: Individuals residing in lower income neighbourhoods experienced rising CRC mortality despite residing in a jurisdiction with universal health care and should receive increased efforts to reduce CRC mortality. These findings should be of particular interest to the provincial CRC screening programs, which may be able to reduce the disparities in CRC mortality by reducing the disparities in CRC screening participation.

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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