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 446. Epidemiology of Necrotizing Fasciitis in Korea: A Nationwide Study Using Claims Data

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S220-S220
Author(s):  
Hee Kyoung Choi ◽  
GiHyeon Seo ◽  
Euna Han
Keyword(s):  
Regression Model ◽  
Necrotizing Fasciitis ◽  
Incidence Rate ◽  
Poisson Regression ◽  
Surgical Intervention ◽  
Annual Incidence ◽  
Poisson Regression Model ◽  
Annual Incidence Rate ◽  
Mean Temperature ◽  
Age And Sex

Abstract Background Necrotizing fasciitis (NF) is a rare but fatal infectious disease that causes economic burdens on patient and the healthcare system. We investigated the incidence of necrotizing fasciitis (NF) and the seasonal variation of necrotizing fasciitis in Korea. Methods We analyzed a nationwide claims database from the Korean Health Insurance Review and Assessment Service from 2011 to 2017. For case definition, we used two different methods. First, patients who hospitalized with NF diagnosis code and received surgical intervention (NF code method) were defined as NF. Second, patients hospitalized with sepsis codes accompanying surgical intervention codes were defined as NF (sepsis code method). The annual incidence rate per 100,000 population of NF was calculated using the number of identified NF cases as numerator and age- and sex-specific midyear population as the denominator. Poisson regression models were used to assess the relationship of crude incidence rates to year, age, and sex. A multivariate Poisson regression model was used to investigate variations in trends in the monthly NF cases. Results The overall average annual incidence rate of NF during 2012–2017 was 0.86/100,000 by NF code method and 1.47/100,000 by the sepsis code method. The incidence of NF increased with age and 2.5 times higher in males than females across all age groups. Two-thirds of episodes occurred in diabetes patients. The incidence of NF occurred the most during summer. A multivariate Poisson regression model using national meteorological variables suggested that higher mean temperature of and larger numbers of NF cases during a prior month increased NF cases. Conclusion The possibility of NF should be suspected for the cases for an elderly man with diabetes in summer. From a national management perspective, the prior information on the number of NF incidences and the mean temperature can help predict NF outbreak. Disclosures All authors: No reported disclosures.

Maximum Likelihood Estimation and Testing of a Poisson Regression Model

1992 ◽  
Vol 31 (03) ◽  
pp. 215-218
Author(s):  
J. Y. Wan ◽  
A. T. Galecki
Keyword(s):  
Maximum Likelihood ◽  
Regression Model ◽  
Poisson Regression ◽  
Likelihood Function ◽  
Likelihood Estimation ◽  
Incidence Rates ◽  
Maximum Likelihood Estimates ◽  
Categorical Variables ◽  
Poisson Regression Model ◽  
Regression Parameters

Abstract:A Poisson regression model is proposed for the analysis of incidence rates presented in a two-way table classified by two categorical variables. It is shown that the likelihood function is the same as that using Glasser’s exponential covariate model. An algorithm is given to solve the maximum likelihood estimates of the regression parameters. The model is evaluated via deviance and the method is illustrated with an example. Some extensions of the model are discussed.

scholarly journals A-35 On-Field Dizziness and Timing of Referral to Vestibular Therapy Following Adolescent Sports-Related Concussion: Initial Findings

2020 ◽  
Vol 35 (5) ◽  
pp. 631-631
Author(s):  
E Reynolds ◽  
K Covert ◽  
M Bennett ◽  
S Driver ◽  
R King ◽  
...  
Keyword(s):  
Regression Model ◽  
Outpatient Clinic ◽  
Poisson Regression ◽  
Time Frame ◽  
Optimal Time ◽  
Poisson Regression Model ◽  
Post Injury ◽  
Adolescent Patients ◽  
Integral Role ◽  
Age And Sex

Abstract Objective To examine if adolescent patients who experienced on-field dizziness immediately following sports-related concussion (SRC) and were referred to vestibular therapy (VT) in 7–9 days returned to play faster when compared to referrals made > 10 days. Method Registry data for an outpatient clinic specializing in adolescent SRC was analyzed. Of the 85 adolescent patients included, 67 (79%) experienced dizziness immediately following injury; 36 (54%) of which were referred to VT. Mean age at time of injury was 15.3 years; 61.1% were male (n = 22); most were injured while participating in football (38.9%), soccer (16.7%), or wrestling (13.9%). Days to initial VT evaluation from time of injury and days from VT to clearance from concussion protocol were analyzed using a Poisson regression model; age and sex were controlled. Results Patients referred to VT 7–9 days post-injury returned to play 16 ± 13.4 days earlier (20.7 ± 18.6 days; p < .0001) than patients who received VT 10–20 days post SRC (36.7 days±32). While non-significant, individuals referred to VT > 21 days post-injury returned to play 6 days later than those referred < 10 days (26.3 ± 32.9; p = .14). Conclusions Appropriate and timely referrals to VT following the presence of on-field dizziness after SRC may play an integral role in recovery, although more research in this area is needed. Initial findings suggest that when determining optimal time frame for referral to VT, 7–9 days post-injury may be most beneficial for adolescents following SRC.

Abstract P214: Twenty-five Years Trends in Stroke Incidence in the Atherosclerosis Risk in Communities (ARIC) Study

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Silvia Koton ◽  
Andrea L Schneider ◽  
Wayne D Rosamond ◽  
Rebecca F Gottesman ◽  
Josef Coresh
Keyword(s):  
African Americans ◽  
Incidence Rate ◽  
Poisson Regression ◽  
Incidence Rates ◽  
Age Group ◽  
Calendar Time ◽  
Stroke Incidence ◽  
Atherosclerosis Risk In Communities ◽  
Atherosclerosis Risk ◽  
Rate Ratios

Introduction: Prior studies have shown a decrease in stroke mortality rates, but data on validated stroke incidence and trends by race are more limited. We aimed to study trends in stroke incidence by age and race in the Atherosclerosis Risk in Communities (ARIC) population from 1987 to 2010. Hypothesis: We assessed the hypothesis that total stroke incidence rates are decreasing over calendar time in all age groups, and in whites and African-Americans. Methods: ARIC participants were recruited in 1987-1989 and followed until 2010. A computer algorithm and physician reviewers identified and adjudicated definite and probable stroke as ischemic, intracerebral hemorrhage and subarachnoid hemorrhage (total stroke). A person-years (PY) approach using 5-year periods for age and calendar time was used for studying rates of total stroke incidence and trends. Poisson regression modeled calendar time in 5-year periods and linearly per decade, adjusted for 5-year age group. Results: From 1987 to 2010, 994 strokes occurred over 274,837 PY in 14,145 participants free of stroke at baseline. Total stroke incidence rates by age and calendar time increased with age and generally decreased with time ( Table ). Poisson regression adjusted for age group showed incidence rate ratios of 2.28 (95% CI 2.01-2.59) for African-Americans, compared to whites. Using the 1992-1996 as reference period, incidence rate ratios were 0.90 (0.70-1.16) for 1987-1991, 0.84 (0.69-1.02) for 1997-2001, 0.69 (0.56-0.85) for 2002-2006, and 0.59 (0.46-0.75) for 2007-2010. Analysis stratified by race shows an incidence rate ratio decrease per decade of -27% (-13% to - 39%) in whites and -22% (-5% to -34%) in African-Americans. Conclusions: Validated total stroke incidence rates decreased over calendar time from 1992 to 2010 in the ARIC cohort in both whites and African-Americans, after taking the aging of the cohort into account. Cohorts like ARIC only represent a limited age range but provide an ability to use validated events in updating stroke incidence trends.

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

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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