The Effect of Data Aggregation on a Poisson Regression Model of Canadian Migration

1992 ◽  
Vol 24 (10) ◽  
pp. 1381-1391 ◽  
Author(s):  
C G Amrhein ◽  
R Flowerdew
Keyword(s):  
Data Aggregation ◽  
Poisson Regression ◽  
Regression Models ◽  
High Spatial Resolution ◽  
Simulation Experiments ◽  
Data Set ◽  
Migration Data ◽  
Statistics Canada ◽  
Aggregate Population ◽  
Canada Data

A Statistics Canada data set for Canadian migration data at the census division level incorporating information on income tax for 1986 has already been presented. This matrix of 260 × 260 flows was used to calibrate a set of Poisson regression models by utilizing flows for the aggregate population. In this paper, the relatively high spatial resolution is used to test for aggregation effects as the original 260 units are combined to form fewer, synthetic regions with larger areas. A series of simulation experiments are performed with three different aggregation algorithms to create 130, 65, and ultimately 10 (corresponding to the provinces) synthetic regions. Average results from the experiments are compared with the original model. Results are obtained that suggest that, in this case, obvious aggregation effects similar to those observed elsewhere (by Openshaw) are not observed.

Statistical Inference for Zero-Inflated Poisson Regression Models with Excess Zeros

2013 ◽  
Vol 367 ◽  
pp. 253-258
Author(s):  
Lin Dai ◽  
Ying Zi Fu
Keyword(s):  
Poisson Regression ◽  
Regression Models ◽  
Test Procedure ◽  
Score Test ◽  
Zero Inflation ◽  
Test Statistic ◽  
Inference Procedure ◽  
Data Set ◽  
Excess Zeros ◽  
Fire Accident

In this paper, we deal with a class of zero-inflated Poisson regression models and propose a score test procedure for assessing whether there exists zero-inflation or not. The sampling distribution and the power of the score test statistic are investigated by a limited simulation study. Furthermore, a Bayesian inference procedure is also presented for comparison. Finally, a data set of fire accident is used to illustrate our methodology and our numerical results show that our approach is useful and appealing for the analysis of count data with zero-inflation.

scholarly journals Climate changes and their effects in the public health: use of poisson regression models

2010 ◽  
Vol 30 (2) ◽  
pp. 427-442 ◽  
Author(s):  
Jonas Bodini Alonso ◽  
Jorge Alberto Achcar ◽  
Luiz Koodi Hotta
Keyword(s):  
Poisson Regression ◽  
Regression Models ◽  
Climate Changes ◽  
Random Effect ◽  
Poisson Regression Model ◽  
Air Humidity ◽  
Data Set ◽  
The Public ◽  
Bayesian Paradigm ◽  
Ischemic Diseases

In this paper, we analyze the daily number of hospitalizations in São Paulo City, Brazil, in the period of January 01, 2002 to December 31, 2005. This data set relates to pneumonia, coronary ischemic diseases, diabetes and chronic diseases in different age categories. In order to verify the effect of climate changes the following covariates are considered: atmosphere pressure, air humidity, temperature, year season and also a covariate related to the week day when the hospitalization occurred. The possible effects of the assumed covariates in the number of hospitalization are studied using a Poisson regression model in the presence or not of a random effect which captures the possible correlation among the hospitalization accounting for the different age categories in the same day and the extra-Poisson variability for the longitudinal data. The inferences of interest are obtained using the Bayesian paradigm and MCMC (Markov chain Monte Carlo) methods.

scholarly journals Anticipated help-seeking for cancer symptoms before and after the coronavirus pandemic: results from the Onco-barometer population survey in Spain

2021 ◽  
Author(s):  
Dafina Petrova ◽  
Marina Pollán ◽  
Miguel Rodriguez-Barranco ◽  
Dunia Garrido ◽  
Josep M. Borrás ◽  
...  
Keyword(s):  
Poisson Regression ◽  
Help Seeking ◽  
Regression Models ◽  
Population Survey ◽  
Waiting Times ◽  
Perceived Barriers ◽  
Older Individuals ◽  
Cancer Symptoms ◽  
Before And After ◽  
Diagnostic Delays

Abstract Background The patient interval—the time patients wait before consulting their physician after noticing cancer symptoms—contributes to diagnostic delays. We compared anticipated help-seeking times for cancer symptoms and perceived barriers to help-seeking before and after the coronavirus pandemic. Methods Two waves (pre-Coronavirus: February 2020, N = 3269; and post-Coronavirus: August 2020, N = 1500) of the Spanish Onco-barometer population survey were compared. The international ABC instrument was administered. Pre–post comparisons were performed using multiple logistic and Poisson regression models. Results There was a consistent and significant increase in anticipated times to help-seeking for 12 of 13 cancer symptoms, with the largest increases for breast changes (OR = 1.54, 95% CI 1.22–1–96) and unexplained bleeding (OR = 1.50, 1.26–1.79). Respondents were more likely to report barriers to help-seeking in the post wave, most notably worry about what the doctor may find (OR = 1.58, 1.35–1.84) and worry about wasting the doctor’s time (OR = 1.48, 1.25–1.74). Women and older individuals were the most affected. Conclusions Participants reported longer waiting times to help-seeking for cancer symptoms after the pandemic. There is an urgent need for public interventions encouraging people to consult their physicians with symptoms suggestive of cancer and counteracting the main barriers perceived during the pandemic situation.

Challenging the Link Between Early Childhood Television Exposure and Later Attention Problems: A Multiverse Approach

2021 ◽  
pp. 095679762097165
Author(s):  
Matthew T. McBee ◽  
Rebecca J. Brand ◽  
Wallace E. Dixon
Keyword(s):  
Early Childhood ◽  
Regression Models ◽  
Propensity Score Analysis ◽  
Harmful Effect ◽  
Attention Problems ◽  
Linear Regression Models ◽  
Data Set ◽  
Logistic Regression Models ◽  
Score Analysis ◽  
Television Exposure

In 2004, Christakis and colleagues published an article in which they claimed that early childhood television exposure causes later attention problems, a claim that continues to be frequently promoted by the popular media. Using the same National Longitudinal Survey of Youth 1979 data set ( N = 2,108), we conducted two multiverse analyses to examine whether the finding reported by Christakis and colleagues was robust to different analytic choices. We evaluated 848 models, including logistic regression models, linear regression models, and two forms of propensity-score analysis. If the claim were true, we would expect most of the justifiable analyses to produce significant results in the predicted direction. However, only 166 models (19.6%) yielded a statistically significant relationship, and most of these employed questionable analytic choices. We concluded that these data do not provide compelling evidence of a harmful effect of TV exposure on attention.

scholarly journals Potential value of the current mental health monitoring of children in state care in England

BJPsych Open ◽  
2018 ◽  
Vol 4 (6) ◽  
pp. 486-491 ◽  
Author(s):  
Christine Cocker ◽  
Helen Minnis ◽  
Helen Sweeting
Keyword(s):  
Mental Health ◽  
Missing Data ◽  
Mental Health Problems ◽  
Population Level ◽  
Cross Sectional ◽  
Data Set ◽  
Looked After Children ◽  
Education Data ◽  
Sample Attrition ◽  
Aggregate Population

BackgroundRoutine screening to identify mental health problems in English looked-after children has been conducted since 2009 using the Strengths and Difficulties Questionnaire (SDQ).AimsTo investigate the degree to which data collection achieves screening aims (identifying scale of problem, having an impact on mental health) and the potential analytic value of the data set.MethodDepartment for Education data (2009–2017) were used to examine: aggregate, population-level trends in SDQ scores in 4/5- to 16/17-year-olds; representativeness of the SDQ sample; attrition in this sample.ResultsMean SDQ scores (around 50% ‘abnormal’ or ‘borderline’) were stable over 9 years. Levels of missing data were high (25–30%), as was attrition (28% retained for 4 years). Cross-sectional SDQ samples were not representative and longitudinal samples were biased.ConclusionsMental health screening appears justified and the data set has research potential, but the English screening programme falls short because of missing data and inadequate referral routes for those with difficulties.Declaration of interestNone.

Do quadratic and Poisson regression models help to predict monthly rainfall?

2021 ◽  
Vol 215 ◽  
pp. 288-318
Author(s):  
Youssef Kassem ◽  
Hüseyin Gökçekuş
Keyword(s):  
Poisson Regression ◽  
Regression Models ◽  
Monthly Rainfall

Association Between Opioid Use and Diabetes in Patients With Ankle Fracture Repair

2021 ◽  
pp. 107110072110581
Author(s):  
Wenye Song ◽  
Naohiro Shibuya ◽  
Daniel C. Jupiter
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Ankle Fracture ◽  
Regression Models ◽  
Fracture Repair ◽  
Opioid Use ◽  
Data Set ◽  
Opioid Prescription ◽  
Logistic Regression Models ◽  
Patients With Diabetes

Background: Ankle fractures in patients with diabetes mellitus have long been recognized as a challenge to practicing clinicians. Ankle fracture patients with diabetes may experience prolonged healing, higher risk of hardware failure, an increased risk of wound dehiscence and infection, and higher pain scores pre- and postoperatively, compared to patients without diabetes. However, the duration of opioid use among this patient cohort has not been previously evaluated. The purpose of this study is to retrospectively compare the time span of opioid utilization between ankle fracture patients with and without diabetes mellitus. Methods: We conducted a retrospective cohort study using our institution’s TriNetX database. A total of 640 ankle fracture patients were included in the analysis, of whom 73 had diabetes. All dates of opioid use for each patient were extracted from the data set, including the first and last date of opioid prescription. Descriptive analysis and logistic regression models were employed to explore the differences in opioid use between patients with and without diabetes after ankle fracture repair. A 2-tailed P value of .05 was set as the threshold for statistical significance. Results: Logistic regression models revealed that patients with diabetes are less likely to stop using opioids within 90 days, or within 180 days, after repair compared to patients without diabetes. Female sex, neuropathy, and prefracture opioid use are also associated with prolonged opioid use after ankle fracture repair. Conclusion: In our study cohort, ankle fracture patients with diabetes were more likely to require prolonged opioid use after fracture repair. Level of Evidence: Level III, prognostic.

How local personal vote-earning attributes affect the aggregate party vote share: Evidence from the Belgian flexible-list PR system (2003–2014)

Politics ◽  
2018 ◽  
Vol 39 (4) ◽  
pp. 464-479
Author(s):  
Gert-Jan Put ◽  
Jef Smulders ◽  
Bart Maddens
Keyword(s):  
Regression Models ◽  
Empirical Test ◽  
Original Data ◽  
Ordinary Least Squares ◽  
Vote Share ◽  
District Level ◽  
National Party ◽  
Data Set ◽  
Geographically Distributed ◽  
Party Vote Shares

This article investigates the effect of candidates exhibiting local personal vote-earning attributes (PVEA) on the aggregate party vote share at the district level. Previous research has often assumed that packing ballot lists with localized candidates increases the aggregate party vote and seat shares. We present a strict empirical test of this argument by analysing the relative electoral swing of ballot lists at the district level, a measure of change in party vote shares which controls for the national party trend and previous party results in the district. The analysis is based on data of 7527 candidacies during six Belgian regional and federal election cycles between 2003 and 2014, which is aggregated to an original data set of 223 ballot lists. The ordinary least squares (OLS) regression models do not show a significant effect of candidates exhibiting local PVEA on relative electoral swing of ballot lists. However, the results suggest that ballot lists do benefit electorally if candidates with local PVEA are geographically distributed over different municipalities in the district.

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