Profiling of county-level foster care placements using random-effects Poisson regression models

2007 ◽  
Vol 7 (3-4) ◽  
pp. 97-108 ◽  
Author(s):  
Robert D. Gibbons ◽  
Kwan Hur ◽  
Dulal K. Bhaumik ◽  
Carl C. Bell
2006 ◽  
Vol 36 (1) ◽  
pp. 285-301 ◽  
Author(s):  
Jean-Philippe Boucher ◽  
Michel Denuit

This paper examines the validity of some stylized statements that can be found in the actuarial literature about random effects models. Specifically, the actual meaning of the estimated parameters and the nature of the residual heterogeneity are discussed. A numerical illustration performed on a Belgian motor third party liability portfolio supports this discussion.


2021 ◽  
pp. 1471082X2110080
Author(s):  
Kangjie Zhang ◽  
Juxin Liu ◽  
Yang Liu ◽  
Peng Zhang ◽  
Raymond J. Carroll

Fatal car crashes are the leading cause of death among teenagers in the USA. The Graduated Driver Licensing (GDL) programme is one effective policy for reducing the number of teen fatal car crashes. Our study focuses on the number of fatal car crashes in Michigan during 1990–2004 excluding 1997, when the GDL started. We use Poisson regression with spatially dependent random effects to model the county level teen car crash counts. We develop a measurement error model to account for the fact that the total teenage population in the county level is used as a proxy for the teenage driver population. To the best of our knowledge, there is no existing literature that considers adjustment for measurement error in an offset variable. Furthermore, limited work has addressed the measurement errors in the context of spatial data. In our modelling, a Berkson measurement error model with spatial random effects is applied to adjust for the error-prone offset variable in a Bayesian paradigm. The Bayesian Markov chain Monte Carlo (MCMC) sampling is implemented in rstan. To assess the consequence of adjusting for measurement error, we compared two models with and without adjustment for measurement error. We found the effect of a time indicator becomes less significant with the measurement-error adjustment. It leads to our conclusion that the reduced number of teen drivers can help explain, to some extent, the effectiveness of GDL.


2014 ◽  
Vol 143 (8) ◽  
pp. 1692-1701
Author(s):  
N. F. REEVE ◽  
T. R. FANSHAWE ◽  
K. LAMDEN ◽  
P. J. DIGGLE ◽  
J. CHEESBROUGH ◽  
...  

SUMMARYMany cases of giardiasis in the UK are undiagnosed and among other things, diagnosis is dependent upon the readiness of GPs to request a specimen. The aim of this study is to assess the rate of specimens requested per GP practice in Central Lancashire, to examine the differences between GP practices and to estimate the pattern of unexplained spatial variation in the practice rate of specimens after adjustment for deprivation. To achieve this, we fitted a set of binomial and Poisson regression models, with random effects for GP practice. Our analysis suggests that there were differences in the rate of specimens by GP practices (P < 0·001) for a single year, but no difference in the proportion of positive tests per specimen submitted or in the rate of positive specimens per practice population. There was a difference in the cumulative rate of positive specimens per practice population over a 9-year period (P < 0·001). Neither the specimen rate per practice for a single year nor the cumulative rate of positive specimens over multiple years demonstrated significant spatial correlation. Hence, spatial variation in the incidence of giardiasis is unlikely to be confounded by variation in GP rate of specimens.


2006 ◽  
Vol 36 (01) ◽  
pp. 285-301 ◽  
Author(s):  
Jean-Philippe Boucher ◽  
Michel Denuit

This paper examines the validity of some stylized statements that can be found in the actuarial literature about random effects models. Specifically, the actual meaning of the estimated parameters and the nature of the residual heterogeneity are discussed. A numerical illustration performed on a Belgian motor third party liability portfolio supports this discussion.


Author(s):  
Dafina Petrova ◽  
Marina Pollán ◽  
Miguel Rodriguez-Barranco ◽  
Dunia Garrido ◽  
Josep M. Borrás ◽  
...  

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.


SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A271-A271
Author(s):  
Azizi Seixas ◽  
Nicholas Pantaleo ◽  
Samrachana Adhikari ◽  
Michael Grandner ◽  
Giardin Jean-Louis

Abstract Introduction Causes of COVID-19 burden in urban, suburban, and rural counties are unclear, as early studies provide mixed results implicating high prevalence of pre-existing health risks and chronic diseases. However, poor sleep health that has been linked to infection-based pandemics may provide additional insight for place-based burden. To address this gap, we investigated the relationship between habitual insufficient sleep (sleep &lt;7 hrs./24 hr. period) and COVID-19 cases and deaths across urban, suburban, and rural counties in the US. Methods County-level variables were obtained from the 2014–2018 American community survey five-year estimates and the Center for Disease Control and Prevention. These included percent with insufficient sleep, percent uninsured, percent obese, and social vulnerability index. County level COVID-19 infection and death data through September 12, 2020 were obtained from USA Facts. Cumulative COVID-19 infections and deaths for urban (n=68), suburban (n=740), and rural (n=2331) counties were modeled using separate negative binomial mixed effects regression models with logarithmic link and random state-level intercepts. Zero-inflated models were considered for deaths among suburban and rural counties to account for excess zeros. Results Multivariate regression models indicated positive associations between cumulative COVID-19 infection rates and insufficient sleep in urban, suburban and rural counties. The incidence rate ratio (IRR) for urban counties was 1.03 (95% CI: 1.01 – 1.05), 1.04 (95% CI: 1.02 – 1.05) for suburban, and 1.02 (95% CI: 1.00 – 1.03) rural counties.. Similar positive associations were observed with county-level COVID-19 death rates, IRR = 1.11 (95% CI: 1.07 – 1.16) for urban counties, IRR = 1.04 (95% CI: 1.01 – 1.06) for suburban counties, and IRR = 1.03 (95% CI: 1.01 – 1.05) for rural counties. Level of urbanicity moderated the association between insufficient sleep and COVID deaths, but not for the association between insufficient sleep and COVID infection rates. Conclusion Insufficient sleep was associated with COVID-19 infection cases and mortality rates in urban, suburban and rural counties. Level of urbanicity only moderated the relationship between insufficient sleep and COVID death rates. Future studies should investigate individual-level analysis to understand the role of sleep mitigating COVID-19 infection and death rates. Support (if any) NIH (K07AG052685, R01MD007716, R01HL142066, K01HL135452, R01HL152453


2021 ◽  
Vol 215 ◽  
pp. 288-318
Author(s):  
Youssef Kassem ◽  
Hüseyin Gökçekuş

2021 ◽  
Author(s):  
Sarah F. Loch ◽  
Bradley D. Stein ◽  
Robin Ghertner ◽  
Elizabeth McNeer ◽  
William D. Dupont ◽  
...  

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