Ageing Prisoners' Disease Burden: Is Being Old a Better Predictor than Time Served in Prison?

Background: The number of older prisoners entering and ageing in prison has increased in the last few decades. Ageing prisoners pose unique challenges to the prison administration as they have differentiated social, custodial and healthcare needs than prisoners who are younger and relatively healthier. Objective: The goal of this study was to explore and compare the somatic disease burden of old and young prisoners, and to examine whether it can be explained by age group and/or time served in prison. Methods: Access to prisoner medical records was granted to extract disease and demographic information of older (>50 years) and younger (≤49 years) prisoners in different Swiss prisons. Predictor variables included the age group and the time spent in prison. The dependent variable was the total number of somatic diseases as reported in the medical records. Results were analysed using descriptive statistics and a negative binomial model. Results: Data of 380 male prisoners from 13 different prisons in Switzerland reveal that the mean ages of older and younger prisoners were 58.78 and 34.26 years, respectively. On average, older prisoners have lived in prison for 5.17 years and younger prisoners for 2.49 years. The average total number of somatic diseases reported by older prisoners was 2.26 times higher than that of prisoners below 50 years of age (95% CI 1.77-2.87, p < 0.001). Conclusion: This study is the first of its kind to capture national disease data of prisoners with a goal of comparing the disease burden of older and younger prisoners. Study findings indicate that older inmates suffer from more somatic diseases and that the number of diseases increases with age group. Results clearly illustrate the poorer health conditions of those who are older, their higher healthcare burden, and raises questions related to the provision of healthcare for inmates growing old in prison. © 2014 S. Karger AG, Basel.

Efficacy Of Canakinumab (Acz885), A Fully Human Anti-Interleukin (Il)-1beta Monoclonal Antibody, In The Prevention Of Flares In Gout Patients Initiating Allopurinol Therapy

Background: Gout patients initiating urate lowering therapy have an increased risk of flares. Inflammation in gouty arthritis is induced by IL-1b. Canakinumab targets and inhibits IL-1b effectively in clinical studies. This study compared different doses of canakinumab vs colchicine in preventing flares in gout patients initiating allopurinol therapy.Methods: In this 24 week double blind study, gout patients (20-79 years) initiating allopurinol were randomized (1:1:1:1:1:1:2) to canakinumab s.c. single doses of 25, 50, 100, 200, 300 mg, or 150 mg divided in doses every 4 weeks (50+50+25+25 mg [q4wk]) or colchicine 0.5 mg p.o. daily for 16 weeks. Primary outcome was to determine the canakinumab dose giving comparable efficacy to colchicine with respect to the number of gout flares occurring during first 16 weeks. Secondary outcomes included number of patients with gout flares and C-reactive protein (CRP) levels during the first 16 weeks.Results: 432 patients were randomized and 391 (91%) completed the study. All canakinumab doses were better than colchicine in preventing flares and therefore, a canakinumab dose comparable to colchicine could not be determined. Based on a negative binomial model, all canakinumab groups, except 25 mg, reduced the flare rate ratio per patient significantly compared to colchicine group (rate ratio estimates 25 mg 0.60, 50 mg 0.34, 100 mg 0.28, 200 mg 0.37, 300 mg 0.29, q4wk 0.38; p<=0.05). The percentage of patients with flares was lower for all canakinumab groups (25 mg 27.3%, 50 mg 16.7%, 100 mg 14.8%, 200 mg 18.5%, 300 mg 15.1%, q4wk 16.7%) compared to colchicine group (44.4%). All patients taking canakinumab were significantly less likely to experience at least one gout flare than patients taking colchicine (odds ratio range [0.22 - 0.47]; p<=0.05 for all). The median baseline CRP levels were 2.86 mg/L for 25 mg, 3.42 mg/L for 50 mg, 1.76 mg/L for 100 mg, 3.66 mg/L for 200 mg, 3.21 mg/L for 300 mg, 3.23 mg/L for q4wk canakinumab groups and 2.69 mg/L for colchicine group. In all canakinumab groups with median CRP levels above the normal range at baseline, median levels declined within 15 days of treatment and were maintained at normal levels (ULN=3 mg/L) throughout the 16 week period. Adverse events (AEs) occurred in 52.7% (25 mg), 55.6% (50 mg), 51.9% (100 mg), 51.9% (200 mg), 54.7% (300 mg), and 58.5% (q4wk) of patients on canakinumab vs 53.7% of patients on colchicine. Serious AEs (SAE) were reported in 2 (3.6%; 25 mg), 2 (3.7%, 50 mg), 3 (5.6%, 100 mg), 3 (5.6%, 200 mg), 3 (5.7%, 300 mg) and 1 (1.9%, q4wk) patients on canakinumab and in 5 (4.6%) patients on colchicine. One fatal SAE (myocardial infarction, not related to study drug) occurred in colchicine group.Conclusion: In this large randomized, double-blind active controlled study of flare prevention in gout patients initiating allopurinol therapy, treatment with canakinumab led to a statistically significant reduction in flares compared with colchicine (standard of care), and was well tolerated.

Canakinumab reduces the risk of acute gouty arthritis flares during initiation of allopurinol treatment: results of a double-blind, randomised study.

Objective This study assessed the efficacy and safety of canakinumab, a fully human anti-interleukin 1 beta monoclonal antibody, for prophylaxis against acute gouty arthritis flares in patients initiating urate-lowering treatment.Methods In this double-blind, double-dummy, dose-ranging study, 432 patients with gouty arthritis initiating allopurinol treatment were randomised 1:1:1:1:1:1:2 to receive: a single dose of canakinumab, 25, 50, 100, 200, or 300 mg subcutaneously; 4 x 4-weekly doses of canakinumab (50 + 50 + 25 + 25 mg subcutaneously); or daily colchicine 0.5 mg orally for 16 weeks. Patients recorded details of flares in diaries. The study aimed to determine the canakinumab dose having equivalent efficacy to colchicine 0.5 mg at 16 weeks.Results A dose-response for canakinumab was not apparent with any of the four predefined dose-response models. The estimated canakinumab dose with equivalent efficacy to colchicine was below the range of doses tested. At 16 weeks, there was a 62% to 72% reduction in the mean number of flares per patient for canakinumab doses >= 50 mg versus colchicine based on a negative binomial model (rate ratio: 0.28-0.38, p <= 0.0083), and the percentage of patients experiencing >= 1 flare was significantly lower for all canakinumab doses (15% to 27%) versus colchicine (44%, p<0.05). There was a 64% to 72% reduction in the risk of experiencing >= 1 flare for canakinumab doses >= 50 mg versus colchicine at 16 weeks (hazard ratio (HR): 0.28-0.36, p <= 0.05). The incidence of adverse events was similar across treatment groups.Conclusions Single canakinumab doses >= 50 mg or four 4-weekly doses provided superior prophylaxis against flares compared with daily colchicine 0.5 mg.

Efficacy of Canakinumab (ACZ885), a Fully Human Anti-Interleukin (IL)-1beta Monoclonal Antibody, in the Prevention of Flares in Gout Patients Initiating Allopurinol Therapy

Background: Gout patients initiating urate lowering therapy have an increased risk of flares. Inflammation in gouty arthritis is induced by IL-1b. Canakinumab targets and inhibits IL-1b effectively in clinical studies. This study compared different doses of canakinumab vs colchicine in preventing flares in gout patients initiating allopurinol therapy.Methods: In this 24 week double blind study, gout patients (20-79 years) initiating allopurinol were randomized (1:1:1:1:1:1:2) to canakinumab s.c. single doses of 25, 50, 100, 200, 300 mg, or 150 mg divided in doses every 4 weeks (50+50+25+25 mg [q4wk]) or colchicine 0.5 mg p.o. daily for 16 weeks. Primary outcome was to determine the canakinumab dose giving comparable efficacy to colchicine with respect to the number of gout flares occurring during first 16 weeks. Secondary outcomes included number of patients with gout flares and C-reactive protein (CRP) levels during the first 16 weeks.Results: 432 patients were randomized and 391 (91%) completed the study. All canakinumab doses were better than colchicine in preventing flares and therefore, a canakinumab dose comparable to colchicine could not be determined. Based on a negative binomial model, all canakinumab groups, except 25 mg, reduced the flare rate ratio per patient significantly compared to colchicine group (rate ratio estimates 25 mg 0.60, 50 mg 0.34, 100 mg 0.28, 200 mg 0.37, 300 mg 0.29, q4wk 0.38; p<=0.05). The percentage of patients with flares was lower for all canakinumab groups (25 mg 27.3%, 50 mg 16.7%, 100 mg 14.8%, 200 mg 18.5%, 300 mg 15.1%, q4wk 16.7%) compared to colchicine group (44.4%). All patients taking canakinumab were significantly less likely to experience at least one gout flare than patients taking colchicine (odds ratio range [0.22 - 0.47]; p<=0.05 for all). The median baseline CRP levels were 2.86 mg/L for 25 mg, 3.42 mg/L for 50 mg, 1.76 mg/L for 100 mg, 3.66 mg/L for 200 mg, 3.21 mg/L for 300 mg, 3.23 mg/L for q4wk canakinumab groups and 2.69 mg/L for colchicine group. In all canakinumab groups with median CRP levels above the normal range at baseline, median levels declined within 15 days of treatment and were maintained at normal levels (ULN=3 mg/L) throughout the 16 week period. Adverse events (AEs) occurred in 52.7% (25 mg), 55.6% (50 mg), 51.9% (100 mg), 51.9% (200 mg), 54.7% (300 mg), and 58.5% (q4wk) of patients on canakinumab vs 53.7% of patients on colchicine. Serious AEs (SAE) were reported in 2 (3.6%; 25 mg), 2 (3.7%, 50 mg), 3 (5.6%, 100 mg), 3 (5.6%, 200 mg), 3 (5.7%, 300 mg) and 1 (1.9%, q4wk) patients on canakinumab and in 5 (4.6%) patients on colchicine. One fatal SAE (myocardial infarction, not related to study drug) occurred in colchicine group.Conclusion: In this large randomized, double-blind active controlled study of flare prevention in gout patients initiating allopurinol therapy, treatment with canakinumab led to a statistically significant reduction in flares compared with colchicine (standard of care), and was well tolerated.

La régression de Poisson multiniveau généralisée au sein d’un devis longitudinal : un exemple de modélisation du nombre d’arrestations de membres de gangs de rue à Montréal entre 2005 et 2007

Les données comptées (count data) possèdent des distributions ayant des caractéristiques particulières comme la non-normalité, l’hétérogénéité des variances ainsi qu’un nombre important de zéros. Il est donc nécessaire d’utiliser les modèles appropriés afin d’obtenir des résultats non biaisés. Ce mémoire compare quatre modèles d’analyse pouvant être utilisés pour les données comptées : le modèle de Poisson, le modèle binomial négatif, le modèle de Poisson avec inflation du zéro et le modèle binomial négatif avec inflation du zéro. À des fins de comparaisons, la prédiction de la proportion du zéro, la confirmation ou l’infirmation des différentes hypothèses ainsi que la prédiction des moyennes furent utilisées afin de déterminer l’adéquation des différents modèles. Pour ce faire, le nombre d’arrestations des membres de gangs de rue sur le territoire de Montréal fut utilisé pour la période de 2005 à 2007. L’échantillon est composé de 470 hommes, âgés de 18 à 59 ans. Au terme des analyses, le modèle le plus adéquat est le modèle binomial négatif puisque celui-ci produit des résultats significatifs, s’adapte bien aux données observées et produit une proportion de zéro très similaire à celle observée.

Geoadditive models for regional count data: an application to industrial location

We propose a geoadditive negative binomial model (Geo-NB-GAM) for regional count data that allows us to address simultaneously some important methodological issues, such as spatial clustering, nonlinearities, and overdispersion. This model is applied to the study of location determinants of inward greenfield investments that occurred during 2003–2007 in 249 European regions. After presenting the data set and showing the presence of overdispersion and spatial clustering, we review the theoretical framework that motivates the choice of the location determinants included in the empirical model, and we highlight some reasons why the relationship between some of the covariates and the dependent variable might be nonlinear. The subsequent section first describes the solutions proposed by previous literature to tackle spatial clustering, nonlinearities, and overdispersion, and then presents the Geo-NB-GAM. The empirical analysis shows the good performance of Geo-NB-GAM. Notably, the inclusion of a geoadditive component (a smooth spatial trend surface) permits us to control for spatial unobserved heterogeneity that induces spatial clustering. Allowing for nonlinearities reveals, in keeping with theoretical predictions, that the positive effect of agglomeration economies fades as the density of economic activities reaches some threshold value. However, no matter how dense the economic activity becomes, our results suggest that congestion costs never overcome positive agglomeration externalities.

Probability distribution models for daily rainfall data for an interior station of Brazil

Two stochastic models have been fitted to daily rainfall data for an interior station of Brazil. Of these two models, the results show a better fit to describe the data, by truncated negative probability model in comparison with Markov chain probability model. Kolmogorov-Smirnov test is applied for significance for these models. © 1983 Springer-Verlag.

The impact of climate on Leptospirosis in Sao Paulo, Brazil

In this work, we correlate the daily number of human leptospirosis cases with several climatic factors. We used a negative binomial model that considers hospital daily admissions due to leptospirosis as the dependent variable, and the climatic variables of daily precipitation pattern, and maximum and minimum temperature as independent variables. We calculated the monthly leptospirosis admission probabilities from the precipitation and maximum temperature variables. The month of February showed the highest probability, although values were also high during the spring months. The month of February also showed the highest number of hospital admissions. Another interesting result is that, for every 20 mm precipitation, there was an average increase of 31.5% in hospital admissions. Additionally, the relative risk of leptospirosis varied from 1.1 to 2.0 when the precipitation varied from 20 to 140 mm.

Migration background is associated with caries in Viennese school children, even if parents have received a higher education.

BACKGROUND A low level of education and the migration background of parents are associated with the development of caries in children. The aim of this study was to evaluate whether a higher educational level of parents can overcome risks for the development of caries in immigrants in Vienna, Austria. METHODS The educational level of the parents, the school type, and the caries status of 736 randomly selected twelve-year-old children with and without migration background was determined in this cross sectional study. In children attending school in Vienna the decayed, missing, and filled teeth (DMFT) index was determined. For statistical analysis, a mixed negative-binomial-model was used. RESULTS The caries status of the children with migration background was significantly worse compared to that of the native Viennese population. A significant interaction was found between migration background and the educational level of the parents (p = 0.045). No interaction was found between the school type and either the migration background (p = 0.220) or the education level of the parents (p = 0.08). In parents with a higher scholarly education level, migration background (p < 0.01) and school type (p = 0.018) showed an association with DMFT values. In parents with a low education level, however, migration background and school type had no significant association with DMFT values. CONCLUSION These data indicate that children with a migration background are at higher risk to acquire caries than other Viennese children, even when the parents have received a higher education.

Determinant factors of senior tourists’ length of stay

The current tendency to undertake more trips, but of shorter duration, throughout the year, has meant that the tourist industry has started to show greater interest in attracting those market segments that opt for more prolonged stays, as they are especially profitable. One of these segments is that of seniors. Given the aging demographic of the population worldwide, which is particularly noticeable in Spain, the object of this study is to identify the variables that determine the length of stay of Spanish seniors at their destination. The Negative Binomial model was adapted to the context of length of stay by Spanish seniors and the determinant factors identified were: age, travel purpose, climate, type of accommodation, group size, trip type and the activities carried out at the destination. This study is a contribution to this field from an empirical point of view, given the scarcity of studies of this type and their eminently descriptive character; as well as from a practical level, with interesting implications for the sector.

SURVEILLANCE OF, AND THE IMPACT OF COMMUNITY POLICING ON ARREST-RELATED DEATHS (ARDS): Exploring the surveillance of ARDs and the opportunities to prevent them in the United States

Thesis (Master's)--University of Washington, 2016-08

A flexible model for survival data with a cure rate: a Bayesian approach

In this paper we deal with a Bayesian analysis for right-censored survival data suitable for populations with a cure rate. We consider a cure rate model based on the negative binomial distribution, encompassing as a special case the promotion time cure model. Bayesian analysis is based on Markov chain Monte Carlo (MCMC) methods. We also present some discussion on model selection and an illustration with a real dataset.

A Bayesian Long-term Survival Model Parametrized in the Cured Fraction

The main goal of this paper is to investigate a cure rate model that comprehends some well-known proposals found in the literature. In our work the number of competing causes of the event of interest follows the negative binomial distribution. The model is conveniently reparametrized through the cured fraction, which is then linked to covariates by means of the logistic link. We explore the use of Markov chain Monte Carlo methods to develop a Bayesian analysis in the proposed model. The procedure is illustrated with a numerical example.

Differential expression analysis for sequence count data via mixtures of negative binomials

The recent advent of Next-generation sequencing technologies has revolutionized the way of analyzing the genome. This innovation allows to get deeper information at a lower cost and in less time, and provides data that are discrete measurements. One of the most important applications with these data is the differential analysis, that is investigating if one gene exhibit a different expression level in correspondence of two (or more) biological conditions (such as disease states, treatments received and so on). As for the statistical analysis, the final aim will be statistical testing and for modeling these data the Negative Binomial distribution is considered the most adequate one especially because it allows for "over dispersion". However, the estimation of the dispersion parameter is a very delicate issue because few information are usually available for estimating it. Many strategies have been proposed, but they often result in procedures based on plug-in estimates, and in this thesis we show that this discrepancy between the estimation and the testing framework can lead to uncontrolled first-type errors. We propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Afterwards, three consistent statistical tests are developed for differential expression analysis. We show that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it is the best one in reaching the nominal value for the first-type error, while keeping elevate power. The method is finally illustrated on prostate cancer RNA-seq data.

An empirical model to estimate the demand for primary care in urban settings

Objective. To measure the demand for primary care and its associated factors by building and estimating a demand model of primary care in urban settings.^ Data source. Secondary data from 2005 California Health Interview Survey (CHIS 2005), a population-based random-digit dial telephone survey, conducted by the UCLA Center for Health Policy Research in collaboration with the California Department of Health Services, and the Public Health Institute between July 2005 and April 2006.^ Study design. A literature review was done to specify the demand model by identifying relevant predictors and indicators. CHIS 2005 data was utilized for demand estimation.^ Analytical methods. The probit regression was used to estimate the use/non-use equation and the negative binomial regression was applied to the utilization equation with the non-negative integer dependent variable.^ Results. The model included two equations in which the use/non-use equation explained the probability of making a doctor visit in the past twelve months, and the utilization equation estimated the demand for primary conditional on at least one visit. Among independent variables, wage rate and income did not affect the primary care demand whereas age had a negative effect on demand. People with college and graduate educational level were associated with 1.03 (p < 0.05) and 1.58 (p < 0.01) more visits, respectively, compared to those with no formal education. Insurance was significantly and positively related to the demand for primary care (p < 0.01). Need for care variables exhibited positive effects on demand (p < 0.01). Existence of chronic disease was associated with 0.63 more visits, disability status was associated with 1.05 more visits, and people with poor health status had 4.24 more visits than those with excellent health status. ^ Conclusions. The average probability of visiting doctors in the past twelve months was 85% and the average number of visits was 3.45. The study emphasized the importance of need variables in explaining healthcare utilization, as well as the impact of insurance, employment and education on demand. The two-equation model of decision-making, and the probit and negative binomial regression methods, was a useful approach to demand estimation for primary care in urban settings.^