Unemployment Duration: Competing and Defective Risks

This paper examines the determinants of unemployment duration in the framework of a competing risks model, where the destination states are employment and inactivity. The major innovation is the use of a split-population approach to accommodate the presence of defective risks in the context of the competing risks model. Certain of the regressors that affect the conditional hazards are allowed to influence defective risks. Unobserved individual heterogeneity among the susceptible populations is also controlled for. Access to unemployment benefits and age are accorded special emphasis because of their influence on defective risks and escape rates.

A simulation study of maximum likelihood estimation in logistic regression with cured individuals

The logistic regression model is widely used to investigate the relationship between a binary outcome Y and a set of potential predictors X. Diop et al. (2011) present some conditions under which the maximum likelihood estimator is consistent and asymptotically normal in the logistic regression model with a cure fraction. So far, however, only limited simulation results are available to judge the quality of this estimator in finite samples. Therefore in this paper, we conduct a detailed simulation study of its numerical properties. We evaluate its accuracy and the quality of the normal approximation of its asymptotic distribution. We also study the quality of the approximation for constructing asymptotic Wald-type tests of hypothesis. Finally, we consider the problem of estimating the conditional probability of the outcome. Our results indicate that when the proportion of cured individuals is moderate to moderately large, and the sample size is large enough, reliable statistical inferences can be obtained for the regression effects and the probability of the outcome. Our results also indicate that the approximations can be problematic when the cure fraction is very large

SIR epidemics with long range infection in one dimension

We study epidemic processes with immunization on very large 1-dimensional lattices, where at least some of the infections are non-local, with rates decaying as power laws p(x) ~ x^{-sigma-1} for large distances x. When starting with a single infected site, the cluster of infected sites stays always bounded if $\sigma >1$ (and dies with probability 1, of its size is allowed to fluctuate down to zero), but the process can lead to an infinite epidemic for sigma <1. For sigma <0 the behavior is essentially of mean field type, but for 0 < sigma <= 1 the behavior is non-trivial, both for the critical and for supercritical cases. For critical epidemics we confirm a previous prediction that the critical exponents controlling the correlation time and the correlation length are simply related to each other, and we verify detailed field theoretic predictions for sigma --> 1/3. For sigma = 1 we find generic power laws with continuously varying exponents even in the supercritical case, and confirm in detail the predicted Kosterlitz-Thouless nature of the transition. Finally, the mass N(t) of supercritical clusters seems to grow for 0 < sigma < 1 like a stretched exponential. The latter implies that networks embedded in 1-d space with power-behaved link distributions have infinite intrinsic dimension (based on the graph distance), but are not small world.Comment: 16 pages, including 28 figures; minor changes from version v

Maladie coeliaque associée à une maladie de Basedow et un déficit sélectif en IgA chez une fille de 4 ans

La maladie coeliaque est une entéropathie auto-immune, induite par l'ingestion du gluten chez des sujets génétiquement prédisposés. Son association à d'autres maladies auto-immunes est décrite. Néanmoins l'association de la maladie céliaque, la maladie de Basedow et le déficit en IgA sélectif est rarement relevée chez l'enfant. Nous rapportons l'observation exceptionnelle d'une fille âgée de 4 ans qui présente une maladie c'liaque associée à une maladie de basedow et un déficit sélectif en IgA.Pan African Medical Journal 2015; 2

Overheard at Gettysburg

Monday. In Old TKE. A student of color is called in the hallway to hear the “funniest thing ever.” (giggling) “Night night little nigglet.” Tuesday. In an AFS class. “I’m pretty sure the majority of black students in my private school were there because of sports.” Wednesday. In Musselman. Woman: “I can’t believe Trayvon Martin got shot because someone thought skittles was a weapon.” Man: “To be honest, he did look suspicious because he was black.” [excerpt

Maximum likelihood estimation in the logistic regression model with a cure fraction

International audienceLogistic regression is widely used in medical studies to investigate the relationship between a binary response variable Y and a set of potential predictors X. The binary response may represent, for example, the occurrence of some outcome of interest (Y=1 if the outcome occurred and Y=0 otherwise). In this paper, we consider the problem of estimating the logistic regression model with a cure fraction. A sample of observations is said to contain a cure fraction when a proportion of the study subjects (the so-called cured individuals, as opposed to the susceptibles) cannot experience the outcome of interest. One problem arising then is that it is usually unknown who are the cured and the susceptible subjects, unless the outcome of interest has been observed. In this setting, a logistic regression analysis of the relationship between X and Y among the susceptibles is no more straightforward. We develop a maximum likelihood estimation procedure for this problem. We establish the consistency and asymptotic normality of the resulting estimator, and we conduct a simulation study to investigate its finite-sample behavior

ACT Now or Later: The Economics of Malaria Resistance

In the past, malaria control efforts in sub-Saharan Africa have relied on a combination of vector control and effective treatment using chloroquine. With increasing resistance to chloroquine, attention has now turned to alternative treatment strategies to replace this failing drug. Although there are strong theoretical arguments in favor of switching to more expensive artemisinin-based combination treatments (ACTs), the validity of these arguments in the face of financial constraints has not been previously analyzed. In this paper, we use a bioeconomic model of malaria transmission and evolution of drug resistance to examine questions of optimal treatment strategy and coverage when drug resistance places an additional constraint on choices available to the policymaker. Our main finding is that introducing ACTs sooner is more economically efficient if the planner had a relatively longer time horizon. However, for shorter planning horizons, delaying the introduction of ACTs is preferable.Malaria; mathematical models; drug resistance; bioeconomics