A Spatio-temporal model for cancer incidence data with zero-inflation

In this work we consider a joint space-time model for cancer incidence, using data on prostate cancer collected between 1988 and 2005 in a specific area of France. Our aim is to take into account possible non linear effects of some covariates and zero-inflation due to data aggregation for Poisson regression. We assume that counts of cancer cases follow zero-inflated Poisson distribution, where the probability of zero inflation is a monotonic function of the mean. The purpose of our analysis is to check whether the French prostate screening program, which begins in 1994, results in a spatial or a spatial-temporal change of the pattern of the disease

An efficient record linkage scheme using graphical analysis for identifier error detection

Integration of information on individuals (record linkage) is a key problem in healthcare delivery, epidemiology, and "business intelligence" applications. It is now common to be required to link very large numbers of records, often containing various combinations of theoretically unique identifiers, such as NHS numbers, which are both incomplete and error-prone

Modelling Space-time variation of cancer incidence data: a case study

Cancer incidence data are typically available as rates or counts for contiguous geographical regions and are collected over time. Recent methodological developments have moved in the direction of univariate space-time modeling of incidence data especially in a Bayesian context. Based on an example of data on cancer incidence collected between 1988 and 2005 in a specific area of France, this work describes an approach to analyze the space-time evolution of the disease taking into account also of possible non linear effects of other covariates. For this purpose, we consider Generalized Additive Mixed Models (GAMMs) with a Poisson response. The proposed method allows to incorporate a wide range of correlation structures. Besides one dimensional smooth functions accounting for non-linear effects of covariates, the space-time interaction can be modeled using scale invariant tensor product smooths, where the smoothness parameter is estimated and does not depend on the different scales of the covariate axes. Another possibility investigated to account for space-time dependency is to use varying coefficient models. In such case, to explore spatio-temporal patterns, analyzes focused on six time periods, each 3 years in length, between 1988 and 2005. For model implementations we use the R package mgc

Modelling spatial hospital recruitment via integrated nested Laplace approximations

We propose different spatial models to study hospital recruitment, including some potentially explicative variables. Data analysed concern the hospital recruitment of the Haute Alsace a region in the north-east of France. Spatial models can be employed to show current patterns of healthcare utilization and to monitor changes in primary care access. Interest is on the distribution per geographical unit of the ratio between the number of patients living in this geographical unit and the population in the same unit. Models considered are within the framework of Bayesian latent Gaussian models. We assume that our response variable, the number of patients, follows, independently, a binomial distribution, with logit link, whose parameters are the population in each geographical unit and the corresponding risk. A flexible geoaddittive predictor is considered. To approximate posterior marginals, we use integrated nested Laplace approximations (INLA), recently proposed for approximate Bayesian inference in latent Gaussian models. Model comparisons are assessed using Deviance Information Criterion

Bayesian semi-parametric ZIP models with space-time interactions: an application to cancer registry data

We analyse lymphoid leukemia incidence data collected between 1988 and 2002 from the cancer registry of Haut-Rhin, a region in north-east France. For each patient, sex, area of residence, date of birth and date of diagnosis are available. Incidence summaries in the registry are grouped by 3-year periods. A disproportionately large frequency of zeros in the data leads to a lack of fit for Poisson models of relative risk. The aim of our analysis was to model the spatio-temporal variations of the disease taking into account some non-standard requirements, such as count data with many zeros and space-time interactions. For this purpose, we consider a flexible zero-inflated Poisson model for semi-parametric regression which incorporates space-time interactions (modelled by means of varying coefficient model) using an extension of the methodology proposed in Fahrmeir & Osuna (2006, Structured additive regression for overdispersed and zero-inflated count data. Stoc. Models Bus. Ind., 22, 351-369). Inference is carried out from a Bayesian perspective using Markov chain Monte Carlo methods by means of the BayesX software. Our analysis of the geographical distribution of the disease and its evolution in time may be considered as a starting point for further studies

Space-time variation of cancer incidence data and healthcare resources allocation

In this work we analyze cancer incidence data collected between 1992 and 2005 in a specific area of France. Our primary aim is to monitor changes of cancer incidence in space and time taking into account of age group categories. We assume that the distribution of the different age categories is not the same in all of the geographical units of the region and change in time. Such analysis can be useful for healthcare resources allocation, in order to replan the allocation of hospitals according to patient age. To do so we use Generalized Additive Mixed Models (GAMMs) with a Poisson response, using the methodology presented in Wood (2006)

Resources allocations in healthcare for cancer: a case study using generalized additive mixed models

Our aim is to develop a method for helping resources re-allocation in healthcare linked to cancer, in order to replain the allocation of providers. Aging of the population has a considerable impact on the use of health resources because aged people require more specialised medical care due notably to cancer. We propose a method useful to monitor changes of cancer incidence in space and time taking into account of age in two group categories, according to healthcare general organisation. We use generalised additive mixed models with a Poisson response, according to the methodology presented in Wood, 2006. Besides one dimensional smooth functions accounting for non-linear effects of covariates, the space-time interaction can be modelled using scale invariant smoothers. Incidence data collected by a general cancer registry between 1992 and 2007 in a specific area of France is studied. A best model exhibits a strong increase of the incidence of cancer along time and an obvious spatial pattern for people more than 70 years with an higher incidence in the central band of the region. This is a strong argument for re-allocating resources for old people cancer care in this sub-region