Evaluation des méthodes statistiques en épidémiologie spatiale : cas des méthodes locales de détection d'agrégats

Although performance assessment of cluster detection tests is a critical issue in spatial epidemiology, there is a lack of consensus regarding how it should be carried out. Nowadays, with the spread of new technologies in network systems, data sources for epidemiology are undergoing radical changes that will increase the need for performance evaluation. Field specialists are currently evaluating cluster detection tests with multiple complementary performance indicators such as conditional powers or indicators derived from the field of diagnostic tools evaluation. These evaluations are performed following classical protocols for power assessment and are often limited to a few number of simulated alternative hypotheses, thus restricting results interpretation and scope. Furthermore, with the use of multiple varying indicators, comparisons between studies is difficult at best. This work proposes and compares different global performance indicators that take into account both usual power and location accuracy. Their benefit for cluster detection tests evaluation is illustrated with a systematic spatial assessment enabling performance mapping. In addition to the evaluation of performance when clusters exist, we also propose a method for the spatial evaluation of type I error, together with a new statistical test for edge effect.L'évaluation des performances des méthodes de détection d'agrégats de maladie est fondamentale dans le domaine de l'épidémiologie spatiale et, paradoxalement, on déplore une absence de consensus quant à sa conduite. Cette problématique est d'autant plus importante que les nouvelles technologies de partage d'informations promettent une évolution importante des signaux disponibles pour l'épidémiologie et la veille sanitaire. Les spécialistes du domaine ont adopté un mode d'évaluation fondé sur l'utilisation concomitante de plusieurs indicateurs de performances complémentaires tels que des indicateurs dérivés de l'évaluation des méthodes diagnostiques ou encore diverses définitions de puissance conditionnelle. Cependant, ces évaluations issues de schémas de simulation classiques reposent sur le choix de quelques hypothèses alternatives particulières et ne permettent qu'une interprétation limitée à ces hypothèses. De plus, la démultiplication des indicateurs évaluant la performance, différents selon les protocoles, gêne la comparaison des études entres elles et complique l'interprétation des résultats. Notre travail propose et évalue plusieurs indicateurs de performance prenant en compte à la fois puissance et précision de localisation. Leur intérêt dans l'évaluation spatiale systématique des méthodes est illustré par la création de cartes de performance. En complément de l'évaluation des performances lorsqu'une détection est attendue, nous proposons également une méthode d'évaluation de la répartition spatiale de l'erreur de type I complétée par la construction d'une nouvelle inférence statistique testant l'éventualité d'un effet de bord

Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment

International audienceIn cluster detection of disease, the use of local cluster detection tests (CDTs) is current. These methods aim both at locating likely clusters and testing for their statistical significance. New or improved CDTs are regularly proposed to epidemiologists and must be subjected to performance assessment. Because location accuracy has to be considered, performance assessment goes beyond the raw estimation of type I or II errors. As no consensus exists for performance evaluations, heterogeneous methods are used, and therefore studies are rarely comparable. A global indicator of performance, which assesses both spatial accuracy and usual power, would facilitate the exploration of CDTs behaviour and help between-studies comparisons. The Tanimoto coefficient (TC) is a well-known measure of similarity that can assess location accuracy but only for one detected cluster. In a simulation study, performance is measured for many tests. From the TC, we here propose two statistics, the averaged TC and the cumulated TC, as indicators able to provide a global overview of CDTs performance for both usual power and location accuracy. We evidence the properties of these two indicators and the superiority of the cumulated TC to assess performance. We tested these indicators to conduct a systematic spatial assessment displayed through performance maps

Performance map of a cluster detection test using extended power.

International audienceBACKGROUND: Conventional power studies possess limited ability to assess the performance of cluster detection tests. In particular, they cannot evaluate the accuracy of the cluster location, which is essential in such assessments. Furthermore, they usually estimate power for one or a few particular alternative hypotheses and thus cannot assess performance over an entire region. Takahashi and Tango developed the concept of extended power that indicates both the rate of null hypothesis rejection and the accuracy of the cluster location. We propose a systematic assessment method, using here extended power, to produce a map showing the performance of cluster detection tests over an entire region. METHODS: To explore the behavior of a cluster detection test on identical cluster types at any possible location, we successively applied four different spatial and epidemiological parameters. These parameters determined four cluster collections, each covering the entire study region. We simulated 1,000 datasets for each cluster and analyzed them with Kulldorff's spatial scan statistic. From the area under the extended power curve, we constructed a map for each parameter set showing the performance of the test across the entire region. RESULTS: Consistent with previous studies, the performance of the spatial scan statistic increased with the baseline incidence of disease, the size of the at-risk population and the strength of the cluster (i.e., the relative risk). Performance was heterogeneous, however, even for very similar clusters (i.e., similar with respect to the aforementioned factors), suggesting the influence of other factors. CONCLUSIONS: The area under the extended power curve is a single measure of performance and, although needing further exploration, it is suitable to conduct a systematic spatial evaluation of performance. The performance map we propose enables epidemiologists to assess cluster detection tests across an entire study region

Spatial heterogeneity of type I error for local cluster detection tests.

International audienceBACKGROUND: Just as power, type I error of cluster detection tests (CDTs) should be spatially assessed. Indeed, CDTs' type I error and power have both a spatial component as CDTs both detect and locate clusters. In the case of type I error, the spatial distribution of wrongly detected clusters (WDCs) can be particularly affected by edge effect. This simulation study aims to describe the spatial distribution of WDCs and to confirm and quantify the presence of edge effect. METHODS: A simulation of 40 000 datasets has been performed under the null hypothesis of risk homogeneity. The simulation design used realistic parameters from survey data on birth defects, and in particular, two baseline risks. The simulated datasets were analyzed using the Kulldorff's spatial scan as a commonly used test whose behavior is otherwise well known. To describe the spatial distribution of type I error, we defined the participation rate for each spatial unit of the region. We used this indicator in a new statistical test proposed to confirm, as well as quantify, the edge effect. RESULTS: The predefined type I error of 5% was respected for both baseline risks. Results showed strong edge effect in participation rates, with a descending gradient from center to edge, and WDCs more often centrally situated. CONCLUSIONS: In routine analysis of real data, clusters on the edge of the region should be carefully considered as they rarely occur when there is no cluster. Further work is needed to combine results from power studies with this work in order to optimize CDTs performance

Impact of Vitamin D Supplementation on Influenza Vaccine Response and Immune Functions in Deficient Elderly Persons: A Randomized Placebo-Controlled Trial

Background: Immunosenescence contributes to reduced vaccine response in elderly persons, and is worsened by deficiencies in nutrients such as Vitamin (Vit-D). The immune system is a well-known target of Vit-D, which can both potentiate the innate immune response and inhibit the adaptive system, and so modulate vaccination response.Objective: This randomized placebo-controlled double-blind trial investigated whether Vit-D supplementation in deficient elderly persons could improve influenza seroprotection and immune response.Design: Deficient volunteers (Vit-D serum <30 ng/mL) were assigned (V1) to receive either 100,000 IU/15 days of cholecalciferol (D, n = 19), or a placebo (P, n = 19), over a 3 month period. Influenza vaccination was performed at the end of this period (V2), and the vaccine response was evaluated 28 days later (V3). At each visit, serum cathelicidin, immune response to vaccination, plasma cytokines, lymphocyte phenotyping, and phagocyte ROS production were assessed.Results: Levels of serum 25-(OH)D increased after supplementation (D group, V1 vs. V2: 20.7 ± 5.7 vs. 44.3 ± 8.6 ng/mL, p < 0.001). No difference was observed for serum cathelicidin levels, antibody titers, and ROS production in D vs. P groups at V3. Lower plasma levels of TNFα (p = 0.040) and IL-6 (p = 0.046), and higher ones for TFGβ (p = 0.0028) were observed at V3. The Th1/Th2 ratio was lower in the D group at V2 (D: 0.12 ± 0.05 vs. P: 0.18 ± 0.05, p = 0.039).Conclusions: Vit-D supplementation promotes a higher TGFβ plasma level in response to influenza vaccination without improving antibody production. This supplementation seems to direct the lymphocyte polarization toward a tolerogenic immune response. A deeper characterization of metabolic and molecular pathways of these observations will aid in the understanding of Vit-D's effects on cell-mediated immunity in aging. This clinical trial was registered at clinicaltrials.gov as NCT01893385

Evaluation of statistical methods in spatial epidemiology : the case of cluster detection tests

L'évaluation des performances des méthodes de détection d'agrégats de maladie est fondamentale dans le domaine de l'épidémiologie spatiale et, paradoxalement, on déplore une absence de consensus quant à sa conduite. Cette problématique est d'autant plus importante que les nouvelles technologies de partage d'informations promettent une évolution importante des signaux disponibles pour l'épidémiologie et la veille sanitaire. Les spécialistes du domaine ont adopté un mode d'évaluation fondé sur l'utilisation concomitante de plusieurs indicateurs de performances complémentaires tels que des indicateurs dérivés de l'évaluation des méthodes diagnostiques ou encore diverses définitions de puissance conditionnelle. Cependant, ces évaluations issues de schémas de simulation classiques reposent sur le choix de quelques hypothèses alternatives particulières et ne permettent qu'une interprétation limitée à ces hypothèses. De plus, la démultiplication des indicateurs évaluant la performance, différents selon les protocoles, gêne la comparaison des études entres elles et complique l'interprétation des résultats. Notre travail propose et évalue plusieurs indicateurs de performance prenant en compte à la fois puissance et précision de localisation. Leur intérêt dans l'évaluation spatiale systématique des méthodes est illustré par la création de cartes de performance. En complément de l'évaluation des performances lorsqu'une détection est attendue, nous proposons également une méthode d'évaluation de la répartition spatiale de l'erreur de type I complétée par la construction d'une nouvelle inférence statistique testant l'éventualité d'un effet de bord.Although performance assessment of cluster detection tests is a critical issue in spatial epidemiology, there is a lack of consensus regarding how it should be carried out. Nowadays, with the spread of new technologies in network systems, data sources for epidemiology are undergoing radical changes that will increase the need for performance evaluation. Field specialists are currently evaluating cluster detection tests with multiple complementary performance indicators such as conditional powers or indicators derived from the field of diagnostic tools evaluation. These evaluations are performed following classical protocols for power assessment and are often limited to a few number of simulated alternative hypotheses, thus restricting results interpretation and scope. Furthermore, with the use of multiple varying indicators, comparisons between studies is difficult at best. This work proposes and compares different global performance indicators that take into account both usual power and location accuracy. Their benefit for cluster detection tests evaluation is illustrated with a systematic spatial assessment enabling performance mapping. In addition to the evaluation of performance when clusters exist, we also propose a method for the spatial evaluation of type I error, together with a new statistical test for edge effect

Simulated dataset for I = 2.26 % and RR = 3

<p>A collection of 221 datasets in R format (rda), each corresponding to 1000 simulations of one cluster with a relative risk of 3 for a base incidence of 2.26 % births per year.</p> <p>Each dataset is a table of 221 000 rows and 6 columns.<br>The rows contain:</p> <p>-the coordinates (longitude and latitude) of a SU, the observed number of cases,</p> <p>-the size of the at-risk population (i.e., the number of live births),</p> <p>-the expected number of cases in the specified SU assuming an inhomogeneous Poisson process for the cases distribution and</p> <p>-an indicator for the simulation ranging from 1 to 1000.</p

Simulated dataset for I = 2.26 % and RR = 6

<p>A collection of 221 datasets in R format (rda), each corresponding to 1000 simulations of one cluster with a relative risk of 6 for a base incidence of 2.26 % births per year.</p> <p>Each dataset is a table of 221 000 rows and 6 columns.<br>The rows contain:</p> <p>-the coordinates (longitude and latitude) of a SU, the observed number of cases,</p> <p>-the size of the at-risk population (i.e., the number of live births),</p> <p>-the expected number of cases in the specified SU assuming an inhomogeneous Poisson process for the cases distribution and</p> <p>-an indicator for the simulation ranging from 1 to 1000.</p> <p> </p

Simulated dataset for I = 0.48% and RR = 6

<p>A collection of 221 datasets in R format (rda), each corresponding to 1000 simulations of one cluster with a relative risk of 6 for a base incidence of 0.48 % births per year.</p> <p>Each dataset is a table of 221 000 rows and 6 columns.<br>The rows contain:</p> <p>-the coordinates (longitude and latitude) of a SU, the observed number of cases,</p> <p>-the size of the at-risk population (i.e., the number of live births),</p> <p>-the expected number of cases in the specified SU assuming an inhomogeneous Poisson process for the cases distribution and</p> <p>-an indicator for the simulation ranging from 1 to 1000.</p

Détection d'agrégats spatio-temporels de malformations congénitales (mise en place d'un système de surveillance et d'alerte en Auvergne)

La recherche d'agrégats de maladie est une question centrale de la veille sanitaire. Le choix d'une ou plusieurs méthodes statistiques parmi la centaine existant aujourd'hui dépend des données disponibles, de l'objectif de l'analyse et des éventuelles contraintes matérielles. La mise en place d'un système de surveillance-alerte, et en particulier dès que la dimension spatiale de la détection d'agrégat est concernée, ne peut s'affranchir de l'étape de l'étude du comportement des méthodes statistiques utilisées qui dépendent pour grande part des caractéristiques de la région d'étude. C'est l'étude de puissance qui fournira des arguments précis et fiables pour consolider les résultats des analyses et donnera des éléments d'interprétation indispensables lors de la surveillance de routine. Plus largement, la qualité globale du système est conditionnée par les performances de chacune de ses composantes, les méthodes statistiques utilisées n'en sont qu'une parmi d'autres. Notre travail avait pour but d'une part de sélectionner les méthodes statistiques les plus appropriées à la détection d'agrégats dans le cadre d'une surveillance spatio-temporelle prospective et d'en étudier les performances dans le contexte des malformations congénitales en Auvergne, et d'autre part d'assurer l'ensemble des étapes nécessaires à la mise en place effective de ces méthodes au sein du système de surveillance. Ainsi, ce travail s'est d'abord intéressé à la qualité des données en réévaluant et en mettant à jour la base de données du CEMC-Auvergne, puis l'étude de puissance et la construction du protocole de surveillance ont été effectuées en parallèle, enfin, l'implémentation du logiciel destiné à la réalisation des analyses a été réalisée. La mise en place du système et son utilisation feront l'objet d'une formation à destination de l'ensemble des acteurs concernés.CLERMONT FD-BCIU-Santé (631132104) / SudocSudocFranceF