Aline Guttmann

Performance map of a cluster detection test using extended power

BackgroundConventional 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.MethodsTo 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.ResultsConsistent 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.ConclusionsThe 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.RésuméContexteLes études de puissance ont montré leurs limites dans l’évaluation des performances des tests de détection d’agrégats. En raison de la nécessité de prendre en compte à la fois la capacité du test à rejeter l’hypothèse nulle et à localiser correctement l’agrégat, la puissance usuelle ne peut refléter la véritable performance de ces tests. De plus, ces évaluations ne traitent en général qu’un nombre limité d’hypothèses alternatives ignorant donc le comportement de ces tests sur l’ensemble d’une région d’étude. Takahashi et Tango ont proposé le concept de puissance étendue qui, au-delà de la puissance usuelle, reflète également la précision de localisation de l’agrégat. Nous proposons une méthode d’évaluation systématique, fondée ici sur la puissance étendue, pour produire une carte offrant une visualisation synoptique des performances des tests de détection d’agrégats sur l’ensemble d’une région.MéthodesDe façon à explorer le comportement d’un test de détection d’agrégats sur un même type d’agrégat pour toutes les localisations possibles, nous avons fixé quatre jeux de paramètres spatiaux et épidémiologiques, de façon à simuler quatre collections d’agrégats, chacune couvrant l’ensemble de la région d’étude. Mille jeux de données ont été simulés pour chaque agrégat et soumis au scan spatial de Kulldorff. A partir de l’aire sous la courbe de puissance étendue, nous avons produit une carte de performance pour chaque jeu de paramètres.RésultatsConformément aux précédentes études, la performance du scan spatial croît avec l’incidence de base de la maladie, la taille de la population à risque et la force de l’agrégat (i.e., le risque relatif). Cependant, même pour des agrégats très similaires, la performance du test est hétérogène, suggérant l’influence potentielle d’autres facteurs.ConclusionsL’aire sous la courbe de puissance étendue est une mesure unique de performance et, bien qu’elle nécessite des évaluations plus poussées, elle convient à l’évaluation spatiale systématique de la performance. La carte de performance que nous proposons autorise les épidémiologistes à évaluer les tests de détection d’agrégats sur l’ensemble d’une région d’étude.

The Role of Psychological Factors in Persistent Pain After Cesarean Delivery.

UNLABELLED This French multicenter prospective cohort study recruited 391 patients to investigate the risk factors for persistent pain after elective cesarean delivery, focusing on psychosocial aspects adjusted for other known medical factors. Perioperative data were collected and specialized questionnaires were completed to assess reports of pain at the site of surgery. Three dependent outcomes were considered: pain at the third month after surgery (M3, n = 268; risk = 28%), pain at the sixth month after surgery (M6, n = 239; risk = 19%), and the cumulative incidence (up to M6) of neuropathic pain, as assessed using the Douleur Neuropathique 4 questionnaire (n = 218; risk = 24.5%). The neuropathic aspect of reported pain changed over time in more than 60% of cases, pain being more intense if associated with neuropathic features. Whatever the dependent outcome, a high mental component of quality of life (SF-36) was protective. Pain at M3 was also predicted by pain reported during current pregnancy and a history of miscarriage. Pain at M6 was also predicted by report of a postoperative complication. Incident neuropathic pain was predicted by pain reported during current pregnancy, a previous history of a peripheral neuropathic event, and preoperative anxiety. TRIAL REGISTRATION ClinicalTrials.gov, NCT00812734. PERSPECTIVE Persistent pain after cesarean delivery has a relatively frequent neuropathic aspect but this is less stable than that after other surgeries. When comparing the risk factor analyses with published data for hysterectomy, the influence of preoperative psychological factors seems less important, possibly because of the different context and environment.

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

In 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.

Spatial heterogeneity of type I error for local cluster detection tests

BackgroundJust 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.MethodsA 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.ResultsThe 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.ConclusionsIn 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.RésuméContexteLes tests de détection de clusters (CDT) permettent à la fois de détecter et de localiser les clusters. Au même titre que pour la puissance, il est donc nécessaire d’étudier la répartition spatiale de l’erreur de type I de ces CDT. Dans le cas de l’erreur de type I, la répartition spatiale des clusters détectés à tort (WDC) peut être particulièrement concernée par un effet de bord. Cette étude de simulation a pour objectif de décrire la distribution spatiale des WDCs et de confirmer et quantifier la présence de cet effet de bord.MéthodesCe travail s’appuie sur la synthèse de 40 000 jeux de données simulant l’hypothèse nulle d’homogénéité spatiale des risques. Les simulations étaient fondées sur les paramètres réels de données d’un registre de malformations congénitales, et notamment sur deux risques de base réels. La description de la distribution spatiale de l’erreur de type I nous a conduits à définir le concept de taux de participation de chaque unité spatiale de la région. Cet indicateur a ensuite été intégré pour la construction d’un nouveau test statistique destiné à confirmer et quantifier l’effet de bord.RésultatsLa valeur globale de l’erreur de type I à 5% a bien été retrouvée. Les résultats montraient un très net effet de bord avec un gradient décroissant du taux de participation depuis le centre vers le bord, les WDC étant plus souvent situés en zone centrale.ConclusionsLors de la mise en œuvre des CDT sur données réelles, les détections de clusters près du bord d’une région d’étude doivent être examinées avec la plus grande attention, ces dernières étant très rares en l’absence de cluster réel. Il est maintenant nécessaire d’orienter de futurs développements vers la combinaison de ces résultats à ceux des études de puissance, et ce dans le but d’optimiser les performances des CDT.

General Practitioners' Barriers to Prescribe Physical Activity: The Dark Side of the Cluster Effects on the Physical Activity of Their Type 2 Diabetes Patients.

Aims/hypothesis To describe barriers to physical activity (PA) in type 2 diabetes patients and their general practitioners (GPs), looking for practitioner’s influence on PA practice of their patients. Methods We conducted a cross-sectional study on GPs (n = 48) and their type 2 diabetes patients (n = 369) measuring respectively barriers to prescribe and practice PA using a self-assessment questionnaire: barriers to physical activity in diabetes (BAPAD). Statistical analysis was performed accounting hierarchical data structure. Similar practitioner’s patients were considered a cluster sharing common patterns. Results The higher the patient’s BAPAD score, the higher the barriers to PA, the higher the risk to declare practicing no PA (p<0.001), low frequency and low duration of PA (p<0.001). A high patient’s BAPAD score was also associated with a higher risk to have HbA1c ≥7% (53 mmol/mol) (p = 0.001). The intra-class correlation coefficient between type 2 diabetes patients and GPs was 34%, indicating a high cluster effect. A high GP’s BAPAD score, regarding the PA prescription, is predictive of a high BAPAD score with their patients, regarding their practice (p = 0.03). Conclusion/interpretation Type 2 diabetes patients with lower BAPAD score, thus lower barriers to physical activity, have a higher PA level and a better glycemic control. An important and deleterious cluster effect between GPs and their patients is demonstrated: the higher the GP’s BAPAD score, the higher the type 2 diabetes patients’ BAPAD score. This important cluster effect might designate GPs as a relevant lever for future interventions regarding patient’s education towards PA and type 2 diabetes management.

Do estimates of blood loss differ between student midwives and midwives? A multicenter cross-sectional study

OBJECTIVE the principal objective of this study was to assess the quality of blood loss estimates by midwives and student midwives. The secondary objectives were: to assess the intraobserver agreement of visual blood estimates and the rate of underestimation of blood loss by participants, and to estimate the sensitivity, specificity, and negative likelihood ratio of these estimates for clinically pertinent blood losses (≥ 500mL and ≥ 1000mL). DESIGN multicenter cross-sectional study. SETTING thirty-three French maternity units and 35 French midwifery schools participated in this study. PARTICIPANTS volunteer French midwifery students (n = 463) and practicing midwives (n = 578). INTERVENTION an online survey showed 16 randomly ordered photographs of 8 different simulated blood quantities (100, 150, 200, 300, 500, 850, 1000, and 1500mL) with a reference 50-mL image in each photo and asked participants to estimate the blood loss. The visual blood loss estimates were compared with Fishers exact test. Intraobserver agreement for these estimates was assessed with a weighted kappa coefficient, and the negative predictive values (probability of no hemorrhage when visual estimate was negative) were calculated from prevalence rates in the literature. FINDINGS of the 16,656 estimates obtained, 34.1% were accurate, 37.2% underestimated the quantity presented, and 28.7% overestimated it. Analyses of the intraobserver reproducibility between the two estimates of the same photograph showed that agreement was highest (weighted kappa ≥ 0.8) for the highest values (1000mL, 1500mL). For each volume considered, students underestimated blood loss more frequently than midwives. In both groups, the negative predictive values regarding postpartum hemorrhage (PPH) diagnosis (severe or not) were greater than 98%. KEY CONCLUSIONS AND IMPLICATIONS FOR PRACTICE student midwives tended to underestimate the quantity of blood loss more frequently than the midwives. Postpartum hemorrhage (≥ 500mL) was always identified, but severe postpartum hemorrhage (≥ 1000mL) was identified in fewer than half the cases. These results should be taken into account in training both student midwives and practicing professionals.