Int J Health Geogr - Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters.

Tópicos

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Resumo

CKGROUND: Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi-objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function.RESULTS & DISCUSSION: We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under-populated disconnection nodes in candidate clusters, the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function, the most geographically meaningful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is used. In this paper we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function. We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas' disease in puerperal women in Minas Gerais state, Brazil.CONCLUSIONS: We show that, compared to the other single-objective algorithms, multi-objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi-objective non-connectivity scan is faster and better suited for the detection of moderately irregularly shaped clusters. The multi-objective cohesion scan is most effective for the detection of highly irregularly shaped clusters.

Resumo Limpo

ckground irregular shape spatial cluster difficult delin cluster found algorithm often spread larg portion map impact geograph mean penal likelihood method kulldorff spatial scan statist use control excess freedom shape cluster penalti function base cluster geometri nonconnect propos recent anoth approach involv use multiobject algorithm maxim two object spatial scan statist geometr penalti functionresult discuss present novel scan statist algorithm employ function base graph topolog penal presenc underpopul disconnect node candid cluster disconnect node cohes function disconnect node defin region within cluster remov disconnect cluster appli function geograph meaning cluster sift immens set possibl irregular shape candid cluster solut evalu statist signific solut multiobject scan statist approach base concept attain function use paper compar differ penal likelihood employ geometr nonconnect regular function novel disconnect node cohes function also build multiobject scan use three function compar previous penal likelihood scan applic present use comprehens statewid data chaga diseas puerper women mina gerai state brazilconclus show compar singleobject algorithm multiobject scan present better perform regard power sensit posit predict valu multiobject nonconnect scan faster better suit detect moder irregular shape cluster multiobject cohes scan effect detect high irregular shape cluster

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