Int J Health Geogr - Detecting cancer clusters in a regional population with local cluster tests and Bayesian smoothing methods: a simulation study.


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CKGROUND: There is a rising public and political demand for prospective cancer cluster monitoring. But there is little empirical evidence on the performance of established cluster detection tests under conditions of small and heterogeneous sample sizes and varying spatial scales, such as are the case for most existing population-based cancer registries. Therefore this simulation study aims to evaluate different cluster detection methods, implemented in the open source environment R, in their ability to identify clusters of lung cancer using real-life data from an epidemiological cancer registry in Germany.METHODS: Risk surfaces were constructed with two different spatial cluster types, representing a relative risk of RR = 2.0 or of RR = 4.0, in relation to the overall background incidence of lung cancer, separately for men and women. Lung cancer cases were sampled from this risk surface as geocodes using an inhomogeneous Poisson process. The realisations of the cancer cases were analysed within small spatial (census tracts, N = 1983) and within aggregated large spatial scales (communities, N = 78). Subsequently, they were submitted to the cluster detection methods. The test accuracy for cluster location was determined in terms of detection rates (DR), false-positive (FP) rates and positive predictive values. The Bayesian smoothing models were evaluated using ROC curves.RESULTS: With moderate risk increase (RR = 2.0), local cluster tests showed better DR (for both spatial aggregation scales > 0.90) and lower FP rates (both < 0.05) than the Bayesian smoothing methods. When the cluster RR was raised four-fold, the local cluster tests showed better DR with lower FPs only for the small spatial scale. At a large spatial scale, the Bayesian smoothing methods, especially those implementing a spatial neighbourhood, showed a substantially lower FP rate than the cluster tests. However, the risk increases at this scale were mostly diluted by data aggregation.CONCLUSION: High resolution spatial scales seem more appropriate as data base for cancer cluster testing and monitoring than the commonly used aggregated scales. We suggest the development of a two-stage approach that combines methods with high detection rates as a first-line screening with methods of higher predictive ability at the second stage.

Resumo Limpo

ckground rise public polit demand prospect cancer cluster monitor littl empir evid perform establish cluster detect test condit small heterogen sampl size vari spatial scale case exist populationbas cancer registri therefor simul studi aim evalu differ cluster detect method implement open sourc environ r abil identifi cluster lung cancer use reallif data epidemiolog cancer registri germanymethod risk surfac construct two differ spatial cluster type repres relat risk rr rr relat overal background incid lung cancer separ men women lung cancer case sampl risk surfac geocod use inhomogen poisson process realis cancer case analys within small spatial census tract n within aggreg larg spatial scale communiti n subsequ submit cluster detect method test accuraci cluster locat determin term detect rate dr falseposit fp rate posit predict valu bayesian smooth model evalu use roc curvesresult moder risk increas rr local cluster test show better dr spatial aggreg scale lower fp rate bayesian smooth method cluster rr rais fourfold local cluster test show better dr lower fps small spatial scale larg spatial scale bayesian smooth method especi implement spatial neighbourhood show substanti lower fp rate cluster test howev risk increas scale most dilut data aggregationconclus high resolut spatial scale seem appropri data base cancer cluster test monitor common use aggreg scale suggest develop twostag approach combin method high detect rate firstlin screen method higher predict abil second stage

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