RESUMO
BACKGROUND: With more and more medical device databases being developed, there is an increasing interest in learning the geographical patterns of medical-device-related adverse events (AEs). For a specific medical device and an adverse event (AE) of interest, our aim is to detect a spatial-cluster signal that has a significantly higher AE rate than the AE rates for other regions, when the exposure information is available. METHODS: We develop a likelihood ratio test (LRT) method incorporating exposure information, by geographical region, for spatial-cluster signal detection when the underlying observed health outcome associated with the medical device of interest is a Poisson-modeled count data (e.g., an outcome of AE count). RESULTS: An extensive simulation study shows that this method has good power, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). The application of the method is demonstrated by two hypothetical case studies regarding a medical device that is used for patients who have reached end-stage heart failure. DISCUSSION: A methodological framework for exploring geographic patterns in device safety surveillance is discussed, including safety data collection, statistical tools, and display of the analysis results. The proposed statistical method can be used for spatial-cluster signal detection for an AE of interest from medical device registries or other databases that have patient-level geographical information.