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A simulation study for geographic cluster detection analysis on population-based health survey data using spatial scan statistics
DC Field | Value | Language |
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dc.contributor.author | 정인경 | - |
dc.date.accessioned | 2022-12-22T04:05:36Z | - |
dc.date.available | 2022-12-22T04:05:36Z | - |
dc.date.issued | 2022-09 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/192039 | - |
dc.description.abstract | Background: In public health and epidemiology, spatial scan statistics can be used to identify spatial cluster patterns of health-related outcomes from population-based health survey data. Although it is appropriate to consider the complex sample design and sampling weight when analyzing complex sample survey data, the observed survey responses without these considerations are often used in many studies related to spatial cluster detection. Methods: We conducted a simulation study to investigate which data type from complex survey data is more suitable for use by comparing the spatial cluster detection results of three approaches: (1) individual-level data, (2) weighted individual-level data, and (3) aggregated data. Results: The results of the spatial cluster detection varied depending on the data type. To compare the performance of spatial cluster detection, sensitivity and positive predictive value (PPV) were evaluated over 100 iterations. The average sensitivity was high for all three approaches, but the average PPV was higher when using aggregated data than when using individual-level data with or without sampling weights. Conclusions: Through the simulation study, we found that use of aggregate-level data is more appropriate than other types of data, when searching for spatial clusters using spatial scan statistics on population-based health survey data. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | BioMed Central | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Cluster Analysis | - |
dc.subject.MESH | Computer Simulation | - |
dc.subject.MESH | Health Surveys | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Public Health* | - |
dc.subject.MESH | Research Design* | - |
dc.title | A simulation study for geographic cluster detection analysis on population-based health survey data using spatial scan statistics | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) | - |
dc.contributor.googleauthor | Jisu Moon | - |
dc.contributor.googleauthor | Inkyung Jung | - |
dc.identifier.doi | 10.1186/s12942-022-00311-6 | - |
dc.contributor.localId | A03693 | - |
dc.relation.journalcode | J01119 | - |
dc.identifier.eissn | 1476-072X | - |
dc.identifier.pmid | 36085072 | - |
dc.subject.keyword | Geographic surveillance | - |
dc.subject.keyword | Health survey | - |
dc.subject.keyword | Sampling design | - |
dc.subject.keyword | Sampling weight | - |
dc.subject.keyword | Spatial cluster detection | - |
dc.contributor.alternativeName | Jung, In Kyung | - |
dc.contributor.affiliatedAuthor | 정인경 | - |
dc.citation.volume | 21 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 11 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS, Vol.21(1) : 11, 2022-09 | - |
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