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Disease mapping models relate the observed number of cases to an expected number of cases per area. Expected numbers are often calculated by internal standardization, which requires both accurate population numbers and disease rates per gender and\/or age group. However, confidentiality issues or the absence of high\u2010quality information about the characteristics of a population\u2010at\u2010risk can hamper those calculations. Based on methods in point process analysis for situations without accurate population data, we propose the use of a case\u2010control approach in the context of lattice data, in which an unrelated, spatially unstructured disease is used as a control disease. We correct for the uncertainty in the estimation of the expected values, which arises by using the control\u2010disease's observed number of cases as a representation of a fraction of the total population. We apply our methods to a Belgian study of mesothelioma risk, where pancreatic cancer serves as the control disease. The analysis results are in close agreement with those coming from traditional disease mapping models based on internally standardized expected counts. The simulation study results confirm our findings for different spatial structures. We show that the proposed method can adequately address the problem of inaccurate or unavailable population data in disease mapping\u00a0analysis.<\/jats:p>","DOI":"10.1002\/bimj.202000246","type":"journal-article","created":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T09:37:51Z","timestamp":1644572271000},"page":"733-757","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Disease mapping method comparing the spatial distribution of a disease with a control disease"],"prefix":"10.1002","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1802-9640","authenticated-orcid":false,"given":"Oana","family":"Petrof","sequence":"first","affiliation":[{"name":"I\u2010BioStat Data Science Institute Hasselt University  Hasselt Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2364-7555","authenticated-orcid":false,"given":"Thomas","family":"Neyens","sequence":"additional","affiliation":[{"name":"I\u2010BioStat Data Science Institute Hasselt University  Hasselt Belgium"},{"name":"L\u2010BioStat Department of Public Health and Primary Care Faculty of Medicine KU Leuven  Leuven Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6509-7777","authenticated-orcid":false,"given":"Maren","family":"Vranckx","sequence":"additional","affiliation":[{"name":"I\u2010BioStat Data Science Institute Hasselt University  Hasselt Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valerie","family":"Nuyts","sequence":"additional","affiliation":[{"name":"Centre for Environment and Health Department of Public Health and Primary Care KU Leuven  Leuven 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