Systematic mapping review of statistical methods applied to the relationships between cancer diagnosis and...

We examined studies that analysed the spatial association of cancers with demographic, environmental, behavioural and/or socioeconomic factors and the statistical methods applied.

Systematic mapping review.

Web of Science (SSCI) (search on 28 July 2022), MEDLINE, SocINDEX and CINAHL (search on 4 August 2022), additional searches included grey literature.

(1) Focused on the constituent countries of the UK (England, Wales, Scotland and Northern Ireland) and its major regions (eg, the North West); (2) compared cancer(s) outcomes with demographic, environmental, behavioural and socioeconomic characteristics by applying methods to identify their spatial association; (3) reported cancer prevalence, incidence rates, relative risk or ORs for a risk factor or to an average level of cancer.

A standardised data extraction form was developed and for all studies, core data were extracted including bibliographic information, study design, geographical factors analysed, data aggregation level, methods applied and main findings. We described and synthesised the characteristics of the studies using summary tables, charts and graphs.

52 studies were included covering a variety of objectives and geographical scales. These studies considered different types of cancer, with the most common cancer types analysed being blood and lymphoid cell cancers. The most common methods used to assess the association between cancers and geographical level factors were regression analyses, with the majority being Poisson regression, then logistic and linear regression. Studies were usually conducted at ward and local authority level, or by exact point location when distances from putative risk sources were considered. The results were usually presented in plots or as tables, instead of maps.

Our results highlight the lack of consideration of spatially explicit models in the analysed studies, with the risk of having failed the assumption of independence in the data.

CRD42022349165.