{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["van Schaik P"],"funding":["Public Health England"],"pagination":["152"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6636096"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["19(1)"],"pubmed_abstract":["Background
In recent years, the availability of publicly available data related to public health has significantly increased. These data have substantial potential to develop public health policy; however, this requires meaningful and insightful analysis. Our aim is to demonstrate how data analysis techniques can be used to address the issues of data reduction, prediction and explanation using online available public health data, in order to provide a sound basis for informing public health policy.Methods
Observational suicide prevention data were analysed from an existing online United Kingdom national public health database. Multi-collinearity analysis and principal-component analysis were used to reduce correlated data, followed by regression analyses for prediction and "],"journal":["BMC medical research methodology"],"pubmed_title":["Explainable statistical learning in public health for policy development: the case of real-world suicide data."],"pmcid":["PMC6636096"],"funding_grant_id":["NA"],"pubmed_authors":["Peng Y","Ojelabi A","van Schaik P","Ling J"],"additional_accession":[]},"is_claimable":false,"name":"Explainable statistical learning in public health for policy development: the case of real-world suicide data.","description":"Background
In recent years, the availability of publicly available data related to public health has significantly increased. These data have substantial potential to develop public health policy; however, this requires meaningful and insightful analysis. Our aim is to demonstrate how data analysis techniques can be used to address the issues of data reduction, prediction and explanation using online available public health data, in order to provide a sound basis for informing public health policy.Methods
Observational suicide prevention data were analysed from an existing online United Kingdom national public health database. Multi-collinearity analysis and principal-component analysis were used to reduce correlated data, followed by regression analyses for prediction and ","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 Jul","modification":"2025-04-05T10:41:14.838Z","creation":"2019-07-30T07:07:02Z"},"accession":"S-EPMC6636096","cross_references":{"pubmed":["31315579"],"doi":["10.1186/s12874-019-0796-7"]}}