001     277109
005     20240229155010.0
024 7 _ |a 10.1016/j.ypmed.2023.107583
|2 doi
024 7 _ |a pmid:37352940
|2 pmid
024 7 _ |a 0091-7435
|2 ISSN
024 7 _ |a 1096-0260
|2 ISSN
024 7 _ |a altmetric:151701095
|2 altmetric
037 _ _ |a DKFZ-2023-01274
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Carrera, Pricivel
|0 P:(DE-He78)220ae95e502090f81d486cc30b17836d
|b 0
|e First author
|u dkfz
245 _ _ |a Knowledge of cancer risk factors and risk-reduction in high-income countries.
260 _ _ |a Amsterdam
|c 2023
|b Elsevier
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1706776154_16102
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a #EA:B210#LA:C060# /Short Communication
520 _ _ |a According to the International Public Opinion Survey on Cancer 2020, on average, nearly 1 in 3 individuals in high-income countries (HIC) did not engage in risk reduction. Meanwhile, only 1 in 4 individuals reported being aware that eating red and processed meat was a cancer risk factor. We explored relations between risk-reduction behavior and self-perceived knowledge of cancer risk factors in HIC using data from the survey. The average effect of knowledge, and interaction effects with country and risk factor were estimated using a linear model fit. The model included main and two-way interaction terms between the proportion of respondents who knew about a specific risk factor, and risk factor and country. The overall significance of knowledge impact and interaction terms was tested using type III tests in ANCOVA. Based on our analysis, we found that knowledge of cancer risk factors was positively associated with risk reduction in HIC. Every unit increase in the proportion of the population knowledgeable about a cancer risk factor, on average across risk factors and HIC, significantly increases the proportion of people engaging in risk reduction by approximately 16.91%. A significant interaction effect was found between knowledge and country, but not between knowledge and risk factor. Using respondents' non-response options to represent lack of risk factor knowledge Japan had the largest percentage of individuals lacking knowledge about risk factors as well as the largest percentage of individuals not engaging in risk reduction.
536 _ _ |a 313 - Krebsrisikofaktoren und Prävention (POF4-313)
|0 G:(DE-HGF)POF4-313
|c POF4-313
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
650 _ 7 |a Disease prevention
|2 Other
650 _ 7 |a Health promotion
|2 Other
650 _ 7 |a High-income countries
|2 Other
650 _ 7 |a Primary cancer prevention
|2 Other
650 _ 7 |a cancer awareness
|2 Other
650 _ 7 |a cancer risk factors
|2 Other
650 _ 7 |a cancer risk reduction
|2 Other
700 1 _ |a Calderazzo, Silvia
|0 P:(DE-He78)b5d9469407737829d5348adb615655c6
|b 1
|e Last author
|u dkfz
773 _ _ |a 10.1016/j.ypmed.2023.107583
|g Vol. 173, p. 107583 -
|0 PERI:(DE-600)1471564-8
|p 107583
|t Preventive medicine
|v 173
|y 2023
|x 0091-7435
909 C O |p VDB
|o oai:inrepo02.dkfz.de:277109
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 0
|6 P:(DE-He78)220ae95e502090f81d486cc30b17836d
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 1
|6 P:(DE-He78)b5d9469407737829d5348adb615655c6
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-313
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Krebsrisikofaktoren und Prävention
|x 0
914 1 _ |y 2023
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2022-11-26
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2022-11-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2022-11-26
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2023-08-19
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2023-08-19
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2023-08-19
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2023-08-19
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2023-08-19
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2023-08-19
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
|d 2023-08-19
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b PREV MED : 2022
|d 2023-08-19
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2023-08-19
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2023-08-19
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b PREV MED : 2022
|d 2023-08-19
920 2 _ |0 I:(DE-He78)C060-20160331
|k C060
|l C060 Biostatistik
|x 0
920 1 _ |0 I:(DE-He78)B210-20160331
|k B210
|l B210 NWG Somatische Evolution und Früherkennung
|x 0
920 1 _ |0 I:(DE-He78)C060-20160331
|k C060
|l C060 Biostatistik
|x 1
920 0 _ |0 I:(DE-He78)B210-20160331
|k B210
|l B210 NWG Somatische Evolution und Früherkennung
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)B210-20160331
980 _ _ |a I:(DE-He78)C060-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21