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An overview of cervical cancer epidemiology and prevention in Scandinavia.

Kine PedersenSara FogelbergLise H ThamsborgMark ClementsMari NygårdIvar S KristiansenElsebeth LyngePär SparénJane J KimEmily A Burger
Published in: Acta obstetricia et gynecologica Scandinavica (2018)
New technologies such as human papillomavirus (HPV) testing and vaccination necessitate comprehensive policy analyses to optimize cervical cancer prevention. To inform future Scandinavian-specific policy analyses, we aimed to provide an overview of cervical cancer epidemiology and existing prevention efforts in Denmark, Norway and Sweden. We compiled and summarized data on current prevention strategies, population demography and epidemiology (for example, age-specific HPV prevalence and cervical cancer incidence over time) for each Scandinavian country by reviewing published literature and official guidelines, performing registry-based analyses using primary data and having discussions with experts in each country. In Scandinavia, opportunistic screening occurred as early as the 1950s and by 1996, all countries had implemented nationwide organized cytology-based screening. Prior to implementation of widespread screening and during 1960-66, cervical cancer incidence was considerably higher in Denmark than in Norway and Sweden. Decades of cytology-based screening later (i.e. 2010-2014), cervical cancer incidence has been considerably reduced and has converged across the countries since the 1960s, although it still remains lowest in Sweden. Generally, Scandinavian countries face similar cervical cancer burdens and utilize similar prevention approaches; however, important differences remain. Future policy analyses will need to evaluate whether these differences warrant differential prevention policies or whether efforts can be streamlined across Scandinavia.
Keyphrases
  • risk factors
  • public health
  • healthcare
  • high grade
  • systematic review
  • randomized controlled trial
  • quality improvement
  • artificial intelligence
  • childhood cancer
  • deep learning
  • data analysis