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Italian public's views on sharing genetic information and medical information: findings from the 'Your DNA, Your Say' study.

Virginia RomanoRichard MilneDeborah Mascalzoni
Published in: Wellcome open research (2021)
Background : The collection and sharing of genomic and health data underpins global efforts to develop genomic medicine services. 'Your DNA, Your Say' is a cross-sectional survey with the goal of gathering lay public attitudes toward the access and sharing of deoxyribonucleic acid (DNA) information and medical information. It suggests significant international variation in the willingness to share information, and in trust in the actors associated with the collection and use of this information. This paper explores these questions in the Italian context. Methods : The Italian Your DNA, Your Say campaign led to the collection of 1229 valid questionnaires. The sample was analysed using standard descriptive statistics. We described the sample in terms of gender, age ranges and self-reported religiosity, and split the sample amongst the five typically studied Italian macro-areas to explore regional variation. We analysed the relationship between these factors and trust and willingness to share medical and DNA information.  Results : The majority of the sample, across all socio-demographics, were willing to share DNA and health information with all entities considered except for-profit researchers. Respondents tended not to trust institutions beyond their own doctor. There was no difference between Italian regions. Conclusions : Despite the generally positive attitude towards sharing, we suggest that the lack of trust in non-profit researchers and the government needs to be better understood to inform public communication projects around genomics in the future and to enhance awareness of DNA and medical information in Italy.
Keyphrases
  • health information
  • healthcare
  • circulating tumor
  • social media
  • cell free
  • single molecule
  • mental health
  • nucleic acid
  • primary care
  • copy number
  • machine learning
  • dna methylation
  • genome wide
  • cross sectional