Right Here Right Now (RHRN) pilot study: testing a method of near-real-time data collection on the social determinants of health

Lynn Naven, Greig Inglis, Rachel Harris, Gillian Fergie, Gemma Teal, Rebecca Phipps, Sally Stewart, Lorna Kelly, Shona Hilton, Madeline Smith, Gerry McCartney, David Walsh, Matthew Tolan, James Egan

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Abstract

Background Informing policy and practice with up-to-date evidence on the social determinants of health is an ongoing challenge. One limitation of traditional approaches is the time-lag between identification of a policy or practice need and availability of results. The Right Here Right Now (RHRN) study piloted a near-real-time data-collection process to investigate whether this gap could be bridged. Methods A website was developed to facilitate the issue of questions, data capture and presentation of findings. Respondents were recruited using two distinct methods – a clustered random probability sample, and a quota sample from street stalls. Weekly four-part questions were issued by email, Short Messaging Service (SMS or text) or post. Quantitative data were descriptively summarised, qualitative data thematically analysed, and a summary report circulated two weeks after each question was issued. The pilot spanned 26 weeks. Results It proved possible to recruit and retain a panel of respondents providing quantitative and qualitative data on a range of issues. The samples were subject to similar recruitment and response biases as more traditional data-collection approaches. Participants valued the potential to influence change, and stakeholders were enthusiastic about the findings generated, despite reservations about the lack of sample representativeness. Stakeholders acknowledged that decision-making processes are not flexible enough to respond to weekly evidence. Conclusion RHRN produced a process for collecting near-real-time data for policy-relevant topics, although obtaining and maintaining representative samples was problematic. Adaptations were identified to inform a more sustainable model of near-real-time data collection and dissemination in the future.
Original languageEnglish
JournalEvidence and Policy
DOIs
Publication statusPublished - 18 Jul 2017

Keywords

  • technology
  • real-time
  • evidence
  • policy

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