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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

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.

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.

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.

Key messages

RHRN aimed to capture people’s everyday experiences to provide timely insights for policy-makers.

It proved feasible to run a multi-mode weekly data-collection process to inform decision-makers.

Difficulties recruiting a representative sample limited the utility of the quantitative data.

Decision-making processes were not flexible enough to respond to rapid weekly evidence generation.
Original languageEnglish
Pages (from-to)301-321
Number of pages21
JournalEvidence & Policy
Issue number2
Early online date18 Jul 2017
Publication statusPublished - 31 May 2018


  • Evidence
  • Policy
  • Real-time
  • Technology


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