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Assessing the potential return on investment of the proposed UK NHS diabetes prevention programme in different population subgroups: an economic evaluation
  1. Chloe Thomas,
  2. Susi Sadler,
  3. Penny Breeze,
  4. Hazel Squires,
  5. Michael Gillett,
  6. Alan Brennan
  1. School of Health and Related Research, University of Sheffield, Sheffield, UK
  1. Correspondence to Chloe Thomas; c.thomas{at}sheffield.ac.uk

Abstract

Objectives To evaluate potential return on investment of the National Health Service Diabetes Prevention Programme (NHS DPP) in England and estimate which population subgroups are likely to benefit most in terms of cost-effectiveness, cost-savings and health benefits.

Design Economic analysis using the School for Public Health Research Diabetes Prevention Model.

Setting England 2015–2016.

Population Adults aged ≥16 with high risk of type 2 diabetes (HbA1c 6%–6.4%). Population subgroups defined by age, sex, ethnicity, socioeconomic deprivation, baseline body mass index, baseline HbA1c and working status.

Interventions The proposed NHS DPP: an intensive lifestyle intervention focusing on dietary advice, physical activity and weight loss. Comparator: no diabetes prevention intervention.

Main outcome measures Incremental costs, savings and return on investment, quality-adjusted life-years (QALYs), diabetes cases, cardiovascular cases and net monetary benefit from an NHS perspective.

Results Intervention costs will be recouped through NHS savings within 12 years, with net NHS saving of £1.28 over 20 years for each £1 invested. Per 100 000 DPP interventions given, 3552 QALYs are gained. The DPP is most cost-effective and cost-saving in obese individuals, those with baseline HbA1c 6.2%–6.4% and those aged 40–74. QALY gains are lower in minority ethnic and low socioeconomic status subgroups. Probabilistic sensitivity analysis suggests that there is 97% probability that the DPP will be cost-effective within 20 years. NHS savings are highly sensitive to intervention cost, effectiveness and duration of effect.

Conclusions The DPP is likely to be cost-effective and cost-saving under current assumptions. Prioritising obese individuals could create the most value for money and obtain the greatest health benefits per individual targeted. Low socioeconomic status or ethnic minority groups may gain fewer QALYs per intervention, so targeting strategies should ensure the DPP does not contribute to widening health inequalities. Further evidence is needed around the differential responsiveness of population subgroups to the DPP.

  • public health
  • diabetes and endocrinology
  • health economics

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Footnotes

  • Contributors CT contributed to planning the project, carried out the model adaptation and wrote the manuscript. She is guarantor. SS contributed to planning the project, adapting the model and writing the manuscript. PB developed the model and revised the draft paper. HS contributed to the conceptual development of the model adaptation and revised the draft paper. MG provided specialist knowledge around model inputs and revised the draft paper. AB was principle investigator for the project and contributed to the analysis and manuscript.

  • Funding This abstract presents independent research commissioned and funded by Public Health England (PHE) with support from NHS England, Diabetes UK and the Department of Health. Model development was funded by the National Institute for Health Research (NIHR)’s School for Public Health Research (SPHR). The views expressed are those of the authors and not necessarily those of PHE, NHS England, Diabetes UK, the NIHR or the Department of Health.

  • Competing interests The authors have no financial relationships with any organisations that might have an interest in the submitted work in the previous three years other than Public Health England and NHS England and no other relationships or activities that could appear to have influenced the submitted work.

  • Ethics approval Ethical approval was not needed for this study because the model is based on publicly available data and analysis of secondary data.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement Detailed results for each subgroup analysed in the model are available on request by email from the corresponding author.

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