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Quasi-experimental evaluation of a nationwide diabetes prevention programme

Nature volume 624, pages 138–144 (2023)Cite this article

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Abstract

Diabetes is a leading cause of morbidity, mortality and cost of illness1,2. Health behaviours, particularly those related to nutrition and physical activity, play a key role in the development of type 2 diabetes mellitus3. Whereas behaviour change programmes (also known as lifestyle interventions or similar) have been found efficacious in controlled clinical trials4,5, there remains controversy about whether targeting health behaviours at the individual level is an effective preventive strategy for type 2 diabetes mellitus6 and doubt among clinicians that lifestyle advice and counselling provided in the routine health system can achieve improvements in health7,8,9. Here we show that being referred to the largest behaviour change programme for prediabetes globally (the English Diabetes Prevention Programme) is effective in improving key cardiovascular risk factors, including glycated haemoglobin (HbA1c), excess body weight and serum lipid levels. We do so by using a regression discontinuity design10, which uses the eligibility threshold in HbA1c for referral to the behaviour change programme, in electronic health data from about one-fifth of all primary care practices in England. We confirm our main finding, the improvement of HbA1c, using two other quasi-experimental approaches: difference-in-differences analysis exploiting the phased roll-out of the programme and instrumental variable estimation exploiting regional variation in programme coverage. This analysis provides causal, rather than associational, evidence that lifestyle advice and counselling implemented at scale in a national health system can achieve important health improvements.

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Fig. 1: Association between baseline HbA1c and intensive lifestyle counselling and potential confounders.
Fig. 2: Robustness of the effects of being referred to intensive lifestyle counselling on HbA1c and BMI across bandwidth choices.
Fig. 3: Difference-in-differences effect estimates of NHS DPP implementation.

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Data availability

This study used data from the CPRD Aurum and NHS England HES APC database. The data are available from CPRD (https://cprd.com) but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. Owing to CPRD license restrictions, we are unable to share data.

Code availability

All medical codes and algorithms to define variables and R analysis code are available in the Supplementary Information or at the OSF repository (https://osf.io/rqz6x/?view_only=abc4c7a3abcb457596cec9fe2664f542).

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Acknowledgements

This study is based on data from the Clinical Practice Research Datalink obtained under license from the UK Medicines and Healthcare products Regulatory Agency. The data are provided by patients and collected by the NHS as part of their care and support. The interpretation and conclusions contained in this study are those of the authors alone. This work was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professorship awarded to T.B. Data storage and computing resources used in this work were supported by the Ministry of Science, Research and the Arts Baden-Wuerttemberg, Germany, German Research Foundation, the state of Baden-Wuerttemberg, Germany and the German Research Foundation grant no. INST 35/1314-1 FUGG. P.G. was supported by the National Institute of Allergy and Infectious Diseases (1DP2AI171011) and the Chan Zuckerberg Biohub investigator award. J.M.L. acknowledges support from the German Academic Scholarship Foundation.

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Authors and Affiliations

  1. Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg, Germany

    Julia M. Lemp, Christian Bommer, Min Xie, Felix Michalik, Anant Jani & Till Bärnighausen

  2. Division of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, CA, USA

    Julia M. Lemp, Min Xie, Felix Michalik & Pascal Geldsetzer

  3. Department of Economics and Centre for Modern Indian Studies, University of Goettingen, Göttingen, Germany

    Christian Bommer & Sebastian Vollmer

  4. University of Oxford, Oxford, UK

    Anant Jani

  5. Institute of Applied Health Research, University of Birmingham, Birmingham, UK

    Justine I. Davies

  6. Centre for Global Surgery, Department of Global Health, Stellenbosch University, Cape Town, South Africa

    Justine I. Davies

  7. Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

    Justine I. Davies

  8. Africa Health Research Institute, Somkhele, South Africa

    Till Bärnighausen

  9. Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA

    Till Bärnighausen

  10. Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA

    Pascal Geldsetzer

  11. Chan Zuckerberg Biohub—San Francisco, San Francisco, CA, USA

    Pascal Geldsetzer

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Contributions

J.M.L., C.B., A.J., J.I.D., S.V. and P.G. conceived of the research. T.B., S.V. and P.G. acquired funding for the research project. J.M.L., M.X. and F.M. curated the data. J.M.L., M.X., C.B. and P.G. designed the statistical analyses with consults from F.M., T.B. and S.V. J.M.L. and M.X. analysed the data. C.B. and P.G. supervised the analysis. J.M.L. and P.G. wrote the paper with edits from C.B., M.X., F.M., A.J., J.I.D., T.B. and S.V. All authors approved the final manuscript.

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Correspondence to Pascal Geldsetzer.

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Extended data figures and tables

Extended Data Fig. 1 Trends in glycated haemoglobin (HbA1c) before, during and after programme roll-out.

Weighted average HbA1c in one-year intervals from April 2015 to March 2020, for (a) wave 1 and (b) wave 2 practices (intervention) compared to wave 3 practices (control). The y-axis does not start from 0, weighting by number of individuals for each practice, by year. The roll-out of the NHS DPP started in June 2016 for wave 1, in April 2017 for wave 2 and in April 2018 for wave 3.

Extended Data Fig. 2 Regional and temporal variation in NHS DPP programme implementation.

(a) Share of patients eligible for NHS DPP derived via official practice eligibility by roll-out wave and (b) share of patients referred to NHS DPP in each of the nine Strategic Health Authorities.

Extended Data Table 1 Sample characteristics
Full size table
Extended Data Table 2 Placebo tests with baseline characteristics (Balance tests)
Full size table
Extended Data Table 3 Regression discontinuity results of being eligible for and of being referred to, intensive lifestyle counselling
Full size table
Extended Data Table 4 Regression discontinuity results with robust bias-corrected confidence intervals of being eligible for and of being referred to, intensive lifestyle counselling
Full size table
Extended Data Table 5 Negative outcome controls
Full size table
Extended Data Table 6 Aggregated treatment effect estimates of NHS DPP implementation on HbA1c from difference-in-differences analysis
Full size table
Extended Data Table 7 Overview of estimated effects of intensive lifestyle counselling on glycemic control
Full size table

Supplementary information

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Lemp, J.M., Bommer, C., Xie, M. et al. Quasi-experimental evaluation of a nationwide diabetes prevention programme. Nature 624, 138–144 (2023). https://doi.org/10.1038/s41586-023-06756-4

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