Article Text

Download PDFPDF

Prepayment meters strongly associated with multiple types of deprivation and emergency respiratory hospital admissions: an observational, cross-sectional study
  1. Xuejie Ding1,2,
  2. Evelina T Akimova1,
  3. Bo Zhao1,
  4. Kasimir Dederichs1,3,
  5. Melinda C Mills1,4,5
  1. 1 Leverhulme Centre for Demographic Science, Nuffield Department of Population Health, University of Oxford and Nuffield College, Oxford, UK
  2. 2 WZB Berlin Social Science Center, Berlin, Germany
  3. 3 Department of Sociology, University of Oxford, Oxford, UK
  4. 4 Department of Genetics, University Medical Centre Groningen, Groningen, The Netherlands
  5. 5 Department of Economics, Econometrics and Finance, Faculty of Economics and Business, University of Groningen, Groningen, The Netherlands
  1. Correspondence to Professor Melinda C Mills, University of Oxford, Oxford OX1 1JD, UK; melinda.mills{at}; Dr Xuejie Ding; xuejie.ding{at}


Background Prepayment meters (PPMs) require energy to be paid in advance. Action groups and media contend that PPMs are concentrated in the most vulnerable groups, prone to run out of credit and experience financial burden. This led to forced installation for those over age 85 being banned in April 2023 and a ‘prepayment premium’ scrapped in July 2023. Yet, we lack empirical evidence of which groups PPMs are concentrated. This ecological study examines the extent to which PPMs are associated with multiple measures of structural social, economic and health deprivation to establish evidence-based policy.

Methods Combining multiple regional data and census estimates at the Lower Layer Super Output Area and the Middle Layer Super Output Area level from England and Wales, we use Spearman’s rank correlation, Pearson correlation and multivariate linear regression to empirically establish associations between PPMs and multiple types of deprivation.

Results Higher PPM prevalence is strongly associated with: lower income, receipt of employment benefits, ethnic minorities, lower education and higher health deprivation. Higher PPM prevalence is strongly associated with higher income deprivation affecting children, the elderly and social rental properties. PPMs are significantly associated with emergency hospital admissions for respiratory diseases in England, even after controlling for confounders (coefficient=1.81; 95% CI 1.51 to 2.11).

Conclusions We found empirical evidence that PPM users are concentrated among the population who already experience multiple disadvantages. Furthermore, PPM concentrated areas are associated with higher emergency hospital admissions for respiratory diseases.

  • deprivation
  • health inequalities
  • policy
  • public health
  • social sciences

Data availability statement

Data are available upon reasonable request.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:

Statistics from

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Data availability statement

Data are available upon reasonable request.

View Full Text

Supplementary materials

  • Supplementary Data

    This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.


  • Twitter @XuejieDing, @akimova_eva, @Kasimir_D, @melindacmills

  • Correction notice This article has been corrected since it first published. The open access licence type has been updated to CC BY.

  • Contributors Conceptualisation: all authors. Original draft preparation: XD and MCM. Main analysis: XD and MCM. Additional data preparation and analysis: BZ, ETA and KD. Writing, review and editing: MCM. All authors provided substantial critical input to improve the manuscript and all authors approved the final draft. XD and MCM are responsible for the overall content as guarantors.

  • Funding Funding was provided by the ERC (835069), ESRC Connecting Generations (ES/V0141988/1); Leverhulme Trust (Grant RC-2018-003) for the Leverhulme Centre for Demographic Science.

  • Map disclaimer The depiction of boundaries on this map does not imply the expression of any opinion whatsoever on the part of BMJ (or any member of its group) concerning the legal status of any country, territory, jurisdiction or area or of its authorities. This map is provided without any warranty of any kind, either express or implied.

  • Competing interests None declared.

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

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Linked Articles