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BackgroundPrepayment 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.MethodsCombining 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.ResultsHigher 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).ConclusionsWe 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.

Original publication

DOI

10.1136/jech-2023-220793

Type

Journal article

Journal

Journal of epidemiology and community health

Publication Date

10/2023

Addresses

Leverhulme Centre for Demographic Science, Nuffield Department of Population Health, University of Oxford and Nuffield College, Oxford, UK melinda.mills@demography.ox.ac.uk xuejie.ding@demography.ox.ac.uk.