Pioneering a new era in assessing hearing health inequalities: monitoring burden and distribution of hearing loss in older adults using routine health information systems

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Abstract

Introduction: hearing loss presents a significant public health challenge, with prevalence estimates based on projected age demographics rather than actual health needs. Validating these estimates with real-world data from routine health information systems has been lacking globally. This study aimed to quantify hearing loss severity using real-world evidence from English primary care and explore localised patterns and trends.

Methods: information on hearing loss prevalence was gathered from primary care records in the Cheshire and Merseyside Integrated Care System (ICS). We measured the annual percentage of adults diagnosed with any type of hearing loss in Lower Super Output Areas (LSOAs) from 2013 to 2022. We applied Cluster and Outlier Analysis using the Local Moran's I algorithm and examined the local effects of deprivation on hearing loss using Geographically Weighted Regression (GWR).

Results: the analysis revealed significant spatial clusters and an increasing trend in hearing loss prevalence. Cheshire had the highest concentration of High-High clusters of hearing loss (42% of the area), and Halton had the highest percentage of areas with a significant rate of increase (40.4%). Deprivation explained up to 35% of the variance in hearing loss across the ICS in 2020.

Conclusion: since 2013, the study has identified a rapid increase in hearing health inequalities within areas of the Cheshire and Merseyside Integrated Care System (ICS). Monitoring spatial patterns and trends of hearing loss using routine health information systems is crucial for understanding these disparities. This data can guide the development of targeted prevention and intervention strategies, helping to identify priority areas for action. By systematically documenting, analysing, and interpreting hearing health indicators within patient records, equitable long-term care models can be crafted to align with the genuine needs of diverse populations. Policymakers can gain valuable insights from this data to allocate resources efficiently and implement context-sensitive strategies to address these inequalities effectively.

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Contributors

Author: Dialechti Tsimpida

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