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Exploring the Impact of Diagnostic Timeframes on Multimorbidity Prevalence in England

Our study in published in BMJ Medicine in February 2024 examined the effect of defining timeframes for long-term conditions on the prevalence of multimorbidity in England, and on the role played by sociodemographic factors. Using primary care electronic health records from the Clinical Practice Research Datalink Aurum, the study included over 9.7 million adults registered in England as of 1 January 2020, focusing on 212 long-term conditions.

Key Findings

Varying Prevalence Rates: The prevalence of multimorbidity, defined as the coexistence of two or more long-term conditions, varied widely based on the timeframe used for definition. It ranged from 41% with stricter criteria (requiring three codes within any 12-month period) to a 74% when a single diagnostic code was deemed sufficient. Using conditions marked as active problems resulted in the lowest prevalence rate at 35%.

Sociodemographic Influences: The study revealed that younger individuals, certain minority ethnic groups, and those living in areas of lower socioeconomic deprivation were more likely to be reclassified as not multimorbid under timeframes that required more than one diagnostic code. This suggests that these groups are disproportionately affected by the criteria used to define long-term conditions.

Implications for Healthcare Policy and Research: The substantial variation in multimorbidity prevalence underscores the challenges in directly comparing estimates of multimorbidity between studies. It highlights the need for clear rationales behind the choice of timeframe and suggests a potential bias introduced by definitions requiring multiple codes. We recommended that researchers provide their reasoning for the timeframe choice and consider sensitivity analyses to explore the impact on different patient groups.

Addressing Multimorbidity in Healthcare

The findings emphasize the complexity of measuring multimorbidity and the influence of methodological decisions on prevalence estimates. This has important implications for healthcare policy, practice, and research; stressing the importance of adopting a nuanced approach to understanding and addressing the needs of people with multiple health conditions. It calls for a balance between the granularity of condition definitions and the practicality of healthcare delivery, ensuring that healthcare systems can adequately respond to the nuanced needs of its diverse patient population.

Conclusions

The study serves as a critical reminder of the dynamic nature of health conditions and the need for healthcare systems to adapt their approaches to effectively manage multimorbidity. It opens avenues for further research into optimising care for individuals with multiple long-term conditions, ultimately aiming to enhance clinical outcomes, patient experience quality of life, and healthcare efficiency.

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