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Informing better healthcare policy for an ageing population

With an ageing population in the UK, healthcare providers are increasingly having to plan and deliver care and treatments for elderly patients with multiple ongoing conditions. But which patient groups are most at risk? How can decision makers design effective services to reduce inequalities? The Health Economics Unit’s was commissioned to help answer these questions with an in-depth analysis of the elderly population in Northwest London.

The project aimed to understand the patterns of multi-morbidity onset among the elderly population in Northwest London; crucial in shedding a light on the backdrop of the UK’s ageing population, and the challenges it poses to the health and social care system. Multi-morbidity is the presence of two or more long-term health conditions, which can include defined physical or mental health conditions, such as diabetes or schizophrenia, as well as ongoing conditions, such as a learning disability.

The Health Economics Unit was commissioned to carry out this work by the Health Foundation’s Research and Economic Analysis for the Long term (REAL) Centre, which provides independent analysis and research to support better long-term decision making in health and social care. The Centre uses independent analysis to inform policymakers and system leaders within the UK government and NHS, and help them make the best decisions based on the most recent evidence.

Building a picture with expert analysis

To ensure a comprehensive understanding of multi-morbidity patterns in the elderly population of Northwest London, a meticulous methodology was adopted. The primary approach was a retrospective analysis, which delved into historical data to discern patterns and trends. This analysis was rooted in the robust Whole Systems Integrated Care (WSIC)/Discover database, ensuring a wide coverage and reliable data points. The study was not just quantitative but also qualitative in its approach. While it quantitatively assessed the number of cases, it qualitatively evaluated variables such as age, gender, ethnicity, and deprivation. These variables were chosen based on their potential impact on health outcomes and their relevance to the study’s objectives.

Furthermore, to ensure the data was interpreted accurately and provided actionable insights, advanced statistical methods were employed. Survival analysis, a statistical approach that deals with the expected duration of time until one or more events happen, was the primary tool. Two specific survival analysis methods were used: the Kaplan-Meier method and the Cox regression. The Kaplan-Meier method was instrumental in estimating the survival function from lifetime data, while the Cox regression model helped in understanding the impact of several variables on the survival time.

Additionally, rigorous data cleaning procedures were implemented to ensure the accuracy and relevance of the data. Records were filtered for ages between 65 to 100, spanning from 2015 to 2021, and only recognized gender classes were considered. This meticulous approach ensured that the findings were both reliable and pertinent to the study’s objectives.

Identifying those at risk

Over the span from 2015 to 2021, the dataset reflected a notable increase in the elderly population, growing from 318,967 to 394,460 individuals, with a predominant representation from female and white demographics. The Kaplan-Meier Analysis revealed that as time progressed, the likelihood of individuals having fewer than two long-term conditions diminished. Interestingly, gender did not significantly influence this probability. However, factors such as deprivation, age, and ethnicity emerged as influential, with the most deprived segment showing the lowest probability and age and ethnicity proving to be significant determinants of multi-morbidity onset.

The Cox Regression Analysis further elucidated these patterns, indicating that males exhibited a 7% heightened risk of multi-morbidity compared to females. Additionally, as age increased, so did the risk, with Asian and Black ethnic groups facing a higher risk than their White counterparts. Alarmingly, the most deprived individuals faced a 19% escalated risk. Complementing these findings, the Sensitivity Analysis highlighted that the probability of an individual having at least one long-term condition or facing mortality was significantly higher than the chances of having two or more conditions.

Improving policy with evidence

The insights derived from understanding these multi-morbidity patterns are invaluable for system leaders within health and care. They pave the way for more nuanced policy formulation, enabling the crafting of health and social care policies that cater specifically to the unique needs of diverse demographic groups. Equally crucial is the guidance they offer in optimizing resource allocation, ensuring that areas or groups with heightened risks receive prioritized attention. Beyond policy and resources, these findings are instrumental in sculpting targeted awareness campaigns, ensuring that the most vulnerable groups are adequately informed. Moreover, this study isn’t just an endpoint; it’s a springboard. It lays a robust groundwork for future research, setting the stage for deeper dives into specific long-term conditions and the intricate ways they intersect.

Shining a light on the needs of our ageing population

The research has shed light on the intricate patterns of multi-morbidity onset within Northwest London’s elderly community. Armed with these insights, health and social care providers are now better positioned to make strategic decisions, ensuring enhanced care and support tailored to this demographic’s unique needs.

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