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| Name | Class |
|---|---|
| University of Leeds | OTHER |
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Life expectancy at age 65 in the most deprived fifth of the English population was about 4 years shorter than of the most affluent fifth in 2010. The inverse gradient between mortality and social position is well established. But how disease patterns and multimorbidity (having two or more long term conditions at the same time) impact on differential mortality rates is inconclusive: is it because disadvantaged groups acquire more or more lethal combinations of, diseases over their life course; or, simply, become ill at ages younger than more affluent groups?
The association between social inequality and cause-specific mortality and single disease morbidity has been studied extensively. However, it remains unclear whether having two or more chronic diseases concurrently (or 'multimorbidity') plays a role in contributing to the inequalities gap in survival. This is particularly relevant given an ageing population and the trend of a widening in the life expectancy gap across several European countries.
Multimorbidity incidence increases rapidly with age. Estimates of the prevalence of multimorbidity in older people range from 55% to 98%, mainly due to the selection of diseases included, population coverage (hospital, community) and data source (self-reported surveys or clinical records). However, across all studies there is a clear and consistent pattern of higher prevalence rates at older ages, with multimorbidity.
Many aspects of the patient health trajectory remain under-explored. Patient case-mixes are likely to vary across socioeconomic groups, alongside a host of prognostic factors, including the clustering of multiple risk factors, age of onset, and disease presentation, progression and management in the presence of multiple health conditions.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Healthy | Patients without any of the pre-specified chronic diseases |
| |
| Multimorbid | Patients having any two or more of the pre-specified chronic diseases |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| This is not an intervention study | Other | This study is based on the retrospective analysis of linked electronic health records. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Yearly multimorbidity incidence rate | Numerator: Of patients with either 0 or 1 chronic diseases as on 1st Jan of the year, all those who become multimorbid by 31st Dec of the year. Denominator: patient years at risk of patients with either 0 or 1 chronic diseases as on 1st Jan of the year | 10 years |
| Yearly multimorbidity prevalence | Numerator: all those with 2 or more listed diseases on 1st July of the year. Denominator: All eligible patients on 1st July of the year, irrespective of disease status on that date. | 10 years |
| Yearly all-cause mortality rates | Numerator: number of deaths until 31st Dec of the year amongst the patients included in the denominator. Denominator: Person years at risk of patients with 0,1,2 or more diseases on 1st Jan of the year. | 10 years |
| Overall life expectancy | Incident rates of transitions between no disease, 1 disease, 2+ diseases, and death. | 10 years |
| Health state-specific life expectancies | Incident rates of transitions between four health states - no disease, 1 disease, 2+ diseases, and death. | 10 years |
| Measure | Description | Time Frame |
|---|---|---|
| Yearly non-accidental mortality rates | As outcome 3 but excluding accidental deaths from the numerator | 10 years |
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Inclusion Criteria:
Exclusion Criteria:
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Patients included in the CALIBER dataset.
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| Name | Affiliation | Role |
|---|---|---|
| Madhavi Bajekal, PhD | University College, London | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University College London | London | WC1 E7H | United Kingdom |
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