Socioeconomic gradient in mortality of working age and older adults with multiple long-term conditions in England and Ontario, Canada
Project Status: Published
The number of people living with multiple long-term conditions is rising in many western countries and is socially patterned; those living in deprived areas have higher prevalence with a faster and earlier acquisition rate. There is currently mixed evidence on the influence of number of conditions and deprivation on mortality. We aimed to explore whether number of long-term conditions contribute to socioeconomic inequalities in mortality, examine whether the influence of number of conditions on mortality is consistent across socioeconomic groups and assess whether these associations vary by working age (18-64 years) and older adults (65+ years). We provide a cross-jurisdiction comparison between England and Ontario, by replicating the analysis using comparable representative data from both jurisdictions.
Survival time based on deaths from all causes is the primary outcome for this study. Cox regression models were used to estimate hazards of mortality by number of long-term conditions, deprivation and their interaction, with adjustment for age and sex and stratified between working age and older adults in England
We used data from the Clinical Practice Research Datalink (CPRD Aurum) linked to Hospital Episode Statistics, IMD mortality and Indices of Multiple Depriavtion (eRAP 20_000239).
The codelists we used to derive conditions for patients are from Anna Head at the University of Liverpool and can be found here.
This study used health administrative data from Ontario (information to be added)
The process to create an analysis dataset from our CPRD sample and clinical codelists, can be found in our project exploring MLTC's and mortality in diverse ethnic groups and is 01_Process_data.R
There are three R scripts that create variables and then run models to test our hypotheses:
- 02SES_prepdata.R - Derives variables
- 03survival_SES_descriptives.R- Creates summary statistics used in the paper
- 04survival_SES.R- Runs the survival analyses
- ONanalysis.do - Stata code for the Ontario analysis. Modelling only. Descriptive statistics were done in SAS using in-house macros.
Results from England and Ontario are outputted as excel docs and results were visualised for the paper.
- 05_survival_graphs.R- Creates the visualisation of results
If you have access to a CPRD Aurum sample then you can run the scripts in order to re-produce this work. For Ontario, the computer programs may rely upon coding templates or macros that are unique to ICES and are therefore either inaccessible or may require modification.
These scripts were written in R version 4.0.5 and RStudio Version 1.1.383.
The following R packages (available on CRAN) are needed:
In addition these scripts make use of our in house package aurumpipeline and THFstyle available here on GitHub.
Link to paper will be added [here]
This project is licensed under the MIT License