|Title||Comparisons of Disease Cluster Patterns, Prevalence and Health Factors in the USA, Canada, England and Ireland.|
|Publication Type||Journal Article|
|Year of Publication||Forthcoming|
|Authors||Hernandez, B, Voll, S, Lewis, N, McCrory, C, White, A, Stirland, L, Kenny, RAnne, Reilly, R, Hutton, CP, Griffith, LE, Kirkland, S, Terrera, GMuñiz, Hofer, S|
|Keywords||ELSA, multimorbidity, TILDA|
Background Identification of those who are most at risk of developing specific patterns of disease across different populations is required for directing public health policy. Here, we contrast prevalence and patterns of cross-national disease incidence, co-occurrence and related risk factors across population samples from the U.S., Canada, England and Ireland.
Methods Participants (n=62,111) were drawn from the US Health and Retirement Study (n=10,858); the Canadian Longitudinal Study on Ageing (n=36,647); the English Longitudinal Study of Ageing (n=7,938) and The Irish Longitudinal Study on Ageing (n=6,668). Self-reported lifetime prevalence of 10 medical conditions, predominant clusters of multimorbidity and their specific risk factors were compared across countries using latent class analysis.
Results The U.S. had significantly higher prevalence of multimorbid disease patterns and nearly all diseases when compared to the three other countries, even after adjusting for age, sex, BMI, income, employment status, education, alcohol consumption and smoking history. For the U.S. the most at-risk group were younger on average compared to Canada, England and Ireland. Socioeconomic gradients for specific disease combinations were more pronounced for the U.S., Canada and England than they were for Ireland. The rates of obesity trends over the last 50 years align with the prevalence of eight of the ten diseases examined. While patterns of disease clusters and the risk factors related to each of the disease clusters were similar, the probabilities of the diseases within each cluster differed across countries.
Conclusions This information can be used to better understand the complex nature of multimorbidity and identify appropriate prevention and management strategies for treating multimorbidity across countries.