Towards a Research Agenda on Living Well with Multiple Chronic Conditions: A Resilience Model and Multi-level Profile AUTHORS KATHERINE COATTA & ANDREW WISTER DEPARTMENT OF GERONTOLOGY, SIMON FRASER UNIVERSITY VANCOUVER CAMPUS, CANADA

Age Pyramids of the Canadian Population, 2009 & 2036

Purpose  Cross-cultural and generational comparative analysis of multiple chronic illness patterns  Demographic, health and social profile  “The Canadian Case”  Identify areas for multivariate analyses

Background  Attention devoted to multiple chronic illnesses (multiple morbidity), given potential synergistic effects, population aging & health care discourse  Research is still in infancy  Multiple chronic conditions have been correlated with longer hospital stays, increased use of health care resources, and decreased productivity  The ‘well-being paradox’ (Windle, Woods & Markland, 2010) - life satisfaction maintained in the face of poor health  Has led to ‘living well’ with multiple chronic illnesses

Defining Multiple Morbidity  1) Simple dichotomies: 0 vs. 1+ Illnesses; OR 0,1 vs. 2+  2) Additive Scales (counts of illnesses)  3) Weighted based on HRQL or diagnostic criteria (onset, severity)  4) Comorbidity (index disease)  5) Combinations of selected illnesses  90% of older adults have 1+ chronic illness; 70% have 2+ (2008/09 Canadian Community Health Survey CCHS)  [asthma, arthritis, osteoporosis, back problems, blood pressure, migraine headaches, bronchitis, emphysema, COPD, diabetes, heart disease, cancer, ulcers, stroke, urinary incontinence, bowel disorder, cataracts, glaucoma and thyroid problems]

Theoretical Frame for Living Well with Multiple Chronic Illnesses  Adaptation (Homeostasis; Person-environment)  Connections among individuals, community & health policies (Socio-ecological theory, e.g. Stokols, 1991)  Behavioural change and action (TOPB, Social learning, Transtheoretical model, etc.)  Interconnectedness of hardiness and resources at individual, community & policy spheres (Resilience Theory)  Population health and health care interface (Chronic Care Models)

Adapted from: Barr, V., Robsinson, S., Marin-Link, B., Underhill, L., Dotts, A., Ravensdale, D. & Salivaras, S. (2003) The expanded chronic care model: An integration of concepts and strategies from population health promotion and the chronic care model. Hospital Quarterly, 7(1), 73-82

Methods  Analyses of the CCHS 4.2 Healthy Aging 2008/09  45+ (N = 30, 639)  Weighted to Canadian population and rescaled to limit overpowering analyses  Chronic illness additive measure (selected 8 illnesses common across CCHS and Australian HILDA survey)  Age Groups: 45-64; 65-74; 75+  Gender

Prevalence of Chronic Illnesses

Mean Number of Chronic Illnesses by Selected Variables, Age Group and Gender

Odds Ratios for Gender Differences in Mean # of Chronic Illnesses for Selected Outcomes * Numbers shown in columns are female/male odds ratios for the mean number of chronic illnesses

Odds Ratios for Age Differences in Mean # of Chronic Illnesses for Selected Outcomes * Numbers shown in columns are age group A (older)/ age group B (younger) odds ratios for number of chronic illnesses

Mean Number of Chronic Illnesses by Selected Variables, Age Group and Gender

Odds Ratios for Age Differences in Mean # of Chronic Illnesses for Selected Outcomes

Mean Number of Chronic Illnesses by Selected Variables, Age Group and Gender SELF PERCEIVED HEALTHHOSPITAL ADMITTANCE

Odds Ratios for Gender Differences in Mean # of Chronic Illnesses for Selected Outcomes SELF RATED HEALTH * Numbers shown in columns are female/male odds ratios for the mean number of chronic illnesses HOSPITAL ADMITTANCE

Mean Number of Chronic Illnesses by Selected Variables, Age Group and Gender VISIBLE MINORITY STATUSCOUNTRY OF BIRTH

Odds Ratios for Gender Differences in Mean # of Chronic Illnesses for Selected Outcomes * Numbers shown in columns are female/male odds ratios for the mean number of chronic illnesses COUNTRY OF BIRTHVISIBLE MINORITY STATUS

Mean Number of Chronic Illnesses by Selected Variables, Age Group and Gender MOBILITYPERSONAL CARE ASSISTANCE

Odds Ratios for Gender Differences in Mean # of Chronic Illnesses for Selected Outcomes MOBILITY PERSONAL CARE * Numbers shown in columns are female/male odds ratios for the mean number of chronic illnesses

Odds Ratios for Age Differences in Mean # of Chronic Illnesses for Selected Outcomes MOBILITYPERSONAL CARE

Mean Number of Chronic Illnesses by Selected Variables, Age Group and Gender

Odds Ratios for Gender Differences in Mean # of Chronic Illnesses for Selected Outcomes FORMAL INFORMAL * Numbers shown in columns are female/male odds ratios for the mean number of chronic illnesses

Correlations between Chronic Illnesses Scale and Selected Outcomes by Age Group and Gender

Odds Ratios for Age Differences by Correlation Coefficients of Chronic Illness Scale and Selected Variables

Correlations between Chronic Illnesses Scale and MOS Sub-scales Age Group and Gender

Summary  Age effect on chronic illness prevalence  Gender differences depend on specific illness Analysis of Multiple Chronic Illness Patterns:  Gender difference in multiple morbidity within marital status is largest for widowed boomers (female/male OR= 1.3)  The 75+/boomer age difference is large for males in all marital categories; but only for married females  Education effect on chronic illness is largest for boomers (45-64), declines with age  Gender difference in self-rated health & hospital admittance is largest for boomers

Summary – Con’t  Significantly higher gender difference in multiple morbidity for visible minority boomers  Large gender difference in multiple morbidity and personal care association for those aged 75+  Strong correlations between multiple morbidity and medication use (r=.45 to.55); slightly higher for boomers than seniors  Gender difference in chronic illness and formal and informal care is highest for boomers, smallest for seniors  Small (r=-.15 to -.25) correlations between multiple morbidity and life satisfaction, but boomers and young-old are most likely to be negatively affected  Correlations between multiple morbidity and social support dimensions are very low