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Abstract

Self-reported health is a common measure predictive of morbidity and mortality among adults. Many factors are known to be associated with self-reported health including the number of chronic conditions (i.e., multimorbidity). While the association between self-reported health and morbidity and mortality has been well-established, the factors that shape the relationship with self-reported health (e.g., modify and mediate) are poorly understood. Further, it is unknown why some older adults, despite having high numbers of chronic conditions, continue to rate their health positively. This is known as the well-being paradox. This mixed methods research study was designed to address these knowledge gaps. The objectives of the proposed research are to (1) determine what factors shape the relationship between multimorbidity and self-reported health and how they do so; (2) describe the ways that older adults define and perceive their individual health; and (3) explain the well-being paradox. Informed by a multimorbidity resilience framework, the quantitative component of research will analyze Canadian Longitudinal Study on Aging data while the qualitative component will collect and analyze interview data from 12 to 20 community-dwelling older adults using a case study design. Findings from this study have the potential to inform and advance future health intervention programs or services aimed at improving health-related quality of life for community-dwelling older adults.

Keywords

case study mixed methods secondary data analysis cross-sectional

Introduction

Multimorbidity is defined as the co-occurrence of two or more chronic conditions and is recognized as a significant public health issue that negatively affects health-related quality of life and increases the use of costly health and social services—especially among older adults (Wister, Kendig, et al., 2016). The increasing prevalence and complexity of multimorbidity has resulted in a shift in focus of gerontological research away from single conditions toward the study of multimorbidity (Wister, Kendig et al., 2016).

Aligned with this shift is an increasing interest in the ways that older adults respond to the health-related adversity associated with multimorbidity and how this influences their self-reported health (Lehti et al., 2021; Whitmore et al., 2020; Wister et al., 2019). Self-reported health is a subjective assessment of health based upon individual perceptions (Banerjee et al., 2010). Typically captured as a response to the question, “In general, would you say that your health is excellent, very good, good, fair, or poor?” self-reported health is a widely used and well-validated epidemiological health measure. This is because there is extensive evidence for self-reported health as a predictor of morbidity and mortality in adult populations (Banerjee et al., 2010; Idler & Benyamini, 1997E. L. Idler & Benyamini, 1997), including older adults (Benyamini et al., 2003; Schüz et al., 2011; Vuorisalmi et al., 2005).

Disease counts are a common approach to defining multimorbidity (Johnston et al., 2018) and can include a broad spectrum of health conditions including risk factors and symptoms (Griffith et al., 2019; Johnston et al., 2018; Willadsen et al., 2016). Regardless of the health conditions included in the disease count, evidence suggests that as the number of conditions increase (i.e., as the level of multimorbidity increases) the level of self-reported health decreases, or becomes more negative (Heller et al., 2008; Perruccio et al., 2012; Pinquart, 2001; Schüz et al., 2011; Terner et al., 2011). While this relationship is well-established, little is known, however, about the role of other factors, such as demographic or other health variables, in shaping this relationship. Further, a subset of older adults with high levels of multimorbidity describe positive self-reported health. This apparent contradiction, the presence of wellness amidst illness, is termed the well-being paradox and pertains to those individuals who achieve and report positive levels of subjective health (e.g., self-reported health) despite having poorer health according to objective indicators (Rowe & Kahn, 1987; Wister, Coatta et al., 2016). The purpose of this paper is to describe a study protocol designed to address these knowledge gaps.

Background

First used in sociological health research, self-reported health is increasingly prevalent in medical and epidemiological research because it has been shown to be a strong predictor of morbidity and mortality and is simple to use (Kaplan & Camacho, 1983; Mossey & Shapiro, 1982). Broadly, self-reported health is conceptualized as a cognitive appraisal process examining individual perceptions of health, health and illness experiences, and future health expectations (Idler et al., 2004; Jylhä, 2009; Knäuper & Turner, 2003).

As adults age, the likelihood of developing chronic conditions and, in turn, multimorbidity, increases (Marengoni et al., 2011; Suls et al., 2019; Vertrano et al., 2018; Wister, Kendig, et al., 2016). The presence of multimorbidity among older adults is associated with poorer health-related quality of life, increased use of costly health and social services, increased caregiver burden, and an increased risk for adverse events (e.g., falls)—often necessitating more complex clinical care (Marengoni et al., 2011; Suls et al., 2019).

The rising prevalence of multimorbidity has resulted in increasing research emphasis on multimorbidity. Traditionally, successful aging was believed to be limited to the absence of disease and the presence of physical and cognitive capacity (Rowe & Kahn, 1987; Wister, Kendig et al., 2016). In recent years, however, this conceptualization has been challenged as there remains a sub-group of older adults with higher levels of self-reported health despite multimorbidity. Termed the well-being paradox, these individuals achieve higher self-reported health despite objective measures of high multimorbidity using biomedical indicators such as disease diagnoses or medications (Netuveli & Blane, 2008). This discrepancy between objective and subjective measures of health suggests that factors other than multimorbidity shape how community-dwelling older adults view their health.

Summary of Pilot Work

A diverse range of factors (e.g., sociodemographic, physical, psychiatric, health-related behavior, and emotional) associated with self-reported health among community-dwelling older adults have been identified in the literature (Whitmore et al., 2020). These include the number of chronic conditions, the ability to perform activities of daily living, the presence of depressive symptoms, and social participation (Whitmore et al., 2020). As reported in a recent scoping review by the authors, while the relationship between the number of chronic conditions (i.e., multimorbidity) and self-reported health is consistent, there remains a lack of consistency in the literature regarding the nature of the relationship between other factors (e.g., sex, age, and body mass index) and self-reported health (Whitmore et al., 2020). Further, little is known about how these various factors shape or explain self-reported health among older adults. This includes a need to understand whether certain factors modify or mediate this relationship.

This scoping review also highlighted the opportunity to explore the relationship between resilience and self-reported health. This is due to the fact that many of the factors reported to be associated with self-reported health in the literature (e.g., social integration, coping, and locus of control) are the same factors that comprise common resilience measures (Whitmore et al., 2020). To our knowledge, only one study has reported resilience as a factor associated with higher levels of self-reported health among older adults. For this reason, there are compelling reasons to explore the role of resilience among other factors in shaping or explaining self-reported health. This understanding may help to explain why and how older adults self-reported their health positively despite the presence of multimorbidity (i.e., the well-being paradox).

Multimorbidity, Resilience, and Self-Reported Health

One explanation for this discrepancy between objective measures of health and the subjective experience of well-being may be the presence of multimorbidity resilience. Resilience has been defined as the ability of an individual to both navigate and negotiate psychological, social, cultural, and physical resources (Ungar, 2008) and to “bounce back” in the face of adversity, such as the adversity of multimorbidity (Windle, 2011; Windle et al., 2020). Multimorbidity resilience which specifically refers to the ways that individuals regain wellness following illness draws upon coping strategies, available resources, and previous life experiences including those related to health and illness at both the individual and environmental level (Wister, Coatta, et al., 2016). Given the increasing prevalence of multimorbidity in older adult populations (Gijzel et al., 2019), the recent paradigmatic shift away from the idea that healthy aging occurs in the absence of illness, as well as the overlap of factors associated with self-reported health and resilience, the concept of resilience can enhance understanding of how individual, social, and environmental factors shape self-reported health in older adults with multimorbidity (Wister, Coatta et al., 2016).

Multimorbidity Resilience Framework

The Lifecourse Model of Multimorbidity Resilience (Wister, Coatta et al., 2016) was selected to guide this mixed methods research. Rooted in theoretical foundations that underlie resilience research, the Lifecourse Model of Multimorbidity Resilience broadly positions the individual at the center of an interrelated social and environmental context (Wister, Coatta et al., 2016). While multimorbidity resilience is thought to be experienced at the level of the individual, this model posits that it is best understood within an individual’s broad socioenvironmental landscape (Wister, Coatta et al., 2016). The three overlapping circles represent wellness, a concept that involves a full integration between the individual, social, and environmental systems of which the person exists (Wister, Coatta et al., 2016). Wellness in the context of this model is described as achieving meaningful engagement in life, opportunity to achieve potential, and a core element of healthy aging.

The Lifecourse Model of Multimorbidity Resilience was selected as the conceptual framework for this proposed research because it is specific to an older adult population, describes resilience within the context of multimorbidity and health-related adversity, and recognizes the importance of the lifecourse in acquiring resources required to foster a higher level of resilience (Wister, Coatta et al., 2016). This model will be used to guide data collection, data analysis, and interpretation of the findings in both the quantitative and qualitative components of study.

Objective

The purpose of this mixed methodological study is to determine how individual, social, and environmental factors shape the relationship between multimorbidity and self-reported health. The study will also attempt to understand and explain the well-being paradox among community-dwelling older adults by exploring how older adults define and the factors that shape their health. A mixed methods study design will be employed to address these objectives which include (1) a secondary analysis of a large Canadian database (the Canadian Longitudinal Study on Aging [CLSA]) and (2) a qualitative case study to understand the way these community-dwelling older adults explain the influence of certain factors on their self-reported health. Following the Good Reporting of a Mixed Methods Study (GRAMMS) (O’Cathain et al., 2008), each component of the study will be described.

Method

Convergent Mixed Methods Research Design

Mixed methodological research uses both quantitative and qualitative research approaches in a single study (Creswell & Plano Clark, 2018). This methodology emphasizes the integration or linking of findings from both individual approaches (Creswell and Plano Clark, 2018; Tashakkori &Creswell, 2007). A convergent design will be used where both quantitative and qualitative data will be collected separately and then integrated. This integrated data will contribute to a richer understanding of the factors shaping the relationship between multimorbidity and self-reported health, advance understanding of the well-being paradox, and facilitate a range of study findings that explain one another in a way that each approach alone could not (Creswell & Plano Clark, 2018). This mixed methods approach ensures that the mechanisms by which these factors shape health as well as their described importance among older adults can be explored. Further, in using a mixed methods approach, constructs identified in the resilience model but not captured in the chosen dataset, such as those that relate to previous life experience (e.g., experiences with illness), can be explored through qualitative analyses (see Figure 1 for a detailed schematic of the study design).

Figure 1.

Study schematic.

Specific research questions have been developed for each component of the study.

Quantitative Questions

1. What is the relationship between multimorbidity and self-reported health in community-dwelling older adults, and what factors are associated with this relationship?

2. What is the prevalence of the well-being paradox among community-dwelling older adults?

3. What are the characteristics of community-dwelling older adults with the well-being paradox, and what factors are associated with the well-being paradox in this population?

Qualitative Questions

4. How do community-dwelling older adults define their health?

5. How do community-dwelling older adults explain how individual, social, and environmental factors shape their self-reported health?

Overarching Mixed Methodological Question

6. What is the relationship between multimorbidity, resilience, and self-reported health among community-dwelling older adults, and how does this understanding help to explain the well-being paradox?

Quantitative Component

Design

To address the quantitative research questions, a secondary analysis of CLSA data will be completed. The CLSA is a large, population-based, prospective longitudinal cohort study that is following over 50,000 adults aged 45–85 years at baseline (Raina et al., 2009). Designed as a 20-year longitudinal study, the CLSA collects comprehensive data to understand the maintenance of health and development of disease as people age (Raina et al., 2009). CLSA baseline data are separated into two groups: tracking participant data and comprehensive participant data. The tracking group (stratified random sample of 10 Canadian provinces), comprised of over 21,000 adults, complete a telephone questionnaire, while the comprehensive group (stratified random sample of individuals recruited in the surround geographical area of the 11 data collection sites), comprised of just over 30,000 adults, complete an in-home interview as well as a visit to a data collection site (Raina et al., 2019).

Participants were excluded from CLSA if they resided in one of Canada’s three territories, lived on a federal First Nations reserve, were full-time members of the Canadian Armed Forces, lived in an institutional setting, were not fluent in French or English, or had a cognitive impairment precluding them from providing informed consent or providing data on their own (Raina et al., 2009).

Sample

All community-dwelling older adults aged 65 years or older from the baseline tracking (version 3.4) and comprehensive (version 4.0) datasets will be included.

Data Collection

Use of the CLSA tracking and comprehensive databases provides the opportunity for robust analysis of individual, social, and environmental level factors that may shape the relationship between multimorbidity and self-reported health among the study population. Drawing upon the Lifecourse Model of Multimorbidity Resilience (Wister, Coatta et al., 2016), a validated multimorbidity resilience index (Wister et al., 2018), and a comprehensive scoping review of the literature (Whitmore et al., 2020), a broad range of variables available within the CLSA database will be examined. These variables include factors known to be associated with self-reported health, such as level of multimorbidity, sociodemographic factors (e.g., age, sex, gender, economic status, and education); functional status; health behavior (e.g., exercise, nutrition, and activities of daily living); social engagement (e.g., social participation, social network, and social support); and emotional well-being (e.g., life satisfaction, emotional distress, and depressive symptoms) (Whitmore et al., 2020).

Variables of Interest

Based on a completed scoping review and the selected multimorbidity resilience framework, several variables available in CLSA will support the proposed quantitative analysis.

Demographics

These include sex, age, marital or partner status, race, education, economic status, and language spoken at home.

Health

Health variables will include self-reported health, depressive symptoms (Centre for Epidemiologic Studies Depression Scale—10 item) activities and instrumental activities of daily living (ADL and IADL), Older Americans Resource Scale (OARS), various physical mobility measures, as well as social and environmental variables inclusive of social support availability, satisfaction with life, and the life space index.

In addition, a count of chronic conditions and a level of multimorbidity will be calculated. Using a list of highly prevalent chronic conditions (Fortin et al., 2017), the level of multimorbidity will be calculated using the number of self-reported chronic conditions available in the CLSA (see Supplement Table 1 for a full list of all variables of interest). Each of the chronic condition groups will count as one chronic condition (e.g., the presence of depression and diabetes counts as two conditions, whereas the presence of two types of cancer and diabetes also counts as two conditions).

Multimorbidity Resilience

The level of multimorbidity resilience will be determined using the validated resilience index developed from CLSA data by Wister and colleagues (2018). This resilience index maps functional (e.g., activities of daily living), psychological (e.g., distress), and social variables (e.g., social support) to multimorbidity resilience (Wister et al., 2018) using health variables included in these analyses. A summative score, reflective of the functional, social, and psychosocial sub-domain scores, will provide an overall score of multimorbidity resilience with a higher score reflecting a higher level of resilience.

Variables of interest and the corresponding CLSA variable identification codes are provided in Supplementary Table 1. Full descriptions of these variables and the data available in CLSA is described in the CLSA cohort profile (Raina et al., 2019) and in the paper by Wister et al., (2018).

Analysis

Data will be analyzed using SAS version 9.4.

Relationship Between Self-Reported Health and Multimorbidity

To address the first research question, all community-dwelling adults aged 65 years and older in the CLSA baseline dataset (n = 21,503) will be included in the analyses. Descriptive analyses will determine the relationship between differing levels of multimorbidity (0, 1, 2, 3, 4, 5, and 6 + chronic conditions) and levels of self-reported health (excellent, very good, good, fair, and poor). Consistent with the literature, it is hypothesized that the higher the level of multimorbidity, the more negative the level of self-reported health (Pinquart, 2001). Further stratified analyses of the relationship between multimorbidity and self-reported health will occur based on selected sub-group variables including factors known to be associated with self-reported health, such as sex, age, economic status, and resilience (Pinquart, 2001; Whitmore et al., 2020). This analysis will employ effect modification, interaction, and mediation analysis techniques (Corraini et al., 2017; Frazier et al., 2004), mediator pathway analysis, and logistic regression (Rijnhart et al., 2019) to determine the mechanism by which associated factors shape self-reported health, as well as determine independent predictors of self-reported health. Additionally, differences between men and women will be examined via sex disaggregated analyses.

Factors Shaping the Well-Being Paradox

The second and third research questions will explore, in greater detail, the factors that can explain the well-being paradox. Older adults will be classified as exhibiting the well-being paradox if they have high levels of multimorbidity (4 + chronic conditions) and positive self-reported health (very good or excellent). This definition reflects both a higher-than-average response to both the number of chronic conditions for the sample as well as a number of chronic conditions that is likely to include individuals with chronic conditions that extend beyond those that are merely common and often asymptomatic (e.g., hypertension and obesity). Sensitivity analyses will be completed to test whether this operational definition of the well-being paradox is appropriate.

To answer the second research question, descriptive analysis will be completed to identify characteristics of those with the well-being paradox. This will include comparing older adults with the well-being paradox to those without the well-being paradox (i.e., those older adults with 4 + chronic conditions and negative self-reported health) using inferential statistics (i.e., independent t-tests or x² tests of independence). Bivariate analyses to identify candidate predictors of the well-being paradox (i.e., positive self-reported health and four or more chronic conditions) will be further explored in the third research question using logistic regression. In these analyses, the criterion for inclusion of any independent variable in the multiple regression model will be relaxed to p = 0.10 to not eliminate potential associations early in the analysis. It is hypothesized that there are several individual, social, and environmental factors that shape (e.g., modify, mediate, and interact with) the presence of the well-being paradox among the study population.

Qualitative Component

Design

An explanatory qualitative single case study design will build upon and inform interpretation of findings in the quantitative component of this research. Case study research aims to understand a phenomenon within its real-world context (Yin, 2014).

Case Under Study

The case or unit of analysis is the way in which community-dwelling older adults explain the influence of individual, social, and/or environmental factors on how they perceive their health. An understanding of this process will help to explain why some community-dwelling older adults with high levels of multimorbidity self-report their health positively (i.e., the well-being paradox).

Propositions

Propositions in a case study direct attention toward what requires further examination within the study, limit the scope of the research, and guide the project (Yin, 2014). Propositions have been developed using the completed scoping review (Whitmore et al., 2020), the existing literature, and the conceptual model (see Supplementary Table 2).

Case Binding

The population under study is bound by the definitions of community-dwelling and older adults and is further bound by the operational definitions of self-reported health, multimorbidity, and well-being paradox. The study will include older adults living in Southwestern Ontario.

Sampling and Recruitment

Purposive sampling strategies for older adults will include criterion sampling as well as maximum variation sampling to obtain a sample of 12–20 older adults. Inclusion criteria include adults (1) 65 years of age and older; (2) living in the community (e.g., not in long-term care or hospital); and (3) English speaking. Maximum variation sampling will be used to sample older adults with higher levels of multimorbidity via strategic recruitment through specifically selected recruitment sites as well as initial screening questions during participant intake.

Recruitment of community-dwelling older adults will involve both in-person and virtual strategies. Recruitment posters will be distributed at various community-based organizations such as local libraries, fitness facilities, and seniors’ centers as well as other community-facing locations such as grocery stores and laundry mats. Virtual means of recruitment will involve use of existing email listservs and social media to share study recruitment information. In addition, study participants will be encouraged to share recruitment materials within their own social circles using snowball methods.

Data Collection

One-on-one telephone interviews will serve as the main data collection approach and will be completed by the first author. The interviews will be semi-structured, using pre-developed questions that are open-ended and flexible (see Supplementary Table 2). Additional prompts and follow-up questions will be used as appropriate. Interview questions will address the broad range of concepts in the resilience model (Wister, Coatta et al., 2016) and literature. Demographic data as well as data related to self-reported health, multimorbidity, and depressive symptoms will be collected. To measure the level of self-reported health, the same question used in the CLSA will be asked of study participants. To measure the level of multimorbidity, a multimorbidity index, based on Fortin and colleague’s (2017) work, aligned with the variables available in the CLSA database, will be used. This multimorbidity measure will be the same one used in the quantitative component of this proposed research. The level of depressive symptoms will be measured using the validated 10-item Centre for Epidemiologic Studies Depression Scale (Andreson et al., 1994).

Researcher reflection notes will be written and analyzed to add context and understanding to the qualitative data collected. These will be completed during the interview (e.g., emphasis on specific experiences or observations made during the interview) as well as after the completion of the interview. All interviews and documents (e.g., observation notes) will be transcribed verbatim or scanned and imported into NVivo v.12^©.

Analysis

All data sources will be reviewed and analyzed together so that study findings are based upon data convergence and not merely conclusions from individual data sources (Yin, 2014). The qualitative data will be collected and analyzed concurrently to inform future interviews, the quantitative analysis, and the need for further sampling (Yin, 2014; Baxterand Jack, 2008). Data analysis will follow Yin’s five iterative stages: compiling, disassembling, reassembling, interpreting, and concluding (Yin, 2014), and will be guided by Braun and Clarke’s reflexive thematic analysis (Braun & Clarke, 2006). Quantitative data collected (e.g., demographic data and depressive symptoms score) in this case study component of the proposed research will be analyzed and serve as context for interpreting and understanding qualitative study findings.

Mixed Methods Analysis and Interpretation

An explanatory bidirectional approach will be used for data integration (Moseholm & Fetters, 2017). This approach uses an initially quantitative framed analysis, followed by a qualitatively framed analysis, with iterative and repeated feedback between components before reaching a final interpretation (Moseholm & Fetters, 2017). As such, the quantitative findings will inform the qualitative sample (e.g., participant characteristics for maximum variation sampling) and the semi-structured interview guide (e.g., probes for specific relationships identified in the quantitative findings). As qualitative data are collected, analyzed, and interpreted, this initial quantitative framing will then be reversed to inform quantitative analyses that may not have been explored initially.

Once all data have been collected and analyzed, the findings from both components will be integrated in a matrix analysis. The intent of convergent mixed methodological data integration is to develop interpretations of the data collected that expand understanding and confirm or disconfirm hypotheses and propositions. To this end, this research will compare findings from the quantitative and qualitative components using the following four steps: (1) look for common concepts across both sets of findings; (2) develop tables that organize the findings so that the comparisons are easily observed; (3) compare the results of the tables to determine the ways in which the findings confirm, disconfirm, or expand upon one another; and (4) advance interpretation of the ways in which the converging, diverging, and expanding evidence enhances understanding of the phenomenon under study (Creswell & Plano Clarke, 2018). Following this comparison, integrated findings will be presented in a narrative discussion. This discussion will offer an interpretation of how the data answers the mixed methodological research question identified.

Rigor

Guided by Dellinger and Leech’s (2007) validation framework, the rigour of this convergent mixed methods research study will be reflected in the methodological decisions and considerations made in each component.

Quantitative Validity

Data collected and made available by CLSA is held to rigorous standards (Raina et al., 2019). Validated instruments, where available, and standardized processes are utilized. Where validated instruments or definitions do not exist (e.g., operationalizing the well-being paradox), a critical review of the literature and consultation with experts will be completed alongside sensitivity analyses. This critical review of the literature has also contributed to the formation of a priori study purpose, design, methodology, and sampling strategies (Dellinger & Leech, 2007; O’Cathain et al., 2010).

Qualitative Credibility

Credibility of the qualitative component of this proposed research will be enhanced through multiple methodological decisions made. This includes clear and justified statements regarding the research questions of interest, the guiding propositions, as well as the sampling approach (Baxter & Jack, 2008). In addition, case study research promotes the use of data triangulation through the inclusion of multiple data sources (interview data, researcher reflections, demographic data, and quantitative data) to support overall data integration (Yin, 2014). This triangulation contributes to the phenomenon being viewed and explored from multiple perspectives and overall enhances overall data and idea convergence (Baxter & Jack, 2008). Analytic logic, evidenced through the maintenance of a documented audit trail, will also be employed and include careful documentation of decisions made during data collection and analysis.

Mixed Methods Validity and Quality

One of the challenges that impact the potential meta-inferences made in this study is the limitations associated with conducting a secondary analysis. As the quantitative component of this study will use a large, pre-existing database, the research team will not be in control of the representativeness of the sample, variables collected, or the instruments used to measure constructs. Considerations for enhancing inferential consistency and to mitigate these challenges include using the extant literature to guide study decisions (Creswell, 2013); investigating all associations, whether statistically significant, expected, or not (Ivankova et al., 2006); further clarifying those meta-inferences made; and describing differences in the sample between the quantitative and qualitative components.

Ethical Considerations

Institutional ethics approval was granted for this work through the Hamilton Integrated Research Ethics Board (#8271V3; #8337V4). An application for CLSA data access was reviewed and accepted in May 2019 and an amendment was approved in August 2019. Informed consent, either written or verbal, will be obtained by the first author for all participants in the qualitative component. Participants can discontinue the study at any time without consequence as outlined in the consent form.

Significance and Implications

The proposed research is timely and important for several reasons. The quantitative, qualitative, and mixed methods findings will contribute novel understanding of the relationship between multimorbidity and self-reported health. This understanding will enhance knowledge about the nature and role of certain factors in this relationship and enrich understanding of the well-being paradox among community-dwelling older adults. As the demography of Canada shifts with a growing older adult population, high quality evidence is required to address the needs of older adults to promote optimal aging at home. To date, most studies that have explored self-reported health and the well-being paradox have not focused on how resilience or other factors shape self-reported health or have provided an explanation of the well-being paradox. This research, informed by a model of multimorbidity resilience, will address these gaps to contribute novel understanding for practice and policy application.

Conclusion

Findings from this proposed research aim to extend beyond the average experience of health among community-dwelling older adults by examining factors that shape self-reported health—a widely used but poorly understood health indicator. Further, these findings will contribute novel understanding of why some older adults report positive health despite health-related adversity.

Supplemental Material

sj-pdf-1-ijq-10.1177_16094069211050166 – Supplemental Material for The Relationship Between Multimorbidity and Self-Reported Health Among Community-Dwelling Older Adults and the Factors that Shape This Relationship: A Mixed Methods Study Protocol Using CLSA Baseline Data

Supplemental Material, sj-pdf-1-ijq-10.1177_16094069211050166 for The Relationship Between Multimorbidity and Self-Reported Health Among Community-Dwelling Older Adults and the Factors that Shape This Relationship: A Mixed Methods Study Protocol Using CLSA Baseline Data by Carly Whitmore, Maureen Markle-Reid, Kathryn Fisher, Jenny Ploeg, Carrie McAiney, Lauren Griffith, Susan Phillips and Andrew Wister in International Journal of Qualitative Methods

Supplemental Material

sj-pdf-2-ijq-10.1177_16094069211050166 – Supplemental Material for The Relationship Between Multimorbidity and Self-Reported Health Among Community-Dwelling Older Adults and the Factors that Shape This Relationship: A Mixed Methods Study Protocol Using CLSA Baseline Data

Supplemental Material, sj-pdf-2-ijq-10.1177_16094069211050166 for The Relationship Between Multimorbidity and Self-Reported Health Among Community-Dwelling Older Adults and the Factors that Shape This Relationship: A Mixed Methods Study Protocol Using CLSA Baseline Data by Carly Whitmore, Maureen Markle-Reid, Kathryn Fisher, Jenny Ploeg, Carrie McAiney, Lauren Griffith, Susan Phillips and Andrew Wister in International Journal of Qualitative Methods

Footnotes

Acknowledgments

This research is made possible using the data collected by the Canadian Longitudinal Study on Aging (CLSA). The CLSA is led by Drs. Parminder Raina, Christina Wolfson, and Susan Kirkland. The opinions expressed in this manuscript are the authors’ own and do not reflect the views of the Canadian Longitudinal Study on Aging.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is funded through a Canadian Institutes of Health Research (CIHR) Catalyst Grant (funding reference number 170309). In addition, CW is grateful for the funding support awarded through CIHR and the Vanier Canada Graduate Scholarship. This research was also undertaken, in part, thanks to the funding from Dr. Markle-Reid’s Tier 2 CIHR Canada Research Chair. Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference: LSA 94473 and the Canada Foundation for Innovation. This research will be conducted using the CLSA Baseline Tracking and Comprehensive Datasets, under application identification #190241.

Ethical Approval

ORCID iDs

Carly Whitmore

Maureen Markle-Reid

Jenny Ploeg

Supplemental Material

Supplemental material for this article is available online.

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