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Geriatric medicine
Original research
Impact of multimorbidity and complex multimorbidity on mortality among older Australians aged 45 years and over: a large population-based record linkage study
  1. Alamgir Kabir1,
  2. An Tran1,
  3. Sameera Ansari2,3,
  4. Damian P Conway4,5,
  5. Margo Barr1
  1. 1Centre for Primary Health Care and Equity, University of New South Wales, Sydney, NSW, Australia
  2. 2School of Population Health, University of New South Wales, Sydney, NSW, Australia
  3. 3Faculty of Health Sciences and Medicine, Bond University, Robina, QLD, Australia
  4. 4The Kirby Institute, University of New South Wales, Sydney, NSW, Australia
  5. 5Population and Community Health Directorate, South Eastern Sydney Local Health District, Sydney, NSW, Australia
  1. Correspondence to Alamgir Kabir; a.kabir{at}unsw.edu.au

Abstract

Objectives Multimorbidity (MM, co-occurrence of two or more chronic conditions) and complex multimorbidity (CMM, three or more chronic conditions affecting three or more different body systems) are used in the assessment of complex healthcare needs and their impact on health outcomes. However, little is known about the impacts of MM and CMM on mortality in Australia.

Design Community-based prospective cohort study.

Setting New South Wales, Australia.

Participants People aged 45 years and over who completed the baseline survey of the 45 and Up Study.

Measures Baseline survey data from the 45 and Up Study were linked with deaths registry data. Deaths that occurred within 8 years from the baseline survey date were the study outcome. Eleven self-reported chronic conditions (cancer, heart disease, diabetes, stroke, Parkinson’s disease, depression/anxiety, asthma, allergic rhinitis, hypertension, thrombosis and musculoskeletal conditions) from the baseline survey were included in the MM and CMM classifications. Cox proportional hazard models were used to estimate adjusted and unadjusted 8-year mortality hazard ratios (HRs).

Results Of 251 689 people (53% female and 54% aged ≥60 years) in the cohort, 111 084 (44.1%) were classified as having MM and 39 478 (15.7%) as having CMM. During the 8-year follow-up, there were 25 891 deaths. Cancer (34.7%) was the most prevalent chronic condition and the cardiovascular system (50.9%) was the body system most affected by a chronic condition. MM and CMM were associated with a 37% (adjusted HR 1.36, 95% CI 1.32 to 1.40) and a 22% (adjusted HR 1.22, 95% CI 1.18 to 1.25) increased risk of death, respectively. The relative impact of MM and CMM on mortality decreased as age increased.

Conclusion MM and CMM were common in older Australian adults; and MM was a better predictor of all-cause mortality risk than CMM. Higher mortality risk in those aged 45–59 years indicates tailored, person-centred integrated care interventions and better access to holistic healthcare are needed for this age group.

  • GERIATRIC MEDICINE
  • Adult intensive & critical care
  • INTERNAL MEDICINE
  • EPIDEMIOLOGY
  • PRIMARY CARE
  • PUBLIC HEALTH

Data availability statement

No data are available. Data that support the findings of this study are available from the Sax Institute, but restrictions apply to the availability of these data, which were used under licence for the current study and so are not publicly available. The data, however, are available from the authors upon reasonable request and with permission from the Sax Institute.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2021-060001

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    Strengths and limitations of this study

    • A large population-based prospective cohort study of people aged 45 years and over was used to evaluate the effect of multimorbidity (MM) and complex multimorbidity (CMM) on 8-year mortality.

    • Self-reported chronic health conditions were used to define MM and CMM.

    • Deaths registry data were probabilistically linked to the cohort data by the New South Wales Centre for Health Record Linkage for mortality ascertainment.

    • Though the study cohort has been shown to be generally representative of the population from which it is drawn, non-response at recruitment may mean the cohort varies slightly from the broader population.

    • Our analysis was restricted to the conditions listed in the 45 and Up Study baseline survey questionnaire; however, this included all of the most important chronic conditions.

    Introduction

    Multimorbidity (MM), the co-occurrence of two or more chronic health conditions in an individual, is often used in the assessment of complex healthcare needs and their impact on health outcomes.1 As life expectancy increases over time due to advances in healthcare and living standards, the burden of chronic conditions is increasing globally.2 Consequently, the proportion of national healthcare expenditure spent caring for people with MM has increased substantially. For example, managing MM accounts for 71% of total US healthcare spending.3 While overall prevalence of MM is 33% globally,4 prevalence among those aged 65 years or more is estimated to be 55%–98% in high-income countries.5 In Australia, estimated prevalence of MM is 20% overall and 51% among those aged 65 years or more.6

    MM is associated with increased risk of adverse mental and physical health outcomes7 8 and poor quality of life overall.9 10 However, reported effects of MM on mortality in older adults are mixed: some studies reported MM was associated with greater risk of mortality,11–16 while others reported no significant association.17 18 A systematic review found an increased risk of mortality among those with MM, but noted the majority of studies were not population-based, had relatively small sample sizes and/or lacked internal validity.14 Also, many studies reported the impact of MM on mortality among older adults overall without stratifying outcomes by age group. Though some Australian studies demonstrated an association between MM and mortality, these studies were not population-based and have limited generalisability.19 20

    Some authors have proposed complex multimorbidity (CMM; co‐occurrence of three or more chronic conditions affecting three or more different body systems) as an alternative and more specific metric to assess complexity of individual healthcare needs.19 21 This metric provides lower prevalence estimates than MM and allows greater differentiation among older adults.19 However, whether it enables more targeted patient care and health resource planning requires further investigation. To our knowledge, there have not been any published evaluations comparing the impact of MM and CMM on older adult mortality in Australia. Hence, we conducted a large population-based data linkage study to (1) compare the effect of MM and CMM on mortality among older adults aged 45 and above; and (2) assess whether any observed effect on mortality varies by age group.

    Methods

    Study design and population

    We conducted a prospective cohort study of people aged 45 years and over from New South Wales (NSW), Australia, enrolled in the Sax Institute’s 45 and Up Study with 8-year follow-up from recruitment. People who completed the baseline survey questionnaire of the 45 and Up Study and who did not withdraw from the study were our study population. The 45 and Up Study is a large-scale population-based cohort study comprising 267 153 men and women aged 45 years and over. Details of the study have been described elsewhere.22 In brief, potential study participants aged 45 years or older in NSW were randomly sampled from the Services Australia (formerly the Australian Government Department of Human Services) Medicare enrolment database, which provides near-complete coverage of the population and invited to participate between 2006 and 2009. However, people aged 80+ years from rural and remote areas were oversampled; about 18% of those invited participated, and participants included about 11% of the NSW population aged 45 years and over. Participants consented to self-completing the baseline questionnaire and to long-term follow-up with linkage of survey data to other administrative health records. Data collected via baseline survey included sociodemographic and lifestyle characteristics, and self-reported chronic conditions. We excluded people from the study population who had completed their baseline data before 20 February 2006 as the death records were only available from that date.

    Data linkage and outcome ascertainment

    All deaths between 20 February 2006 and 30 September 2018 recorded in the NSW Registry of Births, Deaths and Marriages were probabilistically linked to 45 and Up Study data by the NSW Centre for Health Record Linkage. Follow-up time for mortality was set at 8 years from baseline survey as baseline data collection was completed in December 2009 and the latest available deaths registry data were from September 2018. All-cause mortality occurring within 8 years of recruitment was our outcome of interest.

    MM and CMM ascertainment

    Self-reported chronic health conditions were ascertained from responses to two 45 and Up Study baseline survey questions: ‘Has a doctor ever told you that you have (name of condition)?’ and ‘In the last month, have you been treated for (name of condition)?’. If the response was ‘yes’ for either question for a condition, we considered the person had the condition. These included cancer (all types), heart disease, diabetes, stroke, Parkinson’s disease, depression, anxiety, asthma, allergic rhinitis, hypertension, thrombosis and musculoskeletal conditions. Accordingly, participants were classified as having MM (two or more chronic conditions at baseline) and/or CMM (three or more chronic conditions affecting three or more body systems at baseline, table 1).23 To define CMM, we first classified the 11 chronic conditions into nine groups according to the body system: cardiovascular, musculoskeletal, neurological, psychological, respiratory, skin, endocrine/metabolic, female genital and male genital.24 Conditions that occurred in different body parts (eg, cancer at different sites) were grouped into one condition for the MM measure but were classified into different body systems, depending on the sites.

    View this table:
    Table 1

    Percentage of self-reported chronic conditions among older adults aged 45 years and over by condition and by body system

    Statistical analysis

    Continuous variables were categorised and included one additional category for missing values if there were≥5% missing values. Psychological distress was measured using the Kessler Psychological Distress Scale (K10) and categorised as low or moderate (<22) and high (≥22).25 Participant characteristics were compared for those with and without MM or CMM using χ2 tests. We conducted a time-to-event analysis to measure impacts of MM and CMM on mortality. Follow-up time started at the date of baseline data collection and was censored at death or the date when participants completed the 8-year follow-up, whichever came first. We generated Kaplan-Meier survival curves for people with and without MM or CMM and used log-rank tests for comparison. Death rate was calculated using person-time at-risk as the denominator. Crude and adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated using univariate and multiple Cox proportional hazard models. The potential confounders were selected based on the following steps: first, we selected those variables for the base model which were found to be associated with MM or CMM at p<0.20 using a χ2 test; second, we applied the change-in-estimate strategy using the ‘chest’ package in R.26 27 We then assessed potential effect modification for age groups, as there was a large variation in age at baseline, by adding an interaction term into the Cox proportional hazard model. If the interaction term was significant at p<0.05, we did a stratified analysis by age. We set 5% as the significance level for all statistical tests. We used R V.3.6.3 software (R Foundation, Vienna, Austria) for data analysis and SAS V.9.4 for data management.

    Patient and public involvement

    Patients or public were not involved in design, management or reporting of our study.

    Results

    The analytical cohort comprised 251 689 people aged 45 years and over (figure 1). The percentage of the cohort assessed to have MM was 44.1% (95% CI 43.9% to 44.3%) with the most frequent chronic conditions being cancer (34.7%), followed by hypertension (31.0%) and depression or anxiety (18.4%) (table 1). The percentage of the cohort assessed to have complex MM was 15.7% (95% CI 15.5% to 15.8%), and the cardiovascular system was the most prevalent body system (50.9%) followed by respiratory (22.0%) and psychological (18.4%).

    Figure 1
    Figure 1

    Assembly of the analytical cohort.

    Participants’ baseline characteristics (see the online supplemental material for detailed descriptions) by morbidity status are presented in table 2. All baseline characteristics except current smoking status were significantly (p<0.001) different between people with and without MM, while all baseline characteristics were significantly different between people with and without CMM. The proportion of the cohort categorised as having MM and CMM increased with increasing age, but the proportion decreased with increasing household income. People not working, self-reporting fair/poor quality of life or having high levels of psychological distress had significantly higher proportions of MM and CMM than those not in these groups.

    View this table:
    Table 2

    Percentage of MM and CMM by characteristics for the study participants

    Survival probability for people with MM or CMM was significantly lower than for those without MM or CMM (p<0.001; figure 2). Mortality was 2.5 times higher among people with MM compared with those without (20.3 vs 8.3 deaths/1000 person-years, table 3). When adjusted for confounding, mortality was 36% (HR 1.36, 95% CI 1.32 to 1.40) higher among people with MM compared with those without. Absolute difference in deaths/1000 person-years between people with and without MM increased as age increased: 2.3 for 45–59 years, 6.0 for 60–74 years and 12.4 for 75 years and over, respectively. However, impact of MM on mortality decreased as age increased: adjusted HRs were 1.59 (95% CI 1.46 to 1.73) for 45–59 years, 1.49 (95% CI 1.41 to 1.57) for 60–74 years and 1.15 (95% CI 1.11 to 1.19) for 75 years and over.

    View this table:
    Table 3

    Impact of MM on 8-year mortality (from recruitment)

    Figure 2
    Figure 2

    Kaplan-Meier curve: impact of MM and CMM on 8-year (from recruitment) mortality. CMM, complex multimorbidity; MM, multimorbidity.

    Mortality was 2.2 times higher among people with CMM compared with those without (25.3 vs 11.4 deaths/1000 person-years, table 4). When adjusted for confounding, mortality was 22% (HR 1.22, 95% CI 1.18 to 1.25) higher among people with CMM compared with those without. Absolute difference in deaths/1000 person-years between people with and without CMM increased as age increased: 3.4 for 45–59 years, 6.3 for 60–74 years and 13.2 for 75 years and over, respectively. However, impact of CMM on mortality decreased as age increased: adjusted HRs were 1.49 (95% CI 1.33 to 1.67) for 45–59 years, 1.29 (95% CI 1.22 to 1.36) for 60–74 years and 1.08 (95% CI 1.04 to 1.12) for 75 years and over.

    View this table:
    Table 4

    Impact of CMM on 8-year mortality (from recruitment)

    Discussion

    This is the first population-based analysis of the effect of CMM on mortality in Australia. MM and CMM were present in 44.1% and 15.7% of people within this cohort, respectively. During 8 years of follow-up, mortality in MM and CMM sub-groups was at least twice that of those without MM and CMM; 20.3 vs 8.3 deaths/1000 person-years, and 25.3 vs 11.4 deaths/1000 person-years, respectively. Adjusted risk of all-cause mortality was 36% higher for people with MM and 22% higher for people with CMM, compared with people not in either group. When adjusted risk of all-cause mortality due to MM and CMM was stratified by age, risk was highest in the youngest age group (45–59 years) and decreased towards the oldest age group (75 years or more). In our analysis, MM was found to have a greater impact on mortality than CMM, and both MM and CMM had the greatest impact on all-cause mortality in the youngest age group (45–59 years).

    Our prevalence estimate for MM depends on self-reported survey data for 11 chronic conditions and is comparable with other Australian and international studies.6 28 29 Prevalence of MM in the Australian 2017–2018 National Health Survey involving 10 self-reported chronic conditions was 47%, which is similar to our estimate.6 However, 5 out of 10 conditions were different from those available in the 45 and Up Study baseline survey. Another Australian study estimated 37.4% prevalence for MM and 8.7% prevalence for CMM using the 45 and Up Study baseline survey data, but unlike our analysis, they did not include allergic rhinitis, thrombosis and musculoskeletal conditions and included only participants with consistent concession cardholder status in the Pharmaceutical Benefits Scheme (PBS) dataset (n=90 352).28 A cross-sectional Scottish study reported MM prevalence ranging between 39% for people aged 55–66 years and 76% for people aged 75 years and over (which is similar to our estimates), while other studies have reported much higher prevalence among older adults.5

    Several international studies have reported that MM and CMM are associated with a greater risk of mortality, but the effect size in most of the previous studies may not be directly comparable to our study because of the number and type of chronic conditions, different study designs, varying follow-up time and study population of different age groups.14–16 30–32 Consistent with our study, a 2015 meta-analysis of 26 studies demonstrated greater mortality risk among older adults aged ≥65 years of age with MM compared with those without (HR 1.44, 95% CI 1.34 to 1.55).14 More recently, the English Longitudinal Study of Ageing reported lower mortality risk associated with MM (HR 1.27, 95% CI 1.14 to 1.43) among 9171 people aged ≥50 years, which may be related to a relatively older population (90% were aged ≥60 years) who were at a greater risk of mortality.30 However, other US and Scottish studies reported significantly greater effects of MM on mortality compared with our analysis.15 16 The Scottish study involved younger people (≥18 years of age) and considered severe conditions to be those needing hospitalisation, while the US study had different classes of MM. Though several studies have evaluated the effect of MM on mortality, relatively few have evaluated the effect of CMM on mortality.31 32 A Japanese population-based cohort study evaluating the effects of MM and CMM on mortality among adults aged 65 and over reported lower and similar effects for both MM and CMM compared with our study (HR 1.07, 95% CI 1.01 to 1.14 for MM and HR 1.07, 95% CI 0.99 to 1.16 for CMM). However, a Norwegian population-based cohort study found 22% higher mortality risk in those with CMM aged 60–69 years (relative risk (RR) 1.22, 95% CI 1.12 to 1.33).32

    While all-cause mortality overall was higher in our study for the older age groups the relative effect sizes for mortality risk for both MM and CMM were higher in adults aged 45–59 years compared with the older age groups. A study using the UK Biobank data (n=502 640) found similar results and concluded that this may be because most interventions to date have been directed at middle-aged populations.33 They therefore highlighted the need for algorithms that could identify these younger people with MM to provide earlier, more targeted care. This phenomenon may also be explained, in part, because early-onset disease is often more aggressive and people are presenting later.34 35 This again highlights the need for early diagnosis, treatment and targeted care.

    Risk of all-cause mortality associated with MM was found to be higher than mortality risk associated with CMM. This was unexpected, given that CMM was proposed to be more specific in assessing the complexity of individual healthcare needs.21 A possible reason for this finding is that our and other studies focused on prevalence of CMM and not the severity of illness.23 36 For example, as Harrison et al identified, those with mild chronic conditions affecting three body systems could have less healthcare needs than someone diagnosed with one severe chronic condition.21 Also, the most prevalent MM and CMM combinations may not necessarily be the most severe. Another potential explanation for this finding could relate to cancer conditions being split and allocated by affected body system for CMM categorisation, rather than kept as a group. This meant those with cancer and other chronic conditions affecting less than three body systems were excluded from being categorised with CMM.

    The finding MM was a better predictor of all-cause mortality risk than CMM suggests individuals with MM should be prioritised for intervention in clinical practice. That all-cause mortality risk was highest in the youngest age group (45–59 years) suggests tailored innovative healthcare interventions and better access to integrated care are needed for this age group; for example, the delivery of a nurse-led self-management programme for COPD in the context of MM implemented in Australian general practice.37 A holistic approach is required for healthcare management of MM, involving shared decision-making and care coordination across all levels of the health system; particularly cardiovascular, respiratory, and mental health conditions, which were the most prevalent domains in our study. Though CMM was not a better predictor of mortality than MM in our analysis, this finding needs further exploration and confirmation. As suggested in another Australian study, CMM could be used to ‘examine the relationship between the number of diagnosed chronic conditions/body systems affected and overall severity of illness, complexity of care and health resource utilisation’.21

    The major strength of this study was our use of a large community-dwelling cohort of older adults which was not restricted only to those engaged with health services, thus providing a more realistic denominator. Recruitment of individuals across the age spectrum to the 45 and Up Study at baseline enabled us to assess the impacts of MM and CMM on mortality across that age range. Also, to our knowledge, this is the first analysis to compare the effect of MM versus CMM on mortality.

    Our study had several limitations. Though the 45 and Up Study cohort has been shown to be generally representative of the population from which it is drawn, non-response at baseline may mean the cohort varies slightly from the broader population. However, studies with relatively low response rates provide similar estimates to the studies with higher response rate.38 There were some other important confounding variables, such as functional disability that we were unable to adjust for, and thus residual confounding is possible. Self-reported chronic conditions were considered without any clinical diagnosis, so misclassification might occur. We defined CMM based on participants’ self-reported chronic conditions in the 45 and Up Study and used the International Classification of Primary Care 2nd edition for classification of disease, so it was not possible to classify the 11 chronic conditions reported in our study according to the exact body system, as in the clinically coded and single disease-focussed International Classification of Diseases, 10th Revision.

    For example, treatment of cancer can affect the whole body, taking into account the side effects of anticancer drugs and radiotherapy; therefore, lung cancer (though not reported separately in our study) would have the potential to affect the body in more ways than can be categorised as a respiratory disease. Another limitation was that we considered only those chronic conditions which were listed in the baseline survey, but some other important chronic conditions, such as dyslipidaemia, chronic kidney disease, blood disorders and rheumatic diseases, which also increase the risk of mortality, were not included in this study. As a result, the effect of MM or CMM might be underestimated due to non-differential misclassification bias. However, an Australian study exploring the concordance between the 45 and Up Study baseline survey and administrative healthcare datasets found that over 70% of individuals classified as having MM were identified from the baseline survey.28 A systematic review has also found that self-report is a valid method for capturing MM.39 There might have been some losses to follow-up in our study cohort due to overseas or interstate migration, but the estimated migration rate in 2011 in the NSW population was ~3%, which had unlikely to have any impact.40

    Further research exploring patterns of healthcare use, such as uptake of primary care chronic disease management plans, between those with MM and CMM would provide better understanding of our findings. Survey data could be combined with other data sources (PBS, Medicare Benefits Schedule, general practice clinic records and hospital administrative datasets) to assess whether our findings can be replicated when diverse data sources and a more extensive list of chronic conditions are used. Conducting research to explore how these associations may differ across health service regions in NSW, particularly between urban and rural settings, would also be beneficial. This would enable us to determine what works and does not work when managing those with MM across different settings.

    Conclusion

    MM and CMM were common in this large population-based cohort study of older adults in NSW, Australia. Mortality among people in MM and CMM subgroups was high, with MM being a better predictor of all-cause mortality risk than CMM. However, further research is required with additional data on chronic conditions to confirm that MM is a better predictor for mortality than CMM. All-cause mortality risk being highest in the youngest age group (45–59 years) is an important finding which indicates the need for tailored, person-centred integrated care interventions and better access to holistic healthcare for this age group.

    Data availability statement

    No data are available. Data that support the findings of this study are available from the Sax Institute, but restrictions apply to the availability of these data, which were used under licence for the current study and so are not publicly available. The data, however, are available from the authors upon reasonable request and with permission from the Sax Institute.

    Ethics statements

    Patient consent for publication

    Ethics approval

    This study involves human participants and was approved by NSW Population and Health Services Research Ethics Committee (reference number 2016/06/642). The participants gave informed consent to participate in the study before taking part.

    Acknowledgments

    The research was completed using data collected through the 45 and Up Study (www.saxinstitute.org.au/our-work/45-up-study/). The 45 and Up Study is managed by the Sax Institute in collaboration with major partner Cancer Council NSW; and partners: the Heart Foundation, NSW Ministry of Health, NSW Department of Communities and Justice and Australian Red Cross Lifeblood. We thank the many thousands of people participating in the 45 and Up Study. We acknowledge the NSW Centre for Health Record Linkage for linkage and provision of the death data (http://www.cherel.org.au/). We acknowledge the Secure Unified Research Environment for the provision of secure data access. We also thank Katherine E Meikle, who reviewed the manuscript and provided some feedback.

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    Footnotes

    • Contributors All authors substantially contributed to this article and met the authorship criteria. AK, AT, SA and MB conceived the study. AK, AT, SA, DPC and MB contributed to the design, analysis and interpreting the results. AK drafted the manuscript and coordinated its revision, and all authors critically reviewed the manuscript. All authors read and approved the final version of the manuscript. AK acts as the guarantor for the overall content.

    • Funding This research was funded by Sydney Local Health District, South Eastern Sydney Local Health District and the South and Eastern Sydney Primary Health Network.

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.