Elsevier

Maturitas

Volume 109, March 2018, Pages 53-62
Maturitas

Multimorbidity and quality of life at mid-life: A systematic review of general population studies

https://doi.org/10.1016/j.maturitas.2017.12.004Get rights and content

Highlights

  • Eight articles were identified from four databases. Multimorbidity is consistently associated with poorer HrQoL at mid-life.

  • Two cross-sectional studies found adults with multimorbidity at early mid-life reported poorer HrQoL than those at late mid-life, while another found the reverse.

  • Two distinct disease clusters were identified at mid-life: mental health conditions, cardiovascular disease (CVD).

  • Those in mental health clusters reported poorer HrQoL than those in CVD clusters at mid-life.

  • Future research using weighted disease severity index and multimorbidity trajectories based on longitudinal data is needed.

Abstract

There is substantial multimorbidity at mid-life but little is known about the strength of evidence on multimorbidity and health-related quality of life (HrQoL) at mid-life. This review addresses this gap, focusing on studies of the general population. PubMed, Web of Science, Embase and APA PsycNET databases were screened on 6 March 2017 for original research on multimorbidity and HrQoL in adults aged 40–65 years from the general population. Studies focused on index conditions, using single-item HrQoL measures, unlikely to represent the general population (e.g. primary care), and papers that were not in the English language were excluded. A narrative synthesis was presented due to heterogeneity in the measurement of multimorbidity. Of the 2557 articles, 83 underwent full text screening and 8 were included in the review. Included studies were of moderate to high quality and no exclusions were made on the basis of quality or bias. Multimorbidity was associated with poorer HrQoL at mid-life. Two cross-sectional studies found that adults with multimorbidity at early mid-life reported poorer HrQoL than adults with multimorbidity at late mid-life, while another found the reverse. Two distinct disease clusters were identified: mental health conditions and cardiovascular disease (CVD). Those in the mental health cluster reported poorer HrQoL than those in the CVD cluster, women more so than men. Limitations of the selected studies include lack of longitudinal evidence, use of self-reported conditions and no assessment of disease severity. Multimorbidity is associated with poor HrQoL at mid-life at the population level, with some evidence of differences in association with age and disease cluster and sparse evidence on sex differences. Longitudinal research using a weighted disease severity index and multimorbidity trajectories is needed to strengthen the evidence base.

Introduction

Multimorbidity, the co-occurrence of at least two health conditions in an individual [[1], [2], [3], [4], [5], [6]], is an increasing health problem. It is brought about by population aging, unhealthy lifestyle habits, emerging chronic conditions, reduced mortality from improved medical care and technologies, and earlier detection and treatment of conditions [7]. Globally, multimorbidity prevalence is estimated to be between 3.5% and 100% [8]. The large variation is estimates observed is driven by differences definition and measurement of multimorbidity, population setting, participant age range, and country income levels [9]. Despite this, there is consensus that multimorbidity represents significant and growing burden to society [7]. Increased multimorbidity burden can lead to greater complexity in patient health management, reduced health related quality of life (HrQoL), and increased health care use and costs [[10], [11], [12]].

While multimorbidity is common in older adults, studies have shown that those under 65 years old also experience a substantial multimorbidity burden [[1], [13], [14], [15]]. A recent systematic review highlighted a ‘S’ shape curve of multimorbidity prevalence with age [2], where prevalence increased steeply at mid-life, and plateaued in those age 75 years and above. The onset of conditions at mid-life may affect HrQoL. Previous systematic reviews on multimorbidity and HrQoL have focused on primary care populations[[12], [16]] or older adults (age 65+) [17] and have shown that multimorbidity is negatively associated with HrQoL in these populations. However little is known on the strength of evidence on multimorbidity and HrQoL at mid-life or at the general population level. Given the substantially higher prevalence and disease burden of multimorbidity in primary care compared to the general populations [18], there need for synthesis of evidence at a population level to assist in health service planning.

It is unclear whether multimorbidity rates differ between males and females. The prevalence of multimorbidity is slightly higher in females compared to males, however findings are inconsistent [[7], [14], [19]]. Some patterns of multimorbidity differ between males and females [19]. For example, depressive symptoms is more common in females while psychiatric and substance abuse is more common in young males [19]. These sex differences in prevalence and patterns of multimorbidity coupled with the onset of conditions at midlife may modify the association between multimorbidity and HrQoL.

This review aims to quantify the relationship between multimorbidity and HrQoL at mid-life, at the population level. It will clarify whether the relationship is consistent between sexes, for different methods to measure multimorbidity, and between preference weighted and non-preference weighted HrQoL instruments.

Section snippets

Protocol and registration

The corresponding review protocol is registered at PROSPERO (CRD42017056911).

Study selection

This review focused on original quantitative epidemiological research that evaluated the association between multimorbidity and HrQoL in mid-age adults (aged 40–65 years) in the general population. We also included cohort or cross-sectional studies on adults where separate estimates of multimorbidity and HrQoL were available for adults aged 40–65 years. For the purpose of this review, multimorbidity is defined as

Study selection: overall description of screening/assessment process

A total of 3698 articles were identified from the four databases, and 2557 were screened based on title and abstract (Fig. 1). After full text screening and quality and risk of bias assessment, 8 articles were included in the review, of which one article was identified from the reference list of an included article. All articles included in synthesis were moderate to high quality based on the Fortin and NOS assessments (Table 1). No articles were excluded on the basis of quality or risk of

Discussion

This review highlights that recent data on multimorbidity and HrQoL at mid-life, using studies based on the general population is sparse, limited to cross-sectional research, and with substantial heterogeneity in the measurement and reporting of multimorbidity [4]. There were consistent findings across adult and mid-life studies that multimorbidity was associated with poorer HrQoL at mid-life. While there has been no review of the literature on multimorbidity and HrQoL focused at mid-life to

Conclusion

This review identified eight relevant studies and found consistently, multimorbidity was associated with poorer HrQoL at mid-life. There was some cross-sectional evidence of a difference in this association between early and late mid-life, and disease cluster, however more research is needed for conclusive findings. Given that the onset of morbidity at mid-life can affect HrQoL, research from longitudinal studies which measure multimorbidity using indices that incorporate severity of disease

Contributors

Jeeva Kanesarajah performed the literature review, designed the search strategy, screened titles and abstracts and full text, determined quality of the articles and performed the data extraction, and drafted the manuscript.

Michael Waller determined quality of the articles

All authors contributed to study conception and designed the review, participated in data synthesis/analysis and data interpretation. All authors contributed to the critical revision of the manuscript, and saw and approved the

Conflict of interest

The authors declare that they have no conflict of interest.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Jeeva Kanesarajah is funded by the Australian Government Research Training Program. Gita D. Mishra is supported by NHMRC Principal Research Fellowship.

Provenance and peer review

This article has undergone peer review.

Acknowledgements

Thanks to Scott McIntyre, Xiaolin Xu and Louise Wilson for their valuable advice during the systematic review process.

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