Ageing is a key determinant of developing multiple diseases (Barnett et al., 2012; Marengoni et al., 2011) . The term multimorbidity is used to define the co-occurrence of multiple diseases, specifically two or more chronic conditions within the same individual (Diederichs et al., 2011; Johnston et al., 2019) . The global prevalence of multimorbidity is expected to increase through the 21 st century, as a result of global population dynamics, including increases in life expectancy, ageing populations, and the expansion of morbidity (United Nations Department of Economic and Social Affairs Population Division, 2020). Within the United Kingdom, the prevalence of individuals with complex multimorbidity (four or more co-occurring chronic conditions) is projected to almost double in the next 15 years, increasing to 17% by 2035 (Kingston et al., 2018) . The implications of increasing multimorbidity for individuals and societies are stark: multimorbidity is predictive of poorer subsequent outcomes, including increased mortality (Nunes et al., 2016) , poorer quality of life (Makovski et al., 2019) , and greater functional decline (Ryan et al., 2015) . Additionally, multimorbidity places a considerable economic burden on health services (Wang et al., 2018) . Because of these population and epidemiological trends, and the fact that health care systems are typically organised around the single disease model, multimorbidity is one of the most significant future challenges for health systems around the world.
Given the increasing prevalence and serious implications of multimorbidity, much (predominantly cross-sectional) research has investigated the complex patterns of multimorbidity within individuals and attempted to identify risk factors for severity. For example, systematic reviews have identified that certain diseases and conditions are more likely to co-occur (Ng et al., 2018; Prados-Torres et al., focuses on reviewing longitudinal studies. In recent years, there has been a growing academic and funder interest in longitudinal multimorbidity research and identification of trajectories (Alaeddini et al., 2017; Ashworth et al., 2019; Jensen et al., 2014; Quiñones et al., 2011; Ryan et al., 2018; Strauss et al., 2014; The Academy of Medical Science, 2018) . While 'snap-shot' analyses are useful for understanding the prevalence and, to some extent, clustering of diseases, they provide little information about how multimorbidity develops over time within individuals, and how the determinants and consequences of multimorbidity change with these trajectories. Such approaches also allow for a better understanding of sequencing of diseases, which may have important implications from a clinical and intervention perspective. To date, there has been no review synthesising longitudinal studies on multimorbidity. Therefore, this paper systematically reviews the emerging body of longitudinal studies that have investigated multimorbidity trajectories. Our research questions for the review are:
1. What type and range of longitudinal methods are used to analyse multimorbidity over time within individuals? 2. What are the risk/protective factors identified to be associated with individual multimorbidity trajectories?
Our narrative synthesis focuses on the commonalities and differences in methodological and analytical approaches, and the associated factors that influence specific disease trajectories. Therefore, the paper provides a methodological summary and a comprehensive synthesis of the evidence on factors affecting multimorbidity pathways.
As explained, this review aims to scope specific aspects of the emerging body of literature on longitudinal studies exploring multimorbidity trajectories. In line with this objective, we choose to systematically review the literature via a scoping review approach rather than a systematic review approach (Munn et al., 2018) . In reporting, we follow the recently developed Preferred Reporting Items for Systematic Reviews and Meta-Analyses for scoping reviews (PRISMA-ScR) (Tricco et al., 2018) (Appendix A).
Eligibility criteria for the review were set prior to database searches and study identification (Table 1) .
A primary eligibility criterion for the review was that the study measured multimorbidity longitudinally within the same sample of adults (18+ years old) using a quantitative approach. Reviews and studies that were only cross-sectional or qualitative were not eligible. Studies had to measure multimorbidity primarily through recognised diseases and/or conditions rather than solely a collection of symptoms/states (such as disability, frailty, or quality of life/functioning) or disease risk factors (such as obesity, smoking, or alcohol consumption). Alternatively, studies could also use a defined multimorbidity measure such as the Charlson or Elixhauser comorbidity indexes (Charlson et al., 1987; Elixhauser et al., 1998) . Studies were required to examine change in multimorbidity from distinguishable diseases rather than changes or progression within a single disease category (such as different types of cancer). Studies that only examined transitions from one index disease into a secondary disease (such as development of comorbidities of diabetes) were not included.
Four online databases were searched: Medline, Scopus, Web of Science, and Embase. The search strategy for this review was developed in consultation with an academic librarian. Scoping searches were initially conducted within each database, with relevant terms such as 'multimorbidity', 'disease trajectory', and 'longitudinal', in order to refine the search strategy and identify additional relevant search terms.
The final search strategy is summarised in Table 2 . Three primary searches were developed and then combined. The first search focused on the concept of multimorbidity, the second search concentrated on the methodological approach of disease trajectories, and the third search aimed to identify studies with longitudinal designs. Initial searches highlighted a large number of irrelevant references, focusing on cellular medicine, genetics, and Covid-19. The searches were re-run with an exclusive search to remove these irrelevant references and database search limiting tools were used to refine the search results in line with review eligibility criteria, including English language, research with adult humans, and peer-reviewed journal articles only. No publication date restrictions were set on the searches. All searches were conducted in May 2020. The full search syntax is included in Appendix B.
Several relevant papers were known to the authors prior to beginning the searches for this review.
Furthermore, a group of potentially relevant papers were identified through external collaborators after database searches were completed. The initial database search results were searched for these . CC-BY-ND 4.0 International license It is made available under a perpetuity.
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The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101 https://doi.org/10. /2020 additional papers. If they were not identified in the database searches, they were included as 'identified through other sources' and were subject to the same screening procedure as papers identified through database searches.
Final search results were de-duplicated and then screened for eligibility in the review by title, abstract and finally full text. This work was shared between GC, CM, and KK. A double screening process was performed at full text level to minimise evidence selection bias (Buscemi et al., 2006) . Each author independently reviewed their share of the articles and then passed them onto one of the other authors for a second independent review. Any disagreements were resolved through discussion and consensus between GC, CM, and KK. The reference lists of the selected studies were searched to identify any related studies that may have been missed during the main database search. All studies identified through reference list searches were cross-checked with the initial database search results, to ensure they had not been previously identified, and were subject to the same screening and data extraction processes as those identified through the primary search. Figure 1 depicts the full study selection process. Online database searches returned a total of 11,420 papers and 9 additional papers were identified from other sources. Of the combined 11,429 papers, 4,705 were duplicate references and subsequently removed. The remaining 6,724 papers were subject to screening. A total of 6,315 papers were removed during title screening and a further 361 papers were excluded during abstract screening. The most common reasons for paper exclusion throughout screening was study design (e.g. studies did not analyse multimorbidity longitudinally, multimorbidity was not the focus of the study, trajectories were only followed within a single or index disease) and study methodology (e.g. cross-sectional analysis, did not follow the same cohort longitudinally). The remaining 48 papers were subject to full text screening and 19 were removed during this process, leaving 29 papers for inclusion in this review. Searching the reference lists of the 29 included papers identified 11 potentially relevant papers not identified by initial searches. After screening these 11 papers, 6 were excluded leaving 5 additional papers for inclusion in the review. In total, 34 papers were selected for inclusion in this scoping review. Table 3 shows the characteristics of the 34 selected studies. All were published in the last decade (i.e. since 2011). The studies used data from 14 different countries, primarily based on European data (N=15) and North American data (N=11) ( Table 3 ). The review included four studies based on data from high-income Asian countries (e.g. Taiwan, South Korea, Singapore) (Chang et al., 2011; Hsu, 2015; Kim et al., 2018; Zhu et al., 2018) . Apart from one study based on Chinese data (Ruel et al., 2014b) , none of the included studies explored multimorbidity trajectory patterns in low-and middle-income settings, and none in Africa or Latin America. Table 3 shows a great variability in sample characteristics. Sample size ranged from 756 in a survey of older participants to 6.2 million in a nation-wide study based on administrative data sources. Selected studies had a wide range of follow-up periods, between 2 and 20 years. Most studies had age restrictions while a few had none (Beck et al., 2016; Chang et al., 2011; Jensen et al., 2014; Zhu et al., 2018) . About half of the selected studies focused on older populations (50 years and over), with one study focused on the very old (80 years and over) (Gellert et al., 2018) . Population samples included both males and females in most studies apart from two studies including males only (Hiyoshi et al., 2017; Lindhagen et al., 2015) and three studies including females only (Jackson et al., 2015; Rocca et . CC-BY-ND 4 .0 International license It is made available under a perpetuity.
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The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101 https://doi.org/10. /2020 https://doi.org/10. .11.16.20232363 doi: medRxiv preprint al., 2016 Xu et al., 2018) . Furthermore, three studies focused on U.S. veterans, a predominantly male population (Alaeddini et al., 2017; Faruqui et al., 2018; Pugh et al., 2016) .
Administrative data sources, including primary or secondary care data, disease registries, and health insurance data, were used in 23 of the selected articles, and survey data were used in 17 studies (Table   3 ). In six studies, survey data was combined with administrative data sources such as patient registry, hospital discharge, and health claims records (Calderon-Larranaga et al., 2019; Calderón-Larrañaga et al., 2018; Dekhtyar et al., 2019; Perez et al., 2019; Strauss et al., 2014; Zeng et al., 2014) . In another five studies based on survey data, interviews were followed by either medical examination, biomedical assessment, or extra cognitive or laboratory tests, improving disease identification (Fabbri et al., 2015; Fabbri et al., 2016; Ruel et al., 2014a; Ruel et al., 2014b; Ryan et al., 2018) . Informed consent of participants was mentioned in 10 out of the 17 studies using survey data (Calderon-Larranaga et al., 2019; Calderón-Larrañaga et al., 2018; Dekhtyar et al., 2019; Fabbri et al., 2015; Fabbri et al., 2016; Perez et al., 2019; Ruel et al., 2014a; Ruel et al., 2014b; Ryan et al., 2018; Strauss et al., 2014) .
Administrative data-based research meant that the diseases studied were mostly clinician-diagnosed and based on standardised diagnosis codes, such as the International Classification of Diseases (ICD)
i.e. ICD-9, ICD-9-CM, or ICD-10, or classifications of primary care codes (Table 3) . All studies based on survey data only used participant self-report for disease identification. In the studies that combined survey data with complementary data sources, diseases were ascertained in a variety of ways: mostly through clinician-diagnosed administrative coding (Calderon-Larranaga et al., 2019; Calderón-Larrañaga et al., 2018; Dekhtyar et al., 2019; Perez et al., 2019; Strauss et al., 2014; Zeng et al., 2014) but also from a combination of clinician-diagnosis, laboratory and cognitive tests, and self-report (Fabbri et al., 2015; Fabbri et al., 2016; Ruel et al., 2014a; Ruel et al., 2014b; Ryan et al., 2018) .
The number of diseases which were considered to contribute to a measure of multimorbidity varied widely within included studies, ranging from three (Siriwardhana et al., 2018; Xu et al., 2018) to a very large number based on all three level ICD10 codes (Beck et al., 2016; Jensen et al., 2014) (Table 3) .
Studies investigating disease transitions tended to consider either a very narrow or very wide range of diseases, while those measuring multimorbidity using counts of diseases typically used between 13 and 60 chronic disease categories. Studies based on survey data used a narrower range of diseases (between 3 and 18). is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101 https://doi.org/10. /2020 The precise list of diseases considered also varied considerably (Appendix C). The rationale for choosing a specific list of diseases was usually described. For example, some studies explained that they included chronic diseases with high prevalence and high risk of disability and mortality (Fabbri et al., 2015; Fabbri et al., 2016) and others included diseases that were assessed and validated by clinical consensus (Calderon-Larranaga et al., 2019; Calderón-Larrañaga et al., 2018; Dekhtyar et al., 2019; Perez et al., 2019; Strauss et al., 2014) . Some studies included diseases based on the common Charlson and Elixhauser multimorbidity indices (Fraccaro et al., 2016; Gellert et al., 2018; Hanson et al., 2015; Hiyoshi et al., 2017; Kim et al., 2018; Lindhagen et al., 2015; Zeng et al., 2014) but lists were sometimes adapted, either by augmenting with extra (perceived relevant) conditions (Hiyoshi et al., 2017) or by removing diseases due to data sensitivity restrictions (Fraccaro et al., 2016) .
To identify disease trajectories, studies tended to take one of two approaches. In the first approach, studies attempted to characterise multimorbidity status at repeated intervals for each individual (N=26). Most used repeated measures of counts of diseases, or weighted counts of diseases (to account for severity), and some also considered the accumulation or development of multimorbidity as a base for analysis. Thus, multimorbidity was often defined as a count of diseases or as a continuum which starts from a healthy state into developing chronic disease (Ruel et al., 2014a; Ruel et al., 2014b) rather than defining multimorbidity using a binary cut-off of two or more chronic conditions (Canizares et al., 2017; Ryan et al., 2018) . The second approach explored disease sequences or took a disease transition approach (N=9). Studies focusing on a limited number of diseases (3 to 6) considered diseases either on their own or combined in health states as a unit for analysis (Alaeddini et al., 2017; Faruqui et al., 2018; Lappenschaar et al., 2013; Siriwardhana et al., 2018; Xu et al., 2018; Zhu et al., 2018) while those analysing a larger number of diseases considered disease pairs to assess their significant association and directionality and further construct trajectories (Beck et al., 2016; Jensen et al., 2014) . Only one study on multimorbidity acquisition sequence explored the order of disease occurrence (Ashworth et al., 2019) . Finally, one study took both approaches of using a multimorbidity count and assessing transitions (Xu et al., 2018) .
For the purpose of this review, we categorised analytical approaches into five groups: constructed variables of multimorbidity change, regression approaches, longitudinal group-based methodologies, transition and data mining methodologies, and visual approaches.
In the first group, four selected articles created measures of multimorbidity development (Table 4a ).
In one study, multimorbidity development was measured by the intra-individual change in Charlson is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10. 1101 /2020 Comorbidity Index (CCI) between baseline and later time points (Fraccaro et al., 2016) , and in another study, by the transition into multimorbidity (i.e. zero or one condition to two or more conditions), or by the worsening of multimorbidity (from established multimorbidity to additional conditions) (Ryan et al., 2018) . Another two studies constructed morbidity trajectory groups ('constant high', 'constant medium', 'constant low', 'decreasing', 'increasing', and 'erratic') (Chang et al., 2011) and chronic disease transition stages ('healthy', 'healthy to a single chronic disease', 'stable with a single chronic disease', 'healthy to multimorbidity', 'stable multimorbidity', and 'increasing multimorbidity') (Ruel et al., 2014b) . The creation of these variables or trajectory groups allowed the authors to use them in regression analysis to assess their association with health expenditures (Chang et al., 2011 ), food categories (Ruel et al., 2014b , and level of physical activity and functions (Ryan et al., 2018) .
A larger set of articles employed regression analysis, such as random effect models or linear mixed models (also named growth curve models, hierarchical linear models or multilevel models) (Table 4b,   N=13 ). These studies tended to analyse repeated measures over time within each individual; hence within-individual change can be referred to as a "trajectory" or "growth curve". In these studies, the dependent variable was typically a count of diseases variable or a multimorbidity index measured repeatedly, and the coefficients assessed an increase in the number of diseases, the speed of disease accumulation, or a change in multimorbidity. In some studies, models used the regression estimates (i.e. intercept and slope of changes in multimorbidity) to create categories capturing speed of multimorbidity e.g. 'rapidly accumulating' and 'slowly accumulating' (Calderón-Larrañaga et al., 2018) , which were then used in additional association analysis. Other studies directly explore the association between factors such as biomarkers (Fabbri et al., 2015; Perez et al., 2019) or life experiences (Dekhtyar et al., 2019) and the change in multimorbidity by including an interaction term between time and these factors and using a multimorbidity count or indicator as the outcome variable.
The third approach aimed to construct meaningful groups or categories of longitudinal multimorbidity patterns (Table 4c , N=7). Methodologies employed include latent class analysis, latent class growth analysis, growth mixture modelling, or group-based trajectory modelling. These methods produced between four and six groups of distinct longitudinal multimorbidity patterns which were often used in additional association analysis. Two studies took an associative approach, exploring which diseases associate or cluster longitudinally (Hsu, 2015; Pugh et al., 2016) . For example, Hsu (2015) found four trajectory groups ('low risk', 'cardiovascular risk only', 'gastrointestinal and chronic non-specific lung disease', and 'multiple risks'). In contrast, the other five studies focused on typical multimorbidity patterns and accumulation stages rather than the longitudinal combination of diseases (Hanson et al., 2015; Hiyoshi et al., 2017; Jackson et al., 2015; Kim et al., 2018; Strauss et al., 2014) . is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted November 16, 2020. acute increase trajectory', 'medium start and a slow increase trajectory', 'a high start and a slow increase trajectory'. Despite small variations, these clusters all have information on the initial level, accumulation over time, and in some cases, whether pace of accumulation was rapid or not. These usually showed increases over time. Only one study of the five found groups with a decrease in multimorbidity (Kim et al., 2018) .
The next group of studies focussed on modelling disease transitions (Table 4d , N=9). These methodologies are generally more varied and complex and, in some cases, more orientated towards data mining approaches. Perhaps because these methods are more data intensive, six of these studies focused on a limited number of diseases (3 to 6) (Alaeddini et al., 2017; Faruqui et al., 2018; Lappenschaar et al., 2013; Siriwardhana et al., 2018; Xu et al., 2018; Zhu et al., 2018) . Some studies were based on a transition structure using methodologies such as transition matrix based on Markov modelling (Alaeddini et al., 2017) , acyclic multistate models to define an interconnected progressive chronic disease system (Siriwardhana et al., 2018) or state transition modelling, aimed at capturing CCI changes and based on a series of regression analyses to identify disease transition probabilities (Lindhagen et al., 2015) . Two studies employed Bayesian techniques including a multilevel temporal Bayesian networks (Lappenschaar et al., 2013) and a longest path algorithm to identify the most probable sequence from/to a specific disease following an unsupervised multi-level temporal Finally, two studies created temporal disease trajectories by combining significant temporal directed pairs from all disease pairs possible in a data-based discovery-driven analysis (Beck et al., 2016; Jensen et al., 2014) . Analysing complex transitions between diseases also enabled the identification of clusters. For example, Alaeddini et al. (2017) found two clusters, one of stability, which was predominant, and one of change, both described in great depth through transition matrices.
The final group of studies used visual methods to represent disease sequence or transitions (Table 4e, N=3). Alluvial plots illustrated multimorbidity acquisition sequences based on date of disease onset and although useful to understand the order of diseases (co-)occurrence, the visualisations did not account for time between the occurrence of two diseases, thus cannot distinguish between slow and rapid multimorbidity progression (Ashworth et al., 2019) . Aalen-Johansen curves (a multistate generalization of cumulative incidence curves) were used to represent the accumulation of is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
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Most of the studies sought to use multimorbidity trajectories to predict subsequent outcomes (Table   5a , N=7) or to associate multimorbidity trajectories with risk factors (Table 5b, N=18) . Predicted outcomes included self-reported health, disability, medical utilisation, and mortality but also standardized neurocognitive tests evaluating mental status, memory, executive function, processing speed, and verbal fluency. Among older adults, results showed that an increase in multimorbidity over 10 years was associated with worse reported health (Zeng et al., 2014) , and that those who developed multimorbidity faster had greater risk of disability (Calderón-Larrañaga et al., 2018) . In one study, changes in multimorbidity were found to be a greater predictor of mortality than baseline multimorbidity in adults (Fraccaro et al., 2016) . By contrast, another study confirmed that a change in CCI predicts mortality but not necessarily better than a one-time point 'snapshot' of multimorbidity in Over half of the selected articles explored some risk factors and determinants of multimorbidity trajectories (Table 5b) . Age was often accounted for in analyses but also referred to as a prominent risk factor. For example, age was associated with greater development of multimorbidity in those with no multimorbidity at baseline but also greater worsening of multimorbidity in those already multimorbid (Ryan et al., 2018) . With a different approach, Xu et al. (2018) also showed that the risk of multimorbidity progression increased with age. As expected, younger age groups were more likely to belong to a non-chronic healthier cluster (Strauss et al., 2014) . However, trajectories starting with depression were more likely in younger individuals (Ashworth et al., 2019) .
A few studies reported gender differences. While one study found that those in the 'multiple risks' group were more likely to be female (Hsu, 2015) , another found that men were more likely to transition between states than women, based on eight states and three diseases (diabetes, ischemic heart disease, and chronic kidney disease) (Siriwardhana et al., 2018) .
The relationship between ethnicity and multimorbidity was addressed by four studies (Ashworth et al., 2019; Quiñones et al., 2011; Quiñones et al., 2019; Siriwardhana et al., 2018) . In two US studies, baseline multimorbidity profile and accumulation patterns differed by race/ethnicity. Compared to non-Hispanic white participants, Black American participants had a higher rate of multimorbidity at baseline along with a slow rate of disease accumulation over time while Hispanic participants tended to start their trajectory with less diseases but to increase at a faster rate ( is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101/2020.11.16.20232363 doi: medRxiv preprint white individuals were more likely to transition from ischaemic heart disease to death, while Asian and Native Hawaiian and Pacific Islander individuals were more likely to transition from diabetes to diabetes plus chronic kidney disease (Siriwardhana et al., 2018) . In the UK, disease specific sequences /pathways also differed by ethnicity (Ashworth et al., 2019) . For example, the white ethnic group was dominated by depression as a starting point, while diabetes was the most common starting point in the black ethnic group along with depression and serious mental illness.
The selected studies also explored a range of socio-demographic determinants including area-based deprivation, education, occupation, income, and marital status. Results largely confirm those found with cross-sectional analyses, with lower socioeconomic status associated with worse multimorbidity trajectories. For example, lower levels of education were associated with higher rate of multimorbidity accumulation (Dekhtyar et al., 2019; Xu et al., 2018) or worse multimorbidity trajectories (Jackson et al., 2015) . People living in the more deprived areas were more likely to be in an evolving or multichronic multimorbidity cluster (Strauss et al., 2014) and to have trajectories with diabetes and depression as the most common starting point (Ashworth et al., 2019) . Being married was also found to be protective of greater multimorbidity accumulation (Hiyoshi et al., 2017; Xu et al., 2018) .
Health behaviours such as diet, alcohol consumption, smoking, and physical activity were investigated as risk factors of multimorbidity trajectory. One Chinese study showed that a greater consumption of fruits and vegetables and grain (other than rice and wheat) slowed the development of multimorbidity (Ruel et al., 2014b) . Alcohol consumption, smoking, and physical inactivity was associated with worse multimorbidity trajectory patterns (Jackson et al., 2015; Xu et al., 2018) .
Other health factors were also investigated. For example, physical function (measured by gait speed and grip strength at baseline) was associated with development and worsening of multimorbidity over 2 years in a sample of older adults, aged 50 years and over (Ryan et al., 2018) . Being overweight or obese was also a determinant of a developing or worsening multimorbidity trajectory (Jackson et al., 2015; Ryan et al., 2018; Xu et al., 2018) . Two studies investigated the role of specific biomarkers, taken as marker of chronic inflammation, system dysregulation, and multisystem failure, on the rate of multimorbidity accumulation (Fabbri et al., 2015; Perez et al., 2019) . Findings showed that some of these biomarkers were associated with higher rate of multimorbidity development and supported that chronic inflammation and multisystem failure are markers of future development of multimorbidity.
Other determinants found be to associated with development and worsening multimorbidity trajectories were being a member of a more recent birth cohort compared to their predecessors at corresponding ages (Canizares et al., 2017) , young parenthood (younger than 25) and extremely high . CC-BY-ND 4.0 International license It is made available under a perpetuity.
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The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10. 1101 /2020 parity (nine of more births) (Hanson et al., 2015) , and negative attitude towards life and health such as low life satisfaction and negative health outlook (Calderon-Larranaga et al., 2019) .
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Understanding multimorbidity trajectories is an important public health priority for clinicians, academics, and funders alike (The Academy of Medical Science, 2018). This review aimed to scope existing research in the field with a focus on methodology and substantive knowledge about inequalities in multimorbidity trajectories. By doing so it addresses a gap in the multitude of multimorbidity reviews which focus on cross-sectional and prevalence studies (Johnston et al., 2019; Nguyen et al., 2019; Violan et al., 2014; Xu et al., 2017) . Our systematic scoping review shows that despite this widespread interest, relatively few published studies take a longitudinal approach to multimorbidity. All the selected studies were published within the last decade and the vast majority using data from high-income countries. The 34 studies identified showed a great variability in sampling and methodologies for identifying multimorbidity (e.g. the list of diseases included). We also identified several types of methodological approaches to characterise multimorbidity longitudinally. Statistical methodologies included longitudinal regression-based approaches such as mixed modelling, longitudinal group-based methodologies providing multimorbidity trajectory clusters, and analyses of disease transitions, including some data mining approaches. From a substantive perspective, these studies highlighted that worse multimorbidity trajectories were linked to poorer outcomes including worse self-reported health, greater disability, and higher mortality. A range of multimorbidity trajectory determinants were also identified as important for multimorbidity trajectories, including socio-demographic factors, health behaviours, physical function, biomarkers, cohort effects, fertility history, and attitudinal factors.
A limitation of narrative reviews is that they might select evidence to support a particular stance and do not necessarily take enough steps to eliminate selection bias. However, we selected a comprehensive set of items to extract and we engaged in double screening and extraction. Therefore, our methodological approach should limit selection and extraction bias. In addition, our review didn't engage in a critical appraisal of the quality of the selected studies. However, the aim of the review was to scope the published evidence on longitudinal approaches to multimorbidity.
Our review has highlighted gaps in geographical coverage when it comes to multimorbidity research.
There is an underrepresentation of longitudinal multimorbidity research in low-and middle-income countries, which reflects the geographical focus of multimorbidity research more generally (Xu et al., 2017) . This could be due to fewer resources and investment in multimorbidity research in these countries, coupled with difficulties in collecting or accessing relevant data. For example, most of the studies in the review make use of electronic medical records or large-scale longitudinal studies, which are scarce in developing countries. Nevertheless, due to the rapidly ageing nature of many low-and middle-income populations, multimorbidity is a major public health issue, with potentially more . CC-BY-ND 4.0 International license It is made available under a perpetuity.
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The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101/2020.11.16.20232363 doi: medRxiv preprint complex comorbidity patterns (e.g. undiagnosed conditions or interactions with higher levels of infectious diseases), which deserves research using a longitudinal approach. Recently published work in low and middle-income countries has tended to employ a cross-sectional design to analyse multimorbidity (Rivera-Almaraz et al., 2018; Stubbs et al., 2018) , and therefore were not eligible for inclusion in this review.
In addition, none of the selected studies in our review made cross-country comparisons. Comparative studies have the ability to generate stronger evidence about disease trajectories and can help in generating new hypotheses about the mechanisms involved in multimorbidity development and progression. For example, consistent findings across different country settings may render findings more generalizable and suggest common biological mechanisms. However, divergent findings for different countries could suggest moderation of disease processes by policy approaches to treatment, health care settings, and institutional structures which may provide clues to how specific trajectories could be prevented. Given that there are multiple harmonised ageing studies (e.g. the Health and Retirement Study in the US and the Survey of Health, Ageing and Retirement in Europe) and many comparable administrative datasets using similar validated disease classification such as ICD-10, we would expect to see more comparative studies in the future.
The selected studies used a great variety of data sources including administrative data (primary and secondary care data, health insurance claim data, patient and disease registries) and survey data, leading to variations in sample size and population characteristics. As expected, sample sizes were typically smaller in survey-based studies. However, issues of small sample size were only discussed in a limited number of studies, mostly in relation to subgroups (Ashworth et al., 2019; Hiyoshi et al., 2017; Quiñones et al., 2011; Quiñones et al., 2019) . In some cases, the small sample size issue was dealt with by excluding some ethnic minority groups from the analysis (Ashworth et al., 2019; Quiñones et al., 2011; Quiñones et al., 2019) . However, health trajectories can differ by ethnic group due to cultural and social factors (e.g. different life exposure to social deprivation or different health behaviours), different forms of discrimination, processes of health selection and acculturation, and biological/genetic factors. Therefore, a clear gap is to ensure a large enough sample size to address multimorbidity patterns among ethnic minority populations in order to feed into our broader understanding of ethnic inequalities in health.
Despite the use of large surveys or administrative data, the majority of studies expressed doubts about the generalisability of their findings, due to their study sample being unrepresentative. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101/2020.11.16.20232363 doi: medRxiv preprint data sources typically investigated multimorbidity based on complete follow-up and excluding those who died, generating immortal time bias and investigating potentially healthier population (Chang et al., 2011; Faruqui et al., 2018) . In other studies, the choice of data sources induced bias, for example where samples were based on health service users which meant population samples were more likely to be less healthy, more deprived, and less well-off than the general population (Fraccaro et al., 2016; Pugh et al., 2016) . Other explained their sample might be representative but only of a particular group in a specific region (Hanson et al., 2015; Hsu, 2015) .
In line with previous multimorbidity reviews (Xu et al., 2017) and reflecting the variety of multimorbidity measures used (Stirland et al., 2020), a wide variety of diseases were included for the exploration of multimorbidity trajectories and a few studies used 'standard' measurement of multimorbidity like the Charlson or Elixhauser indexes. As a result of the diversity of data sources used in each study, diseases were also ascertained in various ways, using clinical-diagnosis, laboratory results, medication use, and self-report. The only common measurement feature was that the longitudinal studies in this review tended not to define multimorbidity as the presence or two or more diseases.
The choice of statistical methods served to highlight or obscure different aspects of multimorbidity such as disease accumulation, disease sequence, or disease combination. For example, the most common approach, using regression analysis, typically multilevel modelling or growth curve modelling, takes advantage of the repeated measures per individual (intra-individual change) and models the outcome as a count of diseases or weighted multimorbidity score. One advantage of this approach is the capacity to simultaneously evaluate the baseline level of multimorbidity and the (slope) change in multimorbidity, and how this differs between groups with different characteristics.
These approaches highlight differing accumulation speeds but tends to obscure the role of specific diseases by collapsing all in a single count or index. We cannot tell, for example, whether this faster accumulation is predominantly occurring among certain types of disorders.
Complementary to the multilevel regression approach, grouped-based methodologies aimed to classify individuals into types of multimorbidity progression or accumulation. However, a minority of studies employed the cluster-based approach to understand how specific diseases co-occur over time (Hsu, 2015; Pugh et al., 2016) . The latter extends cross-sectional approaches often referred to as associative multimorbidity (Prados-Torres et al., 2014) . Hence, we refer to this as a "longitudinal associative multimorbidity approach". This approach was used by only two studies but has the clear advantage of providing a more detailed understanding of the constellation of diseases that can contribute to distinct trajectories. However, due to the rarity of specific diseases, this technique will tend to find mostly common clusters rather than more uncommon disease trajectories. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10. 1101 /2020 Many papers in our review conceptualised the issue of multimorbidity development as one of transitioning between different disease states, utilizing either structured frameworks (e.g. Markov models with transition matrix (Alaeddini et al., 2017) , a state transition model (Lindhagen et al., 2015) , and acyclic multistate model defining an interconnected disease system (Siriwardhana et al., 2018)) or a data driven (unsupervised) approach based on all possible disease combinations on a populationlevel sample (Jensen et al., 2014; Perez et al., 2019) . The more structured approach to disease transition tends to provide a very detailed knowledge of the interactions between (usually a small set of) diseases. The sharper focus on a limited number of diseases can provide useful evidence for targeting prevention but does not provide a comprehensive picture of the overall process of accumulation for wider policy strategies that we could get from a broader set of diseases. By contrast, using a data-driven approach is very comprehensive providing evidence for population-based strategies, but relies on large datasets collected over a number of years, and appropriate clinical expertise to interpret the results of patterns identified through artificial intelligence (AI). Given the growing interdisciplinary collaborations between epidemiology and computer science, data-driven research will continue to expand in the coming years and extend to prediction modelling and projections. One of the strengths of computer science, and the recent new developments in AI with machine learning, is the ability to work towards solutions that can combine prediction models and compare different treatment options for cohorts of patients (e.g. what is the likelihood that a medication commonly used for one chronic condition may speed up the progression of another condition or lead to the development of a new condition).
We also found a group of studies which uses more complex visual tools, such as Aalen-Johansen curves, useful to visualize different trajectories of disease accumulation (Rocca et al., 2016) or flow diagrams (alluvial plots and Sankey diagram), using the notion of flow to understand disease and multimorbidity transitions (Ashworth et al., 2019; Xu et al., 2018) . These visual tools succeed at describing and identifying trajectories, but complementary analysis to these visualisations are required to relate trajectories to risk factors.
The selected studies confirm expected patterns that initial multimorbidity as well as worsening of multimorbidity was linked to poorer outcomes such as worse reported health, greater risk of disability, is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted November 16, 2020. 
With this review, we have identified a small but developing body of literature attempting to describe multimorbidity longitudinally. There was a notable lack of longitudinal multimorbidity trajectory research in low and middle-income countries, as well as in many minority ethnic groups. A wide variety of complementary methods are employed, which provide a broad set of evidence on multimorbidity trajectories including factors associated with greater disease accumulation, speed of accumulation, and disease transition processes. Methodologies based on disease ordering or sequence was seldom explored by the studies selected in this review. By taking this approach, researchers may face the challenge to accurately identify disease onset. Nevertheless, future research should seek to develop greater understanding of the ordering of disease trajectories or sequences that underlie the accumulation process. Longitudinal associative multimorbidity research and further disease sequence and transition research can help in this endeavor. Risk factors for each disease trajectory type could then be analysed in depth to inform future intervention and prevention strategies at critical life course periods and disease progression turning points. Initiatives to enable researchers greater access to . CC-BY-ND 4.0 International license It is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101/2020.11.16.20232363 doi: medRxiv preprint relevant data sources, such as the HDR UK initiative to harmonise datasets for multimorbidity research, is crucial and should become more generalised worldwide in order to gain the insight on multimorbidity processes required to feed into prevention and policy makers strategies at a larger scale.
. CC-BY-ND 4.0 International license It is made available under a perpetuity.
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The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101/2020.11.16.20232363 doi: medRxiv preprint
KK was the principal investigator. Funding was acquired by KK, supported by FS, and JB. KK conceptualised the idea for a scoping review on longitudinal multimorbidity studies and discussed it with all other authors. KK and GC developed the search terms and eligibility criteria and refined them with CM. GC, CM, and KK shared the screening of titles, abstracts, and full text, and the data extraction.
GC analysed the extracted data. GC, CM, and KK wrote the first draft of different sections of the paper.
All authors reviewed and provided feedback on all drafts. All approved the final version for submission. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101 https://doi.org/10. /2020 is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101 https://doi.org/10. /2020 is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101 https://doi.org/10. /2020 is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101 https://doi.org/10. /2020 is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101 https://doi.org/10. /2020 is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10. 1101 /2020 (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) Survey data (self-reported) 7 Self-reported Quinones et al. Survey (with telephone interview, self-completed questionnaire, and clinic biomedical assessment including blood sample) 8
Combined information from selfreport and biomedical measurements Fraccaro et al. (2016) Time to death from any cause Change in comorbidities was examined by calculating differences between baseline Charlson Comorbidity Index (CCI) and 1, 5, 10-year follow-up CCI scores as a proportion of mortality rate. Ruel et al. (2014) Six chronic disease transition stages groups Constructed six groups of chronic disease transition stages (healthy, healthy to a single chronic disease, stable with a single chronic disease, healthy to multimorbidity, stable multimorbidity, and increasing multimorbidity) Ryan et al. (2018) Development/Worsening of multimorbidity Constructed measures of development of multimorbidity (from no multimorbidity i.e. zero or one condition at baseline to established multimorbidity i.e. two or more conditions) and of worsening of multimorbidity (from established multimorbidity at baseline to additional conditions at follow-up) Level of disability (number of limitations combining activities of daily living (ADL) and instrumental activities of daily living (IADL))
Linear mixed models to estimate the rate of multimorbidity accumulation. Predicted slopes are examined by quartile and this is further dichotomised into "rapidly accumulating" (upper quartile) and "slowly accumulating" (three lower quartile). Calderon-Larranaga et al. (2019) Multimorbidity (chronic disease accumulation) and level of disability Linear mixed models were used to assess the association between baseline level of the psychological factors and the number of chronic diseases and disability over time. The interaction terms between time and each of the psychological factor were included as a fixed effect. Canizares et al. (2017) Presence of multimorbidity Multilevel logistic growth modelling (Proc GLIMMIX in SAS) with 2 levels was used to examine the age, period, and cohort effects on multimorbidity. Observations were nested in individuals and age and birth cohort entered as fixed effect. The modelling strategy was sequential, including additional variables and complexity. Dekhtyar et al.
Linear mixed models were used to assess the association between life experiences and the speed of chronic disease accumulation over time. Interaction terms between follow-up time and life experiences were included as fixed effects. Each life experience was considered in separate models and all together in fully adjusted models. Fabbri et al. (2015) Number of chronic diseases Linear mixed models were used to assess the association between baseline age and biomarkers with the cross-sectional number of diseases and with steeper increase in number of diseases over follow-up. This method was also used to test whether increase in IL-6 over follow-up would predict steeper increase in number of diseases over the same time, independent of baseline IL-6. 
A finite mixture modelling approach (Proc TRAJ in SAS) was used to model simultaneously comorbidity trajectories and probability of death. The groupbased approach identified distinct clusters of individual trajectories.
Six comorbidities trajectories were identified: 'robust' (no comorbid conditions), 'initiates' (none at baseline and increase over time), 'slow initiates' (some at baseline and gradual increase over time), 'accelerated initiates' (none at baseline and quick increase followed by deceleration), 'chronic low' (steady comorbidity over time), 'ailing' (moderate levels of comorbidity at baseline and steady increase over time), 'frail' (high comorbidity at baseline, remaining high over time).
Hiyoshi et al. (2017) A group-based trajectory modelling identified distinct clusters of individual trajectories.
Four trajectories were identified: 'a constant low trajectory', 'a low start and an acute increase trajectory', 'medium start and a slow increase trajectory', 'a high start and a slow increase trajectory'. Hsu (2015) A multiple group-based trajectory model (Proc TRAJ in SAS) was used to identify multimorbidity patterns across time. Morbidity was set to follow a logistic model. The optimal group number was determined using the Bayesian Information Criteria and the parsimony principle.
Four chronic disease trajectories were identified: 'low risk', 'cardiovascular risk only', 'gastrointestinal and chronic non-specific lung disease', and 'multiple risks' Jackson et al.
Latent class growth analysis to identify trajectories of multimorbidity.
Five trajectory groups were identified: 'no morbidity, constant', 'low morbidity, constant', 'moderate morbidity, constant', 'no morbidity, increasing', and 'low morbidity, increasing'. Kim et al. (2018) Growth mixture modelling (GMM) was used to estimate trajectory groups of combined comorbidities over time.
Mortality and comorbidity trajectory groups Strauss et al. (2014) Latent class growth analysis (LCGA) models were fitted starting with a onecluster model, assuming that all subjects have the same trajectory, and then successively increasing the number of clusters until most of the heterogeneity in the data was explained. Counts in each period were assumed to be Poisson distributed. Quadratic growth curves were applied for all clusters identified within the LCGA models. Both the Bayesian Information Criterion (BIC) and the adjusted likelihood ratio test proposed by Lo, Mendell, and Rubin (LMR) were used to determine optimal number of clusters. Analysis was applied in both Phase 1 (trajectory development) and phase 2 (testing the trajectories).
Five distinct trajectories of multimorbidity for chronic diseases over time in older adults were identified and validated identified: 'those who had no recorded chronic problems' (40%), 'those who developed a first chronic morbidity over 3 years' (10%), 'a developing multimorbidity group' (37%), 'a group with increasing number of chronic morbidities' (12%), and 'a multi-chronic group with many chronic morbidities' (1%). The four models combined yielded parameter estimates to calculate changes in CCI with their confidence intervals using a simulation approach. Lappenschaar et al. (2013) Progression of six chronic cardiovascular disease progression (cumulative disease incidence and probability of having particular disease combinations)
Multilevel temporal Bayesian Networks (MBN) was used to model the patient's disease status at baseline using the first 5 years of the data in retrospect, with a registration minimum of 3 years. Disease status 3 and 5 years after the baseline was also included in the model. The variance induced by the urbanization level (a practice level variable), age, and gender, in the multilevel model was explained by using Markov Chain Monte Carlo simulation.
Siriwardhana et al.
Disease state occupation probability and transition probability from a single disease state to a multiple disease state.
Acyclic multistate model to define an interconnected progressive chronic disease system for the elderly population. Aalen and Johansen estimator to estimate marginal state occupational probabilities, which is a nonparametric technique that provides great flexibility in handling a complex multi-state system without strict model assumptions. Xu et al. (2018) Cumulative incidence of diseases and accumulation of multimorbidity 
Multimorbidity Alluvial plots based on date of onset of each long-term condition, tabulated as first, second and third to visualise the acquisition sequence Rocca et al. (2016) 18 chronic conditions and the development of multimorbid conditions over time.
The accumulation of multimorbidity was represented graphically using Aalen-Johansen curves (a multistate generalization of cumulative incidence curves; unadjusted curves considering all 18 conditions equally). Xu et al. (2018) Cumulative incidence of diseases and accumulation of multimorbidity Sankey diagram was constructed to characterize the dynamic changes of different combinations of the three conditions over time.
Study, year Outcomes predicted by multimorbidity accumulation, speed, and trajectory
Calderon-Larranaga et al. (2018) Disability
The speed of multimorbidity (as a proxy of multisystem decline) is a strong predictor for disability in older adults, even when accounting for baseline number of chronic conditions Chang et al. (2011) Medical utilisation A simple morbidity stratification classification across 3 years was as predictive a tool in explaining medical utilization as more complex risk adjusters. Fraccaro et al.
Change over time of Charlson Comorbidity Index (CCI) was a stronger predictor of mortality than baseline CCI Fabbri et al. (2016) Standardized neurocognitive tests evaluating mental status, memory, executive function, processing speed and verbal fluency Accumulation of multimorbidity (accelerated deterioration of physical health) was associated with faster decline in verbal fluency but seems to have no effect on memory decline, in older adults without mild cognitive impairment or dementia. Kim et al. (2018) Mortality
The 'consistently high' trajectory pattern the highest risk of mortality at 1, 3 and 5 year follow-ups.
Zeng et al. (2014) Self-reported health, number of primary care visit, inpatient and emergency admissions, mortality
Using growth curve models for trajectories gave marginally better fitting models for the outcomes of SRH, but mortality and inpatient status was best predicted by multimorbidity snapshot the year before the survey. Overall, limited evidence that taking into account longitudinal trajectories of multimorbidity (over snapshot) better predicts outcomes. -Adults over 60 years old with higher than elementary education, lifelong active occupations and richer social networks had slower chronic disease accumulation.
-The association between childhood circumstances and disease accumulation speed was attenuated by subsequent (mid-and late-) life experiences.
-Rich social networks reduced the speed of disease accumulation irrespective of lifelong job stress and level of education Fabbri et al. (2015) Biomarkers: IL-6, IL-1ra, TNF-α receptor II, and DHEAS (as a marker of chronic inflammation and system dysregulation)
-The rise of multimorbidity with age was not linear (significant interaction age*time) but rather significantly accelerate at older ages.
-Higher IL-6, IL-1ra, and TNF-α receptor II and low DHEAS were associated with higher multimorbidity at baseline, independent of age, sex, BMI, and education.
-Higher IL-6 and steeper increase in IL-6 predicted an accelerated rise in multimorbidity over 9 years of follow-up.
-This supports that chronic inflammation is a predisposing factor to development of multiple diseases with aging.
Hanson et al. (2015) Parity, timing of childbearing, birth outcomes of offspring -Parity, timing of childbearing, and birth outcomes of offspring are significantly related to the shape of later-life comorbidity patterns and trajectories, when controlling for early-life conditions (age at parental death, childhood socioeconomic status, familial excess longevity, religious participation) Hiyoshi et al. (2017) Income, marital status -The constant low trajectory had the most favourable developmental and socioeconomic characteristics.
-Income, and physical, cognitive, and psychological function were associated with trajectory group membership in unadjusted analysis but no longer so in fully adjusted analysis. Hsu (2015) Sex, education, physical function, depressive symptoms, life satisfaction, number of health examination, smoking, and drinking -Those in the 'multiple risks' group were more likely to be female, less educated, with more physical function difficulties, more depressive symptoms, lower life satisfaction, more health examinations, and not to smoke or drink. Note that very few elderly Taiwanese women smoke or drink.
-Differences in the multimorbidity trajectory groups reflected their initial successful aging status at baseline. Members in the 'CVD risk only' and 'multiple risks' groups were more likely to have physical function difficulties and depressive symptoms.
Overweight or obesity, education, difficulty managing income, smoking alcohol consumption, physical activity -Being overweight or obese, having a lower education level and difficulty managing on income emerged as key risk factors for belonging to a trajectory where conditions accumulated over time. Smoking, alcohol intake and physical activity level also appeared to be important risk factors for the development of some trajectories. Lappenschaar et al. (2013) Urbanization, multimorbidity at baseline -Urbanization level of a general practice is associated with the cumulative incidence of chronic cardiovascular conditions, in particular those with a high prevalence, that is, obesity, hypertension, dyslipidaemia, diabetes mellitus, and ischemic heart disease.
-Overall multimorbidity rate of chronic cardiovascular (related) disorders rapidly increases when multimorbidity is already present at baseline. Perez et al. (2020) tGSH (biomarker of multisystem failure)
-Lower baseline levels of tGSH were associated with a higher rate of multimorbidity development over 6 years, independent of covariates.
-It supports tGSH as a marker of multisystem failure which could lead to multimorbidity. Quinones et al. (2011) Race/ethnicity -White Americans differ from Black and Mexican Americans in terms of level and rate of change of multimorbidity. Mexican Americans demonstrate lower initial levels and slower accumulation of comorbidities relative to White American. In contrast, Black Americans showed an elevated level of multimorbidity throughout the 11-year period of observation, although their rate of change slowed relative to White Americans.
Quinones et al. (2019) Race/ethnicity -Non-Hispanic black respondents had initial chronic disease counts that were 28% higher than non-Hispanic white respondents, while Hispanic respondents had initial chronic disease counts that were 15% lower than non-Hispanic white respondents.
-Non-Hispanic black respondents had rates of chronic disease accumulation that were 1.1% slower than non-Hispanic whites and Hispanic respondents had rates of chronic disease accumulation that were 1.5% faster than non-Hispanic white respondents.
-Chronic disease for white respondents who begin the study period with 0.98 chronic diseases and end with 2.8 chronic diseases; black respondents who begin the study period with 1.3 chronic diseases and end with 3.3 chronic diseases; and Hispanic respondents who begin the study period with 0.84 chronic diseases and end with 2.7 chronic diseases.
Ruel et al. (2014) Nutrition -The study provides first evidence of the relationship between nutrition and evolution of multimorbidity.
-Greater amount of fruits and vegetables and grain other than rice and wheat prevent evolution of multimorbidity in a Chinese cohort Ryan et al. (2018) Multimorbidity at baseline, age, obesity, gait speed, and grip strength, access to government funded primary care
In participants with no multimorbidity at baseline, age, obesity, gait speed, and grip strength were significantly associated with development of multimorbidity. Age, access to government funded primary care, gait speed, and grip strength were significantly associated with worsening of multimorbidity in those with multimorbidity at baseline. Gait speed and age were significantly associated with new condition development in people with complex multimorbidity.
-> (Overall) Gait speed, grip strength, and age were significantly associated with both the development of multimorbidity and accrual of additional conditions with evidence of a dose-response relationship.
Siriwardhana et al.
Age, sex, race/ethnicity Various covariate relationships. For example, men were more likely to transition between states than women. White had the highest risk of transitioning from ischemic heart disease to death. Asian and Native Hawaiian and Pacific Islander were more likely to transition from diabetes to diabetes+chronic kidney disease. Strauss et al. (2014) Age, deprivation -Younger age groups were more likely to be in the non-chronic cluster than older groups. Females were more likely to develop or start with multimorbidity than males. More deprived individuals were more likely to be in the evolving multimorbidity cluster. -These trajectories also had different self-reported physical and mental health profiles. Xu et al. (2018) Sex, age, marital status, income, education, obesity, physical activity, smoking, and immigrant status.
-Odds of progression increased over time and with age.
-Women with stroke were more likely to progress to another disease and become multimorbid than other baseline characteristics.
-In adjusted models, accumulation of MM was associated with non-married status, low income, lower education, obesity, sedentary and smoking, immigrant status. Obesity differently associated with different sequences.