Studying trajectories of multimorbidity: a systematic scoping review of longitudinal approaches and evidence

Recommendations on Methods for Assessing Multimorbidity Changes Over Time: Aligning the Method to the Purpose

BACKGROUND

The rapidly growing field of multimorbidity research demonstrates that changes in multimorbidity in mid- and late-life have far reaching effects on important person-centered outcomes, such as health-related quality of life. However, there are few organizing frameworks and comparatively little work weighing the merits and limitations of various quantitative methods applied to the longitudinal study of multimorbidity.

METHODS

We identify and discuss methods aligned to specific research objectives with the goals of (i) establishing a common language for assessing longitudinal changes in multimorbidity, (ii) illuminating gaps in our knowledge regarding multimorbidity progression and critical periods of change, and (iii) informing research to identify groups that experience different rates and divergent etiological pathways of disease progression linked to deterioration in important health-related outcomes.

RESULTS

We review practical issues in the measurement of multimorbidity, longitudinal analysis of health-related data, operationalizing change over time, and discuss methods that align with 4 general typologies for research objectives in the longitudinal study of multimorbidity: (i) examine individual change in multimorbidity, (ii) identify subgroups that follow similar trajectories of multimorbidity progression, (iii) understand when, how, and why individuals or groups shift to more advanced stages of multimorbidity, and (iv) examine the coprogression of multimorbidity with key health domains.

CONCLUSIONS

This work encourages a systematic approach to the quantitative study of change in multimorbidity and provides a valuable resource for researchers working to measure and minimize the deleterious effects of multimorbidity on aging populations.

Trajectories of chronic multimorbidity patterns in older patients: MTOP study

BACKGROUND

Multimorbidity is associated with negative results and poses difficulties in clinical management. New methodological approaches are emerging based on the hypothesis that chronic conditions are non-randomly associated forming multimorbidity patterns. However, there are few longitudinal studies of these patterns, which could allow for better preventive strategies and healthcare planning. The objective of the MTOP (Multimorbidity Trajectories in Older Patients) study is to identify patterns of chronic multimorbidity in a cohort of older patients and their progression and trajectories in the previous 10 years.

METHODS

A retrospective, observational study with a cohort of 3988 patients aged > 65 was conducted, including suspected and confirmed COVID-19 patients in the reference area of Parc Taulí University Hospital. Real-world data on socio-demographic and diagnostic variables were retrieved. Multimorbidity patterns of chronic conditions were identified with fuzzy c-means cluster analysis. Trajectories of each patient were established along three time points (baseline, 5 years before, 10 years before). Descriptive statistics were performed together with a stratification by sex and age group.

RESULTS

3988 patients aged over 65 were included (58.9% females). Patients with ≥ 2 chronic conditions changed from 73.6 to 98.3% in the 10-year range of the study. Six clusters of chronic multimorbidity were identified 10 years before baseline, whereas five clusters were identified at both 5 years before and at baseline. Three clusters were consistently identified in all time points (Metabolic and vascular disease, Musculoskeletal and chronic pain syndrome, Unspecific); three clusters were only present at the earliest time point (Male-predominant diseases, Minor conditions and sensory impairment, Lipid metabolism disorders) and two clusters emerged 5 years before baseline and remained (Heart diseases and Neurocognitive). Sex and age stratification showed different distribution in cluster prevalence and trajectories.

CONCLUSIONS

In a cohort of older patients, we were able to identify multimorbidity patterns of chronic conditions and describe their individual trajectories in the previous 10 years. Our results suggest that taking these trajectories into consideration might improve decisions in clinical management and healthcare planning.

TRIAL REGISTRATION NUMBER

NCT05717309.

10-Year Multimorbidity Trajectories in Older People Have Limited Benefit in Predicting Short-Term Health Outcomes in Comparison to Standard Multimorbidity Thresholds: A Population-Based Study

Purpose

To identify multimorbidity trajectories among older adults and to compare their health outcome predictive performance with that of cross-sectional multimorbidity thresholds (eg, ≥2 chronic conditions (CCs)).

Patients and Methods

We performed a population-based longitudinal study with a random sample of 99,411 individuals aged >65 years on April 1, 2019. Using health administrative data, we calculated for each individual the yearly CCs number from 2010 to 2019 and constructed the trajectories with latent class growth analysis. We used logistic regression to determine the increase in predictive capacity (c-statistic) of multimorbidity trajectories and traditional cross-sectional indicators (≥2, ≥3, or ≥4 CCs, assessed in April 2019) over that of a baseline model (including age, sex, and deprivation). We predicted 1-year mortality, hospitalization, polypharmacy, and frequent general practitioner, specialist, or emergency department visits.

Results

We identified eight multimorbidity trajectories, each representing between 3% and 25% of the population. These trajectories exhibited trends of increasing, stable, or decreasing number of CCs. When predicting mortality, the 95% CI for the increase in the c-statistic for multimorbidity trajectories [0.032-0.044] overlapped with that of the ≥3 indicator [0.037-0.050]. Similar results were observed when predicting other health outcomes and with other cross-sectional indicators.

Conclusion

Multimorbidity trajectories displayed comparable health outcome predictive capacity to those of traditional cross-sectional multimorbidity indicators. Given its ease of calculation, continued use of traditional multimorbidity thresholds remains relevant for population-based multimorbidity surveillance and clinical practice.

Plant-Based Diets and Risk of Multimorbidity: The Health and Retirement Study

BACKGROUND

Plant-based diets have gained attention due to their beneficial effects against major chronic diseases, although their association with multimorbidity is mostly unknown.

OBJECTIVES

We examined the association between the healthful (hPDI) and unhealthful plant-based diet indices (uPDI) with multimorbidity among middle-aged and older adults from the United States.

METHODS

Data on 4262 adults aged >50 y was obtained from the 2012-2020 Health and Retirement Study (HRS) and 2013 Health Care and Nutrition Study (HCNS). Food consumption was collected at baseline with a food frequency questionnaire and 2 PDIs were derived: the hPDI, with positive scores for healthy plant foods and reverse scores for less healthy plant foods and animal foods; and the uPDI, with only positive scoring for less healthy plant foods. Complex multimorbidity, defined as ≥3 coexistent conditions, was ascertained from 8 self-reported conditions: hypertension, diabetes, cancer, chronic lung disease, heart disease, stroke, arthritis, and depression. Cox proportional hazards models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs).

RESULTS

After a median follow-up of 7.8 y, we documented 1202 incident cases of multimorbidity. Compared with the lowest quartile, higher adherence to the hPDI was inversely associated with multimorbidity (HR for quartile 3: 0.77; 95% CI: 0.62, 0.96 and HR for quartile 4: 0.79; 95% CI, 0.63, 0.98; P-trend = 0.02). In addition, a 10-point increment in the hPDI was associated with a 11% lower incidence of multimorbidity (95% CI: 1, 20%). No significant associations were found for the uPDI after adjusting for sociodemographic and lifestyle factors.

CONCLUSIONS

Higher adherence to the hPDI was inversely associated with multimorbidity among middle-aged and older adults. Plant-based diets that emphasize consumption of high-quality plant foods may help prevent the development of complex multimorbidity.

Determinants of central obesity in children and adolescents and associated complications in South Africa: a systematic review

Background

Central obesity in children is a global health concern associated with cardiovascular risk factors. In 2019 the World Obesity Federation predicted that in 2025, 206 million children and adolescents aged 5 to 19 will be obese, and the number is estimated to reach 254 million by 2030. There is limited literature on the factors that are associated with the development of central obesity in children. We report a systematic review, aimed to describe the current literature on determinants of central obesity and its associated health outcomes in children and adolescents in the South African population.

Methods

We searched for peer-reviewed studies in Google Scholar, PubMed, and Science Direct search engines, and about seven studies were included. This systematic review has been registered with the International Prospective Register of Systematic Reviews (PROSPERO) (Registration number: CRD42023457012). This systematic review was conducted and reported according to an updated version of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. The quality of the included studies was assessed by following guidelines from the Newcastle-Ottawa Scale (NOS). The method considered three main domains: selection, comparability, and outcome across different study designs.

Results

The prevalence of central obesity in children and adolescents by waist-to-height ratio (WHtR) ranged from 2.0 to 41.0%; waist-to-hip [WHR ranged from 10 to 25%; waist circumference (WC) ranged from 9 to 35%]. Central obesity was associated with age, physical inactivity, gender socio, and demographic profiles of the household. Central obesity in children was associated with cardiovascular diseases and mental health issues.

Conclusion

Central obesity in children and adolescents was determined by gender, pubertal development, and age of the parents, households with high socioeconomic status, dietary practices, and overweight/obesity. Given the high prevalence of central obesity in children which can ultimately result in cardiometabolic diseases, cardiovascular risk factors, and mental health issues. This highlights the need for systems, jointly initiated by healthcare providers, policymakers, and the general society aimed at reducing the burden of central obesity such as introducing children and adolescents to health-promoting lifestyles.

Younger but sicker? Cohort trends in disease accumulation among middle-aged and older adults in Scotland using health-linked data from the Scottish Longitudinal Study

BACKGROUND

In the United Kingdom, rising prevalence of multimorbidity-the co-occurrence of two or more chronic conditions- is coinciding with stagnation in life expectancy. We investigate patterns of disease accumulation and how they vary by birth cohort, social and environmental inequalities in Scotland, a country which has long suffered from excess mortality and poorer health outcomes relative to its neighbours.

METHODS

Using a dataset which links census data from 1991, 2001 and 2011 to disease registers and hospitalization data, we follow cohorts of adults aged 30-69 years for 18 years. We model physical and mental disease accumulation using linear mixed-effects models.

RESULTS

Recent cohorts experience higher levels of chronic disease accumulation compared to their predecessors at the same ages. Moreover, in more recently born cohorts we observe socioeconomic status disparities emerging earlier in the life course, which widen over time and with every successive cohort. Patterns of chronic conditions are also changing, and the most common diseases suffered by later born cohorts are cancer, hypertension, asthma, drug and alcohol problems and depression.

CONCLUSION

We recommend policies which target prevention of chronic disease in working age adults, considering how and why certain conditions are becoming more prevalent across time and space.

Unraveling cradle-to-grave disease trajectories from multilayer comorbidity networks

We aim to comprehensively identify typical life-spanning trajectories and critical events that impact patients' hospital utilization and mortality. We use a unique dataset containing 44 million records of almost all inpatient stays from 2003 to 2014 in Austria to investigate disease trajectories. We develop a new, multilayer disease network approach to quantitatively analyze how cooccurrences of two or more diagnoses form and evolve over the life course of patients. Nodes represent diagnoses in age groups of ten years; each age group makes up a layer of the comorbidity multilayer network. Inter-layer links encode a significant correlation between diagnoses (p < 0.001, relative risk > 1.5), while intra-layers links encode correlations between diagnoses across different age groups. We use an unsupervised clustering algorithm for detecting typical disease trajectories as overlapping clusters in the multilayer comorbidity network. We identify critical events in a patient's career as points where initially overlapping trajectories start to diverge towards different states. We identified 1260 distinct disease trajectories (618 for females, 642 for males) that on average contain 9 (IQR 2-6) different diagnoses that cover over up to 70 years (mean 23 years). We found 70 pairs of diverging trajectories that share some diagnoses at younger ages but develop into markedly different groups of diagnoses at older ages. The disease trajectory framework can help us to identify critical events as specific combinations of risk factors that put patients at high risk for different diagnoses decades later. Our findings enable a data-driven integration of personalized life-course perspectives into clinical decision-making.

Association of Depression With the Progression of Multimorbidity in Older Adults: A Population-Based Cohort Study

BACKGROUND

The relationship between depression and the risk of multimorbidity progression has rarely been studied in older adults. This study was aimed to determine whether depression is associated with progression in the severity and complexity of multimorbidity, considering the influence of depression's severity and subtype.

METHODS

As a part of the Korean Longitudinal Study on Cognitive Aging and Dementia, this population-based cohort study followed a random sample of community-dwelling Koreans aged 60 and older for 8 years at 2-year intervals starting in 2010. Participants included those who completed mood and multimorbidity assessments and did not exhibit complex multimorbidity at the study's outset. Depression was assessed using the Geriatric Depression Scale, while multimorbidity was evaluated using the Cumulative Illness Rating Scale. The study quantified multimorbidity complexity by counting affected body systems and measured multimorbidity severity by averaging scores across 14 body systems.

FINDINGS

The 2,486 participants (age = 69.1 ± 6.5 years, 57.6% women) were followed for 5.9 ± 2.4 years. Linear mixed models revealed that participants with depression had a faster increase in multimorbidity complexity score (β = .065, SE = 0.019, p = 0.001) than those without depression, but a comparable increase in multimorbidity severity score (β = .001, SE = .009, p = 0.870) to those without depression. Cox proportional hazard models revealed that depression was associated with the risk of developing highly complex multimorbidity affecting five or more body systems, particularly in severe or anhedonic depression.

INTERPRETATION

Depression was associated with the worsening of multimorbidity in Korean older adults, particularly when severe or anhedonic. Early screening and management of depression may help to reduce the burden of multimorbidity in older adults.

Risk factors of multimorbidity among older adults in India: A systematic review and meta-analysis

Background

Multimorbidity among older adults is a growing concern in India. Multimorbidity is defined as the coexistence of two or more chronic health conditions in an individual. Primary studies have been conducted on risk factors of multimorbidity in India, but no systematic review has been conducted on this topic. This systematic review aimed to synthesize the existing evidence on risk factors of multimorbidity among older adults in India.

Methods

The JBI and Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines were followed. Several databases were searched for published and unpublished studies until August 03, 2022. The screening of titles and abstracts and full texts, data extraction, and quality assessment were conducted by two independent reviewers. Any disagreements were resolved through discussion or by involving a third reviewer. Data synthesis was conducted using narrative synthesis and random effects meta-analysis, where appropriate.

Results

Out of 8781 records identified from the literature search, 16 and 15 studies were included in the systematic review and meta-analysis, respectively. All included studies were cross-sectional, and 10 met a critical appraisal score of more than 70%. Broadly, sociodemographic, lifestyle, and health conditions-related factors were explored in these studies. The pooled odds of multimorbidity were higher in people aged ≥70 years compared to 60-69 years (odds ratio (OR) 1.51; 95% confidence interval (CI) 1.20-1.91), females compared to males (1.38; 1.09-1.75), single, divorced, separated, and widowed compared to married (1.29; 1.11-1.49), economically dependent compared to economically independent (1.54; 1.21-1.97), and smokers compared to non-smokers (1.33; 1.16-1.52) and were lower in working compared to not working (0.51; 0.36-0.72).

Conclusion

This systematic review and meta-analysis provided a comprehensive picture of the problem by synthesizing the existing evidence on risk factors of multimorbidity among older adults in India. These synthesized sociodemographic and lifestyle factors should be taken into consideration when developing health interventions for addressing multimorbidity among older adults in India.

Learning prevalent patterns of co-morbidities in multichronic patients using population-based healthcare data

The prevalence of longstanding chronic diseases has increased worldwide, along with the average age of the population. As a result, an increasing number of people is affected by two or more chronic conditions simultaneously, and healthcare systems are facing the challenge of treating multimorbid patients effectively. Current therapeutic strategies are suited to manage each chronic condition separately, without considering the whole clinical condition of the patient. This approach may lead to suboptimal clinical outcomes and system inefficiencies (e.g. redundant diagnostic tests and inadequate drug prescriptions). We develop a novel methodology based on the joint implementation of data reduction and clustering algorithms to identify patterns of chronic diseases that are likely to co-occur in multichronic patients. We analyse data from a large adult population of multichronic patients living in Tuscany (Italy) in 2019 which was stratified by sex and age classes. Results demonstrate that (i) cardio-metabolic, endocrine, and neuro-degenerative diseases represent a stable pattern of multimorbidity, and (ii) disease prevalence and clustering vary across ages and between women and men. Identifying the most common multichronic profiles can help tailor medical protocols to patients' needs and reduce costs. Furthermore, analysing temporal patterns of disease can refine risk predictions for evolutive chronic conditions.

A scoping review finds a growing trend in studies validating multimorbidity patterns and identifies five broad types of validation methods

OBJECTIVES

Multimorbidity, the presence of two or more long-term conditions, is a growing public health concern. Many studies use analytical methods to discover multimorbidity patterns from data. We aimed to review approaches used in published literature to validate these patterns.

STUDY DESIGN AND SETTING

We systematically searched PubMed and Web of Science for studies published between July 2017 and July 2023 that used analytical methods to discover multimorbidity patterns.

RESULTS

Out of 31,617 studies returned by the searches, 172 were included. Of these, 111 studies (64%) conducted validation, the number of studies with validation increased from 53.13% (17 out of 32 studies) to 71.25% (57 out of 80 studies) in 2017-2019 to 2022-2023, respectively. Five types of validation were identified: assessing the association of multimorbidity patterns with clinical outcomes (n = 79), stability across subsamples (n = 26), clinical plausibility (n = 22), stability across methods (n = 7) and exploring common determinants (n = 2). Some studies used multiple types of validation.

CONCLUSION

The number of studies conducting a validation of multimorbidity patterns is clearly increasing. The most popular validation approach is assessing the association of multimorbidity patterns with clinical outcomes. Methodological guidance on the validation of multimorbidity patterns is needed.

Tackling diet inequalities in the UK food system: is food insecurity driving the obesity epidemic? (The FIO Food project)

By 2050 the number of adults living with obesity in the UK will rise with approximately one in four in the adult population. This rising trend is not equitable, with higher prevalence in socially disadvantaged groups. There is an apparent paradox of not being able to provide food for the family to eat, a feature of food insecurity and living with obesity. With the current cost-of-living crisis, there is a challenge to afford both food and fuel bills. Environmentally sustainable and healthy diets are proposed to improve public health and reduce the impact of the food system on the environment, while also improving diet quality. However, healthier foods tend to be nearly three times more expensive than unhealthy foods, and this provides a challenge for citizens on low incomes. In this review, we explore some of the evidence for solutions in the retail food environment to support the UK food system to be safe, nutritious, environmentally friendly and fair for all. We highlight the value of co-production in research, to give value and power to the lived experience to address these inequalities. Our multidisciplinary research approach within the FIO Food research grant will generate new insights into modifiable and potentially impactful changes to the UK food system, specifically for the retail food sector. We believe that the co-creation, design and delivery of research with those living with obesity and food insecurity will help to transform the UK food system for health and the environment in this vulnerable group.

Burden, determinants, consequences and care of multimorbidity in rural and urbanising Telangana, India: protocol for a mixed-methods study within the APCAPS cohort

INTRODUCTION

The epidemiological and demographic transitions are leading to a rising burden of multimorbidity (co-occurrence of two or more chronic conditions) worldwide. Evidence on the burden, determinants, consequences and care of multimorbidity in rural and urbanising India is limited, partly due to a lack of longitudinal and objectively measured data on chronic health conditions. We will conduct a mixed-methods study nested in the prospective Andhra Pradesh Children and Parents' Study (APCAPS) cohort to develop a data resource for understanding the epidemiology of multimorbidity in rural and urbanising India and developing interventions to improve the prevention and care of multimorbidity.

METHODS AND ANALYSIS

We aim to recruit 2100 APCAPS cohort members aged 45+ who have clinical and lifestyle data collected during a previous cohort follow-up (2010-2012). We will screen for locally prevalent non-communicable, infectious and mental health conditions, alongside cognitive impairments, disabilities and frailty, using a combination of self-reported clinical diagnosis, symptom-based questionnaires, physical examinations and biochemical assays. We will conduct in-depth interviews with people with varying multimorbidity clusters, their informal carers and local healthcare providers. Deidentified data will be made available to external researchers.

ETHICS AND DISSEMINATION

The study has received approval from the ethics committees of the National Institute of Nutrition and Indian Institute of Public Health Hyderabad, India and the London School of Hygiene and Tropical Medicine, UK. Meta-data and data collection instruments will be published on the APCAPS website alongside details of existing APCAPS data and the data access process (www.lshtm.ac.uk/research/centres-projects-groups/apcaps).

Sociodemographic characteristics and longitudinal progression of multimorbidity: A multistate modelling analysis of a large primary care records dataset in England

BACKGROUND

Multimorbidity, characterised by the coexistence of multiple chronic conditions in an individual, is a rising public health concern. While much of the existing research has focused on cross-sectional patterns of multimorbidity, there remains a need to better understand the longitudinal accumulation of diseases. This includes examining the associations between important sociodemographic characteristics and the rate of progression of chronic conditions.

METHODS AND FINDINGS

We utilised electronic primary care records from 13.48 million participants in England, drawn from the Clinical Practice Research Datalink (CPRD Aurum), spanning from 2005 to 2020 with a median follow-up of 4.71 years (IQR: 1.78, 11.28). The study focused on 5 important chronic conditions: cardiovascular disease (CVD), type 2 diabetes (T2D), chronic kidney disease (CKD), heart failure (HF), and mental health (MH) conditions. Key sociodemographic characteristics considered include ethnicity, social and material deprivation, gender, and age. We employed a flexible spline-based parametric multistate model to investigate the associations between these sociodemographic characteristics and the rate of different disease transitions throughout multimorbidity development. Our findings reveal distinct association patterns across different disease transition types. Deprivation, gender, and age generally demonstrated stronger associations with disease diagnosis compared to ethnic group differences. Notably, the impact of these factors tended to attenuate with an increase in the number of preexisting conditions, especially for deprivation, gender, and age. For example, the hazard ratio (HR) (95% CI; p-value) for the association of deprivation with T2D diagnosis (comparing the most deprived quintile to the least deprived) is 1.76 ([1.74, 1.78]; p < 0.001) for those with no preexisting conditions and decreases to 0.95 ([0.75, 1.21]; p = 0.69) with 4 preexisting conditions. Furthermore, the impact of deprivation, gender, and age was typically more pronounced when transitioning from an MH condition. For instance, the HR (95% CI; p-value) for the association of deprivation with T2D diagnosis when transitioning from MH is 2.03 ([1.95, 2.12], p < 0.001), compared to transitions from CVD 1.50 ([1.43, 1.58], p < 0.001), CKD 1.37 ([1.30, 1.44], p < 0.001), and HF 1.55 ([1.34, 1.79], p < 0.001). A primary limitation of our study is that potential diagnostic inaccuracies in primary care records, such as underdiagnosis, overdiagnosis, or ascertainment bias of chronic conditions, could influence our results.

CONCLUSIONS

Our results indicate that early phases of multimorbidity development could warrant increased attention. The potential importance of earlier detection and intervention of chronic conditions is underscored, particularly for MH conditions and higher-risk populations. These insights may have important implications for the management of multimorbidity.

Molecular bases of comorbidities: present and future perspectives

Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under study. The study of similarities between diseases using 'omics data, such as genes altered in diseases, gene expression, proteome, and microbiome, are fundamental to uncovering the origin of, and potential treatment for, comorbidities. Recent studies have produced a first generation of genetic interpretations for as much as 46% of the comorbidities described in large cohorts. Integrating different sources of molecular information and using artificial intelligence (AI) methods are promising approaches for the study of comorbidities. They may help to improve the treatment of comorbidities, including the potential repositioning of drugs.

[Estimated multimorbity among young Brazilians: results of the 2019 National Health Survey]

Multimorbidity, namely the presence of two or more chronic non-communicable diseases, is directly associated with behavioral factors. This study sought to estimate the prevalence of multimorbidity among young Brazilians by linking it to different social and lifestyle determinants. It involved a cross-sectional study of the data source, namely the 2019 National Health Survey. Data from individuals aged between 15 and 24 years (n = 10,460) were selected. Associated factors were investigated by calculating the Prevalence Ratio with robust variance, suitable for bivariate and multivariate analysis. The prevalence of multimorbidity in young people was estimated at 7.84% (95%CI: 7.01-8.75; N: 2,455,097). The most common conditions were mental illness, depression, asthma or bronchitis and chronic back problems. In the adjusted model, young females (PR: 1.84; 95%CI: 1.44-2.36), obese youths (PR: 1.97; 95%CI: 1.45-2.68) and former smokers (PR: 1.46; 95%CI: 1.12-1.90) showed a higher prevalence of multimorbidity. It was also revealed that the prevalence ratio for multimorbidity increased by 5% for each year of the individual's life. This study identified an association of multimorbidity with social determinants and lifestyle.

Spanish version of the self-care self-efficacy scale: A validation study in community-dwelling older adults with chronic multimorbidity

OBJECTIVE

To test the psychometric properties of the Spanish version of the Self-Care Self-Efficacy Scale (SCSES-Sp) in community-dwelling older adults with chronic multimorbidity.

METHODS

A sample of 1013 community-dwelling older adults with chronic multimorbidity participated in an observational cross-sectional study that was carried out in 3 phases.

RESULTS

Confirmatory factor analysis showed that the SCSES-Sp has 4 dimensions: "self-efficacy in self-care behaviours based on clinical knowledge", "self-efficacy in self-care maintenance", "self-efficacy in self-care monitoring", and "self-efficacy in self-care management". A panel of independent experts considered the content of the SCSES-Sp valid. Convergent validity analysis showed moderate-strong correlations between all of the SCSES-Sp's dimensions and the reference criteria chosen. Reliability was good for the SCSES-Sp and all its dimensions. Test-retest reliability analysis showed that the SCSES-Sp was temporally stable.

CONCLUSIONS

The SCSES-Sp is a valid and reliable tool to assess self-efficacy in self-care in Spanish-speaking, community-dwelling older adults with chronic multimorbidity.

Healthy immigrants, unhealthy ageing? Analysis of health decline among older migrants and natives across European countries

The probability of having multiple chronic conditions simultaneously, or multimorbidity, tends to increase with age. Immigrants face a particularly high risk of unhealthy ageing. This study investigates the immigrant-native disparities in the speed of age-related chronic disease accumulation, focusing on the number of chronic health conditions; and considers the heterogeneity of this trajectory within immigrant populations by origin and receiving country. We use data from the Survey of Health, Ageing and Retirement in Europe from 2004 to 2020 on adults aged 50 to 79 from 28 European countries and employ both cross-sectional and longitudinal analyses. For longitudinal panel analyses, we use fixed-effects regression models to account for the unobserved heterogeneity related to individual characteristics including migration background. Our results indicate that immigrants report a higher number of chronic conditions at all ages relative to their native-born peers, but also that the immigrant-native differential in the number of chronic conditions decreases from age 65 onwards. When considering differences by origin country, we find that the speed of chronic disease accumulation is slower among immigrants from the Americas and the Asia and Oceania country groups than it is among natives. When looking at differences by receiving country group, we observe that the speed of accumulating chronic diseases is slower among immigrants in Eastern Europe than among natives, particularly at older ages. Our findings suggest that age-related trajectories of health vary substantially among immigrant populations by origin and destination country, which underscore that individual migration histories play a persistent role in shaping the health of ageing immigrant populations throughout the life course.

Effect on life expectancy of temporal sequence in a multimorbidity cluster of psychosis, diabetes, and congestive heart failure among 1·7 million individuals in Wales with 20-year follow-up: a retrospective cohort study using linked data

BACKGROUND

To inform targeted public health strategies, it is crucial to understand how coexisting diseases develop over time and their associated impacts on patient outcomes and health-care resources. This study aimed to examine how psychosis, diabetes, and congestive heart failure, in a cluster of physical-mental health multimorbidity, develop and coexist over time, and to assess the associated effects of different temporal sequences of these diseases on life expectancy in Wales.

METHODS

In this retrospective cohort study, we used population-scale, individual-level, anonymised, linked, demographic, administrative, and electronic health record data from the Wales Multimorbidity e-Cohort. We included data on all individuals aged 25 years and older who were living in Wales on Jan 1, 2000 (the start of follow-up), with follow-up continuing until Dec 31, 2019, first break in Welsh residency, or death. Multistate models were applied to these data to model trajectories of disease in multimorbidity and their associated effect on all-cause mortality, accounting for competing risks. Life expectancy was calculated as the restricted mean survival time (bound by the maximum follow-up of 20 years) for each of the transitions from the health states to death. Cox regression models were used to estimate baseline hazards for transitions between health states, adjusted for sex, age, and area-level deprivation (Welsh Index of Multiple Deprivation [WIMD] quintile).

FINDINGS

Our analyses included data for 1 675 585 individuals (811 393 [48·4%] men and 864 192 [51·6%] women) with a median age of 51·0 years (IQR 37·0-65·0) at cohort entry. The order of disease acquisition in cases of multimorbidity had an important and complex association with patient life expectancy. Individuals who developed diabetes, psychosis, and congestive heart failure, in that order (DPC), had reduced life expectancy compared with people who developed the same three conditions in a different order: for a 50-year-old man in the third quintile of the WIMD (on which we based our main analyses to allow comparability), DPC was associated with a loss in life expectancy of 13·23 years (SD 0·80) compared with the general otherwise healthy or otherwise diseased population. Congestive heart failure as a single condition was associated with mean a loss in life expectancy of 12·38 years (0·00), and with a loss of 12·95 years (0·06) when preceded by psychosis and 13·45 years (0·13) when followed by psychosis. Findings were robust in people of older ages, more deprived populations, and women, except that the trajectory of psychosis, congestive heart failure, and diabetes was associated with higher mortality in women than men. Within 5 years of an initial diagnosis of diabetes, the risk of developing psychosis or congestive heart failure, or both, was increased.

INTERPRETATION

The order in which individuals develop psychosis, diabetes, and congestive heart failure as combinations of conditions can substantially affect life expectancy. Multistate models offer a flexible framework to assess temporal sequences of diseases and allow identification of periods of increased risk of developing subsequent conditions and death.

FUNDING

Health Data Research UK.

Contribution of Frailty to Multimorbidity Patterns and Trajectories: Longitudinal Dynamic Cohort Study of Aging People

BACKGROUND

Multimorbidity and frailty are characteristics of aging that need individualized evaluation, and there is a 2-way causal relationship between them. Thus, considering frailty in analyses of multimorbidity is important for tailoring social and health care to the specific needs of older people.

OBJECTIVE

This study aimed to assess how the inclusion of frailty contributes to identifying and characterizing multimorbidity patterns in people aged 65 years or older.

METHODS

Longitudinal data were drawn from electronic health records through the SIDIAP (Sistema d'Informació pel Desenvolupament de la Investigació a l'Atenció Primària) primary care database for the population aged 65 years or older from 2010 to 2019 in Catalonia, Spain. Frailty and multimorbidity were measured annually using validated tools (eFRAGICAP, a cumulative deficit model; and Swedish National Study of Aging and Care in Kungsholmen [SNAC-K], respectively). Two sets of 11 multimorbidity patterns were obtained using fuzzy c-means. Both considered the chronic conditions of the participants. In addition, one set included age, and the other included frailty. Cox models were used to test their associations with death, nursing home admission, and home care need. Trajectories were defined as the evolution of the patterns over the follow-up period.

RESULTS

The study included 1,456,052 unique participants (mean follow-up of 7.0 years). Most patterns were similar in both sets in terms of the most prevalent conditions. However, the patterns that considered frailty were better for identifying the population whose main conditions imposed limitations on daily life, with a higher prevalence of frail individuals in patterns like chronic ulcers &peripheral vascular. This set also included a dementia-specific pattern and showed a better fit with the risk of nursing home admission and home care need. On the other hand, the risk of death had a better fit with the set of patterns that did not include frailty. The change in patterns when considering frailty also led to a change in trajectories. On average, participants were in 1.8 patterns during their follow-up, while 45.1% (656,778/1,456,052) remained in the same pattern.

CONCLUSIONS

Our results suggest that frailty should be considered in addition to chronic diseases when studying multimorbidity patterns in older adults. Multimorbidity patterns and trajectories can help to identify patients with specific needs. The patterns that considered frailty were better for identifying the risk of certain age-related outcomes, such as nursing home admission or home care need, while those considering age were better for identifying the risk of death. Clinical and social intervention guidelines and resource planning can be tailored based on the prevalence of these patterns and trajectories.

Multimorbidity and adverse longitudinal outcomes among patients attending chronic outpatient medical care in Bahir Dar, Northwest Ethiopia

Background

Multimorbidity is becoming more prevalent in low-and middle-income countries (LMICs). However, the evidence base on the burden and its longitudinal outcomes are limited. This study aimed to determine the longitudinal outcomes of patients with multimorbidity among a sample of individuals attending chronic outpatient non communicable diseases (NCDs) care in Bahir Dar, northwest Ethiopia.

Methods

A facility-based longitudinal study was conducted among 1,123 participants aged 40+ attending care for single NCD (n = 491) or multimorbidity (n = 633). Data were collected both at baseline and after 1 year through standardized interviews and record reviews. Data were analyzed using Stata V.16. Descriptive statistics and longitudinal panel data analyzes were run to describe independent variables and identify factors predicting outcomes. Statistical significance was considered at p-value <0.05.

Results

The magnitude of multimorbidity has increased from 54.8% at baseline to 56.8% at 1 year. Four percent (n = 44) of patients were diagnosed with one or more NCDs and those having multimorbidity at baseline were more likely than those without multimorbidity to develop new NCDs. In addition, 106 (9.4%) and 22 (2%) individuals, respectively were hospitalized and died during the follow up period. In this study, about one-third of the participants had higher quality of life (QoL), and those having higher high activation status were more likely to be in the higher versus the combined moderate and lower QoL [AOR1 = 2.35, 95%CI: (1.93, 2.87)] and in the combined higher and moderate versus lower level of QoL [AOR2 = 1.53, 95%CI: (1.25, 1.88)].

Conclusion

Developing new NCDs is a frequent occurrence and the prevalence of multimorbidity is high. Living with multimorbidity was associated with poor progress, hospitalization and mortality. Patients having a higher activation level were more likely than those with low activation to have better QoL. If health systems are to meet the needs of the people with chronic conditions and multimorbidity, it is essential to understand diseases trajectories and of impact of multimorbidity on QoL, and determinants and individual capacities, and to increase their activation levels for better health improve outcomes through education and activation.

Evaluation of the reported data linkage process and associated quality issues for linked routinely collected healthcare data in multimorbidity research: a systematic methodology review

OBJECTIVE

The objective of this systematic review was to examine how the record linkage process is reported in multimorbidity research.

METHODS

A systematic search was conducted in Medline, Web of Science and Embase using predefined search terms, and inclusion and exclusion criteria. Published studies from 2010 to 2020 using linked routinely collected data for multimorbidity research were included. Information was extracted on how the linkage process was reported, which conditions were studied together, which data sources were used, as well as challenges encountered during the linkage process or with the linked dataset.

RESULTS

Twenty studies were included. Fourteen studies received the linked dataset from a trusted third party. Eight studies reported variables used for the data linkage, while only two studies reported conducting prelinkage checks. The quality of the linkage was only reported by three studies, where two reported linkage rate and one raw linkage figures. Only one study checked for bias by comparing patient characteristics of linked and non-linked records.

CONCLUSIONS

The linkage process was poorly reported in multimorbidity research, even though this might introduce bias and potentially lead to inaccurate inferences drawn from the results. There is therefore a need for increased awareness of linkage bias and transparency of the linkage processes, which could be achieved through better adherence to reporting guidelines.

PROSPERO REGISTRATION NUMBER

CRD42021243188.

Multimorbidity and determinants for initiating outpatient trajectories: A population-based study

INTRODUCTION

Individuals with multimorbidity often receive high numbers of hospital outpatient services in concurrent trajectories. Nevertheless, little is known about factors associated with initiating new hospital outpatient trajectories; identified as the continued use of outpatient contacts for the same medical condition.

PURPOSE

To investigate whether the number of chronic conditions and sociodemographic characteristics in adults with multimorbidity is associated with entering a hospital outpatient trajectory in this population.

METHODS

This population-based register study included all adults in Denmark with multimorbidity on January 1, 2018. The exposures were number of chronic conditions and sociodemographic characteristics, and the outcome was the rate of starting a new outpatient trajectory during 2018. Analyses were stratified by the number of existing outpatient trajectories. We used Poisson regression analysis, and results were expressed as incidence rates and incidence rate ratios with 95% confidence intervals. We followed the individuals during the entire year of 2018, accounting for person-time by hospitalization, emigration, and death.

RESULTS

Incidence rates for new outpatient trajectories were highest for individuals with low household income and ≥3 existing trajectories and for individuals with ≥3 chronic conditions and in no already established outpatient trajectory. A high number of chronic conditions and male gender were found to be determinants for initiating a new outpatient trajectory, regardless of the number of existing trajectories. Low educational level was a determinant when combined with 1, 2, and ≥3 existing trajectories, and increasing age, western ethnicity, and unemployment when combined with 0, 1, and 2 existing trajectories.

CONCLUSION

A high number of chronic conditions, male gender, high age, low educational level and unemployment were determinants for initiation of an outpatient trajectory. The rate was modified by the existing number of outpatient trajectories. The results may help identify those with multimorbidity at greatest risk of having a new hospital outpatient trajectory initiated.

Generating Older Adult Multimorbidity Trajectories Using Various Comorbidity Indices and Calculation Methods

Background and Objectives

Older adult multimorbidity trajectories are helpful for understanding the current and future health patterns of aging populations. The construction of multimorbidity trajectories from comorbidity index scores will help inform public health and clinical interventions targeting those individuals that are on unhealthy trajectories. Investigators have used many different techniques when creating multimorbidity trajectories in prior literature, and no standard way has emerged. This study compares and contrasts multimorbidity trajectories constructed from various methods.

Research Design and Methods

We describe the difference between aging trajectories constructed with the Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI). We also explore the differences between acute (single-year) and chronic (cumulative) derivations of CCI and ECI scores. Social determinants of health can affect disease burden over time; thus, our models include income, race/ethnicity, and sex differences.

Results

We use group-based trajectory modeling (GBTM) to estimate multimorbidity trajectories for 86,909 individuals aged 66-75 in 1992 using Medicare claims data collected over the following 21 years. We identify low-chronic disease and high-chronic disease trajectories in all 8 generated trajectory models. Additionally, all 8 models satisfied prior established statistical diagnostic criteria for well-performing GBTM models.

Discussion and Implications

Clinicians may use these trajectories to identify patients on an unhealthy path and prompt a possible intervention that may shift the patient to a healthier trajectory.

HOW TO IDENTIFY SUBGROUPS IN LONGITUDINAL CLINICAL DATA: TREATMENT RESPONSE PATTERNS IN PATIENTS WITH A SHORTENED DENTAL ARCH

BACKGROUND

When dental patients seek care, treatments are not always successful,that is patients' oral health problems are not always eliminated or substantially reduced. Identifying these patients (treatment non-responders) is essential for clinical decision-making. Group-based trajectory modeling (GBTM) is rarely used in dentistry, but a promising statistical technique to identify non-responders in particular and clinical distinct patient groups in general in longitudinal data sets.

AIM

Using group-based trajectory modeling, this study aimed to demonstrate how to identify oral health-related quality of life (OHRQoL) treatment response patterns by the example of patients with a shortened dental arch (SDA).

METHODS

This paper is a secondary data analysis of a randomized controlled clinical trial. In this trial SDA patients received partial removable dental prostheses replacing missing teeth up to the first molars (N = 79) either or the dental arch ended with the second premolar that was present or replaced by a cantilever fixed dental prosthesis (N = 71). Up to ten follow-up examinations (1-2, 6, 12, 24, 36, 48, 60, 96, 120, and 180 months post-treatment) continued for 15 years. The outcome OHRQoL was assessed with the 49-item Oral Health Impact Profile (OHIP). Exploratory GBTM was performed to identify treatment response patterns.

RESULTS

Two response patterns could be identified - "responders" and "non-responders." Responders' OHRQoL improved substantially and stayed primarily stable over the 15 years. Non-responders' OHRQoL did not improve considerably over time or worsened. While the SDA treatments were not related to the 2 response patterns, higher levels of functional, pain-related, psychological impairment in particular, and severely impaired OHRQoL in general predicted a non-responding OHRQoL pattern after treatment. Supplementary, a 3 pattern approach has been evaluated.

CONCLUSIONS

Clustering patients according to certain longitudinal characteristics after treatment is generally important, but specifically identifying treatment in non-responders is central. With the increasing availability of OHRQoL data in clinical research and regular patient care, GBTM has become a powerful tool to investigate which dental treatment works for which patients.

A conceptual framework for characterising lifecourse determinants of multiple long-term condition multimorbidity

Objective

Social, biological and environmental factors in early-life, defined as the period from preconception until age 18, play a role in shaping the risk of multiple long-term condition multimorbidity. However, there is a need to conceptualise these early-life factors, how they relate to each other, and provide conceptual framing for future research on aetiology and modelling prevention scenarios of multimorbidity. We develop a conceptual framework to characterise the population-level domains of early-life determinants of future multimorbidity.

Method

This work was conducted as part of the Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity (MELD-B) study. The conceptualisation of multimorbidity lifecourse determinant domains was shaped by a review of existing research evidence and policy, and co-produced with public involvement via two workshops.

Results

Early-life risk factors incorporate personal, social, economic, behavioural and environmental factors, and the key domains discussed in research evidence, policy, and with public contributors included adverse childhood experiences, socioeconomics, the social and physical environment, and education. Policy recommendations more often focused on individual-level factors as opposed to the wider determinants of health discussed within the research evidence. Some domains highlighted through our co-production process with public contributors, such as religion and spirituality, health screening and check-ups, and diet, were not adequately considered within the research evidence or policy.

Conclusions

This co-produced conceptualisation can inform research directions using primary and secondary data to investigate the early-life characteristics of population groups at risk of future multimorbidity, as well as policy directions to target public health prevention scenarios of early-onset multimorbidity.

Treatment of missing data in Bayesian network structure learning: an application to linked biomedical and social survey data

BACKGROUND

Availability of linked biomedical and social science data has risen dramatically in past decades, facilitating holistic and systems-based analyses. Among these, Bayesian networks have great potential to tackle complex interdisciplinary problems, because they can easily model inter-relations between variables. They work by encoding conditional independence relationships discovered via advanced inference algorithms. One challenge is dealing with missing data, ubiquitous in survey or biomedical datasets. Missing data is rarely addressed in an advanced way in Bayesian networks; the most common approach is to discard all samples containing missing measurements. This can lead to biased estimates. Here, we examine how Bayesian network structure learning can incorporate missing data.

METHODS

We use a simulation approach to compare a commonly used method in frequentist statistics, multiple imputation by chained equations (MICE), with one specific for Bayesian network learning, structural expectation-maximization (SEM). We simulate multiple incomplete categorical (discrete) data sets with different missingness mechanisms, variable numbers, data amount, and missingness proportions. We evaluate performance of MICE and SEM in capturing network structure. We then apply SEM combined with community analysis to a real-world dataset of linked biomedical and social data to investigate associations between socio-demographic factors and multiple chronic conditions in the US elderly population.

RESULTS

We find that applying either method (MICE or SEM) provides better structure recovery than doing nothing, and SEM in general outperforms MICE. This finding is robust across missingness mechanisms, variable numbers, data amount and missingness proportions. We also find that imputed data from SEM is more accurate than from MICE. Our real-world application recovers known inter-relationships among socio-demographic factors and common multimorbidities. This network analysis also highlights potential areas of investigation, such as links between cancer and cognitive impairment and disconnect between self-assessed memory decline and standard cognitive impairment measurement.

CONCLUSION

Our simulation results suggest taking advantage of the additional information provided by network structure during SEM improves the performance of Bayesian networks; this might be especially useful for social science and other interdisciplinary analyses. Our case study show that comorbidities of different diseases interact with each other and are closely associated with socio-demographic factors.

Associations Among Multimorbid Conditions in Hospitalized Middle-aged and Older Adults in China: Statistical Analysis of Medical Records

BACKGROUND

Multimorbidity has become a new challenge for medical systems and public health policy. Understanding the patterns of and associations among multimorbid conditions should be given priority. It may assist with the early detection of multimorbidity and thus improve quality of life in older adults.

OBJECTIVE

This study aims to comprehensively analyze and compare associations among multimorbid conditions by age and sex in a large number of middle-aged and older Chinese adults.

METHODS

Data from the home pages of inpatient medical records in the Shenzhen National Health Information Platform were evaluated. From January 1, 2017, to December 31, 2018, inpatients aged 50 years and older who had been diagnosed with at least one of 40 conditions were included in this study. Their demographic characteristics (age and sex) and inpatient diagnoses were extracted. Association rule mining, Chi-square tests, and decision tree analyses were combined to identify associations between multiple chronic conditions.

RESULTS

In total, 306,264 hospitalized cases with available information on related chronic conditions were included in this study. The prevalence of multimorbidity in the overall population was 76.46%. The combined results of the 3 analyses showed that, in patients aged 50 years to 64 years, lipoprotein metabolism disorder tended to be comorbid with multiple chronic conditions. Gout and lipoprotein metabolism disorder had the strongest association. Among patients aged 65 years or older, there were strong associations between cerebrovascular disease, heart disease, lipoprotein metabolism disorder, and peripheral vascular disease. The strongest associations were observed between senile cataract and glaucoma in men and women. In particular, the association between osteoporosis and malignant tumor was only observed in middle-aged and older men, while the association between anemia and chronic kidney disease was only observed in older women.

CONCLUSIONS

Multimorbidity was prevalent among middle-aged and older Chinese individuals. The results of this comprehensive analysis of 4 age-sex subgroups suggested that associations between particular conditions within the sex and age groups occurred more frequently than expected by random chance. This provides evidence for further research on disease clusters and for health care providers to develop different strategies based on age and sex to improve the early identification and treatment of multimorbidity.

12-year evolution of multimorbidity patterns among older adults based on Hidden Markov Models

BACKGROUND

The evolution of multimorbidity patterns during aging is still an under-researched area. We lack evidence concerning the time spent by older adults within one same multimorbidity pattern, and their transitional probability across different patterns when further chronic diseases arise. The aim of this study is to fill this gap by exploring multimorbidity patterns across decades of age in older adults, and longitudinal dynamics among these patterns.

METHODS

Longitudinal study based on the Swedish National study on Aging and Care in Kungsholmen (SNAC-K) on adults ≥60 years (N=3,363). Hidden Markov Models were applied to model the temporal evolution of both multimorbidity patterns and individuals' transitions over a 12-year follow-up.

FINDINGS

Within the study population (mean age 76.1 years, 66.6% female), 87.2% had ≥2 chronic conditions at baseline. Four longitudinal multimorbidity patterns were identified for each decade. Individuals in all decades showed the shortest permanence time in an Unspecific pattern lacking any overrepresented diseases (range: 4.6-10.9 years), but the pattern with the longest permanence time varied by age. Sexagenarians remained longest in the Psychiatric-endocrine and sensorial pattern (15.4 years); septuagenarians in the Neuro-vascular and skin-sensorial pattern (11.0 years); and octogenarians and beyond in the Neuro-sensorial pattern (8.9 years). Transition probabilities varied across decades, sexagenarians showing the highest levels of stability.

INTERPRETATION

Our findings highlight the dynamism and heterogeneity underlying multimorbidity by quantifying the varying permanence times and transition probabilities across patterns in different decades. With increasing age, older adults experience decreasing stability and progressively shorter permanence time within one same multimorbidity pattern.

Understanding multimorbidity trajectories in Scotland using sequence analysis

Understanding how multiple conditions develop over time is of growing interest, but there is currently limited methodological development on the topic, especially in understanding how multimorbidity (the co-existence of at least two chronic conditions) develops longitudinally and in which order diseases occur. We aim to describe how a longitudinal method, sequence analysis, can be used to understand the sequencing of common chronic diseases that lead to multimorbidity and the socio-demographic factors and health outcomes associated with typical disease trajectories. We use the Scottish Longitudinal Study (SLS) linking the Scottish census 2001 to disease registries, hospitalisation and mortality records. SLS participants aged 40–74 years at baseline were followed over a 10-year period (2001–2011) for the onset of three commonly occurring diseases: diabetes, cardiovascular disease (CVD), and cancer. We focused on participants who transitioned to at least two of these conditions over the follow-up period (N = 6300). We use sequence analysis with optimal matching and hierarchical cluster analysis to understand the process of disease sequencing and to distinguish typical multimorbidity trajectories. Socio-demographic differences between specific disease trajectories were evaluated using multinomial logistic regression. Poisson and Cox regressions were used to assess differences in hospitalisation and mortality outcomes between typical trajectories. Individuals who transitioned to multimorbidity over 10 years were more likely to be older and living in more deprived areas than the rest of the population. We found seven typical trajectories: later fast transition to multimorbidity, CVD start with slow transition to multimorbidity, cancer start with slow transition to multimorbidity, diabetes start with slow transition to multimorbidity, fast transition to both diabetes and CVD, fast transition to multimorbidity and death, fast transition to both cancer and CVD. Those who quickly transitioned to multimorbidity and death were the most vulnerable, typically older, less educated, and more likely to live in more deprived areas. They also experienced higher number of hospitalisations and overnight stays while still alive. Sequence analysis can strengthen our understanding of typical disease trajectories when considering a few key diseases. This may have implications for more active clinical review of patients beginning quick transition trajectories.

Does group-based trajectory modeling estimate spurious trajectories?

Background

Group-based trajectory modelling (GBTM) is increasingly used to identify subgroups of individuals with similar patterns. In this paper, we use simulated and real-life data to illustrate that GBTM is susceptible to generating spurious findings in some circumstances.

Methods

Six plausible scenarios, two of which mimicked published analyses, were simulated. Models with 1 to 10 trajectory subgroups were estimated and the model that minimized the Bayes criterion was selected. For each scenario, we assessed whether the method identified the correct number of trajectories, the correct shapes of the trajectories, and the mean number of participants of each trajectory subgroup. The performance of the average posterior probabilities, relative entropy and mismatch criteria to assess classification adequacy were compared.

Results

Among the six scenarios, the correct number of trajectories was identified in two, the correct shapes in four and the mean number of participants of each trajectory subgroup in only one. Relative entropy and mismatch outperformed the average posterior probability in detecting spurious trajectories.

Conclusion

Researchers should be aware that GBTM can generate spurious findings, especially when the average posterior probability is used as the sole criterion to evaluate model fit. Several model adequacy criteria should be used to assess classification adequacy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01622-9.

Multimorbidity

Multimorbidity (two or more coexisting conditions in an individual) is a growing global challenge with substantial effects on individuals, carers and society. Multimorbidity occurs a decade earlier in socioeconomically deprived communities and is associated with premature death, poorer function and quality of life and increased health-care utilization. Mechanisms underlying the development of multimorbidity are complex, interrelated and multilevel, but are related to ageing and underlying biological mechanisms and broader determinants of health such as socioeconomic deprivation. Little is known about prevention of multimorbidity, but focusing on psychosocial and behavioural factors, particularly population level interventions and structural changes, is likely to be beneficial. Most clinical practice guidelines and health-care training and delivery focus on single diseases, leading to care that is sometimes inadequate and potentially harmful. Multimorbidity requires person-centred care, prioritizing what matters most to the individual and the individual's carers, ensuring care that is effectively coordinated and minimally disruptive, and aligns with the patient's values. Interventions are likely to be complex and multifaceted. Although an increasing number of studies have examined multimorbidity interventions, there is still limited evidence to support any approach. Greater investment in multimorbidity research and training along with reconfiguration of health care supporting the management of multimorbidity is urgently needed.

Multimorbidity and mortality: A data science perspective

Background

With multimorbidity becoming the norm rather than the exception, the management of multiple chronic diseases is a major challenge facing healthcare systems worldwide.

Methods

Using a large, nationally representative database of electronic medical records from the United Kingdom spanning the years 2005–2016 and consisting over 4.5 million patients, we apply statistical methods and network analysis to identify comorbid pairs and triads of diseases and identify clusters of chronic conditions across different demographic groups. Unlike many previous studies, which generally adopt cross-sectional designs based on single snapshots of closed cohorts, we adopt a longitudinal approach to examine temporal changes in the patterns of multimorbidity. In addition, we perform survival analysis to examine the impact of multimorbidity on mortality.

Results

The proportion of the population with multimorbidity has increased by approximately 2.5 percentage points over the last decade, with more than 17% having at least two chronic morbidities. We find that the prevalence and the severity of multimorbidity, as quantified by the number of co-occurring chronic conditions, increase progressively with age. Stratifying by socioeconomic status, we find that people living in more deprived areas are more likely to be multimorbid compared to those living in more affluent areas at all ages. The same trend holds consistently for all years in our data. In general, hypertension, diabetes, and respiratory-related diseases demonstrate high in-degree centrality and eigencentrality, while cardiac disorders show high out-degree centrality.

Conclusions

We use data-driven methods to characterize multimorbidity patterns in different demographic groups and their evolution over the past decade. In addition to a number of strongly associated comorbid pairs (e.g., cardiac-vascular and cardiac-metabolic disorders), we identify three principal clusters: a respiratory cluster, a cardiovascular cluster, and a mixed cardiovascular-renal-metabolic cluster. These are supported by established pathophysiological mechanisms and shared risk factors, and largely confirm and expand on the results of existing studies in the medical literature. Our findings contribute to a more quantitative understanding of the epidemiology of multimorbidity, an important pre-requisite for developing more effective medical care and policy for multimorbid patients.

Multimorbidity matters in low and middle-income countries

Multimorbidity is a complex challenge affecting individuals, families, caregivers, and health systems worldwide. The burden of multimorbidity is remarkable in low- and middle-income countries (LMICs) given the many existing challenges in these settings. Investigating multimorbidity in LMICs poses many challenges including the different conditions studied, and the restriction of data sources to relatively few countries, limiting comparability and representativeness. This has led to a paucity of evidence on multimorbidity prevalence and trends, disease clusters, and health outcomes, particularly longitudinal outcomes. In this paper, based on our experience of investigating multimorbidity in LMICs contexts, we discuss how the structure of the health system does not favor addressing multimorbidity, and how this is amplified by social and economic disparities and, more recently, by the COVID-19 pandemic. We argue that generating epidemiologic data around multimorbidity with similar methods and definition is essential to improve comparability, guide clinical decision-making and inform policies, research priorities, and local responses. We call for action on policy to refinance and prioritize primary care and integrated care as the center of multimorbidity.

Life.course digital T.wins – I.ntelligent M.onitoring for E.arly and continuous intervention and prevention (LifeTIME): Proposal for a proof-of-concept study (Preprint)

Background

Multimorbidity, which is associated with significant negative outcomes for individuals and health care systems, is increasing in the United Kingdom. However, there is a lack of knowledge about the risk factors (including health, behavior, and environment) for multimorbidity over time. An interdisciplinary approach is essential, as data science, artificial intelligence, and engineering concepts (digital twins) can identify key risk factors throughout the life course, potentially enabling personalized simulation of life-course risk for the development of multimorbidity. Predicting the risk of developing clusters of health conditions before they occur would add clinical value by enabling targeted early preventive interventions, advancing personalized care to improve outcomes, and reducing the burden on health care systems.

Objective

This study aims to identify key risk factors that predict multimorbidity throughout the life course by developing an intelligent agent using digital twins so that early interventions can be delivered to improve health outcomes. The objectives of this study are to identify key predictors of lifetime risk of multimorbidity, create a series of simulated computational digital twins that predict risk levels for specific clusters of factors, and test the feasibility of the system.

Methods

This study will use machine learning to develop digital twins by identifying key risk factors throughout the life course that predict the risk of later multimorbidity. The first stage of the development will be the training of a base predictive model. Data from the National Child Development Study, the North West London Integrated Care Record, the Clinical Practice Research Datalink, and Cerner’s Real World Data will be split into subsets for training and validation, which will be done following the k-fold cross-validation procedure and assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST). In addition, 2 data sets—the Early-Life Data Cross-linkage in Research study and the Children and Young People’s Health Partnership randomized controlled trial—will be used to develop a series of digital twin personas that simulate clusters of factors to predict different risk levels of developing multimorbidity.

Results

The expected results are a validated model, a series of digital twin personas, and a proof-of-concept assessment.

Conclusions

Digital twins could provide an individualized early warning system that predicts the risk of future health conditions and recommends the most effective intervention to minimize that risk. These insights could significantly improve an individual’s quality of life and healthy life expectancy and reduce population-level health burdens.

International Registered Report Identifier (IRRID)

PRR1-10.2196/35738