Distinct trajectories of multimorbidity in primary care were identified using latent class growth analysis

Association of physical activity trajectories over 8 years and risk of knee replacement: data from the osteoarthritis initiative

BACKGROUND

To identify physical activity (PA) trajectories in adults with or at risk of knee osteoarthritis and to evaluate the association of PA trajectories with incident knee replacement (KR).

METHODS

This study used data from the Osteoarthritis Initiative. The Physical Activity Scale for the Elderly and the KR were assessed annually from baseline to 9 years. Individuals were included if they did not undergo KR surgery at baseline and had data on PA at ≥ 1 visit before KR. Latent class growth mixture Modeling was used to identify the optimal trajectories of PA before KR. Log-binomial regression models were used to assess the association between PA trajectories and the risk of KR. Data analyses were conducted in all individuals and those with radiographic osteoarthritis (ROA) and significant knee pain (Western Ontario and McMaster Osteoarthritis Index pain score of ≥ 5 on a 0-20 scale) at baseline, respectively.

RESULTS

Of 4731 participants (mean age 61.1 years, 58.5% female), four distinct and slightly declined PA trajectories were identified. Compared to individuals with a "Low" PA trajectory, those with "Medium-low", "Medium-high", or "High" PA trajectories were not significantly associated with the risk of KR (risk ratios: 0.97-1.19, all p > 0.05). Similar PA trajectories and associations with the risk of KR were observed in the subgroups of individuals with radiographic osteoarthritis and those with significant knee pain at baseline, respectively.

CONCLUSION

In participants with or at risk of knee osteoarthritis, PA slightly declines over time and may play no role in the risk of KR.

Trajectories in long-term condition accumulation and mortality in older adults: a group-based trajectory modelling approach using the English Longitudinal Study of Ageing

OBJECTIVES

To classify older adults into clusters based on accumulating long-term conditions (LTC) as trajectories, characterise clusters and quantify their associations with all-cause mortality.

DESIGN

We conducted a longitudinal study using the English Longitudinal Study of Ageing over 9 years (n=15 091 aged 50 years and older). Group-based trajectory modelling was used to classify people into clusters based on accumulating LTC over time. Derived clusters were used to quantify the associations between trajectory memberships, sociodemographic characteristics and all-cause mortality by conducting regression models.

RESULTS

Five distinct clusters of accumulating LTC trajectories were identified and characterised as: 'no LTC' (18.57%), 'single LTC' (31.21%), 'evolving multimorbidity' (25.82%), 'moderate multimorbidity' (17.12%) and 'high multimorbidity' (7.27%). Increasing age was consistently associated with a larger number of LTCs. Ethnic minorities (adjusted OR=2.04; 95% CI 1.40 to 3.00) were associated with the 'high multimorbidity' cluster. Higher education and paid employment were associated with a lower likelihood of progression over time towards an increased number of LTCs. All the clusters had higher all-cause mortality than the 'no LTC' cluster.

CONCLUSIONS

The development of multimorbidity in the number of conditions over time follows distinct trajectories. These are determined by non-modifiable (age, ethnicity) and modifiable factors (education and employment). Stratifying risk through clustering will enable practitioners to identify older adults with a higher likelihood of worsening LTC over time to tailor effective interventions to prevent mortality.

Twenty-year trajectories of morbidity in individuals with and without osteoarthritis

OBJECTIVES

To identify multimorbidity trajectories over 20 years among incident osteoarthritis (OA) individuals and OA-free matched references.

METHODS

Cohort study using prospectively collected healthcare data from the Skåne region, Sweden (~1.4 million residents). We extracted diagnoses for OA and 67 common chronic conditions. We included individuals aged 40+ years on 31 December 2007, with incident OA between 2008 and 2009. We selected references without OA, matched on birth year, sex, and year of death or moving outside the region. We employed group-based trajectory modelling to capture morbidity count trajectories from 1998 to 2019. Individuals without any comorbidity were included as a reference group but were not included in the model.

RESULTS

We identified 9846 OA cases (mean age: 65.9 (SD 11.7), female: 58%) and 9846 matched references. Among both cases and references, 1296 individuals did not develop chronic conditions (no-chronic-condition class). We identified four classes. At the study outset, all classes exhibited a low average number of chronic conditions (≤1). Class 1 had the slowest progression towards multimorbidity, which increased progressively in each class. Class 1 had the lowest count of chronic conditions at the end of the follow-up (mean: 2.9 (SD 1.7)), while class 4 had the highest (9.6 (2.6)). The presence of OA was associated with a 1.29 (1.12, 1.48) adjusted relative risk of belonging to class 1 up to 2.45 (2.12, 2.83) for class 4.

CONCLUSIONS

Our findings suggest that individuals with OA face an almost threefold higher risk of developing severe multimorbidity.

Factors associated with trajectories of bone marrow lesions over 4 years: data from the Osteoarthritis Initiative

OBJECTIVE

To identify bone marrow lesion (BML) trajectories over 4 years and their demographic and structural predictors in middle-aged and older adults with or at increased risk of knee osteoarthritis (OA).

METHODS

A total of 614 participants (mean age 61 years, 62% female) from the Osteoarthritis Initiative cohort (OAI) were included. BMLs in 15 anatomical locations of the knee were measured annually from baseline to 4 years using the Magnetic Resonance Imaging Osteoarthritis Knee Score (MOAKS) method. BML trajectories were determined using latent class mixed models (LCMMs). Multinomial logistic regression was used to examine baseline characteristics that predicted BML trajectories.

RESULTS

Three distinct BML trajectories were identified: "Mild-stable BMLs" (25.9%), "Moderate-stable BMLs" (66.4%), and "Rapid-rise BMLs" (7.7%). Compared to the "Mild-stable BMLs" trajectory, current smokers were more likely to be in the "Moderate-stable BMLs" (odds ratio [OR] 2.089, P < 0.001) and "Rapid-rise" (OR 2.462, P < 0.001) trajectories. Moreover, female sex and meniscal tears were associated with an increased risk of being in the "Rapid-rise BMLs" trajectory (OR 2.023 to 2.504, P < 0.05). Participants who had higher education levels and drank more alcohol were more likely to be in the "Rapid-rise BMLs" trajectory (OR 1.624 to 3.178, P < 0.05) and less likely to be in the "Moderate-stable BMLs" trajectory (OR 0.668 to 0.674, P < 0.05).

CONCLUSIONS

During the 4-year follow-up, most participants had relatively stable BMLs, few had enlarged BMLs, and no trajectory of decreased BMLs was identified. Sociodemographic factors, lifestyle, and knee structural pathology play roles in predicting distinct BML trajectories.

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.

Exposure to early-life adversity and long-term trajectories of multimorbidity among older adults in China: analysis of longitudinal data from the China Health and Retirement Longitudinal Study

OBJECTIVES

This study aimed to identify long-term distinct trajectories of multimorbidity with ageing from 50 to 85 years among Chinese older adults and examine the relationship between exposure to early-life adversity (ELA; including specific types of adversity and accumulation of different adversities) and these long-term multimorbidity trajectories.

DESIGN

The group-based trajectory models identified long-term multimorbidity trajectories. Multinomial logistic regression models were used to examine the relationship between ELA and the identified multimorbidity trajectories.

SETTING

This study used data from the China Health and Retirement Longitudinal Study (CHARLS, 2011-2018) and the 2014 Life History Survey.

PARTICIPANTS

We used data from 9112 respondents (aged 60 and above) of the 2018 wave of CHARLS.

OUTCOME MEASURES

Each respondent's history of chronic conditions and experiences of ELA were collected from the 2011-2018 waves of CHARLS and the 2014 Life History Survey.

RESULTS

Four heterogeneous long-term trajectories of multimorbidity development were identified: 'maintaining-low' (19.1%), 'low onset-rapidly increasing' (23.3%), 'middle onset-moderately increasing' (41.5%) and 'chronically-high' (16.2%). Our findings indicated that the heterogeneity can be explained by ELA experiences. Across various types of different ELA experiences, exposure to food insufficiency (relative risk ratios from 1.372 (95% CI 1.190 to 1.582) to 1.780 (95% CI 1.472 to 2.152)) and parental quarrel/divorce (relative risk ratios from 1.181 (95% CI 1.000 to 1.394) to 1.262 (95% CI 1.038 to 1.536)) had the most prominent associations with health deterioration. The accumulation of more different ELA experiences was associated with a higher relative risk of developing more severe multimorbidity trajectories (relative risk ratio for five to seven ELAs and chronically high trajectory: 7.555, 95% CI 4.993 to 11.431).

CONCLUSIONS

There are heterogeneous long-term trajectories of multimorbidity in Chinese older adults, and the risk of multimorbidity associated with ELA accumulates over the lifespan. Our findings highlight the role of a supportive early-life family environment in promoting health development across the lifespan, advocating for the integration of life-course approaches to implementing health disparity interventions.

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.

Multimorbidity trajectories in early adulthood and middle age: Findings from the CARDIA prospective cohort study

Background

Multimorbidity research has focused on the prevalence and consequences of multimorbidity in older populations. Less is known about the accumulation of chronic conditions earlier in the life course.

Methods

We identified patterns of longitudinal multimorbidity accumulation using 30 years of data from in-person exams, annual follow-ups, and adjudicated end-points among 4,945 participants of the Coronary Artery Risk Development in Young Adults (CARDIA) study. Chronic conditions included arthritis, asthma, atrial fibrillation, cancer, end stage renal disease, chronic obstructive pulmonary disease, coronary heart disease, diabetes, heart failure, hyperlipidemia, hypertension, and stroke. Trajectory patterns were identified using latent class growth curve models.

Results

Mean age (SD) at baseline (1985-6) was 24.9 (3.6), 55% were female, and 51% were Black. The median follow-up was 30 years (interquartile range 25-30). We identified six trajectory classes characterized by when conditions began to accumulate and the rapidity of accumulation: (1) early-fifties, slow, (2) mid-forties, fast, (3) mid-thirties, fast, (4) late-twenties, slow, (5) mid-twenties, slow, and (6) mid-twenties, fast. Compared with participants in the early-fifties, slow trajectory class, participants in mid-twenties, fast were more likely to be female, Black, and currently smoking and had a higher baseline mean waist circumference (83.6 vs. 75.6 cm) and BMI (27.0 vs. 23.4 kg/m2) and lower baseline physical activity (414.1 vs. 442.4 exercise units).

Conclusions

A life course approach that recognizes the heterogeneity in patterns of accumulation of chronic conditions from early adulthood into middle age could be helpful for identifying high risk subgroups and developing approaches to delay multimorbidity progression.

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.

Complexity in Epidemiology and Public Health. Addressing Complex Health Problems Through a Mix of Epidemiologic Methods and Data

Public health and the underlying disease processes are complex, often involving the interaction of biologic, social, psychologic, economic, and other processes that may be nonlinear and adaptive and have other features of complex systems. There is therefore a need to push the boundaries of public health beyond single-factor data analysis and expand the capacity of research methodology to tackle real-world complexities. This article sets out a way to operationalize complex systems thinking in public health, with a particular focus on how epidemiologic methods and data can contribute towards this end. Our proposed framework comprises three core dimensions-patterns, mechanisms, and dynamics-along which complex systems may be conceptualized. These dimensions cover seven key features of complex systems-emergence, interactions, nonlinearity, interference, feedback loops, adaptation, and evolution. We relate this framework to examples of methods and data traditionally used in epidemiology. We conclude that systematic production of knowledge on complex health issues may benefit from: formulation of research questions and programs in terms of the core dimensions we identify, as a comprehensive way to capture crucial features of complex systems; integration of traditional epidemiologic methods with systems methodology such as computational simulation modeling; interdisciplinary work; and continued investment in a wide range of data types. We believe that the proposed framework can support the systematic production of knowledge on complex health problems, with the use of epidemiology and other disciplines. This will help us understand emergent health phenomena, identify vulnerable population groups, and detect leverage points for promoting public health.

Maternal weight latent trajectories and associations with adverse pregnancy outcomes using a smoothing mixture model

Class membership is a critical issue in health data sciences. Different types of statistical models have been widely applied to identify participants within a population with heterogeneous longitudinal trajectories. This study aims to identify latent longitudinal trajectories of maternal weight associated with adverse pregnancy outcomes using smoothing mixture model (SMM). Data were collected from the Khuzestan Vitamin D Deficiency Screening Program in Pregnancy. We applied the data of 877 pregnant women living in Shooshtar city, whose weights during the nine months of pregnancy were available. In the first step, maternal weight was classified and participants were assigned to only one group for which the estimated trajectory is the most similar to the observed one using SMM; then, we examined the associations of identified trajectories with risk of adverse pregnancy endpoints by applying logistic regression. Three latent trajectories for maternal weight during pregnancy were identified and named as low, medium and high weight trajectories. Crude estimated odds ratio (OR) for icterus, preterm delivery, NICU admission and composite neonatal events shows significantly higher risks in trajectory 1 (low weight) compared to trajectory 2 (medium weight) by 69% (OR = 1.69, 95%CI 1.20, 2.39), 82% (OR = 1.82, 95%CI 1.14, 2.87), 77% (OR = 1.77, 95%CI 1.17, 2.43), and 85% (OR = 1.85, 95%CI 1.38, 2.76), respectively. Latent class trajectories of maternal weights can be accurately estimated using SMM. It is a powerful means for researchers to appropriately assign individuals to their class. The U-shaped curve of association between maternal weight gain and risk of maternal complications reveals that the optimum place for pregnant women could be in the middle of the growth curve to minimize the risks. Low maternal weight trajectory compared to high had even a significantly higher hazard for some neonatal adverse events. Therefore, appropriate weight gain is critical for pregnant women.Trial registration International Standard Randomized Controlled Trial Number (ISRCTN): 2014102519660N1; http://www.irct.ir/searchresult.php?keyword=&id=19660&number=1&prt=7805&total=10&m=1 (Archived by WebCite at http://www.webcitation.org/6p3lkqFdV ).

Racial, ethnic, and socioeconomic disparities in trajectories of morbidity accumulation among older Americans

Introduction

Multimorbidity, the presence of multiple chronic health conditions, generally starts in middle and older age but there is considerable heterogeneity in the trajectory of morbidity accumulation. This study aimed to clarify the number of distinct trajectories and the potential associations between race/ethnicity and socioeconomic status and these trajectories.

Methods

Data from 13,699 respondents (age ≥51) in the Health and Retirement Study between 1998 and 2016 were analyzed with growth mixture models. Nine prevalent self-reported morbidities (arthritis, cancer, cognitive impairment, depressive symptoms, diabetes, heart disease, hypertension, lung disease, stroke) were summed for the morbidity count.

Results

Three trajectories of morbidity accumulation were identified: low [starting with few morbidities and accumulating them slowly (i.e., low intercept and low slope); 80% of sample], increasing (i.e., low intercept and high slope; 9%), and high (i.e., high intercept and low slope; 11%). Compared to non-Hispanic (NH) White adults in covariate-adjusted models, NH Black adults had disadvantages while Hispanic adults had advantages. Our results suggest a protective effect of education for NH Black adults (i.e., racial health disparities observed at low education were ameliorated and then eliminated at increasing levels of education) and a reverse pattern for Hispanic adults (i.e., increasing levels of education was found to dampen the advantages Hispanic adults had at low education). Compared with NH White adults, higher levels of wealth were protective for both NH Black adults (i.e., reducing or reversing racial health disparities observed at low wealth) and Hispanic adults (i.e., increasing the initial health advantages observed at low wealth).

Conclusion

These findings have implications for addressing health disparities through more precise targeting of public health interventions. This work highlights the imperative to address socioeconomic inequalities that interact with race/ethnicity in complex ways to erode health.

Trajectories of multiple long-term conditions and mortality in older adults: A retrospective cohort study using English Longitudinal Study of Ageing (ELSA)

Objectives

To classify older adults with MLTC into clusters based on accumulating conditions as trajectories over time, characterise clusters and quantify associations between derived clusters and all-cause mortality.

Design

We conducted a retrospective cohort study using the English Longitudinal Study of Ageing (ELSA) over nine years (n=15,091 aged 50 years and older). Group-based trajectory modelling was used to classify people into MLTC clusters based on accumulating conditions over time. Derived clusters were used to quantify the associations between MLTC trajectory memberships, sociodemographic characteristics, and all-cause mortality.

Results

Five distinct clusters of MLTC trajectories were identified and characterised as: "no-LTC" (18.57%), "single-LTC" (31.21%), "evolving MLTC" (25.82%), "moderate MLTC" (17.12%), and "high MLTC" (7.27%). Increasing age was consistently associated with an increased number of MLTC. Female sex (aOR = 1.13; 95%CI 1.01 to 1.27) and ethnic minority (aOR = 2.04; 95%CI 1.40 to 3.00) were associated with the "moderate MLTC" and "high MLTC" clusters, respectively. Higher education and paid employment were associated with a lower likelihood of progression over time towards an increased number of MLTC. All the clusters had higher all-cause mortality than the "no-LTC" cluster.

Conclusions

The development of MLTC and the increase in the number of conditions over time follow distinct trajectories. These are determined by non-modifiable (age, sex, ethnicity) and modifiable factors (education and employment). Stratifying risk through clustering will enable practitioners to identify older adults with a higher likelihood of worsening MLTC over time to tailor effective interventions.

Clinical response trajectories and drug persistence in systemic lupus erythematosus patients on belimumab treatment: A real-life, multicentre observational study

Objective

To obtain real-world data on outcomes of belimumab treatment and respective prognostic factors in patients with systemic lupus erythematosus (SLE).

Methods

Observational study of 188 active SLE patients (median disease duration 6.2 years, two previous immunosuppressive/biological agents) treated with belimumab, who were monitored for SLEDAI-2K, Physician Global Assessment (PGA), LLDAS (lupus low disease activity state), remission (DORIS/Padua definitions), SELENA-SLEDAI Flare Index, SLICC/ACR damage index and treatment discontinuations. Group-based disease activity trajectories were modelled followed by multinomial regression for predictive variables. Drug survival was analysed by Cox-regression.

Results

At 6, 12 and 24 months, LLDAS was attained by 36.2%, 36.7% and 33.5%, DORIS-remission by 12.3%, 11.6% and 17.8%, and Padua-remission by 21.3%, 17.9% and 29.0%, respectively (attrition-corrected). Trajectory analysis of activity indices classified patients into complete (25.5%), partial (42.0%) and non-responder (32.4%) groups, which were predicted by baseline PGA, inflammatory rash, leukopenia and prior use of mycophenolate. During median follow-up of 15 months, efficacy-related discontinuations occurred in 31.4% of the cohort, especially in patients with higher baseline PGA (hazard ratio [HR] 2.78 per 1-unit; 95% CI 1.32-5.85). Conversely, PGA improvement at 3 months predicted longer drug retention (HR 0.57; 95% CI 0.33-0.97). Use of hydroxychloroquine was associated with lower risk for safety-related drug discontinuation (HR 0.33; 95% CI 0.13-0.85). Although severe flares were reduced, flares were not uncommon (58.0%) and contributed to treatment stops (odds ratio [OR] 1.73 per major flare; 95% CI 1.09-2.75) and damage accrual (OR 1.83 per mild/moderate flare; 95% CI 1.15-2.93).

Conclusions

In a real-life setting with predominant long-standing SLE, belimumab was effective in the majority of patients, facilitating the achievement of therapeutic targets. Monitoring PGA helps to identify patients who will likely benefit and stay on the treatment. Vigilance is required for the prevention and management of flares while on belimumab.

Comorbidity clusters associated with newly treated type 2 diabetes mellitus: a Bayesian nonparametric analysis

Type 2 diabetes mellitus (T2DM) is associated with the development of chronic comorbidities, which can lead to high drug utilization and adverse events. We aimed to identify common comorbidity clusters and explore the progression over time in newly treated T2DM patients. The IQVIA Medical Research Data incorporating data from THIN, a Cegedim database of anonymized electronic health records, was used to identify all patients with a first-ever prescription for a non-insulin antidiabetic drug (NIAD) between January 2006 and December 2019. We selected 58 chronic comorbidities of interest and used Bayesian nonparametric models to identify disease clusters and model their progression over time. Among the 175,383 eligible T2DM patients, we identified the 20 most frequent comorbidity clusters, which were comprised of 14 latent features (LFs). Each LF was associated with a primary disease (e.g., 98% of patients in cluster 2, characterized by LF2, had congestive heart failure [CHF]). The presence of certain LFs increased the probability of having another LF active. For example, LF2 (CHF) frequently appeared with LFs related to chronic kidney disease (CKD). Over time, the clusters associated with cardiovascular diseases, such as CHF, progressed rapidly. Moreover, the onset of certain diseases led to further complications. Our models identified established T2DM complications and previously unknown connections, thus, highlighting the potential for Bayesian nonparametric models to characterize complex comorbidity patterns.

Multimorbidity burden and developmental trajectory in relation to later‐life dementia: A prospective study

INTRODUCTION

This study assessed the associations of multimorbidity burden and its developmental trajectory with later-life dementia.

METHODS

Among 5923 Health and Retirement Study participants, major chronic conditions including hypertension, diabetes mellitus, cancer, lung diseases, heart disease, stroke, psychological disorders, and arthritis were self- or proxy-reported in 1994-2008. Dementia diagnosis was self- or proxy-reported in 2008-2018. We used Cox regression to assess the associations of multimorbidity with incident dementia.

RESULTS

During follow-up (median = 8 years), 701 participants developed dementia. Each additional chronic condition in 2008 was related to 15% (confidence interval: 9% to 22%) higher hazard of dementia. Multimorbidity trajectories in 1994-2008 were classified as "rapid growth", "steady growth", "slow growth", and "no new condition" by the group-based trajectory modelling methods. Compared to "no new condition", the "rapid growth" trajectory was related to 32% (3% to 69%) higher dementia risk.

CONCLUSIONS

Both multimorbidity burden and its developmental trajectory were prospectively associated with risk of dementia.

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.

Construction of Life-Cycle Simulation Framework of Chronic Diseases and Their Comorbidities Based on Population Cohort

Life-cycle population follow-up data collection is time-consuming and often takes decades. General cohort data studies collect short-to-medium-term data from populations of different age groups. The purpose of constructing a life-cycle simulation method is to find an efficient and reliable way to achieve the way to characterize life-cycle disease metastasis from these short-to-medium-term data. In this paper, we have presented our effort at construction of a full lifetime population cohort simulation framework. The design aim is to generate a comprehensive understanding of the disease transition for full lifetime when we only have short-or-medium term population cohort data. We have conducted several groups of experiments to show the effectiveness of our method.

Multimorbidity Accumulation Among Middle-Aged Americans: Differences by Race/Ethnicity and Body Mass Index

BACKGROUND

Obesity and multimorbidity are more prevalent among U.S. racial/ethnic minority groups. Evaluating racial/ethnic disparities in disease accumulation according to body mass index (BMI) may guide interventions to reduce multimorbidity burden in vulnerable racial/ethnic groups.

METHOD

We used data from the 1998-2016 Health and Retirement Study on 8 106 participants aged 51-55 at baseline. Disease burden and multimorbidity (≥2 co-occurring diseases) were assessed using 7 chronic diseases: arthritis, cancer, heart disease, diabetes, hypertension, lung disease, and stroke. Four BMI categories were defined per convention: normal, overweight, obese class 1, and obese class 2/3. Generalized estimating equations models with inverse probability weights estimated the accumulation of chronic diseases.

RESULTS

Overweight and obesity were more prevalent in non-Hispanic Black (82.3%) and Hispanic (78.9%) than non-Hispanic White (70.9 %) participants at baseline. The baseline burden of disease was similar across BMI categories, but disease accumulation was faster in the obese class 2/3 and marginally in the obese class 1 categories compared with normal BMI. Black participants across BMI categories had a higher initial burden and faster accumulation of disease over time, while Hispanics had a lower initial burden and similar rate of accumulation, compared with Whites. Black participants, including those with normal BMI, reach the multimorbidity threshold 5-6 years earlier compared with White participants.

CONCLUSIONS

Controlling weight and reducing obesity early in the lifecourse may slow the progression of multimorbidity in later life. Further investigations are needed to identify the factors responsible for the early and progressing nature of multimorbidity in Blacks of nonobese weight.

Charlson Comorbidity Index: A Critical Review of Clinimetric Properties

The present critical review was conducted to evaluate the clinimetric properties of the Charlson Comorbidity Index (CCI), an assessment tool designed specifically to predict long-term mortality, with regard to its reliability, concurrent validity, sensitivity, incremental and predictive validity. The original version of the CCI has been adapted for use with different sources of data, ICD-9 and ICD-10 codes. The inter-rater reliability of the CCI was found to be excellent, with extremely high agreement between self-report and medical charts. The CCI has also been shown either to have concurrent validity with a number of other prognostic scales or to result in concordant predictions. Importantly, the clinimetric sensitivity of the CCI has been demonstrated in a variety of medical conditions, with stepwise increases in the CCI associated with stepwise increases in mortality. The CCI is also characterized by the clinimetric property of incremental validity, whereby adding the CCI to other measures increases the overall predictive accuracy. It has been shown to predict long-term mortality in different clinical populations, including medical, surgical, intensive care unit (ICU), trauma, and cancer patients. It may also predict in-hospital mortality, although in some instances, such as ICU or trauma patients, the CCI did not perform as well as other instruments designed specifically for that purpose. The CCI thus appears to be clinically useful not only to provide a valid assessment of the patient's unique clinical situation, but also to demarcate major diagnostic and prognostic differences among subgroups of patients sharing the same medical diagnosis.

Trajectories of Insomnia in Adults After Traumatic Brain Injury

IMPORTANCE

Insomnia is common after traumatic brain injury (TBI) and contributes to morbidity and long-term sequelae.

OBJECTIVE

To identify unique trajectories of insomnia in the 12 months after TBI.

DESIGN, SETTING, AND PARTICIPANTS

In this prospective cohort study, latent class mixed models (LCMMs) were used to model insomnia trajectories over time and to classify participants into distinct profile groups. Data from the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study, a longitudinal, multisite, observational study, were uploaded to the Federal Interagency Traumatic Brain Injury Repository (FITBIR) database. Participants were enrolled at 1 of 18 participating level I trauma centers and enrolled within 24 hours of TBI injury. Additional data were obtained directly from the TRACK-TBI investigators that will be uploaded to FITBIR in the future. Data were collected from February 26, 2014, to August 8, 2018, and analyzed from July 1, 2020, to November 15, 2021.

EXPOSURES

Traumatic brain injury.

MAIN OUTCOMES AND MEASURES

Insomnia Severity Index assessed serially at 2 weeks and 3, 6, and 12 months thereafter.

RESULTS

The final sample included 2022 participants (1377 [68.1%] men; mean [SD] age, 40.1 [17.2] years) from the FITBIR database and the TRACK-TBI study. The data were best fit by a 5-class LCMM. Of these participants, 1245 (61.6%) reported persistent mild insomnia symptoms (class 1); 627 (31.0%) initially reported mild insomnia symptoms that resolved over time (class 2); 91 (4.5%) reported persistent severe insomnia symptoms (class 3); 44 (2.2%) initially reported severe insomnia symptoms that resolved by 12 months (class 4); and 15 (0.7%) initially reported no insomnia symptoms but had severe symptoms by 12 months (class 5). In a multinomial logistic regression model, several factors significantly associated with insomnia trajectory class membership were identified, including female sex (odds ratio [OR], 1.65 [95% CI, 1.02-2.66]), Black race (OR, 2.36 [95% CI, 1.39-4.01]), history of psychiatric illness (OR, 2.21 [95% CI, 1.35-3.60]), and findings consistent with intracranial injury on computed tomography (OR, 0.36 [95% CI, 0.20-0.65]) when comparing class 3 with class 1.

CONCLUSIONS AND RELEVANCE

These results suggest important heterogeneity in the course of insomnia after TBI in adults. More work is needed to identify outcomes associated with these insomnia trajectory class subgroups and to identify optimal subgroup-specific treatment approaches.

The effect of disease onset chronology on mortality among patients with multimorbidity: A Danish nationwide register study

Background

Multimorbidity is associated with increased mortality. Certain combinations of diseases are known to be more lethal than others, but the limited knowledge of how the chronology in which diseases develop impacts mortality may impair the development of effective clinical interventions for patients with multimorbidity.

Objective

To explore if in multimorbidity the chronology of disease onset is associated with mortality. Design: A prospective nationwide cohort study, including 3,986,209 people aged ≥18 years on 1 January 2000, was performed. We included ten diagnosis groups: lung, musculoskeletal, endocrine, mental, cancer, neurological, gastrointestinal, cardiovascular, kidney, and sensory organs. We defined multimorbidity as the presence of at least two diagnoses from two diagnosis groups (out of ten). To determine mortality, logistic regression models were used to calculate odds ratios (OR) and ratio of ORs (RORs).

Results

For most combinations of multimorbidity, the chronology of disease onset does not change mortality. However, when multimorbidity included mental health diagnoses, mortality was in general higher if the mental health diagnosis appeared first. If multimorbidity included heart and sensory diagnoses, mortality was higher if these developed second. For the majority of multimorbidity combinations, there was excess mortality if multimorbidity was diagnosed simultaneously, rather than consecutively, for example, heart and kidney (3.58 ROR; CI 2.39–5.36), or mental health and musculoskeletal diagnoses (2.38 ROR; CI 1.70–3.32).

Conclusions

Overall, in multimorbidity, the chronology in which diseases develop is not associated with mortality, with few exceptions. For almost all combinations of multimorbidity, diagnoses act synergistically in relation to mortality if diagnosed simultaneously.

The Differential Impact of Multimorbidity Patterns and Subsequent Accumulation on Longitudinal Trajectories of Physical Function Decline in a Population-based Cohort of Older People

BACKGROUND

Although both the patterns and accumulation of multimorbidity are important for predicting physical function, studies have not simultaneously examined their impact on functional decline. This study aimed to associate multimorbidity patterns and subsequently developed conditions with longitudinal trajectories of functional decline, and it tested whether the effects of newly developed conditions on functional decline varied across distinct multimorbidity patterns.

METHODS

We included 6,634 participants aged at least 60 years from the China Health and Retirement Longitudinal Survey. Latent class analysis identified multimorbidity patterns from 14 chronic conditions. Mixed negative binomial models estimated the changes in physical function measured across four waves as a function of multimorbidity patterns, subsequently developed conditions and their interactions.

RESULTS

Five distinct patterns were identified three years before wave 1: stomach/arthritis (15.7%), cardiometabolic (6.7%), arthritis/hypertension (47.9%), hepatorenal/multi-system (18.3%), and lung/asthma (11.4%). The hepatorenal/multi-system and the lung/asthma pattern were associated with worse baseline physical function, and the hypertension/arthritis pattern was associated with greater decline of physical function. The effect of developing new conditions on decline of physical function over time was most evident for individuals from the cardiometabolic pattern.

DISCUSSION

Considering both the combinations and progressive nature of multimorbidity is important for identifying individuals at greater risk of disability. Future studies are warranted to differentiate the factors responsible for the progression of chronic conditions in distinct multimorbidity patterns and investigate the potential implications for improved prediction of functional decline.

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

OBJECTIVES

Multimorbidity-the co-occurrence of at least two chronic diseases in an individual-is an important public health challenge in ageing societies. The vast majority of multimorbidity research takes a cross-sectional approach, but longitudinal approaches to understanding multimorbidity are an emerging research area, being encouraged by multiple funders. To support development in this research area, the aim of this study is to scope the methodological approaches and substantive findings of studies that have investigated longitudinal multimorbidity trajectories.

DESIGN

We conducted a systematic search for relevant studies in four online databases (Medline, Scopus, Web of Science and Embase) in May 2020 using predefined search terms and inclusion and exclusion criteria. The search was complemented by searching reference lists of relevant papers. From the selected studies, we systematically extracted data on study methodology and findings and summarised them in a narrative synthesis.

RESULTS

We identified 35 studies investigating multimorbidity longitudinally, all published in the last decade, and predominantly in high-income countries from the Global North. Longitudinal approaches employed included constructing change variables, multilevel regression analysis (eg, growth curve modelling), longitudinal group-based methodologies (eg, latent class modelling), analysing disease transitions and visualisation techniques. Commonly identified risk factors for multimorbidity onset and progression were older age, higher socioeconomic and area-level deprivation, overweight and poorer health behaviours.

CONCLUSION

The nascent research area employs a diverse range of longitudinal approaches that characterise accumulation and disease combinations and to a lesser extent disease sequencing and progression. Gaps include understanding the long-term, life course determinants of different multimorbidity trajectories, and doing so across diverse populations, including those from low-income and middle-income countries. This can provide a detailed picture of morbidity development, with important implications from a clinical and intervention perspective.

Health trajectories after age 60: the role of individual behaviors and the social context

Background: This study aimed to detect health trajectories after age 60, and to explore to what extent individual and social factors may contribute to healthier aging.

Methods: Twelve-year health trajectories were identified in subjects from the Swedish National Study on Aging and Care in Kungsholmen (N=3108), integrating five indicators of disease, physical and cognitive function, and disability through nominal response models. Growth mixture models were applied to explore health trajectories in terms of rate and pattern of change. Baseline information about health-related behaviors and the social context was collected through standardized questionnaires. The strength of the associations was estimated using logistic regression, and their impact through population attributable fractions (PAF).

Results: Three trajectories were identified grouping 78%, 18%, and 4% of people with respectively increasing rates of health decline. Compared to the best trajectory, subjects in the middle and worst trajectories became functionally dependent 12.0 (95% CI: 11.4-12.6) and 12.1 (95% CI: 11.5-12.7) years earlier, respectively. Insufficient physical activity (OR: 3.38, 95% CI: 2.58-4.42), financial strain (OR: 2.76, 95% CI: 1.77-4.30), <12 years education (OR: 1.53, 95% CI: 1.14-2.04), low social connections (OR: 1.45, 95% CI: 1.09-1.94), low social participation (OR: 1.39, 95% CI: 1.06-1.83) and a body mass index ≥25 (OR: 1.34, 95% CI: 1.03-1.75) were associated with belonging to the middle/worst trajectories. The highest PAFs were observed for insufficient physical activity (27.1%), low education (19.3%) and low social participation (15.9%); a total PAF of 66.1% was obtained.

Conclusions: Addressing the social determinants of health in its broadest sense, complementarily considering life-long factors belonging to the socioeconomic, psychosocial, and behavioral dimensions, should be central to any strategy aimed at fostering health in older age.

Patterns of multimorbidity trajectories and their correlates among Korean older adults

OBJECTIVE

This study aims to identify distinct patterns of 10-year multimorbidity trajectory among Korean older adults and examine factors associated with the patterns.

METHODS

Data were drawn from the six waves of the Korean Longitudinal Study of Ageing (KLoSA, 2006-2016). We examined trajectories of multimorbidity of 1,705 older adults aged 65 and older using Growth Mixture Modeling. Then, the identified patterns were used as dependent variables to examine the correlates of multimorbidity trajectories. Explanatory variables considered were sociodemographic, psychological, health behavioural and interpersonal factors at baseline.

RESULTS

Four distinct patterns of multimorbidity trajectories were identified: 'maintaining-low' (59.4%), 'chronically-high' (7.5%), 'moderately-increasing' (26.0%) and 'rapidly- increasing' (7.1%). Gender, depressive symptoms, life satisfaction and frequency of contacts with others were associated with trajectory membership. Specifically, women were more likely to be in the 'chronically-high' group than any other groups. Compared to the 'maintaining-low' group, those with higher levels of depressive symptoms and lower levels of life satisfaction were more likely to belong to the 'chronically-high' group and 'moderately-increasing' group, respectively. Respondents who had less frequent meetings with others in close relationships were more likely to be in the 'rapidly-increasing' group than the 'maintaining-low' group.

DISCUSSION

These findings are suggestive of distinct trajectories of multimorbidity across older adulthood, indicating that multimorbidity experiences might differ among older adults. Moreover, results suggest that there may be gender inequalities in multimorbidity trajectories, and that levels of psychological well-being and social engagement could be useful in identifying older adults who are at higher risk of worsening multimorbidity.

Racial and Ethnic Differences in Multimorbidity Changes Over Time

BACKGROUND

Our understanding of how multimorbidity progresses and changes is nascent.

OBJECTIVES

Assess multimorbidity changes among racially/ethnically diverse middle-aged and older adults.

DESIGN, SETTING, AND PARTICIPANTS

Prospective cohort study using latent class analysis to identify multimorbidity combinations over 16 years, and multinomial logistic models to assess change relative to baseline class membership. Health and Retirement Study respondents (age 51 y and above) in 1998 and followed through 2014 (N=17,297).

MEASURES

Multimorbidity latent classes of: hypertension, heart disease, lung disease, diabetes, cancer, arthritis, stroke, high depressive symptoms.

RESULTS

Three latent classes were identified in 1998: minimal disease (45.8% of participants), cardiovascular-musculoskeletal (34.6%), cardiovascular-musculoskeletal-mental (19.6%); and 3 in 2014: cardiovascular-musculoskeletal (13%), cardiovascular-musculoskeletal-metabolic (12%), multisystem multimorbidity (15%). Remaining participants were deceased (48%) or lost to follow-up (12%) by 2014. Compared with minimal disease, individuals in cardiovascular-musculoskeletal in 1998 were more likely to be in multisystem multimorbidity in 2014 [odds ratio (OR)=1.78, P<0.001], and individuals in cardiovascular-musculoskeletal-mental in 1998 were more likely to be deceased (OR=2.45, P<0.001) or lost to follow-up (OR=3.08, P<0.001). Hispanic and Black Americans were more likely than White Americans to be in multisystem multimorbidity in 2014 (OR=1.67, P=0.042; OR=2.60, P<0.001, respectively). Black compared with White Americans were more likely to be deceased (OR=1.62, P=0.01) or lost to follow-up (OR=2.11, P<0.001) by 2014.

CONCLUSIONS AND RELEVANCE

Racial/ethnic older adults are more likely to accumulate morbidity and die compared with White peers, and should be the focus of targeted and enhanced efforts to prevent and/or delay progression to more complex multimorbidity patterns.

Glutathione Serum Levels and Rate of Multimorbidity Development in Older Adults

We aimed to investigate the association between baseline levels of total serum glutathione (tGSH) and rate of chronic disease accumulation over time. The study population (n = 2,596) was derived from a population-based longitudinal study on ≥60-year-olds living in Stockholm. Participants were clinically assessed at baseline, 3- and 6-year follow-ups. Multimorbidity was measured as the number of chronic conditions from a previously built list of 60 diseases. Linear mixed models were applied to analyze the association between baseline tGSH levels and the rate of multimorbidity development over 6 years. We found that at baseline, participants with ≥4 diseases had lower tGSH levels than participants with no chronic conditions (3.3 vs 3.6 µmol/L; p < .001). At follow-up, baseline levels of tGSH were inversely associated with the rate of multimorbidity development (β * time: -0.044, p < .001) after adjusting for age, sex, education, levels of serum creatinine, C-reactive protein, albumin, body mass index, smoking, and time of dropout or death. In conclusion, serum levels of tGSH are inversely associated with multimorbidity development; the association exists above and beyond the link between tGSH and specific chronic conditions. Our findings support the hypothesis that tGSH is a biomarker of multisystem dysregulation that eventually leads to multimorbidity.

The incidence of multimorbidity and patterns in accumulation of chronic conditions: A systematic review

Multimorbidity, the presence of 1+ chronic condition in an individual, remains one of the greatest challenges to health on a global scale. Although the prevalence of multimorbidity has been well-established, its incidence is not fully understood. This systematic review determined the incidence of multimorbidity across the lifespan; the order in which chronic conditions accumulate to result in multimorbidity; and cataloged methods used to determine and report accumulation of chronic conditions resulting in multimorbidity. Studies were identified by searching MEDLINE, Embase, CINAHL, and Cochrane electronic databases. Two independent reviewers evaluated studies for inclusion and performed quality assessments. Of 36 included studies, there was high heterogeneity in study design and operational definitions of multimorbidity. Studies reporting incidence (n = 32) reported a median incidence rate of 30.7 per 1,000 person-years (IQR 39.5 per 1,000 person-years) and a median cumulative incidence of 2.8% (IQR 28.7%). Incidence was notably higher for persons with older age and 1+ chronic conditions at baseline. Studies reporting patterns in accumulation of chronic conditions (n = 5) reported hypertensive and heart diseases, and diabetes, as among the common starting conditions resulting in later multimorbidity. Methods used to discern patterns were highly heterogenous, ranging from the use of latent growth trajectories to divisive cluster analyses, and presentation using alluvial plots to cluster trajectories. Studies reporting the incidence of multimorbidity and patterns in accumulation of chronic conditions vary greatly in study designs and definitions used. To allow for more accurate estimations and comparison, studies must be transparent and consistent in operational definitions of multimorbidity applied.

Learning multimorbidity patterns from electronic health records using Non-negative Matrix Factorisation

Multimorbidity, or the presence of several medical conditions in the same individual, has been increasing in the population - both in absolute and relative terms. Nevertheless, multimorbidity remains poorly understood, and the evidence from existing research to describe its burden, determinants and consequences has been limited. Previous studies attempting to understand multimorbidity patterns are often cross-sectional and do not explicitly account for multimorbidity patterns' evolution over time; some of them are based on small datasets and/or use arbitrary and narrow age ranges; and those that employed advanced models, usually lack appropriate benchmarking and validations. In this study, we (1) introduce a novel approach for using Non-negative Matrix Factorisation (NMF) for temporal phenotyping (i.e., simultaneously mining disease clusters and their trajectories); (2) provide quantitative metrics for the evaluation of these clusters and trajectories; and (3) demonstrate how the temporal characteristics of the disease clusters that result from our model can help mine multimorbidity networks and generate new hypotheses for the emergence of various multimorbidity patterns over time. We trained and evaluated our models on one of the world's largest electronic health records (EHR) datasets, containing more than 7 million patients, from which over 2 million where relevant to, and hence included in this study.

Untangling the complexity of multimorbidity with machine learning

The prevalence of multimorbidity has been increasing in recent years, posing a major burden for health care delivery and service. Understanding its determinants and impact is proving to be a challenge yet it offers new opportunities for research to go beyond the study of diseases in isolation. In this paper, we review how the field of machine learning provides many tools for addressing research challenges in multimorbidity. We highlight recent advances in promising methods such as matrix factorisation, deep learning, and topological data analysis and how these can take multimorbidity research beyond cross-sectional, expert-driven or confirmatory approaches to gain a better understanding of evolving patterns of multimorbidity. We discuss the challenges and opportunities of machine learning to identify likely causal links between previously poorly understood disease associations while giving an estimate of the uncertainty on such associations. We finally summarise some of the challenges for wider clinical adoption of machine learning research tools and propose some solutions.

Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models

Objectives

The time-dependent study of comorbidities provides insight into disease progression and trajectory. We hypothesize that understanding longitudinal disease characteristics can lead to more timely intervention and improve clinical outcomes. As a first step, we developed an efficient and easy-to-install toolkit, the Time-based Elixhauser Comorbidity Index (TECI), which pre-calculates time-based Elixhauser comorbidities and can be extended to common data models (CDMs).

Methods

A Structured Query Language (SQL)-based toolkit, TECI, was built to pre-calculate time-specific Elixhauser comorbidity indices using data from a clinical data repository (CDR). Then it was extended to the Informatics for Integrating Biology and the Bedside (I2B2) and Observational Medical Outcomes Partnership (OMOP) CDMs.

Results

At the University of Arkansas for Medical Sciences (UAMS), the TECI toolkit was successfully installed to compute the indices from CDR data, and the scores were integrated into the I2B2 and OMOP CDMs. Comorbidity scores calculated by TECI were validated against: scores available in the 2015 quarter 1–3 Nationwide Readmissions Database (NRD) and scores calculated using the comorbidities using a previously validated algorithm on the 2015 quarter 4 NRD. Furthermore, TECI identified 18,846 UAMS patients that had changes in comorbidity scores over time (year 2013 to 2019). Comorbidities for a random sample of patients were independently reviewed, and in all cases, the results were found to be 100% accurate.

Conclusions

TECI facilitates the study of comorbidities within a time-dependent context, allowing better understanding of disease associations and trajectories, which has the potential to improve clinical outcomes.

Multimorbidity and consultation time: a systematic review

BACKGROUND

Multimorbidity (MM) is one of the major challenges health systems currently face. Management of time length of a medical consultation with a patient with MM is a matter of concern for doctors.

METHODS

A systematic review was performed to describe the impact of MM on the average time of a medical consultation considering the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines. The systematic online searches of the Embase and PubMed databases were undertaken, from January 2000 to August 2018. The studies were independently screened by two reviewers to decide which ones met the inclusion criteria. (Kappa = 0.84 and Kappa = 0.82). Differing opinions were solved by a third person. This systematic review included people with MM criteria as participants (two or more chronic conditions in the same individual). The type of outcome included was explicitly defined - the length of medical appointments with patients with MM. Any strategies aiming to analyse the impact of MM on the average consultation time were considered. The length of time of medical appointment for patients without MM was the comparator criteria. Experimental and observational studies were included.

RESULTS

Of 85 articles identified, only 1 observational study was included, showing a clear trend for patients with MM to have longer consultations than patients without MM criteria (p < 0.001).

CONCLUSIONS

More studies are required to better assess allocation length-time for patients with MM and to measure other characteristics like doctors' workload.

Depression and self-care in older adults with multiple chronic conditions: A multivariate analysis

AIMS

To investigate the relationship between depression and self-care behaviours in older individuals with multimorbidity.

DESIGN

Cross-sectional study. Data were collected between April 2017 - June 2019.

METHODS

Patients were enrolled from community and outpatient settings and included if they were ≥65 years, affected by heart failure, diabetes mellitus or chronic obstructive pulmonary disease and at least another chronic condition. They were excluded if they had dementia and/or cancer. Patient Health Questionnaire-9 was used to measure depression and Self-Care of Chronic Illness Inventory was used to measure self-care maintenance, monitoring, and management. The relationship between depression and self-care was evaluated by performing two sets of univariate analyses, followed by multivariate and step-down analyses. The second set was performed to control for the number of chronic conditions, age, and cognitive function.

RESULTS

The sample (N = 366) was mostly female (54.2%), with a mean age of 76.4 years. Most participants (65.6%) had mild to very severe depressive symptoms. Preliminary analysis indicated a significant negative association between depression and self-care maintenance and monitoring and a significant negative association between depression and multivariate self-care. Step-down analysis showed that self-care maintenance was the only dimension negatively associated with depression, even after controlling for the number of chronic conditions, age, and cognitive function.

CONCLUSION

In multimorbid populations, depression is more likely to be associated with self-care maintenance than the other self-care dimensions. Therefore, self-care maintenance behaviours (e.g., physical activity and medication adherence) should be prioritized in assessment and focused on when developing interventions targeting depressed older adults with multimorbidity.

IMPACT

The results of this study may help guide clinical practice. In patients with depressive symptoms, self-care maintenance behaviours should be assessed first, as a potential first indicator of poor self-care.

Socio-economic inequalities in life expectancy of older adults with and without multimorbidity: a record linkage study of 1.1 million people in England

Background

Age of onset of multimorbidity and its prevalence are well documented. However, its contribution to inequalities in life expectancy has yet to be quantified.

Methods

A cohort of 1.1 million English people aged 45 and older were followed up from 2001 to 2010. Multimorbidity was defined as having 2 or more of 30 major chronic diseases. Multi-state models were used to estimate years spent healthy and with multimorbidity, stratified by sex, smoking status and quintiles of small-area deprivation.

Results

Unequal rates of multimorbidity onset and subsequent survival contributed to higher life expectancy at age 65 for the least (Q1) compared with most (Q5) deprived: there was a 2-year gap in healthy life expectancy for men [Q1: 7.7 years (95% confidence interval: 6.4–8.5) vs Q5: 5.4 (4.4–6.0)] and a 3-year gap for women [Q1: 8.6 (7.5–9.4) vs Q5: 5.9 (4.8–6.4)]; a 1-year gap in life expectancy with multimorbidity for men [Q1: 10.4 (9.9–11.2) vs Q5: 9.1 (8.7–9.6)] but none for women [Q1: 11.6 (11.1–12.4) vs Q5: 11.5 (11.1–12.2)]. Inequalities were attenuated but not fully attributable to socio-economic differences in smoking prevalence: multimorbidity onset was latest for never smokers and subsequent survival was longer for never and ex smokers.

Conclusions

The association between social disadvantage and multimorbidity is complex. By quantifying socio-demographic and smoking-related contributions to multimorbidity onset and subsequent survival, we provide evidence for more equitable allocation of prevention and health-care resources to meet local needs.

Identifying typical trajectories in longitudinal data: modelling strategies and interpretations

Individual-level longitudinal data on biological, behavioural, and social dimensions are becoming increasingly available. Typically, these data are analysed using mixed effects models, with the result summarised in terms of an average trajectory plus measures of the individual variations around this average. However, public health investigations would benefit from finer modelling of these individual variations which identify not just one average trajectory, but several typical trajectories. If evidence of heterogeneity in the development of these variables is found, the role played by temporally preceding (explanatory) variables as well as the potential impact of differential trajectories may have on later outcomes is often of interest. A wide choice of methods for uncovering typical trajectories and relating them to precursors and later outcomes exists. However, despite their increasing use, no practical overview of these methods targeted at epidemiological applications exists. Hence we provide: (a) a review of the three most commonly used methods for the identification of latent trajectories (growth mixture models, latent class growth analysis, and longitudinal latent class analysis); and (b) recommendations for the identification and interpretation of these trajectories and of their relationship with other variables. For illustration, we use longitudinal data on childhood body mass index and parental reports of fussy eating, collected in the Avon Longitudinal Study of Parents and Children.

Psychological correlates of multimorbidity and disability accumulation in older adults

BACKGROUND/OBJECTIVES

attitudes toward life and health are emerging as important psychological contributors to health heterogeneity in ageing. We aimed to explore whether different psychological factors were associated with the rate of chronic disease and disability accumulation over time.

DESIGN

population-based cohort study between 2001 and 2010.

SETTING

Swedish National study on aging and care in Kungsholmen.

SUBJECTS

adults aged 60 and older (N = 2293).

METHODS

linear mixed models were employed to study the association of life satisfaction, health outlook, resistance to illness, sickness orientation, and health worry with the rate of accumulation of chronic diseases and impaired basic and instrumental activities of daily living. Models were adjusted for demographic, clinical, social, personality and lifestyle factors. Analyses were repeated after excluding individuals with multimorbidity or disability at baseline.

RESULTS

high life satisfaction and positive health outlook were consistently associated with a lower rate of accumulation and progression of multimorbidity (β -0.064 95% confidence interval [CI] -0.116, -0.011; β -0.065 95% CI -0.121, -0.008, respectively) and disability (β -0.063 95% CI -0.098, -0.028; β -0.042 95% CI -0.079, -0.004, respectively) over time. This was true even for people without multimorbidity or disability at baseline and after adjusting for all covariates.

CONCLUSIONS

positive attitudes toward life in general and health in particular may be especially important in old age, when the cumulative effects of biological and environmental deficits lead to accelerated health decline. These findings should encourage researchers to use measures of psychological well-being to better understand the multifactorial and diverse process of ageing.

Association Between Speed of Multimorbidity Accumulation in Old Age and Life Experiences: A Cohort Study

Rapidly accumulating multiple chronic conditions (multimorbidity) during aging are associated with many adverse outcomes. We explored the association between 4 experiences throughout life-childhood socioeconomic circumstances, early-adulthood education, midlife occupational stress, and late-life social network-and the speed of chronic disease accumulation. We followed 2,589 individuals aged ≥60 years from the Swedish National Study on Aging and Care in Kungsholmen for 9 years (2001-2013). Information on life experiences was collected from detailed life-history interviews. Speed of disease accumulation was operationalized as the change in the count of chronic conditions obtained from clinical examinations, medical histories, laboratory data, drug use, and register linkages over 9 years. Linear mixed models were used to analyze the data. Speed of disease accumulation was lower in individuals with more than elementary education (for secondary, β × time = -0.065, 95% CI: -0.126, -0.004; for university, β × time = -0.118, 95% CI: -0.185, -0.050); for active occupations compared with high-strain jobs (β × time = -0.078, 95% CI: -0.138, -0.017); and for richer social networks (for moderate tertile, β × time = -0.102, 95% CI: -0.149, -0.055; for highest tertile, β × time = -0.135, 95% CI: -0.182, -0.088). The association between childhood circumstances and speed of disease accumulation was attenuated by later-life experiences. Diverse experiences throughout life might decelerate chronic disease accumulation during aging.

Prevalence, characteristics, and patterns of patients with multimorbidity in primary care: a retrospective cohort analysis in Canada

BACKGROUND

Multimorbidity is a complex issue in modern medicine and a more nuanced understanding of how this phenomenon occurs over time is needed.

AIM

To determine the prevalence, characteristics, and patterns of patients living with multimorbidity, specifically the unique combinations (unordered patterns) and unique permutations (ordered patterns) of multimorbidity in primary care.

DESIGN AND SETTING

A retrospective cohort analysis of the prospectively collected data from 1990 to 2013 from the Canadian Primary Care Sentinel Surveillance Network electronic medical record database.

METHOD

Adult primary care patients who were aged ≥18 years at their first recorded encounter were followed over time. A list of 20 chronic condition categories was used to detect multimorbidity. Computational analyses were conducted using the Multimorbidity Cluster Analysis Tool to identify all combinations and permutations.

RESULTS

Multimorbidity, defined as two or more and three or more chronic conditions, was prevalent among adult primary care patients and most of these patients were aged <65 years. Among female patients with two or more chronic conditions, 6075 combinations and 14 891 permutations were detected. Among male patients with three or more chronic conditions, 4296 combinations and 9716 permutations were detected. While specific patterns were identified, combinations and permutations became increasingly rare as the total number of chronic conditions and patient age increased.

CONCLUSION

This research confirms that multimorbidity is common in primary care and provides empirical evidence that clinical management requires a tailored, patient-centred approach. While the prevalence of multimorbidity was found to increase with increasing patient age, the largest proportion of patients with multimorbidity in this study were aged <65 years.

Divergent Six Month Functional Recovery Trajectories and Predictors after Traumatic Brain Injury: Novel Insights from the Citicoline Brain Injury Treatment Trial Study

Cross-sectional approaches to outcome assessment may not adequately capture heterogeneity in recovery after traumatic brain injury (TBI). Using latent class mixed models (LCMM), a data-driven analytic that identifies groups of patients with similar trajectories, we identified distinct 6 month functional recovery trajectories in a large cohort (n = 1046) of adults 18-70 years of age with complicated mild to severe TBI who participated in the Citicoline Brain Injury Treatment Trial (COBRIT). We used multinomial logistic fixed effect models and backward elimination, forward selection, and forward stepwise selection with several stopping rules to explore baseline predictors of functional recovery trajectory. Based on statistical and clinical considerations, the seven-class model was deemed superior. Visualization of these seven functional recovery trajectories revealed that each trajectory class started at one of three recovery levels at 1 month, which, for ease of reference we labeled groups A-C: Group A, good recovery (two classes; A1 and A2); Group B, moderate disability (two classes; B1 and B2); and Group C, severe disability (three classes; C1, C2, and C3). By 6 months, these three groups experienced dramatically divergent trajectories. Group A experienced stable good recovery (A1, n = 115) or dramatic decline (A2, n = 4); Group B experienced rapid complete recovery (B1, n = 71) or gradual recovery (B2, n = 742); Group C experienced dramatic rapid recovery (C1, n = 12), no recovery (C2, n = 91), or death (C3, n = 11). Trajectory class membership was not predicted by citicoline treatment (p = 0.57). The models identified demographic, pre-injury, and injury-related predictors of functional recovery trajectory, including: age, race, education, pre-injury employment, pre-injury diabetes, pre-injury psychiatric disorder, site, Glasgow Coma Scale (GCS) score, post-traumatic amnesia, TBI mechanism, major extracranial injury, hemoglobin, and acute computed tomographic (CT) findings. GCS was the most consistently selected predictor across all models. All models also selected at least one demographic or pre-injury medical predictor. LCMM successfully identified dramatically divergent, clinically meaningful 6 month recovery trajectories with utility to inform clinical trial design.

Unfolding of hidden white blood cell count phenotypes for gene discovery using latent class mixed modeling

Resting-state white blood cell (WBC) count is a marker of inflammation and immune system health. There is evidence that WBC count is not fixed over time and there is heterogeneity in WBC trajectory that is associated with morbidity and mortality. Latent class mixed modeling (LCMM) is a method that can identify unobserved heterogeneity in longitudinal data and attempts to classify individuals into groups based on a linear model of repeated measurements. We applied LCMM to repeated WBC count measures derived from electronic medical records of participants of the National Human Genetics Research Institute (NHRGI) electronic MEdical Record and GEnomics (eMERGE) network study, revealing two WBC count trajectory phenotypes. Advancing these phenotypes to GWAS, we found genetic associations between trajectory class membership and regions on chromosome 1p34.3 and chromosome 11q13.4. The chromosome 1 region contains CSF3R, which encodes the granulocyte colony-stimulating factor receptor. This protein is a major factor in neutrophil stimulation and proliferation. The association on chromosome 11 contain genes RNF169 and XRRA1; both involved in the regulation of double-strand break DNA repair.

Lifetime trajectory simulation of chronic disease progression and comorbidity development

INTRODUCTION

Comorbidity is common in elderly patients and it imposes heavy burden on both individual and the whole healthcare system. This study aims to gain insights of comorbidity development by simulating the lifetime trajectory of disease progression from single chronic disease to comorbidity.

METHODS

Eight health states spanning from no chronic condition to comorbidity are considered in this study. Disease progression network is constructed based on the seven-year retrospective data of around 700,000 residents living in Singapore central region. Microsimulation is applied to simulate the process of aging and disease progression of a synthetic new-born cohort for the entire lifetime.

RESULTS

Among the 40 unique trajectories observed from the simulation, the top 10 trajectories covers 60% of the cohort. Timespan of most trajectories from birth to death is 80 years. Most people progress to at risk at late 30 s, develop the first chronic condition at 50 s or 60 s, and then progress to complications at 70 s. It is also observed that the earlier one person develops chronic conditions, the more life-year-lost is incurred.

DISCUSSION

The lifetime disease progression trajectory constructed for each person in the cohort describes how a person starts healthy, becomes at risk, then progresses to one or more chronic conditions, and finally deteriorates to various complications over the years. This study may help us have a better understanding of chronic disease progression and comorbidity development, hence add values to chronic disease prevention and management.

Multi-trajectory modeling of home blood pressure telemonitoring utilization among hypertensive patients in China: A latent class growth analysis

BACKGROUND

Home blood pressure telemonitoring (HBPT) has great potential in improving blood pressure (BP) control among patients with hypertension. However, the longitudinal use trajectories of HBPT have not been identified yet. In addition, there has been a lack of understanding of the relationship between developmental trajectories of HBPT and BP control over time. The primary goal of this study was to identify the longitudinal trajectories of using HBPT among hypertensive patients and to explore the relationship between longitudinal trajectories of HBPT use patterns and BP control.

METHODS

A total of 122 hypertensive patients were enrolled consecutively in Xiling, Huayan, Baisha and Xueyuan communities in Yichang City, Hubei Province, China. Each patient was provided with a portable monitoring device which has unlimited data service at the time of enrollment. Socio-demographics (e.g. name, age, sex, marital status) were collected at baseline. Real-time data including systolic and diastolic blood pressure were automatically uploaded to cloud platform through devices. Latent class growth analysis was conducted to determine the latent trajectory of HBPT use. Joint trajectory method was used to correlate the longitudinal trajectories of HBPT utilization and BP control status.

RESULTS

Five trajectories were identified which are persistently low (47.1%), moderate with decreasing (23.9%), sharply decreasing (11.2%), high with decreasing (11.3%) and persistently high with increasing (6.6%). There was no statistically significant difference among 5 trajectories in the baseline survey in terms of age, marital status, BP (both SBP and DBP) and BP control status. However, there was a strong positive correlation between the HBPT utilization pattern and BP control status over time.

CONCLUSIONS

The latent trajectories of HBPT utilization were identified in our study. However, no predictors of trajectory membership were identified. Nevertheless, we have demonstrated that HBPT was to some extent positively correlated with improved BP control, and this correlation still needs to be further proved.

Rapidly developing multimorbidity and disability in older adults: does social background matter?

BACKGROUND

Multimorbidity is among the most disabling geriatric conditions. In this study, we explored whether a rapid development of multimorbidity potentiates its impact on the functional independence of older adults, and whether different sociodemographic factors play a role beyond the rate of chronic disease accumulation.

METHODS

A random sample of persons aged ≥60 years (n = 2387) from the Swedish National study on Aging and Care in Kungsholmen (SNAC-K) was followed over 6 years. The speed of multimorbidity development was estimated as the rate of chronic disease accumulation (linear mixed models) and further dichotomized into the upper versus the three lower rate quartiles. Binomial negative mixed models were used to analyse the association between speed of multimorbidity development and disability (impaired basic and instrumental activities of daily living), expressed as the incidence rate ratio (IRR). The effect of sociodemographic factors, including sex, education, occupation and social network, was investigated.

RESULTS

The risk of new activity impairment was higher among participants who developed multimorbidity faster (IRR 2.4, 95% CI 1.9-3.1) compared with those who accumulated diseases more slowly overtime, even after considering the baseline number of chronic conditions. Only female sex (IRR for women vs. men 1.6, 95% CI 1.2-2.0) and social network (IRR for poor vs. rich social network 1.7, 95% CI 1.3-2.2) showed an effect on disability beyond the rate of chronic disease accumulation.

CONCLUSIONS

Rapidly developing multimorbidity is a negative prognostic factor for disability. However, sociodemographic factors such as sex and social network may determine older adults' reserves of functional ability, helping them to live independently despite the rapid accumulation of chronic conditions.

Development of a research tool to document self-reported chronic conditions in primary care

BACKGROUND

Researchers interested in multimorbidity often find themselves in the dilemma of identifying or creating an operational definition in order to generate data. Our team was invited to propose a tool for documenting the presence of chronic conditions in participants recruited for different research studies.

OBJECTIVE

To describe the development of such a tool.

DESIGN

A scoping review in which we identified relevant studies, selected studies, charted the data, and collated and summarized the results. The criteria considered for selecting chronic conditions were: (1) their relevance to primary care services; (2) the impact on affected patients; (3) their prevalence among the primary care users; and (4) how often the conditions were present among the lists retrieved from the scoping review.

RESULTS

Taking into account the predefined criteria, we developed a list of 20 chronic conditions/categories of conditions that could be self-reported. A questionnaire was built using simple instructions and a table including the list of chronic conditions/categories of conditions.

CONCLUSIONS

We developed a questionnaire to document 20 self-reported chronic conditions/categories of conditions intended to be used for research purposes in primary care. Guided by previous literature, the purpose of this questionnaire is to evaluate the self-reported burden of multimorbidity by participants and to encourage comparability among research studies using the same measurement.

Beyond Return to Work: The Effect of Multimorbidity on Work Functioning Trajectories After Sick Leave due to Common Mental Disorders

Objectives Patients with common mental disorders (CMDs) often suffer from comorbidities, which may limit their functioning at work. We assessed the longitudinal impact of multimorbidity, defined as two or more co-occurring chronic health conditions, on work functioning over time among workers who had returned to work after sick leave due to CMDs. Methods Prospective cohort study of 156 workers followed for 1 year after return to work from sick leave due to CMDs. A multimorbidity score was computed by counting severity-weighted chronic health conditions measured at baseline. Work functioning was measured at baseline and at 3, 6 and 12 months follow-up with the Work Role Functioning Questionnaire. Work functioning trajectories, i.e. the course of work functioning after return to work over time, were identified through latent class growth analysis. Results A total of 44 % of workers had multimorbidity. Four work functioning trajectories were identified: one (12 % of the workers) showed increasing work functioning scores during follow-up, whereas the other trajectories showed low, medium and high scores (23, 41 and 25 %, respectively) that remained stable across time points. Although multimorbidity did not predict membership in any trajectory, within the increasing score trajectory levels of work functioning were lower among those with high baseline multimorbidity score (p < 0.001). Conclusions Over time, multimorbidity negatively impacts work functioning after return to work from sick leave due to CMDs.

A Latent Class Analysis of Multimorbidity and the Relationship to Socio-Demographic Factors and Health-Related Quality of Life. A National Population-Based Study of 162,283 Danish Adults

Objectives

To identify patterns of multimorbidity in the general population and examine how these patterns are related to socio-demographic factors and health-related quality of life.

Study design and setting

We used latent class analysis to identify subgroups with statistically distinct and clinically meaningful disease patterns in a nationally representative sample of Danish adults (N = 162,283) aged 16+ years. The analysis was based on 15 chronic diseases.

Results

Seven classes with different disease patterns were identified: a class with no or only a single chronic condition (59% of the population) labeled “1) Relatively Healthy” and six classes with a very high prevalence of multimorbidity labeled; “2) Hypertension” (14%); “3) Musculoskeletal Disorders” (10%); “4) Headache-Mental Disorders” (7%); “5) Asthma-Allergy” (6%); “6) Complex Cardiometabolic Disorders” (3%); and “7) Complex Respiratory Disorders” (2%). Female gender was associated with an increased likelihood of belonging to any of the six multimorbidity classes except for class 2 (Hypertension). Low educational attainment predicted membership of all of the multimorbidity classes except for class 5 (Asthma-Allergy). Marked differences in health-related quality of life between the seven latent classes were found. Poor health-related quality of life was highly associated with membership of class 6 (Complex Cardiometabolic Disorders) and class 7 (Complex Respiratory Disorders). Despite different disease patterns, these two classes had nearly identical profiles in relation to health-related quality of life.

Conclusion

The results clearly support that diseases tend to compound and interact, which suggests that a differentiated public health and treatment approach towards multimorbidity is needed.