Multimorbidity Trajectories Research Articles

Association between household solid fuel usage and trajectories of multimorbidity among middle-aged and older adults: a nationwide population-based cohort study

BackgroundThis study aimed to explore the effect of household solid fuel usage on the multimorbidity trajectories among middle-aged and older adults.MethodsBased on the 2011–2018 China Health and Retirement Longitudinal Study, the group-based trajectory modeling and the multinomial logistic regression model were used to explore the relationship between multimorbidity trajectories of older adults with different fuel types, duration of solid fuel usage, and potential interaction with PM2.5. Three multimorbidity trajectory patterns were identified by group-based trajectory modeling and labeled as “non-chronic morbidity” (no disease increase), “newly developing multimorbidity” (diseases grew from 0 to 2), and “multi-chronic multimorbidity” (diseases grew from 2 to 4).ResultsCompared to “Non-chronic morbidity,” solid fuel was significantly associated with adverse multimorbidity trajectories, with an odds ratio (OR) and 95% confidence interval (CI) of 1.33 (1.11, 1.60) and 1.35 (1.18, 1.55) for newly developing and multi-chronic group, respectively. An adverse multimorbidity trajectory tended to be established with longer durations of solid fuel usage than “Non-chronic morbidity.” For “Newly-developing multimorbidity,” the ORs (95% CI) for 1–7 years and ≥ 8 years of solid fuel usage were 1.16 (0.94, 1.42) and 1.41 (1.12, 1.76), respectively, with P trend=0.001, while in “Multi-chronic multimorbidity,” those were 1.25 (1.07, 1.47) and 1.68 (1.41, 2.00), respectively, with P trend <0.001. In the interaction analysis, the association between solid fuel usage and trajectories was significant only in areas where PM2.5 was lower than 50 μg/m3.ConclusionFor the middle-aged and older Chinese population, a higher risk of multimorbidity trajectory is associated with household solid fuel usage, especially in the areas with lower PM2.5.

Association of Short-term Pain and Chronic Pain Intensity With Cardiometabolic Multimorbidity Progression: A Multistate Markov Model Analysis.

The impact of pain intensity on the progression trajectories of cardiometabolic multimorbidity (CMM) is not well understood. We attempted to dissect the relationship of short-term pain (STP) and chronic pain intensity with the temporal progression of CMM. We conducted a prospective cohort study based on the UK Biobank participants. Incident cases of cardiometabolic diseases (CMDs) were identified based on self-reported information and multiple health-related records in the UK Biobank. CMM was defined as the occurrence of at least 2 CMDs, including heart failure (HF), ischemic heart disease (IHD), stroke, and type 2 diabetes (T2D). The pain intensity was categorized into 5 levels based on pain duration and the number of sites involved, including chronic widespread pain (CWSP), chronic multilocation pain (CMLP), chronic single-location pain (CSLP), STP, and free-of-pain (FOP). Multistate models were used to assess the impact of pain intensity on the CMM trajectories from enrollment to initial cardiometabolic disease (ICMD), subsequently to CMM, and ultimately to death. A total of 429,145 participants were included. Over the course of a 12.8-year median follow-up, 13.1% (56,137/429,145) developed ICMD, 19.6% (10,979/56,137) further progressed to CMM, and a total of 5.3% (22,775/429,145) died. Compared with FOP, CMLP (hazard ratio [HR], 1.11; 95% confidence interval [CI], 1.06-1.17) and CWSP (HR, 1.26; 95% CI, 1.13-1.42) elevated the risk of transitioning from ICMD to CMM. STP (HR, 0.89; 95% CI, 0.82-0.96), CSLP (HR, 0.88; 95% CI, 0.82-0.95), and CMLP (HR, 0.87; 95% CI, 0.81-0.93) lowered the risk of transition from ICMD to mortality, and STP also reduced the risk of transition from enrollment to mortality (HR, 0.94; 95% CI, 0.89-0.98). The results of disease-specific transitions revealed that the influence of pain intensity varied across transitional stages. Specifically, CMLP and CWSP heightened the risk of conversion from T2D or IHD to CMM, whereas only CWSP substantially elevated the transition risk from HF to CMM. Our results highlighted reductions in chronic pain may mitigate both the onset and progression of CMM, potentially having an important impact on future revisions of cardiometabolic and pain-related guidelines.

Unique genetic and risk-factor profiles in clusters of major depressive disorder-related multimorbidity trajectories

The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients.

The association of a low-inflammatory diet with the trajectory of multimorbidity: a large community-based longitudinal study

BackgroundA proinflammatory diet has been associated with a risk of individual chronic diseases, however, evidence on the association between inflammatory dietary patterns and the trajectory of chronic disease multimorbidity is sparse. ObjectivesWe aimed to investigate the associations of a low-inflammatory diet with the multimorbidity trajectory. MethodsWithin the UK Biobank, 102,424 chronic disease-free participants (mean age 54.7 ± 7.9 y, 54.8% female) were followed up to detect multimorbidity trajectory (annual change in the number of 59 chronic diseases). Baseline inflammatory diet index (IDI) and empirical dietary inflammatory pattern (EDIP) were separately calculated from the weighted sum of 32 posteriori-derived (15 anti-inflammatory) and 18 prior-defined (9 anti-inflammatory) food groups, and tertiled as low-, moderate-, and high-inflammatory diet. Data were analyzed using linear mixed effects model, Cox model, and Laplace regression with adjustment for potential confounders. ResultsDuring the follow-up (median 10.23 y), 15,672 and 35,801 participants developed 1 and 2+ chronic conditions, respectively. Adherence to a low-inflammatory diet was associated with decreased multimorbidity risk (hazard ratio [HRIDI] = 0.84, 95% confidence interval [CI]: 0.81, 0.86; HREDIP = 0.91, 95% CI: 0.89, 0.94) and a slower multimorbidity accumulation (βIDI = −0.033, 95% CI: −0.036, −0.029; βEDIP = −0.006, 95% CI: −0.010, −0.003) compared with a high-inflammatory diet, especially in participants aged > 60 y (βIDI = −0.051, 95% CI: −0.059, −0.042; βEDIP = −0.020, 95% CI: −0.029, −0.012; both P-interactions < 0.05). The 50th percentile difference (95% CI) of chronic disease-free survival time was prolonged by 0.81 (0.64, 0.97) and 0.49 (0.34, 0.64) y for participants with a low IDI and EDIP, respectively. Higher IDI and EDIP were associated with the development of 4 and 3 multimorbidity clusters (especially for cardiometabolic diseases), respectively. ConclusionsA low-inflammatory diet is associated with a lower risk and slower accumulation of multimorbidity (especially in participants aged > 60 y). A low-inflammatory diet may prolong chronic disease-free survival time.

Multimorbidity measures associated with cognitive function among community-dwelling older Chinese adults.

Older adults with multimorbidity are at high risk of cognitive impairment development. There is a lack of research on the associations between different multimorbidity measures and cognitive function among older Chinese adults living in the community. We used the Chinese Longitudinal Healthy Longevity Survey from 2002 to 2018 and included data on dementia-free participants aged ≥65 years. Multimorbidity measures included condition counts, multimorbidity patterns, and trajectories. The association of multimorbidity measures with cognitive function was examined by generalized estimating equation and linear and logistic regression models. Among 14,093 participants at baseline, 43.2% had multimorbidity. Multimorbidity patterns were grouped into cancer-inflammatory, cardiometabolic, and sensory patterns. Multimorbidity trajectories were classified as "onset-condition," "newly developing," and "severe condition." The Mini-Mental State Examination scores were significantly lower for participants with more chronic conditions, with cancer-inflammatory/cardiometabolic/sensory patterns, and with developing multimorbidity trajectories. Condition counts, sensory pattern, cardiometabolic pattern, cancer-inflammatory pattern,and multimorbidity developmental trajectories were prospectively associated with cognitive function. Elderly individuals with a higher number of chronic conditions were associated with lower MMSE scores in the Chinese Longitudinal Healthy Longevity Survey data. MMSE scores were significantly lower for participants with specific multimorbidity patterns. Individuals with developing trajectories of multimorbidity were associated with lower MMSE scores and a higher risk of mild cognitive impairment.

Mining Multimorbidity Trajectories and Co-Medication Effects from Patient Data to Predict Post–Hip Fracture Outcomes

Hip fractures have profound impacts on patients’ conditions and quality of life, even when they receive therapeutic treatments. Many patients face the risk of poor prognosis, physical impairment, and even mortality, especially older patients. Accurate patient outcome estimates after an initial fracture are critical to physicians’ decision-making and patient management. Effective predictions might benefit from analyses of patients’ multimorbidity trajectories and medication usages. If adequately modeled and analyzed, then they could help identify patients at higher risk of recurrent fractures or mortality. Most analytics methods overlook the onset, co-occurrence, and temporal sequence of distinct chronic diseases in the trajectory, and they also seldom consider the combined effects of different medications. To support effective predictions, we develop a novel deep learning–based method that uses a cross-attention mechanism to model patient progression by obtaining “contextual information” from multimorbidity trajectories. This method also incorporates a nested self-attention network that captures the combined effects of distinct medications by learning the interactions among medications and how dosages might influence post-fracture outcomes. A real-world patient dataset is used to evaluate the proposed method, relative to six benchmark methods. The comparative results indicate that our method consistently outperforms all the benchmarks in precision, recall, F-measures, and area under the curve. The proposed method is generalizable and can be implemented as a decision support system to identify patients at greater risk of recurrent hip fractures or mortality, which should help clinical decision-making and patient management.

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

ObjectivesTo identify multimorbidity trajectories over 20 years among incident osteoarthritis (OA) individuals and OA-free matched references.MethodsCohort study using prospectively collected healthcare data from the Skåne region, Sweden (~1.4 million residents)....

Trajectories of chronic multimorbidity patterns in older patients: MTOP study

BackgroundMultimorbidity 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.MethodsA 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.Results3988 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.ConclusionsIn 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 numberNCT05717309.

Impact of gene-by-trauma interaction in MDD-related multimorbidity clusters

BackgroundMajor depressive disorder (MDD) is considerably heterogeneous in terms of comorbidities, which may hamper the disentanglement of its biological mechanism. In a previous study, we classified the lifetime trajectories of MDD-related multimorbidities into seven distinct clusters, each characterized by unique genetic and environmental risk-factor profiles. The current objective was to investigate genome-wide gene-by-environment (G × E) interactions with childhood trauma burden, within the context of these clusters. MethodsWe analyzed 77,519 participants and 6,266,189 single-nucleotide polymorphisms (SNPs) of the UK Biobank database. Childhood trauma burden was assessed using the Childhood Trauma Screener (CTS). For each cluster, Plink 2.0 was used to calculate SNP × CTS interaction effects on the participants' cluster membership probabilities. We especially focused on the effects of 31 candidate genes and associated SNPs selected from previous G × E studies for childhood maltreatment's association with depression. ResultsAt SNP-level, only the high-multimorbidity Cluster 6 revealed a genome-wide significant SNP rs145772219. At gene-level, MPST and PRH2 were genome-wide significant for the low-multimorbidity Clusters 1 and 3, respectively. Regarding candidate SNPs for G × E interactions, individual SNP results could be replicated for specific clusters. The candidate genes CREB1, DBH, and MTHFR (Cluster 5) as well as TPH1 (Cluster 6) survived multiple testing correction. LimitationsCTS is a short retrospective self-reported measurement. Clusters could be influenced by genetics of individual disorders. ConclusionsThe first G × E GWAS for MDD-related multimorbidity trajectories successfully replicated findings from previous G × E studies related to depression, and revealed risk clusters for the contribution of childhood trauma.

Multimorbidity patterns and trajectories in young and middle-aged adults: a large-scale population-based cohort study.

The presence of multiple chronic conditions, also referred to as multimorbidity, is a common finding in adults. Epidemiologic research can help identify groups of individuals with similar clinical profiles who could benefit from similar interventions. Many cross-sectional studies have revealed the existence of different multimorbidity patterns. Most of these studies were focused on the older population. However, multimorbidity patterns begin to form at a young age and can evolve over time following distinct multimorbidity trajectories with different impact on health. In this study, we aimed to identify multimorbidity patterns and trajectories in adults 18-65 years old. We conducted a retrospective longitudinal epidemiologic study in the EpiChron Cohort, which includes all inhabitants of Aragón (Spain) registered as users of the Spanish National Health System, linking, at the patient level, information from electronic health records from both primary and specialised care. We included all 293,923 patients 18-65 years old with multimorbidity in 2011. We used cluster analysis at baseline (2011) and in 2015 and 2019 to identify multimorbidity patterns at four and eight years of follow-up, and we then created alluvial plots to visualise multimorbidity trajectories. We performed age- and sex-adjusted logistic regression analysis to study the association of each pattern with four- and eight-year mortality. We identified three multimorbidity patterns at baseline, named dyslipidaemia & endocrine-metabolic, hypertension & obesity, and unspecific. The hypertension & obesity pattern, found in one out of every four patients was associated with a higher likelihood of four- and eight-year mortality (age- and sex-adjusted odds ratio 1.11 and 1.16, respectively) compared to the unspecific pattern. Baseline patterns evolved into different patterns during the follow-up. Well-known preventable cardiovascular risk factors were key elements in most patterns, highlighting the role of hypertension and obesity as risk factors for higher mortality. Two out of every three patients had a cardiovascular profile with chronic conditions like diabetes and obesity that are linked to low-grade systemic chronic inflammation. More studies are encouraged to better characterise the relatively large portion of the population with an unspecific disease pattern and to help design and implement effective and comprehensive strategies towards healthier ageing.

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.

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)). 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. 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. 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.

Abstract MP65: Multimorbidity Trajectories From Early Adulthood and Current and Subsequent Accelerometer-Measured Sedentary and Physical Activity Behaviors in Midlife: Coronary Artery Risk Development in Young Adults (CARDIA) Study

Introduction: Multimorbidity, having two or more chronic diseases, can lead to functional limitations. However limited data exist on the associations of multimorbidity during the early life-course with physical activity (PA). We assessed the hypothesis that patterns of multimorbidity accumulation from early to mid- adulthood is associated with differences in 1) concurrent PA and 2) changes in PA during midlife. Methods: We analyzed subsamples of CARDIA participants who wore an accelerometer (ActiGraph GT3X) on the waist ≥4 days with ≥10 hr/day during the Year 30 (2015-16; n = 1425) and again at Year 35 (2020-22; n = 745 of 1425) follow-up exams. Accelerometer estimates included time (min/day) spent in sedentary (SB), light intensity (LPA), or moderate to vigorous intensity PA (MVPA) behaviors. Six identified latent trajectories of early- to mid- life multimorbidity based on age and rate of accumulation included: 1) mid-20s fast; 2) mid-20s slow; 3) late-20s fast; 4) mid-30s fast; 5) mid-40s fast; and 6) early-50s slow [referent]. Linear models were used to estimate differences in 1) behaviors at Year 30 and 2) relative change (%) from Year 30 to Year 35 by multimorbidity trajectory, after adjustment. Results: CARDIA adults (overall: 62% female, 45% Black, mean age = 55 at Year 30) in the early-50s slow group had the highest MVPA (54.5 min/day) and LPA (348 min/day) and least SB (515 min/day) (unadjusted χ 2 : p &lt; 0.01). At Year 30, when compared to early-50s slow, mid-20s slow had lower LPA (β = -17.1, p = 0.003) and late-20s fast had lower MVPA (β = -12.0, p &lt; 0.001) after covariate adjustment. No significant differences in 5-year relative change in any activity were found by multimorbidity trajectory (Figure). Conclusions: In this prospective cohort, adults who accumulated disease later (early-50s) had higher PA at midlife. Changes in midlife PA and SB did not differ by multimorbidity trajectory, however this was a relatively short follow-up. Additional follow-up will provide clarity as CARDIA participants transition into older adulthood.

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

ObjectivesThis 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...

SYNTHESIS AND APPLICATION OF QUANTITATIVE METHODS FOR MODELING CHANGE IN MULTIMORBIDITY AND RELATED HEALTH DOMAINS

Abstract The longitudinal examination of multimorbidity (≥ 2 chronic conditions) and related health outcomes (e.g., cognition, disability, mortality) has the potential to identify disparities and intervention points in the age-related progression of chronic disease. This symposium will organize discussion of methodological and applied research projects that review and demonstrate the use of longitudinal methods to address critical questions in the study of multimorbidity and related health domains. The lead presentation “Longitudinal Methods for Assessing Progression of Multimorbidity and Related Health Outcomes” will review the use and limitations of several longitudinal methods that can be used to study the progression of multimorbidity and linked health outcomes. Following presentations will demonstrate the application of specific longitudinal methods to key research questions in the study of multimorbidity. “Emerging Cohort Disparities in Multimorbidity in U.S. Adults Entering Early Older Adulthood” presents evidence of cohort disparities in multimorbidity trajectories using mixed-effects regression to estimate age, period, and cohort effects, “Personalized and Typical Concurrent Risk: Joint Models of Recurrent Outcomes and Mortality by Race/Ethnicity” applies joint models of self-rated health, disability, and mortality to examine disparities among minoritized groups, and “Sequence Analysis of Cardiometabolic Multimorbidity and Association With Subsequent Dementia” identifies common patterning of cardiometabolic conditions over a 5-year period and their relationship with prospective dementia classification. This symposium will encourage attendees to apply the presented methods to their own multimorbidity research questions and demonstrate how these methods are being applied to address key gaps in our understating of multimorbidity progression in older adulthood. This is a Measurement, Statistics, and Research Design Interest Group Sponsored Symposium.

JOINT TRAJECTORIES OF MULTIMORBIDITY AND FRAILTY AMONG OLDER AMERICANS

Abstract Little is known about how health status considering both multimorbidity and frailty develops over time. This study aims to investigate joint trajectories of multimorbidity and frailty and examine their associated factors among older American adults.We used data from the National Health and Aging Trends Study (2011–2019). Fourteen chronic conditions were ascertained each year. Frailty was measured annually using the Fried frailty phenotype. Group-based trajectory models were fitted to identify joint trajectories of multimorbidity and frailty. Multinomial logistic regression model was adapted to examine the association of baseline characteristics with the trajectories. We identified six distinct joint trajectories of multimorbidity and frailty, including four concordant and two discordant trajectories. Compared with the Newly-developing condition and Constant non-frailty group, increasing age, low education, dual Medicare-Medicaid status, former or current smoker, mild or severe insomnia symptoms, and bothersome or activity-limiting pain were significantly associated with an increased likelihood of belonging to the other five groups with higher levels of multimorbidity or frailty (all P&amp;lt; 0.05). Multiple distinct joint trajectories of multimorbidity and frailty offer complementary evidence to the heterogeneous patterns in aging, as well as inform clinicians, policymakers, and researchers to develop targeted interventions and provide comprehensive care management in community settings.

MULTIMORBIDITY PREDICTS REMAINING LIFESPAN IN NONHUMAN PRIMATE MODEL OF ACCELERATED AGING

Abstract Multimorbidity is common among older adults and linked to shorter lifespans, but it is unclear how naturally-occurring multimorbidity impacts lifespan in non-human primate models of aging. We developed a clinical multimorbidity index based on standardized veterinary diagnoses in 288 rhesus macaques (Macaca mulatta) from the Wake Forest Radiation Late Effects Cohort, animals that survived an early-life insult from whole-body radiation and controls (73 female, 237 irradiated, ages 2-24). Our index is based on diagnosis of arthritis, low bone density, cataracts, diabetes, gastrointestinal disorders, heart disease, hepatic dysfunction, hypertension, kidney disease, lung fibrosis, skin disorders, tumors, and overweight/underweight. All diseases were seen in both control and irradiated animals and there was a broad distribution of multimorbidity trajectories across the lifespan both in number and combinations of diseases. On average, the first diagnosis occurred at age 9.0 (equal to 35% of median lifespan) and the second at age 10.7. However, 20% of animals that lived to age 12 were without any diagnoses while others had as many as 9 comorbidities, highlighting broad variability in multimorbidity with aging. Cox proportional-hazard ratios (HR) show significant negative associations (p &amp;lt; 0.05) between survival and multimorbidity. Ages 3-6 years, each additional morbidity doubles likelihood of death (HR &amp;gt; 2) with HR falling from 1.9 to 1.4 from ages 6-15. Based on receiver operating characteristic (ROC) curves, multimorbidity predicts 2-year mortality with AUC &amp;gt; 0.65 from ages 7-14. Our findings suggest multimorbidity is prevalent and predicts lifespan in NHP survivors of radiation. Research was funded by NIHU01AI150578.

Long-term multimorbidity trajectories in older adults: The role of cancer, demographics, and health behaviors.

Multimorbidity is associated with premature mortality and excess health care costs. The burden of multimorbidity is highest among patients with cancer, yet trends and determinants of multimorbidity over time are poorly understood. Via Medicare claims linked to Cancer Prevention Study II data, group-based trajectory modeling was used to compare National Cancer Institute comorbidity index score trends for cancer survivors and older adults without a cancer history. Among cancer survivors, multinomial logistic regression analyses evaluated associations between demographics, health behaviors, and comorbidity trajectories. In 82,754 participants (mean age, 71.6 years [SD,5.1 years]; 56.9% female), cancer survivors (n=11,265) were more likely than older adults without a cancer history to experience the riskiest comorbidity trajectories: (1) steady, high comorbidity scores (remain high; odds ratio [OR],1.36; 95% CI, 1.29-1.45), and (2) high scores that increased over time (start high and increase; OR,1.51; 95% CI, 1.38-1.65). Cancer survivors who were physically active postdiagnosis were less likely to fall into these two trajectories (OR,0.73; 95% CI, 0.64-0.84, remain high; OR,0.42; 95% CI, 0.33-0.53, start high and increase) compared to inactive survivors. Cancer survivors with obesity were more likely to have a trajectory that started high and increased (OR,2.83; 95% CI, 2.32-3.45 vs. normal weight), although being physically active offset some obesity-related risk. Cancer survivors who smoked postdiagnosis were also six times more likely to have trajectories that started high and increased (OR,6.86; 95% CI, 4.41-10.66 vs. never smokers). Older cancer survivors are more likely to have multiple comorbidities accumulated at a faster pace than older adults without a history of cancer. Weight management, physical activity, and smoking avoidance postdiagnosis may attenuate that trend.

Trajectories of Chronic Disease and Multimorbidity Among Middle-aged and Older Patients at Community Health Centers

Health-related consequences of multimorbidity (≥2 chronic diseases) are well documented. However, the extent and rate of accumulation of chronic diseases among US patients seeking care in safety-net clinics are not well understood. These insights are needed to enable clinicians, administrators, and policy makers to mobilize resources for prevention of disease escalations in this population. To identify the patterns and rate of chronic disease accumulation among middle-aged and older patients seeking care in community health centers, as well as any sociodemographic differences. This cohort study used electronic health record data from January 1, 2012, to December 31, 2019, on 725 107 adults aged 45 years or older with 2 or more ambulatory care visits in 2 or more distinct years at 657 primary care clinics in the Advancing Data Value Across a National Community Health Center network in 26 US states. Statistical analysis was performed from September 2021 to February 2023. Race and ethnicity, age, insurance coverage, and federal poverty level (FPL). Patient-level chronic disease burden, operationalized as the sum of 22 chronic diseases recommended by the Multiple Chronic Conditions Framework. Linear mixed models with patient-level random effects adjusted for demographic characteristics and ambulatory visit frequency with time interactions were estimated to compare accrual by race and ethnicity, age, income, and insurance coverage. The analytic sample included 725 107 patients (417 067 women [57.5%]; 359 255 [49.5%] aged 45-54 years, 242 571 [33.5%] aged 55-64 years, and 123 281 [17.0%] aged ≥65 years). On average, patients started with a mean (SD) of 1.7 (1.7) morbidities and ended with 2.6 (2.0) morbidities over a mean (SD) of 4.2 (2.0) years of follow-up. Compared with non-Hispanic White patients, patients in racial and ethnic minoritized groups had marginally lower adjusted annual rates of accrual of conditions (-0.03 [95% CI, -0.03 to -0.03] for Spanish-preferring Hispanic patients; -0.02 [95% CI, -0.02 to -0.01] for English-preferring Hispanic patients; -0.01 [95% CI, -0.01 to -0.01] for non-Hispanic Black patients; and -0.04 [95% CI, -0.05 to -0.04] for non-Hispanic Asian patients). Older patients accrued conditions at higher annual rates compared with patients 45 to 50 years of age (0.03 [95% CI, 0.02-0.03] for 50-55 years; 0.03 [95% CI, 0.03-0.04] for 55-60 years; 0.04 [95% CI, 0.04-0.04] for 60-65 years; and 0.05 [95% CI, 0.05-0.05] for ≥65 years). Compared with those with higher income (always ≥138% of the FPL), patients with income less than 138% of the FPL (0.04 [95% CI, 0.04-0.05]), mixed income (0.01 [95% CI, 0.01-0.01]), or unknown income levels (0.04 [95% CI, 0.04-0.04]) had higher annual accrual rates. Compared with continuously insured patients, continuously uninsured and discontinuously insured patients had lower annual accrual rates (continuously uninsured, -0.003 [95% CI, -0.005 to -0.001]; discontinuously insured, -0.004 [95% CI, -0.005 to -0.003]). This cohort study of middle-aged patients seeking care in community health centers suggests that disease accrued at high rates for patients' chronological age. Targeted efforts for chronic disease prevention are needed for patients near or below the poverty line.

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

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. 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. 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. 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.

Adverse childhood experiences and the development of multimorbidity across adulthood-a national 70-year cohort study.

To examine impact of adverse childhood experiences (ACE) on rates and development of multimorbidity across three decades in adulthood. Sample: Participants from the 1946 National Survey of Health and Development, who attended the age 36 assessment in 1982 and follow-up assessments (ages 43, 53, 63, 69; N = 3,264, 51% males). Prospectively collected data on nine ACEs was grouped into (i) psychosocial, (ii) parental health and (iii) childhood health. For each group, we calculated cumulative ACE scores, categorised into 0, 1 and ≥2 ACEs. Multimorbidity was estimated as the total score of 18 health disorders.Serial cross-sectional linear regression was used to estimate associations between grouped ACEs and multimorbidity during follow-up. Longitudinal analysis of ACE-associated changes in multimorbidity trajectories across follow-up was estimated using linear mixed-effects modelling for ACE groups (adjusted for sex and childhood socioeconomic circumstances). Accumulation of psychosocial and childhood health ACEs were associated with progressively higher multimorbidity scores throughout follow-up. For example, those with ≥2 psychosocial ACEs experienced 0.20(95% CI 0.07, 0.34) more disorders at age 36 than those with none, rising to 0.61(0.18, 1.04) disorders at age 69.All three grouped ACEs were associated with greater rates of accumulation and higher multimorbidity trajectories across adulthood. For example, individuals with ≥2 psychosocial ACEs developed 0.13(-0.09, 0.34) more disorders between ages 36 and 43, 0.29(0.06, 0.52) disorders between ages 53 and 63, and 0.30(0.09, 0.52) disorders between ages 63 and 69 compared with no psychosocial ACEs. ACEs are associated with widening inequalities in multimorbidity development in adulthood and early old age. Public health policies should aim to reduce these disparities through individual and population-level interventions.