Multimorbidity Combinations Research Articles

A Multimorbidity Analysis of Hospitalized Patients With COVID-19 in Northwest Italy: Longitudinal Study Using Evolutionary Machine Learning and Health Administrative Data.

Multimorbidity is a significant public health concern, characterized by the coexistence and interaction of multiple preexisting medical conditions. This complex condition has been associated with an increased risk of COVID-19. Individuals with multimorbidity who contract COVID-19 often face a significant reduction in life expectancy. The postpandemic period has also highlighted an increase in frailty, emphasizing the importance of integrating existing multimorbidity details into epidemiological risk assessments. Managing clinical data that include medical histories presents significant challenges, particularly due to the sparsity of data arising from the rarity of multimorbidity conditions. Also, the complex enumeration of combinatorial multimorbidity features introduces challenges associated with combinatorial explosions. This study aims to assess the severity of COVID-19 in individuals with multiple medical conditions, considering their demographic characteristics such as age and sex. We propose an evolutionary machine learning model designed to handle sparsity, analyzing preexisting multimorbidity profiles of patients hospitalized with COVID-19 based on their medical history. Our objective is to identify the optimal set of multimorbidity feature combinations strongly associated with COVID-19 severity. We also apply the Apriori algorithm to these evolutionarily derived predictive feature combinations to identify those with high support. We used data from 3 administrative sources in Piedmont, Italy, involving 12,793 individuals aged 45-74 years who tested positive for COVID-19 between February and May 2020. From their 5-year pre-COVID-19 medical histories, we extracted multimorbidity features, including drug prescriptions, disease diagnoses, sex, and age. Focusing on COVID-19 hospitalization, we segmented the data into 4 cohorts based on age and sex. Addressing data imbalance through random resampling, we compared various machine learning algorithms to identify the optimal classification model for our evolutionary approach. Using 5-fold cross-validation, we evaluated each model's performance. Our evolutionary algorithm, utilizing a deep learning classifier, generated prediction-based fitness scores to pinpoint multimorbidity combinations associated with COVID-19 hospitalization risk. Eventually, the Apriori algorithm was applied to identify frequent combinations with high support. We identified multimorbidity predictors associated with COVID-19 hospitalization, indicating more severe COVID-19 outcomes. Frequently occurring morbidity features in the final evolved combinations were age>53, R03BA (glucocorticoid inhalants), and N03AX (other antiepileptics) in cohort 1; A10BA (biguanide or metformin) and N02BE (anilides) in cohort 2; N02AX (other opioids) and M04AA (preparations inhibiting uric acid production) in cohort 3; and G04CA (Alpha-adrenoreceptor antagonists) in cohort 4. When combined with other multimorbidity features, even less prevalent medical conditions show associations with the outcome. This study provides insights beyond COVID-19, demonstrating how repurposed administrative data can be adapted and contribute to enhanced risk assessment for vulnerable populations.

Multimorbidity and associated informal care receiving characteristics for US older adults: a latent class analysis

BackgroundOlder adults with varying patterns of multimorbidity may require distinct types of care and rely on informal caregiving to meet their care needs. This study aims to identify groups of older adults with distinct, empirically-determined multimorbidity patterns and compare characteristics of informal care received among estimated classes.MethodsData are from the 2011 National Health and Aging Trends Study (NHATS). Ten chronic conditions were included to estimate multimorbidity patterns among 7532 individuals using latent class analysis. Multinomial logistic regression model was estimated to examine the association between sociodemographic characteristics, health status and lifestyle variables, care-receiving characteristics and latent class membership.ResultsA four-class solution identified the following multimorbidity groups: some somatic conditions with moderate cognitive impairment (30%), cardiometabolic (25%), musculoskeletal (24%), and multisystem (21%). Compared with those who reported receiving no help, care recipients who received help with household activities only (OR = 1.44, 95% CI 1.05–1.98), mobility but not self-care (OR = 1.63, 95% CI 1.05–2.53), or self-care but not mobility (OR = 2.07, 95% CI 1.29–3.31) had greater likelihood of being in the multisystem group versus the some-somatic group. Having more caregivers was associated with higher odds of being in the multisystem group compared with the some-somatic group (OR = 1.09, 95% CI 1.00-1.18), whereas receiving help from paid helpers was associated with lower odds of being in the multisystem group (OR = 0.36, 95% CI 0.19–0.77).ConclusionsResults highlighted different care needs among persons with distinct combinations of multimorbidity, in particular the wide range of informal needs among older adults with multisystem multimorbidity. Policies and interventions should recognize the differential care needs associated with multimorbidity patterns to better provide person-centered care.

Mental-somatic multimorbidity in trajectories of cognitive function for middle-aged and older adults.

Multimorbidity may confer higher risk for cognitive decline than any single constituent disease. This study aims to identify distinct trajectories of cognitive impairment probability among middle-aged and older adults, and to assess the effect of changes in mental-somatic multimorbidity on these distinct trajectories. Data from the Health and Retirement Study (1998-2016) were employed to estimate group-based trajectory models identifying distinct trajectories of cognitive impairment probability. Four time-varying mental-somatic multimorbidity combinations (somatic, stroke, depressive, stroke and depressive) were examined for their association with observed trajectories of cognitive impairment probability with age. Multinomial logistic regression analysis was conducted to quantify the association of sociodemographic and health-related factors with trajectory group membership. Respondents (N = 20,070) had a mean age of 61.0 years (SD = 8.7) at baseline. Three distinct cognitive trajectories were identified using group-based trajectory modelling: (1) Low risk with late-life increase (62.6%), (2) Low initial risk with rapid increase (25.7%), and (3) High risk (11.7%). For adults following along Low risk with late-life increase, the odds of cognitive impairment for stroke and depressive multimorbidity (OR:3.92, 95%CI:2.91,5.28) were nearly two times higher than either stroke multimorbidity (OR:2.06, 95%CI:1.75,2.43) or depressive multimorbidity (OR:2.03, 95%CI:1.71,2.41). The odds of cognitive impairment for stroke and depressive multimorbidity in Low initial risk with rapid increase or High risk (OR:4.31, 95%CI:3.50,5.31; OR:3.43, 95%CI:2.07,5.66, respectively) were moderately higher than stroke multimorbidity (OR:2.71, 95%CI:2.35, 3.13; OR: 3.23, 95%CI:2.16, 4.81, respectively). In the multinomial logistic regression model, non-Hispanic Black and Hispanic respondents had higher odds of being in Low initial risk with rapid increase and High risk relative to non-Hispanic White adults. These findings show that depressive and stroke multimorbidity combinations have the greatest association with rapid cognitive declines and their prevention may postpone these declines, especially in socially disadvantaged and minoritized groups.

RACIAL/ETHNIC AND MULTIMORBIDITY DIFFERENCES IN DEMENTIA ONSET AMONG MIDDLE-AGED AND OLDER ADULTS

Abstract Racial/ethnic minoritized older adults in the U.S. have higher prevalence of multimorbidity and experience higher risk of dementia relative to non-Hispanic White older adults. This study examined the relationship between race/ethnicity and dementia onset within the context of multimorbidity during middle and later life in the U.S. Data from the Health & Retirement Study (1998-2018, HRS and War Babies cohorts, N=8,031) were applied to discrete-time survival models to estimate differences in the risk of dementia onset using the Langa-Weir Classification for up to ten time intervals (1998-2018). Models were adjusted for race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic), multimorbidity (no/one disease, advanced cardiovascular multimorbidity, metabolic multimorbidity, advanced cardiovascular-metabolic multimorbidity, and neither advanced cardiovascular nor metabolic multimorbidity), age at baseline, sex, education, and whether a proxy provided the interview. Over the 20 years, 9.8% of the respondents (n=785) developed dementia. In covariate-adjusted models, non-Hispanic Black (adjusted Odds Ratio (aOR) =2.27, 95%CI: 1.90, 2.72) and Hispanic (aOR=1.59, 95%CI: 1.26, 2.01) respondents had higher risk of developing dementia compared with non-Hispanic White respondents. Regarding multimorbidity categories, respondents with advanced cardiovascular-metabolic multimorbidity (aOR=1.80, 95%CI: 1.45, 2.24) had significantly higher odds of dementia onset relative to the neither advanced cardiovascular nor metabolic multimorbidity category. Significant differences were observed in dementia onset for up to 20 years between racial/ethnic minoritized and White adults, as well as among multimorbidity combinations. Our findings highlight the need to intervene earlier on modifiable risk factors, such as multimorbidity, to reduce the risk of developing dementia among racial/ethnic minoritized adults.

CARE-RECEIVING CHARACTERISTICS AMONG OLDER ADULTS WITH DIFFERENT MENTAL–SOMATIC MULTIMORBIDITY COMBINATIONS

Abstract Older adults with distinct multimorbidity combinations require different care with varying intensities. This study aimed to examine sociodemographic, health, and care-receiving characteristics among older adults with varying multimorbidity patterns. A total of 4,719 participants from the National Health and Aging Trends Study in 2017 were included, and five clinically-informed multimorbidity categories were assessed: 169 (3.58%) had no conditions, 3031 (64.23%) had somatic conditions only, 347 (7.35%) had depression without cognitive impairment (depression group), 852 (18.05%) had cognitive impairment without depression (cognitive impairment group), and 320 (6.78%) had both depression and cognitive impairment. Compared with the depression group and cognitive impairment group, those with both depression and cognitive impairment were significantly older, more likely to be non-Hispanic Black or Hispanic, widowed, U.S. born, Medicaid recipients, underweight BMI, had lower levels of education and more functional limitations. Compared to the depression group, those with both depression and cognitive impairment had lower self-rated health, less likely to be a smoker, received 118 more hours of care per month on average with more caregivers in ADLs, banking, and money management. Compared with the cognitive impairment group, those with both depression and cognitive impairment had more somatic conditions, received an average of 70 more hours of care per month with more caregivers in ADLs. Results highlighted the substantially greater caregiving hours and intensities pertaining to the combination of depression and/or cognitive impairment, and suggested the potential of addressing mental health among older adults with or without dementia to improve their own and caregivers’ health outcomes.

Findings from the Indonesian family life survey on patterns and factors associated with multimorbidity

The prevalence of multimorbidity tends to increase with age, but it is now also reported in the middle-aged population, which has a negative impact on healthcare systems and health outcomes. This study aims to analyze the patterns and factors associated with multimorbidity in Indonesia. This national cross-sectional population-based survey used publicly available data from the Indonesian Family Life Survey (IFLS-5) for 2014 among middle-aged (40–59 years old) and elderly (≥ 60 years old) respondents. Information on all chronic diseases was assessed using a self-reported questionnaire. Sociodemographic and health-related behavioral factors were obtained from self-reported data. Binary logistic regression analysis was used to identify the factors associated with multimorbidity. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported. The study recruited 11,867 respondents. The prevalence of multimorbidity was 18.6% (95% CI 17.9–19.3) with which 15.6% among middle age (95% CI 14.95–16.25) and 24.9% among the elderly (95% CI 24.12–25.68). Hypertension was the most commonly reported disease (23.2%) in all combinations of multimorbidity and among all age groups. Socio-demographic factors: elderly (AOR: 1.66; 95% CI 1.46–1.89), female (AOR: 1.42; 95% CI 1.20–1.69), living in the urban area (AOR: 1.22; 95% CI 1.09–1.38), higher educational level (AOR: 2.49; 95% CI 1.91–3.26), unemployed (AOR: 1.63; 95% CI 1.44–1.84), and higher economic level (AOR: 1.41; 95% CI 1.18–1.68) were associated with multimorbidity. Poor health behavior factors: being former smokers (AOR: 2.03; 95% CI 1.65–2.51) and obesity (AOR: 1.53; 95% CI 1.35–1.75) were also associated with multimorbidity. The prevalence of multimorbidity in the middle-aged and elderly population in Indonesia is relatively high, particularly in populations with poor health behaviors. Therefore, healthcare professionals should integrate more patient-specific factors when designing and implementing tailored interventions to manage multimorbidity in Indonesia.

Multimorbidity patterns in older migrants and natives across Europe

Abstract Background Multimorbidity patterns in immigrants may differ from the native population due to their biological traits and experience in origin and receiving countries. This study addresses the descriptive patterns of multimorbidity by gender and immigration background and compares the prevalence of each multimorbidity pattern between immigrants and natives. Methods This study utilizes longitudinal data from the Survey of Health, Ageing, and Retirement in Europe (SHARE) from waves 2 to 8 in individuals aged 50 to 79 from 28 European countries. We estimate the prevalence of single chronic diseases and two-disease combinations. Further, we estimate the risk ratio of the estimated prevalence between immigrants and natives for comparison. Results This study finds varying multimorbidity patterns based on immigration background. Our results show that combinations including stomach ulcers or diabetes are distinctly more prevalent in immigrants than in natives for both genders, except for the “diabetes-cardiovascular” patterns among men. Further, we observe a higher prevalence of the “dementia-diabetes” combination among immigrant men at their younger ages, which eventually converges toward the native level in older age groups. However, these patterns of multimorbidity combinations in immigrants versus natives vary by origin and the receiving country. Conclusions A chronic health condition in the immigrant population can have a stronger amplifying or buffering effect on the risk of having another chronic disease than their native-born counterparts. Key messages • Multimorbidity patterns in immigrants differ from the native population due to their biological traits and experience in origin and receiving countries. • A chronic health condition in the immigrant population can have a stronger amplifying or buffering effect on the risk of having another chronic disease than their native-born counterparts.

Racial differences in healthcare expenditures for prevalent multimorbidity combinations in the USA: a cross-sectional study

BackgroundWe aimed to model total charges for the most prevalent multimorbidity combinations in the USA and assess model accuracy across Asian/Pacific Islander, African American, Biracial, Caucasian, Hispanic, and Native American populations.MethodsWe used Cerner HealthFacts data from 2016 to 2017 to model the cost of previously identified prevalent multimorbidity combinations among 38 major diagnostic categories for cohorts stratified by age (45–64 and 65 +). Examples of prevalent multimorbidity combinations include lipedema with hypertension or hypertension with diabetes. We applied generalized linear models (GLM) with gamma distribution and log link function to total charges for all cohorts and assessed model accuracy using residual analysis. In addition to 38 major diagnostic categories, our adjusted model incorporated demographic, BMI, hospital, and census division information.ResultsThe mean ages were 55 (45–64 cohort, N = 333,094) and 75 (65 + cohort, N = 327,260), respectively. We found actual total charges to be highest for African Americans (means $78,544 [45–64], $176,274 [65 +]) and lowest for Hispanics (means $29,597 [45–64], $66,911 [65 +]). African American race was strongly predictive of higher costs (p < 0.05 [45–64]; p < 0.05 [65 +]). Each total charge model had a good fit. With African American as the index race, only Asian/Pacific Islander and Biracial were non-significant in the 45–64 cohort and Biracial in the 65 + cohort. Mean residuals were lowest for Hispanics in both cohorts, highest in African Americans for the 45–64 cohort, and highest in Caucasians for the 65 + cohort. Model accuracy varied substantially by race when multimorbidity grouping was considered. For example, costs were markedly overestimated for 65 + Caucasians with multimorbidity combinations that included heart disease (e.g., hypertension + heart disease and lipidemia + hypertension + heart disease). Additionally, model residuals varied by age/obesity status. For instance, model estimates for Hispanic patients were highly underestimated for most multimorbidity combinations in the 65 + with obesity cohort compared with other age/obesity status groupings.ConclusionsOur finding demonstrates the need for more robust models to ensure the healthcare system can better serve all populations. Future cost modeling efforts will likely benefit from factoring in multimorbidity type stratified by race/ethnicity and age/obesity status.

Multimorbidity, Treatment, and Determinants among Chronic Patients Attending Primary Health Facilities in Tshwane, South Africa.

The growing burden of non-communicable diseases amidst the largest burden of HIV in South Africa leads to disease combinations of multimorbidity with the complexity of care. We conducted a cross-sectional study to assess multimorbidity, medication adherence, and associated factors among out-patients with chronic diseases in primary health care (PHC) facilities in Tshwane, South Africa. A structured questionnaire was used to collect data on comorbidities and medication adherence, along with socio-demographic and lifestyle factors. Logistic regression models were used to analyse the determinants of multimorbidity and medication adherence. In all 400 patients with chronic diseases (mean age: 47 ± 12 years) living in poor environments, common chronic conditions were hypertension (62%), diabetes (45%), HIV (44%), TB (33%), hypercholesterolemia (18%), and gout (13%). The proportion of concordant comorbidity (i.e., diseases with similar risk profiles and management) was 72%, more than 28% of discordant comorbidity (i.e., diseases not related in pathogenesis or management). Most patients had two coexisting chronic conditions (75%), while few had more than two chronic conditions (23%) and single-occurring conditions (2%). Prevalence rates for common multimorbidity patterns were 25% (HIV and TB), 17% (hypertension and diabetes), 9% (hypertension, diabetes, and hypercholesterolemia), and 2% (hypertension diabetes and HIV), while medication adherence was estimated at 74%. In multivariate analysis, multimorbidity was associated with an older age and lower socio-economic status, while medication non-adherence was associated with a younger age and socio-economic factors. The study highlights the presence of multimorbidity among primary care patients attributed to hypertension, diabetes, HIV, and TB in South Africa with non-adherence to medication in one-third of patients. Policies are needed for education on multimorbidity with a need to optimize lifestyle modifications, perhaps proactive outreach or nursing contact with high-risk patients with public-health-sensitive conditions, such as HIV and/or TB, as well as patients with a history of non-adherence to medications. Considerations should be given to the development of a medication adherence scale for multiple chronic conditions beyond assessing adherence to a single index medication.

Health Status of US Patients With One or More Health Conditions: Using a Novel Electronic Patient-reported Outcome Measure Producing Single Metric Measures.

Most existing research studying health status impacted by morbidity has focused on a specific health condition, and most instruments used for measuring health status are neither patient-centered nor preference-based. This study aims to report on the health status of patients impacted by one or more health conditions, measured by a patient-centered and preference-based electronic patient-reported outcome measure. A cross-sectional study was conducted among patients with one or more health conditions in the United States. A novel generic, patient-centered, and preference-based electronic patient-reported outcome measure: Château Santé-Base, was used to measure health status. Individual health state was expressed as a single metric number (value). We compared these health-state values between sociodemographic subgroups, between separate conditions, between groups with or without comorbidity, and between different combinations of multimorbidity. The total sample comprised 3913 patients. Multimorbidity was present in 62% of the patients. The most prevalent health conditions were pain (50%), fatigue/sleep problems (40%), mental health problems (28%), respiratory diseases (22%), and diabetes (18%). The highest (best) and lowest health-state values were observed in patients with diabetes and mental health problems. Among combinations of multimorbidity, the lowest values were observed when mental health problems were involved, the second lowest values were observed when fatigue/sleep problems and respiratory diseases coexisted. This study compared health status across various single, and multiple (multimorbidity and comorbidity) health conditions directly, based on single metric health-state values. The insights are valuable in clinical practice and policy-making.

Cardiometabolic multimorbidity and associated patterns of healthcare utilization and quality of life: Results from the Study on Global AGEing and Adult Health (SAGE) Wave 2 in Ghana.

Understanding the patterns of multimorbidity, defined as the co-occurrence of more than one chronic condition, is important for planning health system capacity and response. This study assessed the association of different cardiometabolic multimorbidity combinations with healthcare utilization and quality of life (QoL). Data were from the World Health Organization (WHO) study on global AGEing and adult health Wave 2 (2015) conducted in Ghana. We analysed the clustering of cardiometabolic diseases including angina, stroke, type 2 diabetes, and hypertension with unrelated conditions such as asthma, chronic lung disease, arthritis, cataract and depression. The clusters of adults with cardiometabolic multimorbidity were identified using latent class analysis and agglomerative hierarchical clustering algorithms. We used negative binomial regression to determine the association of multimorbidity combinations with outpatient visits. The association of multimorbidity clusters with hospitalization and QoL were assessed using multivariable logistic and linear regressions. Data from 3,128 adults aged over 50 years were analysed. We identified four distinct classes of multimorbidity: relatively "healthy class" with no multimorbidity (47.9%): abdominal obesity only (40.7%): cardiometabolic and arthritis class comprising participants with hypertension, type 2 diabetes, stroke, abdominal and general obesity, arthritis and cataract (5.7%); and cardiopulmonary and depression class including participants with angina, chronic lung disease, asthma, and depression (5.7%). Relative to the class with no multimorbidity, the cardiopulmonary and depression class was associated with a higher frequency of outpatient visits [β = 0.3; 95% CI 0.1 to 0.6] and higher odds of hospitalization [aOR = 1.9; 95% CI 1.0 to 3.7]. However, cardiometabolic and arthritis class was associated with a higher frequency of outpatient visits [β = 0.8; 95% CI 0.3 to 1.2] and not hospitalization [aOR = 1.1; 95% CI 0.5 to 2.9]. The mean QoL scores was lowest among participants in the cardiopulmonary and depression class [β = -4.8; 95% CI -7.3 to -2.3] followed by the cardiometabolic and arthritis class [β = -3.9; 95% CI -6.4 to -1.4]. Our findings show that cardiometabolic multimorbidity among older persons in Ghana cluster together in distinct patterns that differ in healthcare utilization. This evidence may be used in healthcare planning to optimize treatment and care.

Community-Level Social Support to Mitigate the lmpact of Combined Frailty and Multimorbidity on Psychological Distress Among Rural Chinese Older Adults: Multilevel Modeling Study During the COVID-19 Pandemic.

Accumulating research provides evidence that the psychological health of older people deteriorated from the prior to during the COVID-19 pandemic. Unlike robust individuals, co-existing frailty and multimorbidity would expose older adults to more complicated and wide-ranging stressors. Community-level social support is also an important impetus for age-friendly interventions, and it is one of the components of social capital which is seen as an ecological-level property. To date, we did not find research that examined whether community social support buffer the adverse impact of the combined frailty and multimorbidity on psychological distress in rural setting during the COVID-19 in China. This study explores the combined effect of frailty and multimorbidity on psychological distress in rural Chinese older adults during the COVID-19 pandemic, and to examine whether community-level social support would buffer the afore-mentioned association. Data used in this study were extracted from two waves of the Shandong Rural Elderly Health Cohort (SREHC), the final analytic sample included 2,785 respondents who participated both of the baseline and follow-up surveys. Multilevel linear mixed-effects models were employed to quantify the strength of the longitudinal association between frailty and multimorbidity combinations and psychological distress using two waves of data for each participant, and then, the cross-level interactions between community-level social support and the combination of frailty and multimorbidity were included to test whether the community-level social support would buffer the adverse impact of the coexisting frailty and multimorbidity on psychological distress. Frail older adults with multimorbidity reported the most psychological distress compared with individuals with only one or without any of the conditions (β=0.68; 95% CI: 0.60 to 0.77; P<.001), and baseline coexisting frailty and multimorbidity predicted the most psychological distress during the COVID-19 pandemic (β=0.32; 95% CI: 0.22 to 0.43; P<.001). Further, community social support moderated the afore-mentioned association (β=-0.16; 95% CI: -0.23 to -0.09; P<.001), and the increased community-level social support buffered the adverse effect of coexisting frailty and multimorbidity on psychological distress during the COVID-19 pandemic (β=-0.11; 95% CI: -0.22 to -0.01; P=.035). Our findings suggest that more public health and clinical attention should be paid to psychological distress among multimorbid older adults with frailty when facing public health emergencies. This research also suggests that community-level interventions prioritizing social support mechanisms, specifically, improving the averaged levels of social support within communities, may be an effective approach to alleviate psychological distress for rural older adults who concurrently manifest frailty and multimorbidity.

Multimorbidity and Functional Disability among Older Adults: The Role of Inflammation and Glycemic Status – An Observational Longitudinal Study

Introduction: Specific multimorbidity combinations, in particular those including arthritis, stroke, and cognitive impairment, have been associated with high burden of activities of daily living (ADL)-instrumental activities of daily living (IADL) disability in older adults. The biologic underpinnings of these associations are still unclear. Methods: Observational longitudinal study using data from the Health and Retirement Study (N = 8,618, mean age = 74 years, 58% female, 25% non-white) and negative binomial regression models stratified by sex to evaluate the role of inflammatory and glycemic biomarkers (high-sensitivity C-reactive protein (hs-CRP) and HbA1c) in the association between specific multimorbidity combinations (grouped around one of eight index diseases: arthritis, cancer, cognitive impairment, diabetes, heart disease, hypertension, lung disease, and stroke; assessed between 2006 and 2014) and prospective ADL-IADL disability (2 years later, 2008–2016). Results were adjusted for sociodemographic characteristics, body mass index, number of coexisting diseases, and baseline ADL-IADL score. Results: Multimorbidity combinations indexed by arthritis (IRR = 1.1, 95% CI = 1.01–1.20), diabetes (IRR = 1.19, 95% CI = 1.09–1.30), and cognitive impairment (IRR = 1.11, 95% CI = 1.01–1.23) among men and diabetes-indexed multimorbidity combinations (IRR = 1.07, 95% CI = 1.01–1.14) among women were associated with higher ADL-IADL scores at increasing levels of HbA1c. Across higher levels of hs-CRP, multimorbidity combinations indexed by arthritis (IRR = 1.06, 95% CI = 1.02–1.11), hypertension (IRR = 1.06, 95% CI = 1.02–1.11), heart disease (IRR = 1.06, 95% CI = 1.01–1.12), and lung disease (IRR = 1.14, 95% CI = 1.07–1.23) were associated with higher ADL-IADL scores among women, while there were no significant associations among men. Conclusion: The findings suggest potential for anti-inflammatory management among older women and optimal glycemic control among older men with these particular multimorbidity combinations as focus for therapeutic/preventive options for maintaining functional health.

Multimorbidity combinations and their association with functional disabilities among Indian older adults: evidence from Longitudinal Ageing Study in India (LASI)

ObjectiveThis study aims to identify the unique multimorbidity combinations (MMCs) and their associations with the functional disability of Indian older adults. Moreover, the population attributable fractions (PAFs) were calculated to...

Agrupamento de doenças crônicas não transmissíveis em adultos e idosos brasileiros: correlações como multimorbidade

Abstract Background Health has dynamic conditions and overlapping pathophysiological factors. For health prevention and promotion, actions are necessary to understand the most common risk combinations. Objective Describe noncommunicable chronic diseases (NCDs) clusters and investigate specific multimorbidity combinations in Brazilian adults and older adults. Method This study used data from Vigitel 2013 survey held in the Brazilian capitals (52,929 interviews). A self-report of diabetes, dyslipidemia, hypertension, and obesity was used. The analyses were the descriptive cluster of NCDs and an adjusted binary logistic regression (odds ratio [OR]), stratified by age. Results Among adults, the clusters of diabetes, dyslipidemia, hypertension, and obesity (O/E = 18.74) and diabetes, hypertension, and obesity (O/E = 16.83) were higher. There was a higher clustering between diabetes and obesity (O/E = 7.25). Among adults, diabetes was associated with dyslipidemia (OR: 3.04), hypertension (OR: 3.84), and hypertension with obesity (OR: 3.34). In older adults, hypertension was associated with diabetes (OR: 2.79), dyslipidemia (OR: 2.06), and obesity (OR: 2.26). Conclusion Other diseases combined with diabetes and hypertension were more frequent in adults and older adults. It is suggested to combine preventive and control measures for these diseases for the non-occurrence of new diagnoses.

MENTAL-SOMATIC MULTIMORBIDITY IN GROUP-BASED TRAJECTORIES OF COGNITIVE FUNCTION FOR MIDDLE-AGED AND OLDER ADULTS

Abstract Somatic and mental multimorbidity contributes to cognitive decline. The study aims to identify distinct trajectories of cognitive performance among middle-aged and older adults, and to examine the contribution of specific somatic and mental multimorbidity combinations on subsequent risk of cognitive impairment. We used group-based trajectory modeling to identify trajectories of cognitive impairment risk among participants in the Health &amp; Retirement Study during years 1998-2016 (N=20,372). We included time-invariant sociodemographic factors (sex, race/ethnicity, education) to quantify their association with trajectory class membership, and time-varying multimorbidity combinations to examine their relative impact on observed trajectories. Four somatic-mental multimorbidity combinations were analyzed: somatic multimorbidity (two or more of the following: heart disease, lung disease, hypertension, arthritis, diabetes, cancer), stroke-multimorbidity (any somatic including stroke); depressive-multimorbidity (any somatic including high depressive symptoms); and stroke and depressive multimorbidity (including both stroke and high depressive symptoms). We identified three trajectory classes of cognitive impairment: low baseline risk with gradual increase (55.1%); low baseline risk with rapid increase (32.8%); and high baseline risk with gradual increase (12.1%). In the group with low baseline risk with rapid increase, stroke-multimorbidity (OR: 2.40, 95%CI: 2.11, 2.74) and depressive-multimorbidity (OR: 1.65, 95%CI: 1.50,1.81) each had higher odds of cognitive impairment relative to somatic multimorbidity. Similarly, stroke and depressive multimorbidity (OR: 3.46, 95%CI: 2.85, 4.21) was associated with higher odds of cognitive impairment compared with somatic multimorbidity. This study highlights the importance of risk modification for somatic and mental multimorbidity combinations from mid-life to inform interventions to sustain cognitive performance.

Prevalence of multimorbidity combinations and their association with medical costs and poor health: A population-based study of U.S. adults.

Multimorbidity is common, but the prevalence and burden of the specific combinations of coexisting disease has not been systematically examined in the general U.S. adult population. To identify and estimate the burden of highly prevalent combinations of chronic conditions that are treated among one million or more adults in the United States. Cross-sectional analysis of U.S. households in the Medical Expenditure Panel Survey (MEPS), 2016-2019, a large nationally-representative sample of the community-dwelling population. Association rule mining was used to identify the most common combinations of 20 chronic conditions that have high relevance, impact, and prevalence in primary care. The main measures and outcomes were annual treated prevalence, total medical expenditures, and perceived poor health. Logistic regression models with poor health as the outcome and each multimorbidity combination as the exposure were used to calculate adjusted odds ratios and 95% confidence intervals. Frequent pattern mining yielded 223 unique combinations of chronic disease, including 74 two-way (dyad), 115 three-way (triad), and 34 four-way combinations that are treated in one million or more U.S. adults. Hypertension-hyperlipidemia was the most common two-way combination occurring in 30.8 million adults. The combination of diabetes-arthritis-cardiovascular disease was associated with the highest median annual medical expenditures ($23,850, interquartile range: $11,593-$44,616), and the combination of diabetes-arthritis-asthma/COPD had the highest age-race-sex adjusted odds ratio of poor self-rated health (adjusted odd ratio: 6.9, 95%CI: 5.4-8.8). This study demonstrates that many multimorbidity combinations are highly prevalent among U.S. adults, yet most research and practice-guidelines remain single disease focused. Highly prevalent and burdensome multimorbidity combinations could be prioritized for evidence-based research on optimal prevention and treatment strategies.

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

BackgroundMultimorbidity 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.ObjectiveTo 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).ResultsFor 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).ConclusionsOverall, 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.

Multimorbidity patterns across race/ethnicity as stratified by age and obesity

The objective of our study is to assess differences in prevalence of multimorbidity by race/ethnicity. We applied the FP-growth algorithm on middle-aged and elderly cohorts stratified by race/ethnicity, age, and obesity level. We used 2016–2017 data from the Cerner HealthFacts electronic health record data warehouse. We identified disease combinations that are shared by all races/ethnicities, those shared by some, and those that are unique to one group for each age/obesity level. Our findings demonstrate that even after stratifying by age and obesity, there are differences in multimorbidity prevalence across races/ethnicities. There are multimorbidity combinations distinct to some racial groups—many of which are understudied. Some multimorbidities are shared by some but not all races/ethnicities. African Americans presented with the most distinct multimorbidities at an earlier age. The identification of prevalent multimorbidity combinations amongst subpopulations provides information specific to their unique clinical needs.

Association of Complex Multimorbidity and Long-term Survival After Emergency General Surgery in Older Patients With Medicare

Although nearly 1 million older patients are admitted for emergency general surgery (EGS) conditions yearly, long-term survival after these acute diseases is not well characterized. Many older patients with EGS conditions have preexisting complex multimorbidity defined as the co-occurrence of at least 2 of 3 key domains: chronic conditions, functional limitations, and geriatric syndromes. The hypothesis was that specific multimorbidity domain combinations are associated with differential long-term mortality after patient admission with EGS conditions. To examine multimorbidity domain combinations associated with increased long-term mortality after patient admission with EGS conditions. This cohort study included community-dwelling participants aged 65 years and older from the Medicare Current Beneficiary Survey with linked Medicare data (January 1992 through December 2013) and admissions for diagnoses consistent with EGS conditions. Surveys on health and function from the year before EGS conditions were used to extract the 3 domains: chronic conditions, functional limitations, and geriatric syndromes. The number of domains present were summed to calculate a categorical rank: no multimorbidity (0 or 1), multimorbidity 2 (2 of the 3 domains present), and multimorbidity 3 (all 3 domains present). Whether operative treatment was provided during the admission was also identified. Data were cleaned and analyzed between January 16, 2020, and April 29, 2021. Mutually exclusive multimorbidity domain combinations (functional limitations and geriatric syndromes; functional limitations and chronic conditions; chronic conditions and geriatric syndromes; or functional limitations, geriatric syndromes, and chronic conditions). Time to death (up to 3 years from EGS conditions admission) in patients with multimorbidity combinations was analyzed using a Cox proportional hazards model and compared with those without multimorbidity; hazard ratios (HRs) and 95% CIs are presented. Models were adjusted for age, sex, and operative treatment. Of 1960 patients (median [IQR] age, 79 [73-85] years; 1166 [59.5%] women), 383 (19.5%) had no multimorbidity, 829 (42.3%) had 2 multimorbidity domains, and 748 (38.2%) had all 3 domains present. A total of 376 (19.2%) were known to have died in the follow-up period, with a median (IQR) follow-up of 377 (138-621) days. Patients with chronic conditions and geriatric syndromes had a mortality risk similar to those without multimorbidity. However, all domain combinations with functional limitations were associated with significantly increased risk of death: functional limitations and chronic conditions (HR, 1.83; 95% CI, 1.03-3.23); functional limitations and geriatric syndromes (HR, 2.91; 95% CI, 1.37-6.18); and functional limitations, geriatric syndromes, and chronic conditions (HR, 2.08; 95% CI, 1.49-2.89). Findings of this study suggest that a patient's baseline complex multimorbidity level efficiently identifies risk stratification groups for long-term survival. Functional limitations are rarely considered in risk stratification paradigms for older patients with EGS conditions compared with chronic conditions and geriatric syndromes. However, functional limitations may be the most important risk factor for long-term survival.