Multimorbidity measures differentially predicted mortality among older Chinese adults

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

INTRODUCTION

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.

METHODS

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.

RESULTS

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.

DISCUSSION

Condition counts, sensory pattern, cardiometabolic pattern, cancer-inflammatory pattern, and multimorbidity developmental trajectories were prospectively associated with cognitive function.

HIGHLIGHTS

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.

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.

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

OBJECTIVES

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

STUDY DESIGN AND SETTING

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

RESULTS

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

CONCLUSION

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

Association between sleep problems and multimorbidity patterns in older adults

BACKGROUND

Sleep problems are frequent in older adults and are associated with chronic diseases. However, the association with multimorbidity patterns is still unknown. Considering the negative impacts that multimorbidity patterns can have on older adults' life, knowledge of this association can help in the screening and early identification of older adults with sleep problems. The objective was to verify the association between sleep problems and multimorbidity patterns in older Brazilian adults.

METHODS

This was a cross-sectional study conducted with data from 22,728 community-dwelling older adults from the 2019 National Health Survey. The exposure variable was self-reported sleep problems (yes/no). The study outcomes were: multimorbidity patterns, analyzed by self-report of the coexistence of two or more chronic diseases with similar clinical characteristics: (1) cardiopulmonary; (2) vascular-metabolic; (3) musculoskeletal; (4) coexisting patterns.

RESULTS

Older adults with sleep problems had 1.34 (95%CI: 1.21; 1.48), 1.62 (95%CI: 1.15; 2.28), 1.64 (95%CI: 1.39; 1.93), and 1.88 (95%CI: 1.52; 2.33) greater odds of presenting vascular-metabolic, cardiopulmonary, musculoskeletal, and coexisting patterns, respectively.

CONCLUSIONS

These results suggest that public health programs aimed at preventing sleep problems in older adults are essential to reduce possible adverse health outcomes, including multimorbidity patterns and their negative consequences for older adults' health.

Multimorbidity patterns and the relation to self-rated health among older Japanese people: a nationwide cross-sectional study

OBJECTIVES

Classifying individuals into multimorbidity patterns can be useful to identify the target population with poorer clinical outcomes. Self-rated health (SRH) is one of the core outcomes in multimorbidity patients. Although studies have reported that multimorbidity is associated with poor SRH, whether certain patterns have stronger associations remains unknown. Therefore, this study aimed to identify the prevalence and patterns of multimorbidity and investigate the association between multimorbidity patterns and SRH in an older Japanese population.

DESIGN

Cross-sectional study.

SETTING

Data were obtained from the 2013 Comprehensive Survey of Living Conditions, a nationally representative survey of the general Japanese population.

PARTICIPANTS

This study mainly examined 23 730 participants aged ≥65 years who were not hospitalised or institutionalised.

PRIMARY OUTCOME MEASURE

Poor SRH was defined as choosing 'not very good' or 'bad' from five options: 'excellent', 'fairly good', 'average', 'not very good' and 'bad'.

RESULTS

The prevalence of multimorbidity was 40.9% and that of poor SRH was 23.8%. Three multimorbidity patterns were identified by exploratory factor analysis: (1) degenerative/mental health, (3) malignant/digestive/urological/haematological and (3) cardiovascular/metabolic. Multivariable modified Poisson regression analysis revealed that high malignant/digestive/urological/haematological, degenerative/mental health and cardiovascular/metabolic pattern scores, corresponding to the number of affected body systems in each pattern, were significantly associated with poor SRH (adjusted risk ratio (aRR)=1.68, 95% CI: 1.60 to 1.76; aRR=1.63, 95% CI: 1.58 to 1.69; and aRR=1.31, 95% CI: 1.26 to 1.36, respectively). When including the Kessler 6 score, a screening scale for psychological distress, in the analysis, the association between each multimorbidity pattern score and poor SRH decreased.

CONCLUSIONS

Malignant/digestive/urological/haematological and degenerative/mental health patterns may be associated with a high risk for poor SRH. Further research should focus on interventions to improve SRH in multimorbidity patients.