Multimorbidity patterns in old adults and their associated multi-layered factors: a cross-sectional study

The number of chronic diseases in old patients increases with age, which laid disease burdens to old patients and government [1, 2]. A study revealed the risk of death was 1.73 (95% CI: 1.41; 2.13) and 2.72 (95% CI: 1.81; 4.08) for individuals with 2+ and 3+ co-existing chronic diseases respectively compared with individuals with ≤1 chronic disease [3]. The coexistence of two or more chronic diseases in the same individual is called as “multimorbidity” [4]. This term originated from “comorbidity” [5] but differed from the conception of “comorbidity” that signified a “distinct additional clinical entity” occurring in the setting of an index disease [6, 7]. The conception of “multimorbidity” remains human-centered and focuses on exploring the systematic methods of prevention and intervention of the common risk factors. Frailty can also be used to measure the risk profile of old adults in order to support clinical decisions and design tailored interventions. Frailty refers to a medical syndrome with multiple causes and contributors. It is characterized by diminished strength, endurance, and reduced physiologic function that increases an individual’s vulnerability for developing increased dependency and/or death [8]. Compared with frailty, multimorbidity has a more recognizable measuring standard and implies a more holistic evaluation of the individual’s clinical complexity [9]. To reflect this complexity, it is needed to analyze the differences among co-existing chronic diseases patterns but not the discrepancy in the number of chronic diseases [10, 11]. Besides, the probability of occurrence through specific patterns was higher than that of random disease combinations among patients with multimorbidity.

Recognition of specific patterns will help clinicians predict the possible occurrence of multimorbidity risks among patients and prevent or intervene those risks at group level [12]. However, due to differences in contextual features (ethnic characteristics, living habits, etc.), multimorbidity patterns in different regions and countries also presented differently [13]. Garin et al. analyzed the data from the Collaborative Research on Ageing in Europe project and the World Health Organization’s Study on Global Ageing and Adult Health [14]. They discovered three prevalent multimorbidity patterns around the world, which were “cardio-respiratory” (angina, asthma, and chronic obstructive pulmonary disease), “metabolic” (diabetes, adiposis, and hypertension), and “mental-articular” (arthritis and depression). Sheridan et al. conducted a prospective analysis by using the data from the Survey of Health, Ageing and Retirement in Europe in both 2013 and 2015 [15]. Their results identified 380 unique combinations of chronic disease in old adults with multimorbidity, which proved that hypertension almost existed in each prevalent disease combination. The study of Hernández et al. in Ireland found hypertension and hypercholesterolemia were the most common co-existing diseases by analyzing the chronic diseases combinations from 6101 old adults aged 50+ [12]. Gu et al. identified three multimorbidity patterns (“degenerative disorders”, “digestive/respiratory disorders”, and “cardiovascular/metabolic disorders”) by analyzing the data of old adults aged 60+ in Nanjing, China through exploratory factor analysis [16]. Furthermore, some studies discovered that multimorbidity patterns were also influenced by individual characteristics, such as socio-demographic characteristics, behavioral lifestyles and socio-economic factors excepting for contextual features [17‐20].

To accurately determine multimorbidity patterns and their risk factors, some studies introduced theories of cognitive psychology as the factor screening basis, but they rarely involved comprehensive context features. For example, Singer et al. assessed the relationships between multimorbidity and material, psycho-social and behavioural factors based on the theories of social determinants of health (SDoH), but ignored the role of social and cultural contexts [21]. These weaknesses led to the incomplete understanding of the different types of factors associated with multimorbidity, which calls on future researchers to establish effective multimorbidity prevention and intervention strategy. As to this point, this study introduces health ecological model by involving individual-, inter-personal-, community-, organization-, government- and other multi-layered factors associated with individual’s health [22, 23]. The model, which evolved from an ecological model, shows individuals’ health is affected by innate personal characteristics, psychological behavior characteristics, and macro-environmental factors. Health is the result of the interaction of individual characteristics and contextual features [24, 25]. Thus, in this study, we initially tried to identify multimorbidity patterns in old adults, then analyzed the possible multi-layered factors associated with them based on health ecological model.

We identified “degenerative/digestive diseases”, “metabolic diseases” and “cardiovascular diseases” as the three specific patterns of multimorbidity among Chinese old patients with chronic disease in this study. The pattern of degenerative/digestive diseases is comprised of similar proportions of arthritis, hypertension, hearing loss, osteoporosis, digestive system diseases, eye diseases, and its prevalence was 40.86%. Degenerative diseases as a separate pattern were observed in Chinese old populations but not in other countries or regions [16, 35‐40]. Gu et al. used data from the community-dwelling old persons in Nanjing, China and discovered hearing disorder, cataract, joint disease and cancer were included in the degenerative diseases group [16, 38], but cancer was not included in our and other studies [35‐37, 39]. Meanwhile, the digestive system diseases and arthritis diseases were often clustered (stomach or other digestive disease, arthritis) among older Chinese [40, 41]. Moreover, the patterns of cardiovascular diseases and metabolic diseases were observed in almost all studies focusing on older Chinese [16, 38, 39, 41, 42]. In this study, the pattern of metabolic diseases was characterized by having 100% probability to suffer from diabetes and 78.9% probability of being ill with hypertension, and its prevalence was 22.96%. While more than 1 in 5 persons in the pattern of cardiovascular diseases had coronary heart-hypertension combination, and the prevalence of this pattern was 36.18%. The study of She et al. found that the pattern of cardiovascular disorders (arrhythmia, ischemic heart disease, and heart failure) and the pattern of metabolic disorders (hypertension, obesity, diabetes, and dyslipidemia) were identified by the 1497 rural community older from the Confucius Hometown Aging Project (2014-2016) in Qufu, Shandong, China [39]. Hypertension, diabetes, coronary heart disease were also the major diseases of the cardiovascular and metabolic diseases patterns in other studies [16, 38, 41]. However, mental illness did not appear in disease combination in this study. This finding was inconsistent with some previous studies. Garin et al. observed a “Mental-articular” pattern in China, Ghana and India, which included arthritis and depression and “Mental-articular” pattern in Spain, which included arthritis, depression and anxiety [14, 43]. This may be limited by the sample size. The small sample size of mental illnesses leads to the low joint probability and conditional probability of mental illnesses and other chronic diseases [34], resulting in a low probability of mental illnesses appearing in the multimorbidity patterns in our study.

Remarkable heterogeneity in the number, types, and assessment approaches of chronic conditions [39], as well as the aging characteristics of different study samples [16], has led to some differences in the combination of patterns across studies, but specific common multimorbidity patterns have been identified, such as metabolic syndromes, degenerative and cardiovascular diseases.

What’s more interesting, some specific multi-layered factors were protective factors for one pattern, but risk factors for another. An imbalanced diet might cause gastrointestinal mucosal damage and dysfunction which might increase the incidence of “degenerative/digestive diseases”. While, “metabolic diseases” always require an accurate and strict restriction on the intake of foods rich in fats, protein, starch and sugar, so a reasonable diet might increase the probability of this disease. A great scale and close-contact of friendship network might enhance the patients’ mutual assistance and information exchange in chronic disease management to reduce the incidence of such “degenerative/digestive diseases” pattern [21, 44, 45]. But individuals may be opposed to disease management because of the negative emotions brought about by excessive constraints from family members. Some unreasonable lifestyle habits might be aggrandized with the expansion of the friendship network which increase the incidence of “metabolic diseases” pattern. It is worth mentioning that good medication adherence might no longer be beneficial for all disease patterns. In the early stages of disease, rational administration might prevent functional injuries and reduce the incidence of “degenerative/digestive diseases”. However, long-term use of certain drugs would lead to the “metabolic diseases” pattern [46]. For example, long-term use of atypical antipsychotic drugs (AAP) might increase the risk of weight gain, blood sugar and blood lipids rise and insulin resistance, which would lead to adiposis, diabetes, high blood pressure, etc. [47]. In addition, medication adherence might also increase the risk of “cardiovascular diseases” in multimorbidity patients. Ferdinandy et al. discovered that some comorbidities and their medications might have potential cardiotoxity of a drug [48]. Medication is the preferred way to control disease progression, but to avoid adverse drug interactions that further complicate the disease, we should pay attention to the adverse effects of long-term medication on certain multimorbidity patterns. In addition, except for the factors above, genetic/family history might increase the risk of “metabolic diseases” [49, 50]. Aging and heavy oil and high-salt diet were the risk factors for “cardiovascular disease” [51, 52], while high-intensity physical exercise was a protective factor [53].

There were four limitations in this study. First, cross-sectional data in this study failed to consider the time effect on multimorbidity, and the causality inference could not be estimated. Meanwhile, although our investigators were strictly trained and had medical professional backgrounds, self-reported variables in our study also may be misestimated inevitably. A large-scale longitudinal study should be designed to validate these possible causal hypotheses in future more accurately. Second, the data in this study were investigated from a single geographical region, which might be difficult to verify the effect of macro-environmental factors effectively. Thus, the conclusion of this study should be applicable to regions with the same political and cultural environment. Future studies should expand the research area to the whole China or even more countries. Third, the proportion of old adults with multimorbidity may be underestimated because respondents who have suffered from chronic illnesses but not diagnosed by doctors may be excluded in our study. Moreover, although most Chinese older people choose home-based care, the chronically ill older people who lived in nursing homes or hospitals were not interviewed. Fourth, the variables involved in this study might not be comprehensive enough to cover all associated factors, although these variables were selected based on health ecological model and systemic literature review. Some other variables such as home and community living environment should be further analyzed in future studies.

In this study, we identified three specific multimorbidity patterns, “degenerative/digestive diseases”, “metabolic diseases” and “cardiovascular diseases”, among old Chinese patients. The behavioral lifestyles-layered factors mostly explained three multimorbidity patterns, but specific factors of different layers in the three multimorbidity patterns were significantly different, and some factors might even have opposite effects on different multimorbidity patterns. Therefore, a multi-level “individual-community-government” systematic management strategy should be established to manage old patients’ multimorbidity, considering both the “lifestyle change”-centered systematic management strategy and group-customized intervention program for different multimorbidity patterns. From the perspective of individuals, the self-management ability of old patients with chronic diseases should be improved in continuous visualized education and decision-making to provide more support in health-promotion and disease-prevention behaviors. From the perspective of communities, the multimorbidity care service delivery system should be strengthened by firstly building a knowledge-sharing network to facilitate the peer effect of patient group with multimorbidity, and by secondly establishing classified and graded archives and remote dynamic risk monitoring and early warning system for multimorbidity patients to help formulate group-customized intervention programs. From the perspective of the government, a unified and coordinated two-way referral tactics should be developed to improve the management of multimorbidity, and give full play to the catch-all and mutual-aid roles of basic medical insurance.