Silver Age Health | Elderly comorbidity model and management methods

With the trend of global population aging, the problem of chronic disease comorbidity has gradually become a major challenge in the field of global health. As the number of patients with chronic diseases continues to increase, the prevalence of comorbidities has also increased. Chronic disease comorbidity is not only associated with higher mortality, more disability and decreased functional status, but also leads to a decline in quality of life. In addition, chronic disease comorbidity consumes a lot of health care resources, including costs, hospital stays and number of visits. Therefore, understanding the specific factors and processes of comorbidity, the interaction of diseases and possible synergies is very important to promote diagnosis, improve the quality of life of patients, improve prevention and reduce the cost of the health care system.

▏The definition and evolution of comorbidity

The concept of "comorbidity" was first proposed by an American medical professor in 1970. It refers to a person who suffers from a certain disease while also having other diseases. The key point of this concept is that researchers need to pay attention to the disease being studied and the impact of other diseases on it. Over time, this concept has been updated and extended. For example, the National Institute on Drug Abuse of the United States believes that two or more diseases or conditions can occur in a person at the same time, which is called comorbidity. In 2008, the World Health Organization (WHO) clearly defined "comorbidity" as two or more chronic diseases that exist in a person at the same time.

▏Identification methods of comorbidity patterns and related research

Diseases do not cluster randomly, and comorbidity patterns reveal statistically significant associations between diseases and pathophysiological relationships between different diseases. Mining comorbidity patterns and exploring their distribution patterns will help classify diseases, reduce the complexity of chronic disease prevention and treatment, and make chronic disease prevention measures more accurate and efficient. A systematic review of multi-disease epidemiological studies showed that different studies have different findings on the nature and patterns of comorbidity. At present, there is no unified definition of methods for identifying comorbidity patterns. Commonly used methods include odds ratios, factor analysis, cluster analysis, association rules, etc. The application of these identification methods in comorbidity pattern research varies, and they need to be selected and optimized based on specific research questions and data characteristics.

1. Odds ratio The odds ratio is a probability analysis method used to measure the ratio of the probability of something happening to not happening. It is often used to analyze the relative risks of two diseases in comorbidity pattern analysis, thereby judging the strength of the correlation between the two diseases. A study in the Netherlands used the odds ratio method to analyze the correlation between chronic diseases. The results showed that the three pairs of chronic diseases, depression and anxiety, coronary heart disease and heart failure, and chronic obstructive pulmonary disease and heart failure, had the greatest relative risk of correlation. Because the odds ratio algorithm is simple and easy to apply, it has gradually become one of the commonly used methods in comorbidity pattern research.

2. Factor analysis Factor analysis is a widely used analytical method that aims to identify complex relationships between multiple diseases. This method can combine multiple related factors into a few comprehensive indicators, thereby simplifying the data structure and revealing the associations between variables. A study of Japanese adults included 17 chronic diseases, and through the application of factor analysis, different chronic disease comorbidity patterns were identified. In the end, the study obtained 5 groups of comorbidity pattern groups. Compared with traditional grouping assumptions, this method no longer relies on them, but extracts important information by reducing the dimension and simplifying the data. The advantage of the factor analysis method is that it can more effectively reveal the relationship between variables and help us better understand the disease comorbidity pattern.

3. Cluster analysis Cluster analysis is a data mining technique and a set of algorithms for identifying clusters of data. It uses arbitrary distance metrics to identify groups. The two most common methods are hierarchical clustering and K-means clustering. The comorbidity patterns of patients with COPD can include comorbidity with cardiovascular disease, comorbidity with allergic diseases, and comorbidity with other representative diseases. These comorbidity patterns may be related to genetic, environmental or behavioral risk factors. Through cluster analysis, we can explore the commonalities of these potential factors in order to better understand the comorbidity phenomenon of chronic diseases.

4. Association rules When we talk about the application of association rules in chronic disease relationship analysis, it can not only analyze the direct association between multiple chronic diseases, but also reveal the strong association of comorbidity patterns. In a laboratory in the UK, they used cluster analysis and association rule mining methods to evaluate the comorbidity patterns of middle-aged and elderly people in the UK. After in-depth analysis, they found 3 clusters and 30 disease patterns. These patterns not only reveal the complex relationship between different diseases, but also show the central role in disease clusters, such as diabetes, hypertension, and asthma. These chronic diseases are not just independent existences, there are complex interactions and associations between them. Therefore, we need to start from a holistic perspective and comprehensively evaluate and treat chronic diseases to achieve better health results.

▏The impact of different factors on comorbidity patterns

1. Geography: The comorbidity patterns of different countries vary with geographical regions. A German study found that the most common comorbidity patterns include cardiovascular and metabolic diseases, joint, liver, lung and eye diseases, mental and neurological diseases, gastrointestinal diseases and cancer. In Spain, a study of people over 50 years old found that cardiopulmonary diseases, mental and degenerative diseases, and a combination called "clustered diseases" were the main comorbidity patterns.

Domestic studies have shown that chronic disease comorbidity patterns mainly include hypertension and cerebrovascular disease, diabetes and dyslipidemia, coexistence of diabetes and dyslipidemia, and hypertension. In cross-sectional data studies, the two most common chronic disease comorbidity patterns are hypertension and dyslipidemia, and among the three chronic disease comorbidity patterns, hypertension, dyslipidemia and diabetes are the most common. It is worth noting that different countries have different population characteristics, so their comorbidity patterns will also vary. Exploring how geographical characteristics affect the comorbidity mechanism of the elderly will not only help us understand the cause of the disease, but also help improve clinical practice. At the same time, this also provides an important reference for the rational allocation of medical resources.

2. Gender: There are obvious gender differences in the comorbidity patterns between men and women. Studies have found that women over 65 are more likely to show patterns specific to mental and degenerative diseases. The most common patterns for men are those related to cardiovascular and metabolic diseases. This was also confirmed by a report from China involving 1,480 subjects, with a higher prevalence of metabolic disorders (such as diabetes, hypertension, and obesity) in women than in men. Currently, there is relatively little data, so the impact of gender on comorbidity patterns needs further study.

3. As time goes by, we find that the prevalence of comorbidities is showing an upward trend. The emergence of this trend may be closely related to the increase in the long-term disease burden brought about by rapid economic development. A health and pension tracking survey report based on my country from 2011 to 2018 provides an in-depth observation perspective. In 2011, no traces of comorbidities were found in specific populations. However, in 2018, we found four comorbidity patterns, namely heart and metabolic disease patterns, digestion and arthritis patterns, cardiometabolic and cerebrovascular disease patterns, and respiratory diseases. In order to better understand the true situation of multiple diseases and their determinants and determine causal relationships, we need more longitudinal data so that we can make better decisions for future health and pension.

4. Mental Psychology In recent years, the impact of multiple diseases on psychological conditions has gradually become a research hotspot. However, the relationship between multiple diseases and mental health is not clear at a glance, but presents a more complex trend. A previous study showed that the prevalence of mental illness increased with the increase in the number of chronic diseases, which revealed the potential impact of multiple diseases on patients' mental health, not just physical health. Studies have shown that respiratory diseases and arthritis, digestive, and vision diseases may be powerful predictors of future depressive symptoms in middle-aged and elderly people in China. This provides a new direction for future research, that is, to focus on the specific mechanisms between certain physiological multiple diseases and depression and anxiety. This will help us better provide patients with more precise and personalized treatment plans.

Strategies for managing comorbidities

1. Prevention strategy Taking effective preventive measures against the relevant risk factors of the disease can help reduce the incidence of the disease and its complications. For example, smoking cessation has been proven to be effective in preventing cardiovascular disease, respiratory diseases and some tumors; reasonable blood pressure control is important for preventing coronary heart disease, ischemic stroke, hemorrhagic stroke, congestive heart failure and chronic kidney disease; lowering low-density lipoprotein cholesterol helps prevent coronary heart disease and ischemic stroke. Considering that many diseases often occur at the same time, the above three intervention measures can effectively reduce the risk of aggregation of complications. Therefore, early preventive intervention of risk factors plays a key role in reducing the incidence of complications.

2. Management strategy In-depth exploration of the comorbidity patterns of chronic disease populations is crucial for customizing personalized comorbidity intervention strategies and prevention and control measures. However, due to the different risk factors and protective factors of different comorbidity patterns, there are still some differences in the current clinical guidelines. Take an unbalanced diet as an example. It may cause gastrointestinal mucosal damage and dysfunction, thereby increasing the incidence of degenerative diseases/digestive system diseases. For metabolic diseases, the intake of foods rich in fat, protein, starch and sugar is precisely and strictly restricted, and an unreasonable diet may lead to an increased risk of disease. In the early stages of the disease, rational medication may help prevent functional damage and reduce the incidence of degenerative/digestive system diseases. However, long-term use of certain drugs may lead to the emergence of metabolic disease patterns, which reminds us that while controlling the progression of the disease, we should also pay attention to the possible adverse effects of long-term medication on certain multi-disease patterns to avoid adverse reactions or interactions of drugs that further complicate the condition. Therefore, it is particularly important to establish a multi-level "individual-family-community-organization" system management strategy to manage chronic comorbidities in elderly patients.

(1) Self-management: Encourage and support patients to manage themselves through lifestyle changes, self-decision-making, and self-education, so as to improve the health of the elderly, such as chronic pain, self-care, physical activity, and self-confidence.

(2) Family support As one of the important resources for patients with comorbidities, the family provides them with necessary emotional and behavioral support. Family caregivers not only play the role of coordinating transitional care, obtaining and coordinating medical care services, and communicating with doctors and services, but also provide information about the patient's medical history. Without active family support, the effects of health interventions may be difficult to maintain, which in turn has a negative impact on the patient's psychological state.

(3) Community management As the main management place, the community is crucial for the health management of these two groups of people. Therefore, incorporating comorbidity prevention and control into the community chronic disease management system and establishing a comprehensive chronic disease management system, including comorbidity health management, will have far-reaching significance for the health management of patients with comorbidities. Community medical service personnel should:

1) Establish a knowledge-sharing network to promote the peer effect among patients with comorbidities and enhance communication and cooperation among them; 2) Establish classified and graded archives and a remote dynamic risk monitoring and early warning system for patients with comorbidities, so as to develop personalized intervention plans for each patient and conduct continuous follow-up management; 3) Carry out diversified health guidance services, including lectures on knowledge of chronic disease comorbidity prevention and control, and use of online information platforms to monitor the health status of patients with chronic disease comorbidities in real time, so as to provide them with timely and accurate information.

(4) Improve the quality of health care services. The current health care system is mainly designed around the treatment of a single disease, which obviously cannot meet the needs of the coexistence of multiple diseases. Health management departments should attach importance to the construction of a multidisciplinary medical and health information platform, establish a multidisciplinary team for the elderly, carry out interdisciplinary team training, and promote multi-departmental collaboration. In combination with the characteristics of chronic disease comorbidity in my country, clinical guidelines that are in line with my country's national conditions should be formulated and continuously improved in practice. Understanding the epidemiological distribution of comorbidities and the comorbidity patterns will help improve our ability to predict the demand for medical and health resources, so that elderly patients with comorbidities can get the most benefits, improve their functional status and improve their quality of life, thereby achieving healthy aging.

Author | Han Mei was born in Dunhua City, Jilin Province. She is a practicing pharmacist and has worked in a well-known national tertiary hospital for more than 30 years. She has rich medical care experience. She has represented the hospital to go out for exchanges and study many times. She is an expert in food hygiene and nutrition, has a national nutritionist qualification, and is a science enthusiast.