Comorbidity Patterns and Influencing Factors in Elderly Chronic Disease Patients: A Five-Year Retrospective Study

Comorbidity Patterns and Influencing Factors in Elderly Chronic Disease Patients: A Five-Year Retrospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comorbidity Patterns and Influencing Factors in Elderly Chronic Disease Patients: A Five-Year Retrospective Study Yugao Wu, Rongyue Li, Guanghui Guo, Zhuo Cheng, Mingwei Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6168743/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: This study aims to analyze the comorbidity patterns of chronic diseases in elderly patients aged 65 and above at Panzhihua Central Hospital and their relationships with gender and age, revealing the strength of associations between common chronic diseases, thereby providing data support for clinical management and public health strategies. Methods: This retrospective study analyzed the medical records of 88,742 hospitalized patients aged 65 and above at Panzhihua Central Hospital from January 2019 to December 2023. Patient data, including age, gender, primary diagnosis, and other disease diagnoses, were collected through the hospital's medical information system. Diseases were classified and statistically analyzed according to the International Classification of Diseases, 10th Revision (ICD-10). The comorbidity patterns among 13 common chronic diseases were analyzed. Results: Hypertension, diabetes, and coronary heart disease are the most common chronic diseases among elderly patients. The most common three-way comorbidity pattern is HTN + DM + CA, with a prevalence of 41.05%. Gender and age have significant impacts on comorbidity patterns. Chronic obstructive pulmonary disease (COPD) and heart failure (HF) comorbidity patterns are more common in male patients, while gastrointestinal and renal diseases are predominant in females. As age increases, the comorbidity rate of cardiovascular and neurological diseases rises significantly, particularly in elderly individuals aged 80 and above. Hypertension shows a significant association with chronic diseases like diabetes and coronary heart disease, and the comorbidity relationship between atrial fibrillation and heart failure is particularly strong, suggesting a potential shared pathological mechanism between these diseases. Conclusions: Hypertension, diabetes, and coronary heart disease are the most common chronic diseases among elderly patients, and there is a high degree of comorbidity between these diseases. Gender and age significantly influence comorbidity patterns, and there are strong associations between chronic diseases. The findings provide important clinical evidence for the management of chronic diseases in the elderly population, suggesting the development of personalized disease management strategies based on gender and age characteristics to optimize the allocation of public health resources. Elderly patients Chronic diseases Comorbidity patterns Hypertension Diabetes Gender Age Figures Figure 1 1. Background With the intensification of global aging, the elderly population is increasing rapidly, and chronic diseases have become a major health threat to this group. According to the World Health Organization (WHO), chronic diseases account for over 70% of the global disease burden, and this issue is particularly prominent among the elderly[ 1 ]. The high incidence of chronic diseases and the coexistence of multiple diseases (i.e., comorbidities) significantly affect patients' quality of life and increase the burden on healthcare resources[ 2 ]. China is the world’s most populous country and has the largest aging population. The population aged 65 years and older has markedly increased in recent years, and there were approximately 190 million people aged 65 years and older inChina in 2020[ 3 , 4 ]. Comorbidity not only increases the complexity of diseases but also demands higher standards for disease diagnosis, treatment, and public health strategies[ 5 – 7 ]. Particularly in resource-limited areas, effectively identifying high-risk comorbidity combinations and formulating targeted intervention measures is an urgent issue to be addressed in current clinical practice and public health policy. Currently, studies have analyzed the comorbidity phenomena and influencing factors of chronic diseases in the elderly, identifying hypertension (HTN), diabetes (DM), and coronary heart disease (CHD) as core components in comorbidity patterns[ 8 – 13 ]. However, most studies have focused on a single region or small sample sizes, and have lacked in-depth analysis of the effects of gender and age[ 14 ]. Furthermore, the strength of disease associations and potential mechanisms within comorbidity patterns have not been systematically explored, limiting the applicability of the findings in precision medicine and public health. Panzhihua, as an important resource-based city in China, has a high proportion of elderly population, with a disease spectrum and healthcare burden that show significant regional characteristics[ 15 ]. This study utilizes the big data of inpatient cases at Panzhihua Central Hospital, classifying chronic diseases according to the ICD-10 standards and systematically analyzing the comorbidity patterns and influencing factors in elderly patients aged 65 and above[ 16 ]. This study aims to describe the prevalence and comorbidity patterns of 13 major chronic diseases in elderly inpatients aged 65 and above at Panzhihua Central Hospital through a retrospective analysis. It explores the impact of gender and age on comorbidity distribution and association strength, providing scientific evidence for the precise prevention and control of chronic diseases in the elderly. The significance of this research lies in revealing comorbidity patterns, clarifying the main comorbidity combinations and their distribution characteristics in elderly chronic diseases. It explores the impact of gender and age, analyzing significant differences in comorbidity patterns based on gender and age to support stratified management. Additionally, it guides precision medicine and provides data support for the development of personalized health management strategies for the elderly population. 2. Methods 2.1 Study Design This study is a retrospective cross-sectional study, based on the medical records of hospitalized patients at Panzhihua Central Hospital from January 2019 to December 2023. It analyzes the comorbidity patterns of chronic diseases in elderly patients aged 65 and above and their influencing factors. Inclusion criteria: Age ≥65 years; hospitalization and discharge during the study period; discharge records containing clear primary and secondary diagnosis information. Exclusion criteria: Patients with incomplete or missing medical records; patients with multiple hospitalizations in the same year, only the last hospitalization record is included. Data were obtained from the medical information system of Panzhihua Central Hospital, covering all eligible discharged patients. Data collection included demographic characteristics (age, gender), disease diagnostic information (all primary and secondary diagnoses), and disease classification: all diagnoses were categorized and coded according to the International Classification of Diseases, 10th Revision (ICD-10). Chronic disease inclusion criteria: This study includes 13 types of chronic diseases based on the China Health and Retirement Longitudinal Study (CHARLS)[17]: hypertension (HTN), coronary heart disease (CHD), diabetes (DM), atrial fibrillation (AF), heart failure (HF), cerebrovascular disease (CVA), chronic kidney disease (CKD), chronic gastrointestinal diseases (GERD), malignant tumors (CA), skeletal joint diseases (ARTH), chronic obstructive pulmonary disease (COPD), chronic liver diseases (LIV), and prostate disease (BPH, applicable only to males). 2.2 Study Variables Main variables include: prevalence of chronic diseases, comorbidity patterns, and common combinations of binary, ternary, and higher-order comorbidities. Influencing factors include: gender, comparing the differences in chronic disease prevalence and comorbidity patterns between males and females. Age, categorized into four groups (65–70 years, 70–75 years, 75–80 years, ≥80 years), was explored for its impact on comorbidity patterns. 2.3 Statistical Analysis Descriptive statistics were used to analyze the demographic characteristics of patients and the prevalence of single diseases and comorbidities for 13 chronic diseases. The distribution of chronic diseases and comorbidity patterns across different gender and age groups was analyzed. Inferential statistics, including χ² tests, were used to compare the differences in chronic disease distribution and comorbidity patterns by gender and age. The observed-to-expected ratio (O/E) and odds ratio (OR) with 95% confidence intervals were calculated to assess the strength of disease associations. Visualization analysis was performed using Yule’s Q heatmap to display the strength of associations between chronic diseases, with color intensity reflecting the strength of the association. 2.4 Ethical Statement This study is based on anonymized medical record data from hospitalized patients and does not require direct contact with patients, adhering to ethical principles. The study protocol has been approved by the Ethics Committee of Panzhihua Central Hospital (Ethics number:2025-013). 2.5 Data Management and Quality Control Data collection and organization were carried out by two independent researchers to ensure consistency in classification and coding. A double-checking method was employed to reduce data entry and classification errors, with 10% of the samples randomly selected for rechecking. All data analyses were conducted using professional statistical software (e.g., SPSS or R) to ensure the scientific accuracy and reliability of the results. 3. Results 3.1 Basic Characteristics of the Study Subjects A total of 88,742 hospitalized patients from Panzhihua Central Hospital, from January 2019 to December 2023, were included in this study. All patients were aged ≥65 years. Among them, 50,476 were male (56.88%) and 38,266 were female (43.12%). The age distribution of the patients was as follows: 65–70 years 27.31%, 70–75 years 29.24%, 75–80 years 24.19%, and ≥80 years 19.27% (Table 1). There were significant differences in the distribution of chronic disease count based on gender and age (χ²=371.82, P<0.001; χ²=480.60, P<0.001). The proportion of male patients with ≥2 chronic diseases was 58.26%, higher than that of females at 56.84%. . As age increased, the severity of comorbidity also increased (Table 1). 3.2 Differences in the Characteristics of Patients With Multiorbidity As shown in Table 2, The prevalence of Chronic disease in the overall population was 85.79% ，37.65% of the study population had HTN, which was the highest prevalence Among all chronic diseases.This was followed by cancer(CA, 36.64%) and diabetes (DM, 21.70%).Patients with these chronic conditions had an average multimorbidity burden of ≥3 chronic conditions per patient. As shown in Table 3 ,The three most prevalent chronic comorbidities were chronic kidney disease (CKD, 97.69%), atrial fibrillation (AF, 94.9%), and coronary heart disease (CHD, 93.82%). The prevalence of chronic obstructive pulmonary disease (COPD, 14.47%) and benign prostatic hyperplasia (BPH, 18.9%) was significantly higher in male patients, while gastroesophageal reflux disease (GERD, 27%) and CKD (8.16%) were significantly more common in females. As age increased, the prevalence of most chronic diseases significantly increased, such as heart failure (HF, from 20.73% to 32.64%) and cerebrovascular disease (CVA, from 19.19% to 30.31%). 3.3 Analysis of Comorbidity Patterns of Chronic Diseases Among binary comorbidity patterns, HTN+CA and HTN+DM were the most common, with prevalences of 12.54% and 11.87%, respectively (Table 4). Additionally, HTN+CVA and HTN+CHD were also relatively common, with prevalences of 7.72% and 6.69%, respectively. Among ternary comorbidity patterns, HTN+DM+CA was the most common, with a prevalence of 41.05%. It was followed by HTN+DM+CVA (26.67%) and HTN+HF+CA (24.94%) (Table 4). 3.4 Analysis of the Strength of Associations Between Chronic Diseases The observed/expected ratio (O/E) and odds ratio (OR) for HTN+DM were 1.45 and 2.28 (95% CI: 2.21–2.36), respectively, indicating a significant strength of association between the two diseases (Table 4). For AF+HF, the O/E ratio was 2.95, and the OR was 4.47 (95% CI: 4.22–4.75), which was the strongest association in this study. 3.5 Heatmap Analysis The heatmap based on the Yule’s Q correlation coefficient matrix (Figure 1) shows that HTN has the strongest correlation with multiple chronic diseases (such as DM, CA, and CHD), making it a core disease in comorbidity patterns. The darker the color, the stronger the correlation. The correlations of AF+HF and CKD+HF were the most prominent in the matrix (Figure 1). 4. Discussion 4.1 Comorbidity Patterns and Influencing Factors in Elderly Chronic Disease Patients Among hospitalized patients aged ≥65, hypertension (HTN), diabetes mellitus (DM), and coronary heart disease (CHD) are the most common chronic diseases, indicating the high burden of cardiovascular and metabolic diseases in the elderly population. Comorbidity is prominent, with HTN+DM+CA being the most common three-way comorbidity pattern, with HTN at the core of both binary and three-way comorbidities. Gender and age significantly impact comorbidity, with male patients primarily affected by respiratory diseases, while females are more likely to have digestive and renal diseases. As age increases, the comorbidity rate of cardiovascular and neurological diseases significantly rises. The strongest associations between chronic diseases are seen in AF+HF and CKD+HF, suggesting that some chronic diseases may share common pathological mechanisms or synergistic effects. 4.2 The Significance of Hypertension as a Core Chronic Disease This study shows that hypertension is the most common chronic disease and plays a central role in comorbidity patterns[18]. This may be because hypertension is a common risk factor for several diseases, such as coronary heart disease, cerebrovascular diseases, and chronic kidney disease. Hypertension affects the vascular system over time, leading to organ damage and metabolic disorders, thereby increasing the likelihood of comorbidities[19, 20]. This suggests that clinicians should focus on potential comorbidity risks when managing patients with hypertension. 4.3 Possible Reasons for Gender Differences The study found that the prevalence of chronic obstructive pulmonary disease (COPD) and benign prostatic hyperplasia (BPH) is significantly higher in male patients, while female patients have higher rates of chronic gastrointestinal diseases (GERD) and chronic kidney disease (CKD). These differences may be related to gender-specific physiological characteristics and lifestyle factors, with men typically having higher smoking rates, which increases the risk of COPD[21, 22]. Women, influenced by hormonal levels, are more prone to gastrointestinal and renal dysfunction[23]. Cultural background and gender roles may also impact the diagnosis and management of diseases, as women tend to seek medical services more often[24]. 4.4 Cumulative Effects of Age on Comorbidity Patterns As age increases, the comorbidity rate of cardiovascular, neurological, and musculoskeletal diseases significantly rises. This trend may be related to factors such as organ function degeneration, as the heart, kidneys, and nervous systems in the elderly decline with age, increasing the risk of multiple comorbidities[25]. The progression of chronic diseases and their longer duration make existing chronic diseases more likely to trigger other diseases. The complexity of treatment also contributes to comorbidity, as interactions between treatments or complications caused by treatment may arise[26]. 4.5 Clinical Significance of Comorbidity Association Strength The study found that the associations between AF+HF and CKD+HF have the highest strength, suggesting that some chronic diseases may share similar pathological mechanisms, such as inflammation and immunity. Atrial fibrillation (AF) and heart failure (HF) may be driven by chronic inflammation and myocardial remodeling. In terms of metabolic disorders, the synergistic effect between chronic kidney disease (CKD) and heart failure (HF) may be caused by metabolic dysfunction and the imbalance of the kidney-heart axis[27]. These findings provide important guidance for clinical practice, suggesting that a comprehensive disease management strategy should be employed when dealing with high-risk comorbidity patterns. 4.6 Public Health and Clinical Practice Implications Layered management and personalized interventions should be implemented, with strategies tailored according to gender and age differences. For male patients, screening and intervention for smoking-related diseases (such as COPD) should be strengthened[28]. For female patients, early detection of digestive and renal diseases should be prioritized. For elderly patients, special attention should be given to managing cardiovascular and neurological complications. Precision medicine for high-risk comorbidity patterns, such as HTN+DM+CA, should include the establishment of multidisciplinary teams to optimize joint treatment plans for chronic diseases. Personalized health education and prevention plans based on disease combinations should be promoted, along with long-term health monitoring and follow-up management aimed at multimorbidity[29]. Optimizing resource allocation is necessary, as comorbidity increases the medical resource demand of elderly patients. Policymakers should increase chronic disease prevention investments and optimize medical resource distribution, particularly in primary healthcare settings[30, 31], to promote integrated disease management models. 4.7 Limitations and Future Directions The study data were sourced from Panzhihua Central Hospital, and the results may have limited generalizability. As a cross-sectional study, it is difficult to determine causal relationships between comorbidities. The study did not include potential influencing factors such as lifestyle and socioeconomic status. Future research should expand the scope to include multicenter data to improve the generalizability of results. Longitudinal studies should be conducted to track the dynamic changes in chronic disease progression and comorbidity patterns. Exploring the potential biological mechanisms between chronic diseases could provide new treatment targets. 5. Conclusion Based on data from hospitalized patients at Panzhihua Central Hospital from January 2019 to December 2023, this study systematically analyzed the comorbidity patterns and the effects of gender and age in elderly patients aged 65 and above. The study revealed that the high burden of chronic diseases and the prominent phenomenon of comorbidity in the elderly were significant. Hypertension, diabetes, and coronary heart disease are the most common chronic diseases among elderly patients, with hypertension occupying a central role in comorbidity patterns. HTN+DM+CA is the most common three-way comorbidity pattern, and comorbidity significantly increases the complexity of diseases and medical burden in elderly patients. Gender and age have a significant impact on comorbidity patterns, with male comorbidities primarily involving the respiratory system (e.g., COPD), while female comorbidities are more often associated with the digestive system (e.g., GERD) and renal system (e.g., CKD). As age increases, the comorbidity rate of cardiovascular and neurological diseases significantly rises, suggesting that elderly patients need more comprehensive disease management. The high association strength between chronic diseases, particularly AF+HF and CKD+HF, suggests that these diseases may share common pathological mechanisms or synergistic effects. Understanding the associations between chronic diseases helps more accurately identify high-risk populations and optimize intervention strategies. Clinically, the key role of hypertension as the core of comorbidity should be emphasized, and personalized management plans should be developed for high-risk combinations (e.g., HTN+DM+CA). At the public health level, layered management strategies should be developed based on gender and age characteristics, optimizing resource allocation and disease prevention strategies. This study provides scientific evidence for the comorbidity patterns and management of elderly chronic diseases but further research, including multicenter data and longitudinal studies, is needed to explore causal relationships and mechanisms between chronic diseases and promote the development of precision medicine. Abbreviations ICD-10 the International Classification of Diseases CHARLS the China Health and Retirement Longitudinal Study hypertension HTN coronary heart disease CHD diabetes DM atrial fibrillation AF heart failure HF cerebrovascular disease CVA chronic kidney disease CKD chronic gastrointestinal diseases GERD malignant tumors CA skeletal joint diseases ARTH chronic obstructive pulmonary disease COPD chronic liver diseases LIV and prostate disease BPH applicable only to males . Declarations Ethics approval and consent to participate The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.This study is based on anonymized medical record data from hospitalized patients and does not require direct contact with patients, adhering to ethical principles. The study protocol has been approved by the Ethics Committee of Panzhihua Central Hospital (Ethics number:2025-013). Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding Not applicable. Author contributions YGW and MWL wrote the manuscript and conducted statistical analysis. RYL and ZC searched the data. RYL and GHG designed the study, and revised the manuscript. MWL supervised the overall work and is the guarantor of the review.All authors read and approved the final manuscript. Acknowledgements Not applicable. Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s. References Chen X, Giles J, Yao Y, Yip W, Meng Q, Berkman L, Chen H, Chen X, Feng J, Feng Z ,et al. The path to healthy ageing in China: a Peking University-Lancet Commission . Lancet 2022, 400 (10367):1967-2006. Nugent R. Preventing and managing chronic diseases . BMJ 2019, 364 :l459. Piacenza F, Di Rosa M, Soraci L, Montesanto A, Corsonello A, Cherubini A, Fabbietti P, Provinciali M, Lisa R, Bonfigli AR ,et al. Interactions between patterns of multimorbidity and functional status among hospitalized older patients: a novel approach using cluster analysis and association rule mining . J Transl Med 2024, 22 (1):669. Blum MR, Sallevelt BTGM, Spinewine A, O'Mahony D, Moutzouri E, Feller M, Baumgartner C, Roumet M, Jungo KT, Schwab N ,et al. 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Tables Table 1 Characteristics of the population and comparison （n[%]） Variable Total Number of comorbidities χ 2 p 0 1 2 ≥3 Sex 371.82 0.000 Male 50476(56.88) 7047(13.96) 14023(27.78) 12912(25.58) 16494(32.68) female 38266(43.12) 5564(14.53) 10950(28.62) 9856(25.76) 11896(31.09) Age(years) 480.6 0.000 65-70 24232(27.31) 4560(18.82) 8236(33.99) 6033(24.9) 5403(22.3) 70-75 25944(29.24) 3770(14.53) 7669(29.56) 6950(26.79) 7555(29.12) 75-80 21466(24.19) 2597(12.10) 5453(25.4) 5559(25.90) 7857(36.60) ≥80 17100(19.27) 1685(9.85) 3615(21.14) 4225(24.71) 7575(44.30) Table 2 Characteristics of the population and comparison （n[%]） Rank Chronic disease Presence in all elderly hospitalized patients, n (%) Number of co-occurring conditions, mean (SD 1 HTN 33413(37.65) 3.76(1.23) 2 CA 32513(36.64) 3.69(1.37) 3 DM 19258(21.7) 3.38(1.27) 4 HF 14295(16.11) 3.83(1.44) 5 CVA 14200(16) 3.65(1.41) 6 GERD 13678(15.41) 3.2(1.47) 7 CHD 12385(13.96) 3.45(1.26) 8 ARTH 9900(11.16) 4.25(1.49) 9 COPD 6553(7.38) 3.49(1.36) 10 BPH 6251(7.04) 3.63(1.44) 11 AF 5470(6.16) 3.13(1.26) 12 CKD 5012(5.65) 3.6(1.41) 13 HID 4654(5.24) 3.5(1.42) Total 76130(85.79) 2.98(1.16) Table 3 Trends of comorbidities on different genders and age groups（n[%]） Variable prevalence Sex Age P Male female χ2 P 65-70 70-75 75-80 ≥80 χ2 HTN 83.74 15700(53.39) 12281(56.46) 47.47 0.00 6396(55.93) 8140(56.12) 7607(56.7) 5838(49.47) 170.45 0.00 CA 71.11 13799(46.93) 9322(42.86) 83.37 0.00 5622(49.16) 6637(45.76) 5895(43.94) 4967(42.09) 128.82 0.00 DM 90.64 9384(31.91) 8071(37.11) 149.85 0.00 3836(33.54) 5048(34.8) 4828(35.99) 3743(31.72) 55.72 0.00 HF 90.79 7332(24.93) 5647(25.96) 6.93 0.01 2371(20.73) 3235(22.3) 3522(26.25) 3851(32.64) 536.46 0.00 CVA 89.54 7215(24.54) 5499(25.28) 3.68 0.06 2195(19.19) 3364(23.19) 3579(26.68) 3576(30.31) 429.25 0.00 GERD 89.36 6349(21.59) 5873(27) 200.97 0.00 2596(22.7) 3322(22.9) 3152(23.49) 3152(26.71) 69.51 0.00 CHD 93.82 7004(23.82) 4616(21.22) 47.86 0.00 2012(17.59) 2933(20.22) 3176(23.67) 3499(29.65) 552.81 0.00 ARTH 88.79 4568(15.53) 4222(19.41) 131.75 0.00 1805(15.78) 2328(16.05) 2213(16.5) 2444(20.71) 136.56 0.00 COPD 89.07 4255(14.47) 1582(7.27) 639.9 0.00 900(7.87) 1529(10.54) 1703(12.69) 1705(14.45) 282.32 0.00 BPH 89.44 5559(18.9) 0.00(0.00) 4611.22 0.00 683(5.97) 1231(8.49) 1574(11.73) 2071(17.55) 922.34 0.00 AF 94.9 2751(9.36) 2440(11.22) 47.37 0.00 684(5.98) 1212(8.36) 1478(11.02) 1817(15.4) 636.75 0.00 CKD 97.69 3122(10.62) 1774(8.16) 87.21 0.00 874(7.64) 1196(8.25) 1272(9.48) 1554(13.17) 255.27 0.00 HID 90.42 1741(5.92) 2467(11.34) 486.08 0.00 1291(11.29) 1387(9.56) 942(7.02) 588(4.98) 366.6 0.00 Table 4. Top 10 Dyad and Triad Comorbidity Patterns in Elderly Patients with Chronic Diseases (Ranked by Prevalence) Rank Comorbidity Patterns Cases (n) Proportion (%) Triad Comorbidity Patterns Cases (n) Proportion (%) 1 HTN+CA 11128 12.54% HTN+DM+CA 3642 41.05% 2 HTN+DM 10532 11.87% HTN+DM+CVA 2366 26.67% 3 HTN+CVA 6848 7.72% HTN+HF+CA 2213 24.94% 4 DM+CA 6708 7.56% HTN+CHD+DM 2176 24.53% 5 HTN+CHD 5935 6.69% HTN+DM+HF 2093 23.59% 6 HTN+HF 5777 6.51% HTN+CHD+CA 1843 20.77% 7 HF+CA 5503 6.20% HTN+CHD+HF 1738 19.59% 8 HTN+GERD 4843 5.46% HTN+CVA+CA 1730 19.50% 9 GERD+CA 4543 5.12% HTN+GERD+CA 1542 17.38% 10 DM+CVA 4155 4.68% DM+HF+CA 1506 16.97% Table 5. Observed vs. Expected Ratios and Association Strengths of Dyad Comorbidity Patterns Dyad Comorbidity Patterns Cases, n Prevalence (%) Observed/Expected Ratio OR(95% CI) Observed Expected HTN+DM 10532 11.87 8.17 1.45 2.28(2.21-2.36) HTN+CVA 6848 7.72 6.02 1.28 2.37(2.20-2.55) HTN+CHD 5935 6.69 5.25 1.27 1.42(1.41-1.53) HTN+HF 5777 6.51 6.07 1.07 2.33(2.18-2.49 HTN+GERD 4843 5.46 5.80 0.94 0.81(0.78-0.84) CHD+DM 3905 4.40 3.03 1.45 1.59(1.52-1.66) AF+HF 2595 2.92 0.99 2.95 4.47(4.22-4.75) CKD+HF 1925 2.17 0.91 2.38 2.72(2.55-2.90) HID+CVA 1436 1.62 0.84 1.93 2.35(2.20-2.55) CKD+ARTH 1228 1.38 0.63 2.19 1.50(1.45-1.56) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6168743","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":425498796,"identity":"9c53654c-d8a5-4254-b1a6-90d6c3b9e672","order_by":0,"name":"Yugao Wu","email":"","orcid":"","institution":"Panzhihua Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yugao","middleName":"","lastName":"Wu","suffix":""},{"id":425498797,"identity":"80228b7b-b99c-400b-b744-8bb14d707fb2","order_by":1,"name":"Rongyue Li","email":"","orcid":"","institution":"Panzhihua Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rongyue","middleName":"","lastName":"Li","suffix":""},{"id":425498798,"identity":"8ef98345-f9ce-4c41-8f83-ca2ac9c0618a","order_by":2,"name":"Guanghui Guo","email":"","orcid":"","institution":"Panzhihua Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guanghui","middleName":"","lastName":"Guo","suffix":""},{"id":425498799,"identity":"cd1ff63c-4a51-4628-8649-20c6a057ae52","order_by":3,"name":"Zhuo Cheng","email":"","orcid":"","institution":"Panzhihua Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhuo","middleName":"","lastName":"Cheng","suffix":""},{"id":425498800,"identity":"939a9882-4594-4e8b-a7a4-3c8110307b91","order_by":4,"name":"Mingwei Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBACNv7mgw8SKmzq+9nBjBrCWvgkjiUbfDiTxjizB8h4cOYYYS1yDDlmkjPbDjNuuAFkPGxhJsJhDGeMjXnY0pgle86YVSQ2sDHwt3cn4NfC3Fb4mIfHho2fva3sRuIOGQaJM2c3ELDl8GZjHok0Hsmew9tuJJ5hYzCQyCWkJcFMmsfgsITBjQSzgsQ2ZmK0pJhJzkg4bGBwI8WMgTgt4EA+kJYgCQxkiYQzx3gI+kW+HxiDif9sEviBUfnxR0WNHH97L34tGICHNOWjYBSMglEwCrACAN+cTpFEcvg6AAAAAElFTkSuQmCC","orcid":"","institution":"Panzhihua Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Mingwei","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2025-03-06 08:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6168743/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6168743/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78225770,"identity":"0db7db0a-9a2d-4fe9-8fc0-0b930c54ecc9","added_by":"auto","created_at":"2025-03-11 06:54:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52434,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap visualization of pairwise disease associations based on Yule's Q correlation coefficients.（The upper triangular matrix displays Yule's Q values (color gradient from white [Q=0] to dark red [Q=+1]), while the lower triangular matrix shows statistical significance levels (*P\u0026lt;0.05, **P\u0026lt;0.01).）\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6168743/v1/891372bb80e7dd453c4fa5d6.png"},{"id":78229158,"identity":"329d3ccf-c4b1-4915-a490-649339f39b23","added_by":"auto","created_at":"2025-03-11 07:18:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2533492,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6168743/v1/dab4eb40-bdf8-4959-8ea9-e6914cb0f096.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comorbidity Patterns and Influencing Factors in Elderly Chronic Disease Patients: A Five-Year Retrospective Study","fulltext":[{"header":"1. Background","content":"\u003cp\u003eWith the intensification of global aging, the elderly population is increasing rapidly, and chronic diseases have become a major health threat to this group. According to the World Health Organization (WHO), chronic diseases account for over 70% of the global disease burden, and this issue is particularly prominent among the elderly[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The high incidence of chronic diseases and the coexistence of multiple diseases (i.e., comorbidities) significantly affect patients' quality of life and increase the burden on healthcare resources[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChina is the world\u0026rsquo;s most populous country and has the largest aging population. The population aged 65 years and older has markedly increased in recent years, and there were approximately 190\u0026nbsp;million people aged 65 years and older inChina in 2020[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Comorbidity not only increases the complexity of diseases but also demands higher standards for disease diagnosis, treatment, and public health strategies[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Particularly in resource-limited areas, effectively identifying high-risk comorbidity combinations and formulating targeted intervention measures is an urgent issue to be addressed in current clinical practice and public health policy.\u003c/p\u003e \u003cp\u003eCurrently, studies have analyzed the comorbidity phenomena and influencing factors of chronic diseases in the elderly, identifying hypertension (HTN), diabetes (DM), and coronary heart disease (CHD) as core components in comorbidity patterns[\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, most studies have focused on a single region or small sample sizes, and have lacked in-depth analysis of the effects of gender and age[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, the strength of disease associations and potential mechanisms within comorbidity patterns have not been systematically explored, limiting the applicability of the findings in precision medicine and public health.\u003c/p\u003e \u003cp\u003ePanzhihua, as an important resource-based city in China, has a high proportion of elderly population, with a disease spectrum and healthcare burden that show significant regional characteristics[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This study utilizes the big data of inpatient cases at Panzhihua Central Hospital, classifying chronic diseases according to the ICD-10 standards and systematically analyzing the comorbidity patterns and influencing factors in elderly patients aged 65 and above[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aims to describe the prevalence and comorbidity patterns of 13 major chronic diseases in elderly inpatients aged 65 and above at Panzhihua Central Hospital through a retrospective analysis. It explores the impact of gender and age on comorbidity distribution and association strength, providing scientific evidence for the precise prevention and control of chronic diseases in the elderly. The significance of this research lies in revealing comorbidity patterns, clarifying the main comorbidity combinations and their distribution characteristics in elderly chronic diseases. It explores the impact of gender and age, analyzing significant differences in comorbidity patterns based on gender and age to support stratified management. Additionally, it guides precision medicine and provides data support for the development of personalized health management strategies for the elderly population.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a retrospective cross-sectional study, based on the medical records of hospitalized patients at Panzhihua Central Hospital from January 2019 to December 2023. It analyzes the comorbidity patterns of chronic diseases in elderly patients aged 65 and above and their influencing factors. \u003c/p\u003e\n\u003cp\u003eInclusion criteria: Age \u0026ge;65 years; hospitalization and discharge during the study period; discharge records containing clear primary and secondary diagnosis information. Exclusion criteria: Patients with incomplete or missing medical records; patients with multiple hospitalizations in the same year, only the last hospitalization record is included.\u003c/p\u003e\n\u003cp\u003eData were obtained from the medical information system of Panzhihua Central Hospital, covering all eligible discharged patients. Data collection included demographic characteristics (age, gender), disease diagnostic information (all primary and secondary diagnoses), and disease classification: all diagnoses were categorized and coded according to the International Classification of Diseases, 10th Revision (ICD-10).\u003c/p\u003e\n\u003cp\u003eChronic disease inclusion criteria: This study includes 13 types of chronic diseases based on the China Health and Retirement Longitudinal Study (CHARLS)[17]: hypertension (HTN), coronary heart disease (CHD), diabetes (DM), atrial fibrillation (AF), heart failure (HF), cerebrovascular disease (CVA), chronic kidney disease (CKD), chronic gastrointestinal diseases (GERD), malignant tumors (CA), skeletal joint diseases (ARTH), chronic obstructive pulmonary disease (COPD), chronic liver diseases (LIV), and prostate disease (BPH, applicable only to males).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Study Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMain variables include: prevalence of chronic diseases, comorbidity patterns, and common combinations of binary, ternary, and higher-order comorbidities. Influencing factors include: gender, comparing the differences in chronic disease prevalence and comorbidity patterns between males and females. Age, categorized into four groups (65\u0026ndash;70 years, 70\u0026ndash;75 years, 75\u0026ndash;80 years, \u0026ge;80 years), was explored for its impact on comorbidity patterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were used to analyze the demographic characteristics of patients and the prevalence of single diseases and comorbidities for 13 chronic diseases. The distribution of chronic diseases and comorbidity patterns across different gender and age groups was analyzed. Inferential statistics, including \u0026chi;\u0026sup2; tests, were used to compare the differences in chronic disease distribution and comorbidity patterns by gender and age. The observed-to-expected ratio (O/E) and odds ratio (OR) with 95% confidence intervals were calculated to assess the strength of disease associations. Visualization analysis was performed using Yule\u0026rsquo;s Q heatmap to display the strength of associations between chronic diseases, with color intensity reflecting the strength of the association.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Ethical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is based on anonymized medical record data from hospitalized patients and does not require direct contact with patients, adhering to ethical principles. The study protocol has been approved by the Ethics Committee of Panzhihua Central Hospital (Ethics number:2025-013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Data Management and Quality Control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection and organization were carried out by two independent researchers to ensure consistency in classification and coding. A double-checking method was employed to reduce data entry and classification errors, with 10% of the samples randomly selected for rechecking. All data analyses were conducted using professional statistical software (e.g., SPSS or R) to ensure the scientific accuracy and reliability of the results.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Basic Characteristics of the Study Subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 88,742 hospitalized patients from Panzhihua Central Hospital, from January 2019 to December 2023, were included in this study. All patients were aged \u0026ge;65 years. Among them, 50,476 were male (56.88%) and 38,266 were female (43.12%). The age distribution of the patients was as follows: 65\u0026ndash;70 years 27.31%, 70\u0026ndash;75 years 29.24%, 75\u0026ndash;80 years 24.19%, and \u0026ge;80 years 19.27% (Table 1). There were significant differences in the distribution of chronic disease count based on gender and age (\u0026chi;\u0026sup2;=371.82, P\u0026lt;0.001; \u0026chi;\u0026sup2;=480.60, P\u0026lt;0.001). The proportion of male patients with \u0026ge;2 chronic diseases was 58.26%, higher than that of females at 56.84%. . As age increased, the severity of comorbidity also increased (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Differences in the Characteristics of Patients With Multiorbidity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, The prevalence of Chronic disease in the overall population was 85.79% ，37.65% of the study population had HTN, which was the highest prevalence Among all chronic diseases.This was followed by cancer(CA, 36.64%) and diabetes (DM, 21.70%).Patients with these chronic conditions had an average multimorbidity burden of \u0026ge;3 chronic conditions per patient.\u003c/p\u003e\n\u003cp\u003eAs shown in Table 3 ,The three most prevalent chronic comorbidities were chronic kidney disease (CKD, 97.69%), atrial fibrillation (AF, 94.9%), and coronary heart disease (CHD, 93.82%). The prevalence of chronic obstructive pulmonary disease (COPD, 14.47%) and benign prostatic hyperplasia (BPH, 18.9%) was significantly higher in male patients, while gastroesophageal reflux disease (GERD, 27%) and CKD (8.16%) were significantly more common in females. As age increased, the prevalence of most chronic diseases significantly increased, such as heart failure (HF, from 20.73% to 32.64%) and cerebrovascular disease (CVA, from 19.19% to 30.31%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Analysis of Comorbidity Patterns of Chronic Diseases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong binary comorbidity patterns, HTN+CA and HTN+DM were the most common, with prevalences of 12.54% and 11.87%, respectively (Table 4). Additionally, HTN+CVA and HTN+CHD were also relatively common, with prevalences of 7.72% and 6.69%, respectively. Among ternary comorbidity patterns, HTN+DM+CA was the most common, with a prevalence of 41.05%. It was followed by HTN+DM+CVA (26.67%) and HTN+HF+CA (24.94%) (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Analysis of the Strength of Associations Between Chronic Diseases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe observed/expected ratio (O/E) and odds ratio (OR) for HTN+DM were 1.45 and 2.28 (95% CI: 2.21\u0026ndash;2.36), respectively, indicating a significant strength of association between the two diseases (Table 4). For AF+HF, the O/E ratio was 2.95, and the OR was 4.47 (95% CI: 4.22\u0026ndash;4.75), which was the strongest association in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Heatmap Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe heatmap based on the Yule\u0026rsquo;s Q correlation coefficient matrix (Figure 1) shows that HTN has the strongest correlation with multiple chronic diseases (such as DM, CA, and CHD), making it a core disease in comorbidity patterns. The darker the color, the stronger the correlation. The correlations of AF+HF and CKD+HF were the most prominent in the matrix (Figure 1).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1 Comorbidity Patterns and Influencing Factors in Elderly Chronic Disease Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong hospitalized patients aged \u0026ge;65, hypertension (HTN), diabetes mellitus (DM), and coronary heart disease (CHD) are the most common chronic diseases, indicating the high burden of cardiovascular and metabolic diseases in the elderly population. Comorbidity is prominent, with HTN+DM+CA being the most common three-way comorbidity pattern, with HTN at the core of both binary and three-way comorbidities. Gender and age significantly impact comorbidity, with male patients primarily affected by respiratory diseases, while females are more likely to have digestive and renal diseases. As age increases, the comorbidity rate of cardiovascular and neurological diseases significantly rises. The strongest associations between chronic diseases are seen in AF+HF and CKD+HF, suggesting that some chronic diseases may share common pathological mechanisms or synergistic effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 The Significance of Hypertension as a Core Chronic Disease\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study shows that hypertension is the most common chronic disease and plays a central role in comorbidity patterns[18]. This may be because hypertension is a common risk factor for several diseases, such as coronary heart disease, cerebrovascular diseases, and chronic kidney disease. Hypertension affects the vascular system over time, leading to organ damage and metabolic disorders, thereby increasing the likelihood of comorbidities[19, 20]. This suggests that clinicians should focus on potential comorbidity risks when managing patients with hypertension.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Possible Reasons for Gender Differences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study found that the prevalence of chronic obstructive pulmonary disease (COPD) and benign prostatic hyperplasia (BPH) is significantly higher in male patients, while female patients have higher rates of chronic gastrointestinal diseases (GERD) and chronic kidney disease (CKD). These differences may be related to gender-specific physiological characteristics and lifestyle factors, with men typically having higher smoking rates, which increases the risk of COPD[21, 22]. Women, influenced by hormonal levels, are more prone to gastrointestinal and renal dysfunction[23]. Cultural background and gender roles may also impact the diagnosis and management of diseases, as women tend to seek medical services more often[24].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Cumulative Effects of Age on Comorbidity Patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs age increases, the comorbidity rate of cardiovascular, neurological, and musculoskeletal diseases significantly rises. This trend may be related to factors such as organ function degeneration, as the heart, kidneys, and nervous systems in the elderly decline with age, increasing the risk of multiple comorbidities[25]. The progression of chronic diseases and their longer duration make existing chronic diseases more likely to trigger other diseases. The complexity of treatment also contributes to comorbidity, as interactions between treatments or complications caused by treatment may arise[26].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Clinical Significance of Comorbidity Association Strength\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study found that the associations between AF+HF and CKD+HF have the highest strength, suggesting that some chronic diseases may share similar pathological mechanisms, such as inflammation and immunity. Atrial fibrillation (AF) and heart failure (HF) may be driven by chronic inflammation and myocardial remodeling. In terms of metabolic disorders, the synergistic effect between chronic kidney disease (CKD) and heart failure (HF) may be caused by metabolic dysfunction and the imbalance of the kidney-heart axis[27]. These findings provide important guidance for clinical practice, suggesting that a comprehensive disease management strategy should be employed when dealing with high-risk comorbidity patterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6 Public Health and Clinical Practice Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLayered management and personalized interventions should be implemented, with strategies tailored according to gender and age differences. For male patients, screening and intervention for smoking-related diseases (such as COPD) should be strengthened[28]. For female patients, early detection of digestive and renal diseases should be prioritized. For elderly patients, special attention should be given to managing cardiovascular and neurological complications. Precision medicine for high-risk comorbidity patterns, such as HTN+DM+CA, should include the establishment of multidisciplinary teams to optimize joint treatment plans for chronic diseases. Personalized health education and prevention plans based on disease combinations should be promoted, along with long-term health monitoring and follow-up management aimed at multimorbidity[29]. Optimizing resource allocation is necessary, as comorbidity increases the medical resource demand of elderly patients. Policymakers should increase chronic disease prevention investments and optimize medical resource distribution, particularly in primary healthcare settings[30, 31], to promote integrated disease management models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e 4.7 Limitations and Future Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study data were sourced from Panzhihua Central Hospital, and the results may have limited generalizability. As a cross-sectional study, it is difficult to determine causal relationships between comorbidities. The study did not include potential influencing factors such as lifestyle and socioeconomic status. Future research should expand the scope to include multicenter data to improve the generalizability of results. Longitudinal studies should be conducted to track the dynamic changes in chronic disease progression and comorbidity patterns. Exploring the potential biological mechanisms between chronic diseases could provide new treatment targets.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eBased on data from hospitalized patients at Panzhihua Central Hospital from January 2019 to December 2023, this study systematically analyzed the comorbidity patterns and the effects of gender and age in elderly patients aged 65 and above. The study revealed that the high burden of chronic diseases and the prominent phenomenon of comorbidity in the elderly were significant. Hypertension, diabetes, and coronary heart disease are the most common chronic diseases among elderly patients, with hypertension occupying a central role in comorbidity patterns. HTN+DM+CA is the most common three-way comorbidity pattern, and comorbidity significantly increases the complexity of diseases and medical burden in elderly patients. Gender and age have a significant impact on comorbidity patterns, with male comorbidities primarily involving the respiratory system (e.g., COPD), while female comorbidities are more often associated with the digestive system (e.g., GERD) and renal system (e.g., CKD). As age increases, the comorbidity rate of cardiovascular and neurological diseases significantly rises, suggesting that elderly patients need more comprehensive disease management. The high association strength between chronic diseases, particularly AF+HF and CKD+HF, suggests that these diseases may share common pathological mechanisms or synergistic effects. Understanding the associations between chronic diseases helps more accurately identify high-risk populations and optimize intervention strategies.\u003c/p\u003e\n\u003cp\u003eClinically, the key role of hypertension as the core of comorbidity should be emphasized, and personalized management plans should be developed for high-risk combinations (e.g., HTN+DM+CA). At the public health level, layered management strategies should be developed based on gender and age characteristics, optimizing resource allocation and disease prevention strategies.\u003c/p\u003e\n\u003cp\u003eThis study provides scientific evidence for the comorbidity patterns and management of elderly chronic diseases but further research, including multicenter data and longitudinal studies, is needed to explore causal relationships and mechanisms between chronic diseases and promote the development of precision medicine.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eICD-10 the International Classification of Diseases\u003c/p\u003e\n\u003cp\u003eCHARLS the China Health and Retirement Longitudinal Study\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehypertension\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHTN \u0026nbsp;coronary heart disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCHD \u0026nbsp;diabetes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDM \u0026nbsp;atrial fibrillation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAF \u0026nbsp;heart failure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHF \u0026nbsp;cerebrovascular disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCVA \u0026nbsp;chronic kidney disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCKD \u0026nbsp;chronic gastrointestinal diseases\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGERD \u0026nbsp;malignant tumors\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCA \u0026nbsp;skeletal joint diseases\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eARTH \u0026nbsp;chronic obstructive pulmonary disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCOPD \u0026nbsp;chronic liver diseases\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLIV \u0026nbsp;and prostate disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBPH \u0026nbsp;applicable only to males .\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.This study is based on anonymized medical record data from hospitalized patients and does not require direct contact with patients, adhering to ethical principles. The study protocol has been approved by the Ethics Committee of Panzhihua Central Hospital (Ethics number:2025-013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYGW and MWL wrote the manuscript and conducted statistical analysis. RYL and ZC searched the data. RYL and GHG designed the study, and revised the manuscript. MWL supervised the overall work and is the guarantor of the review.All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChen X, Giles J, Yao Y, Yip W, Meng Q, Berkman L, Chen H, Chen X, Feng J, Feng Z\u003cem\u003e ,et al.\u003c/em\u003e \u003cstrong\u003eThe path to healthy ageing in China: a Peking University-Lancet Commission\u003c/strong\u003e. \u003cem\u003eLancet \u003c/em\u003e2022, \u003cstrong\u003e400\u003c/strong\u003e(10367):1967-2006.\u003c/li\u003e\n\u003cli\u003eNugent R. \u003cstrong\u003ePreventing and managing chronic diseases\u003c/strong\u003e. \u003cem\u003eBMJ \u003c/em\u003e2019, \u003cstrong\u003e364\u003c/strong\u003e:l459.\u003c/li\u003e\n\u003cli\u003ePiacenza F, Di Rosa M, Soraci L, Montesanto A, Corsonello A, Cherubini A, Fabbietti P, Provinciali M, Lisa R, Bonfigli AR\u003cem\u003e ,et al.\u003c/em\u003e \u003cstrong\u003eInteractions between patterns of multimorbidity and functional status among hospitalized older patients: a novel approach using cluster analysis and association rule mining\u003c/strong\u003e. \u003cem\u003eJ Transl Med \u003c/em\u003e2024, \u003cstrong\u003e22\u003c/strong\u003e(1):669.\u003c/li\u003e\n\u003cli\u003eBlum MR, Sallevelt BTGM, Spinewine A, O\u0026apos;Mahony D, Moutzouri E, Feller M, Baumgartner C, Roumet M, Jungo KT, Schwab N\u003cem\u003e ,et al.\u003c/em\u003e \u003cstrong\u003eOptimizing Therapy to Prevent Avoidable Hospital Admissions in Multimorbid Older Adults (OPERAM): cluster randomised controlled trial\u003c/strong\u003e. \u003cem\u003eBMJ \u003c/em\u003e2021, \u003cstrong\u003e374\u003c/strong\u003e:n1585.\u003c/li\u003e\n\u003cli\u003eGao S, Sun S, Sun T, Lu T, Ma Y, Che H, Liu M, Xue W, He K, Wang Y\u003cem\u003e ,et al.\u003c/em\u003e \u003cstrong\u003eChronic diseases spectrum and multimorbidity in elderly inpatients based on a 12-year epidemiological survey in China\u003c/strong\u003e. \u003cem\u003eBMC Public Health \u003c/em\u003e2024, \u003cstrong\u003e24\u003c/strong\u003e(1):509.\u003c/li\u003e\n\u003cli\u003eHan S, Mo G, Gao T, Sun Q, Liu H, Zhang M. \u003cstrong\u003eAge, sex, residence, and region-specific differences in prevalence and patterns of multimorbidity among older Chinese: evidence from Chinese Longitudinal Healthy Longevity Survey\u003c/strong\u003e. \u003cem\u003eBMC Public Health \u003c/em\u003e2022, \u003cstrong\u003e22\u003c/strong\u003e(1):1116.\u003c/li\u003e\n\u003cli\u003eHe K, Zhang W, Hu X, Zhao H, Guo B, Shi Z, Zhao X, Yin C, Shi S. \u003cstrong\u003eRelationship between multimorbidity, disease cluster and all-cause mortality among older adults: a retrospective cohort analysis\u003c/strong\u003e. \u003cem\u003eBMC Public Health \u003c/em\u003e2021, \u003cstrong\u003e21\u003c/strong\u003e(1):1080.\u003c/li\u003e\n\u003cli\u003eSaito Y, Igarashi A, Nakayama T, Fukuma S. \u003cstrong\u003ePrevalence of multimorbidity and its associations with hospitalisation or death in Japan 2014-2019: a retrospective cohort study using nationwide medical claims data in the middle-aged generation\u003c/strong\u003e. \u003cem\u003eBMJ Open \u003c/em\u003e2023, \u003cstrong\u003e13\u003c/strong\u003e(5):e063216.\u003c/li\u003e\n\u003cli\u003eRodrigues LP, Fran\u0026ccedil;a DG, Vissoci JRN, Caruzzo NM, Batista SR, de Oliveira C, Nunes BP, Silveira EA. \u003cstrong\u003eAssociations of hospitalisation - admission, readmission and length to stay - with multimorbidity patterns by age and sex in adults and older adults: the ELSI-Brazil study\u003c/strong\u003e. \u003cem\u003eBMC Geriatr \u003c/em\u003e2023, \u003cstrong\u003e23\u003c/strong\u003e(1):504.\u003c/li\u003e\n\u003cli\u003eRodrigues LP, de Oliveira Rezende AT, Delpino FM, Mendon\u0026ccedil;a CR, Noll M, Nunes BP, de Oliviera C, Silveira EA. \u003cstrong\u003eAssociation between multimorbidity and hospitalization in older adults: systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eAge Ageing \u003c/em\u003e2022, \u003cstrong\u003e51\u003c/strong\u003e(7).\u003c/li\u003e\n\u003cli\u003eRodrigues LP, Rezende ATdO, Moura LdANE, Nunes BP, Noll M, de Oliveira C, Silveira EA. \u003cstrong\u003eWhat is the impact of multimorbidity on the risk of hospitalisation in older adults? 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needs\u003c/strong\u003e. \u003cem\u003ePatient Educ Couns \u003c/em\u003e2020, \u003cstrong\u003e103\u003c/strong\u003e(2):398-404.\u003c/li\u003e\n\u003cli\u003eAllegrante JP, Wells MT, Peterson JC. \u003cstrong\u003eInterventions to Support Behavioral Self-Management of Chronic Diseases\u003c/strong\u003e. \u003cem\u003eAnnu Rev Public Health \u003c/em\u003e2019, \u003cstrong\u003e40\u003c/strong\u003e:127-146.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Characteristics of the population and comparison\u0026nbsp;（n[%]）\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"490\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003eNumber of comorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e371.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50476(56.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7047(13.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14023(27.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12912(25.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16494(32.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38266(43.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5564(14.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10950(28.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9856(25.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11896(31.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e480.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e65-70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24232(27.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4560(18.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8236(33.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6033(24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5403(22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e70-75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25944(29.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3770(14.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7669(29.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6950(26.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7555(29.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e75-80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21466(24.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2597(12.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5453(25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5559(25.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7857(36.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17100(19.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1685(9.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3615(21.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4225(24.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7575(44.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 Characteristics of the population and comparison （n[%]）\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"659\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eChronic disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003ePresence in all elderly hospitalized patients, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eNumber of co-occurring conditions, mean (SD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eHTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e33413(37.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e3.76(1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e32513(36.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e3.69(1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e19258(21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e3.38(1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e14295(16.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e3.83(1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eCVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e14200(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e3.65(1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eGERD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e13678(15.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e3.2(1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eCHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e12385(13.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e3.45(1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eARTH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e9900(11.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e4.25(1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e6553(7.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e3.49(1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e6251(7.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e3.63(1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e5470(6.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e3.13(1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e5012(5.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e3.6(1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eHID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e4654(5.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e3.5(1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e76130(85.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e2.98(1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 Trends of comorbidities on different genders and age groups（n[%]）\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"688\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 47px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eprevalence \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 137px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 191px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026chi;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e65-70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e70-75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e75-80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026ge;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026chi;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eHTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e83.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e15700(53.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e12281(56.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e47.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e6396(55.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e8140(56.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e7607(56.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e5838(49.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e170.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e71.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e13799(46.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e9322(42.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e83.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e5622(49.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e6637(45.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e5895(43.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4967(42.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e128.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e90.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e9384(31.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e8071(37.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e149.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3836(33.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e5048(34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4828(35.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3743(31.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e55.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e90.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e7332(24.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e5647(25.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e6.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2371(20.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3235(22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3522(26.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3851(32.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e536.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eCVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e89.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e7215(24.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e5499(25.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2195(19.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3364(23.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3579(26.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3576(30.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e429.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eGERD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e89.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e6349(21.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e5873(27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e200.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2596(22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3322(22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3152(23.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3152(26.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e69.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eCHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e93.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e7004(23.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4616(21.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e47.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2012(17.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2933(20.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3176(23.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3499(29.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e552.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eARTH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e88.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4568(15.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4222(19.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e131.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1805(15.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2328(16.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2213(16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2444(20.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e136.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e89.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4255(14.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1582(7.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e639.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e900(7.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1529(10.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1703(12.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1705(14.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e282.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e89.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e5559(18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.00(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e4611.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e683(5.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1231(8.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1574(11.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2071(17.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e922.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e94.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2751(9.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2440(11.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e47.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e684(5.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1212(8.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1478(11.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1817(15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e636.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e97.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3122(10.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1774(8.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e87.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e874(7.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1196(8.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1272(9.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1554(13.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e255.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eHID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e90.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1741(5.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2467(11.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e486.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1291(11.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1387(9.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e942(7.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e588(4.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e366.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 4. Top 10 Dyad and Triad Comorbidity Patterns in Elderly Patients with Chronic Diseases (Ranked by Prevalence)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"611\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eComorbidity Patterns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eCases (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eProportion (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eTriad Comorbidity Patterns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eCases (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eProportion (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eHTN+CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e11128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e12.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eHTN+DM+CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e41.05%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eHTN+DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e10532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e11.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eHTN+DM+CVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e26.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eHTN+CVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e6848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e7.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eHTN+HF+CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e24.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eDM+CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e6708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e7.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eHTN+CHD+DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e24.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eHTN+CHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e5935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e6.69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eHTN+DM+HF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e23.59%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eHTN+HF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e5777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e6.51%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eHTN+CHD+CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e20.77%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eHF+CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e5503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e6.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eHTN+CHD+HF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e19.59%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eHTN+GERD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e4843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e5.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eHTN+CVA+CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e19.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eGERD+CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e4543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e5.12%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eHTN+GERD+CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e17.38%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eDM+CVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e4155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e4.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eDM+HF+CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e16.97%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 5. Observed vs. Expected Ratios and Association Strengths of Dyad Comorbidity Patterns\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"516\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 116px;\"\u003e\n \u003cp\u003eDyad Comorbidity Patterns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 43px;\"\u003e\n \u003cp\u003eCases, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePrevalence (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003eObserved/Expected Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;OR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eObserved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eExpected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eHTN+DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e10532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e11.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e8.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e2.28(2.21-2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eHTN+CVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e6848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e7.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e2.37(2.20-2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eHTN+CHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e5935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e1.42(1.41-1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eHTN+HF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e5777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e6.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e2.33(2.18-2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eHTN+GERD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e4843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e5.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e5.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.81(0.78-0.84)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eCHD+DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e3905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e4.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.59(1.52-1.66)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eAF+HF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e2595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e4.47(4.22-4.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eCKD+HF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e1925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e2.72(2.55-2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003eHID+CVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e1436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e2.35(2.20-2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eCKD+ARTH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e1228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e1.50(1.45-1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Elderly patients, Chronic diseases, Comorbidity patterns, Hypertension, Diabetes, Gender, Age","lastPublishedDoi":"10.21203/rs.3.rs-6168743/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6168743/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThis study aims to analyze the comorbidity patterns of chronic diseases in elderly patients aged 65 and above at Panzhihua Central Hospital and their relationships with gender and age, revealing the strength of associations between common chronic diseases, thereby providing data support for clinical management and public health strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis retrospective study analyzed the medical records of 88,742 hospitalized patients aged 65 and above at Panzhihua Central Hospital from January 2019 to December 2023. Patient data, including age, gender, primary diagnosis, and other disease diagnoses, were collected through the hospital's medical information system. Diseases were classified and statistically analyzed according to the International Classification of Diseases, 10th Revision (ICD-10). The comorbidity patterns among 13 common chronic diseases were analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Hypertension, diabetes, and coronary heart disease are the most common chronic diseases among elderly patients. The most common three-way comorbidity pattern is HTN + DM + CA, with a prevalence of 41.05%. Gender and age have significant impacts on comorbidity patterns. Chronic obstructive pulmonary disease (COPD) and heart failure (HF) comorbidity patterns are more common in male patients, while gastrointestinal and renal diseases are predominant in females. As age increases, the comorbidity rate of cardiovascular and neurological diseases rises significantly, particularly in elderly individuals aged 80 and above. Hypertension shows a significant association with chronic diseases like diabetes and coronary heart disease, and the comorbidity relationship between atrial fibrillation and heart failure is particularly strong, suggesting a potential shared pathological mechanism between these diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Hypertension, diabetes, and coronary heart disease are the most common chronic diseases among elderly patients, and there is a high degree of comorbidity between these diseases. Gender and age significantly influence comorbidity patterns, and there are strong associations between chronic diseases. The findings provide important clinical evidence for the management of chronic diseases in the elderly population, suggesting the development of personalized disease management strategies based on gender and age characteristics to optimize the allocation of public health resources.\u003c/p\u003e","manuscriptTitle":"Comorbidity Patterns and Influencing Factors in Elderly Chronic Disease Patients: A Five-Year Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-11 06:54:47","doi":"10.21203/rs.3.rs-6168743/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"db486c55-dcf1-475b-a208-954897a8828b","owner":[],"postedDate":"March 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-13T02:38:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-11 06:54:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6168743","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6168743","identity":"rs-6168743","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}