Published on in Vol 9 (2023)

Preprints (earlier versions) of this paper are available at, first published .
Disease Burden and the Accumulation of Multimorbidity of Noncommunicable Diseases in a Rural Population in Henan, China: Cross-sectional Study

Disease Burden and the Accumulation of Multimorbidity of Noncommunicable Diseases in a Rural Population in Henan, China: Cross-sectional Study

Disease Burden and the Accumulation of Multimorbidity of Noncommunicable Diseases in a Rural Population in Henan, China: Cross-sectional Study

Original Paper

1Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China

2China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China

3Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia

4Central Clinical School, Faculty of Medicine, Monash University, Melbourne, Australia

*these authors contributed equally

Corresponding Author:

Lei Zhang, PhD

China-Australia Joint Research Center for Infectious Diseases

School of Public Health

Xi’an Jiaotong University Health Science Center

No. 76, Yanta West Road

Xi’an, Shaanxi, 710061


Phone: 86 29 82655135


Background: Multimorbidity causes substantial disease and economic burdens on individuals and the health care system.

Objective: This study aimed to explore the disease burden of multimorbidity and the potential correlations among chronic noncommunicable diseases (NCDs) in a rural population in Henan, China.

Methods: A cross-sectional analysis was performed using the baseline survey of the Henan Rural Cohort Study. Multimorbidity was defined as the simultaneous occurrence of at least two NCDs in a participant. This study examined the multimorbidity pattern of 6 NCDs, including hypertension, dyslipidemia, type 2 diabetes mellitus, coronary heart disease, stroke, and hyperuricemia.

Results: From July 2015 to September 2017, a total of 38,807 participants (aged 18-79 years; 15,354 men and 23,453 women) were included in this study. The overall population prevalence of multimorbidity was 28.1% (10,899/38,807), and the multimorbidity of hypertension and dyslipidemia was the most common (8.1%, 3153/38,807). Aging, higher BMI, and unfavorable lifestyles were significantly associated with a higher risk of multimorbidity (multinomial logistic regression, all P<.05). The analysis of the mean age at diagnosis suggested a cascade of interrelated NCDs and their accumulation over time. Compared with participants without 2 conditional NCDs, participants with 1 conditional NCD would have higher odds of another NCD (1.2-2.5; all P<.05), and those with 2 conditional NCDs would elevate the odds of the third NCD to 1.4-3.5 (binary logistic regression, all P<.05).

Conclusions: Our findings indicate a plausible tendency for the coexistence and accumulation of NCDs in a rural population in Henan, China. Early prevention of multimorbidity is essential to reduce the NCD burden in the rural population.

JMIR Public Health Surveill 2023;9:e43381



Multimorbidity is the coexistence of multiple disease conditions over time. Many of these conditions shared similar age-associated structural, physiologic, and biological changes in predispose patients with multimorbidity, resulting in compound risks and adversities [1-4]. The occurrence of multimorbidity is often a powerful predictor of poor health outcomes in the future. Recent studies have demonstrated that people with multimorbidity were at significantly increased risks of depression, disability, and premature death. The physiological interaction between multiple disease conditions increases the difficulty of treating individual conditions [5]. Multimorbidity often leads to a substantial financial burden on individuals, families, and the health care system [6-10].

In China, this prevalence ranged from 6.4% to 76.5%, as reported in 2015 [11]. Previous studies have reported that aging, physical inactivity, and higher BMI were contributors to noncommunicable diseases (NCDs) and their multimorbidity [12]. However, as the study population and the targeted NCDs varied, the prevalence and associating factors of multimorbidity also substantially varied across different studies [11-13]. Further, in China, most studies have focused on older adults and were conducted among the community-dwelling population [14,15], whereas research on multimorbidity in rural adults remains limited. With an aging population and high levels of risk factors, multimorbidity is becoming a public health issue in China. However, understanding the trend and characteristics of the coexistence and accumulation of NCDs is limited.

The United States has emphasized the importance of timely diagnosis of multiple NCDs to support the development of effective clinical guidelines for multimorbidity [16]. In addition, previous studies found that NCDs can mutually interact with each other [17-22]. However, to our knowledge, the study of the accumulation of NCDs based on the temporal sequence of NCD onset was limited. A better understanding of the accumulation of NCDs can help explore complex interactions between NCDs and develop interventions for managing multimorbidity to diminish the public health burden.

Our study aimed to explore the disease burden of multimorbidity and determine the pattern of NCD accumulation among rural adults in Henan, China. We recruited 39,259 participants aged 18-79 years in Henan province for this study.

Study Population

The baseline survey of the Henan Rural Cohort Study enrolled a total of 39,259 participants aged from 18 to 79 years between July 2015 and September 2017 in the counties of Yuzhou, Xinxiang, Tongxu, Yima, and Suiping in Henan Province by using the stratified cluster sampling method. Detail description of the cohort had been previously published [23]. In this study, participants were excluded if they (1) had cancer or renal failure (n=350; for abnormal metabolism [protein, glucose, and lipid]) [24,25] or (2) did not have complete NCD information on hypertension, dyslipidemia, type 2 diabetes mellitus (T2DM), coronary heart disease (CHD), stroke, or hyperuricemia (n=102). Ultimately, 38,807 participants were included in this study (see the study flowchart about the data process in Multimedia Appendix 1).

Ethics Approval

This study was approved by the Zhengzhou University Life Science Ethics Committee (ethics approval 2015 MEC [S128]). Informed consent was signed by all participants.

Data Collection and Measurements

Information was collected through face-to-face interviews using a standardized questionnaire. Demographic characteristics included age (18-44, 45-59, and ≥60 years, according to the classification provided by the World Health Organization [WHO] [26]), gender, educational level (elementary school or below, junior high school, and senior high school or above), marital status (married or cohabiting and living alone), and average monthly individual income (<500 RMB [<US $72.4], 500-1000 RMB [US $72.4-144.8], and ≥1000 RMB [≥US $144.8], according to the average monthly income of Chinese rural populations [27]). Lifestyle factors included smoking status (never, former, and current), drinking status (never, former, and current), and physical activity (low, moderate, and high) [28]. Dietary status included vegetable and fruit intake (low [<500 g/day] and high [≥500 g/day]), salt diet (low and high according to dietary flavor habits), and fat diet (low [<75 g/day] and high [≥75 g/day] according to an average taking of meat from livestock and poultry). Family history of hypertension, dyslipidemia, diabetes, CHD, stroke, and gout were also recorded.

BMI was calculated as weight in kilograms divided by height in meters squared, and further categorized as underweight (<18.5 kg/m2), normal (18.5-23.9 kg/m2), overweight (24.0-27.9 kg/m2), and obesity (≥28.0 kg/m2) [29]. Systolic blood pressure and diastolic blood pressure were obtained by using an electronic sphygmomanometer (HEM-770afuzzy, Omron). Venous blood samples were obtained from individuals after at least eight hours of overnight fasting. Total cholesterol, triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, fasting blood glucose, and serum uric acid (SUA) level were measured by a chemistry analyzer (Cobas C501, Roche Diagnostics GmbH).

Definitions of NCDs and Multimorbidity

In light of the lack of a standard approach to measure multimorbidity, the selection of morbidities to be included is inevitably subjective and relies on the previous studies. We have selected 6 NCDs—hypertension, dyslipidemia, T2DM, CHD, stroke, and hyperuricemia—that have been reported to be the core diseases in multimorbidity studies [30,31]. These 6 NCDs have been defined in our original survey for the cohort [23]. The definition of hypertension was average systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or self-reported hypertension diagnosed by a physician and receiving antihypertensive therapy within the past 2 weeks [32]. Dyslipidemia was defined as one of the following conditions: elevated total cholesterol level (≥6.2 mmol/L); elevated low-density lipoprotein cholesterol (≥4.1 mmol/L); low high-density lipoprotein cholesterol (<1.0 mmol/L); elevated triglyceride (≥2.3 mmol/L); or self-reported dyslipidemia diagnosed by a physician and the use of anti-dyslipidemia medications in the past 2 weeks [33]. T2DM was defined as fasting blood glucose ≥7.0 mmol/L or self-reported T2DM diagnosed by a physician and the use of antidiabetic medications in the past 2 weeks [33]. CHD and stroke were defined as self-reports of a previous diagnosis by specialist physicians according to criteria recommended by the WHO [34,35]. The definition of hyperuricemia was SUA >7.0 mg/dL (417.0 μmol/L) in men and SUA >6.0 mg/dL (357.0 μmol/L) in women. Multimorbidity was defined as the simultaneous occurrence of at least two NCDs in a single patient [36-39].

Statistical Analysis

The participants were divided into healthy individuals, individuals living with 1 NCD, those with multimorbidities of 2 NCDs, and those with multimorbidities of ≥3 NCDs. Continuous variables were expressed as mean (SD), and ANOVA was used for statistical analysis. Categorical variables were presented as counts and percentages, and the chi-square test was used for statistical analysis.

The prevalence of multimorbidity was assessed for each characteristic group, and we quantified the number of events for each NCD separately and visualized all observed combinations of NCDs with an intersection diagram. Multinomial logistic regression analysis was used to analyze the relationship between demographic factors, lifestyle, BMI, or family history of NCDs and 1 NCD or multimorbidity. When one factor was analyzed, all other factors were adjusted as covariance.

Based on the mean age at diagnosis, the temporal order of NCDs was determined, and binary logistic regression was used to explore the associations between NCD pairs. An NCD with an earlier onset was regarded as a predictor of an NCD with a later onset [40]. Following the cascade of NCD onset, binary logistic regression was used to assess the combined association of 2 conditional NCDs with a third, later NCD by creating a new combined variable with 4 groups of both conditional NCDs, in which the combined group without both conditional NCDs served as the reference. Demographic characteristics, lifestyle factors, dietary status, BMI, and family history of NCDs were adjusted in the regression models. In addition, in the regression of assessing the combined association, when 3 of the conditions were analyzed, the others were adjusted.

Results from the logistic regression models were presented as odds ratio (OR) with a 95% CI. The level of statistical significance was set at P<.05. Statistical analysis was conducted using SPSS (version 21.0; IBM Corp) and R (version 4.0.4; R Foundation for Statistical Computing).

Our study was implemented according to the Strengthening of the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [41] (see the study STROBE checklist for cross-sectional studies in Multimedia Appendix 2).

Characteristics of Study Participants

Table 1 showed the characteristics of the participants of the baseline survey from July 2015 to September 2017. The mean age was 55.6 (SD 12.2) years among the 38,807 participants. Across the NCD groups, age, gender, educational level, average monthly individual income, marital status, physical activity, smoking status, drinking status, high-salt diet, high vegetable and fruit intake, high-fat diet, and BMI were all significantly different (ANOVA and chi-square test, all P values were <.001, except high-salt diet [P=.008]). Compared with the healthy individuals, participant groups with 1, 2, or 3 or more NCDs were more likely to be older and women and have lower education and income, higher BMI, poorer marital status, a higher-salt diet, and lower physical activity level (ANOVA and chi-square test, all P<.001). They were also less likely to have a high-fat diet and adequate fruit and vegetable intake than healthy individuals (chi-square test, all P<.001).

Table 1. Demographic characteristics of study participants according to multimorbidity.
VariablesTotal participants (n=38,807)No NCDsa (n=14,918)1 NCD (n=12,990)2 NCDs (n=7285)≥3 NCDs (n=3614)P value
Age (year), mean (SD)55.6 (12.2)52.2 (12.7)52.3 (11.7)58.6 (11.0)60.8 (9.9)<.001
Gender, women, n (%)23,453 (60.4)9552 (64)7623 (58.7)4200 (57.6)2078 (57.5)<.001
Educational level, n (%)<.001

Elementary school or below17,366 (44.8)5912 (39.6)5869 (45.2)3592 (49.3)1993 (55.2)

Junior high school15,457 (39.8)6408 (43)5241 (40.3)2643 (36.3)1165 (32.2)

Senior high school or above5984 (15.4)2598 (17.4)1880 (14.5)1050 (14.4)456 (12.6)
Marital status, living alone, n (%)3979 (10.3)1335 (8.9)1314 (10.1)884 (12.1)446 (12.3)<.001
Average monthly income, n (%)<.001

<500 RMB (<US $72.4)13,825 (35.6)4949 (33.2)4693 (36.1)2705 (37.1)1478 (40.9)

500-1000 RMB (US $72.4-144.8)12,778 (32.9)4966 (33.3)4251 (32.7)2482 (34.1)1079 (29.9)

≥1000 RMB (≥US $144.8)12,204 (31.5)5003 (33.5)4046 (31.2)2098 (28.8)1057 (29.2)
Physical activity, n (%)<.001

Low12,544 (32.3)4126 (27.7)4132 (31.8)2699 (37.1)1587 (43.9)

Moderate14,631 (37.7)5899 (39.5)4905 (37.8)2587 (35.5)1240 (34.3)

High11,632 (30)4893 (32.8)3953 (30.4)1999 (27.4)787 (21.8)
Smoking, n (%)<.001

Never28,225 (72.7)11,180 (75)9319 (71.7)5147 (70.6)2579 (71.4)

Former3131 (8.1)901 (6)1019 (7.9)726 (10)485 (13.4)

Current7451 (19.2)2837 (19)2652 (20.4)1412 (19.4)550 (15.2)
Drinking, n (%)<.001

Never29,981 (77.3)11,860 (79.5)9958 (76.7)5463 (75)2700 (74.7)

Former1794 (4.6)523 (3.5)549 (4.2)432 (5.9)290 (8)

Current7032 (18.1)2535 (17)2483 (19.1)1390 (19.1)624 (17.3)
High-salt diet, n (%)6937 (17.9)2609 (17.5)2319 (17.9)1351 (18.6)658 (18.2).008
More vegetable and fruit intake, n (%)16,183 (41.7)6541 (43.8)5367 (41.3)2880 (39.5)1395 (38.6)<.001
High-fat diet, n (%)7421 (19.1)3136 (21)2447 (18.8)1263 (17.3)575 (15.9)<.001
BMI (kg/m2), mean (SD)24.8 (3.6)23.51 (3.2)25.03 (3.4)26.2 (3.6)27.0 (3.5)<.001

aNCD: noncommunicable disease.

Prevalence of NCDs and Their Multimorbidities

Among all participants, 33.5% (12,990/38,807) were living with 1 NCD, among which dyslipidemia was the most common (5858/38,807, 15.1%). In contrast, 18.8% (7285/38,807) of participants were living with 2 NCDs, and the most frequent cluster of NCDs was dyslipidemia and hypertension (3153/38,807, 8.1%). Moreover, 9.3% (3614/38,807) of participants were living with 3 or more NCDs, and the cluster of dyslipidemia, hypertension, and hyperuricemia (818/38,807, 2.1%) was the most common in participants (Figure 1 and Table 2).

Overall, the prevalence of multimorbidity (2 or ≥3 NCDs) was 28.1% (10,899/38,807), and it was significantly higher in participants who were aged over 60 years (1330/3982, 36%), men (4621/15,354, 30.1%), educated in elementary school or below (5585/17,366, 32.2%), living alone (1330/3982, 33.4%), had low physical activity (4286/12,544, 34.2%), and obese (3280/6878, 47.7%; chi-square test, all P<.001; Table 2).

Figure 1. NCDs multimorbidity. The left panel displayed bars for each NCD separately that quantify the total number of events per disease, and the line chart showed the cumulative prevalence of NCDs. CHD: coronary heart disease; DYS: dyslipidemia; HTN: hypertension; HUA: hyperuricemia; NCDs: noncommunicable diseases; T2DM: type 2 diabetes mellitus.
Table 2. The prevalence of 1 noncommunicable disease (NCD) and multimorbidity.
Variables1 NCD, n (%)2 NCDs, n (%)≥3 NCDs, n (%)
Total (n=38,807)12,990 (33.5)7285 (18.8)3614 (9.3)
Age (years)

18-44 (n=6805)1963 (28.6)728 (10.7)222 (3.3)

45-59 (n=15,181)5225 (34.4)2734 (18)1156 (7.6)

≥60 (n=16,821)5802 (34.5)3823 (22.7)2236 (13.3)
Gender, women (n=23,453)7623 (32.5)4200 (17.9)2078 (8.9)
Educational level

Elementary school or below (n=17,366)5869 (33.8)3592 (20.7)1993 (11.5)

Junior high school (n=15,457)5241 (33.9)2643 (17.1)1165 (7.5)

Senior high school or above (n=5984)1880 (31.4)1050 (17.6)456 (7.6)
Marital status, living alone (n=3979)1314 (33)884 (22.2)446 (11.2)
Average monthly income

<500 RMB (<US $72.4; n=13,825)4693 (34)2705 (19.6)1478 (10.7)

500-1000 RMB (US $72.4-144.8; n=12,778)4251 (33.3)2482 (19.4)1079 (8.4)

≥1000 RMB (≥US $144.8; n=12,204)4046 (33.2)2098 (17.2)1057 (8.7)
Physical activity

Low (n=12,544)4132 (32.9)2699 (21.5)1587 (12.7)

Moderate (n=14,631)4905 (33.5)2587 (17.7)1240 (8.5)

High (n=11,632)3953 (34)1999 (17.2)787 (6.8)

Never (n=28,225)9319 (33)5147 (18.2)2579 (9.1)

Former (n=3131)1019 (32.6)726 (23.2)485 (15.5)

Current (n=7451)2652 (35.6)1412 (19)550 (7.4)

Never (n=29,981)9958 (33.2)5463 (18.2)2700 (9)

Former (n=1794)549 (30.6)432 (24.1)290 (16.2)

Current (n=7032)2483 (35.3)1390 (19.8)624 (8.9)
High-salt diet (n=6937)2319 (33.4)1351 (19.5)658 (9.5)
More vegetable and fruit intake (n=16,183)5367 (33.2)2880 (17.8)1395 (8.6)
High-fat diet (n=7421)2447 (33)1263 (17)575 (7.8)

Underweight (n=938)233 (24.8)67 (7.1)19 (2)

Normal (n=15,567)4867 (31.3)1938 (12.5)687 (4.4)

Overweight (n=15,306)5546 (36.2)3243 (21.2)1611 (10.5)

Obesity (n=6878)2309 (33.6)2012 (29.3)1268 (18.4)
Family history of hypertension (n=7522)2490 (33.1)1657 (22)1023 (13.6)
Family history of diabetes (n=1618)523 (32.3)339 (21)194 (12)
Family history of CHDa (n=3106)1053 (33.9)534 (17.2)306 (9.9)
Family history of hyperlipemia (n=1378)420 (30.5)249 (18.1)152 (11)
Family history of stroke (n=3313)1089 (32.9)582 (17.6)360 (10.9)
Family history of gout (n=80)26 (32.5)13 (16.3)7 (8.8)

aCHD: coronary heart disease.

Associating Factors of Multimorbidity

Overall, participants with older age (all P<.001), those who are living alone (P<.001 and P=.007), those who are overweight and obese (all P<.001), former drinkers (all P<.001), current drinkers (P=.006 and P=.047), and those with a history of family hypertension and diabetes (all P<.001) were at a significantly higher risk of multimorbidity (2 or ≥3 NCDs, respectively; multinomial logistic regression). Although women (all P<.001) and participants with moderate and high physical activity (all P<.001), high-fat diet (P=.004 and P=.008), and who are underweight (all P<.001) were less likely to report multimorbidity (2 or ≥3 NCDs, respectively), individuals who currently smoke were less likely to report multimorbidity (≥3 NCDs; multinomial logistic regression, P<.001; Table 3).

Table 3. The associating factors of 1 noncommunicable disease (NCD) and multimorbidity.
Variables1 NCD2 NCDs≥3 NCDs

ORa (95% CI)P valueOR (95% CI)P valueOR (95% CI)P value
Age (years)


45-591.7 (1.6-1.8)<.0012.5 (2.3-2.7)<.0013.5 (3.0-4.1)<.001

≥602.6 (2.4-2.8)<.0015.2 (4.7-5.8)<.00110.7 (9.1-12.7)<.001
Gender, women0.8 (0.7-0.8)<.0010.8 (0.7-0.8)<.0010.7 (0.6-0.8)<.001
Educational level

Elementary school or below1.0

Junior high school1.0 (0.9-1.0).530.9 (0.8-0.9).0050.8 (0.7-0.9)<.001

Senior high school or above1.0 (0.9-1.0).241.0 (0.9-1.1).640.9 (0.8-1.0).08
Marital status, living alone1.1 (1.0-1.2).081.3 (1.2-1.4)<.0011.2 (1.1-1.4).007
Average monthly income

<500 RMB (<US $72.4)1.00

500-1000 RMB (US $72.4-144.8)1.0 (0.9-1.0).081.0 (0.9-1.1).750.8 (0.7-0.9)<.001

≥1000 RMB (≥US $144.8)1.0 (0.9-1.0).280.9 (0.9-1.0).121.0 (0.9-1.1).42
Physical activity


Moderate0.9 (0.8-0.9)<.0010.8 (0.7-0.8)<.0010.6 (0.6-0.7)<.001

High0.8 (0.7-0.8)<.0010.6 (0.6-0.7)<.0010.4 (0.4-0.5)<.001


Former1.0 (0.9-1.1).521.1 (0.9-1.2).261.2 (1.1-1.4).02

Current1.0 (0.9-1.1).511.0 (0.9-1.1).620.8 (0.7-0.9)<.001


Former1.0 (0.9-1.2).751.4 (1.2-1.7)<.0011.8 (1.5-2.1)<.001

Current1.1 (1.0-1.2).071.2 (1.1-1.3).0061.1 (1.1-1.3).047
High-salt diet1.0 (1.0-1.1).701.1 (1.0-1.1).261.0 (0.9-1.1).55
More vegetable and fruit intake1.0 (0.9-1.0).101.0 (0.9-1.0).131.0 (0.9-1.1).43
High-fat diet0.9 (0.9-0.9).030.9 (0.8-0.9).0040.9 (0.8-0.9).009

Underweight0.6 (0.5-0.7)<.0010.4 (0.3-0.5)<.0010.3 (0.2-0.5)<.001


Overweight2.0 (1.9-2.1)<.0013.0 (2.8-3.2)<.0014.4 (4.0-4.9)<.001

Obesity3.3 (3.1-3.6)<.0017.8 (7.1-8.6)<.00114.8 (13.1-16.6)<.001
Family history of hypertension1.4 (1.3-1.5)<.0011.9 (1.8-2.1)<.0012.7 (2.4-2.9)<.001
Family history of diabetes1.2 (1.1-1.3).041.4 (1.2-1.6)<.0011.6 (1.3-1.9)<.001
Family history of CHDb1.0 (1.0-1.1).410.9 (0.8-1.1).291.1 (0.9-1.3).27
Family history of hyperlipemia0.9 (0.8-1.1).201.0 (0.8-1.2).771.1 (0.9-1.4).33
Family history of stroke1.0 (0.9-1.0).250.9 (0.8-0.9).021.1 (0.9-1.2).36
Family history of gout0.9 (0.5-1.5).610.8 (0.4-1.5).470.8 (0.3-1.9).55

aOR: odds ratio.

bCHD: coronary heart disease.

Age Gaps Between NCDs

A cascade of NCDs was depicted by the mean age of participants at diagnosis. Among the study participants, 10.2% (3973/38,807) were diagnosed with hyperuricemia at a mean age of 54.0 years, followed by dyslipidemia (14,569/38,807, 37.5% at 55.1 years), hypertension (12,692/38,807, 32.7% at 55.4 years), CHD (1708/38,807, 4.4% at 56.1 years), T2DM (3664/38,807, 9.4% at 56.7 years), and stroke (2613/38,807, 6.7% at 59.1 years). In addition, we observed additional connections between NCD pairs (Figure 2 and Multimedia Appendix 3).

Figure 2. Age gaps between NCDs and the accumulation of NCDs in individuals based on mean age at diagnosis. NCDs along the x-axis were in order of increasing mean age at diagnosis. The number of the connected edge reflected the OR (95% CI) of the NCD pairs. The forest plot displayed the independent and combined effects of NCDs. The dots and lines exhibited OR and 95% CI, respectively. The model reference was "no/no," which meant without 2 conditional NCDs; "yes/no" meant having the first NCD of the 2 conditional NCDs, "no/yes" meant having the second NCD, and "yes/yes" meant having both conditional NCDs. CHD: coronary heart disease; DYS: dyslipidemia; HTN: hypertension; HUA: hyperuricemia; NCD: noncommunicable disease; OR: odds ratio; STR: stroke; T2DM: type 2 diabetes mellitus.

Impact of Multimorbidity on Hypertension Occurrence

Compared with individuals without 2 conditional NCDs, individuals with 1 conditional NCD (hyperuricemia or dyslipidemia) would have significantly higher odds of hypertension (1.3-1.4, logistic regression; both P<.001). Individuals with 2 conditional NCDs (hyperuricemia and dyslipidemia) would further elevate their odds of hypertension to 2.1 (P<.001; Figure 2 and Multimedia Appendix 4).

Impact of Multimorbidity on CHD Occurrence

Compared with individuals without 2 conditional NCDs, individuals with one of the conditional NCDs (hyperuricemia, dyslipidemia, or hypertension) would have significantly higher odds of CHD (1.2-1.5; the combination of hyperuricemia without dyslipidemia: P=.008; the combination of no hyperuricemia but dyslipidemia: P<.001; the combination of hyperuricemia without hypertension: P=.001; and the combination of no hyperuricemia but hypertension: P=.02). Individuals with 2 conditional NCDs (hyperuricemia and dyslipidemia combination: P<.001; or hyperuricemia and hypertension combination: P=.002) would further elevate their odds of CHD to 1.4-1.7 (Figure 2 and Multimedia Appendix 4).

Impact of Multimorbidity on T2DM Occurrence

Compared with individuals without 2 conditional NCDs, individuals with 1 conditional NCDs (dyslipidemia, hypertension, or CHD) would have a significantly higher odd of T2DM (1.3-2.2; all P values were <.001 except the combination of no dyslipidemia but CHD [P=.01]), and individuals with these 2 conditional NCDs (dyslipidemia and hypertension combination or dyslipidemia and CHD combination) would further elevate their odds of T2DM to 2.7-3.1 (all P<.001; Figure 2 and Multimedia Appendix 4).

Impact of Multimorbidity on Stroke Occurrence

Compared with individuals without 2 conditional NCDs, individuals with one of the conditional NCDs (dyslipidemia, hypertension, CHD, or T2DM) would have significantly higher odds of stroke (1.4-2.5; all P<.001). Individuals with these 2 conditional NCDs (6 NCD combinations) would further elevate their odds of a stroke to 2.3-3.5 (all P<.001; Figure 2 and Multimedia Appendix 4).

Principal Findings

Our findings indicated a strong tendency for the coexistence and accumulation of NCDs in a rural population in Henan, China. The prevalence of multimorbidity was 28.1% (10,899/38,807) and increased with age, unfavorable lifestyles, and a higher BMI among Henan rural adults. The multimorbidity of dyslipidemia and hypertension was the most prevalent in study participants of all age groups. In addition, the onset of NCDs was associated, and a later NCD is often predictable by 1 or more preceding NCDs. The tendency of NCD accumulation indicates that compared with participants without conditional NCDs, participants with 1 existing NCD are more likely to develop a second NCD, whereas participants with 2 NCDs have an even greater likelihood to develop a third NCD.

Our study finding demonstrated a lower multimorbidity prevalence than previous studies [14]. A younger age, existing recall and reporting biases, and a fewer number of NCDs included may have contributed to the lower prevalence observed in our study. Further, consistent with previous studies [2,42,43], the prevalence of multimorbidity increases with age, and approximately one-third of older adults aged 60-79 years develop multimorbidity. Nevertheless, multimorbidity in the young and middle-aged population should not be neglected, given the increasing trend of risk factors in the young and middle-aged population [13,44]. Multimorbidity research and preventive strategies should not focus on older adults only but should instead recognize its importance throughout the whole life cycle.

Our study found that unfavorable lifestyles and a higher BMI were associated with a higher risk of multimorbidity. Our finding is supported by evidence in other settings. A multinational cohort study found that a prediagnostic healthy lifestyle, such as a healthy diet, lower BMI, and higher physical activity, could reduce the risk of multimorbidity [45]. A pooled study from 16 cohort studies demonstrates that a high BMI significantly contributes to the risk of cardiometabolic multimorbidity [46]. Further, a systematic review identifies that physical activity may improve immunity and reduce systemic inflammation and thereby ameliorate multimorbidity [47]. Overall, many modifiable risk factors associated with multimorbidity, including drinking, overweight and obesity, and physical inactivity, could be targets of behavioral prevention and interventions. Differing from previous studies [48-50], our study found that a high-fat diet, current smoking status, and being underweight may reduce the risk of multimorbidity. Given our study is a cross-sectional study with certain biases, the power to explain the causative effects of risk factors on diseases is limited. Further investigation is necessary to confirm this particular finding. Our findings support the WHO’s recommendation to implement evidence-based strategies to reduce poor use of alcohol, encourage physical activity, and maintain a healthy weight.

Our study suggested a tendency for NCD accumulation among the participants. The clustering of dyslipidemia and hypertension was the most prevalent multimorbidity observed in our study, consistent with previous studies [30,51]. A Japanese study further describes hypertension as the most common comorbidity among individuals with dyslipidemia [52]. In addition, the temporal sequence reflects the order of NCDs onset and suggests a potential causal association between the occurrence of these NCDs and the development of other comorbidities [40]. The accumulation of NCDs reflects that participants who lived with 1 conditional NCD are more likely to develop a second NCD, whereas individuals with 2 conditional NCDs are more likely to develop a third NCD than otherwise. Similar results have also been reported in a large-series prospective study: participants with the multimorbidity of T2DM and hypertension have a higher risk of CHD or stroke than participants with hypertension or T2DM [53]. This observation might be partly explained by obesity and the variations of metabolites that have accumulating side effects on health over time [54,55]. Kivimaki et al [54] found that obesity-related disease predicted or was predicted by 1 or more other obesity-related diseases, leading to the accumulation of NCDs. A study of 11,000 participants to examine metabolic pathways associated with 27 NCDs shows that two-thirds of metabolites are associated with more than 1 NCD. In addition, 420 metabolites shared between multimorbidity have been found, revealing several key common pathways in NCDs [55]. Our study suggests that taking reasonable health management and therapies of early NCDs can reduce the incidence or slow down the progression of multimorbidities effectively and ultimately improve the quality of life of the population [56,57].

Our findings have important implications for research and public health policy for multimorbidity. This study indicates that multimorbidity is an important health concern in rural Henan. In light of the WHO Sustainable Development Goals for preventing and controlling NCDs [58], existing health care needs to be improved to cope with the burden of multimorbidity. First, the prevalence of multimorbidity is higher in those older than 45 years. A life cycle approach to multimorbidity and to the challenges it poses to public health is vitally important [59]. Second, there is a clear need to increase the awareness of the importance of a healthy lifestyle in the rural population in Henan. Measures of propagating publicity and education on relevant health knowledge, improving health literacy, and changing unfavorable lifestyles should be implemented. Third, disease-specific guidelines are insufficient to effectively manage patients with multimorbidity, and new detailed guidelines for multimorbidity need to be developed [6]. Given the tendency of the coexistence and accumulation of NCDs, preventing multimorbidity will become difficult if the NCDs are treated separately. Integrated strategies for multimorbidity should be developed, such as monitoring at the individual level with electronic health records and increasing the screening management of high-risk populations.


This study has several limitations. First, the causal relationship between NCDs may not be reliable on account of the data being from the baseline survey of a prospective cohort study in Henan Province. Therefore, prospective studies are needed to confirm the results of this study. Second, we define hypertension, dyslipidemia, hyperuricemia, and T2DM by self-report with laboratory measurements, whereas CHD and stroke are defined only by self-report, which might underestimate their prevalence. Finally, although several potential confounders are controlled, there may be other unknown confounders affecting the results of this study.


In conclusion, our study indicates that multimorbidity is an important health issue in rural Henan. Older age, unfavorable lifestyles, and a higher BMI are associated with a higher risk of multimorbidity. In addition, our findings highlight a plausible explanation for the coexistence and accumulation of NCDs. Care for multimorbidity is complex, and concerted efforts and early interventions to prevent the occurrence of multimorbidity are essential to improve health in the rural population in Henan, China.


The authors would like to give great gratitude to the participants and all research staff of this study.

Authors' Contributions

L Zhang and CW designed this study. YC, MP, YH, XD, ZH, JH, JY, YY, RL, L Zhu, SL, NK, and WL directed the data collection. YC analyzed the data and wrote the manuscript. JH and YB provided writing assistance. All authors listed have read, corrected, and approved the final manuscript.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Study flowchart about the data process.

PNG File , 47 KB

Multimedia Appendix 2

The Strengthening of the Reporting of Observational Studies in Epidemiology (STROBE) statement checklist for cross-sectional studies.

DOCX File , 29 KB

Multimedia Appendix 3

The association between noncommunicable disease pairs based on mean age at diagnosis.

DOC File , 40 KB

Multimedia Appendix 4

The accumulation of noncommunicable diseases in individuals based on mean age at diagnosis.

DOC File , 100 KB

  1. Nobili A, Garattini S, Mannucci PM. Multiple diseases and polypharmacy in the elderly: challenges for the internist of the third millennium. J Comorb 2011 Dec 27;1:28-44 [FREE Full text] [CrossRef] [Medline]
  2. Chen H, Cheng M, Zhuang Y, Broad JB. Multimorbidity among middle-aged and older persons in urban China: prevalence, characteristics and health service utilization. Geriatr Gerontol Int 2018 Oct;18(10):1447-1452. [CrossRef] [Medline]
  3. Zou S, Wang Z, Bhura M, Zhang G, Tang K. Prevalence and associated socioeconomic factors of multimorbidity in 10 regions of China: an analysis of 0.5 million adults. J Public Health (Oxf) 2022 Mar 07;44(1):36-50. [CrossRef] [Medline]
  4. Barnes PJ. Mechanisms of development of multimorbidity in the elderly. Eur Respir J 2015 Mar;45(3):790-806 [FREE Full text] [CrossRef] [Medline]
  5. Whitty CJM, MacEwen C, Goddard A, Alderson D, Marshall M, Calderwood C, et al. Rising to the challenge of multimorbidity. BMJ 2020 Jan 06;368:l6964 [FREE Full text] [CrossRef] [Medline]
  6. Kernick D, Chew-Graham CA, O'Flynn N. Clinical assessment and management of multimorbidity: NICE guideline. Br J Gen Pract 2017 May;67(658):235-236 [FREE Full text] [CrossRef] [Medline]
  7. Vogeli C, Shields AE, Lee TA, Gibson TB, Marder WD, Weiss KB, et al. Multiple chronic conditions: prevalence, health consequences, and implications for quality, care management, and costs. J Gen Intern Med 2007 Dec;22 Suppl 3(Suppl 3):391-395 [FREE Full text] [CrossRef] [Medline]
  8. Hu RH, Hsiao FY, Chen LJ, Huang PT, Hsu WWY. Increasing age- and gender-specific burden and complexity of multimorbidity in Taiwan, 2003-2013: a cross-sectional study based on nationwide claims data. BMJ Open 2019 Jun 09;9(6):e028333 [FREE Full text] [CrossRef] [Medline]
  9. Marengoni A, Angleman S, Melis R, Mangialasche F, Karp A, Garmen A, et al. Aging with multimorbidity: a systematic review of the literature. Ageing Res Rev 2011 Sep;10(4):430-439. [CrossRef] [Medline]
  10. Valderas JM, Starfield B, Sibbald B, Salisbury C, Roland M. Defining comorbidity: implications for understanding health and health services. Ann Fam Med 2009 Jul;7(4):357-363 [FREE Full text] [CrossRef] [Medline]
  11. Hu X, Huang J, Lv Y, Li G, Peng X. Status of prevalence study on multimorbidity of chronic disease in China: systematic review. Geriatr Gerontol Int 2015 Jan;15(1):1-10. [CrossRef] [Medline]
  12. Khan N, Rahman M, Mitra D, Afsana K. Prevalence of multimorbidity among Bangladeshi adult population: a nationwide cross-sectional study. BMJ Open 2019 Nov 28;9(11):e030886 [FREE Full text] [CrossRef] [Medline]
  13. The Lancet. Making more of multimorbidity: an emerging priority. Lancet 2018 Apr 28;391(10131):1637. [CrossRef] [Medline]
  14. Yao SS, Cao GY, Han L, Chen ZS, Huang ZT, Gong P, et al. Prevalence and Patterns of Multimorbidity in a Nationally Representative Sample of Older Chinese: Results From the China Health and Retirement Longitudinal Study. J Gerontol A Biol Sci Med Sci 2020 Sep 25;75(10):1974-1980. [CrossRef] [Medline]
  15. Zhang R, Lu Y, Shi L, Zhang S, Chang F. Prevalence and patterns of multimorbidity among the elderly in China: a cross-sectional study using national survey data. BMJ Open 2019 Aug 18;9(8):e024268 [FREE Full text] [CrossRef] [Medline]
  16. Parekh AK, Goodman RA, Gordon C, Koh HK, HHS Interagency Workgroup on Multiple Chronic Conditions. Managing multiple chronic conditions: a strategic framework for improving health outcomes and quality of life. Public Health Rep 2011 Jul 1;126(4):460-471 [FREE Full text] [CrossRef] [Medline]
  17. Wu Y, Hu H, Cai J, Chen R, Zuo X, Cheng H, et al. Association of hypertension and incident diabetes in Chinese adults: a retrospective cohort study using propensity-score matching. BMC Endocr Disord 2021 Apr 29;21(1):87 [FREE Full text] [CrossRef] [Medline]
  18. Rho YH, Lu N, Peloquin CE, Man A, Zhu Y, Zhang Y, et al. Independent impact of gout on the risk of diabetes mellitus among women and men: a population-based, BMI-matched cohort study. Ann Rheum Dis 2016 Jan;75(1):91-95 [FREE Full text] [CrossRef] [Medline]
  19. Mortada I. Hyperuricemia, type 2 diabetes mellitus, and hypertension: an emerging association. Curr Hypertens Rep 2017 Sep;19(9):69. [CrossRef] [Medline]
  20. Wang J, Qin T, Chen J, Li Y, Wang L, Huang H, et al. Hyperuricemia and risk of incident hypertension: a systematic review and meta-analysis of observational studies. PLoS One 2014;9(12):e114259 [FREE Full text] [CrossRef] [Medline]
  21. Kopin L, Lowenstein C. Dyslipidemia. Ann Intern Med 2017 Dec 05;167(11):ITC81-ITC96. [CrossRef] [Medline]
  22. Wang L, Qiu H, Luo L, Zhou L. Age- and sex-specific differences in multimorbidity patterns and temporal trends on assessing hospital discharge records in Southwest China: network-based study. J Med Internet Res 2022 Feb 25;24(2):e27146 [FREE Full text] [CrossRef] [Medline]
  23. Liu X, Mao Z, Li Y, Wu W, Zhang X, Huo W, et al. Cohort profile: The Henan Rural Cohort: a prospective study of chronic non-communicable diseases. Int J Epidemiol 2019 Dec 01;48(6):1756-1756j. [CrossRef] [Medline]
  24. Gyamfi J, Kim J, Choi J. Cancer as a metabolic disorder. Int J Mol Sci 2022 Jan 21;23(3):1155 [FREE Full text] [CrossRef] [Medline]
  25. Rubenfeld S, Garber AJ. Abnormal carbohydrate metabolism in chronic renal failure. the potential role of accelerated glucose production, increased gluconeogenesis, and impaired glucose disposal. J Clin Invest 1978 Jul;62(1):20-28 [FREE Full text] [CrossRef] [Medline]
  26. Chen Y, Bao J, Yan Q, Wu C, Yang H, Zou J. Distribution of Modic changes in patients with low back pain and its related factors. Eur J Med Res 2019 Oct 09;24(1):34 [FREE Full text] [CrossRef] [Medline]
  27. China Statistical Yearbook 2018. National Bureau of Statistics. 2018.   URL: [accessed 2020-10-24]
  28. Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, Bassett DR, Tudor-Locke C, et al. 2011 Compendium of Physical Activities: a second update of codes and MET values. Med Sci Sports Exerc 2011 Aug;43(8):1575-1581. [CrossRef] [Medline]
  29. Wang Y, Li Y, Liu X, Zhang H, Abdulai T, Tu R, et al. Prevalence and influencing factors of coronary heart disease and stroke in Chinese rural adults: The Henan Rural Cohort Study. Front Public Health 2019 Jan 21;7:411 [FREE Full text] [CrossRef] [Medline]
  30. Gu J, Chao J, Chen W, Xu H, Wu Z, Chen H, et al. Multimorbidity in the community-dwelling elderly in urban China. Arch Gerontol Geriatr 2017 Jan;68:62-67. [CrossRef] [Medline]
  31. He L, Biddle SJH, Lee JT, Duolikun N, Zhang L, Wang Z, et al. The prevalence of multimorbidity and its association with physical activity and sleep duration in middle aged and elderly adults: a longitudinal analysis from China. Int J Behav Nutr Phys Act 2021 Jun 10;18(1):77 [FREE Full text] [CrossRef] [Medline]
  32. Li N, Chen G, Liu F, Mao S, Liu Y, Hou Y, et al. Associations of long-term exposure to ambient PM with hypertension and blood pressure in rural Chinese population: The Henan Rural Cohort Study. Environ Int 2019 Jul;128:95-102 [FREE Full text] [CrossRef] [Medline]
  33. Dong X, Zhang H, Wang F, Liu X, Yang K, Tu R, et al. Epidemiology and prevalence of hyperuricemia among men and women in Chinese rural population: The Henan Rural Cohort Study. Mod Rheumatol 2020 Sep;30(5):910-920. [CrossRef] [Medline]
  34. Joint International Society and Federation of Cardiology/World Health Organization Task Force. Nomenclature and criteria for diagnosis of ischemic heart disease. report of the Joint International Society and Federation of Cardiology/World Health Organization task force on standardization of clinical nomenclature. Circulation 1979 Mar;59(3):607-609. [CrossRef] [Medline]
  35. Aho K, Harmsen P, Hatano S, Marquardsen J, Smirnov VE, Strasser T. Cerebrovascular disease in the community: results of a WHO collaborative study. Bull World Health Organ 1980;58(1):113-130 [FREE Full text] [Medline]
  36. Forman DE, Maurer MS, Boyd C, Brindis R, Salive ME, Horne FM, et al. Multimorbidity in older adults with cardiovascular disease. J Am Coll Cardiol 2018 May 15;71(19):2149-2161 [FREE Full text] [CrossRef] [Medline]
  37. Multiple long-term conditions (Multimorbidity): a priority for global health research. The Academy of Medical Sciences. 2018.   URL: [accessed 2020-10-24]
  38. Ho IS, Azcoaga-Lorenzo A, Akbari A, Black C, Davies J, Hodgins P, et al. Examining variation in the measurement of multimorbidity in research: a systematic review of 566 studies. Lancet Public Health 2021 Aug;6(8):e587-e597 [FREE Full text] [CrossRef] [Medline]
  39. Shi X, Lima SMDS, Mota CMDM, Lu Y, Stafford RS, Pereira CV. Prevalence of multimorbidity of chronic noncommunicable diseases in Brazil: population-based study. JMIR Public Health Surveill 2021 Nov 25;7(11):e29693 [FREE Full text] [CrossRef] [Medline]
  40. Kivimäki M, Batty GD, Pentti J, Shipley MJ, Sipilä PN, Nyberg ST, et al. Association between socioeconomic status and the development of mental and physical health conditions in adulthood: a multi-cohort study. Lancet Public Health 2020 Mar;5(3):e140-e149 [FREE Full text] [CrossRef] [Medline]
  41. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med 2007 Oct 16;4(10):e296 [FREE Full text] [CrossRef] [Medline]
  42. Zhang L, Ma L, Sun F, Tang Z, Chan P. A multicenter study of multimorbidity in older adult inpatients in China. J Nutr Health Aging 2020 Jan 10;24(3):269-276 [FREE Full text] [CrossRef] [Medline]
  43. Keetile M, Navaneetham K, Letamo G. Prevalence and correlates of multimorbidity among adults in Botswana: a cross-sectional study. PLoS One 2020;15(9):e0239334 [FREE Full text] [CrossRef] [Medline]
  44. Pefoyo AJK, Bronskill SE, Gruneir A, Calzavara A, Thavorn K, Petrosyan Y, et al. The increasing burden and complexity of multimorbidity. BMC Public Health 2015 Apr 23;15:415 [FREE Full text] [CrossRef] [Medline]
  45. Freisling H, Viallon V, Lennon H, Bagnardi V, Ricci C, Butterworth AS, et al. Lifestyle factors and risk of multimorbidity of cancer and cardiometabolic diseases: a multinational cohort study. BMC Med 2020 Jan 10;18(1):5 [FREE Full text] [CrossRef] [Medline]
  46. Kivimäki M, Kuosma E, Ferrie JE, Luukkonen R, Nyberg ST, Alfredsson L, et al. Overweight, obesity, and risk of cardiometabolic multimorbidity: pooled analysis of individual-level data for 120 813 adults from 16 cohort studies from the USA and Europe. Lancet Public Health 2017 Jun;2(6):e277-e285 [FREE Full text] [CrossRef] [Medline]
  47. Duggal NA, Niemiro G, Harridge SDR, Simpson RJ, Lord JM. Can physical activity ameliorate immunosenescence and thereby reduce age-related multi-morbidity? Nat Rev Immunol 2019 Sep;19(9):563-572. [CrossRef] [Medline]
  48. Ng R, Sutradhar R, Yao Z, Wodchis WP, Rosella LC. Smoking, drinking, diet and physical activity-modifiable lifestyle risk factors and their associations with age to first chronic disease. Int J Epidemiol 2020 Feb 01;49(1):113-130 [FREE Full text] [CrossRef] [Medline]
  49. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet 2017 Dec 16;390(10113):2627-2642 [FREE Full text] [CrossRef] [Medline]
  50. Eckel RH, Jakicic JM, Ard JD, de Jesus JM, Houston Miller N, Hubbard VS, American College of Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 AHA/ACC guideline on lifestyle management to reduce cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines. Circulation 2014 Jun 24;129(25 Suppl 2):S76-S99 [FREE Full text] [CrossRef] [Medline]
  51. Mitsutake S, Ishizaki T, Teramoto C, Shimizu S, Ito H. Patterns of co-occurrence of chronic disease among older adults in Tokyo, Japan. Prev Chronic Dis 2019 Jan 31;16:E11 [FREE Full text] [CrossRef] [Medline]
  52. Otsuka T, Takada H, Nishiyama Y, Kodani E, Saiki Y, Kato K, et al. Dyslipidemia and the risk of developing hypertension in a working-age male population. J Am Heart Assoc 2016 Mar 25;5(3):e003053 [FREE Full text] [CrossRef] [Medline]
  53. Yen FS, Wei JC, Chiu L, Hsu CC, Hwu CM. Diabetes, hypertension, and cardiovascular disease development. J Transl Med 2022 Jan 03;20(1):9 [FREE Full text] [CrossRef] [Medline]
  54. Kivimäki M, Strandberg T, Pentti J, Nyberg ST, Frank P, Jokela M, et al. Body-mass index and risk of obesity-related complex multimorbidity: an observational multicohort study. Lancet Diabetes Endocrinol 2022 Apr;10(4):253-263 [FREE Full text] [CrossRef] [Medline]
  55. Pietzner M, Stewart ID, Raffler J, Khaw K, Michelotti GA, Kastenmüller G, et al. Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med 2021 Mar;27(3):471-479 [FREE Full text] [CrossRef] [Medline]
  56. Chudasama YV, Khunti KK, Zaccardi F, Rowlands AV, Yates T, Gillies CL, et al. Physical activity, multimorbidity, and life expectancy: a UK Biobank longitudinal study. BMC Med 2019 Jun 12;17(1):108 [FREE Full text] [CrossRef] [Medline]
  57. Muth C, Blom JW, Smith SM, Johnell K, Gonzalez-Gonzalez AI, Nguyen TS, et al. Evidence supporting the best clinical management of patients with multimorbidity and polypharmacy: a systematic guideline review and expert consensus. J Intern Med 2019 Mar 10;285(3):272-288 [FREE Full text] [CrossRef] [Medline]
  58. Global action plan for the prevention and control of NCDs 2013–2020. World Health Organization. 2013 Nov 14.   URL: [accessed 2023-05-09]
  59. Zhang Y, Chen C, Huang L, Liu G, Lian T, Yin M, et al. Associations among multimorbid conditions in hospitalized middle-aged and older adults in China: statistical analysis of medical records. JMIR Public Health Surveill 2022 Nov 24;8(11):e38182 [FREE Full text] [CrossRef] [Medline]

CHD: coronary heart disease
NCD: noncommunicable disease
OR: odds ratio
STROBE: Strengthening of the Reporting of Observational Studies in Epidemiology
SUA: serum uric acid
T2DM: type 2 diabetes mellitus
WHO: World Health Organization

Edited by Y Khader; submitted 11.10.22; peer-reviewed by M He, R Bai; comments to author 03.02.23; revised version received 07.03.23; accepted 18.03.23; published 22.05.23


©Ying Chen, Mingming Pan, Yaling He, Xiaokang Dong, Ze Hu, Jian Hou, Yining Bao, Jing Yang, Yinghao Yuchi, Ruiying Li, Linghui Zhu, Ning Kang, Wei Liao, Shuoyi Li, Chongjian Wang, Lei Zhang. Originally published in JMIR Public Health and Surveillance (, 22.05.2023.

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