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. 2022 Feb 18;22:342. doi: 10.1186/s12889-022-12722-y

Sociodemographic and behavioral influences on multimorbidity among adult residents of northeastern China

Jikang Shi 1, Yanbo Guo 1, Zhen Li 1, Zhuoshuai Liang 1, Lingfeng Pan 1, Yang Yu 2, Wenfei Zhu 1, Aiyu Shao 1, Wenjun Chen 1, Chao Gao 1, Siyu Liu 1, Yawen Liu 1,✉, Yi Cheng 2,✉
PMCID: PMC8855562  PMID: 35177044

Abstract

Background

Multimorbidity is defined as two or more chronic health conditions existing in an individual simultaneously. Multimorbidity has been associated with poor conditions, such as higher health care costs and the poor quality of life. Thus, identifying the risk factors of the multimorbidity is required for multimorbidity prevention.

Methods

This study was based on the Comprehensive Demonstration Research Project of Major Chronic Noncommunicable Disease Prevention and Control Technology in Northeast China initiated by China Medical University. The investigation was a cross-sectional study under a multistage stratified cluster random sampling design. Associations between multimorbidity and sociodemographic and behavioral factors in adult residents were investigated using univariate analysis and multivariate logistic regression analysis.

Results

A total of 6706 participants were enrolled in this investigation, and the prevalence of multimorbidity was 21.2% among the adult residents of northeastern China. There existed differences of association between age and multimorbidity risks (65–69 years old: OR = 3.53, 95%CI: 2.04–6.12; 70–74 years old: OR = 5.26, 95%CI: 3.02–9.17). Participants who are overweight had significantly high multimorbidity risk (OR = 2.76, 95%CI: 1.50–5.24). Family history of hypertension and family history of diabetes were significantly associated with high multimorbidity risk (family history of hypertension: OR = 2.34, 95%CI: 1.96–2.79; family history of diabetes: OR = 1.77, 95%CI: 1.38–2.26). Compared with the frequency of fatigue (< 1 time/week or 1–2 times/week), that (≥3 times/week) was associated with high multimorbidity risk (OR = 1.39, 95%CI: 1.07–1.81). For fresh fruit consumption, compared with eating fruits regularly, eating rarely had a higher risk of multimorbidity (OR = 2.33, 95%CI: 1.90–2.85).

Conclusions

Sociodemographic indices (age, BMI, family history of hypertension, and family history of diabetes) and behavioral indices (fatigue status and fresh fruit consumption) increase the risks of multimorbidity. This study provides a necessary route to prevent and control multimorbidity in northeast China.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-12722-y.

Keywords: Adults, China, Influencing factors, Multimorbidity

Introduction

Multimorbidity is defined as two or more chronic health conditions existing in an individual simultaneously [1–4]. Multimorbidity increases with aging [5]. Aging is a risk factor of multimorbidity; moreover, the number and proportion of the elderly are increasing sharply in China. Thus, China has to face a heavy burden of the multimorbidity in future decades [6, 7].

Multimorbidity has been associated with adverse events, including longer hospitalizations, multiple medical treatments, more complications, psychological distress, higher health care costs, and the poorer quality of life [8–15]. A higher number of chronic conditions in an individual is associated with higher mortality [16–18]. In addition, multimorbidity is associated with a higher risk of unemployment [19], and multimorbidity leads to a substantial economic burden on health care systems [20–22]. Therefore, identifying the risk factors for multimorbidity to further address the major public health problems.

To date, the prevalence and pattern of multimorbidity has been investigated worldwide. The prevalence of multimorbidity are reported as following: 28% in Americans [23], 37.1% in Australia [24], 58.2% in women who are more than 50 years old in Brazil [25], and 6.4–76.5% in the population aged 60 years or more in China [26]. The difference of multimorbidity prevalence may arise from population, data sources, and eating habits from different areas. The major patterns of multimorbidity are identified as cardiovascular and metabolic diseases, mental health problems, and musculoskeletal disorders in the elderly who lived in Europe, the United States (U.S.), and Australia [27]. In contrast, cardiopulmonary-mental-degenerative disorder and cerebrovascular-metabolic disorder are the patterns identified in China [28]. Indeed, different methods, population, and chronic diseases have been used in defining multimorbidity pattern, affording that there exists no consensus on the determination and classification of multimorbidity pattern.

The prevention and control of chronic disease are necessary for multimorbidity management, underscoring the identification of risk factors of the multimorbidity. The risk factors for multimorbidity have been identified in studies, including age, female, and low socioeconomic status [29–31]. Moreover, influencing factors of multimorbidity, such as racial and ethnic, remain controversial [32, 33]. Thus, more studies are needed to investigate risk factors for multimorbidity. In this paper, we investigated the prevalence of multimorbidity and further evaluated the sociodemographic and behavioral influences on multimorbidity among adult residents to identify the risk factors for multimorbidity in Changchun, China.

Materials and methods

Ethical statement

The study was approved by the Ethics Committee of China Medical University. The study protocol was performed in accordance with the principles outlined in the Declaration of Helsinki, and informed consent was collected from each of participants.

Study population

The study was affiliated to the Comprehensive Demonstration Research Project of Major Chronic Noncommunicable Disease Prevention and Control Technology in Northeast China initiated by China Medical University. The investigation, which was conducted from January 1, 2019 to November 31, 2019, was a cross-sectional study under a multistage stratified cluster random sampling design. The data were collected from residents of 10 districts in Changchun city, Jilin Province. The adult residents were enrolled according to following inclusion criteria: (1) over the age of 35 years; (2) with registered permanent residence (a record officially identifying area residents); (3) living in Changchun for more than 6 months; (4) with consciousness and no communication barriers; (5) good compliance. The exclusion criteria satisfied the followings: (1) incomplete information; (2) data with outliers. (Supplemental Fig. 1).

Questionnaire and health examination

The questionnaire was designed by the China Medical University and the School of Public Health, Jilin University. Direct face-to-face interview survey was performed by uniformly trained investigators. Questionnaires and data of anthropometric measurements were collected from each participant. Demographic information (sex, age, ethnicity, marital status, occupation, annual income, and level of education), health behaviors (smoking, drinking, diet, sleep status, and physical activity), and history of chronic diseases (hypertension, diabetes, coronary heart disease, and stroke), were collected from the questionnaires. In addition, the information of anthropometric measurements (height, weight, blood pressure, fasting blood glucose, and blood lipids) were obtained from health examination. Every physical measurement was checked by two medical staffs together. Blood samples were collected and transported to a central laboratory via a cold chain transport system.

Statistical analysis

Constituent ratio was used to represent the composition of prevalence of chronic diseases for classified participants according to sociodemographic and behavioral characteristics. Chi-square (χ2) test was used to identify the relationship of multimorbidity with sociodemographic and behavioral characteristics. Multivariate logistic regression was performed to analyze odds ratios (OR) for multimorbidity. The predictive models were built on the basis of risk factors and visualized using nomograms, and the performance of our models was evaluated using the Harrell’s concordance index (c-index). SPSS version 24.0 and R version 4.1.0 were used for statistical analysis, and P-values < 0.05 was considered statistically significant.

Results

A total of 6706 participants were enrolled in this investigation. The mean age of the participants was 68.79 years old, and the prevalence of multimorbidity was 21.2%. The participants were divided into four groups according to the number of chronic disease (1 disease, 2 diseases, and ≥ 3 diseases), and corresponding data of prevalence are showed in Table 1. Significant differences of prevalence classified by number of chronic diseases existed in age, BMI, marital status, family history of hypertension, family history of diabetes, educational level, occupation, annual income, physical exercise, sleep status, fatigue status, stay up late, salt taste, edible oil taste, carbonated drinks, fresh fruit consumption, meat consumption (red meat and poultry), consumption of fish, and consumption of eggs and beans (P < 0.05).

Table 1.

Prevalence of number of chronic diseases by sociodemographic and behavioral characteristics of the study population

Variables Total (n) 0disease 1disaese 2diseases ≥3diseases χ2 P
n % n % n % n %
Sex
 Male 2677 1082 40.4 1045 39.0 450 16.8 100 3.7 3.481 0.323
 Female 4029 1551 38.5 1604 39.8 699 17.3 175 4.3
Age (year)
  ≤ 64 191 114 59.7 62 32.5 12 6.3 3 1.6 94.721 <0.001*
 65–69 3929 1643 41.8 1519 38.7 637 16.2 130 3.3
 70–74 2586 876 33.9 1068 41.3 500 19.3 142 5.5
Ethnicity
 Han 6517 2558 39.3 2576 39.5 1121 17.2 262 4.0 4.316 0.229
 Non-Han 189 75 39.7 73 38.6 28 14.8 13 6.9
BMI
 Underweight 102 55 53.9 36 35.3 9 8.8 2 2.0 163.603 <0.001*
 Normal 2568 1230 47.9 911 35.5 351 13.7 76 3.0
 Overweight 3961 1316 33.2 1674 42.3 775 19.6 196 4.9
 Obese 75 32 42.7 28 37.3 14 18.7 1 1.3
Marital status
 Married/Cohabitation 35 18 51.4 14 40.0 3 8.6 0 0.0 14.601 0.024*
 Unmarried 5923 2357 39.8 2325 39.3 1007 17 234 4.0
 Divorced/ Separated 748 258 34.5 310 41.4 139 18.6 41 5.5
Family history of hypertension
 No 5940 2450 41.2 2353 39.6 949 16 188 3.2 203.931 <0.001*
 Yes 766 183 23.9 296 38.6 200 26.1 87 11.4
Family history of diabetes
 No 6064 2427 40 2418 39.9 1004 16.6 215 3.5 109.425 <0.001*
 Yes 349 83 23.8 133 38.1 89 25.5 44 12.6
Educational level
 Primary school or below 162 52 32.1 69 42.6 36 22.2 5 3.1 20.846 0.013*
 Junior middle school 861 300 34.8 378 43.9 151 17.5 32 3.7
 Senior middle schoo 2280 879 38.6 915 40.1 388 17 98 4.3
 Undergraduate or above 3403 1402 41.2 1287 37.8 574 16.9 140 4.1
Occupation
 Agriculture 321 102 31.8 140 43.6 62 19.3 17 5.3 61.841 <0.001*
 Industry 512 212 41.4 198 38.7 79 15.4 23 4.5
 Individual business and service industry 521 231 44.3 192 36.9 86 16.5 12 2.3
 Agency and business unit 592 291 49.2 192 32.4 88 14.9 21 3.5
 Retirement 3638 1410 38.8 1432 39.4 633 17.4 163 4.5
 Unemployment 664 231 34.8 302 45.5 108 16.3 23 3.5
 Other 458 156 34.1 193 42.1 93 20.3 16 3.5
Annual income (¥)
  < 10,000 563 196 34.8 240 42.6 91 16.2 36 6.4 89.176 <0.001*
 10,000 ~ 30,000 2817 960 34.1 1237 43.9 508 18 112 4.0
 30,000 ~ 50,000 2765 1213 43.9 987 35.7 464 16.8 101 3.7
  ≥ ~ 50,000 561 264 47.1 185 33.0 86 15.3 26 4.6
Physical exercise
 Every day 4912 1893 38.5 2002 40.8 827 16.8 190 3.9 30.286 0.003*
 3-4 days/week 220 109 49.5 69 31.4 35 15.9 7 3.2
 2-3 days/week 200 96 48 62 31.0 30 15 12 6.0
 1-2 days/month 39 16 41 15 38.5 5 12.8 3 7.7
 Never 1335 519 38.9 501 37.5 252 18.9 63 4.7
Sleep status
 Worse 76 20 26.3 34 44.7 16 21.1 6 7.9 28.86 0.004*
 Poor 622 216 34.7 237 38.1 131 21.1 38 6.1
 Average 2015 796 39.5 787 39.1 343 17 89 4.4
 Good 3642 1453 39.9 1457 40.0 600 16.5 132 3.6
 Excellent 351 148 42.2 134 38.2 59 16.8 10 2.8
Fatigueness status (time/week)
 <1 5304 2096 39.5 2114 39.9 887 16.7 207 3.9 22.967 0.001*
 1–2 1061 418 39.4 420 39.6 178 16.8 45 4.2
  ≥ 3 341 119 34.9 115 33.7 84 24.6 23 6.7
Stay up late
 Often 214 96 44.9 70 32.7 40 18.7 8 3.7 44.007 <0.001*
 Sometimes 479 229 47.8 162 33.8 71 14.8 17 3.5
 Rarely 1243 548 44.1 447 36.0 194 15.6 54 4.3
 Never 4770 1760 36.9 1970 41.3 844 17.7 196 4.1
Smoking status
 Smoker 945 366 38.7 377 39.9 161 17 41 4.3 0.28 0.964
 Non-smoker 5761 2267 39.4 2272 39.4 988 17.1 234 4.1
Status of alcohol drinking
 Drinker 815 325 39.9 329 40.4 132 16.2 29 3.6 1.407 0.704
 Non-drinker 5891 2308 39.2 2320 39.4 1017 17.3 246 4.2
Salt taste
 Salty 427 181 42.4 134 31.4 85 19.9 27 6.3 96.487 <0.001*
 Insipid 1411 480 34 548 38.8 275 19.5 108 7.7
 Appropriate 4868 1972 40.5 1967 40.4 789 16.2 140 2.9
Edible oil taste
 Greasy 278 120 43.2 89 32.0 56 20.1 13 4.7 77.108 <0.001*
 Thin 1330 488 36.7 486 36.5 251 18.9 105 7.9
 Appropriate 5098 2025 39.7 2074 40.7 842 16.5 157 3.1
Carbonated drinks
 Yes 111 60 54.1 39 35.1 10 9 2 1.8 12.637 0.005*
 No 6595 2573 39 2610 39.6 1139 17.3 273 4.1
Fresh fruit consumption
 Often/Always 4777 2002 41.9 1850 38.7 732 15.3 193 4.0 123.52 <0.001*
 Sometimes 1352 505 37.4 550 40.7 245 18.1 52 3.8
 Rarely/Never 577 126 21.8 249 43.2 172 29.8 30 5.2
Meat consumption (red meet)
 Often/Always 1829 741 40.5 729 39.9 287 15.7 72 3.9 14.157 0.028*
 Sometimes 3593 1441 40.1 1384 38.5 621 17.3 147 4.1
 Rarely/Never 1284 451 35.1 536 41.7 241 18.8 56 4.4
Meat consumption (poultry)
 Often/Always 1375 570 41.5 548 39.9 209 15.2 48 3.5 24.06 0.001*
 Sometimes 3810 1535 40.4 1467 38.6 644 16.9 155 4.1
 Rarely/Never 1530 528 34.5 634 41.4 296 19.3 72 4.7
Consumption of fish
 Often/Always 747 322 43.1 281 37.6 113 15.1 31 4.1 23.499 0.001*
 Sometimes 3881 1580 40.7 1500 38.6 648 16.7 153 3.9
 Rarely/Never 2078 731 35.2 868 41.8 388 18.7 91 4.4
Consumption of eggs and beans
 Often/Always 3837 1569 40.9 1499 39.1 609 15.9 160 4.2 26.452 <0.001*
 Sometimes 2298 853 37.1 927 40.3 440 19.1 78 3.4
 Rarely/Never 571 211 37 223 39.1 100 17.5 37 6.5
Consumption of milk
 Often/Always 2974 1205 40.5 1148 38.6 483 16.2 138 4.6 12.477 0.052
 Sometimes 2265 888 39.2 891 39.3 407 18 79 3.5
 Rarely/Never 1467 540 36.8 610 41.6 259 17.7 58 4.0
Consumption of rice
 Often/Always 5394 2144 39.7 2126 39.4 906 16.8 218 4.0 10.382 0.109
 Sometimes 1060 411 38.8 417 39.3 186 17.5 46 4.3
 Rarely/Never 252 78 31 106 42.1 57 22.6 11 4.4
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*P < 0.05

We used univariate analysis to investigate the influencing factors of multimorbidity on the basis of 26 independent variables listed in the questionnaire, finding that multimorbidity was associated with age, BMI, marital status, family history of hypertension, family history of diabetes, sleep status, fatigue status, salt taste, edible oil taste, carbonated drinks, fresh fruit consumption, meat consumption (poultry), consumption of fish, and consumption of eggs and beans (P < 0.05) (Table 2). The prevalence of multimorbidity increased with aging (P < 0.001). The prevalence of multimorbidity in participants with underweight, normal weight, overweight, or obese was 10.8, 30.0, 24.5, and 20.0%, correspondingly (P < 0.001). There were the significant differences of prevalence in married/cohabitation, unmarried, and divorced/separated (8.6, 21.0, and 24.1%, respectively) (P = 0.027). The prevalence of multimorbidity in participants with family history of hypertension/diabetes was significantly higher than that in participants without the respective/corresponding one (P < 0.001). The prevalence of multimorbidity increased with the deteriorating of sleep status (P < 0.001). The prevalence of multimorbidity increased with the increasing frequency of fatigue (P < 0.001). For salt consumption and edible oil consumption, the prevalence of multimorbidity of appropriate consumption was significantly lower than that of excessive consumption or low consumption (P < 0.001). There also existed significantly differences in the prevalence among current-smokers (45.1%), ex-smokers (46.5%), and non-smokers (35.3%) (P < 0.001). For the consumption of fresh fruit, poultry meat, eggs and beans, and fish, the prevalence of multimorbidity increased with the decreasing frequency of consumption from group (often/always) to group (rarely/never) (all P < 0.05) (Table 2).

Table 2.

Univariate factor analysis of multimorbidity

Variables No Multimorbidity Multimorbidity χ2 P
n % n %
Total 5282 78.8 1424 21.2
Sex
 Male 2127 79.5 550 20.5 1.266 0.261
 Female 3155 78.3 874 21.7
Age (year)
  ≤ 64 176 92.1 15 7.9 47.284 < 0.001*
 65–69 3162 80.5 767 19.5
 70–74 1944 75.2 642 24.8
Ethnicity
 Han 5134 78.8 1383 21.2 0.024 0.876
 Non-Han 148 78.3 41 21.7
BMI
 Underweight 91 89.2 11 10.8 64.783 < 0.001*
 Normal 2141 40.5 427 30.0
 Overweight 2990 75.5 971 24.5
 Obese 60 80.0 15 20.0
Marital status
 Married/Cohabitation 32 91.4 3 8.6 7.219 0.027*
 Unmarried 4682 79.0 1241 21.0
 Divorced/ Separated 568 75.9 180 24.1
Family history of hypertension
 No 4803 80.9 1137 19.1 136.24 < 0.001*
 Yes 479 62.5 287 37.5
Family history of diabetes
 No 4845 79.9 1219 20.1 66.016 < 0.001*
 Yes 216 61.9 133 38.1
Educational level
 Primary school or below 121 74.7 41 25.3 1.747 0.627
 Junior middle school 678 78.7 183 21.3
 Senior middle school 1794 78.7 486 21.3
 Undergraduate or above 2689 79.0 714 21.0
Occupation
 Agriculture 242 75.4 79 24.6 10.973 0.089
 Industry 410 80.1 102 19.9
 Individual business and service industry 423 81.2 98 18.8
 Agency and business unit 483 81.6 109 18.4
 Retirement 2842 78.1 796 21.9
 Unemployment 533 80.3 131 19.7
 Other 349 76.2 109 23.8
Annual income (¥)
  < 10,000 436 77.4 127 22.6 3.201 0.362
 10,000 ~ 30,000 2197 78.0 620 22.0
 30,000 ~ 50,000 2200 79.6 565 20.4
  ≥ ~ 50,000 449 80.0 112 20.0
Physical exercise
 Every day 3895 79.3 1017 20.7 5.898 0.207
 3-4 days/week 178 80.9 42 19.1
 2-3 days/week 158 79.0 42 21.0
 1-2 days/month 31 79.5 8 20.5
 Never 1020 76.4 315 23.6
Sleep status
 Worse 54 71.1 22 28.9 19.187 0.001*
 Poor 453 72.8 169 27.2
 Average 1583 78.6 432 21.4
 Good 2910 79.9 732 20.1
 Excellent 282 80.3 69 19.7
Fatigueness status (time/week)
 <1 4210 79.4 1094 20.6 22.183 < 0.001*
 1–2 838 79.0 223 21.0
  ≥ 3 234 68.6 107 31.4
Stay up late
 Often 166 77.6 48 22.4 4.675 0.197
 Sometimes 391 81.6 88 18.4
 Rarely 995 80.0 248 20.0
 Never 3730 78.2 1040 21.8
Smoking status
 Smoker 743 78.6 202 21.4 0.013 0.909
 Non-smoker 4539 85.9 1222 85.8
Status of alcohol drinking 0.0
 Drinker 654 80.2 161 19.8 1.215 0.27
 Non-drinker 4628 78.6 1263 21.4
Salt taste
 Salty 315 73.8 112 26.2 49.292 < 0.001*
 Insipid 1028 72.9 383 27.1
 Appropriate 3939 80.9 929 19.1
Edible oil taste
 Greasy 209 75.2 69 24.8 34.66 < 0.001*
 Thin 974 73.2 356 26.8
 Appropriate 4099 80.4 999 19.6
Carbonated drinks
 Yes 99 89.2 12 10.8 7.332 0.007*
 No 5183 78.6 1412 21.4
Fresh fruit consumption
 Often/Always 3852 80.6 925 19.4 75.884 < 0.001*
 Sometimes 1055 78.0 297 22.0
 Rarely/Never 375 65.0 202 35.0
Meat consumption (red meat)
 Often/Always 1470 80.4 359 19.6 5.625 0.06
 Sometimes 2825 78.6 768 21.4
 Rarely/Never 987 76.9 297 23.1
Meat consumption (poultry)
 Often/Always 1118 81.3 257 18.7 12.686 0.002*
 Sometimes 3002 79.0 799 21.0
 Rarely/Never 1162 75.9 368 24.1
Consumption of fish
 Often/Always 603 80.7 144 19.3 6.634 0.036*
 Sometimes 3080 79.4 801 20.6
 Rarely/Never 1599 76.9 479 23.1
Consumption of eggs and beans
 Often/Always 3068 80.0 769 20.0 8.208 0.017*
 Sometimes 1780 77.5 518 22.5
 Rarely/Never 434 76.0 137 24.0
Consumption of milk
 Often/Always 2353 79.1 621 20.9 0.412 0.814
 Sometimes 1779 78.5 486 21.5
 Rarely/Never 1150 78.4 317 21.6
Consumption of rice
 Often/Always 4270 79.2 1124 20.8 5.758 0.056
 Sometimes 828 78.1 232 21.9
 Rarely/Never 184 73.0 68 27.0
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*P < 0.05

We further used a multivariate logistic regression analysis, constructing a prediction model to validate multimorbidity-influencing factors. Data of the multiple logistic regression analysis, shown in Fig. 1, are visualized in the form of a nomogram to provide effective and reliable guides (Fig. 2). We identified that the increasing risks of multimorbidity were associated with independent factors (age, BMI, family history of hypertension, family history of diabetes, fatigue status, and fresh fruit consumption) (all P ≤ 0.01). Multimorbidity risks were related to aging (65–69 years old: OR = 3.53, 95%CI: 2.04–6.12; 70–74 years old: OR = 5.26, 95%CI: 3.02–9.17). Overweight participants had significantly high multimorbidity risks (OR = 2.76, 95%CI: 1.50–5.24). Family history of hypertension and family history of diabetes was significantly associated with high multimorbidity risks (family history of hypertension: OR = 2.34, 95%CI: 1.96–2.79; family history of diabetes: OR = 1.77, 95%CI: 1.38–2.26). Compared with the frequency of fatigue (< 1 time/week or 1–2 times/week), that (≥3 times/week) was associated with high multimorbidity risks (OR = 1.39, 95%CI: 1.07–1.81). For fresh fruit consumption, compared with participants eating fruits regularly, those eating rarely had higher risks of multimorbidity (OR = 2.33, 95%CI: 1.90–2.85). The C-index of the nomogram was 0.650.

Fig. 1.

Fig. 1

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Multivariate logistic regression analysis of factors associated with multimorbidity

Fig. 2.

Fig. 2

Open in a new tab

Nomogram for predicting multimorbidity risk. The nomogram was generated based on age, BMI, family history of hypertension, family history of diabetes, fatigue status, and fresh fruit consumption

Discussions

In this paper, we documented that the prevalence of multimorbidity is 21.2% among the adult residents. In addition, the risks of multimorbidity are associated with age, BMI, family history of hypertension, family history of diabetes, fatigue status, and fresh fruit consumption.

The prevalence of multimorbidity in our study in 2019 is substantially lower than that in the study of Wang et al. in 2012 [34]. The decrease in prevalence of multimorbidity in northeastern China may be due to the implementation of chronic disease prevention and control strategies in decades. Actually, chronic disease prevention and control, supported by series projects focusing on chronic noncommunicable disease prevention and control, have been proceeding in northeastern China. With nationally spreading of 5G networks, healthcare systems conduct precise prevention and control for individuals with multimorbidity.

Aging has been widely considered to be associated with risks of multimorbidity [5, 35]. In agreement with other studies [36–38], our study also found that the prevalence of multimorbidity increased dramatically with aging. Moreover, consistent with other studies [25, 39, 40], our study found BMI influenced multimorbidity. Zhang et al. conducted a national investigation, finding that obesity is associated with the risk of multimorbidity in whole China [41]. Surprisingly, we corroborated that obesity was neither protect factor nor risk factor of multimorbidity in Northeastern China.

We identified the risk factors of multimorbidity (the family history of hypertension, family history of diabetes, and fatigue status [≥3 times/week]) in northeast China. These factors confer perception to connections implicated in multimorbidity. Thus, people with these three characteristics should pay more attention to their health and strengthen their awareness of prevention and control. In addition, for fresh fruit consumption, similar to the results of Ruel et al. [42], our results also showed that greater consumption of fruits appears to lower risks of multimorbidity.

Multimorbidity increases the risk of disability and mortality [43–45], necessitating the identification of influencing factors of multimorbidity. Moreover, our nomogram also provides effective and reliable guides for the risk-prediction, prevention, and control of multimorbidity. Overall, the adult residents with three characteristics (family history of hypertension, family history of diabetes, and fatigue status) are the population with high risk of multimorbidity. The three characteristics provide theoretical and precisely practical guidelines to prevent and control multimorbidity, such as controlling weight and increasing consumption of fruits.

There are strengths in this study, including the large sample size, comprehensive sociodemographic and behavioral characteristics, and region representativeness of northeast China. However, some limitations also exist. First, the causality between multimorbidity and risk factors could not be reflected in our cross-sectional design. Second, the data in this study were based on self-reported questionnaires; therefore, the accuracy of the reported results cannot be determined.

Conclusion

In conclusion, the prevalence of multimorbidity is 21.2% among the adult residents of northeastern China. Sociodemographic indices (age, BMI, family history of hypertension, and family history of diabetes) and behavioral indices (fatigue status and fresh fruit consumption) increase the risks of multimorbidity. This study provides a necessary route to prevent and control multimorbidity in northeast China.

Supplementary Information

12889_2022_12722_MOESM1_ESM.docx (22.3KB, docx)

Additional file 1: Supplemental Figure 1. Inclusion and exclusion criteria and selection process of participants.

12889_2022_12722_MOESM2_ESM.docx (17.4KB, docx)

Additional file 2: Supplemental Table 1. Definition of variables.

Acknowledgements

We thank all the participants of the study.

Abbreviations

CI

Confidence Intervals

OR

Odds Ratios

Authors’ contributions

Yawen Liu, Yi Cheng, and Siyu Liu designed the study. Jikang Shi, Yanbo Guo, Zhuoshuai Liang, Lingfeng Pan, and Yang Yu performed the study. Jikang Shi, Yanbo Guo, Weifei Zhu, Aiyu Shao, and Zhen Li analyzed the data, Jikang Shi drafted the manuscript. Wenjun Chen and Chao Gao participated in revising the manuscript. All authors approved the final manuscript.

Funding

Our study was supported by the funds from the National Key R&D Program of China (Grant #2018YFC1311600), National Natural Science Foundation of China (Grant 81973120), and Graduate Innovation Fund of Jilin University (101832020CX267).

Availability of data and materials

All data generated or analysed during this study are included in this published article.

Declarations

Ethics approval and consent to participate

The study was approved by the Ethics Committee of China Medical University. The study protocol is performed in accordance with the principles outlined in the Declaration of Helsinki and informed consent was obtained from all the subjects.

Consent for publication

Not applicable.

Competing interests

The authors declare that there is no competing interests regarding the publication of this article.

Footnotes

Publisher’s Note

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12889_2022_12722_MOESM1_ESM.docx (22.3KB, docx)

Additional file 1: Supplemental Figure 1. Inclusion and exclusion criteria and selection process of participants.

12889_2022_12722_MOESM2_ESM.docx (17.4KB, docx)

Additional file 2: Supplemental Table 1. Definition of variables.

Data Availability Statement

All data generated or analysed during this study are included in this published article.

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