Health and well-being among older adults in Zhejiang Province, China

Abstract

By the end of the 20th century, China had transitioned into an aging society. With the aging population, the health problems of older adults have become a central concern for both families and society. Identifying and addressing these health issues is of paramount importance. The main objective of this study was to investigate the health and well-being of Chinese older adults aged ≥ 60 years. A cross-sectional design was employed, and 16,276 participants from 11 non-randomly selected counties in Zhejiang Province were enrolled in the Zhejiang Healthy Aging Cohort Study. The analysis included a wide range of variables, such as demographic characteristics, family status, social support, medical history, health behaviors, dietary habits, depressive symptoms, activities of daily living, and cognitive function. The results highlighted several significant health challenges among older adults, including a high prevalence of abdominal obesity, limited educational attainment, low income, insufficient physical activity, and inadequate nutritional intake. Additionally, 38.9% reported multimorbidity, 19.8% exhibited cognitive impairment, and 8.2% showed signs of depression. These findings underscore the need for targeted health interventions to improve the well-being of this aging population. Efforts should focus on promoting healthier aging through improved lifestyle choices and comprehensive health management strategies.

Introduction

With economic development, social progress, and improved living standards and healthcare, life expectancy continues to rise, making population aging a significant global social issue. By the end of the 20th century, China had transitioned into an aging society. According to the most recent census conducted in 2020, the proportion of people aged ≥ 60 years in China had reached 18.7%. This demographic is projected to increase from 18.7% in 2020 to 34.6% by 2050^1,2. As the aging population grows, addressing health challenges faced by older adults has become a primary concern for both their families and society.

Older contingents of the population often exhibit poor health indicators due to inadequate living standards, limited opportunities to maintain a healthy lifestyle, and limited access to health and social services. They are particularly susceptible to infectious diseases, co-existing chronic conditions, malnutrition, frailty, falls, disability, dementia, and significant mental health issues, distinguishing them from the general population³. Tanjani et al.⁴. conducted a study on the health status of older adults in Iran in 2012 and found that 5.5% were severely malnourished, while 36.6% exhibited symptoms of depression. In the United States, a recent study estimated that approximately 10.9 million older individuals have diabetes, representing 26.9% of the total older adult population. Annually, 3.1 million older adults with diabetes require emergency care, with associated medical expenditures amounting to approximately 2.9 billion USD⁵. A systematic review of 87 studies revealed a wide range in the prevalence of multimorbidity (4.8–93.1%) and polypharmacy (2.6–86.6%) among older adults⁶. However, despite growing concerns about aging-related health challenges, limited research has comprehensively examined the multidimensional health status of older adults in China, including their physical, mental, social, and dietary health aspects. Previous studies^7–10 have often focused on isolated issues, such as chronic diseases or mental health, leaving significant gaps in understanding the broader determinants of health in this population.

To address these gaps, this study aimed to explore the health and well-being of older adults in China through a comprehensive descriptive analysis of general characteristics, sociological support, dietary intake, behavioral habits, physical health status, and mental health outcomes and to explore the social and cultural factors underlying health issues among older adults and gender disparities in health outcomes, thereby providing evidence to support improvements in health of older adults and the development of targeted health guidance strategies.

Methods

Participants

This study was based on the Zhejiang Healthy Aging Cohort Study (ZHACS) (originally named “Zhejiang Major Public Health Surveillance Program (ZPHS)), an ongoing community-based cohort study focusing on aging and health problems among the older population. Eleven counties were selected from 90 counties in Zhejiang Province, China. Counties were chosen based on local disease patterns, exposure to certain risk factors, population stability, quality of death and disease registries, local commitment, and staff capacity¹¹. Adults aged ≥ 60 years from these counties were enrolled. At least 1,500 participants were randomly recruited from 10 counties, while 600 were enrolled in the remaining district, owing to the sparse population. Furthermore, owing to the random selection of research participants, the study sample may have included partners from the same household. The baseline survey was initiated in 2014 and 2015, encompassing seven counties, and two waves of follow-up surveys were conducted in 2015 and 2016. Since 2018, surveys have been conducted every 3 years in the initial seven counties (follow-up) and four new counties (baseline survey). Ultimately, 11 counties were enrolled in the ZHACS, covering all 11 prefecture-level cities in Zhejiang Province.

In this study, data of 11 counties were collected from a survey conducted in 2018–2020. A face-to-face interview based on a self-designed questionnaire was performed by trained research assistants for each participant. Information regarding demographic characteristics, family status, health behaviors, social support, dietary habits, physical health status, depressive symptoms, activities of daily living (ADLs), and cognitive function was collected. Notably, during the survey of initial 7 counties, demographic information such as gender and date of birth, which does not change over time, was omitted by investigators to reduce respondent burden and improve survey efficiency. In the analysis, this information was supplemented using data from the baseline survey conducted in 2014–2015.

STORBE Statement guidelines (https://www.strobe-statement.org/) were implemented to report this cross-sectional study.

Variable assignment

Demographic characteristics collected included age, gender, body mass index (BMI), weight, waist circumference, hip circumference, education level, marital status, and employment status. Family status encompassed annual household income and number of children. Social support factors involved living arrangements, number of relatives and friends in frequent contact, eating arrangements, and participation in collective activities. Health behaviors assessed were smoking status, exposure to passive smoking, exercise habits, tea and alcohol intake, and sleep patterns (quality, duration, and midday rest). Dietary intake (with two categories of intake frequency: < 3 times/week, and ≥ 3 times/week) was evaluated for consumption of rice or noodles, vegetables, fruits, red and white meat, fish and seafood, eggs, beans, milk and dairy products, nuts, sweet drinks, and cakes (participants were asked how many times per day/week/month they had consumed each food on average during the previous year).

The physical health status section of the questionnaire assessed the presence or absence of 18 common diseases, which supposed to be formally diagnosed by physician, including hypertension, diabetes, hyperlipidemia, coronary artery disease, emphysema, tuberculosis, asthma, chronic bronchitis, gallstones, chronic hepatitis, nephritis, tumors, Parkinson’s disease, arthritis, cataracts, glaucoma, thyroid disease, and gastric disease, all requiring formal physician diagnosis.

Cognitive function was evaluated using the Mini-Mental State Examination (MMSE). In China, the widely accepted education-specific cutoff scores for cognitive impairment are 17–18 for illiteracy, 20–21 for 0–6 years of education, and 24–25 for > 6 years of education¹².

Depressive symptoms were evaluated using the Patient Health Questionnaire-9 (PHQ-9)^13,14, a 9-item self-reported scale designed to assess the frequency of a variety of depressive symptoms over the preceding 2 weeks. Total score for the nine items ranges from 0 to 27, with greater values indicating increased severity. In this study, a cut-off score of five or higher was used to define depressive symptoms.

The Barthel Index (BI)¹⁵ is a widely used standardized measure of independence in ADLs. The total BI score ranged from 0 to 100, with scores of 0–60 indicating severe dependence, 61–90 indicating moderate dependence, 91–99 indicating slight dependence, and 100 indicating independence.

Statistical analysis

Survey data were descriptively presented as percentages and means with standard deviations (SD). Chi-square or Fisher’s exact tests were used to assess differences between categorical variables.

All statistical analyses were performed using SPSS (version 17.0; SPSS Inc., Chicago, IL, USA), and two-tailed p value < 0 0.05 indicated statistical significance.

Results

In total, 16,408 questionnaires were completed. However, 29 of the respondents were aged < 60 years, while 103 had missing information; therefore, the data of 16,276 participants were analyzed in this study.

Sociodemographic and social support

The participants’ sociodemographic and social support characteristics are listed in Table 1.

Table 1.

Sociodemographic and social support characteristics of participants.

Variables	Total (n = 16,276)	Male (n = 7,718)	Female (n = 8,558)	P value
Age (years, Mean ± SD)	69.6 ± 7.1	69.7 ± 7.2	69.4 ± 7.1	0.014
Age groups (years)				0.020
60–69	9,148 (61.5)	4,245 (55.0)	4,903 (57.3)
70–79	5,324 (28.9)	2,580 (33.4)	2,744 (32.1)
80–89	1,689 (9.0)	832 (10.8)	857 (10.0)
≥ 90	115 (0.5)	61 (0.8)	54 (0.6)
Married status				< 0.001
Unmarried	136 (0.8)	118 (1.5)	18 (0.2)
Married	13,319 (81.8)	6,930 (89.8)	6,389 (74.7)
Widowed	2,755 (16.9)	622 (8.1)	2,133 (24.9)
Divorced	66(0.4)	48 (0.6)	18 (0.2)
Body mass index (kg/m2)				< 0.001
Underweight (< 18.5)	836 (5.1)	379 (4.9)	457 (5.3)
Normal (18.5–23.9)	8,429 (51.8)	4,205 (54.5)	4,224 (49.4)
Overweight (24.0–27.9)	5,416 (33.3)	2,505 (32.5)	2,911 (34.0)
Obesity (≥ 28.0)	1,595 (9.8)	629 (8.1)	966 (11.3)
Abdominal obesitya				< 0.001
Yes	11,626 (71.4)	4,513 (58.5)	7,113 (83.1)
No	4,650 (28.6)	3,205 (41.5)	1,445 (16.9)
Education level				< 0.001
No formal education	7,215 (44.3)	1,997 (25.9)	5,218 (61.0)
Primary	7,157 (44.0)	4,281 (55.5)	2,876 (33.6)
Junior middle	1,607 (9.9)	1,217 (15.8)	390 (4.6)
Senior middle	266 (1.6)	199 (2.6)	67 (0.8)
≥College	31 (0.2)	24 (0.3)	7 (0.1)
Annual household income (CNY)
< 10,000	1,121 (6.9)	505 (6.5)	616 (7.2)	0.010
10,001–20,000	2,198 (13.5)	984 (12.7)	1,214 (14.2)
20,001–50,000	4,549 (27.9)	2,162 (28.0)	2,387 (27.9)
50,001–100,000	3,866 (23.8)	1,898 (24.6)	1,968 (23.0)
> 100,000	4,542 (27.9)	2,169 (28.1)	2,373 (27.7)
Employment status				< 0.001
Working	3,692 (22.7)	2,238 (29.0)	1,454 (17.0)
Retired	6,098 (37.5)	3,006 (38.9)	3,092 (36.1)
Never work	6,486 (39.9)	2,474 (32.1)	4,012 (46.9)
Retired age (years, Mean ± SD)	58.6 ± 6.1	60.0 ± 5.4	57.3 ± 6.3	< 0.001
Household size				< 0.001
1	1,822 (11.2)	669 (8.7)	1,153 (13.5)
2–4	10,013 (61.5)	4,904 (63.5)	5,109 (59.7)
5–11	4,425 (27.2)	2,136 (27.7)	2,289 (26.7)
≥ 11	16 (0.1)	9 (0.1)	7 (0.1)
Living with spouse				< 0.001
Yes	12,726 (78.2)	6,583 (85.3)	6,143 (71.8)
No	3,550 (21.8)	1,135 (14.7)	2,415 (28.2)
Living with children				0.003
Yes	6,988 (42.9)	3,221 (41.7)	3,767 (44.0)
No	9,288 (57.1)	4,497 (58.3)	4,791 (56.0)
Living in nursing homes				0.296
Yes	33 (0.2)	19 (0.2)	14 (0.2)
No	16,243 (99.8)	7,699 (99.8)	8,544 (99.8)
Number of children				< 0.001
0	226 (1.4)	184 (2.4)	42 (0.5)
1–2	10,338 (63.5)	5,247 (68.0)	5,091 (59.5)
3–4	4,941 (30.4)	2,022 (26.2)	2,919 (34.1)
≥ 5	771 (4.7)	265 (3.4)	506 (5.9)
Eating arrangement				< 0.001
Eating alone	1,911 (11.7)	707 (9.2)	1,204 (14.1)
With 1 to 3 persons	10,183 (62.6)	5,000 (64.8)	5,183 (60.6)
With 4 to 10 persons	4,163 (25.6)	1,998 (25.9)	2,165 (25.3)
With more than 11 persons	19 (0.1)	13 (0.2)	6 (0.1)
Eating with spouse				< 0.001
Yes	12,655 (77.8)	6,546 (84.8)	6,109 (71.4)
No	3,621 (22.2)	1,172 (15.2)	2,449 (28.6)
Eating with children				0.002
Yes	6,877 (42.3)	3,162 (41.0)	3,715 (43.4)
No	9,399 (57.7)	4,556 (59.0)	4,843 (56.6)
Eating in nursing homes				0.362
Yes	30 (0.2)	17 (0.2)	13 (0.2)
No	16,246 (99.8)	7,701 (99.8)	8,545 (99.8)
Number of relatives and friends in frequent contact				0.007
None	964 (5.9)	439 (5.7)	525 (6.1)
1–3 persons	7,032 (43.2)	3,443 (44.6)	3,589 (41.9)
4–10 persons	5,871 (36.1)	2,728 (35.3)	3,143 (36.7)
≥ 10 persons	2,409 (14.8)	1,108 (14.4)	1,301 (15.2)
Participate group activities				0.022
Never	10,556 (64.9)	5,078 (65.8)	5,478 (64.0)
Occasionally	4,549 (27.9)	2,078 (26.9)	2,471 (28.9)
Often	1,171 (7.2)	562 (7.3)	609 (7.1)

^aWaist-to-height ratio (WHR) greater than 0.90/0.85 (men/women) were considered as abdominal obesity.

The age range of participants was 60–100 years, with a mean age of 69.6 ± 7.1 years. Among them, 7718 were men (mean age: 69.7 ± 7.2 years), and 8558 were women (mean age: 69.4 ± 7.2 years). Of these, 0.4% were divorced and 17.7% were unmarried or widowed. The prevalence of overweight and obesity was 33.2% and 9.8%, respectively, whereas that of abdominal obesity was 71.4%. Most (88.3%) had no more than a primary school education. A little above half (51.7%) had a household income of more than 50,000 per year. Employment status revealed that 37.5% were retired, with a mean retirement age of 58.6 ± 6.1, and 22.7% were still employed. Females had a higher prevalence of abdominal obesity and widowhood and a greater proportion of retired or never-worked individuals than males (P < 0.01). Additionally, females exhibited significantly lower educational attainment (P < 0.01) and annual household income (P = 0.01) than males. Females were also more likely to have abnormal BMI values (P < 0.01) than males.

In addition, 11.2% of participants lived alone, whereas 61.5% had household sizes ranging from 2 to 4 persons. Additionally, 78.2% lived with their spouses, 42.8% lived with their children, and 63.6% had 1–2 children. More than half of the participants met with their children at least once per week. Dining habits revealed that 62.6% ate with 1 to 3 individuals and 77.8% ate with their spouse. Notably, 64.8% did not participate in group activities. Gender differences were significant, compared with male participants, female ones had smaller household sizes (P < 0.01) and were more likely to live and eat with children (P < 0.01) but less likely to live and eat with their spouses (p < 0.01). Female participants maintained more frequent contact with relatives and friends (P < 0.01).

Lifestyle and dietary intake

We analyzed lifestyle and dietary intake characteristics of the participants, including sleep conditions, smoking, alcohol intake, tea intake, and physical exercise. The results are shown in Table 2.

Table 2.

Lifestyle and dietary intake characteristics of participants.

Variables	Total (n = 16,276)	Male (n = 7,718)	Female (n = 8,558)	P value
Sleep quality				< 0.001
Feel poor, ≥ 4 days per week	721 (4.4)	266 (3.4)	455 (5.3)
Feel poor, 1–4 days per week	1,248 (7.7)	448 (5.8)	800 (9.3)
Feel poor, 1–3 days per month	4,069 (25.0)	1,633 (21.2)	2,436 (28.5)
Feel good almost every day	10,238 (62.9)	5,371 (69.6)	4,867 (56.9)
Sleep duration				< 0.001
< 6 h	3,327 (20.5)	1,274 (16.5)	2,053 (24.0)
6–8 h	12,169 (74.9)	6,013 (77.9)	6,156 (71.9)
> 8 h	744 (4.6)	411 (5.3)	333 (3.9)
Unknown	36 (0.2)	20 (55.6)	16 (0.2)
Midday rest in last year				< 0.001
None or occasionally	9,278 (57.0)	4,168 (54.0)	5,110 (59.7)
Have a midday rest in certain months	2,753 (16.9)	1,392 (18.0)	1,361 (15.9)
Have a midday rest every month	4,245 (26.1)	2,158 (28.0)	2,087 (24.4)
Smoking				< 0.001
Never	12,184 (74.9)	3,867 (50.1)	8,317 (97.2)
Smoking currently	2,798 (17.2)	2,600 (33.7)	198 (2.3)
Quit smoking	1,294 (8.0)	1,251 (16.2)	43 (0.5)
Smoking frequency (cigarettes/day)				< 0.001
< 20	1,741 (43.7)	1,596 (42.4)	145 (63.9)
≥ 20	2,247 (56.3)	2,165 (57.6)	82 (36.1)
Passive smoking in the last year				< 0.001
Yes	2,123 (13.0)	731 (9.5)	1,392 (16.3)
No	14,153 (87.0)	6,987 (90.5)	7,166 (83.7)
Drinking alcohol				< 0.001
Never	11,421 (70.2)	3,881 (50.3)	7,540 (88.1)
Quit drinking	695 (4.3)	582 (7.5)	113 (1.3)
Drinking currently	4,160 (25.6)	3,255 (42.2)	905 (10.6)
Drinking tea in a year				< 0.001
Yes	3,711 (22.8)	2,739 (35.5)	972 (11.4)
No	12,565 (77.2)	4,979 (64.5)	7,586 (88.6)
Tea drinking frequency				< 0.001
Not often	305 (8.2)	191 (7.0)	114 (11.7)
At a certain time	120 (3.2)	80 (2.9)	40 (4.1)
Less than once a week	341 (9.2)	212 (7.7)	129 (13.3)
Weekly	2,945 (79.4)	2,256 (82.4)	689 (70.9)
Physical exercise				< 0.001
Yes	2,906 (17.9)	1,284 (16.6)	1,622 (19.0)
No	13,370 (82.1)	6,434 (83.4)	6,936 (81.0)
Form of exercise				< 0.001
Walking/Tai Chi/Qigong	2,465 (84.8)	1,109 (86.4)	1,356 (83.6)
Brisk walking/Yanko	208 (7.2)	104 (8.1)	104 (6.4)
Long-distance running/aerobics	62 (2.1)	26 (2.0)	36 (2.2)
Square dancing	137 (4.7)	20 (1.6)	117 (7.2)
Others	34 (1.2)	25 (2.0)	9 (0.6)
Physical exercise frequency				0.293
1–2 days/week	225 (7.8)	95 (7.4)	130 (8.0)
3–4 days/week	533 (18.3)	234 (18.2)	299 (18.4)
5–6 days/week	751 (25.8)	314 (24.5)	437 (27.0)
7 days/week	1,397 (48.1)	641 (50.0)	756 (46.6)
Dietary intake (≥ 3 times/week, N, %)
Rice, wheat flour and their products	16,168 (99.3)	7,673 (99.4)	8,495 (99.3)	0.247
Fried food	608 (3.7)	351 (4.5)	257 (3.0)	< 0.001
Vegetables	15,887 (97.6)	7,520 (97.4)	8,367 (97.8)	0.166
Fruits	6,025 (37.0)	2,651 (34.4)	3,374 (39.4)	< 0.001
Red meat	5,882 (36.1)	2,972 (38.5)	2,910 (34.0)	< 0.001
White meat	2,154 (13.2)	1,145 (14.8)	1,009 (11.8)	< 0.001
Fish and shrimp	4,025 (24.7)	1,985 (25.7)	2,040 (23.8)	0.005
Eggs	4,752 (29.2)	2,402 (31.1)	2,350 (27.5)	< 0.001
Bean products	3,234 (19.9)	1,599 (20.7)	1,635 (19.1)	0.010
Milk	1,559 (9.6)	698 (9.0)	861 (10.1)	0.028
Pickles	2,428 (14.9)	1,125 (14.6)	1,303 (15.2)	0.246
Nuts	799 (4.9)	469 (6.1)	330 (3.9)	< 0.001
Sweet drinks	267 (1.6)	165 (2.1)	102 (1.2)	< 0.001
Cakes	1,332 (8.2)	638 (8.3)	694 (8.1)	0.715
Oil consumption
Canola oil	7,151 (43.9)	3,437 (44.5)	3,714 (43.4)	0.146
Animal oil	3,160 (19.4)	1,565 (20.3)	1,595 (18.6)	0.008
Blended oil	11,155 (68.5)	5,223 (67.7)	5,932 (69.3)	0.024
Peanut oil	2,120 (13.0)	1,034 (13.4)	1,086 (12.7)	0.181
Soybean oil	825 (5.1)	396 (5.1)	429 (5.0)	0.732

In total, 4.4% of the participants reported consistent poor sleep (feel poor for ≥ 4 days per week), 7.7% reported frequent poor sleep (feel poor for 1–4 days per week), and 20.5% reported a sleep duration < 6 h. In addition, 26.1% of the participants reported taking a midday rest every month; among them, most (77.5%) took a midday rest almost every day. Notably, females exhibited significantly lower sleep quality (P < 0.01) and shorter sleep duration than males (P < 0.01).

Regarding smoking, 74.9% of the participants had never smoked, 7.9% had quit smoking, and 17.2% were current smokers. Additionally, 13.0% of the participants reported exposure to secondhand smoke in the past year. The prevalence and frequency of smoking were significantly higher among males than among females (P < 0.01), whereas the rate of secondhand smoke exposure was significantly higher among females than among males (P < 0.01).

Regarding alcohol intake, 70.2% of participants reported never drinking, 4.3% had ceased drinking, and 25.6% were current drinkers. The prevalence of alcohol consumption was significantly higher in males than in females (P < 0.01).

Among the participants, 22.8% reported tea consumption throughout the year, with 79.4% drinking tea weekly. The prevalence of tea consumption was significantly higher among males than among females (P < 0.01).

Regarding physical exercise, 17.9% of participants had performed physical exercise over the past year. Among those who exercised, walking, Tai Chi, and Qigong were the most popular choices, accounting for 84.8% of participants. Nearly half (48.2%) of the participants exercised daily. The prevalence of physical exercise was significantly higher among females than among males (P < 0.01).

The dietary intake of the participants is presented in Table 2. Almost all the participants consumed staple foods such as rice, wheat, and their products (99.3%), as well as vegetables (97.6%) at least thrice per week. While fewer than 40% of older adults consumed fruit or red meat at least thrice per week, less than 30% consumed eggs or fish, less than 20% consumed white meat or beans, and less than 10% consumed milk or nuts at least thrice per week. Blended oil (68.6%) was most commonly chosen for cooking, followed by canola oil (43.8%). Furthermore, gender differences were observed, as males were more likely to consume drinks, fried foods, red meat, white meat, fish, eggs, and nuts, whereas females were more likely to eat fruits and drink milk (P < 0.05). Additionally, males preferred animal oils over females, while females preferred blended oils (P < 0.05).

Physical and mental health status

The results regarding the prevalence of chronic diseases among the participants are summarized in Table 3. The most prevalent chronic conditions were hypertension (46.3%), diabetes (11.1%), cataracts (8.2%), gastric disease (6.9%), and hyperlipidemia (7.3%). Multimorbidity was observed in 38.9% of respondents, with females being significantly more likely to experience multimorbidity than males (P < 0.01). Additionally, females exhibited a higher prevalence of hypertension, hyperlipidemia, gallstones, cataracts, stomach diseases, diabetes, arthritis, and thyroid disorders than males (P < 0.05). Conversely, males had a significantly higher prevalence of emphysema, chronic bronchitis, and hepatitis than females (P < 0.05).

Table 3.

Physical and mental health of participants.

Variables	Total (n = 16,276)	Male (n = 7,718)	Female (n = 8,558)	P value
Have chronic diseases
0	5,714 (35.1)	2,894 (37.5)	2,820 (33.0)	< 0.001
1	5,806 (35.7)	2,818 (36.5)	2,988 (34.9)
≥ 2	4,756 (29.2)	2,006 (26.0)	2,750 (32.1)
Hypertension				0.039
Yes	7,538 (46.3)	3,509 (45.5)	4,029 (47.1)
No	8,738 (53.7)	4,209 (54.5)	4,529 (52.9)
Hyperlipidemia				< 0.001
Yes	1,185 (7.3)	442 (5.7)	743 (8.7)
No	15,091 (92.7)	7,276 (94.3)	7,815 (91.3)
Coronary artery disease				0.156
Yes	644 (4.0)	323 (4.2)	321 (3.8)
No	1,5632 (96.0)	7,395 (95.8)	8,237 (96.2)
Emphysema				< 0.001
Yes	73 (0.4)	56 (0.7)	17 (0.2)
No	16,203 (99.6)	7,662 (99.3)	8,541 (99.8)
Tuberculosis				0.101
Yes	95 (0.6)	53 (0.7)	42 (0.5)
No	16,181 (99.4)	7,665 (99.3)	8,516 (99.5)
Asthma				0.155
Yes	101 (0.6)	55 (0.7)	46 (0.5)
No	16,175 (99.4)	7,663 (99.3)	8,512 (99.5)
Chronic bronchitis				< 0.001
Yes	438 (2.7)	265 (3.4)	173 (2.0)
No	15,838 (97.3)	7,453 (96.6)	8,385 (98.0)
Gallstones				< 0.001
Yes	859 (5.3)	281 (3.6)	578 (6.8)
No	15,417 (94.7)	7,437 (96.4)	7,980 (93.2)
Nephritis				0.430
Yes	61 (0.4)	32 (0.4)	29 (0.3)
No	16,215 (99.6)	7,686 (99.6)	8,529 (99.7)
Tumor				0.668
Yes	371 (2.3)	180 (2.3)	191 (2.2)
No	15,905 (97.7)	7,538 (97.7)	8,367 (97.8)
Parkinson’s disease				0.220
Yes	48 (0.3)	27 (0.3)	21 (0.2)
No	16,228 (99.7)	7,691 (99.7)	8,537 (99.8)
Cataract				< 0.001
Yes	1,328 (8.2)	488 (6.3)	840 (9.8)
No	14,948 (91.8)	7,230 (93.7)	7,718 (90.2)
Glaucoma				0.209
Yes	54 (0.3)	21 (0.3)	33 (0.4)
No	16,222 (99.7)	7,697 (99.7)	8,525 (99.6)
Gastric disease				< 0.001
Undiagnoseda	1,127 (6.9)	461 (6.0)	666 (7.8)
Diagnosedb	1,149 (7.1)	492 (6.4)	657 (7.7)
No	14,000 (86.0)	6,765 (87.7)	7,235 (84.5)
Diabetes				< 0.001
Yes	1,804 (11.1)	728 (9.4)	1,076 (12.6)
No	14,472 (88.9)	6,990 (90.6)	7,482 (87.4)
Hepatitis				0.017
Yes	141 (0.9)	81 (1.0)	60 (0.7)
No	16,135 (99.1)	7,637 (99.0)	8,498 (99.3)
Arthritis				0.004
Yes	794 (4.9)	337 (4.4)	457 (5.3)
No	15,482 (95.1)	7,381 (95.6)	8,101 (94.7)
Thyroid disease				< 0.001
Yes	402 (2.5)	98 (1.3)	304 (3.6)
No	15,874 (97.5)	7,620 (98.7)	8,254 (96.4)
Depressive symptom				0.033
None	14,953 (91.9)	7,137 (92.5)	7,816 (91.3)
Mild	938 (5.8)	417 (5.4)	521 (6.1)
Moderate	245 (1.5)	100 (1.3)	145 (1.7)
Moderate-severe	53 (0.3)	20 (0.3)	33 (0.4)
Severe	87 (0.5)	44 (0.6)	43 (0.5)
ADL (Barthel index)				0.045
Independence (100)	15,806 (97.1)	7,525 (97.5)	8,281 (96.8)
Slight dependence (91–99)	212 (1.3)	88 (1.1)	124 (1.4)
Moderate Dependence (61–90)	200 (1.2)	80 (1.0)	120 (1.4)
Indicating severe dependence (0–60)	58 (0.4)	25 (0.3)	33 (0.4)
Cognitive impairment				< 0.001
Yes	3,222 (19.8)	1,383 (17.9)	1,839 (21.5)
No	13,054 (80.2)	6,335 (82.1)	6,719 (78.5)
Multimorbidityc				< 0.001
Yes	6,335 (38.9)	2,720 (35.2)	3,615 (42.2)
No	9,941 (61.1)	4,998 (64.8)	4,943 (57.8)

^a “Undiagnosed” indicates that the participant reported symptoms suggestive of gastrointestinal issues in the absence of a formal diagnosis through clinical evaluation at a hospital.

^b “Diagnosed” indicates that the gastric disease of the participants had been diagnosed at a hospital.

^cMultimorbidity, defined as the coexistence of two or more chronic conditions encompassing both physical and mental health disorders.

In addition, 19.8% of the participants presented with cognitive impairment, whereas 8.2% presented with depression (Table 3). With regard to functional independence, 1.3% had a BI of 91–99, 1.2% scored 61–90, while 0.4% had 0–60. Notably, the prevalence of depressive symptoms was significantly higher in males than in females (P = 0.033). Conversely, females exhibited significantly higher rates of dependence and cognitive impairment than males (P < 0.01).

Discussion

This cross-sectional study investigated the general characteristics, sociological support, nutrition, behavioral habits, and physical and mental health status of Chinese individuals aged ≥ 60 years and identified several significant health challenges and gender differences in these aspects, as discussed below.

This study revealed distinctive demographic characteristics among older adults in Zhejiang Province, substantially diverging from international patterns. Specifically, 44.3% of the participants had no formal education or had only received a primary education, significantly exceeding the corresponding rates in many developed countries, such as the United States, Japan, and South Korea^16–18, primary causes include the historical background, national education development levels, and regional economic conditions. This educational disadvantage, particularly evident among female participants, fundamentally constrains health literacy and healthcare accessibility. Economically, over 40% of the participants reported annual household incomes below 50,000 CNY, significantly lower than those of many older adults in developed countries, such as the United States¹⁸. An empirical analysis based on the Chinese Longitudinal Healthy Longevity Survey revealed a significant association between healthcare access and improved health outcomes among older adults¹⁹. These socioeconomic constraints collectively restrict access to nutritious food, healthcare, and other essential resources, thereby exacerbating health inequalities among older adults²⁰.

Regarding social support, approximately 17% of the participants were either widowed or divorced, with 11.2% living alone and 11.7% eating alone. These figures are substantially lower than those reported for some high-income countries^21–23. In several European countries, the proportion of older persons living alone exceeds 50%²⁴. This discrepancy may be attributed to cultural factors, including strong family bonds and intergenerational support systems in Chinese society. However, methodological differences in assessment tools and social desirability bias in self-reporting should also be considered. Additionally, our study revealed that more than half of the participants had never engaged in group activities. Social isolation among older adults in China remains a health-related issue that cannot be ignored and is associated with increased risk of all-cause mortality. Addressing this problem would play a pivotal role in promoting the health and well-being of older adults.

The lifestyle patterns identified in this study revealed concerning trends. Over 80% of the participants did not exercise regularly, a significantly higher figure than the 27.5% reported for adults aged ≥ 50 years in the United States²⁵. Furthermore, gender disparities in lifestyle proved particularly pronounced. Male smoking prevalence (33.7%) exceeded female rates (2.3%) by a factor of 15, consistent with results reported in Asian countries such as Korea and Malaysia^26,27 but surpassing the gender gaps (approximately 1.2:1 to 3:1) observed in several European countries²⁸. This substantial disparity suggests deeply entrenched gender-specific social norms governing tobacco use in Chinese cultural contexts. Similarly, alcohol consumption demonstrated a fourfold gender difference (42.2% among males vs. 10.6% among females), contrasting with the narrower differentials in European populations^29,30.

Dietary intake significantly influences health, quality of life, independence, and economic circumstances, particularly among older adults. Importantly, our study found that less than 40% of older adults consumed fruits or red meat at least thrice per week, while less than 30% consumed eggs or fish, less than 20% consumed white meat or beans, and less than 10% consumed milk or nuts at least thrice per week, far below the recommended intake levels for older adults³¹. The convergence of physical inactivity and nutritional deficiencies creates synergistic risks for metabolic disorders, particularly elucidating the high prevalence of abdominal obesity (83.1% in females) that exceeds rates reported in Malaysia (78.4%) and Spain (69.9%)^32,33.

The physical and mental health profile of Zhejiang Province’s older adults exhibits both concordance with and divergence from global trends. In this study, 38.9% of participants reported multimorbidity, which is consistent with the 36.3% prevalence observed in a meta-analysis of Chinese residents between 1998 and 2019³⁴. However, the prevalence in our study was lower than the global figure reported by a recent systematic review based on data from 126 studies conducted across 54 countries, which found that more than 50% of older adults worldwide experience multimorbidity³⁵. This may be attributed to the relatively low smoking (17.2%) and alcohol (25.6%) consumption rates among older adults in this study. Additionally, the data on chronic conditions were self-reported and not supplemented by clinical records, which may contribute to the observed differences. Nonetheless, multimorbidity has become a growing public health challenge. In addition, 19.8% of participants presented with cognitive impairment in this study, which is consistent with the findings of previous studies conducted in China, the United States (16.0%−22.2%)³⁶, and South Korea (24.1%)^37,38. The prevalence of depressive symptoms among older adults in this study was 8.2%, much lower than the rates reported by most previous studies, at approximately ≥ 20%^39–41. Depression is associated with social isolation⁴².The low depression rate in this study may be related to the low social isolation rate of the participants. In addition, these gaps could be largely attributed to the measurement means of depressive symptoms and differences in the population and sociocultural contexts. The low prevalence of depressive symptoms among older adults in this study suggests that depression in older adults may sometimes be underdiagnosed or underreported, as many older individuals may not seek help because of social stigma or the belief that depressive symptoms are a normal part of aging. This underscores the importance of promoting mental health awareness, reducing stigma, and ensuring that older adults have access to appropriate mental health services.

This study had some limitations. First, the 11 counties enrolled in the ZHACS were not randomly selected, which may have introduced selection bias. Second, the study did not assess specific daily food intake. The observational design may have led to recall bias, as data on dietary intake and chronic conditions were self-reported, and the assessments of cognitive impairment, depressive symptoms, and activities of daily living relied on self-reported questionnaires. The observed significant differences may be attributable to the overly large sample size rather than on actual differences. Finally, this study enrolled an elderly population with limited geographic representation, and caution should be exercised when generalizing the findings to other demographic or regional groups.

Conclusion

The study highlights key health challenges among older adults in China, including a high prevalence of abdominal obesity, limited educational attainment, low income, insufficient physical activity, and inadequate nutritional intake. Additionally, over one-third reported multimorbidity, and approximately one-fifth exhibited cognitive impairment. Future research should prioritize longitudinal studies for a better understanding of the long-term impact of these factors and the development of effective interventions to improve health outcomes and promote healthy aging in this population.

Author contributions

Mengna Wu, Yujia Zhai, and Junfen Lin conceptualized the manuscript; Wei Feng, Fujun Zhou, Xue Gu and Le Xu collected the data; Mengna Wu, Yujia Zhai and Tao Zhang analyzed the data; Mengna Wu wrote the first draft; Wei Feng, Chen Wu, Tao Zhang, Xue Gu, Le Xu, Fujun Zhou, Fudong Li, Yujia Zhai and Junfen Lin revised the manuscript critically. All authors contributed to the manuscript and approved the submitted version.

Funding

This work was supported by the Medical Health Science and Technology Project of the Zhejiang Provincial Health Commission (grant numbers 2024KY891 and 2024KY896), the Science and Technology Project of the Zhejiang Provincial Disease Control and Prevention Administration (grant numbers 2025JK009 and 2025JK171), and the Zhejiang Provincial Public Welfare Technology Application Research Project of China (LTGY24H260004).

Data availability

The raw data supporting the conclusions of this article are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethical statement and consent

All study participants provided written informed consent before the start of the study. The study protocol was approved by the Ethics Committee of Zhejiang Provincial Center for Disease Control and Prevention (No.2021-034-01). In accordance with the principles of the Declaration of Helsinki, all personal data obtained in this study were handled confidentially, and the research procedures followed ethical guidelines.