Abstract
Background
Loneliness is a significant public health issue in China, particularly among the older population. This study sought to explore differences in the incidence of loneliness among older adults in urban versus rural regions and to quantify the contribution of relevant influencing factors.
Methods
This study assessed data from 13,735 older adults derived from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS). The chi-squared test was employed to examine the distribution characteristics of relevant indicators among older adults in the two regions. Logistic regression was utilized to explore the factors affecting loneliness in older adults across different areas. Additionally, the Fairlie decomposition analysis method was applied to quantify the four categories of influencing factors—demographic characteristics, sociological characteristics, personal lifestyle, and health status—that contributed to differences in loneliness and to estimate their respective contributions among urban and rural older adults.
Results
A total of 26.79% of older adults reported feeling lonely, with a higher proportion of rural older adults (27.77%) experiencing loneliness than their urban counterparts (24.27%). In rural regions, risk factors for loneliness included a body mass index (BMI) of < 18.5 kg/m², being widowed, and other marital statuses, as well as self-reported local income status as “rich.” Conversely, protective factors included being male, having an educational level of 1–6 years or ≥ 7 years, self-reported local income status as “average,” alcohol consumption, engaging in exercise, and self-reported health (SRH) as “good.” In urban areas, risk factors included being widowed or in other marital statuses, as well as self-reported local income status as “rich.” In contrast, protective factors comprised being ≥ 100 years old, engaging in exercise, having sleep durations of 6.0–7.9 h, 8.0–9.9 h, or ≥ 10 h, and self-rated health as “good.” The Fairlie decomposition analysis revealed that 95.38% of the disparity in loneliness symptoms stemmed from observable factors, whereas the remaining 4.62% was attributed to urban–rural differences. Factors such as gender, educational attainment, marital status, living conditions, self-reported local income level, alcohol consumption, physical exercise, sleep length, and SRH played a significant role in explaining the disparities in loneliness symptoms between the two groups.
Conclusion
Older adults residing in rural regions exhibited a greater prevalence of loneliness than their counterparts in urban areas. This difference is mainly caused by gender, educational level, marital status, living status, self-reported local income status, drinking, exercise, sleep duration, and SRH. Addressing these influential factors of loneliness would aid in developing targeted and more precise intervention strategies specifically tailored to enhance the mental wellness of high-risk older adults.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-026-26639-3.
Keywords: Loneliness, Old adults, China, Fairlie decomposition
Background
According to the 2020 seventh national census, approximately 264 million people were aged ≥ 60 years in China, accounting for 18.70% of the total population [1]. By 2050, this figure will increase to 487 million, representing 34.9% of the total population [2]. As stated in the updated “Healthy China 2030 Plan Outline,” older adults’ life expectancy is projected to increase markedly, reaching 79 years by 2030 [3]. Against the backdrop of China’s accelerating population aging and rising life expectancy, loneliness—a distressing subjective emotion caused by a discrepancy between one’s desired and perceived social connections [4]—is prevalent among older adults. According to an interview study on lonely older adults in China, they describe loneliness as a feeling of “pain of loneliness,” including a lack of intimacy and companionship, fear of declining health, loss and death, and a sense of disconnection from the outside world [5].
According to a national survey, 29.6% of older individuals report often experiencing loneliness [6]. Several factors account for the high rate of loneliness among older adults: first, this is primarily because the traditional multigenerational family structure is rapidly disintegrating due to the accelerating advancement of urbanization and the long-term impact of the family planning policy. The “4-2-1” family structure (four older people, one couple, and one child) has become the norm, with children leaving home for work and studies, leading to a surge in the number of empty-nest households and a lack of daily companionship for older adults [7, 8]. Second, older adults are exposed to age-related physical changes, such as a higher likelihood of developing illnesses and diminished mobility, as well as various losses, including the death of spouses and friends, worsening health, and the loss of social roles upon retirement [9]. Lastly, with the widespread adoption of mobile Internet, social interaction and entertainment through mobile phones have replaced the traditional form of social interaction. For older adults living alone at home in China, methods such as video communication can help them connect with their families and reduce feelings of loneliness, which has been confirmed by some Chinese scholars [10, 11]. However, with the development of technology and the popularity of mobile Internet, this positive effect is being reversed, and the main reasons include the following two aspects: first, with the emergence of TikTok and other applications, the Internet use time of most people has greatly increased, leading to more serious atomization among people and less face-to-face communication between relatives [12, 13], and second, the “digital divide,” that is, the growing gap between young and old people in their ability to access information and use information technology such as the Internet, has reduced the physical and mental health and social participation level of older adults, suppressed the level of protection for older adults, lowered their overall life satisfaction, and increased their sense of loneliness [14, 15].
A systematic review of the relevant literature revealed that loneliness in older adults is associated not only with age but also with various other factors, such as female sex, unmarried status, low income, low educational level, living alone, social frailty, poor quality of social relationships, poor SRH, declined functional status, living environment (e.g., residing in temporary housing), and special experiences (e.g., falling) [7, 16, 17]. Loneliness in older adults is closely linked to their health status. For instance, Terracciano reported that loneliness may increase the risk of Parkinson disease (PD) [18] and Alzheimer disease (AD) [19, 20]. A British study confirmed that loneliness is linked to an increased risk of coronary heart disease and stroke, independent of traditional cardiovascular disease risk factors [21]. Psychological traits associated with loneliness include poor mental health. Loneliness can trigger sadness, anxiety, and feelings of inferiority, and exacerbate depressive symptoms over time [22–24]. According to empirical studies, loneliness is strongly associated with cognitive decline [25, 26]. Furthermore, loneliness is a significant predictor of early death in older adults, and severe depression is closely associated with an increased mortality rate in those who experience loneliness [27, 28].
Loneliness is a critical issue that requires serious consideration to improve the physical and mental health of older adults in China. Additionally, with the development of urbanization in China, disparities occur not only in terms of unequal physical health outcomes but also in differing mental health statuses between the two areas because of inequalities in income levels, social security, and other factors [29, 30]. Therefore, this may lead to a disparity in loneliness among older people in these two regions of China. However, this aspect has not been investigated sufficiently. This study aimed to explore the differences in the incidence of loneliness among older adults in urban and rural areas of China and to conduct a quantitative analysis of the factors that contribute to loneliness. The findings of this study could provide a basis for formulating targeted prevention and control strategies, thereby helping to narrow the gap in loneliness between urban and rural older adults and improve their quality of life.
Methods
Data sources
This retrospective study extracted relevant information from the 8th wave of the CLHLS, which was implemented by the Peking University Center for Healthy Aging and Development in 2020 and approved by the ethics committee of Peking University (No. IRB00001052-13074). Detailed information on data sources and research designs can be obtained by visiting the following website: 10.18170/DVN/WBO7LK. The survey covered 23 provinces, accounting for approximately two-thirds of the land area of China. To ensure representativeness, the CLHLS adopted a targeted random sampling method, which has been applied in relevant studies [31, 32].
This study excluded two groups of individuals: those aged < 65 years and those lacking data on loneliness, demographic and social characteristics, personal lifestyle choices, or health status indicators.
Participants and sample size
In the 2017–2018 version of the CLHLS, 15,874 people aged ≥ 65 years were surveyed. From this initial pool, 103 participants aged ≤ 65 years, 144 with missing demographic information, and 1,892 with incomplete loneliness-related data were excluded, resulting in a final sample of 13,735 older adults, comprising 3,852 from urban regions and 9,883 from rural regions.
Outcome variable
Loneliness was measured using the question, “Do you feel lonely?” The available answers were “never,” “seldom,” “sometimes,” “often,” and “always.” This one-question assessment tool has been widely used and is highly correlated with multi-item loneliness scales [33, 34]. Based on previous research, given that “seldom” reflects a healthy and well-adjusted state in which loneliness is not a persistent issue, and thus aligns with the psychological benchmark for mental health, they simplified the 5-point Likert scale into two categories: “no loneliness” (including responses of “never” and “seldom”) and “loneliness” (including responses of “sometimes”, “often”, and “always”) [35, 36]. Our study adopted this classification (0 indicating “never/seldom lonely” and 1 indicating “always/often/sometimes lonely”) to ensure comparability with existing literature and to maintain a meaningful distinction between individuals with negligible versus notable loneliness.
Group variables
Individuals who participated in the study were classified as either rural or urban dwellers based on registered residence, a unique population management system in China, when the survey was conducted.
Covariates
To guarantee the trustworthiness of the findings, several potential confounding variables were accounted for: demographic and sociological characteristics, personal lifestyles, and health status were all included in the investigation of loneliness. Age, gender, BMI, education level, and ethnicity were considered demographic characteristics. Sociological characteristics included living conditions, marital status, and self-reported income. Personal lifestyle factors included smoking, alcohol consumption, exercise, and self-reported sleep status. Health status included SRH.
Demographic characteristics
BMI was calculated by dividing weight in kilograms by height in meters squared and then categorized as follows: <18.5, 18.5–23.9, 24.0–27.9, and ≥ 28.0 kg/m2. Age was divided into the following brackets: <70, 70–79, 80–89, 90–99, and ≥ 100 years. Educational level was classified based on years of schooling: 0, 1–6, and ≥ 7 years. Ethnicity was divided into Han and other ethnic groups.
Sociological characteristics
Living status was grouped into three categories: living with household members, alone, and living in a nursing home. For self-reported economic standing, participants were asked, “How would you assess your financial situation in comparison to others in your local area?” Responses to this question were categorized as “poor,” “general,” or “rich.” Marital status was classified as either being married and cohabitating with a spouse, widowed, or “others” (including those who were married but living separately from their spouse, divorced, or never married).
Personal lifestyle
Participants’ smoking, drinking, and exercise habits were classified as “yes” or “no” based on their answers to the following questions: “Do you exercise regularly now?” and “Do you drink alcohol at present?” For sleep duration, participants were asked, “How many hours do you typically sleep each day?” Their responses were grouped into the following categories: <4.0 h, 4.0–5.9 h, 6.0–7.9 h, 8.0–9.9 h, and ≥ 10 h.
Health status
To assess SRH, this survey used a single-item question: “How would you rate your current health?” with five response options. Health status was dichotomized as either “poor” (which included “so-so,” “bad,” or “very bad”) or “good” (“very good” or “good”).
Statistical analysis
Descriptive statistical methods were used to analyze the basic data regarding demographic characteristics, sociological characteristics, personal lifestyle, and health status. The chi-square test was applied to explore how loneliness symptoms were distributed among urban and rural older individuals. A binary logistic regression model was employed to identify the key factors that impact loneliness in older adults from urban and rural areas. The Fairlie model was used to examine the factors contributing to the differences in loneliness symptoms between urban and rural older adults. To ensure the robustness of the study, multiple imputations were used to handle all missing values: each data point was imputed three times, and the imputed data were then used for model fitting and analysis. IBM SPSS Statistics software (version 21.0, IBM Corp., Armonk, New York, the United States of America) and Stata mp16.0 software (STATA Inc., Texas, the United States of America) were used for the analyses. Statistical significance was set at p < 0.05.
Fairlie decomposition analysis
Given that the dependent variable was binary (with two distinct values), the Fairlie nonlinear decomposition method was used. This approach enables the decomposition of variations in loneliness by assigning them to different contributing factors. Further details are provided in our previous study [1, 37].
Results
General characteristics
The overall sample comprised 13,735 participants. Table 1 outlines the results of the descriptive statistical analyses conducted on older adults in urban and rural China: 26.79% of the older adults surveyed reported feeling lonely. A notable contrast emerged between rural and urban populations, with 27.77% of rural older adults experiencing loneliness, higher than the 24.27% in urban areas, and this difference was statistically significant (p < 0.001). Furthermore, a chi-square test was used to analyze the distribution of 12 covariables between rural and urban older adults. The covariates included gender, BMI, educational level, ethnicity, living conditions, marital status, self-reported local income, smoking, drinking, exercise, sleep duration, and SRH.
Table 1.
Basic information of participants of rural and urban respondents
| Variables | Rural [n (%)] | Urban[n (%)] | c2 | P |
|---|---|---|---|---|
| Lonely | 17.33 | < 0.001 | ||
| No | 7138 (72.22) | 2917 (75.72) | ||
| Yes | 2745 (27.78) | 935 (24.27) | ||
| Age (years) | 7.76 | 0.101 | ||
| < 70 | 1076 (10.89) | 429 (11.14) | ||
| 70–79 | 2685 (27.17) | 1025 (26.61) | ||
| 80–89 | 2688 (27.20) | 1064 (27.62) | ||
| 90–99 | 2036 (20.60) | 847 (21.99) | ||
| ≥ 100 | 1398 (14.15) | 487(12.64) | ||
| Gender | 20.31 | < 0.001 | ||
| Female | 4379(44.31) | 1871(48.57) | ||
| Male | 5504(55.69) | 1981(51.43) | ||
| BMI | ||||
| 18.5–23.9 | 5016 (50.75) | 1757 (45.61) | 206.76 | < 0.001 |
| < 18.5 | 1655 (16.75) | 392 (10.18) | ||
| 24.0–27.9 | 2085 (21.10) | 1106 (28.71) | ||
| ≥ 28.0 | 697 (7.05) | 406(10.54) | ||
| Missing | 430(4.35) | 191(4.96) | ||
| Education level | 8500 | < 0.001 | ||
| 0 | 4611 (46.66) | 889 (23.08) | ||
| 1–6 | 2854 (28.88) | 1052 (27.31) | ||
| ≥7 | 807 (8.17) | 1549 (40.21) | ||
| Missing | 1611 (16.30) | 362 (9.40) | ||
| Ethnicity | 98.89 | < 0.001 | ||
| Han nationality | 7742 (78.34) | 3429 (89.02) | ||
| Other | 620 (6.27) | 94 (2.44) | ||
| Missing | 1521(15.39) | 329(8.54) | ||
| Living status | 368.84 | < 0.001 | ||
| Living with household members | 7794 (78.86) | 3017 (78.32) | ||
| Alone | 1764(17.85) | 505(13.11) | ||
| Living in a nursing home | 151(1.53) | 298(7.74) | ||
| Missing | 174(1.76) | 32(0.83) | ||
| Marital status | 24.79 | < 0.001 | ||
| Married and living with spouse | 4058 (41.06) | 1765 (45.82) | ||
| Widowed | 5418(54.82) | 1943(50.44) | ||
| Other | 296 (3.00) | 121 (3.14) | ||
| Missing | 111 (1.12) | 23 (0.60) | ||
| Self-reported local income status | 462.65 | < 0.001 | ||
| Poor | 6989(70.72) | 2493(64.72) | ||
| General | 1584(16.03) | 1162(30.17) | ||
| Rich | 1210(12.24) | 168(4.36) | ||
| Missing | 100(1.01) | 29(0.75) | ||
| Smoking | 63.84 | < 0.001 | ||
| No | 8096 (52.94) | 3357 (24.44) | ||
| Yes | 1692 (12.32) | 446 (3.25) | ||
| Missing | 95 (0.69) | 49 (0.36) | ||
| Drinking | 10.07 | 0.002 | ||
| No | 8199(81.92) | 3276(85.05) | ||
| Yes | 1526(17.12) | 512(13.29) | ||
| Missing | 158(0.96) | 64(1.66) | ||
| Exercise | 562.62 | < 0.001 | ||
| No | 7119(72.03) | 1971(51.17) | ||
| Yes | 2610(26.41) | 1829(47.48) | ||
| Missing | 154(1.56) | 52(1.35) | ||
| Sleep time | 62.41 | < 0.001 | ||
| <4.0 | 353(3.57) | 92(2.39) | ||
| 4.0-5.9 | 1582(16.01) | 579(15.03) | ||
| 6.0-7.9 | 3174(32.12) | 1451(37.67) | ||
| 8.0-9.9 | 2760(27.93) | 1098(28.50) | ||
| ≥10 | 1906(19.29) | 593(15.39) | ||
| Missing | 108(1.09) | 39(1.01) | ||
| Self-reported health | 5.145 | 0.023 | ||
| Bad | 5240(53.02) | 1961(50.91) | ||
| Good | 4623(46.78) | 1886(48.96) | ||
| Missing | 20(0.20) | 5(0.13) |
BMI body mass index
Comparison of variable distributions of different loneliness symptoms
Table 2 shows how covariates are distributed among rural and urban older adults, grouped according to their different experiences of loneliness. Several covariates displayed distinct distribution patterns among older adults who reported loneliness symptoms. These included age, gender, BMI, educational level, ethnicity, marital status, living status, self-reported local income status, smoking, drinking, exercise, and sleep time.
Table 2.
Rural–urban differences in non-loneliness and loneliness symptoms among older adults by select background characteristics
| Variables | Non-loneliness | Loneliness | ||||
|---|---|---|---|---|---|---|
| Rural [n (%)] | City[n (%)] | P | Rural [n (%)] | City [n (%)] | P | |
| Age (years) | 0.365 | 0.006 | ||||
| < 70 | 891(12.48) | 352(12.07) | 185(6.74) | 77(8.24) | ||
| 70–79 | 2054(28.78) | 810(27.77) | 631(22.99) | 215(22.99) | ||
| 80–89 | 1856(26.00) | 758(25.98) | 832(30.30) | 306(32.73) | ||
| 90–99 | 1388(19.44) | 618(21.19) | 648(23.61) | 229(24.49) | ||
| ≥ 100 | 949(13.30) | 379(12.99) | 449(16.36) | 108(11.55) | ||
| Gender | < 0.001 | 0.629 | ||||
| Female | 3288(46.06) | 1491(51.11) | 1091(39.74) | 380(40.64) | ||
| Male | 3850(53.94) | 1426(48.89) | 1654(60.26) | 555(59.36) | ||
| BMI | < 0.001 | < 0.001 | ||||
| 18.5–23.9 | 3670(53.55) | 1339(47.94) | 1346(51.79) | 418(48.16) | ||
| < 18.5 | 1081(15.76) | 282(10.10) | 574(22.08) | 110(12.67) | ||
| 24.0–27.9 | 1579(23.04) | 858(30.72) | 506(19.47) | 248(28.57) | ||
| ≥ 28.0 | 524(7.65) | 314(11.24) | 173(6.66) | 92(10.60) | ||
| Education level | < 0.001 | < 0.001 | ||||
| 0 | 3119(52.38) | 619(23.42) | 1492(64.39) | 270(31.88) | ||
| 1–6 | 2165(36.36) | 809(30.61) | 689(29.74) | 243(28.69) | ||
| ≥7 | 671(11.27) | 1215(45.97) | 136(5.87) | 334(39.43) | ||
| Ethnicity | < 0.001 | < 0.001 | ||||
| Han nationality | 5604(93.14) | 2579(97.14) | 2138(91.17) | 850(97.93) | ||
| Other | 413(6.86) | 76(2.86) | 207(8.83) | 18(2.07) | ||
| Marital status | 0.008 | 0.008 | ||||
| Married and living with spouse | 3418(49.77) | 1500(53.21) | 640(24.53) | 265(29.81) | ||
| Widowed | 3449(50.23) | 1319(46.79) | 1969(75.47) | 624(70.19) | ||
| Other | 187(2.72) | 82(2.91) | 109(4.18) | 39(4.39) | ||
| Living Status | < 0.001 | < 0.001 | ||||
| Living with household members | 5982(85.25) | 2424(83.76) | 1812(67.31) | 593(64.04) | ||
| Alone | 941(13.41) | 287(9.92) | 823(30.57) | 218(23.54) | ||
| Living in a nursing home | 94(1.34) | 183(6.32) | 57(2.12) | 115(12.42) | ||
| Self-reported local income status | < 0.001 | < 0.001 | ||||
| Poor | 5066(71.61) | 1860(64.12) | 1923(70.99) | 633(68.66) | ||
| General | 1321(18.67) | 951(32.78) | 263(9.71) | 211(22.89) | ||
| Rich | 687(9.71) | 90(3.10) | 523(19.31) | 78(8.46) | ||
| Smoking | < 0.001 | < 0.001 | ||||
| No | 5795(81.95) | 2516(87.39) | 2301(84.68) | 841(91.02) | ||
| Yes | 1276(18.05) | 363(12.61) | 416(15.31) | 83(8.98) | ||
| Drinking | 0.002 | 0.136 | ||||
| No | 5821(82.77) | 2445(85.31) | 2378(88.34) | 831(90.13) | ||
| Yes | 1212(17.23) | 421(14.69) | 314(11.66) | 91(9.87) | ||
| Exercise | < 0.001 | < 0.001 | ||||
| No | 5005(71.14) | 1354(47.03) | 2114(78.47) | 617(66.99) | ||
| Yes | 2030(28.86) | 1525(52.97) | 580(21.53) | 304(33.01) | ||
| Sleep time | < 0.001 | < 0.001 | ||||
| <4.0 | 231(3.27) | 53(1.83) | 122(4.51) | 39(4.23) | ||
| 4.0-5.9 | 997(14.10) | 367(12.70) | 585(21.63) | 212(22.97) | ||
| 6.0-7.9 | 2318(32.78) | 1095(37.89) | 856(31.66) | 356(38.57) | ||
| 8.0-9.9 | 2114(29.90) | 886(30.66) | 646(23.89) | 212(22.97) | ||
| ≥10 | 1411(19.95) | 489(16.92) | 495(18.31) | 104(11.27) | ||
| Self-reported health | 0.100 | 0.979 | ||||
| Bad | 3345(46.95) | 1315(45.14) | 1895(69.21) | 646(69.16) | ||
| Good | 3780(53.05) | 1598(54.86) | 843(30.79) | 288(30.84) | ||
BMI body mass index
Logistic model results
Table 3 displays the outcomes of the logistic model calculations for loneliness symptoms among rural and urban older adults. For rural older adults, the risk factors for loneliness symptoms included BMI < 18.5 kg/m2 (odds ratio (OR) = 1.21, 95%CI = 1.04–1.40), being widowed (OR = 3.34, 95%CI = 2.89–3.86) or in “other” marital statuses (married but living apart from a spouse, divorced, or never married) (OR = 2.36, 95%CI = 1.72–3.22), and self-reported local income status as “rich” (OR = 1.76, 95%CI = 1.50–2.06). Conversely, protective factors were as follows: being male (OR = 0.73, 95%CI = 0.64–0.84), having 1–6 years of education (OR = 0.84, 95%CI = 0.73–0.96) or ≥ 7 years of education (OR = 0.71, 95%CI = 0.56–0.90), self-reported local income status as “general” (OR = 0.64, 95%CI = 0.54–0.77), alcohol consumption (OR = 0.78, 95%CI = 0.65–0.92), engaging in exercise (OR = 0.82, 95%CI = 0.71–0.93), and SRH as “good” (OR = 0.45, 95%CI = 0.40–0.51). Among urban older individuals, the risk factors for loneliness symptoms were as follows: being widowed (OR = 3.28, 95%CI = 2.59–4.14) or in “other” marital statuses (OR = 2.72, 95%CI = 1.67–4.45), and self-reported local income status as “rich” (OR = 2.12, 95%CI = 1.43–3.15). Protective factors included age ≥ 100 years (OR = 0.58, 95%CI = 0.37–0.90), engagement in regular exercise (OR = 0.51, 95%CI = 0.42–0.62), sleep durations of 6.0–7.9 h (OR = 0.48, 95%CI = 0.27–0.83), 8.0–9.9 h (OR = 0.39, 95%CI = 0.22–0.69), and ≥ 10 h (OR = 0.29, 95%CI = 0.16–0.52), as well as SRH reported as “good” (OR = 0.42, 95%CI = 0.35–0.51).
Table 3.
Results of logistic model for loneliness symptoms among rural and urban older adults
| Variables | Rural | Urban | ||||
|---|---|---|---|---|---|---|
| OR | 95%CI | P | OR | 95%CI | P | |
| Age (years) | ||||||
| < 70 | Reference | Reference | ||||
| 70–79 | 1.23 | 0.99,1.52 | 0.057 | 1.23 | 0.88,1.71 | 0.234 |
| 80–89 | 1.17 | 0.93.1.46 | 0.175 | 1.15 | 0.81,1.63 | 0.450 |
| 90–99 | 0.95 | 0.95,0.19 | 0.706 | 0.77 | 0.52,1.14 | 0.191 |
| ≥ 100 | 0.91 | 0.70,1.18 | 0.457 | 0.58 | 0.37,0.90 | 0.016 |
| Gender | ||||||
| Female | Reference | Reference | ||||
| Male | 0.73 | 0.64,0.84 | < 0.001 | 0.91 | 0.74,1.13 | 0.391 |
| BMI | ||||||
| 18.5–23.9 | Reference | Reference | ||||
| < 18.5 | 1.21 | 1.04,1.40 | 0.013 | 1.04 | 0.78,1.41 | 0.775 |
| 24.0-27.9 | 1.12 | 0.97,1.23 | 0.128 | 1.04 | 0.84,1.29 | 0.701 |
| ≥ 28.0 | 1.13 | 0.91,1.41 | 0.272 | 0.98 | 0.72,1.33 | 0.905 |
| Education level | ||||||
| 0 | Reference | Reference | ||||
| 1–6 | 0.84 | 0.73,0.96 | 0.010 | 0.83 | 0.65,1.07 | 0.147 |
| ≥ 7 | 0.71 | 0.56,0.90 | 0.005 | 0.87 | 0.67,1.14 | 0.311 |
| Ethnicity | ||||||
| Han nationality | Reference | Reference | ||||
| Other | 1.20 | 0.98,1.47 | 0.083 | 0.84 | 0.45,1.59 | 0.598 |
| Marital status | ||||||
| Married and living with spouse | Reference | Reference | ||||
| Widowed | 3.34 | 2.89,3.86 | < 0.001 | 3.28 | 2.59,4.14 | < 0.001 |
| Other | 2.36 | 1.72,3.22 | < 0.001 | 2.72 | 1.67,4.45 | < 0.001 |
| Living status | ||||||
| Living with household members | Reference | Reference | ||||
| Alone | 0.90 | 0.81,1.01 | 0.076 | 1.11 | 0.69,1.78 | 0.673 |
| Living in a nursing home | 0.72 | 0.49,1.07 | 0.105 | 1.01 | 0.80,1.28 | 0.939 |
| Self-reported local income status | ||||||
| Poor | Reference | Reference | ||||
| General | 0.64 | 0.54,0.77 | < 0.001 | 0.90 | 0.72,1.11 | 0.318 |
| Rich | 1.76 | 1.50,2.06 | < 0.001 | 2.12 | 1.43,3.15 | < 0.001 |
| Smoking | ||||||
| No | Reference | Reference | ||||
| Yes | 1.03 | 0.87,1.21 | 0.758 | 0.96 | 0.70,1.32 | 0.807 |
| Drinking | ||||||
| No | Reference | Reference | ||||
| Yes | 0.78 | 0.65,0.92 | 0.004 | 0.90 | 0.67,1.22 | 0.509 |
| Exercise | ||||||
| No | Reference | Reference | ||||
| Yes | 0.82 | 0.71,0.93 | 0.003 | 0.51 | 0.42,0.62 | < 0.001 |
| Sleep time | ||||||
| < 4.0 | Reference | Reference | ||||
| 4.0-5.9 | 1.32 | 0.99,1.80 | 0.069 | 0.76 | 0.43,1.35 | 0.355 |
| 6.0-7.9 | 1.03 | 0.77,1.38 | 0.846 | 0.48 | 0.27,0.83 | 0.008 |
| 8.0-9.9 | 0.85 | 0.63,1.15 | 0.291 | 0.39 | 0.22,0.69 | 0.001 |
| ≥ 10 | 0.87 | 0.64,1.18 | 0.382 | 0.29 | 0.16,0.52 | < 0.001 |
| Self-reported health | ||||||
| Bad | Reference | Reference | ||||
| Good | 0.45 | 0.40,0.51 | < 0.001 | 0.42 | 0.35,0.51 | < 0.001 |
BMI body mass index
Therefore, the disparities in loneliness symptoms between rural and urban older adults manifested in three aspects: first, a low BMI (OR = 1.21, 95%CI = 1.04–1.40) emerged as a risk factor exclusively among rural older adults; second, several factors acted as protective factors solely in rural areas: being male (OR = 0.73, 95%CI = 0.64–0.84), having 1–6 years of education (OR = 0.84, 95%CI = 0.73–0.96) or ≥ 7 years of education (OR = 0.71, 95%CI = 0.56–0.90), and alcohol consumption (OR = 0.78, 95%CI = 0.65–0.92); third, protective factors unique to urban older adults included being ≥ 100 years of age (OR = 0.58, 95%CI = 0.37–0.90) and sleep durations of 6.0–7.9 h (OR = 0.48, 95%CI = 0.27–0.83), 8.0–9.9 h (OR = 0.39, 95%CI = 0.22–0.69), and ≥ 10 h (OR = 0.29, 95%CI = 0.16–0.52).
Decomposition analysis
Table 4 outlines the insights from the decomposition model regarding disparities in loneliness between rural and urban older adults. To confirm the stability of the results, the decomposition model was run 100 times using the software: 95.38% of the variations in loneliness symptoms stemmed from observable factors, whereas the remaining 4.62% were associated with urban–rural gaps or unobservable factors. Among the observable factors, several specific ones significantly contributed to explaining the differences in loneliness (p < 0.05): gender (-2.18%), educational level (4.86%), marital status (-4.57%), living status (-12.39%), self-reported local income status (4.03%), drinking (-3.15%), exercise (6.92%), sleep time (5.63%), and SRH (16.36%). Additionally, a sensitivity analysis was conducted after addressing the missing values in this section, and the findings were presented in Table 1 of the supplementary materials. The results were nearly consistent with those in Table 4 (only gender showed a change), confirming that our research findings are relatively robust.
Table 4.
Fairlie decomposition of loneliness disparity between rural and urban older populations
| Terms of decomposition | DS | ||||
|---|---|---|---|---|---|
| Difference | 0.03647073 | ||||
| Explained (%) | 0.03478472 (95.38) | ||||
| Non-explained (%) | 0.0016860(4.62) | ||||
| Explained | |||||
| Contribution to difference | p | β | Contribution (%) | (95%CI) | |
| Age | 0.983 | -0.0000026 | -0.02 | -0.0002471, 0.0002419 | |
| Gender | 0.029 | -0.0014942 | -2.18 | -0.0028369, -0.0001516 | |
| BMI | 0.207 | -0.0010637 | -1.26 | -0.0027162, 0.0005888 | |
| Education level | < 0.001 | 0.0189786 | 4.86 | 0.0113234, 0.0266339 | |
| Ethnicity | 0.066 | 0.0016157 | 1.84 | -0.0001082, 0.0033397 | |
| Marital status | < 0.001 | -0.0018861 | -4.57 | -0.0026942, -0.001078 | |
| Living status | < 0.001 | -0.0106363 | -12.39 | -0.0123195, -0.0089532 | |
| Self-reported local income status | < 0.001 | 0.0009232 | 4.03 | 0.0004744, 0.001372 | |
| Smoking | 0.602 | 0.0003326 | 0.52 | -0.0009178,0.001583 | |
| Drinking | 0.002 | -0.0005756 | -3.15 | -0.0009334,-0.0002179 | |
| Exercise | < 0.001 | 0.0119362 | 6.92 | 0.0085554,0.015317 | |
| Sleep time | < 0.001 | 0.0016147 | 5.63 | 0.0010525,0.0021768 | |
| SRH | < 0.001 | 0.0149784 | 16.36 | 0.0131843,0.0167725 | |
BMI body mass index, SRH self-reported health
Discussion
This study provided a comprehensive analysis of variations in loneliness rates among older adults in urban and rural areas of China. It examined the relationships between loneliness and specific factors, such as demographic characteristics, sociological characteristics, personal lifestyle, and health status, while quantifying the extent to which these factors contribute to the existing disparities in loneliness levels between rural and urban older adults. To the best of our knowledge, this is the first cross-sectional study to investigate urban–rural differences in loneliness and the underlying factors driving these differences. Loneliness is more common among people in rural regions than in urban areas. These disparities are largely associated with factors such as gender, educational level, marital status, living status, self-reported local income status, drinking, exercise, sleep time, and SRH. As such, the current research can lay the groundwork for strengthening efforts to prevent loneliness among China’s older population and to implement targeted policies aimed at bridging the mental health gap between older adults in rural and urban areas.
Notably, the prevalence of loneliness among Chinese older adults was 26.79%, which was a significant increase compared to that reported in a 1992 study (approximately 16%) in China [38] and slightly higher than that in a Canadian study in 2019/2020 (19.2%) and an American survey by the American Association of Retired Persons (AARP) (25%) [39]. In many families with only one child, the main reason for this result is that urbanization has accelerated the disintegration of traditional multi-generational families, and the younger generation is more inclined to form independent nuclear families, leading to an increase in the proportion of older adults living alone. Other reasons include the conflict between the lifestyles of young and older adults, the emergence of the digital divide leading to reduced communication between generations, and the current lack of mature social security and support conditions (particularly community psychological services) [7]. The prevalence of loneliness symptoms among older adults in rural areas (27.77%) was higher than that in urban areas (24.27%), corroborating previous findings [40, 41]. This may be due to the following reasons: first, in the past 40 years, many young adults from rural areas in China have migrated to cities for work, which has led to empty nest older adults in rural areas relying only on phone calls for emotional communication, leading to a lack of substantial companionship and care [42, 43]; second, compared with mature urban communities, grassroots organizations (village committees) in rural areas can provide limited cultural, entertainment, and spiritual care services for older adults [44, 45]; third, compared with rural older adults, urban counterparts have more channels (greater proficiency in modern technologies such as the Internet and better basic social psychological services) and better economic conditions (due to better retirement security) to obtain physical and mental health knowledge and participate in social activities [46]. Moreover, the 2.95% gap in the prevalence of loneliness between older adults in urban and rural areas indicates that the public health sector ought to focus on tackling loneliness among older adults, with special attention to those in rural regions.
Our logistic regression analysis further revealed variations in the covariates associated with loneliness between older adults in urban and rural China. According to the results, marital status (widowed: rural area: OR = 3.34, 95%CI = 2.89–3.86 and urban area: OR = 3.28, 95%CI = 2.59–4.14 and other: rural area: OR = 2.36, 95%CI = 1.72–3.22 and urban area: OR = 2.72, 95%CI = 1.67–4.45) was the main common risk factor for older adults in these two areas, which is mainly due to the fact that spouses are the most important partners in modern family structures [47]. Exercise (yes, rural area: OR = 0.82, 95%CI = 0.71–0.93 and urban area: OR = 0.51, 95%CI = 0.42–0.62) and SRH (good, rural area: OR = 0.45, 95%CI = 0.40–0.51 and urban area: OR = 0.42, 95%CI = 0.35–0.51) were the main protective factors associated with loneliness, consistent with previous reports [48, 49]. An interesting phenomenon was found in China: those who were relatively rich in rural and urban areas were relatively lonely compared to the poor, probably because children from wealthy families have relatively better education and more opportunities to work and study abroad, resulting in older people being alone at home and not seeing their children for extended periods. In rural areas, women and those with lower BMI and educational levels showed a higher incidence of loneliness. This could be because women have a stronger dependence on their children, many of whom work in cities. Furthermore, the division of labor between spouses in rural China is still relatively traditional: women are mainly responsible for family affairs, resulting in less communication with the outside world [50, 51]. Second, low body weight in rural China, which generally indicates poor physical and living conditions, makes older adults more likely feel lonely [52]. Third, older adults in rural areas with higher education levels have more channels (including the Internet) and greater ability to deal with loneliness than those with lower education levels [53]. In addition, drinking in rural areas (OR = 0.78, 95%CI = 0.65–0.92) was a protective factor against loneliness, but not in urban areas. This is because there are few recreational activities in rural areas, and drinking can bring friends together to socialize and effectively reduce loneliness. Numerous recreational activities are available in urban areas. Older adults can engage in activities with friends through various channels. In urban areas, sleep time (6.0–7.9 h : OR = 0.48, 95%CI = 0.27–0.83; 8.0–9.9 h: OR = 0.39, 95%CI = 0.22–0.69; ≥10 h: OR = 0.29, 95%CI = 0.16–0.52) is a very important protective factor. Sleep is crucial for the physical and mental health of older adults. Sleep disturbance marks the loss of a fundamentally restorative behavior, thus affecting metabolic, neural, and hormonal processes [54]. Individuals with insomnia are 10 and 17 times more likely than those without insomnia to experience clinically significant levels of negative emotions such as depression and anxiety, respectively [55, 56].
Notable differences in loneliness symptoms exist between older adults in urban and rural parts of China. The results derived from the Fairlie model indicated that gender (-2.18%), educational level (4.86%), marital status (-4.57%), living status (-12.39%), self-reported local income status (4.03%), drinking (-3.15%), exercise (6.92), sleep time (5.63), and SRH (16.36) were factors linked to these discrepancies. All factors, except for gender, were amenable to intervention. Should these intervenable factors be improved, the disparity in loneliness between older individuals in urban and rural areas could be reduced by approximately 93.2%.
Drawing on these findings, our study offers valuable policy suggestions. First, policymakers should pay more attention to loneliness among rural older adults, especially women and those with little education. Offering sports facilities and equipment can promote healthy behaviors and physical activity while also boosting villagers’ social interactions to ease their feelings of loneliness. Second, as numerous studies—including ours—have verified that poor SRH reflects greater dissatisfaction with health challenges when it comes to maintaining a positive health perspective [57], communities and rural areas can organize regular annual physical check-ups for older adults. This would help identify health issues promptly, alleviate health-related anxiety, and enhance SRH. Third, sleep health among older adults —especially those in urban areas—requires focused attention. By conducting health education sessions, offering psychological counseling, and organizing other relevant activities, older adults can understand the importance of sleep and learn methods to improve their sleep quality. Finally, attention should be paid to the families of older adults who are “widowed” or in “other” marital statuses. Their children should be encouraged to dedicate more time to caring for their families, and more social and cultural events should be organized to alleviate feelings of loneliness.
However, this study is not without limitations. As the assessment of loneliness relied on self-reported responses to a single question about loneliness, its capacity to accurately measure loneliness was less robust than that of a standardized scale. Some key variables (such as social support networks, community participation, religiosity, and conservatism versus liberalism) were not included in this study, which may have impacted on loneliness. Furthermore, given that this is a cross-sectional study, it was not possible to track how changes in factors (e.g., marital status and income) dynamically affect loneliness over time.
Conclusions
Our regression and decomposition analyses revealed variations in the prevalence of loneliness among older adults in urban versus rural areas of China. Older adults residing in rural regions exhibited a greater prevalence of loneliness than their counterparts in urban areas. The differences in loneliness between the two regions were shaped by factors including gender, educational level, marital status, living status, self-reported local income status, drinking, exercise, sleep duration, and SRH. The findings of this study have several implications. By providing new evidence of the disparities in loneliness between urban and rural older adults, this study could facilitate the development and adaptation of mental health prevention and treatment programs for older adults in China. To enhance the quality of life of older adults, Chinese government agencies should formulate targeted and more precise intervention strategies based on the differences in factors related to loneliness across various regions.
Supplementary Information
Acknowledgements
CH and FL conceived the study and were responsible for data collection and analysis. CH, FL, and JS performed the data analysis and drafted the initial manuscript. YD was responsible for data interpretation, and LY contributed to the formal data analysis. RX handled the conceptualization, methodology, and editing of the manuscript. The final version of the manuscript was developed with input from all the authors, who also approved it.
Abbreviations
- CLHLS
Chinese Longitudinal Healthy Longevity Survey
- BMI
Body mass index
- SRH
Self-rated health
- AD
Alzheimer disease
- PD
Parkinson disease
- OR
Odds Ratio
- AARP
American Association of Retired Persons
Authors’ contributions
CH and FL conceived the study and were responsible for data collection and analysis. CH, FL, and JS performed the data analysis and drafted the initial manuscript. YD was responsible for data interpretation, and LY contributed to the formal data analysis. RX handled the conceptualization, methodology, and editing of the manuscript. The final version of the manuscript was developed with input from all the authors, who also approved it.
Funding
This research was funded by the National Social Science Fund of China (2023-SKJJ-C-021), the Postgraduate Research Projects of the People’s Liberation Army (Grant No. JY2023B092), and the Talent Plan of Naval Medical University (Grant No. 2025-Lei Yuan, 2025-Chaoqun Hu). None of the authors received any remuneration for preparing this article.
Data availability
The analyzed datasets are publicly accessible and can be retrieved from the Peking University Open Access Research Database at the following link: 10.18170/DVN/WBO7LK.
Declarations
Ethics approval and consent to participate
All protocols in this study were followed in accordance with the Helsinki Declaration (1989 revision). Ethical approval for this study was obtained from the Research Ethics Committees of Peking University and Duke University (reference no. IRB00001052-13074). Written informed consent was obtained from all participants, and all data used in this study are accessible in the public domain.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Chaoqun Hu, Feng Li and Jinhao Shi contributed equally to this work.
Contributor Information
Lei Yuan, Email: yuanleigz@163.com.
Rui Xiao, Email: xiao_rui0809@163.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The analyzed datasets are publicly accessible and can be retrieved from the Peking University Open Access Research Database at the following link: 10.18170/DVN/WBO7LK.