Demographic and socioeconomic inequalities in sleep quality among Chinese aged 15 years and above: a national population-based study.

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Learn more: PMC Disclaimer | PMC Copyright Notice Lancet Reg Health West Pac . 2026 Apr 9;69:101853. doi: 10.1016/j.lanwpc.2026.101853 Search in PMC Search in PubMed View in NLM Catalog Add to search Demographic and socioeconomic inequalities in sleep quality among Chinese aged 15 years and above: a national population-based study Yingchen Sang Yingchen Sang a Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China Find articles by Yingchen Sang a, g , Zhiping Peng Zhiping Peng b NO 984 Hospital of PLA, Beijing, China Find articles by Zhiping Peng b, g , Xinying Zeng Xinying Zeng a Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China Find articles by Xinying Zeng a, g , Ying Liu Ying Liu a Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China Find articles by Ying Liu a , Youjiao Wang Youjiao Wang a Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China Find articles by Youjiao Wang a , Xin Gao Xin Gao a Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China Find articles by Xin Gao a , Chi Zhang Chi Zhang a Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China c Beijing Hospital, National Center of Gerontology, Beijing, China Find articles by Chi Zhang a, c , Ding Zou Ding Zou d Department of Internal Medicine and Clinical Nutrition, Center for Sleep and Vigilance Disorders, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden Find articles by Ding Zou d , Zhili Huang Zhili Huang e Department of Pharmacology, School of Basic Medical Sciences, State Key Laboratory of Medical Neurobiology, Institutes of Brain Science and Collaborative Innovation Center for Brain Science, Joint International Research Laboratory of Sleep, and Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China Find articles by Zhili Huang e, ∗ , Fang Han Fang Han f Department of Sleep Medicine, Peking University People’s Hospital, Beijing, China Find articles by Fang Han f, ∗∗ , Shiwei Liu Shiwei Liu a Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China Find articles by Shiwei Liu a, ∗∗∗ Author information Article notes Copyright and License information a Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China b NO 984 Hospital of PLA, Beijing, China c Beijing Hospital, National Center of Gerontology, Beijing, China d Department of Internal Medicine and Clinical Nutrition, Center for Sleep and Vigilance Disorders, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden e Department of Pharmacology, School of Basic Medical Sciences, State Key Laboratory of Medical Neurobiology, Institutes of Brain Science and Collaborative Innovation Center for Brain Science, Joint International Research Laboratory of Sleep, and Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China f Department of Sleep Medicine, Peking University People’s Hospital, Beijing, China ∗ Corresponding author. [email protected] ∗∗ Corresponding author. [email protected] ∗∗∗ Corresponding author. [email protected] g These authors contribute equally as the co-first author. Received 2026 Jan 16; Revised 2026 Mar 17; Accepted 2026 Mar 23; Collection date 2026 Apr. © 2026 The Authors This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). PMC Copyright notice PMCID: PMC13091300  PMID: 42004585 Summary Background High-quality sleep is essential for health, yet sleep patterns and their prevalence within the Chinese population remain inadequately characterized. We aimed to estimate the prevalence of poor sleep quality and various sleep characteristics within China, and to explore the social gradients and inequalities in sleep quality to identify vulnerable populations. Methods For this nationally representative cross-sectional study, we recruited 46,452 community-based Chinese individuals aged 15 years or older from 420 communities (villages) using a multistage, stratified, cluster-randomized sampling design that considered geographic region, sex and age distribution. Face-to-face questionnaire interviews were conducted to collect self-reported data on demographic characteristics, socioeconomic status, Pittsburgh Sleep Quality Index (PSQI) responses, lifestyle factors, and current chronic disease status. We calculated the prevalence of poor sleep quality (PSQI > 5) and various sleep parameters, as well as analyzed their distribution across various demographic and socioeconomic characteristics. Multivariable logistic regression models were applied to identify factors associated with poor sleep quality in the national sample and by age stratum. Findings Among Chinese individuals aged 15 years and above, the weighted prevalence was 23.9% (95% CI 21.6–26.2) for poor sleep quality under the PSQI > 5 cutoff threshold, along with prevalence of 53.7% (95% CI 49.8–57.6) for reported sleep disturbances and 1.6% (95% CI 1.3–1.9) for use of hypnotic medication. The weighted mean was 27.5 min (95% CI 26.4–28.5) for sleep latency and 7.2 h (95% CI 7.2–7.3) for sleep duration. Multivariable analysis revealed that the likelihood of poor sleep quality was significantly associated with female sex (odds ratio 1.58 [95% CI 1.37–1.82]), advanced age (45–54 years: 1.56 [1.10–2.23]; 55–64 years: 2.06 [1.43–2.96]; ≥65 years: 2.39 [1.68–3.40]), lower household income (50,000–100,000 Chinese yuan [CNY]: 1.25 [1.07–1.46]; 100,000–200,000 CNY: 1.33 [1.01–1.75]), lower education level (junior high school: 0.84 [0.76–0.92]; high school: 0.78 [0.67–0.91]; university: 0.65 [0.54–0.77]), and occupation (government institution employee: 1.89 [1.31–2.73]; unemployed: 1.22 [1.01–1.46]). Women demonstrated significantly higher weighted prevalence of poor sleep quality compared to men. Beginning at age 25, the sex gap of poor sleep quality prevalence progressively widened from 5.2% (95% CI 0.9–9.6) in the 25–34 years age group to 14.3% (95% CI 10.2–18.4) among those aged 65 and older, with escalating statistical significance (P = 0.02 to P < 0.001). No statistically significant associations were found between residence location and the prevalence of poor sleep quality (P > 0.05). The factors associated with poor sleep quality were largely similar across age groups, with several important differences. Higher household income was associated with poorer sleep quality only among adults under 45 years of age (10,000–30,000CNY: 1.50 [1.01–2.20]; 30,000–50,000CNY: 1.40 [1.02–1.91]; 50,000–100,000 CNY: 1.83 [1.38–2.42]; 100,000–200,000 CNY: 2.07 [1.35–3.17]). Government employees under 60 years had higher odds of poor sleep quality (<45 years: 2.45 [1.52–3.94]; 45–60 years: 1.94 [1.21–3.10]), whereas medical personnel aged 60 and above had significantly lower odds (0.14 [0.04–0.56]). Geographically, residents in central China had significantly lower odds of poor sleep quality than those in eastern regions among adults aged 45–60 years (0.83 [0.70–0.99]). Interpretation Poor sleep quality demonstrates remarkably high prevalence across China, with demographic and socioeconomic factors serving as key determinants of sleep health disparities. These findings underscore the urgent need for comprehensive, targeted interventions that address the underlying social and economic drivers of sleep inequality to enhance population sleep health nationwide. Funding Chinese Sleep Research Society, the General Program of the National Natural Science Foundation of China. Keywords: Sleep, Sleep quality, Chinese people, Socioeconomic disparities in health Research in context. Evidence before this study We searched PubMed, the China National Knowledge Internet, and official Chinese Government websites for reports on sleep quality in China published through October 31, 2025, with language restrictions to English and Chinese. A meta-analysis spanning 1996–2011 reported that poor sleep quality prevalence, defined as a Pittsburgh Sleep Quality Index (PSQI) score >7, was 15.1% (95% CI: 11.4%–19.6%) in the general Chinese population. During the COVID-19 pandemic, estimated prevalence of sleep disturbances ranged widely from 4% to 56%. These estimates exhibited substantial heterogeneity, likely reflecting variations in assessment tools and diagnostic criteria. Previous studies were frequently limited to specific regions or specific populations (e.g., the elderly and patients with chronic diseases) and employed diverse sampling methodologies, making accurate national estimates of poor sleep quality difficult to derive. Moreover, although sleep quality has been extensively studied in China, most research has focused on regional populations with small sample sizes and examined specific correlates such as air pollution, internet use, or mental health. To date, no studies have provided detailed characterization of sleep quality patterns across demographic and socioeconomic subgroups within China. Added value of this study To our knowledge, this represents the first study to provide nationally representative estimates of poor sleep quality and various sleep parameters among individuals aged 15 years and above in China, utilizing a standardized and validated instrument (PSQI). Our findings indicated a weighted mean sleep latency of 27.5 min, a mean sleep duration of 7.2 h. The overall prevalence of poor sleep quality was 23.9%, corresponding to approximately 276 million individuals affected by compromised sleep. We comprehensively assessed sleep quality patterns across China according to demographic and socioeconomic characteristics. Our analysis revealed substantial sex disparities in sleep quality that became particularly pronounced after age 25, while higher socioeconomic status appeared to mitigate this gap. We identified factors associated with an increased likelihood of poor sleep quality, including being female, older age, having a lower education level, working in government institution and being unemployed. Additionally, the association of household income with sleep quality varied by age group. Besides, living in Central China (versus East China) was linked to better sleep quality among the 45–60 years group, while a similar tendency observed in those aged 60 and above. Implications of all the available evidence Our study confirms a high prevalence of poor sleep quality in China, shaped by complex interactions among demographic and socioeconomic factors. Evidence from our findings reveals a pronounced and widening sex gap from young adulthood. Additionally, higher socioeconomic status appears to moderate the sex gap in sleep quality, while the role of household income varies significantly across the life course, highlighting the need for life-course approaches to sleep health interventions. These findings demonstrate that sleep health is inequitably distributed and should be addressed through integrated public health strategies that account for socioeconomic context and target vulnerable subgroups, particularly women and middle-aged to older adults. Introduction Sleep, a fundamental biological state that occupies approximately one-third of human life, plays a critical role in optimizing cardiometabolic, immune, neurological, and mental health. 1 Globally, sleep quality exhibits significant disparities across demographic and socioeconomic dimensions, including age, sex and economic status, rather than being uniformly distributed. For instance, women demonstrate higher rates of insufficient sleep duration, particularly during middle age and among higher socioeconomic groups. 2 In contrast, men often experience altered sleep patterns in later life, with these changes influenced by factors such as education level and working hours. 3 Additionally, individuals with lower education levels frequently experience poor sleep quality, typically due to work-related stress and limited resources. 3 , 4 From a health psychology perspective, this association can be understood through frameworks such as the stress process model and cognitive hyperarousal theory. Socioeconomic disadvantage may increase exposure to chronic stressors, deplete coping resources, and lead to sustained emotional and cognitive activation (e.g., worry, rumination), which in turn dysregulate physiological systems (e.g., HPA axis) and disrupt sleep-wake cycles. 5 , 6 , 7 In China, however, sleep research has predominantly treated sleep as an independent variable, examining its associations with cardiovascular disease, cognitive decline, and chronic conditions, 8 , 9 , 10 rather than investigating it as an outcome shaped by social and structural factors. Previous studies reported that the estimated prevalence of poor sleep quality, defined as a Pittsburgh Sleep Quality Index (PSQI) score >7, varied from 4.5% to 29.4%. 11 During the COVID-19 pandemic, reported prevalence of sleep disturbances ranged widely from 4% to 56%, 12 reflecting methodological inconsistencies and lack of standardization. Moreover, the interaction between demographic and socioeconomic factors remains poorly understood within the Chinese context. While some studies suggest sleep quality declines with age, particularly among rural older women, 13 others report that aging does not markedly worsen sleep in Chinese older adults. 14 These inconsistent findings stem partly from a fragmented research approach that focuses on narrow age groups rather than adopting a lifespan perspective. Additionally, few studies have systematically assessed how socioeconomic status, such as income, education and occupation, shapes sleep outcomes. Much of the existing evidence in China has been limited by regional samples, non-standardized measurement tools (such as single-item self-reports or abbreviated PSQI versions), or focus on subpopulations. Crucially, a notable gap exists in large-scale, nationally representative data on sleep quality across the Chinese population. Therefore, using a large, nationally representative sample, this study comprehensively examined the demographic and socioeconomic disparities in sleep quality among the Chinese population aged 15 years and older, with the aim of informing targeted public health strategies to address these inequities. Methods Study design and participants We employed a multistage stratified cluster-sampling design within the national cross-sectional survey “Study on the effect of smoking on sleep disorders” to select participants representative of the civilian, noninstitutionalized Chinese population. Only individuals who had resided in their current location (excluding student dormitories, military barracks, prisons, or hospitals) for at least one month were eligible for participation. In the first stage, we selected seven provinces (municipalities or autonomous regions) representing China’s seven major geographical regions: 1) north, Hebei; 2) northeast, Liaoning; 3) east, Jiangsu; 4) central, Henan; 5) south, Guangdong; 6) southwest, Chongqing; 7) northwest, Shaanxi. In the second stage, we used probability proportional to size (PPS) sampling to select county-level administrative divisions within each province, serving as primary sampling units (PSUs) and stratified by urban-rural classification. A total of 70 PSUs were selected (10 PSUs from each province). In the third stage, three districts in urban areas or townships in rural areas were chosen from each PSU using PPS. Subsequently, we selected two communities or villages from each district or township. In the fourth stage, households within each community or village were enumerated, and 120 households were randomly selected. In the fifth stage, one individual aged at least 15 years was randomly chosen from each household. A total of 50,400 people were selected, and 46,452 participated in the survey, yielding an overall response rate of 92.2% ( Fig. 1 ). The institutional review board of China CDC approved the study protocol (approval number 202410). Verbal informed consent ( Supplementary Appendix pp 2–5) was obtained electronically after explaining study procedures, with participants indicating agreement by proceeding to the survey questions. This consent process was completed approximately 5 min before survey initiation. Fig. 1. Open in a new tab Study profile. PSU = primary sampling unit. Procedures China CDC designed the comprehensive survey protocol and developed a secure, encrypted information collection and management platform that seamlessly integrated sampling procedures, data collection protocols, questionnaire management systems, quality control measures, and real-time progress monitoring capabilities. To ensure rigorous field investigation standards, China CDC organized intensive surveillance training programs for provincial survey personnel, followed by formal competency evaluations to assess their proficiency. Provincial staff who successfully demonstrated qualification were then authorized to conduct analogous training sessions and assessments for investigators at subordinate administrative levels. All field investigators were required to complete standardized training programs and demonstrate competency through comprehensive testing protocols before participating in data collection activities. Trained local CDC staff provided the key sampling information that formed the sampling frames, did face-to-face interviews using portable application devices, recorded the interview process for data quality control, uploaded data to the encrypted information platform. For households or individuals who were unreachable or declined to participate, investigators made up to four contact attempts on different days and at varying times. Only after all attempts failed was the case recorded as a non-response. Non-response adjustment weights were subsequently applied in the final analysis to adjust for potential bias. All interviews were audio-recorded, and a photograph was taken at the end of each session. Supervisors accompanied investigators in the field to verify that the correct households and individuals were surveyed. Each day, supervisors reviewed quality control outcomes via the platform and provided timely feedback to investigators. At the provincial level, supervisors monitored all submitted data and conducted independent re-interviews for 5% of the completed questionnaires using a verification instrument. The resulting database was cross-checked against original submissions. Nationally, all questionnaires underwent a final review. If the verification pass rate for any investigator or monitoring site fell below 70%, a recommendation was made to re-conduct the surveys. Survey funding and expenditure details are provided in the Supplementary Appendix E . To comprehensively evaluate sleep health, we administered the PSQI to assess participants’ sleep patterns. This validated self-reported questionnaire comprises 19 items organized into seven component scores: subjective sleep quality, sleep latency, sleep duration, sleep disturbances, use of hypnotic medication, and daytime dysfunction. Each component receives scores between 0 and 3, with overall PSQI scores ranging from 0 to 21. Higher scores indicate poorer sleep quality and increased sleep disorders. A global score >5 indicates considerably poor sleep quality, while ≤5 indicates good sleep quality. 15 , 16 We selected four key indicators to describe sleep patterns: sleep latency, sleep duration, prevalence of sleep disturbances and prevalence of hypnotic medication use. Sleep latency determined from responses to “How long has it usually taken you to fall asleep each night?”. Sleep duration was derived from “During the past month, how many hours of actual sleep did you get at night?”. Participants who reported one or more sleep-related problems in response to “During the past month, how often have you been troubled by sleep-related issues?” were classified as experiencing sleep disturbances. Hypnotic medication use was defined as a self-reported “yes” to the survey question: “During the past month, have you taken any sleep medication?” Urban residency was defined as living in urban subdistricts, while rural residency as living in rural townships. National region was categorized into three major economic belts: 1) East China, Hebei, Liaoning, Jiangsu, Guangdong, 2) Central China, Henan, 3) West China, Shaanxi, Chongqing, based on comprehensive differences in development levels, resource endowment and geographical conditions. Education level was categorized as primary school or below, junior high school, high school, university/college or above, and unknown. Occupational categories included agricultural workers, government institution staff, business/service employees, teachers, medical personnel, students, unemployed individuals, retirees, others, and unknown. Annual household income was categorized based on quartiles of household income in 2023. Alcohol consumption was defined as drinking alcohol at least once per week within the past year. 17 Physical exercise frequency was categorized as none, 1–2, 3–4, or ≥5 times per week. Smoking status was classified into three mutually exclusive categories: never, current, and former smoker. Following National Health Commission of China regulations, body mass index (BMI) classifications were: underweight (<18.5 kg/m 2 ), normal (18.5–23.9 kg/m 2 ), overweight (24–28 kg/m 2 ), and obese (≥28 kg/m 2 ). Participants were asked, “Have you ever been told by a doctor or other healthcare professional that you had cardiovascular disease (malignant tumor/chronic obstructive pulmonary disease [COPD]/hypertension/diabetes)?” Positive responses identified diagnosed conditions, although we expected a substantial proportion of individuals with these diseases to remain undiagnosed. Depression was defined using the Patient Health Questionnaire-9 (PHQ-9), 18 with a score of ≥5 indicating depressive symptoms. Similarly, anxiety was defined using the Generalized Anxiety Disorder-7 (GAD-7), with a score of ≥5 indicating anxiety symptoms. 19 Statistical analysis All calculations were weighted to represent the overall Chinese population aged 15 years and older, according to the 2020 population census ( Supplementary Appendix C ). Weight coefficients, incorporating base weights, non-response adjustment weight, and post-stratification weight (aligned with national population distributions for sex, age, and urban-rural residence), were derived from the census and the study sample to obtain national estimates. Based on the complex sampling weight, stratification, and clusters, we calculated weighted proportions of the study sample, prevalence of poor sleep quality and other sleep parameters. The prevalence of poor sleep quality, various sleep parameters, PSQI total scores, and component scores were estimated for the total population and subgroups classified by sex, age, residence location, national region, educational level, occupation, and annual household income. We also examined sex differences in poor sleep quality across these subgroups. Variances were calculated using the Taylor series linearization method appropriate for the complex survey design. The Rao-Scott χ 2 test and t test (or one-way ANOVA) were applied to compare prevalence rates and sleep parameters in each subgroup, respectively. We also used the Cochran–Armitage tests to explore age-specific, annual household income-specific and education-specific trends in the prevalence of poor sleep quality and reported sleep disturbances. Polynomial contrast analysis within a one-way ANOVA model was also used to test for linear trends in sleep latency and sleep duration across ordered age groups, annual household income and educational levels. Multivariable modeling of complex survey data was used to explore associations between various demographic, socioeconomic characteristics and poor sleep quality (PSQI > 5). The multivariate model was first performed on the overall population, and then repeated separately for subgroups stratified by age: under 45 years, 45–60 years, and 60 years and over. All models were adjusted for sex, age, residence location, national region, education level, occupation, alcohol consumption, physical exercise frequency, smoking status, BMI, cardiovascular disease, malignant tumor, chronic obstructive pulmonary disease (COPD), hypertension, diabetes, depression and anxiety. Multicollinearity was assessed for all four models, and the results are presented in Supplementary Appendix p 49. To examine the potential interdependence of key social determinants, we conducted tests for two-way interaction effects within our multivariable framework. Based on substantive hypotheses, we evaluated interactions between: (a) national region and annual household income, (b) national region and occupation, (c) education level and occupation, and (d) education level and household income. To assess the robustness of our findings, we performed a sensitivity analysis using a threshold of PSQI > 7, which is more commonly used in Chinese population studies. Furthermore, considering the potential influence of hypnotic medication use on sleep quality assessment, for each PSQI cutoff (>5 and >7), we analyzed both the national sample and a subgroup excluding hypnotic medication users. We then compared the demographic and socioeconomic distribution of poor sleep quality between these two definitions in the respective populations ( Supplementary Appendix p 36–44). We did not impute missing data. All P values were two-tailed, and 0.05 was the threshold for statistical significance. All analyses were performed in SAS (version 9.4). Role of the funding source The study funders played no role in study design, data collection, data analysis, data interpretation, or manuscript preparation. The corresponding author maintained full access to all study data and held final responsibility for the publication decision. Results Between March 2024 and October 2024, we invited 46,452 participants to join the survey. We excluded 245 participants who either failed to answer sleep-related questions or provided questionable responses; thus, 46,207 individuals were included in the final analysis ( Fig. 1 ). Table 1 presents the characteristics of the study population. The mean age was 46.8 years (95% CI 44.9–48.7), with 49.4% (95% CI 48.5–50.2) being women. Table 1. Characteristics of poor sleep quality (PSQI > 5) individuals aged 15 years and older in China. Unweighted frequency of bad sleep quality among survey participants Weighted proportion a Weighted prevalence a χ 2 b P value b Total population 13,865/46,207 96,590,731/404,586,371 23.9% (21.6–26.2) Demographic characteristics Sex 131.72 <0.01 Male 5816/22,334 50.6% (49.8–51.5) 20.4% (18.3–22.5) Female 8049/23,873 49.4% (48.5–50.2) 27.4% (24.8–30.0) Age, years 416.19 <0.01 15–24 175/1625 12.9% (11.3–14.6) 11.3% (9.0–13.5) 25–34 515/3710 13.7% (11.6–15.8) 15.8% (13.2–18.4) 35–44 1071/6558 22.7% (20.2–25.2) 17.0% (14.5–19.5) 45–54 2100/8359 15.5% (14.6–16.3) 23.6% (21.5–25.7) 55–64 3870/11,463 18.9% (16.8–21.1) 32.1% (29.9–34.3) ≥65 6134/14,492 16.3% (13.5–19.0) 41.0% (38.2–43.8) Residence location 6.41 0.01 Urban 7043/24,912 45.8% (35.3–56.2) 21.1% (18.1–24.1) Rural 6822/21,295 54.2% (43.8–64.7) 26.2% (23.6–28.8) Socioeconomic status National region 0.05 0.95 East China 7551/26,428 66.6% (58.4–74.8) 23.9% (20.8–27.1) Central China 1867/6677 18.8% (12.3–25.3) 23.3% (19.3–27.4) West China 4547/13,102 14.6% (9.5–19.7) 24.3% (20.0–28.5) Education level 163.74 <0.01 Primary school or below 6342/15,531 21.9% (18.0–25.9) 37.8% (35.2–40.4) Junior high school 4722/16,678 34.5% (31.0–37.9) 23.6% (20.8–26.3) High school/Vocational school 1642/6876 18.8% (16.8–20.8) 19.1% (17.3–21.0) University/College or above 1137/7024 24.5% (19.3–29.9) 15.6% (13.0–18.2) Unknown 22/98 0.3% (0.2–0.4) 14.8% (3.9–25.7) Occupation 110.17 <0.01 Agriculture worker 5627/16,513 23.2% (17.8–28.5) 29.0% (25.9–32.0) Government institution staff 197/914 2.8% (2.2–3.5) 25.3% (20.5–30.1) Business/service employee 1557/7558 23.6% (18.6–28.6) 17.9% (15.2–20.6) Teacher 106/582 2.1% (1.6–2.5) 17.2% (12.4–22.0) Medical personnel 78/553 1.7% (1.4–2.0) 14.1% (9.5–18.7) Students 97/1013 7.9% (6.9–8.9) 8.9% (6.7–11.1) Unemployed 2062/5656 11.3% (9.5–13.1) 30.9% (27.0–34.7) Retired 2014/5336 7.7% (5.9–9.4) 36.1% (32.3–40.0) Others 2050/7737 19.0% (16.9–21.1) 24.1% (21.1–27.0) Unknown 77/345 0.9% (0.5–1.3) 13.2% (7.8–18.6) Annual household income 14.63 <0.01 <10,000 CNY 4627/12,468 17.5% (14.2–20.8) 30.7% (27.8–33.6) 10,000–30,000 CNY 3388/11,357 20.0% (17.0–23.1) 25.0% (22.9–27.1) 30,000–50,000 CNY 1866/7018 15.4% (13.4–17.4) 22.4% (20.3–24.5) 50,000–100,000 CNY 1447/5679 16.1% (14.2–17.9) 22.2% (19.7–24.7) 100,000–200,000 CNY 555/2312 9.5% (6.5–12.5) 22.0% (16.0–28.1) ≥200,000 CNY 159/731 3.2% (1.8–4.6) 18.8% (9.3–28.3) Refused to answer 1823/6642 18.4% (14.4–22.3) 20.7% (17.8–23.6) Health status Alcohol consumption 26.78 <0.0001 No 12,007/40,564 88.5% (87.3–89.6) 23.2% (20.8–25.6) Yes 1858/5643 11.5% (10.4–12.7) 29.0% (26.7–31.3) Smoking status 19.64 <0.0001 Never smoker 9464/31,763 71.2% (68.9–73.6) 23.3% (20.7–25.9) Current smoker 2917/10,358 22.3% (20.5–24.1) 23.7% (21.8–25.7) Former smoker 1481/4081 6.5% (5.7–7.2) 30.9% (27.7–34.0) Physical exercise frequency 79.27 <0.0001 None 6611/21,242 44.7% (42.4–47.0) 26.0% (23.4–28.6) 1–2 times per week 1144/4889 14.7% (12.3–17.1) 18.3% (16.0–20.6) 3–4 times per week 1516/5859 14.7% (13.7–15.7) 19.2% (16.4–22.0) ≥5 times per week 4594/14,217 25.9% (24.0–27.9) 26.1% (24.0–28.2) BMI <18.5 kg/m2 740/2220 6.2% (5.6–6.7) 21.0% (17.2–24.8) 6.97 0.01 18.5–22.9 kg/m2 4658/16,171 38.6% (36.8–40.5) 22.2% (19.8–24.5) 23–24.9 kg/m2 2815/10,054 22.1% (21.3–22.9) 24.0% (20.8–27.3) ≥25 kg/m2 4280/14,581 33.1% (31.0–35.3) 24.5% (22.8–26.1) Cardiovascular disease 88.93 <0.0001 No 3306/6164 91.8% (90.1–93.5) 21.5% (19.3–23.6) Yes 10,522/39,962 8.1% (6.4–9.8) 51.1% (48.6–53.6) Refused to answer 37/81 0.1% (0.1–0.2) 38.7% (23.9–53.4) Malignant tumor 43.64 <0.0001 No 13,438/45,376 98.7% (98.5–99.0) 23.6% (21.3–25.9) Yes 391/756 1.1% (0.9–1.4) 47.3% (43.1–51.5) Refused to answer 36/75 0.2% (0.1–0.3) 31.3% (9.4–53.2) COPD 39.37 <0.0001 No 13,295/45,277 98.9% (98.6–99.1) 23.5% (21.3–25.8) Yes 535/859 1.0% (0.8–1.3) 54.8% (47.3–62.4) Refused to answer 35/71 0.1% (0.1–0.2) 45.9% (29.5–62.3) Hypertension 205.06 <0.0001 No 8465/34,001 82.2% (79.7–84.6) 20.3% (18.1–22.4) Yes 5382/12,159 17.7% (15.3–20.2) 40.4% (38.0–42.9) Refused to answer 18/47 0.1% (0.1–0.2) 41.1% (28.0–54.2) Diabetes 124.01 <0.0001 No 12,013/42,315 94.2% (93.4–95.1) 22.7% (20.5–24.9) Yes 1830/3836 5.7% (4.8–6.5) 42.8% (39.3–46.3) Refused to answer 22/56 0.1% (0.1–0.2) 30.4% (16.50–44.4) Anxiety 3973.88 <0.0001 No 11,431/43,093 94.7% (93.9–95.5) 21.1% (19.1–23.2) Yes 2428/3099 5.3% (4.5–6.2) 72.7% (68.8–76.5) Depression 15,159.38 <0.0001 No 9759/41,091 91.2% (89.9–92.5) 19.0% (17.2–20.8) Yes 4102/5107 8.8% (7.5–10.1) 74.2% (70.6–77.7) Open in a new tab Data are n/N (%), where n = participants with poor sleep quality and N = total population; or % (95% CI). BMI = body mass index. PSQI = Pittsburgh Sleep Quality Index. a Population of the research was standardized with the 2020 census standard population estimation obtained from the National Bureau of Statistics of China. b From RaoScott χ 2 test. The sensitivity analysis comparing the characteristics of participants with poor sleep quality, defined using the two PSQI cutoffs of >5 and >7, is shown in Supplementary Appendix (Table S14). 5 values were missing on smoking status, 3181 values were missing on BMI, 15 values were missing on anxiety, 9 values were missing on depression. The estimated weighted prevalence of poor sleep quality among Chinese individuals aged 15 years and above was 23.9% (95% CI 21.6–26.2). In sensitivity analysis using a stricter cutoff of >7, the prevalence was 13.9% (95% CI 12.2–15.5) ( Supplementary Appendix pp 39–41). In the main analysis (PSQI > 5), women demonstrated a significantly higher weighted prevalence of poor sleep quality at 27.4% (95% CI 24.8–30.0) compared to men at 20.4% (95% CI 18.3–22.5). The weighted prevalence exhibited a clear increasing trend with age (P for trend < 0.0001), rising from 11.3% (95% CI 9.0–13.5) among individuals aged 15–24 years to 41.0% (95% CI 38.2–43.8) among those aged 65 years and older ( Table 1 ; Supplementary Appendix p 6). Additionally, we observed a significant inverse relationship between education level and weighted prevalence of poor sleep quality, declining from 37.8% (95% CI 35.2–40.4) among those with primary school education or below to 15.6% (95% CI 13.0–18.2) among those with university-level education or above ( Table 1 ; Supplementary Appendix p 6). The weighted prevalence also decreased with higher annual household income, from 30.7% (95% CI 27.8–33.6) among people with annual household income <10,000 Chinese yuan (CNY) to 18.8% (95% CI 9.3–28.3) among those with annual household income exceeding 200,000 CNY ( Table 1 ; Supplementary Appendix p 6). Furthermore, the weighted prevalence of poor sleep quality was highest among people residing in rural areas (26.2% [23.6–28.8]) and retirees (36.1% [32.3–40.0]). However, no significant differences in the prevalence of poor sleep quality were observed across national regions, as shown in Table 1 . Sensitivity analysis using a PSQI cutoff of >7 showed that the distribution patterns across different subgroups remained consistent with those reported using a cutoff of >5 ( Supplementary Appendix pp 36–41). The weighted mean was 27.5 min (95% CI 26.4–28.5) for sleep latency and 7.2 h (95% CI 7.2–7.3) for sleep duration. Furthermore, the weighted prevalence of reported sleep disturbances and use of hypnotic medication were found to be 53.7% (95% CI 49.8–57.6) and 1.6% (95% CI 1.3–1.9), respectively. An adverse sleep profile, characterized by prolonged sleep latency, short sleep duration, high prevalence of self-reported sleep disturbances and increased use of hypnotic medication was consistently associated with being female, older age, rural residence, lower socioeconomic status (indicated by income and education), and being unemployed, retired, or an agricultural worker ( Table 2 ). Table 2. Demographic and socioeconomic characteristics of different sleep parameters among Chinese aged 15 years and above. Sleep latency Sleep duration Sleep disturbance Use of hypnotic medication Mean (95% CI) P value a Mean (95% CI) P value a Weighted prevalence (95% CI) P value b Weighted prevalence (95% CI) P value b Total population 27.5 (26.4–28.5) 7.2 (7.2–7.3) 53.7 (49.8–57.6) 1.6 (1.3–1.9) Demographic characteristics Sex <0.0001 0.03 <0.0001 <0.0001 Male 25.1 (24.1–26.0) 7.3 (7.2–7.3) 50.9 (46.5–55.3) 1.1 (0.8–1.3) Female 29.9 (28.5–31.3) 7.2 (7.1–7.3) 56.6 (53.0–60.2) 2.2 (1.8–2.6) Age, years <0.0001 <0.0001 <0.0001 <0.0001 15–24 25.0 (22.8–27.2) 8.2 (8.1–8.3) 37.5 (34.1–40.8) 0.3 (0.0–0.7) 25–34 25.1 (23.4–26.7) 7.6 (7.5–7.7) 43.1 (38.4–47.8) 0.7 (0.3–1.1) 35–44 24.5 (23.7–25.3) 7.3 (7.3–7.4) 46.7 (41.8–51.5) 0.5 (0.3–0.8) 45–54 25.8 (24.7–26.9) 7.0 (6.9–7.1) 53.7 (50.2–57.2) 1.1 (0.8–1.5) 55–64 29.5 (28.3–30.8) 6.8 (6.7–6.9) 64.8 (61.3–68.3) 2.6 (2.1–3.1) ≥65 34.8 (33.0–36.6) 6.8 (6.7–6.9) 72.4 (69.4–75.4) 4.2 (3.3–5.0) Residence location 0.002 0.16 0.13 0.90 Urban 25.9 (24.6–27.2) 7.3 (7.2–7.4) 50.7 (45.1–56.3) 1.6 (1.1–2.1) Rural 28.8 (27.5–30.0) 7.2 (7.1–7.3) 56.2 (51.6–60.9) 1.6 (1.3–2.0) Socioeconomic status National region 0.02 0.08 0.15 0.67 East China 26.6 (25.2–27.7) 7.2 (7.1–7.3) 55.6 (50.1–61.2) 1.6 (1.3–2.0) Central China 29.6 (28.1–31.2) 7.4 (7.2–7.6) 47.0 (39.0–55.0) 1.7 (1.1–2.4) West China 28.6 (26.5–30.7) 7.3 (7.2–7.5) 53.5 (49.7–57.2) 1.3 (0.8–1.9) Annual household income <0.0001 0.002 0.06 <0.0001 <10,000 CNY 31.4 (29.9–32.9) 7.1 (7.0–7.2) 58.0 (53.9–62.1) 2.6 (2.0–3.3) 10,000–30,000 CNY 27.7 (26.5–28.9) 7.2 (7.1–7.3) 56.4 (53.5–59.4) 1.5 (1.2–1.9) 30,000–50,000 CNY 27.0 (25.2–28.9) 7.2 (7.1–7.3) 53.1 (49.7–56.5) 1.4 (1.0–1.8) 50,000–100,000 CNY 26.4 (24.9–27.9) 7.2 (7.2–7.3) 54.3 (49.5–59.0) 1.6 (1.0–2.2) 100,000–200,000 CNY 24.8 (22.6–27.0) 7.3 (7.1–7.5) 53.3 (43.8–62.8) 1.0 (0.5–1.5) ≥200,000 CNY 25.0 (21.7–28.3) 7.4 (7.1–7.6) 47.3 (35.1–59.6) 2.1 (1.2–3.0) Refused to answer 26.5 (24.7–28.2) 7.4 (7.3–7.5) 47.9 (42.1–53.7) 1.1 (0.7–1.5) Education level <0.0001 <0.0001 <0.0001 <0.0001 Primary school or below 33.8 (32.0–35.5) 6.8 (6.7–6.9) 67.5 (64.3–70.8) 2.8 (2.2–3.3) Junior high school 26.6 (25.5–27.8) 7.2 (7.1–7.3) 53.7 (50.0–57.3) 1.5 (1.1–1.9) High school/Vocational school 25.4 (24.3–26.6) 7.4 (7.3–7.5) 49.0 (44.1–53.9) 1.6 (1.1–2.1) University/College or above 24.6 (23.3–25.8) 7.5 (7.4–7.6) 45.2 (40.3–50.1) 0.7 (0.5–1.0) Unknown 24.0 (20.8–27.1) 8.0 (7.7–8.3) 31.3 (19.4–43.2) 0.5 (0.0–1.2) Occupation <0.0001 <0.0001 <0.0001 <0.0001 Agriculture workers 29.8 (28.3–31.4) 7.0 (6.9–7.1) 57.4 (52.9–61.8) 2.1 (1.6–2.6) Government/public institution staff 25.9 (23.8–28.0) 7.0 (6.8–7.2) 58.9 (51.3–66.6) 0.5 (0.1–0.9) Business/service employee 24.7 (23.6–25.9) 7.3 (7.2–7.4) 48.0 (42.6–53.3) 0.7 (0.4–1.0) Teaching staff 24.2 (22.8–25.6) 7.5 (7.3–7.6) 45.7 (39.5–51.9) 0.4 (0.0–0.7) Medical/health personnel 24.4 (23.0–25.9) 7.4 (7.3–7.5) 35.0 (29.5–40.4) 2.4 (0.0–6.1) Students 23.7 (21.0–26.4) 8.3 (8.2–8.4) 36.5 (31.5–41.4) 0.0 (0.0–0.1) Unemployed 32.5 (30.5–34.5) 7.2 (7.1–7.4) 60.9 (54.7–67.0) 2.0 (1.3–2.6) Retired 29.7 (27.1–32.3) 6.8 (6.7–6.9) 70.6 (65.5–75.8) 5.8 (4.5–7.1) Others 26.7 (25.2–28.3) 7.2 (7.1–7.3) 54.7 (49.7–59.7) 1.1 (0.7–1.5) Unknown 22.1 (19.5–24.6) 7.6 (7.4–7.8) 42.8 (36.1–49.5) 1.4 (0.1–2.7) Open in a new tab Data are % (95% CI) or mean (95% CI). Sleep latency was measured in minutes. Sleep duration was measured in hours. a From survey-weighted ANOVA. b From Rao-Scott χ 2 test. The demographic and socioeconomic patterns of poor sleep quality followed similar distributions across both sexes ( Supplementary Appendix p 9–12). However, beginning at age 25, women demonstrated significantly higher weighted prevalence of poor sleep quality compared to men ( Supplementary Appendix p 9–12, Supplementary Appendix p 34–35). The sex gap of poor sleep quality prevalence progressively widened from 5.2% (95% CI 0.9%–9.6%) at ages 25–34 to 14.3% (95% CI 10.2%–18.4%) for those aged 65 and older, with increasing statistical significance (P = 0.02 to P < 0.001), as shown in Supplementary Appendix p 9–12 and Fig. 2 A. Additionally, among participants with annual family income exceeding 200,000 CNY, the difference in poor sleep quality prevalence between men and women was minimal (2.2% [−5.2% to 9.6%]) and not statistically significant (P = 0.56), as demonstrated in Supplementary Appendix p 9–12 and Fig. 2 B. Furthermore, as the education level increased, the sex disparity in the prevalence of poor sleep quality progressively narrowed, from 11.5% (95% CI 8.4–14.6) with primary school or below education to 3.7% (95% CI 0.7–6.7) with education of university/college or above ( Supplementary Appendix p 9–12, Fig. 2 C). The most pronounced sex disparity in poor sleep quality was observed among retirees (13.1% [8.3–17.8]), followed by government/institution staff (10.0% [0.1–19.9]) and agriculture workers (9.8% [6.9–12.8]). Conversely, no statistically significant sex disparities were identified among teaching staff (4.0% [−6.7 to 14.7]) or medical personnel (1.6% [−11.2 to 14.3]), as illustrated in Supplementary Appendix p 9–12 and Fig. 2 D. Fig. 2. Open in a new tab Sex differences in poor sleep quality by age (A), annual household income (B), education level (C), and occupation (D). Data represent weighted estimates which were standardized with the 2020 population census. Prevalence differences between male and female were presented as the average marginal effect (AME) from regression models controlling age, residence location, national region, annual household income, education level, occupation, alcohol consumption, smoking, physical exercise frequency, BMI, cardiovascular disease, malignant tumor, chronic obstructive pulmonary disease (COPD), hypertension, diabetes, anxiety and depression. Error bars show 95% CIs. P values are shown in Supplementary Appendix ( Table 3 ), with statistical significance indicated as ∗P < 0.05, ∗∗P < 0.01, and ∗∗∗P < 0.001. The distribution of PSQI subdimensions across demographic and socioeconomic characteristics generally aligned with that of poor sleep quality prevalence ( Supplementary Appendix p 13–33). However, after age 45, women exhibited markedly increased component scores, particularly in sleep duration and sleep disturbances components, which indicates shorter sleep duration and higher prevalence of sleep disturbances ( Supplementary Appendix p 34–35). Among men, scores for sleep duration and sleep disturbances components increased gradually with age, while scores for the remaining components remained relatively stable ( Supplementary Appendix p 34–35). Our multivariable regression analysis of the national sample revealed that poor sleep quality was significantly associated with female sex, advanced age, annual household income, education level and occupation, as shown in Table 3 . Among individuals aged < 45 years, poor sleep quality demonstrated significant associations with annual household income and occupation. For individuals aged 45 years and above, the odds of poor sleep quality increased significantly among women, while no significant association was observed between annual household income and sleep quality. Besides, among individuals aged 45–60 years old, residents in central China had significantly lower odds ratio of poor sleep quality compared to those in the eastern regions. This trend persisted in those aged 60 and older, though with limited statistical power. Additionally, among individuals aged 45–60 years old, poor sleep quality showed significant association with government institution staff, while among individuals aged 60 years and above, poor sleep quality was observed being associated with medical/health personnel. Across all four models, residence location was not significantly associated with the prevalence of poor sleep quality (P-values > 0.05), as detailed in Table 3 . Table 3. Factors associated with poor sleep quality (PSQI > 5) among Chinese adults aged 15 years and above. Model 1: all population Model 2: <45 years old Model 3: 45–60 years old Model 4: ≥60 years old OR (95% CI) P value OR (95% CI) P value OR (95% CI) P value OR (95% CI) P value Sex Male 1 (ref) 1 (ref) 1 (ref) 1 (ref) Female 1.58 (1.37–1.82) <0.0001 1.33 (1.02–1.72) 0.04 1.81 (1.46–2.26) <0.0001 1.82 (1.58–2.11) <0.0001 Age, years 15–24 1 (ref) 1 (ref) 25–34 1.03 (0.74–1.44) 0.85 1.02 (0.71–1.45) 0.92 35–44 1.16 (0.78–1.71) 0.45 1.22 (0.80–1.85) 0.35 45–54 1.56 (1.10–2.23) 0.02 1 (ref) 55–64 2.06 (1.43–2.96) 0.0002 1.26 (1.15–1.38) <0.0001 1 (ref) ≥65 2.39 (1.68–3.40) <0.0001 1.11 (0.96–1.27) 0.15 Residence location Urban 1 (ref) 1 (ref) 1 (ref) 1 (ref) Rural 1.06 (0.83–1.36) 0.63 1.05 (0.77–1.43) 0.76 1.07 (0.84–1.36) 0.58 1.04 (0.80–1.34) 0.78 National region East China 1 (ref) 1 (ref) 1 (ref) 1 (ref) Central China 0.91 (0.71–1.17) 0.45 1.11 (0.74–1.67) 0.60 0.83 (0.70–0.99) 0.04 0.80 (0.62–1.02) 0.07 West China 0.89 (0.65–1.22) 0.46 0.93 (0.70–1.24) 0.62 0.91 (0.64–1.30) 0.59 0.83 (0.59–1.18) 0.29 Annual household income <10,000 CNY 1 (ref) 1 (ref) 1 (ref) 1 (ref) 10,000–30,000 CNY 1.02 (0.91–1.15) 0.69 1.50 (1.01–2.20) 0.04 0.95 (0.81–1.13) 0.56 0.90 (0.80–1.02) 0.10 30,000–50,000 CNY 1.08 (0.95–1.23) 0.23 1.40 (1.02–1.91) 0.04 1.08 (0.90–1.30) 0.40 0.98 (0.85–1.14) 0.82 50,000–100,000 CNY 1.25 (1.07–1.46) 0.005 1.83 (1.38–2.42) <0.0001 1.20 (0.95–1.51) 0.13 0.98 (0.76–1.26) 0.86 100,000–200,000 CNY 1.33 (1.01–1.75) 0.04 2.07 (1.35–3.17) 0.001 1.23 (0.92–1.64) 0.16 0.89 (0.57–1.39) 0.61 ≥200,000 CNY 1.18 (0.67–2.10) 0.57 1.68 (0.69–4.08) 0.25 1.14 (0.80–1.62) 0.48 1.32 (0.54–3.25) 0.54 Refused to answer 1.02 (0.86–1.20) 0.84 1.51 (1.09–2.08) 0.01 0.86 (0.67–1.10) 0.21 0.94 (0.78–1.12) 0.48 Education level Primary school or below 1 (ref) 1 (ref) 1 (ref) 1 (ref) Junior high school 0.84 (0.76–0.92) 0.0006 0.64 (0.47–0.87) 0.005 0.88 (0.73–1.06) 0.16 0.89 (0.79–1.01) 0.08 High school/Vocational school 0.78 (0.67–0.91) 0.002 0.57 (0.43–0.77) 0.0004 0.74 (0.59–0.91) 0.006 0.96 (0.80–1.16) 0.67 University/College or above 0.65 (0.54–0.77) <0.0001 0.53 (0.36–0.78) 0.002 0.54 (0.35–0.84) 0.007 0.65 (0.47–0.90) 0.01 Unknown 0.57 (0.20–1.67) 0.30 0.57 (0.14–2.25) 0.42 0.27 (0.02–3.52) 0.31 0.46 (0.17–1.26) 0.13 Occupation Agriculture workers 1 (ref) 1 (ref) 1 (ref) 1 (ref) Government/public institution staff 1.89 (1.31–2.73) 0.001 2.45 (1.52–3.94) 0.0004 1.94 (1.21–3.10) 0.007 1.09 (0.54–2.18) 0.81 Business/service employee 1.05 (0.87–1.26) 0.64 1.19 (0.83–1.70) 0.35 1.10 (0.91–1.33) 0.31 0.99 (0.70–1.40) 0.96 Teaching staff 1.29 (0.91–1.82) 0.15 1.74 (1.12–2.69) 0.01 1.63 (0.86–3.09) 0.13 0.38 (0.14–1.07) 0.07 Medical/health personnel 1.05 (0.69–1.60) 0.83 1.38 (0.79–2.43) 0.26 1.26 (0.67–2.37) 0.48 0.14 (0.04–0.56) 0.006 Students 0.72 (0.47–1.10) 0.13 0.97 (0.56–1.69) 0.92 Unemployed 1.22 (1.01–1.46) 0.04 1.71 (1.17–2.49) 0.006 1.16 (0.91–1.47) 0.23 1.09 (0.86–1.38) 0.48 Retired 1.26 (1.01–1.57) 0.04 1.06 (0.70–1.61) 0.77 1.20 (0.95–1.53) 0.13 Others 1.22 (0.99–1.50) 0.06 1.54 (1.03–2.31) 0.04 1.13 (0.92–1.38) 0.24 1.10 (0.86–1.40) 0.45 Unknown 0.78 (0.51–1.19) 0.24 0.59 (0.31–1.11) 0.10 0.92 (0.29–2.91) 0.88 1.60 (0.82–3.14) 0.16 Open in a new tab The outcome is a binary variable of poor sleep quality (1 = PSQI score > 5; 0 = PSQI score ≤ 5). Multilevel analyses accounted for cluster to randomization, stratification, and complex sampling weight. All models adjusted for alcohol consumption, smoking, physical exercise frequency, BMI, cardiovascular disease, malignant tumor, chronic obstructive pulmonary disease (COPD), hypertension, diabetes, anxiety and depression. In model 1, 5 values were missing on smoking, 3181 values were missing on BMI, 15 values were missing on anxiety and 9 values were missing on depression. In model 2, 2 values were missing on smoking and 252 values were missing on BMI. In model 3, 600 values were missing on BMI, 5 values were missing on anxiety and 3 values were missing on depression. In model 4, 3 values were missing on smoking, 2329 values were missing on BMI, 10 values were missing on anxiety and 6 values were missing on depression. BMI = body mass index. PSQI = Pittsburgh Sleep Quality Index. Analysis of interaction effects indicated significant interactions among key sociodemographic variables. Specifically, statistically significant interactions were observed between national region and household income (Wald χ 2 = 83.29, P < 0.0001), national region and occupation (Wald χ 2 = 41.58, P < 0.0001), education level and occupation (Wald χ 2 = 3394.79, P < 0.0001), and education level and household income (Wald χ 2 = 222.86, P < 0.0001), as detailed in ( Supplementary Appendix pp 45–48). Discussion To our knowledge, this is the first large-scale, nationally representative investigation to document the prevalence of poor sleep quality and various sleep characteristics in China. Our study also provides a comprehensive analysis of how multiple demographic and socioeconomic factors independently and synergistically influence sleep quality disparities among Chinese individuals aged 15 years and older. The weighted prevalence of poor sleep quality in this national sample was 23.9% based on the PSQI > 5 cutoff. To facilitate comparison and assess the robustness of our findings, we conducted a sensitivity analysis using the more stringent PSQI > 7 cutoff, which yielded a prevalence of 13.9%. This estimate aligns closely with those reported in previous Chinese meta-analysis using the same cutoff. 11 The frequent use of the PSQI > 7 cutoff in Chinese studies originates from the early adaptation and validation of the scale in China. Following its introduction by Liu 20 et al., in 1996, the PSQI underwent local psychometric evaluation, which established PSQI > 7 as the validated threshold for distinguishing individuals with insomnia symptoms from healthy controls in Chinese adults. Consequently, this cutoff has been adopted in several Chinese epidemiological research to identify probable insomnia symptoms and derive prevalence estimates comparable to insomnia-specific definitions. In the present study, we primarily employed the standard PSQI > 5 cutoff to ensure broad international comparability and to leverage its utility as an inclusive screening tool. The difference in prevalence between the two cutoffs is methodologically expected and reflects their distinct purposes. Most importantly, the core patterns of association between demographic and socioeconomic factors and sleep quality remained consistent under both definitions, underscoring the robustness of our findings. Our findings revealed a pronounced sex disparity in sleep quality that widened substantially after age 25, with accelerated expansion after age 45. This two-stage pattern indicates that the mechanisms underlying sleep sex gap are both early-forming and sensitive to life-course transition. The substantial widening of sex disparity after age 45 coincides with the menopausal transition, a period characterized by significant hormonal fluctuations and an increased prevalence of vasomotor symptoms (e.g., hot flashes) that severely disrupt sleep architecture. 21 During menopause, declining estrogen and progesterone levels have been demonstrated to exacerbate sleep disturbances in women. 22 Additionally, diminished circadian signaling, including reduced melatonin amplitude and attenuated core body temperature rhythms, further compromises sleep-wake regulation in menopausal women. 23 In contrast, men lack a well-defined physiological milestone equivalent to menopause, which may explain the widening sex disparity in sleep health observed during midlife and beyond 24 Women in midlife often report higher burdens of caregiving and family responsibilities, 21 which may increase stress and activate cognitive patterns of worry and rumination, 25 , 26 processes known to be more prevalent in women and strongly linked to sleep maintenance difficulties. These biological changes, heightened psychosocial demands, and sex-specific cognitive-emotional styles may converge to create a triad of vulnerability that disproportionately impacts women’s sleep. In China specifically, older women have traditionally assumed the primary role in providing family care, including caring for spouses, children, and grandchildren. Research indicates that women, regardless of their own health status, often actively participate in or assume full responsibility for managing their husbands’ health. 27 The implementation of the One-Child Policy intensified this dynamic by increasing caregiving demands on women within households. 28 Although the government has encouraged social organizations to participate in elder care services, targeted support specifically for older women remains inadequate. 29 , 30 Therefore, we recommend establishing systematic sleep health screening for women during perimenopause and integrating sex-sensitive approaches into nationally supported elderly care programs. After adjusting for relevant confounding factors, we found that sleep quality among middle-aged and older adults (aged 45 and above) in central China was better than that in the more economically developed eastern region. Traditionally, higher levels of economic development are associated with better health outcomes through enhanced medical resources and nutritional standards. 31 However, our findings indicates that the health benefits of economic development may reach a threshold beyond which additional gains plateau, or may be counterbalanced by accompanying adverse effects. East China, as the nation’s economic core, exhibits a development model characterized by high-density urbanization, intense competition, and substantial population mobility. While this model generates material prosperity, it simultaneously produces widespread environmental stressors, including light pollution 32 and noise, 33 alongside an accelerated pace of life and erosion of traditional community support networks. 34 These factors collectively create a high-stress environment that may be particularly detrimental for middle-aged and older adults, who face declining physiological reserves and transitioning social roles. Such conditions may disproportionately compromise their sleep quality. In contrast, although central China lags behind the eastern region in total economic output and per capita productivity, its development model—positioned within the Rise of Central China initiative—exhibits distinct characteristics. As a crucial industrial and transportation hub, central China maintains economic vitality while likely preserving a relatively stable demographic structure, 35 stronger community bonds, 36 and a more moderate pace of life. This combination may provide a more protective social and environmental context for the middle-aged and older population, whose adaptive capacities are increasingly sensitive to external stressors. Our findings demonstrated that lower educational attainment and reduced annual household income were independently associated with elevated prevalence of poor sleep quality, consistently reinforcing socioeconomic status (SES) as a fundamental social determinant of sleep health. These results align with previously published research. 37 , 38 Individuals with lower socioeconomic status frequently encounter crowded living conditions, noise pollution, and residence in unsafe neighborhoods, all of which directly compromise sleep quality. 39 Moreover, this population demonstrates higher rates of detrimental behaviors including smoking, excessive alcohol consumption, and physical inactivity—factors significantly linked to poor sleep outcomes. 37 , 40 Additionally, chronic economic insecurity associated with low income triggers persistent activation of the hypothalamic-pituitary-adrenal (HPA) axis, promoting chronic stress states that adversely affect sleep maintenance and reduce deep sleep proportions. 4 , 41 This stress response is further amplified among individuals with lower educational levels, who often face greater job instability and social exclusion. 42 Notably, our results revealed differential associations between annual household income and poor sleep quality across age groups. A positive association was observed among younger individuals, whereas no significant association was found in the middle-aged group or older adults. This finding underscores the dynamic and context-dependent nature of socioeconomic influences on sleep, with age serving as a critical moderator. We hypothesized that among young people, higher income may closely linked to high-pressure professions characterized by long working hours, blurred work-life boundaries, and persistent career anxiety. 43 These “high-pressure sacrifices” and intrusive lifestyle patterns showed an association with poorer sleep quality. Interestingly, this association followed a distinct pattern: poorer sleep quality was primarily observed in the mid-to-high income group (10,000–200,000CNY), while no significant association was detected among the highest earners (≥200,000CNY). This pattern may reflect a dynamic pressure–control balance—mid-to-high income individuals often experience a pronounced mismatch between high job demands and limited autonomy, whereas top earners typically possess greater control and resources to mitigate stress. Among middle-aged and older adults, however, the direct link between income and sleep appears buffered or obscured by more complex factors. 34 On one hand, higher income may offer protective effects through better healthcare access, improved living conditions, and greater investment in health. On the other hand, age-related factors such as physiological decline in sleep architecture, increased chronic disease burden, and non-occupational stresses from caregiving and retirement planning may become dominant correlates of sleep quality. These opposing forces likely counteract each other, potentially rendering the income effect statistically non-significant in these age groups. 2 , 39 , 44 , 45 Additionally, a healthy selection effect may be at play, whereby individuals more susceptible to sleep disturbances may be less likely to sustain the demanding career trajectories required to reach the highest income levels. Nevertheless, as this study employed a cross-sectional design, the observed association between low income and poor sleep quality is also consistent with a reverse causation explanation. It is plausible that sleep problems could lead to impaired cognitive function and increased absenteeism, thereby affecting job performance and income. 46 , 47 This possibility underscores the need for further investigation through longitudinal cohort studies. Occupation emerged as another critical determinant of sleep inequality in our analysis. The most pronounced sex disparities were observed among retirees, government/public institution staff and agricultural workers. Certain professions, including government employees, teachers and the unemployed, demonstrated strong associations with poor sleep quality among younger cohorts (aged < 45). The substantial sleep inequality among retirees and the unemployed likely reflects underlying psycho-social mechanisms, including social isolation, economic insecurity, and loss of meaningful identity—all factors known to compromise sleep health. 48 In contrast, occupation such as government staff, though typically associated with higher socioeconomic status, frequently involve considerable psycho-social demands. These demands include performance pressure, emotional labor, and limited work-time autonomy. 49 , 50 , 51 , 52 According to the Job Demands-Resource model, when such demands chronically outweigh available resources (e.g., decision latitude, social support), they can lead to sustained stress and impede recovery. 49 , 53 , 54 , 55 This dynamic may be especially salient for young adults in these fields, potentially offsetting the benefits of job stability and material security while elevating the risk of sleep disturbance. Furthermore, within the specific context of China’s public sector, pervasive cultural norms of competitive overwork, a phenomenon known as “neijuan” (literally “involution,” referring to intensifying competition without meaningful progress), may intensify these demands and normalize inadequate recovery time, further exacerbating sleep disturbance risk. 49 Additionally, medical/health personnel were found to be associated with better sleep quality compared to their peers. This association may be attributed to a pronounced healthy worker effect, whereby only highly resilient individuals tend to remain in such demanding roles. Their professional health literacy also supports proactive sleep management. Moreover, the continued social engagement and structured daily routine inherent to ongoing work likely provide further protective benefits against age-related sleep decline. 56 , 57 Importantly, our study demonstrated that high socioeconomic status substantially buffers against sex-based sleep inequalities. The progressive widening sex gap with advancing age likely reflects the coverage of multiple psychosocial and biological factors that disproportionately affect women. These include elevated midlife caregiving responsibilities, sex-specific patterns of emotion regulation characterized by heightened worry and rumination, and menopause-related physiological disruptions that directly compromise sleep architecture. 58 , 59 Notably, the pronounced sex gap observed in low-SES groups virtually disappeared among highly educated and high-income individuals. Higher education may empower women with greater health literacy, autonomy, and negotiating power within households, enabling more sleep-conducive routines and reducing domestic burden-sharing inequities. 3 Similarly, high income can provide access to external support services (e.g., domestic help, private healthcare) and reduce financial strain, thereby alleviating stressors that disproportionately affect women’s sleep. 2 , 43 , 60 From a policy perspective, these results highlight that interventions aimed at reducing sleep disparities must be intersectional; enhancing educational and economic opportunities for women may not only improve economic equity but also confer meaningful protective effects on population sleep health. Our analysis further reveals significant interactions among key social determinants, demonstrating that the effects of education, occupation, income, and geographic region on sleep health operate through interconnected rather than independent pathways. This finding underscores that the mechanisms driving sleep inequalities are considerably more complex than single-factor models suggest. Future research should prioritize elucidating the specific pathways, mediating mechanisms, and contextual conditions through which these social factors interact to shape sleep health outcomes. This study has several important limitations that warrant consideration. First, the cross-sectional design precludes establishing causal relationships between socioeconomic factors, demographic characteristics, and sleep quality outcomes. Our weighting procedure included post-stratification adjustment based on the 2020 national census for age, sex, and residence (urban/rural). However, definitive national population distribution data for household income were not accessible, preventing similar calibration for this key socioeconomic indicator. Second, sleep health encompasses multiple dimensions, yet due to methodological constraints inherent in relying on the PSQI, our study captured only aspects related to perceived sleep quality. Although standardized interviews were employed to enhance consistency in questionnaire comprehension, this approach remains susceptible to reporting bias and cannot fully capture objective sleep parameters obtainable through polysomnography. Sleep duration and sleep latency in this study were derived from the PSQI, which relies on self-reported data. While these measures provide useful information on perceived sleep quality, they are subject to potential inaccuracies due to recall bias and individual differences in perception, 61 , 62 particularly as previous studies have shown that discrepancies between subjective and objective sleep measures tend to be larger in women 63 and older adults. 64 In addition, sleep duration and sleep latency were reported as continuous variables, which, while informative, may obscure important subgroups with unhealthy sleep patterns. Additionally, while the PSQI > 5 cutoff provides a broad screening tool, it may not adequately capture age-specific manifestations of sleep problems, particularly among adolescents and older adults who are at distinct physiological stages of sleep development and change. It should be noted that our measure of sleep disturbance, based on the PSQI, is composite in nature and does not distinguish between specific disorder types or their severity. Furthermore, variation in the understanding of certain items, particularly among participants with lower educational attainment, constitutes an additional source of potential measurement error. Future research would benefit from deconstructing the PSQI, incorporating objective measures, and analyzing other sleep features to better capture multidimensional sleep health disparities. Third, despite adjusting for numerous potential confounders, residual confounding may persist due to unmeasured variables including social support networks, neighborhood environmental factors, and healthcare accessibility. Additionally, certain socioeconomic variables were categorized using broad groupings (e.g., household income brackets and education levels), potentially masking more nuanced gradients within these categories. Chronic disease diagnoses relied on self-reported physician diagnoses, which may introduce misclassification and under-ascertainment bias. Although mental health is recognized as an important contributor to sleep quality, this study did not assess its effects in detail. Future research is warranted to clarify the relationships between mental health and sleep. Finally, while these findings represent the Chinese adult population, their applicability to other cultural contexts, healthcare systems or younger populations (i.e., children and adolescents) requires further investigation. In conclusion, poor sleep quality affects an estimated 23.9% of individuals aged 15 years and above in China, representing approximately 276 million people. This substantial public health burden arises from a complex interplay of demographic and socioeconomic factors, including sex, age, educational attainment, household income, and occupational status. Critically, the influence of these factors on sleep quality is not static but evolves dynamically across the life course. These findings underscore the urgent need for targeted public health interventions, including integrated sleep health screening during midlife healthcare visits and comprehensive structural policies designed to reduce sexed caregiving burdens and occupational stress, to address the multifaceted drivers of sleep health disparities in Chinese society. Contributors SL, XZ and YS had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. SL, XZ, ZH conceived and designed the study. All authors acquired, analysed, or interpreted the data. YS and SL drafted the manuscript. DZ, FH, ZP, YL, YW, ZZ, and XG critically revised the manuscript for intellectual content. YS statistically analysed the data. ZH, ZP, and SL obtained funding. YS, SL, FH, and ZH were responsible for the decision to submit the manuscript. All authors discussed the results, commented on the manuscript, and approved the final version. Data sharing statement The data supporting the findings of this study, derived from the national cross-sectional survey “Study on the effect of smoking on sleep disorders,” are not publicly accessible due to participant privacy considerations and ethical requirements. However, anonymised datasets can be made available to qualified researchers upon request to the Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine) (contact email: [email protected] ). Data sharing will be contingent upon obtaining all necessary ethical approvals and signing an institutional data transfer agreement. Researchers may also contact the corresponding author to request access to the analytic methods, study materials, and code used for data processing and analysis. Declaration of interests We declare no competing interests. Acknowledgements We acknowledge financial supports from Chinese Sleep Research Society, and the National Natural Science Foundation of China (Grant No. 81872721). We would like to thank the participants, project staff, and diligent provincial and local staff of the institute of disease control and prevention or health education for their participation and contributions. Footnotes Appendix A Supplementary data related to this article can be found at https://doi.org/10.1016/j.lanwpc.2026.101853 . Contributor Information Zhili Huang, Email: [email protected]. Fang Han, Email: [email protected]. Shiwei Liu, Email: [email protected]. Appendix A. Supplementary data Appendices A–E mmc1.docx (212.7KB, docx) References 1. Lim D.C., Najafi A., Afifi L., et al. The need to promote sleep health in public health agendas across the globe. Lancet Public Health. 2023;8(10):e820–e826. doi: 10.1016/S2468-2667(23)00182-2. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. 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