A Study on the Association Between Early-Life Exposure to Famine and Sleep Quality in Adulthood：evidence from the China health and retirement longitudinal study database

A Study on the Association Between Early-Life Exposure to Famine and Sleep Quality in Adulthood：evidence from the China health and retirement longitudinal study database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Study on the Association Between Early-Life Exposure to Famine and Sleep Quality in Adulthood：evidence from the China health and retirement longitudinal study database Chao Cheng, Xiaoyu Wang, Min Huang, Jiafu He, Yan Li, Yunhua Cui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7938543/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Sleep plays an important role in regulating endocrine function and forming memory in the human body. However, there are few studies on the impact of early-life exposure to famine on sleep. Therefore, the purpose of this study is to assess the effects of early-life exposure to famine on sleep in middle-aged and elderly populations. Methods Using the validated China Health and Retirement Longitudinal Study(CHARLS) baseline questionnaire, a logistic regression model was used to assess the association between famine exposure and sleep. The influence of demographic and health behavior factors on the association was evaluated through stratified analysis and interaction tests. Results Exposure to famine in early life increases the risk of poor sleep in middle-aged and older populations. Compared with the unexposed group, all famine-exposed groups showed a significantly higher risk of poor sleep. Specifically, the ORs (95% CI) for the fetal exposure, childhood exposure, and adolescent/adult exposure were 1.24 (1.10–1.40), 1.21 (1.11–1.32), and 1.58 (1.43–1.75), respectively, with all P-values < 0.001. The co-occurrence of depression and childhood famine exposure further exacerbates the occurrence of poor sleep. Conclusions This population-based cross-sectional study indicates that early-life famine exposure has lasting effects on sleep in middle-aged and older adults. It identifies high-risk groups, including women, those with limited educational attainment, and those with disabilities or depression. These findings underscore the need for public health policies that extend beyond current risk factors and adopt a life-course perspective. famine sleep middle-aged and elderly CHARLS Figures Figure 1 Background Nearly one-third of human life is spent sleeping, and sleep is crucial for endocrine regulation and memory formation[1, 2]. Insufficient or excessive sleep is associated with various conditions, such as obesity[3], metabolic syndrome[4], cardiovascular disease[5], cognitive decline[6], and anxiety[7]. Conversely, adequate sleep is essential for physical and mental health. However, a domestic study targeting middle-aged and older adults aged ≥ 45 reported that in 2011, only 38.7% had an ideal sleep duration (7–9 hours)[8]. A meta-analysis of 13 studies from China further showed a 47.2% prevalence of sleep disorders among those over 60 years[9]. Therefore, identifying factors contributing to sleep problems in middle-aged and older populations and implementing preventive strategies are essential for reducing sleep-related diseases. Sleep regulation involves three primary mechanisms: (1) circadian rhythm; (2) homeostatic sleep drive; and (3) autonomic nervous system[10]. The sleep–wake cycle, the most prominent human circadian rhythm, is regulated by the suprachiasmatic nucleus (SCN) of the hypothalamus. Homeostatic sleep drive refers to the sleep need arising from the accumulation of certain substances or structures during wakefulness[11]. During critical periods, such as fetal or early postnatal stages, malnutrition is hypothesized to lead to lasting metabolic and structural changes[12]. Cintra et al. reported that chronic fetal malnutrition alters homeostatic sleep drive and circadian rhythms[11], while Galler et al. noted that fetal malnutrition may impair adult sleep quality[13]. Furthermore, adverse intrauterine environments can permanently reset the hypothalamic–pituitary–adrenal (HPA) axis[14]. Since the HPA axis regulates alertness and sleep, dysfunction at any level (corticotropin-releasing hormone [CRH], glucocorticoid, or mineralocorticoid receptors) can disrupt sleep. Evidence from humans and animals indicates a bidirectional relationship between sleep disorders and HPA axis dysregulation[15]. China, one of the world’s largest developing countries, experienced a three-year famine from 1959 to 1962[16], creating a 'natural experiment' for assessing the long-term health effects of early-life famine. Prior research has linked early famine exposure to increased risk of diabetes[17], hypertension[18], schizophrenia[19], and metabolic syndrome[20]. However, little is known about its impact on sleep. Therefore, this study aimed to assess the association between early-life famine exposure and sleep quality in middle-aged and older adults. Methods Study design and population Data from the China Health and Retirement Longitudinal Study (CHARLS) project were utilized in this analysis. In 2011, a multi-stage probability sampling method randomly selected 150 county-level and 450 village-level units across 28 Chinese provinces. The baseline survey collected socioeconomic and health data from middle-aged and older individuals, with biennial follow-ups. Details of the study areas and sampling procedures are described elsewhere[21]. This study is based on data from the 2014 Life Course Survey, which included residential and migration history, childhood experiences, and education, and the third national follow-up in 2015, involving 14,582 adults aged ≥ 45 years. After excluding those with missing data, 13,880 participants were included in the final analysis (Fig. 1 ). Ethics approval and consent to participate.This is a retrospective study based on CHARLS database. The patient's information has been hidden before the study. There is no need for the patient's informed consent and no ethical conflict. The original CHARLS was approved by the Ethical Review Committee of Peking University (IRB00001052–11015), and all participants signed the informed consent at the time of participation. This research followed the guidance of the Declaration of Helsinki[21]. Outcome variable definition Outcome variable definition Famine exposure during 1959–1962 was classified by birth year, consistent with prior studies. Participants were divided into four groups: unexposed stage (born from January 1, 1963 to December 31, 1966), fetal exposure (January 1, 1959 to December 31, 1962), childhood exposure (January 1, 1949 to December 31, 1958), and adolescent/adult exposure (January 1, 1921 to December 31, 1948)[22]. Nighttime sleep duration was assessed by the question: 'In the past month, how many hours did you actually sleep at night (average hours per night)?' Based on National Sleep Foundation recommendations[23], those participants reporting 7–9 hours were classified as having adequate sleep, and those with < 7 hours or ≥ 9 hours as poor sleep. Data were collected through face-to-face interviews, covering sociodemographic characteristics (age, gender, residence, marital status, education level, economic status) and health-related factors (body mass index [BMI], smoking, drinking, physical disabilities, health status, adverse childhood experiences, and depression). Gender was defined as male or female, and residence as urban or rural. Marital status was coded as married (living with or without their spouse) or unmarried (divorced, widowed, or unmarried). Education level was grouped into 'primary school and below,' 'middle school,' and 'high school and above.' Economic status is defined based on the question 'How was your family's economic situation before you were 17?' 'Worse than theirs' or 'much worse than theirs' is defined as below average, while 'much better', 'a little better than theirs', or 'the same as theirs' is defined as above or at the average level. BMI was calculated as weight (kilograms) divided by height (square meters). Following Chinese adult standards[24], BMI < 18.5 kg/m² was defined as underweight, 18.5–23.9 as normal, 24.0–27.9 as overweight, and ≥ 28.0 as obese, with normal weight as the reference. Smoking status was classified as non-smoker (never or quit) or current smoker; drinking status as non-drinker (never or quit) or current drinker. Physical disability was assessed by the question 'Do you have any of the following disability issues': 1. Physical disability, 2. Brain damage, 3. Blindness or partial blindness, 4. Deafness or partial deafness, 5. Muteness or severe stuttering; reporting any condition was coded as yes. Self-rated health was assessed with the question: 'How do you feel about your own health?' Adverse Childhood Experiences (ACEs) included 11 negative events before age 17 (bullying, corporal punishment, domestic violence, emotional neglect, parental crime, parental divorce, parental disability, parental substance abuse, poor parental mental health, parental death, unsafe living environment)[25–27]. ACEs are obtained through standardized questionnaires; the specific questions and evaluation criteria are provided in the attachment.ACEs were coded as 0 (absent) or 1 (present). Based on total ACEs (0–11), participants were grouped as 0, 1, 2–3, and ≥ 4[28]. Depression was measured using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10); scores ≥ 10 indicated depressive symptoms[29]. Statistical Analysis For comparisons among sleep duration groups, continuous variables with a normal distribution are expressed as (x̄ ± s), and compared using independent-samples t-tests. Categorical variables were expressed as frequencies or proportions, with group differences assessed by the χ2 test. Logistic regression models were constructed to evaluate the association between famine exposure and sleep. Model 1 was unadjusted, while Model 2 was adjusted for gender, marital status, education level, residence, economic status, physical disability, health condition, ACEs, BMI, smoking and drinking status, and depression. All regression models reported odds ratios (OR) with 95% confidence intervals (CIs). To assess potential confounding, stratified analyses were performed by gender (female or male), residence (rural or urban), physical disability (yes or no), education level (primary school or below, middle school, high school or above), current smoking status (yes or no), current drinking status (yes or no), marital status (married or unmarried), BMI, ACEs, health status, and depression (yes or no). Interactions were further tested to examine modifying effects. Statistical analyses were performed using IBM SPSS Statistics 26, with two-sided P < 0.05 considered statistically significant. Results A total of 13,880 participants were included (Table 1 ), of whom 5,670 (40.8%) reported adequate sleep and 8,210 (59.2%) poor sleep. The poor sleep group was older, with a higher proportion of women, lower education levels (primary school or below), and lower self-assessed economic status. Marital status data indicated a slightly higher proportion of poor sleep among unmarried individuals. The poor sleep group reported lower alcohol consumption. Participants with adequate sleep had marginally higher BMI than those with poor sleep. Poor sleep was significantly associated with higher rates of physical disability, depressive symptoms, and self-rated health as 'poor/very poor'. Moreover, the prevalence of poor sleep increased with the number of ACEs. Table 1 Comparison of the characteristics of middle-aged and elderly people with different sleep durations in China in 2015 Characteristics a Total(N = 13880) Sleep duration P value adequate sleep group(n = 5670) poor sleep group(n = 8210) Age,y 59.49(10.40) 58.19(10.12) 60.39(10.50) <0.001 Gender Male 6516(46.95) 2838(50.05) 3678(44.80) <0.001 Female 7364(53.05) 2832(49.95) 4532(55.20) Marital status Married 13763(99.16) 5635(99.38) 8128(99.00) = 0.016 Unmarried 117(0.84) 35(0.62) 82(1.00) Educational level Primary school or below 8493(61.19) 3197(56.38) 5296(64.51) <0.001 Middle school 3658(26.35) 1658(29.24) 2000(24.36) High school or above 1727(12.46) 814(14.38) 913(11.13) Residence Urban 3373(24.30) 1433(25.27) 1973(24.03) = 0.095 Rural 10507(75.70) 4237(74.73) 6237(75.97) Economic status ≥Average level 8353(60.18) 3551(62.63) 4802(58.49) <0.001 <Average level 5527(39.82) 2119(37.37) 3408(41.51) Current smoking status Yes 4620(33.29) 1939(34.20) 2681(32.66) = 0.058 No 9260(66.71) 3731(65.80) 5529(67.34) Current drinking status Yes 4897(35.28) 2077(36.63) 2820(34.35) = 0.006 No 8983(64.72) 3593(63.37) 5390(65.65) BMI(kg/m 2 ) 23.99(4.01) 24.17(4.01) 23.87(4.00) <0.001 Famine exposure grouping Q1 4215(30.37) 1965(34.66) 2250(27.40) <0.001 Q2 1480(10.66) 618(10.90) 862(10.50) Q3 4790(34.51) 1943(34.27) 2847(34.68) Q4 3395(24.46) 1144(20.17) 2251(27.42) Physical disability Yes 1870(13.47) 616(10.86) 1254(15.27) <0.001 No 12010(86.53) 5054(89.14) 6956(84.73) Health status Q1 3635(26.19) 1779(31.38) 1856(22.61) <0.001 Q2 7760(55.91) 3134(55.27) 4626(56.35) Q3 2485(17.90) 757(13.35) 1728(21.04) Depression Yes 4592(33.08) 1392(24.55) 3200(38.98) <0.001 No 9288(66.92) 4278(75.45) 5010(61.02) Number of ACEs items 0 671(4.83) 305(5.38) 366(4.46) <0.001 1 3301(23.78) 1468(25.89) 1833(22.33) 2 ~ 3 7343(52.90) 2964(52.28) 4379(53.34) ≥ 4 2565(18.49) 933(16.45) 1632(19.87) Abbreviations: ACE, adverse childhood experience; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared) a Continuous data are reported as the mean (SD), and categorical data are reported as the number and percentage of participants. Health status Q1:excellent or very good or good,Q2:fair,Q3:poor or very poor. Famine exposure grouping:Q1:unexposed stage;Q2:fetal exposure;Q3:childhood exposure; Q4:adolescent/adult exposure. A multivariate logistic regression analysis was conducted to investigate the association between early-life famine exposure and poor sleep in middle and older age. Table 2 presents the associations between famine exposure groups and the risk of inadequate sleep. In Model 1 (unadjusted), all famine-exposed groups had significantly elevated risks compared with the unexposed group. The ORs (95% CIs) were 1.22 (1.08–1.37) for the fetal exposure group, 1.28 (1.18–1.39) for the childhood exposure group, and 1.72 (1.57–1.89) for the adolescent/adult exposure group (all P < 0.001). A clear dose–response trend was observed: 'the later the exposure period, the higher the risk,' with the adolescent/adult group showing the most significant risk. In Model 2, after adjustment for gender, marital status, education level, residence, economic status, physical disability, health status, ACEs, BMI, current smoking and drinking status, and depression, the associations remained substantial, and the dose–response pattern persisted. Adjusted ORs (95% CIs) for fetal exposure, childhood exposure, and adolescent/adult exposure were 1.24 (1.10–1.40), 1.21 (1.11–1.32), and 1.58 (1.43–1.75), respectively (P < 0.001). These results indicate that famine exposure in early life is an independent risk factor for poor sleep in later life. Table 2 Multivariate logistic regression model analysis of famine exposure and poor sleep duration Famine exposure grouping Model 1 Model 2 OR (95% CI) P value OR (95% CI) P value Unexposed stage 1 Ref 1 Ref Fetal exposure 1.22(1.08–1.37) <0.001 1.24(1.10–1.40) <0.001 Childhood exposure 1.28(1.18–1.39) <0.001 1.21(1.11–1.32) <0.001 Adolescent/adult exposure 1.72(1.57–1.89) <0.001 1.58(1.43–1.75) <0.001 Abbreviations: ACE, Adverse Childhood Experience; OR, odds ratio.BMI, body mass index (calculated as weight in kilograms divided by height in meters squared) Model 1 was the crude model Model 2 was adjusted for gender, marital status, education level, residence, economic status, physical disability, health status, ACEs, BMI, current smoking and drinking status, and depression To further evaluate independent predictors of poor sleep, multivariate logistic regression including all covariates was performed (Table 3 ). Female gender was associated with a higher risk (OR 1.28, 95% CI 1.16–1.41), whereas higher educational attainment exerted a protective effect. Compared with those with a primary school education or below, individuals with junior high school education (OR = 0.89, 95% CI 0.82–0.98) and high school or above (OR = 0.86, 95% CI 0.76–0.96) had a considerably lower risk of poor sleep. Poor self-rated health was robustly associated with poor sleep: compared with those rating their health as 'good,' the risk increased for those reporting 'average' (OR = 1.23, 95% CI 1.14–1.34) and 'poor/very poor' (OR = 1.51, 95% CI 1.34–1.70). Physical disability also increased the risk (OR = 1.19, 95% CI 1.07–1.32); participants with depression had a substantially higher risk (OR = 1.62, 95% CI 1.49–1.76). Drinkers were more likely to report poor sleep than non-drinkers (OR = 1.09, 95% CI 1.01–1.19), whereas smoking showed no significant association. After full adjustment, the number of ACEs was not considerably related to poor sleep. Table 3 Logistic regression analysis of the incidence of poor sleep duration in middle-aged and elderly people Variable OR (95%CI) P BMI(ref=Normal) Underweight 1.07(0.91–1.27) = 0.39 Overweight 0.93(0.86–1.01) = 0.078 Obese 0.89(0.81–1.01) = 0.066 Gender(ref = male) Female 1.28(1.16–1.41) <0.001 Marital status (ref = unmarried) Married 1.50(0.99–2.24) = 0.052 Educational level(ref = Primary school and below) Middle school 0.89(0.82–0.98) = 0.011 High school and above 0.86(0.76–0.96) = 0.008 Residence(ref = Urban) Rural 0.93(0.85–1.01) = 0.074 Current smoking status(ref = NO) YES 1.08(0.99–1.19) = 0.096 Current drinking status(ref = NO) YES 1.09(1.01–1.19) = 0.036 Health status(ref = excellent and very good and good) Fair 1.23(1.14–1.34) <0.001 Poor and very poor 1.51(1.34–1.70) <0.001 Physical disability(ref = NO) YES 1.19(1.07–1.32) 0.002 Number of ACEs items(ref = 0) 1 0.90(0.76–1.07) 0.24 2 ~ 3 1.02(0.87–1.21) 0.77 ≥ 4 1.08(0.90–1.29) 0.34 Depression(ref = NO) YES 1.62(1.49–1.76) <0.001 Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared);ACE, Adverse Childhood Experience. BMI:Normal:18.5kg/m 2 ≤BMI<24.0kg/m 2 ;Underweight:＜18.5 kg/m 2 ;Overweight:24.0 kg/m 2 ≤ BMI < 28.0 kg/m 2 ;Obese:BMI ≥ 28.0 kg/m 2 . To examine heterogeneity in the famine–sleep relationship, stratified analyses were performed by gender, marital status, education, physical disability, and BMI (Table 4 ). Gender appeared to modify the association: across all famine exposure periods, ORs were higher for females than males, especially in the adolescent/adult exposure group (females: OR = 1.98, 95% CI 1.73–2.26; males: OR = 1.58, 95% CI 1.38–1.80), suggesting greater female susceptibility. Marital status also influenced outcomes; among married individuals, exposure at all stages considerably increased poor sleep risk in a dose-response manner, where associations were not significant in the unmarried population, possibly due to small sample size. Education level showed a buffering effect. In participants with primary or junior high school education, famine exposure was significantly associated with poor sleep. Conversely, in those with senior high school or above, associations were largely insignificant except for adolescence/adulthood exposure (P = 0.063). Stratification by disability revealed that adolescence/adulthood exposure significantly increased risk in both disabled and non-disabled groups. However, risks from fetal and childhood exposure were elevated only among those with disabilities. BMI stratified analysis indicated that the association between famine exposure and poor sleep varied across BMI categories. In the normal weight and overweight groups, the pattern was consistent and significant. Among the underweight, only adolescent/adult exposure was associated with elevated risk, while in the obese group, a considerable increase was also observed only during adolescence/adulthood (OR = 1.91, 95% CI 1.45–2.53). Table 4 Adjusted OR and 95% CI for association between famine exposure and poor sleep, stratified by potential modifier (ref: unexposed stage). Variable Categories Exposure poor sleepOR (95%CI) P Gender Men Fetal exposure 1.09(0.92–1.30) = 0.330 Childhood exposure 1.22(1.08–1.39) = 0.002 Adolescent/adult exposure 1.58(1.38–1.80) <0.001 Womed Fetal exposure 1.36(1.16–1.60) <0.001 Childhood exposure 1.36(1.22–1.53) <0.001 Adolescent/adult exposure 1.98(1.73–2.26) <0.001 Marital status married Fetal exposure 1.22(1.08–1.38) <0.001 Childhood exposure 1.28(1.17–1.39) <0.001 Adolescent/adult exposure 1.72(1.56–1.89) <0.001 Unmarried Fetal exposure 0.79(0.15–4.09) = 0.780 Childhood exposure 1.67(0.65–4.33) = 0.290 Adolescent/adult exposure 1.86(0.62–5.54) = 0.270 Educational level Primary school or below Fetal exposure 1.09(0.91–1.32) = 0.340 Childhood exposure 1.21(1.08–1.36) = 0.001 Adolescent/adult exposure 1.54(1.37–1.74) <0.001 Middle school Fetal exposure 1.49(1.22–1.83) <0.001 Childhood exposure 1.32(1.13–1.54) = 0.001 Adolescent/adult exposure 1.87(1.50–2.32) <0.001 High school or above Fetal exposure 1.15(0.88–1.50) = 0.294 Childhood exposure 1.00(0.78–1.27) = 0.900 Adolescent/adult exposure 1.32(0.99–1.78) = 0.063 Physical disability Yes Fetal exposure 1.22(1.07–1.38) 0.002 Childhood exposure 1.30(1.19–1.42) <0.001 Adolescent/adult exposure 1.67(1.51–1.85) <0.001 No Fetal exposure 1.26(0.84–1.87) 0.26 Childhood exposure 1.02(0.79–1.31) 0.89 Adolescent/adult exposure 1.57(1.20–2.03) 0.001 BMI Normal Fetal exposure 1.27(1.06–1.52) 0.010 Childhood exposure 1.36(1.21–1.54) < 0.001 Adolescent/adult exposure 1.87(1.63–2.14) < 0.001 Underweight Fetal exposure 1.07(0.54–2.10) 0.853 Childhood exposure 1.43(0.91–2.26) 0.122 Adolescent/adult exposure 1.59(1.04–2.45) 0.034 Overweight Fetal exposure 1.22(0.99–1.48) 0.055 Childhood exposure 1.18(1.02–1.36) 0.022 Adolescent/adult exposure 1.41(1.20–1.66) < 0.001 Obese Fetal exposure 1.12(0.84–1.51) 0.441 Childhood exposure 1.15(0.93–1.44) 0.199 Adolescent/adult exposure 1.91(1.45–2.53) < 0.001 As shown in Table 5 , after adjusting for covariates, the cross-group OR values of famine exposure combined with the depression group were all considerably greater than 1, compared with the unexposed and non-depressed groups. In the additive interaction analysis, childhood exposure and depression demonstrated a positive synergistic effect on poor sleep, with relative excess risk due to interaction (RERI; 95% CI) of 0.390 (0.097–0.684), 19.2% of the total effect attributable to the interaction, and synergy index (S; 95% CI) of 1.603 (1.083–2.374). However, interaction effects for other exposure groups with depression were not significant, nor were multiplicative interactions. Table 5 The interaction between the exposure to the Chinese famine and depression on the occurrence of poor sleep Famine exposure Non-depression depression depression P for interaction OR(95%CI) OR(95%CI) RERI(95%CI) AP(95%CI) S(95%CI) Unexposed stage Reference 1.494 (1.300,1.719)*** 0.203 Fetal exposure 1.175 (1.018,1.357)* 2.122 (1.709,2.647)*** 0.453 (-0.036,0.941) 0.213 (0.019,0.408) 1.677 (0.984,2.857) Childhood exposure 1.153 (1.040,1.279)** 2.037 (1.778,2.336)*** 0.390 (0.097,0.684) 0.192 (0.059,0.324) 1.603 (1.083,2.374) Adolescent/adult exposure 1.574 (1.397,1.774)*** 2.389 (2.043,2.798)*** 0.321 (-0.064,0.705) 0.134 (-0.014,0.283) 1.300 (0.949,1.781) * denotes p-value < 0.05; ** denotes p-value < 0.01; *** denotes p-value < 0.001. In the additive interaction, RERI, AP, and S values highlighted in red indicate the presence of an additive interaction effect, using Non-exposed and Non-depression as the reference group. P for interaction indicates the p-value for the multiplicative interaction effect; a value less than 0.05 indicates statistical significance. Model was adjusted for gender, marital status, education level, residence, economic status, physical disability, health status, ACEs, BMI, current smoking and drinking status, and depression. Discussions This cross-sectional study demonstrated that early-life famine exposure increased the risk of poor sleep in middle-aged and older adults. The coexistence of depression and childhood famine exposure further exacerbates this risk. This association remained robust after adjustment for sociodemographic and health-related factors, indicating famine exposure as an independent risk factor for later-life sleep problems. Our findings align with the 'Developmental Origins of Health and Disease' (DOHaD) theory, which posits that adverse exposures during critical developmental windows exert permanent programming effects on physiology and disease susceptibility[30]. Fetal, childhood, and adolescent/adult exposures were all associated with poor sleep, suggesting multiple life stages may significantly influence the maturation of the sleep regulatory system. A significant dose–response relationship was observed, with the highest risk in the adolescent/adult group. This pattern strongly suggests that severe malnutrition at later developmental stages may cause direct, irreversible damage to key brain regions already established or maturing (such as the suprachiasmatic nucleus and amygdala, critical for sleep–wake regulation)[31]. Furthermore, later exposure likely involves greater awareness and memory of the social disruption and psychological trauma from famine, with long-term consequences on stress response systems and sleep quality, potentially mediated through epigenetic mechanisms[32]. This study confirms that female gender is a significant risk factor for poor sleep in middle-aged and older populations, consistent with global evidence of gender differences in sleep disorders[33]. This difference may be influenced by multiple factors, including neuroendocrine changes during menopause (such as fluctuations in estrogen and progesterone), differential stress responses, and higher prevalence of anxiety and depression among women[34]. Conversely, higher education demonstrates a protective effect, supporting the 'healthy social gradient' theory. Education, as a core indicator of socioeconomic status, may enhance sleep quality by improving health literacy, fostering healthier behaviors (such as regular exercise), and strengthening the ability to manage stress and access healthcare resources[35]. A clear gradient relationship was observed between health status and poor sleep. Health status is a comprehensive indicator reflecting undiagnosed physiological discomfort, psychological distress, and limitations in social functioning, all of which can interfere with sleep[36]. Physical disability and depression showed the strongest associations with poor sleep. Physical disabilities are frequently accompanied by chronic pain and limited mobility, directly impacting sleep onset and maintenance. Depression and sleep disorders share neurobiological pathways (including HPA dysfunction and monoamine imbalance), while core depressive symptoms (such as rumination and low mood) are major contributors to insomnia[37]. Alcohol consumption was associated with increased risk of poor sleep, countering the common misconception that 'alcohol helps sleep.' Although alcohol can shorten sleep latency, its metabolites disrupt later sleep stages, leading to fragmentation, rapid eye movement (REM) rebound, and reduced slow-wave sleep, thereby lowering overall sleep quality[37]. In this study, smoking did not show a significant independent association. A possible explanation is that nicotine’s stimulating effects and nocturnal withdrawal responses may be masked by its strong correlation with confounding factors such as depression and poor self-rated health[38]. After adjustment for relevant variables, ACEs were no longer significantly associated with poor sleep in middle-aged and older adults. This observation suggests that the long-term influence of ACEs on sleep may be indirect, primarily mediated through adult risk pathways such as lower socioeconomic status, poorer mental health, and increased chronic disease burden[39]. These findings align with life-course epidemiology, emphasizing that later-life factors may exert more direct effects on current health; however, the impact of early adversity is primarily 'mediated' by its subsequent consequences[40]. The significant association and dose–response relationship observed among the married population suggests that even with early adversity, social support (such as spousal support) may not fully mitigate its adverse effects. However, the association was insignificant among the unmarried population, likely due to the small sample size, indicating that the absence of social support may exacerbate vulnerability; however, this finding should be interpreted with caution[41]. More importantly, the level of education demonstrated an apparent buffering effect. Among individuals with a high school education or above, the adverse impact of famine exposure was essentially eliminated, consistent with the theories of 'cognitive reserve' and 'resource substitution'[42]. Higher education generally confers greater health knowledge, stronger coping skills, and better career and income opportunities, enabling healthier lifestyles and improved access to medical care in adulthood. These advantages may effectively attenuate the long-term consequences of early-life stress on health[43]. Famine exposure during the fetal and childhood periods significantly increased the risk among individuals with physical disabilities but not among those without disabilities. This finding strongly suggests that early malnutrition may act synergistically with acquired physiological defects (such as disabilities), jointly overburdening biological systems and thereby increasing susceptibility to adverse outcomes such as sleep disorders[44]. The BMI-stratified results revealed a more complex pattern. Significant and consistent associations were observed in the normal weight and overweight groups, suggesting these categories adequately capture the core effects of famine exposure. In the obese group, however, increased risk was observed only for exposure during adolescence/adulthood; this observation may reflect the fact that obesity in adulthood is an independent, substantial risk factor for poor sleep (such as obstructive sleep apnea), which could overshadow the subtler influence of earlier famine exposure[45]. The key finding of this study is that childhood famine exposure shows a significant additive interaction with depression in increasing the risk of poor sleep. Additive interaction assesses whether the combined effect of two risk factors exceeds the sum of their individual effects. A significant RERI value (0.390) indicates that, compared with individuals with neither childhood famine exposure nor depression, 0.390 of the excess risk of poor sleep in those with both risk factors is attributable to their synergistic effect[46]. The attributable proportion due to interaction (AP) value (19.2%) highlights the public health relevance, indicating that nearly 20% of the risk of poor sleep among individuals exposed to both factors can be explained by this interaction[47]. These findings have important clinical and public health implications, suggesting that proactive screening for and treatment of depression in individuals with childhood famine exposure may significantly reduce their risk of poor sleep. Why is childhood a critical window for such interactions? These findings align closely with the 'sensitive period' and 'biological embedding' models in life course epidemiology[48]. Childhood represents a key stage for brain development, programming of stress-response systems (such as the HPA axis), and the establishment of health behaviors. Severe nutritional deprivation during this period may cause lasting changes in neurocognitive and emotional regulation, creating an intrinsic vulnerability to psychological stress[49]. When stressors in adulthood trigger depression, this latent susceptibility is activated, and the two risk factors interact, manifesting as sleep disturbances. Advantages and Limitations A key strength of this study is its large sample size, which enabled a robust exploration of the association between famine exposure and poor sleep in middle-aged and older adults. Another important contribution is the identification of a significant additive interaction between childhood famine exposure and depression, highlighting a novel pathway for preventing poor sleep and reduced sleep-related disorders in this population. This study has some notable limitations. First, it is a cross-sectional study, so we cannot draw any causal inferences between the study variables.2. Some of the data for the research variables mainly come from the participants' recollections, which may involve recall bias. 3. Potential selection bias due to missing data. 4. Although some important confounding factors have been adjusted for, there may still be some unknown confounding factors that could affect the research results. Conclusion This population-based cross-sectional study indicates that early-life famine exposure has lasting effects on sleep in middle-aged and older adults. It identifies high-risk groups, including women, those with limited educational attainment, and those with disabilities or depression. These findings underscore the need for public health policies that extend beyond current risk factors and adopt a life-course perspective. At a practical level, community screenings should identify individuals with a history of early-life famine for prioritized care, while integrating psychological health services (especially for depression) into routine management. Such comprehensive strategies may help mitigate the long-term health consequences of early-life famine and enhance the quality of life in aging populations. Declarations Acknowledgements We would like to acknowledge the China Health and Retirement Longitudinal Study team for providing data and the training of using the dataset. Authors’ contributions Concept and design: Cui, Cheng Acquisition, analysis, or interpretation of data: Cui, Cheng, Huang, Wang. Drafting of the manuscript: Cheng. Critical revision of the manuscript for important intellectual content: He, Li, Cui. Statistical analysis: Cheng, Cui. Administrative, technical, or material support: He, Li, Huang. Supervision: Wang, Cui. All authors read and approved the final manuscript. Funding No. Availability of data and material This study used open-access data from the China Health and Retirement Longitudinal Study, which could be downloaded from http://charls.pku.edu.cn/index.htm. Ethics approval and consent to participate This study did not include any animal or human experiments. This study was approved by the Biomedical Ethics Council of Beijing University. The IRB approval number for the main household survey, including anthropometrics, is IRB00001052-11015; the IRB approval number for biomarker collection is IRB00001052-11014. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Clinical trial number Not applicable References Besedovsky L, Lange T, Born J: Sleep and immune function . Pflugers Arch 2012, 463 (1):121-137. Doherty R, Madigan S, Warrington G, Ellis J: Sleep and Nutrition Interactions: Implications for Athletes . Nutrients 2019, 11 (4). Chaput JP, Bouchard C, Tremblay A: Change in sleep duration and visceral fat accumulation over 6 years in adults . Obesity (Silver Spring) 2014, 22 (5):E9-12. Koren D, Taveras EM: Association of sleep disturbances with obesity, insulin resistance and the metabolic syndrome . Metabolism 2018, 84 :67-75. Kwok CS, Kontopantelis E, Kuligowski G, Gray M, Muhyaldeen A, Gale CP, Peat GM, Cleator J, Chew-Graham C, Loke YK et al : Self-Reported Sleep Duration and Quality and Cardiovascular Disease and Mortality: A Dose-Response Meta-Analysis . J Am Heart Assoc 2018, 7 (15):e008552. 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Cheng ST, Chan AC: The Center for Epidemiologic Studies Depression Scale in older Chinese: thresholds for long and short forms . Int J Geriatr Psychiatry 2005, 20 (5):465-470. Gluckman PD, Hanson MA, Cooper C, Thornburg KL: Effect of in utero and early-life conditions on adult health and disease . N Engl J Med 2008, 359 (1):61-73. Van Cauter E, Spiegel K, Tasali E, Leproult R: Metabolic consequences of sleep and sleep loss . Sleep Med 2008, 9 Suppl 1 (0 1):S23-28. McEwen BS: Central effects of stress hormones in health and disease: Understanding the protective and damaging effects of stress and stress mediators . Eur J Pharmacol 2008, 583 (2-3):174-185. Zhang B, Wing YK: Sex differences in insomnia: a meta-analysis . Sleep 2006, 29 (1):85-93. Mallampalli MP, Carter CL: Exploring sex and gender differences in sleep health: a Society for Women's Health Research Report . J Womens Health (Larchmt) 2014, 23 (7):553-562. Whibley D, AlKandari N, Kristensen K, Barnish M, Rzewuska M, Druce KL, Tang NKY: Sleep and Pain: A Systematic Review of Studies of Mediation . Clin J Pain 2019, 35 (6):544-558. Jylhä M: What is self-rated health and why does it predict mortality? Towards a unified conceptual model . Soc Sci Med 2009, 69 (3):307-316. Baglioni C, Battagliese G, Feige B, Spiegelhalder K, Nissen C, Voderholzer U, Lombardo C, Riemann D: Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies . J Affect Disord 2011, 135 (1-3):10-19. Jaehne A, Unbehaun T, Feige B, Lutz UC, Batra A, Riemann D: How smoking affects sleep: a polysomnographical analysis . Sleep Med 2012, 13 (10):1286-1292. Kalmakis KA, Chandler GE: Health consequences of adverse childhood experiences: a systematic review . J Am Assoc Nurse Pract 2015, 27 (8):457-465. Kuh D, Ben-Shlomo Y, Lynch J, Hallqvist J, Power C: Life course epidemiology . J Epidemiol Community Health 2003, 57 (10):778-783. Umberson D, Montez JK: Social relationships and health: a flashpoint for health policy . J Health Soc Behav 2010, 51 Suppl (Suppl):S54-66. Robson M, Chen G, Olsen JA: Explaining subjective social status and health: Beyond education, occupation and income . Soc Sci Med 2025, 371 :117869. Cohen S, Doyle WJ, Baum A: Socioeconomic status is associated with stress hormones . Psychosom Med 2006, 68 (3):414-420. Walker AR, Walker BF: Fetal nutrition and cardiovascular disease in adult life . Lancet 1993, 341 (8857):1421. Young T, Peppard PE, Gottlieb DJ: Epidemiology of obstructive sleep apnea: a population health perspective . Am J Respir Crit Care Med 2002, 165 (9):1217-1239. Knol MJ, VanderWeele TJ: Recommendations for presenting analyses of effect modification and interaction . Int J Epidemiol 2012, 41 (2):514-520. Andersson T, Alfredsson L, Källberg H, Zdravkovic S, Ahlbom A: Calculating measures of biological interaction . Eur J Epidemiol 2005, 20 (7):575-579. Ben-Shlomo Y, Kuh D: A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives . Int J Epidemiol 2002, 31 (2):285-293. Danese A, McEwen BS: Adverse childhood experiences, allostasis, allostatic load, and age-related disease . Physiol Behav 2012, 106 (1):29-39. Additional Declarations No competing interests reported. Supplementary Files annex.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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database","fulltext":[{"header":"Background","content":"\u003cp\u003eNearly one-third of human life is spent sleeping, and sleep is crucial for endocrine regulation and memory formation[1, 2]. Insufficient or excessive sleep is associated with various conditions, such as obesity[3], metabolic syndrome[4], cardiovascular disease[5], cognitive decline[6], and anxiety[7]. Conversely, adequate sleep is essential for physical and mental health. However, a domestic study targeting middle-aged and older adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 reported that in 2011, only 38.7% had an ideal sleep duration (7\u0026ndash;9 hours)[8]. A meta-analysis of 13 studies from China further showed a 47.2% prevalence of sleep disorders among those over 60 years[9]. Therefore, identifying factors contributing to sleep problems in middle-aged and older populations and implementing preventive strategies are essential for reducing sleep-related diseases.\u003c/p\u003e\u003cp\u003eSleep regulation involves three primary mechanisms: (1) circadian rhythm; (2) homeostatic sleep drive; and (3) autonomic nervous system[10]. The sleep\u0026ndash;wake cycle, the most prominent human circadian rhythm, is regulated by the suprachiasmatic nucleus (SCN) of the hypothalamus. Homeostatic sleep drive refers to the sleep need arising from the accumulation of certain substances or structures during wakefulness[11]. During critical periods, such as fetal or early postnatal stages, malnutrition is hypothesized to lead to lasting metabolic and structural changes[12]. Cintra et al. reported that chronic fetal malnutrition alters homeostatic sleep drive and circadian rhythms[11], while Galler et al. noted that fetal malnutrition may impair adult sleep quality[13]. Furthermore, adverse intrauterine environments can permanently reset the hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal (HPA) axis[14]. Since the HPA axis regulates alertness and sleep, dysfunction at any level (corticotropin-releasing hormone [CRH], glucocorticoid, or mineralocorticoid receptors) can disrupt sleep. Evidence from humans and animals indicates a bidirectional relationship between sleep disorders and HPA axis dysregulation[15].\u003c/p\u003e\u003cp\u003eChina, one of the world\u0026rsquo;s largest developing countries, experienced a three-year famine from 1959 to 1962[16], creating a 'natural experiment' for assessing the long-term health effects of early-life famine. Prior research has linked early famine exposure to increased risk of diabetes[17], hypertension[18], schizophrenia[19], and metabolic syndrome[20]. However, little is known about its impact on sleep. Therefore, this study aimed to assess the association between early-life famine exposure and sleep quality in middle-aged and older adults.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and population\u003c/h2\u003e\u003cp\u003eData from the China Health and Retirement Longitudinal Study (CHARLS) project were utilized in this analysis. In 2011, a multi-stage probability sampling method randomly selected 150 county-level and 450 village-level units across 28 Chinese provinces. The baseline survey collected socioeconomic and health data from middle-aged and older individuals, with biennial follow-ups. Details of the study areas and sampling procedures are described elsewhere[21]. This study is based on data from the 2014 Life Course Survey, which included residential and migration history, childhood experiences, and education, and the third national follow-up in 2015, involving 14,582 adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years. After excluding those with missing data, 13,880 participants were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Ethics approval and consent to participate.This is a retrospective study based on CHARLS database. The patient's information has been hidden before the study. There is no need for the patient's informed consent and no ethical conflict. The original CHARLS was approved by the Ethical Review Committee of Peking University (IRB00001052\u0026ndash;11015), and all participants signed the informed consent at the time of participation. This research followed the guidance of the Declaration of Helsinki[21].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eOutcome variable definition\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eOutcome variable definition\u003c/div\u003e\u003cp\u003eFamine exposure during 1959\u0026ndash;1962 was classified by birth year, consistent with prior studies. Participants were divided into four groups: unexposed stage (born from January 1, 1963 to December 31, 1966), fetal exposure (January 1, 1959 to December 31, 1962), childhood exposure (January 1, 1949 to December 31, 1958), and adolescent/adult exposure (January 1, 1921 to December 31, 1948)[22].\u003c/p\u003e\u003cp\u003eNighttime sleep duration was assessed by the question: 'In the past month, how many hours did you actually sleep at night (average hours per night)?' Based on National Sleep Foundation recommendations[23], those participants reporting 7\u0026ndash;9 hours were classified as having adequate sleep, and those with \u0026lt;\u0026thinsp;7 hours or \u0026ge;\u0026thinsp;9 hours as poor sleep.\u003c/p\u003e\u003cp\u003eData were collected through face-to-face interviews, covering sociodemographic characteristics (age, gender, residence, marital status, education level, economic status) and health-related factors (body mass index [BMI], smoking, drinking, physical disabilities, health status, adverse childhood experiences, and depression). Gender was defined as male or female, and residence as urban or rural. Marital status was coded as married (living with or without their spouse) or unmarried (divorced, widowed, or unmarried). Education level was grouped into 'primary school and below,' 'middle school,' and 'high school and above.' Economic status is defined based on the question 'How was your family's economic situation before you were 17?' 'Worse than theirs' or 'much worse than theirs' is defined as below average, while 'much better', 'a little better than theirs', or 'the same as theirs' is defined as above or at the average level. BMI was calculated as weight (kilograms) divided by height (square meters). Following Chinese adult standards[24], BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2; was defined as underweight, 18.5\u0026ndash;23.9 as normal, 24.0\u0026ndash;27.9 as overweight, and \u0026ge;\u0026thinsp;28.0 as obese, with normal weight as the reference. Smoking status was classified as non-smoker (never or quit) or current smoker; drinking status as non-drinker (never or quit) or current drinker. Physical disability was assessed by the question 'Do you have any of the following disability issues': 1. Physical disability, 2. Brain damage, 3. Blindness or partial blindness, 4. Deafness or partial deafness, 5. Muteness or severe stuttering; reporting any condition was coded as yes. Self-rated health was assessed with the question: 'How do you feel about your own health?' Adverse Childhood Experiences (ACEs) included 11 negative events before age 17 (bullying, corporal punishment, domestic violence, emotional neglect, parental crime, parental divorce, parental disability, parental substance abuse, poor parental mental health, parental death, unsafe living environment)[25\u0026ndash;27]. ACEs are obtained through standardized questionnaires; the specific questions and evaluation criteria are provided in the attachment.ACEs were coded as 0 (absent) or 1 (present). Based on total ACEs (0\u0026ndash;11), participants were grouped as 0, 1, 2\u0026ndash;3, and \u0026ge;\u0026thinsp;4[28]. Depression was measured using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10); scores\u0026thinsp;\u0026ge;\u0026thinsp;10 indicated depressive symptoms[29].\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eFor comparisons among sleep duration groups, continuous variables with a normal distribution are expressed as (x̄ \u0026plusmn; s), and compared using independent-samples t-tests. Categorical variables were expressed as frequencies or proportions, with group differences assessed by the χ2 test. Logistic regression models were constructed to evaluate the association between famine exposure and sleep. Model 1 was unadjusted, while Model 2 was adjusted for gender, marital status, education level, residence, economic status, physical disability, health condition, ACEs, BMI, smoking and drinking status, and depression. All regression models reported odds ratios (OR) with 95% confidence intervals (CIs). To assess potential confounding, stratified analyses were performed by gender (female or male), residence (rural or urban), physical disability (yes or no), education level (primary school or below, middle school, high school or above), current smoking status (yes or no), current drinking status (yes or no), marital status (married or unmarried), BMI, ACEs, health status, and depression (yes or no). Interactions were further tested to examine modifying effects. Statistical analyses were performed using IBM SPSS Statistics 26, with two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 13,880 participants were included (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), of whom 5,670 (40.8%) reported adequate sleep and 8,210 (59.2%) poor sleep. The poor sleep group was older, with a higher proportion of women, lower education levels (primary school or below), and lower self-assessed economic status. Marital status data indicated a slightly higher proportion of poor sleep among unmarried individuals. The poor sleep group reported lower alcohol consumption. Participants with adequate sleep had marginally higher BMI than those with poor sleep. Poor sleep was significantly associated with higher rates of physical disability, depressive symptoms, and self-rated health as 'poor/very poor'. Moreover, the prevalence of poor sleep increased with the number of ACEs.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of the characteristics of middle-aged and elderly people with different sleep durations in China in 2015\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristics\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal(N\u0026thinsp;=\u0026thinsp;13880)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eSleep duration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eadequate sleep group(n\u0026thinsp;=\u0026thinsp;5670)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003epoor sleep group(n\u0026thinsp;=\u0026thinsp;8210)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge,y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59.49(10.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.19(10.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.39(10.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6516(46.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2838(50.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3678(44.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7364(53.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2832(49.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4532(55.20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13763(99.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5635(99.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8128(99.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e=\u0026thinsp;0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e117(0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35(0.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e82(1.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary school or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8493(61.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3197(56.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5296(64.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3658(26.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1658(29.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2000(24.36)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1727(12.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e814(14.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e913(11.13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3373(24.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1433(25.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1973(24.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e=\u0026thinsp;0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10507(75.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4237(74.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6237(75.97)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;Average level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8353(60.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3551(62.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4802(58.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;Average level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5527(39.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2119(37.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3408(41.51)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4620(33.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1939(34.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2681(32.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e=\u0026thinsp;0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9260(66.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3731(65.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5529(67.34)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent drinking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4897(35.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2077(36.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2820(34.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e=\u0026thinsp;0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8983(64.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3593(63.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5390(65.65)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.99(4.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.17(4.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.87(4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamine exposure grouping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4215(30.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1965(34.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2250(27.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1480(10.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e618(10.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e862(10.50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4790(34.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1943(34.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2847(34.68)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3395(24.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1144(20.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2251(27.42)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1870(13.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e616(10.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1254(15.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12010(86.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5054(89.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6956(84.73)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3635(26.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1779(31.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1856(22.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7760(55.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3134(55.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4626(56.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2485(17.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e757(13.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1728(21.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4592(33.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1392(24.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3200(38.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9288(66.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4278(75.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5010(61.02)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of ACEs items\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e671(4.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e305(5.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e366(4.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3301(23.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1468(25.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1833(22.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026thinsp;~\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7343(52.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2964(52.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4379(53.34)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2565(18.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e933(16.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1632(19.87)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: ACE, adverse childhood experience; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003e Continuous data are reported as the mean (SD), and categorical data are reported as the number and percentage of participants.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eHealth status Q1:excellent or very good or good,Q2:fair,Q3:poor or very poor.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eFamine exposure grouping:Q1:unexposed stage;Q2:fetal exposure;Q3:childhood exposure;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eQ4:adolescent/adult exposure.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA multivariate logistic regression analysis was conducted to investigate the association between early-life famine exposure and poor sleep in middle and older age. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the associations between famine exposure groups and the risk of inadequate sleep. In Model 1 (unadjusted), all famine-exposed groups had significantly elevated risks compared with the unexposed group. The ORs (95% CIs) were 1.22 (1.08\u0026ndash;1.37) for the fetal exposure group, 1.28 (1.18\u0026ndash;1.39) for the childhood exposure group, and 1.72 (1.57\u0026ndash;1.89) for the adolescent/adult exposure group (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A clear dose\u0026ndash;response trend was observed: 'the later the exposure period, the higher the risk,' with the adolescent/adult group showing the most significant risk. In Model 2, after adjustment for gender, marital status, education level, residence, economic status, physical disability, health status, ACEs, BMI, current smoking and drinking status, and depression, the associations remained substantial, and the dose\u0026ndash;response pattern persisted. Adjusted ORs (95% CIs) for fetal exposure, childhood exposure, and adolescent/adult exposure were 1.24 (1.10\u0026ndash;1.40), 1.21 (1.11\u0026ndash;1.32), and 1.58 (1.43\u0026ndash;1.75), respectively (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results indicate that famine exposure in early life is an independent risk factor for poor sleep in later life.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate logistic regression model analysis of famine exposure and poor sleep duration\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFamine exposure grouping\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnexposed stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFetal exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.22(1.08\u0026ndash;1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.24(1.10\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChildhood exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.28(1.18\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.21(1.11\u0026ndash;1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdolescent/adult exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.72(1.57\u0026ndash;1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.58(1.43\u0026ndash;1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: ACE, Adverse Childhood Experience; OR, odds ratio.BMI, body mass index (calculated as weight in kilograms divided by height in meters squared)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1 was the crude model\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2 was adjusted for gender, marital status, education level, residence, economic status, physical disability, health status, ACEs, BMI, current smoking and drinking status, and depression\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo further evaluate independent predictors of poor sleep, multivariate logistic regression including all covariates was performed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Female gender was associated with a higher risk (OR 1.28, 95% CI 1.16\u0026ndash;1.41), whereas higher educational attainment exerted a protective effect. Compared with those with a primary school education or below, individuals with junior high school education (OR\u0026thinsp;=\u0026thinsp;0.89, 95% CI 0.82\u0026ndash;0.98) and high school or above (OR\u0026thinsp;=\u0026thinsp;0.86, 95% CI 0.76\u0026ndash;0.96) had a considerably lower risk of poor sleep. Poor self-rated health was robustly associated with poor sleep: compared with those rating their health as 'good,' the risk increased for those reporting 'average' (OR\u0026thinsp;=\u0026thinsp;1.23, 95% CI 1.14\u0026ndash;1.34) and 'poor/very poor' (OR\u0026thinsp;=\u0026thinsp;1.51, 95% CI 1.34\u0026ndash;1.70). Physical disability also increased the risk (OR\u0026thinsp;=\u0026thinsp;1.19, 95% CI 1.07\u0026ndash;1.32); participants with depression had a substantially higher risk (OR\u0026thinsp;=\u0026thinsp;1.62, 95% CI 1.49\u0026ndash;1.76). Drinkers were more likely to report poor sleep than non-drinkers (OR\u0026thinsp;=\u0026thinsp;1.09, 95% CI 1.01\u0026ndash;1.19), whereas smoking showed no significant association. After full adjustment, the number of ACEs was not considerably related to poor sleep.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic regression analysis of the incidence of poor sleep duration in middle-aged and elderly people\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(ref=Normal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.07(0.91\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e=\u0026thinsp;0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.93(0.86\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e=\u0026thinsp;0.078\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.89(0.81\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e=\u0026thinsp;0.066\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender(ref\u0026thinsp;=\u0026thinsp;male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.28(1.16\u0026ndash;1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status (ref\u0026thinsp;=\u0026thinsp;unmarried)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.50(0.99\u0026ndash;2.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e=\u0026thinsp;0.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational level(ref\u0026thinsp;=\u0026thinsp;Primary school and below)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.89(0.82\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e=\u0026thinsp;0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.86(0.76\u0026ndash;0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e=\u0026thinsp;0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidence(ref\u0026thinsp;=\u0026thinsp;Urban)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.93(0.85\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e=\u0026thinsp;0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoking status(ref\u0026thinsp;=\u0026thinsp;NO)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.08(0.99\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e=\u0026thinsp;0.096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent drinking status(ref\u0026thinsp;=\u0026thinsp;NO)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.09(1.01\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e=\u0026thinsp;0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth status(ref\u0026thinsp;=\u0026thinsp;excellent and very good and good)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.23(1.14\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor and very poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.51(1.34\u0026ndash;1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical disability(ref\u0026thinsp;=\u0026thinsp;NO)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.19(1.07\u0026ndash;1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of ACEs items(ref\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.90(0.76\u0026ndash;1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026thinsp;~\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.02(0.87\u0026ndash;1.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.08(0.90\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression(ref\u0026thinsp;=\u0026thinsp;NO)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.62(1.49\u0026ndash;1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAbbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared);ACE, Adverse Childhood Experience.\u003c/p\u003e\n\u003cp\u003eBMI:Normal:18.5kg/m\u003csup\u003e2\u003c/sup\u003e\u0026le;BMI\u0026lt;24.0kg/m\u003csup\u003e2\u003c/sup\u003e;Underweight:＜18.5 kg/m\u003csup\u003e2\u003c/sup\u003e ;Overweight:24.0 kg/m\u003csup\u003e2\u003c/sup\u003e \u0026le; BMI \u0026lt; 28.0 kg/m\u003csup\u003e2\u003c/sup\u003e;Obese:BMI \u0026ge; 28.0 kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo examine heterogeneity in the famine\u0026ndash;sleep relationship, stratified analyses were performed by gender, marital status, education, physical disability, and BMI (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Gender appeared to modify the association: across all famine exposure periods, ORs were higher for females than males, especially in the adolescent/adult exposure group (females: OR\u0026thinsp;=\u0026thinsp;1.98, 95% CI 1.73\u0026ndash;2.26; males: OR\u0026thinsp;=\u0026thinsp;1.58, 95% CI 1.38\u0026ndash;1.80), suggesting greater female susceptibility. Marital status also influenced outcomes; among married individuals, exposure at all stages considerably increased poor sleep risk in a dose-response manner, where associations were not significant in the unmarried population, possibly due to small sample size. Education level showed a buffering effect. In participants with primary or junior high school education, famine exposure was significantly associated with poor sleep. Conversely, in those with senior high school or above, associations were largely insignificant except for adolescence/adulthood exposure (P\u0026thinsp;=\u0026thinsp;0.063). Stratification by disability revealed that adolescence/adulthood exposure significantly increased risk in both disabled and non-disabled groups. However, risks from fetal and childhood exposure were elevated only among those with disabilities. BMI stratified analysis indicated that the association between famine exposure and poor sleep varied across BMI categories. In the normal weight and overweight groups, the pattern was consistent and significant. Among the underweight, only adolescent/adult exposure was associated with elevated risk, while in the obese group, a considerable increase was also observed only during adolescence/adulthood (OR\u0026thinsp;=\u0026thinsp;1.91, 95% CI 1.45\u0026ndash;2.53).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAdjusted OR and 95% CI for association between famine exposure and poor sleep, stratified by potential modifier (ref: unexposed stage).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003epoor sleepOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFetal exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.09(0.92\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e=\u0026thinsp;0.330\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChildhood exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.22(1.08\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e=\u0026thinsp;0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdolescent/adult exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.58(1.38\u0026ndash;1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eWomed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFetal exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.36(1.16\u0026ndash;1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChildhood exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.36(1.22\u0026ndash;1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdolescent/adult exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.98(1.73\u0026ndash;2.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003emarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFetal exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.22(1.08\u0026ndash;1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChildhood exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.28(1.17\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdolescent/adult exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.72(1.56\u0026ndash;1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFetal exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.79(0.15\u0026ndash;4.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e=\u0026thinsp;0.780\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChildhood exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.67(0.65\u0026ndash;4.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e=\u0026thinsp;0.290\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdolescent/adult exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.86(0.62\u0026ndash;5.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e=\u0026thinsp;0.270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003eEducational level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePrimary school or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFetal exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.09(0.91\u0026ndash;1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e=\u0026thinsp;0.340\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChildhood exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.21(1.08\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e=\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdolescent/adult exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.54(1.37\u0026ndash;1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMiddle school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFetal exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.49(1.22\u0026ndash;1.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChildhood exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.32(1.13\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e=\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdolescent/adult exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.87(1.50\u0026ndash;2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHigh school or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFetal exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.15(0.88\u0026ndash;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e=\u0026thinsp;0.294\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChildhood exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00(0.78\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e=\u0026thinsp;0.900\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdolescent/adult exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.32(0.99\u0026ndash;1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e=\u0026thinsp;0.063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003ePhysical disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFetal exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.22(1.07\u0026ndash;1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChildhood exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.30(1.19\u0026ndash;1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdolescent/adult exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.67(1.51\u0026ndash;1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFetal exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.26(0.84\u0026ndash;1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChildhood exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.02(0.79\u0026ndash;1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdolescent/adult exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.57(1.20\u0026ndash;2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFetal exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.27(1.06\u0026ndash;1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChildhood exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.36(1.21\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdolescent/adult exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.87(1.63\u0026ndash;2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFetal exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.07(0.54\u0026ndash;2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.853\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChildhood exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.43(0.91\u0026ndash;2.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdolescent/adult exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.59(1.04\u0026ndash;2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFetal exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.22(0.99\u0026ndash;1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChildhood exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.18(1.02\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdolescent/adult exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.41(1.20\u0026ndash;1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eObese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFetal exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.12(0.84\u0026ndash;1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.441\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChildhood exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.15(0.93\u0026ndash;1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdolescent/adult exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.91(1.45\u0026ndash;2.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, after adjusting for covariates, the cross-group OR values of famine exposure combined with the depression group were all considerably greater than 1, compared with the unexposed and non-depressed groups. In the additive interaction analysis, childhood exposure and depression demonstrated a positive synergistic effect on poor sleep, with relative excess risk due to interaction (RERI; 95% CI) of 0.390 (0.097\u0026ndash;0.684), 19.2% of the total effect attributable to the interaction, and synergy index (S; 95% CI) of 1.603 (1.083\u0026ndash;2.374). However, interaction effects for other exposure groups with depression were not significant, nor were multiplicative interactions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe interaction between the exposure to the Chinese famine and depression on the occurrence of poor sleep\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFamine exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-depression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003edepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003edepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP for interaction\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRERI(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAP(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS(95%CI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnexposed stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.494 (1.300,1.719)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFetal exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.175 (1.018,1.357)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.122 (1.709,2.647)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.453 (-0.036,0.941)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.213 (0.019,0.408)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.677 (0.984,2.857)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChildhood exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.153 (1.040,1.279)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.037 (1.778,2.336)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.390 (0.097,0.684)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.192 (0.059,0.324)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.603 (1.083,2.374)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdolescent/adult exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.574 (1.397,1.774)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.389 (2.043,2.798)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.321 (-0.064,0.705)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.134 (-0.014,0.283)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.300 (0.949,1.781)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e* denotes p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** denotes p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** denotes p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001. In the additive interaction, RERI, AP, and S values highlighted in red indicate the presence of an additive interaction effect, using Non-exposed and Non-depression as the reference group. P for interaction indicates the p-value for the multiplicative interaction effect; a value less than 0.05 indicates statistical significance.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel was adjusted for gender, marital status, education level, residence, economic status, physical disability, health status, ACEs, BMI, current smoking and drinking status, and depression.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003eThis cross-sectional study demonstrated that early-life famine exposure increased the risk of poor sleep in middle-aged and older adults. The coexistence of depression and childhood famine exposure further exacerbates this risk.\u003c/p\u003e\u003cp\u003eThis association remained robust after adjustment for sociodemographic and health-related factors, indicating famine exposure as an independent risk factor for later-life sleep problems. Our findings align with the 'Developmental Origins of Health and Disease' (DOHaD) theory, which posits that adverse exposures during critical developmental windows exert permanent programming effects on physiology and disease susceptibility[30]. Fetal, childhood, and adolescent/adult exposures were all associated with poor sleep, suggesting multiple life stages may significantly influence the maturation of the sleep regulatory system. A significant dose\u0026ndash;response relationship was observed, with the highest risk in the adolescent/adult group. This pattern strongly suggests that severe malnutrition at later developmental stages may cause direct, irreversible damage to key brain regions already established or maturing (such as the suprachiasmatic nucleus and amygdala, critical for sleep\u0026ndash;wake regulation)[31]. Furthermore, later exposure likely involves greater awareness and memory of the social disruption and psychological trauma from famine, with long-term consequences on stress response systems and sleep quality, potentially mediated through epigenetic mechanisms[32].\u003c/p\u003e\u003cp\u003eThis study confirms that female gender is a significant risk factor for poor sleep in middle-aged and older populations, consistent with global evidence of gender differences in sleep disorders[33]. This difference may be influenced by multiple factors, including neuroendocrine changes during menopause (such as fluctuations in estrogen and progesterone), differential stress responses, and higher prevalence of anxiety and depression among women[34]. Conversely, higher education demonstrates a protective effect, supporting the 'healthy social gradient' theory. Education, as a core indicator of socioeconomic status, may enhance sleep quality by improving health literacy, fostering healthier behaviors (such as regular exercise), and strengthening the ability to manage stress and access healthcare resources[35]. A clear gradient relationship was observed between health status and poor sleep. Health status is a comprehensive indicator reflecting undiagnosed physiological discomfort, psychological distress, and limitations in social functioning, all of which can interfere with sleep[36]. Physical disability and depression showed the strongest associations with poor sleep. Physical disabilities are frequently accompanied by chronic pain and limited mobility, directly impacting sleep onset and maintenance. Depression and sleep disorders share neurobiological pathways (including HPA dysfunction and monoamine imbalance), while core depressive symptoms (such as rumination and low mood) are major contributors to insomnia[37]. Alcohol consumption was associated with increased risk of poor sleep, countering the common misconception that 'alcohol helps sleep.' Although alcohol can shorten sleep latency, its metabolites disrupt later sleep stages, leading to fragmentation, rapid eye movement (REM) rebound, and reduced slow-wave sleep, thereby lowering overall sleep quality[37]. In this study, smoking did not show a significant independent association. A possible explanation is that nicotine\u0026rsquo;s stimulating effects and nocturnal withdrawal responses may be masked by its strong correlation with confounding factors such as depression and poor self-rated health[38]. After adjustment for relevant variables, ACEs were no longer significantly associated with poor sleep in middle-aged and older adults. This observation suggests that the long-term influence of ACEs on sleep may be indirect, primarily mediated through adult risk pathways such as lower socioeconomic status, poorer mental health, and increased chronic disease burden[39]. These findings align with life-course epidemiology, emphasizing that later-life factors may exert more direct effects on current health; however, the impact of early adversity is primarily 'mediated' by its subsequent consequences[40].\u003c/p\u003e\u003cp\u003eThe significant association and dose\u0026ndash;response relationship observed among the married population suggests that even with early adversity, social support (such as spousal support) may not fully mitigate its adverse effects. However, the association was insignificant among the unmarried population, likely due to the small sample size, indicating that the absence of social support may exacerbate vulnerability; however, this finding should be interpreted with caution[41]. More importantly, the level of education demonstrated an apparent buffering effect. Among individuals with a high school education or above, the adverse impact of famine exposure was essentially eliminated, consistent with the theories of 'cognitive reserve' and 'resource substitution'[42]. Higher education generally confers greater health knowledge, stronger coping skills, and better career and income opportunities, enabling healthier lifestyles and improved access to medical care in adulthood. These advantages may effectively attenuate the long-term consequences of early-life stress on health[43]. Famine exposure during the fetal and childhood periods significantly increased the risk among individuals with physical disabilities but not among those without disabilities. This finding strongly suggests that early malnutrition may act synergistically with acquired physiological defects (such as disabilities), jointly overburdening biological systems and thereby increasing susceptibility to adverse outcomes such as sleep disorders[44]. The BMI-stratified results revealed a more complex pattern. Significant and consistent associations were observed in the normal weight and overweight groups, suggesting these categories adequately capture the core effects of famine exposure. In the obese group, however, increased risk was observed only for exposure during adolescence/adulthood; this observation may reflect the fact that obesity in adulthood is an independent, substantial risk factor for poor sleep (such as obstructive sleep apnea), which could overshadow the subtler influence of earlier famine exposure[45].\u003c/p\u003e\u003cp\u003eThe key finding of this study is that childhood famine exposure shows a significant additive interaction with depression in increasing the risk of poor sleep. Additive interaction assesses whether the combined effect of two risk factors exceeds the sum of their individual effects. A significant RERI value (0.390) indicates that, compared with individuals with neither childhood famine exposure nor depression, 0.390 of the excess risk of poor sleep in those with both risk factors is attributable to their synergistic effect[46]. The attributable proportion due to interaction (AP) value (19.2%) highlights the public health relevance, indicating that nearly 20% of the risk of poor sleep among individuals exposed to both factors can be explained by this interaction[47]. These findings have important clinical and public health implications, suggesting that proactive screening for and treatment of depression in individuals with childhood famine exposure may significantly reduce their risk of poor sleep.\u003c/p\u003e\u003cp\u003eWhy is childhood a critical window for such interactions? These findings align closely with the 'sensitive period' and 'biological embedding' models in life course epidemiology[48]. Childhood represents a key stage for brain development, programming of stress-response systems (such as the HPA axis), and the establishment of health behaviors. Severe nutritional deprivation during this period may cause lasting changes in neurocognitive and emotional regulation, creating an intrinsic vulnerability to psychological stress[49]. When stressors in adulthood trigger depression, this latent susceptibility is activated, and the two risk factors interact, manifesting as sleep disturbances.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eAdvantages and Limitations\u003c/h2\u003e\u003cp\u003eA key strength of this study is its large sample size, which enabled a robust exploration of the association between famine exposure and poor sleep in middle-aged and older adults. Another important contribution is the identification of a significant additive interaction between childhood famine exposure and depression, highlighting a novel pathway for preventing poor sleep and reduced sleep-related disorders in this population.\u003c/p\u003e\u003cp\u003eThis study has some notable limitations. First, it is a cross-sectional study, so we cannot draw any causal inferences between the study variables.2. Some of the data for the research variables mainly come from the participants' recollections, which may involve recall bias. 3. Potential selection bias due to missing data. 4. Although some important confounding factors have been adjusted for, there may still be some unknown confounding factors that could affect the research results.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis population-based cross-sectional study indicates that early-life famine exposure has lasting effects on sleep in middle-aged and older adults. It identifies high-risk groups, including women, those with limited educational attainment, and those with disabilities or depression. These findings underscore the need for public health policies that extend beyond current risk factors and adopt a life-course perspective. At a practical level, community screenings should identify individuals with a history of early-life famine for prioritized care, while integrating psychological health services (especially for depression) into routine management. Such comprehensive strategies may help mitigate the long-term health consequences of early-life famine and enhance the quality of life in aging populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the China Health and Retirement Longitudinal Study team for providing data and the training of using the dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcept and design: Cui, Cheng\u003c/p\u003e\n\u003cp\u003eAcquisition, analysis, or interpretation of data: Cui, Cheng, Huang, Wang.\u003c/p\u003e\n\u003cp\u003eDrafting of the manuscript: Cheng.\u003c/p\u003e\n\u003cp\u003eCritical revision of the manuscript for important intellectual content: He, Li, Cui.\u003c/p\u003e\n\u003cp\u003eStatistical analysis: Cheng, Cui.\u003c/p\u003e\n\u003cp\u003eAdministrative, technical, or material support: He, Li, Huang.\u003c/p\u003e\n\u003cp\u003eSupervision: Wang, Cui.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used open-access data from the China Health and Retirement Longitudinal Study, which could be downloaded from http://charls.pku.edu.cn/index.htm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not include any animal or human experiments. This study was approved by the Biomedical Ethics Council of Beijing University. The IRB approval number for the main household survey, including anthropometrics, is IRB00001052-11015; the IRB approval number for biomarker collection is IRB00001052-11014.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBesedovsky L, Lange T, Born J: \u003cstrong\u003eSleep and immune function\u003c/strong\u003e. \u003cem\u003ePflugers Arch \u003c/em\u003e2012, \u003cstrong\u003e463\u003c/strong\u003e(1):121-137.\u003c/li\u003e\n\u003cli\u003eDoherty R, Madigan S, Warrington G, Ellis J: \u003cstrong\u003eSleep and Nutrition Interactions: Implications for Athletes\u003c/strong\u003e. \u003cem\u003eNutrients \u003c/em\u003e2019, \u003cstrong\u003e11\u003c/strong\u003e(4).\u003c/li\u003e\n\u003cli\u003eChaput JP, Bouchard C, Tremblay A: \u003cstrong\u003eChange in sleep duration and visceral fat accumulation over 6 years in adults\u003c/strong\u003e. \u003cem\u003eObesity (Silver Spring) \u003c/em\u003e2014, \u003cstrong\u003e22\u003c/strong\u003e(5):E9-12.\u003c/li\u003e\n\u003cli\u003eKoren D, Taveras EM: \u003cstrong\u003eAssociation of sleep disturbances with obesity, insulin resistance and the metabolic syndrome\u003c/strong\u003e. \u003cem\u003eMetabolism \u003c/em\u003e2018, \u003cstrong\u003e84\u003c/strong\u003e:67-75.\u003c/li\u003e\n\u003cli\u003eKwok CS, Kontopantelis E, Kuligowski G, Gray M, Muhyaldeen A, Gale CP, Peat GM, Cleator J, Chew-Graham C, Loke YK\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eSelf-Reported Sleep Duration and Quality and Cardiovascular Disease and Mortality: A Dose-Response Meta-Analysis\u003c/strong\u003e. \u003cem\u003eJ Am Heart Assoc \u003c/em\u003e2018, \u003cstrong\u003e7\u003c/strong\u003e(15):e008552.\u003c/li\u003e\n\u003cli\u003eHenry A, Katsoulis M, Masi S, Fatemifar G, Denaxas S, Acosta D, Garfield V, Dale CE: \u003cstrong\u003eThe relationship between sleep duration, cognition and dementia: a Mendelian randomization study\u003c/strong\u003e. \u003cem\u003eInt J Epidemiol \u003c/em\u003e2019, \u003cstrong\u003e48\u003c/strong\u003e(3):849-860.\u003c/li\u003e\n\u003cli\u003eChellappa SL, Aeschbach D: \u003cstrong\u003eSleep and anxiety: From mechanisms to interventions\u003c/strong\u003e. \u003cem\u003eSleep Med Rev \u003c/em\u003e2022, \u003cstrong\u003e61\u003c/strong\u003e:101583.\u003c/li\u003e\n\u003cli\u003eMa Qianqian, He Xianying, Sun Dongxu, Cui Fangfang, Zhao Jie: \u003cstrong\u003eAnalysis of the Dose-Response Relationship Between Sleep Duration and Hypertension in Middle-Aged and Elderly People\u003c/strong\u003e. 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\u003cstrong\u003e106\u003c/strong\u003e(1):29-39.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"famine, sleep, middle-aged and elderly, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-7938543/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7938543/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e Sleep plays an important role in regulating endocrine function and forming memory in the human body. However, there are few studies on the impact of early-life exposure to famine on sleep. Therefore, the purpose of this study is to assess the effects of early-life exposure to famine on sleep in middle-aged and elderly populations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e Using the validated China Health and Retirement Longitudinal Study(CHARLS) baseline questionnaire, a logistic regression model was used to assess the association between famine exposure and sleep. The influence of demographic and health behavior factors on the association was evaluated through stratified analysis and interaction tests.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e Exposure to famine in early life increases the risk of poor sleep in middle-aged and older populations. Compared with the unexposed group, all famine-exposed groups showed a significantly higher risk of poor sleep. Specifically, the ORs (95% CI) for the fetal exposure, childhood exposure, and adolescent/adult exposure were 1.24 (1.10\u0026ndash;1.40), 1.21 (1.11\u0026ndash;1.32), and 1.58 (1.43\u0026ndash;1.75), respectively, with all P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001. The co-occurrence of depression and childhood famine exposure further exacerbates the occurrence of poor sleep.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e This population-based cross-sectional study indicates that early-life famine exposure has lasting effects on sleep in middle-aged and older adults. It identifies high-risk groups, including women, those with limited educational attainment, and those with disabilities or depression. These findings underscore the need for public health policies that extend beyond current risk factors and adopt a life-course perspective.\u003c/p\u003e","manuscriptTitle":"A Study on the Association Between Early-Life Exposure to Famine and Sleep Quality in Adulthood：evidence from the China health and retirement longitudinal study database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 16:58:28","doi":"10.21203/rs.3.rs-7938543/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e4d1118-579f-49a5-96b9-867a84d69c41","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-20T16:19:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 16:58:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7938543","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7938543","identity":"rs-7938543","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}