Associations between reproductive factors and frailty in middle-aged and older women: Evidence from the China Health and Retirement Longitudinal Study

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METHODS Frailty is assessed by the Frailty Index (FI), constructed based on 50 indicators of health deficits. Reproductive factors were extracted from the CHARLS survey. They included the number of biological children, age at menopause, age at menarche, age at first birth, reproductive period, and history of abortion. RESULTS We found that participants with ≥ 3 children had a 26.6% increased risk of frailty compared with participants with 2 children, and those with menarche aged 18 years or older had a 30.1% increased probability of frailty compared with participants with menarche aged 15 years or younger. Participants with menopause at age 55 or older were 57.4 percent more likely to be frail compared to those with menopause between 45 and 55 years of age. In addition, women with a history of abortion were significantly associated with an increased prevalence of frailty, with reproductive period ≤ 33 being a protective factor. The age of the first livebirth did not exhibit a significant association with frailty. CONCLUSIONS The findings suggest that the risk of developing frailty among middle-aged and older Chinese women is strongly associated with specific reproductive factors, such as the number of biological children, age at menarche, history of abortion, and age at menopause. These findings provide potential reproductive health indicators for early identification and prevention of frailty and highlight the importance of further research into these relationships. frailty reproductive factors CHARLS women Figures Figure 1 Figure 2 1. Introduction Frailty is a growing concern in addressing the challenges of global aging( 1 ). Frailty is a clinical condition characterized by a reduction in the body's physiological reserves, a decrease in stress resistance, and an increase in physical vulnerability and disease susceptibility( 2 ).In people over the age of 65 in China, the prevalence of frailty ranges from 4–17%, and in the older age group of 75 and above, it climbs from 10–45%( 3 ). Frailty can significantly increase the risk of adverse health outcomes, including hospitalization, falls, physical disability and even death. This chain reaction not only poses a serious threat to an individual's quality of life but also places a heavy financial burden on the healthcare system and care services( 4 ). Therefore, timely identification and adaptation of variable risk factors for frailty is essential for developing effective prevention strategies( 5 ). It has been demonstrated by previous studies that the prevalence of frailty is significantly higher in women compared to men( 6 ). This may be due to the generally weaker muscular strength of women compared to men, as well as women's greater responsibility for household tasks and greater symptom tolerance( 7 ). In addition, female reproductive characteristics may have a significant gender-specific effect on frailty. Reproductive history, as a uniquely female life experience, can influence hormone levels, overall health, and lifestyle choices. Among the many reproductive factors, the number of biological children plays a key role and may have a significant impact on the health status of older women( 8 ). Extensive literature reviews the correlation between reproductive behaviour and health in later life and reveals the potential adverse effects of having more children and having them earlier on physical and mental health in later life( 9 ). Research conducted on middle-aged and elderly Chinese women has indicated that having more children substantially raises the likelihood of developing co-morbidities and chronic illnesses( 10 , 11 ). During the investigation into the association between the number of children a woman has and cardiovascular disease, it was revealed that the cardiovascular health of women with five or more children is relatively inferior( 12 ) and has a greater likelihood of developing cardiovascular disease( 6 ), while women with no children had a lower prevalence of cardiovascular disease( 7 ). The results of a study of women in the UK showed an upward trend in frailty as the number of biological children increased. This correlation was not only demonstrated in British women but also validated in studies of other populations, further supporting the conclusion that there is a positive association between the number of biological children and frailty( 13 ). Menopause is a key transitional stage in the female life cycle. Changes in levels of estrogen luteinizing, and follicle-stimulating hormones due to ovarian failure during menopause can lead to a range of problems such as mental, physical, and endocrine disorders( 14 ). Regarding the health effects of menopause in women, a study reveals that postmenopausal women show more severe conditions in terms of experiencing negative emotions, musculoskeletal pain, and symptoms such as sleep and memory disorders compared to premenopausal women( 15 ). This provides some evidence that menopause, a reproductive factor, may lead to an increased risk of developing female frailty. The timing of menarche is affected by various contributing elements, including nutritional status( 16 ), physical activity( 17 ), prepubertal growth trajectory( 18 ), stress exposure, and early life events( 19 ). Therefore, the age at which a woman experiences menarche, serving as a comprehensive measure of her early-life environmental influences, could be linked to a higher likelihood of frailty in later years. Although the association between past abortions and frailty continues to be debated, research involving elderly Greek women demonstrated that abortion could elevate the likelihood of developing frailty. In another study using a two-sample Mendelian randomization method, no significant correlation was found between pregnancy abortion and frailty in old age( 20 ). A long-term study conducted in the United Kingdom uncovered a notable association between women's reproductive factors and their likelihood of experiencing frailty in older age( 11 ). The results of this study found that first menarche < 13 years, age at menopause < 45 years, surgical menopause, and shorter reproductive cycles < 35 years were associated with an increased risk of frailty and an increased prevalence of combined frailty, whereas having two or three children and having the first child later in life reduced the risk of frailty. A significant portion of studies exploring the connection between female reproductive factors and frailty has been carried out in Western nations, with research data from Asian populations remaining scarce. This is crucial due to notable racial and ethnic variations in reproductive characteristics( 21 ). For example, Chinese women typically have a later age of menarche and tend to have a comparatively younger age of menopause compared to Caucasian women( 22 ). To our knowledge, the relationship between reproductive factors and frailty risk in Chinese women has not been reported. Based on data from the China Health and Retirement Longitudinal Study (CHARLS), this study aimed to investigate the possible association between reproductive factors (age at first livebirth, reproductive period, age at menopause, number of biological children, age at menarche, and history of abortion) and frailty in women. 2. Materials and Methods 2.1 Population The database for this study is derived from the China Health and Retirement Longitudinal Study (CHARLS)( 23 ). This comprehensive longitudinal survey is designed for residents aged 45 and above in mainland China.CHARLS aims to comprehensively collect key information on participants' socio-economic status, health status, etc.CHARLS employs a stratified multi-stage probability proportional sampling (PPS) approach to achieve a comprehensive and representative sample. The study encompasses 150 districts and 450 rural/urban communities across the country, with a total participation of 17,708 individuals from 10,257 households. To date, CHARLS has successfully implemented four surveys, conducted in 2011, 2013, 2015, and 2018, and detailed survey reports and updates can be accessed through its official website ( http://www.charls.pku.edu.cn/en ) at( 23 ). It is important to highlight that this survey adhered to rigorous ethical standards and received approval from the Institutional Review Board of Peking University (Approval No. IRB00001052-11015). Additionally, informed consent was obtained from all participants before their involvement. In this study, we specifically used the dataset published by CHARLS in 2018. The follow-up survey in that year had a participation count of 19,816 individuals. Excluding male respondents, participants with missing frailty information, and 1,891 participants with missing information on covariates and lack of information on reproductive factors, a final total of 2,948 participants were included in this analysis. See Fig. 1 for details of the selection process. The minimum sample size of the study was determined according to the formula for calculating sample size in cross-sectional studies [N=(Z² (α/2 )-p-q)/δ 2 ]: ( 1 ) N denotes the sample size needed for the study; ( 2 ) p denotes the prevalence rate of frailty among middle-aged and elderly women in China; ( 3 ) q=(1-p); ( 4 ) Z α/2 was set to 1.96, and α was set to 0.05 for the two-sided test; (5 ) δ denotes the permissible error, calculated at 0.1p. According to a previous study, the prevalence of frailty among middle-aged and elderly women in China was 16.57%( 24 ), which was calculated to mean that at least 1,934 participants were needed for this study to achieve the required sample size. Therefore, the sample in this study met the minimum sample size requirement. 2.2 Assessment of reproductive factors The study extracted information on female reproductive factors, including number of biological children, age at menopause, age at menarche, and history of abortion, and calculated age at first livebirth and reproductive period from cross-sectional survey data published in CHARLS. Information on the number of biological children was extracted from the cross-sectional survey data published in CHARLS 2018 (Wave 4). Age at menopause was collected from Wave 1 to Wave 4 data, whichever was recorded first. Age at menarche and history of abortion were extracted from Wave 4 data. Age at first live births was calculated from the biological age and age of the oldest child. The duration reproductive period was calculated as age at menarche to age at menopause. 2.3 Assessment of frailty The Frailty Index (FI), a quantitative assessment tool designed to measure an individual's debilitating condition, is calculated by accumulating age-related health deficits, and its indices can be programmed and freely constructed as needed based on following the principles of health deficit selection, but it must contain at least 30 health deficit items( 25 ). In this study, 50 indicators were selected for the construction of the FI assessment scale (eTable1) based on the criteria for constructing the FI: 18 items for physical limitations, 10 items for depressive symptoms, chronic diseases (hypertension, dyslipidemia, diabetes mellitus or hyperglycemia, cancer or malignant tumor, chronic lung disease, liver disease, heart disease, stroke, kidney disease, gastric or other digestive disorders,, memory-related disorders, arthritis or rheumatic disease, asthma) 13 items, trauma history 2 items, cognitive impairment 4 items, and others including hearing hearing impairment, visual impairment, self-reported health and self-reported pain 4 items. The evaluation of the above dimensions assigns values to the health variables on a scale of 0–1. The process of calculating the FI involves aggregating the individual's scores on the various health deficits and dividing them by the total number of indicators involved in the assessment (in the present study this total number was set at 50 items). The results obtained take values in the range of 0 to 1, where the closer the value converges to 1, the more severe the individual's frailty is.FI has been used in several CHARLS studies( 26 , 27 ). Based on previous research experience, individuals are classified as frail when the Frailty Index (FI) value is greater than 0.25( 13 , 28 ). 2.4 Assessment of Covariates The covariates were divided into four categories, including basic social characteristics (age, education, marital status, place of residence), health status (hypertension, diabetes, heart disease, BMI), lifestyle (social activities, physical labor, smoking, alcohol consumption), and mental status (self-rated health, life satisfaction, depression) (eTable 2). 2.5 Statistical Analysis Categorical variables were expressed using frequency counts and frequencies (n (%)). Continuous variables were subjected to the Kolmogorov-Smirnov test, and if p > 0.05, they were expressed as mean plus or minus standard deviation (M ± SD). Comparisons between groups were made using two independent samples t-test and χ2 test. To investigate the relationship between reproductive factors and frailty, multivariate logistic regression models were used to estimate odds risk (OR) and 95% confidence intervals (CI). Model 1 was unadjusted. Model 2 was adjusted for age, marriage, residence, education level, social activity, physical activity, hypertension, diabetes, heart disease, health self-assessment, smoking, alcohol use, life satisfaction, BMI, and depressive symptoms. The dose-response relationship between reproductive factors and frailty was further examined using restricted cubic spline analysis, with the most appropriate number of nodes determined by the Akaike Information Criterion (AIC). Subgroup analyses were also conducted based on age (< 60 years, ≥ 60 years), area of residence (rural, urban), education (illiterate, literate), and BMI (normal, paradoxical), respectively. Effect size estimates are expressed as OR and 95% CI. All analyses were performed using R 4.3.0. Two-sided p-values less than 0.05 were considered statistically significant 3. Results 3.1 Basic Characteristics of the Study Population A total of 2,948 women were enrolled in the study, out of which 595 (20.18%) participants were adjudged to be frail (Table 1 ), The chi-square test results revealed significant variations in frailty prevalence across different age groups, educational backgrounds, marital statuses, residential locations, and the presence or absence of depression. Additionally, notable differences in frailty prevalence were observed based on the number of biological children and the age at menarche. Table 1 Basic Characteristics of the Study Population Variables Total (n = 2,948) Non-frailty (n = 2,353) Frailty (n = 595) statistic P Age, n(%) χ²=34.01 < .001 <65 553 (18.76) 491 (88.79) 62 (11.21) ≥65 2,395 (81.24) 1,862 (77.75) 533 (22.25) Education, n(%) χ²=39.64 < .001 Illiteracy 1,084 (36.77) 813 (75.00) 271 (25.00) Secondary school and below 1,680 (56.99) 1,368 (81.43) 312 (18.57) High school and above 184 (6.24) 172 (93.48) 12 (6.52) Marital status, n(%) χ²=20.02 < .001 Widow/divorced/unmarried 625 (21.20) 459 (73.44) 166 (26.56) Married 2,323 (78.80) 1,894 (81.53) 429 (18.47) Residence, n(%) χ²=26.29 < .001 Urban 679 (23.03) 589 (86.75) 90 (13.25) Rural 2,269 (76.97) 1,764 (77.74) 505 (22.26) Social activities, n(%) χ²=1.73 0.189 No 1,349 (45.76) 1,091 (80.87) 258 (19.13) Yes 1,599 (54.24) 1,262 (78.92) 337 (21.08) Physical activities, n(%) χ²=0.63 0.428 No 257 (8.72) 210 (81.71) 47 (18.29) Yes 2,691 (91.28) 2,143 (79.64) 548 (20.36) Hypertension, n(%) χ²=0.25 0.616 No 2,594 (87.99) 2,074 (79.95) 520 (20.05) Yes 354 (12.01) 279 (78.81) 75 (21.19) Diabetes, n(%) χ²=0.89 0.345 No 2,802 (95.05) 2,232 (79.66) 570 (20.34) Yes 146 (4.95) 121 (82.88) 25 (17.12) Heart attack, n(%) χ²=0.16 0.692 No 2,749 (93.25) 2,192 (79.74) 557 (20.26) Yes 199 (6.75) 161 (80.90) 38 (19.10) Self-rated health, n(%) χ²=1.58 0.208 Good 2,214 (75.10) 1,779 (80.35) 435 (19.65) Poor 734 (24.90) 574 (78.20) 160 (21.80) Smoke, n(%) χ²=1.43 0.231 No 2,845 (96.51) 2,266 (79.65) 579 (20.35) Yes 103 (3.49) 87 (84.47) 16 (15.53) Drink, n(%) χ²=1.61 0.204 No 1,894 (64.25) 1,525 (80.52) 369 (19.48) Yes 1,054 (35.75) 828 (78.56) 226 (21.44) Life Satisfaction, n(%) χ²=3.79 0.052 Satisfaction 2,563 (86.94) 2,060 (80.37) 503 (19.63) Not satisfaction 385 (13.06) 293 (76.10) 92 (23.90) BMI, n(%) χ²=3.28 0.351 <18.5 161 (5.46) 128 (79.50) 33 (20.50) 18.5–24 1,394 (47.29) 1,109 (79.56) 285 (20.44) 24–28 1,010 (34.26) 821 (81.29) 189 (18.71) ≥28 383 (12.99) 295 (77.02) 88 (22.98) Depression, n(%) χ²=709.51 < .001 No 1,677 (56.89) 1,626 (96.96) 51 (3.04) Yes 1,271 (43.11) 727 (57.20) 544 (42.80) History of abortion, n(%) χ²=0.15 0.694 No 2,228 (75.58) 1,782 (79.98) 446 (20.02) Yes 720 (24.42) 571 (79.31) 149 (20.69) Number of children, n(%) χ²=25.08 < .001 0–1 761 (25.81) 633 (83.18) 128 (16.82) 2 352 (11.94) 306 (86.93) 46 (13.07) ≥3 1,835 (62.25) 1,414 (77.06) 421 (22.94) Age at menarche, n(%) χ²=14.69 < .001 ≤15 1,135 (38.50) 935 (82.38) 200 (17.62) 16–18 1,306 (44.30) 1,042 (79.79) 264 (20.21) ≥18 507 (17.20) 376 (74.16) 131 (25.84) Age at menopause, n(%) χ²=1.72 0.423 ≤45 2,178 (73.88) 1,748 (80.26) 430 (19.74) 45–55 451 (15.30) 359 (79.60) 92 (20.40) ≥55 319 (10.82) 246 (77.12) 73 (22.88) Age at first livebirth, n(%) χ²=0.92 0.632 ≤25 1,022 (34.67) 811 (79.35) 211 (20.65) 25–34 1,766 (59.91) 1,418 (80.29) 348 (19.71) ≥34 160 (5.43) 124 (77.50) 36 (22.50) Duration of reproductive period, n(%) χ²=1.06 0.589 ≤33 1,421 (48.20) 1,123 (79.03) 298 (20.97) 33–43 1,450 (49.19) 1,168 (80.55) 282 (19.45) ≥43 77 (2.61) 62 (80.52) 15 (19.48) Note: χ²: Chi-square test; BMI: body mass index 3.2 Association between reproductive factors and frailty The results of the associations between the six reproductive factors and frailty are presented in Table 2 . After adjusting for all covariates, we found that: participants with ≥ 3 children had a 26.6% increased risk of frailty compared to participants with two children; participants with ≥ 18 years of age at menarche had a 30.1% increased prevalence of frailty compared to participants with age at menarche ≤ 15 years; and participants who experienced menopause at the age of 45–55 years showed a 57.4% higher prevalence of frailty compared to those who reached menopause at or after 55 years of age. Women with a history of miscarriage were found to have a greater association with frailty. In addition, having a reproductive cycle ≤ 33 was a protective factor compared to 33–43. No significant association was found between the age at first livebirth and the likelihood of frailty. Table 2 Association between reproductive factors and frailty Variables Model 1 Model 2 Number of children 0–1 0.743 (0.527,1.069) 0.970 (0.667,1.410) 2 Ref. Ref. ≥3 1.472 (1.183,1.833)*** 1.266 (1.008,1.589)* Age at menarche ≤15 Ref. Ref. 16–18 1.184 (0.966,1.452) 1.071 (0.869,1.319) ≥18 1.629 (1.268,2.093)*** 1.301 (1.002,1.688)* History of abortion No Ref. Ref. Yes 1.043 (0.847,1.284) 1.271 (1.022,1.580)* Age at menopause ≤45 1.042 (0.810,1.341) 0.816(0.609,1.093) 45–55 Ref. Ref. ≥55 1.206 (0.910,1.599) 1.574 (1.109, 2.233)* Age at first livebirth ≤25 0.943 (0.779,1.142) 0.875 (0.696,1.099) 25–34 Ref. Ref. ≥34 1.116 (0.747,1.666) 0.787 (0.491,1.260) Duration of reproductive period ≤33 0.910(0.758,1.092) 0.720 (0.580,0.895)* 33–43 Ref. Ref. ≥43 0.912 (0.511,1.626) 1.013 (0.504,2.037) Note: Ref.:reference group; * :P < 0.05,*** :P < 0.00 3.3 Subgroup analysis of reproductive factors and frailty Table 3 presents the association between various reproductive factors and frailty, categorized by age, residence, education level, and BMI status. The findings indicate that women aged 60 years or older who had three or more children exhibited a significantly higher prevalence of frailty. A history of abortion was connected to a greater chance of frailty, particularly in individuals with a normal BMI. On the other hand, experiencing menopause at an earlier age (≤ 45 years) reduced the risk of frailty in women residing in urban areas, whereas menopause at a later age (55 years and older) increased the risk of frailty in older age, especially among those who were 60 years or older, educated, and had a normal BMI. In addition, shorter reproductive cycles (≤ 33 years) were associated with a reduced risk of frailty, particularly among those ≥ 60 years of age, urban-dwelling, and illiterate. These findings highlight the potential influence of reproductive factors on frailty risk in specific populations. Table 3 Subgroup analysis of reproductive factors and frailty Reproductive factors Age(years) Residence Education BMI < 60 ≥ 60 Rural Urban Illiterate Literate Normal Abnormal OR (95 percent CI) Number of children 0–1 1.094 (0.472,2.536) 1.062 (0.642,1.758) 1.866 (0.535,1.426) 0.874 (0.535,1.426) 1.089 (0.485,2.445) 0.980 (0.593,1.617) 1.257 (0.688,2.296) 0.837 (0.462,1.520) 2 Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. ≥3 0.683 (0.337,1.384) 1.444(1.084,1.924)* 1.410 (0.715,2.784) 1.226 (0.923,1.628) 1.447 (0.952,2.201) 1.196 (0.857,1.670) 1.309 (0.895,1.915) 1.233 (0.862,1.762) Age at menarche ≤15 Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. 16–18 1.224 (0.626,2.393) 0.893 (0.687,1.160) 0.972 (0.563,1.762) 0.975 (0.747,1.273) 0.905 (0.617,1.327) 1.005(0.735,1.374) 1.065 (0.754,1.505) 0.865 (0.617,1.212) ≥18 2.197 (0.727,6.644) 1.016 (0.741,1.395) 1.469 (0.652,3.310) 1.051 (0.757,1.460) 0.947 (0.611,1.468) 1.371 (0.896,2.099) 1.316 (0.845,2.050) 0.999(0.659,1.513) History of abortion No Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. Yes 1.154 (0.569,2.340) 1.175 (0.896,1.539) 0.922 (0.528,1.611) 0.792 (0.596,1.052) 1.082(0.720,1.625) 1.197 (0.869,1.649) 1.445 (1.007,2.075)* 0.919 (0.647,1.305) Age at menopause ≤45 0.539 (0.214,1.361) 0.880 (0.645,1.201) 1.335 (0.601,2.967) 0.715(0.520,0.982)* 0.702 (0.449,1.100) 0.915 (0.620,1.352) 0.816(0.535,1.244) 0.848 (0.564,1.273) 45–55 Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. ≥55 0.850 (0.290,2.495) 1.690 (1.158,2.466)** 1.916 (0.744,4.744) 1.412 (0.962,2.072) 1.163 (0.689,1.961) 2.028 (1.261,3.259)** 1.976 (1.202,3.250)** 1.206 (0.732,1.988) Age at first livebirth ≤25 1.011 (0.492,2.078) 0.855 (0.671,1.089) 1.053 (0.589,1.881) 0.842 (0.654,1.083) 0.816(0.575,1.158) 0.928(0.685,1.256) 0.892 (0.640,1.242) 0.892 (0.649,1.224) 25–34 Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. ≥34 NA 0.927 (0.569,1.511) 1.286 (0.388,4.270) 0.672 (0.400,1.127) 0.934 (0.465,1.876) 0.662 (0.345,1.264) 0.955 (0.463,1.969) 0.735 (0.395,1.367) Duration of reproductive period ≤33 0.802 (0.430,1.498) 0.721(0.572,0.910)** 0.990 (0.572,1.714) 0.656(0.517,0.833)** 0.651(0.468,0.905)* 0.792 (0.592,1.059) 0.575(0.419,0.789)** 0.921 (0.682,1.244) 33–43 Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. ≥43 0.293 (0.033,2.592) 1,437 (0.641,3.226) 1,536 (0.150, 15.747) 0.813 (0.384,1.722) 0.859 (0.275,2.689) 1.163 (0.479,2.822) 0.760 (0.255,2.264) 1.317 (0.529,3.276) Note:Ref.:reference group;NA:data not available;BMI:body mass index * :P < 0.05,** :P < 0.01 3.4 Dose-response association between reproductive factors and frailty Figure 2 illustrates the non-linear association of the four continuous reproductive factor variables with frailty. The findings indicate that the number of biological children and the duration of the reproductive period are nonlinearly associated with frailty, while age at menopause and age at first livebirth are linearly associated with frailty. 4. Discussion It is demonstrated by our findings that middle-aged and elderly women who have three or more biological children face a greater risk of frailty than those with two biological children. Moreover, in comparison to the group with menarche occurring at an earlier age (15 years old and below), no history of abortion, and normal menopausal age (45–55), the subgroup of individuals who had their menarche at 18 years or older, had a history of abortion, and experienced a later menopause demonstrated a significantly elevated risk of frailty development. In contrast, a shorter reproductive period (≤ 33) reduces the risk of frailty, using the 33–43 reproductive period as a reference. Notably, our study found that in the group of middle-aged and older women, those with more biological children (≥ 3) faced a higher risk of developing frailty compared with those with 2 biological children. This finding maintained statistical significance after adjusting for all relevant covariates, thus reinforcing its reliability. This outcome corroborates earlier research findings and reinforces the association between the number of biological children and the risk of frailty in middle-aged and elderly women( 13 , 29 , 30 ). A large number of children means a greater number of pregnancies. The mechanisms involved in why women with more pregnancies are more likely to acquire frailty in old age are not fully understood. It has been shown that a higher number of pregnancies triggers a series of pathophysiological changes in the form of increased insulin resistance, weight gain, and decreased glucose tolerance( 13 ). In addition, a high number of children is strongly linked to a higher likelihood of developing diabetes, specific cancers, and stroke( 31 – 33 ). Furthermore, during pregnancy, some women encounter complications like gestational diabetes, gestational hypertension, pre-eclampsia, and mental health issues. The occurrence of these complications indicates an elevated risk of future cardiovascular and metabolic diseases( 34 ). In addition, women experience fluctuations in blood lipid and blood pressure levels during pregnancy, and these dynamics often indicate an increased risk of coronary heart disease in later life, which may further exacerbate frailty. In addition, several socio-economic and lifestyle factors merit particular attention. Mothers with a large number of children are inclined to be from rural households characterized by low incomes, low educational attainment, and inadequate health insurance coverage( 35 ). Most of them are more likely to be engaged in manual labor and to have experienced unemployment and malnutrition, which may go some way to explaining the association between the number of biological children and the risk of debilitating illnesses found in this study. In addition, the fact that having more children implies greater responsibility and financial burdens may make mothers more prone to anxiety and less sleep, negative effects that may also contribute to increased frailty in old age. Another important finding of this study is that women with a menarche age of ≥ 18 years have a higher risk of developing frailty relative to women with a menarche age of ≤ 15 years. This compares with previous findings in both British women and Chinese women living in Singapore, where younger age at menarche was associated with a higher risk of frailty in later life( 11 , 36 ). In addition, a study conducted on British women revealed a J-shaped correlation between the age of menarche and the incidence of frailty, with both precocious and delayed menarche being linked to an increased prevalence of frailty( 11 ). According to the energetics theory, girls who are continually subjected to environments characterized by food and nutrient deprivation will demonstrate reduced growth rates, their pubertal development (including first menstruation) will be later, and they will be smaller as adults than children who are exposed to better food conditions( 37 ). Thus, delayed age at menarche suggests a delay in female development associated with early nutritional deficiencies( 38 ), which may lead to an elevated risk of frailty later in life. In general, menarche, the onset of the first menstrual period, is considered to be the central event of female puberty( 39 ), and the onset of menarche is complex and controlled by multiple genetic and environmental factors. Explaining the mechanisms linking age at menarche and the prevalence of frailty is beyond the scope of this study, and we believe more research is necessary to explore this. In addition, we determined a significant effect of a history of abortion on Chinese women's frailty. Inducing an abortion can prove to be a psychologically traumatic experience for women, often resulting in emotional distress. Studies have indicated that pregnancy loss is associated with anxiety, depression, and post-traumatic stress disorder( 40 ). The enduring nature of these psychological issues can have long-term consequences for physical and mental well-being, potentially leading to an increased risk of frailty. Evidence on the relationship between age at natural menopause and frailty has historically been divided( 41 ). Nonetheless, research conducted on British women and Chinese females residing in Singapore both revealed a correlation between an earlier onset of menopause and a heightened vulnerability to frailty( 11 , 36 ). To our understanding, this research marks the initial exploration of the association between menopause age and frailty in middle-aged and elderly Chinese women. The results demonstrate that a later age at menopause is associated with an increased risk of frailty. The reasons behind this phenomenon are quite complex with potentially contradictory findings across geographic, cultural, and living environments. We speculate that differences in genetic background between populations may influence the timing of menopause and its long-term effects on health. Some genetic traits may make a certain group more likely to experience early or late menopause, while at the same time, these traits may be associated with other health indicators. In addition, there are significant differences in dietary habits, physical activity, living environment, and healthcare between Chinese women living in China compared to those living in Singapore. In some parts of China, due to historical healthcare constraints, women may face a higher risk of childbearing and disease burden, which in turn leads to frailty in the elderly age, which may be the reason why our results present a later age of menopause associated with a higher risk of frailty prevalence. In addition, environmental factors such as pollution and stress can affect the endocrine system and immune function, indirectly influencing the association between age at menopause and health. Taken together, the association between age at menopause and the risk of frailty is not a simple linear correspondence but is subject to the interaction of many variables. We believe that future studies need to examine the social, economic, and cultural contexts of specific populations in greater detail in order to reveal deeper mechanisms. In addition, unlike findings in other national populations, Our analysis demonstrated that a reduced reproductive period was associated with a protective effect against frailty development among middle-aged and elderly Chinese women. In a separate study conducted on British individuals, it was observed that shorter reproductive periods (<30 years) were associated with a heightened risk of frailty( 11 ). Research on Korean women revealed that a one-year extension in the reproductive period was associated with a 4% reduction in frailty risk( 42 ). It has been shown that the risk of breast cancer increases with age, but the rate of increase in risk is significantly lower with loss of ovarian function, suggesting that the hormones associated with ovarian production are a key risk factor for human breast cancer. The reproductive period reflects the time of exposure to endogenous estrogen( 43 ). A shorter reproductive cycle means that estrogen exposure is relatively limited, which may reduce the incidence of breast cancer as well as other hormone-related diseases, and thus reduce the risk of debilitating disease. 5. Conclusion Based on data from the China Health and Retirement Longitudinal Study (CHARLS), this study examined the association between reproductive factors and frailty in middle-aged and older women. The analysis revealed that middle-aged and elderly women exhibiting three or more children, delayed menarche onset, a history of abortion, or later menopause timing showed elevated frailty risks, whereas a compressed reproductive period demonstrated protective effects against frailty development. These findings highlight the importance of targeting health surveillance and attention to groups of women with specific reproductive factors and provide a scientific basis for the development of future prevention strategies. 6. Limitation The present study has some limitations in exploring the association between reproductive factors and frailty in middle-aged and older women. Firstly, the study relied on cross-sectional data, which did not allow causality to be established, only correlations were observed. Second, the data on reproductive factors relied on participants' self-reports, which may be subject to recall bias. In addition, the study failed to cover all reproductive factors that may influence frailty, such as birth spacing and breastfeeding. Finally, the characteristics of specific populations might have exerted an influence on the study results, thereby restricting their generalizability. Future studies need to use longitudinal designs and collect more comprehensive data to further validate and delve deeper into these relationships. Declarations Ethics approval and consent to participate ： This survey adhered to rigorous ethical standards and received approval from the Institutional Review Board of Peking University (Approval No. IRB00001052-11015). Consent for publication: Not applicable. Availability of data and materials: The datasets analysed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests" in this section. Funding: Not applicable. Authors' contributions: Xiaobing Xian was responsible for Writing-Original Draft, analyzing the experimental data, and visualizing the results of the data. Jie Xiang contribution to the paper was the same as Xiaobing Xian. Shiwei Cao organizes and manages data and visualizes data. Jiaxia Li and Shiqin Ren are responsible for designing the research methodology. Yuanyuan Wang organizes and visualizes data. Kun Shen presented ideas, validated and verified results, and reviewed and revised papers. All authors read and approved the final manuscript. Acknowledgements: Not applicable. Human Ethics and Consent to Participate declarations: Not applicable. Clinical trial number: Not applicable. References Fan J, Yu C, Guo Y, Bian Z, Sun Z, Yang L, et al. Frailty index and all-cause and cause-specific mortality in Chinese adults: a prospective cohort study. LANCET PUBLIC HEALTH. [Journal Article; Research Support, Non-U.S. Gov't]. 2020 2020/12/1;5(12):e650-60. Dent E, Martin FC, Bergman H, Woo J, Romero-Ortuno R, Walston JD. Management of frailty: opportunities, challenges, and future directions. lancet. [ Journal Article; Research Support, Non-U.S. Gov't; Review]. 2019 2019/10/12;394(10206):1376-86. Deng Y, Lai J, Tang L, Li S, Guo X, Kang J, et al. Association between changes of frailty status/frailty components status and rapid loss of kidney function association between changes of frailty status/frailty components status and rapid loss of kidney function in middle- aged and older populations. BMC NEPHROL. 2024 2024/9/13;25(1):306. Vermeiren S, Vella-Azzopardi R, Beckwée D, Habbig AK, Scafoglieri A, Jansen B, et al. Frailty and the Prediction of Negative Health Outcomes: a Meta- Analysis. j AM MED DIR ASSOC. [Journal Article; Meta-Analysis; Review; Systematic Review]. 2016 2016/12/1;17(12):1161-3. Zeng P, Li M, Cao J, Zeng L, Jiang C, Lin F. Association of metabolic syndrome severity with frailty progression among Chinese middle and old-aged adults: a CARDIOVASC DIABETOL. [Journal Article]. 2024 2024/8/16;23(1):302. Ma L, Tang Z, Zhang L, Sun F, Li Y, Chan P. Prevalence of Frailty and Associated Factors in the Community-Dwelling Population of China. J AM GERIATR SOC. [ Journal Article; Research Support, Non-U.S. Gov't]. 2018 2018/3/1;66(3):559-64. Sang N, Liu RC, Zhang MH, Lu ZX, Wu ZG, Zhang MY, et al. Changes in frailty and depressive symptoms among middle-aged and older Chinese people: a nationwide BMC PUBLIC HEALTH. [Journal Article; Research Support, Non-U.S. Gov't]. 2024 2024/1/25;24(1):301. Du Y, Luo Y, Zheng X, Liu J. Number of children and cognitive function among Chinese menopausal women: The mediating role of depressive symptoms and social participation. participation. J AFFECT DISORDERS. [Journal Article; Research Support, Non-U.S. Gov't]. 2023 2023/11/1;340:758-65. Weng Y, Yang X. Fertility behaviours and mid-life health status in China: from a life-course perspective. socci med. [Journal Article]. 2023 2023/12/1;338:116314. Long C, Han J, Yi C. The Health Effect of the Number of Children on Chinese Elders: An Analysis Based on Hukou Category. FRONT PUBLIC HEALTH. 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Chen J, Zhou W, Huang Y. Association between serum uric acid levels and depressive symptoms according to menopausal status. J AFFECT DISORDERS. [Journal Journal]. 2024 2024/4/1;350:240-6. Falkingham J, Evandrou M, Qin M, Vlachantoni A. Chinese women's health and wellbeing in middle life: Unpacking the influence of menopause, lifestyle activities and social participation. Unpacking the influence of menopause, lifestyle activities and social participation. MATURITAS. [Journal Article]. 2021 2021/1/1;143:145-50. Simondon KB, Simon I, Simondon F. Nutritional status and age at menarche of Senegalese adolescents. ann hum biol. [Journal Article]. 1997 1997/11/1;24(6):521-32. Malina RM. Menarche in athletes: a synthesis and hypothesis. ANN HUM BIOL. [Comparative Study; Journal Article]. 1983 1983/1/1;10(1):1-24. Thomas F, Renaud F, Benefice E, de Meeüs T, Guegan JF. International variability of ages at menarche and menopause: patterns and main determinants. HUM BIOL. [Journal Article]. 2001 2001/4/1;73(2):271-90. Liestøl K. Social conditions and menarcheal age: the importance of early years of life. ANN HUM BIOL. [Journal Article; Research Support, Non-U.S. Gov't ]. 1982 1982/11/1;9(6):521-37. Fan M, Wang D, Wu X, Gao W. Exploring the causal relationship between female reproductive traits and frailty: a two-sample mendelian randomisation study . FRONT PHYSIOL. [Journal Article]. 2024 2024/1/20;15:1349952. VanHise K, Wang ET, Norris K, Azziz R, Pisarska MD, Chan JL. Racial and ethnic disparities in polycystic ovary syndrome. FERTIL STERIL. [Journal Article. Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Review]. 2023 2023/3/1;119(3):348-54. Palacios S, Henderson VW, Siseles N, Tan D, Villaseca P. Age of menopause and impact of climacteric symptoms by geographical region. climactic. [ Journal Article]. 2010 2010/10/1;13(5):419-28. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). INT J EPIDEMIOL. [Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't]. 2014 2014/2/1;43(1):61-8. He D, Li J, Li Y, Zhu J, Zhou T, Xu Y, et al. Frailty is associated with the progression of prediabetes to diabetes and elevated risks of cardiovascular disease and all-cause mortality in individuals with prediabetes and diabetes: Evidence from two prospective cohorts. DIABETES RES CLIN PR. [Journal Article]. 2022 2022/12/1;194:110145. Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, et al. A global clinical measure of fitness and frailty in elderly people. CAN MED ASSOC J. [ Journal Article; Research Support, Non-U.S. Gov't; Validation Study]. 2005 2005/8/30;173(5):489-95. Liu X, Dai G, He Q, Ma H, Hu H. Frailty Index and Cardiovascular Disease among Middle-Aged and Older Chinese Adults: a Nationally Representative Cross- Sectional and Follow-Up Study. J CARDIOVASC DEV DIS. 2022 2022/7/18;9(7). He D, Qiu Y, Yan M, Zhou T, Cheng Z, Li J, et al. Associations of metabolic heterogeneity of obesity with frailty progression: results from two prospective cohorts. J CACHEXIA SARCOPENI. [Journal Article; Research Support, Non-U.S. Gov't]. 2023 2023/2/1;14(1):632-41. Mourtzi N, Yannakoulia M, Ntanasi E, Kosmidis MH, Anastasiou CA, Dardiotis E, et al. History of induced abortions and frailty in older Greek women. Results from the HELIAD study. eur GERIATR MED.[Journal Article]. 2018 2018/6/1;9(3):301-10. Gordon EH, Peel NM, Chatfield MD, Lang IA, Hubbard RE. Frailty: a cost incurred by reproduction? SCI REP-UK. [Journal Article]. 2020 2020/6/23;10(1):10139. Hajek A, König HH. The association between the number of life births and certain frailty dimensions. arch gerontol geriat. 2022 2022/9/1;102:104759. Guan HB, Wu QJ, Gong TT. Parity and kidney cancer risk: evidence from epidemiologic studies. canCER EPIDEM BIOMAR. [Journal Article; Meta-Analysis. Review; Systematic Review]. 2013 2013/12/1;22(12):2345-53. Guo P, Xu C, Zhou Q, Zhou J, Zhao J, Si Z, et al. Number of parity and the risk of gallbladder cancer: a systematic review and dose-response meta-analysis of observational studies. ARCH GYNECOL OBSTET. [Journal Article; Meta-Analysis; Review; Systematic Review]. 2016 2016/5/1;293(5):1087-96. Zhang X, Shu XO, Gao YT, Yang G, Li H, Zheng W. Pregnancy, childrearing, and risk of stroke in Chinese women. STROKE. [Comparative Study; Journal Article. Research Support, N.I.H., Extramural]. 2009 2009/8/1;40(8):2680-4. Brunton RJ, Dryer R, Saliba A, Kohlhoff J. Pregnancy anxiety: a systematic review of current scales. j AFFECT DISORDERS. [Journal Article; Research Support, Non-U.S. Gov't; Review; Systematic Review]. 2015 2015/5/1;176:24-34. Dior UP, Hochner H, Friedlander Y, Calderon-Margalit R, Jaffe D, Burger A, et al. Association between number of children and mortality of mothers. Results of a 37-year follow-up study. ANN EPIDEMIOL. [Journal Article; Research Support, N.I.H., Extramural]. 2013 2013/1/1;23(1):13-8. Ho V, Chua KY, Song X, Jin A, Koh WP. Reproductive factors and risk of physical frailty among Chinese women living in Singapore. J NUTR HEALTH AGING. [ Journal Article]. 2024 2024/6/1;28(6):100226. Belachew T, Hadley C, Lindstrom D, Getachew Y, Duchateau L, Kolsteren P. Food insecurity and age at menarche among adolescent girls in Jimma Zone Southwest Ethiopia: a longitudinal study. REPROD BIOL ENDOCRIN. [Comparative Study; Journal Article; Research Support, N.I.H., Extramural. Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.]. 2011 2011/9/13;9:125. Ito M, Yamada M, Hayashi K, Ohki M, Uetani M, Nakamura T. Relation of early menarche to high bone mineral density. CALCIFIED TISSUE INT. [Journal Article]. 1995 1995/7/1;57(1):11-4. Karapanou O, Papadimitriou A. Determinants of menarche. REPROD BIOL ENDOCRIN. [Journal Article; Review]. 2010 2010/9/30;8:115. Farren J, Mitchell-Jones N, Verbakel JY, Timmerman D, Jalmbrant M, Bourne T. The psychological impact of early pregnancy loss. HUM REPROD UPDATE. [ Journal Article; Research Support, Non-U.S. Gov't; Review]. 2018 2018/11/1;24(6):731-49. Kojima G, Taniguchi Y, Aoyama R, Urano T. Earlier menopause is associated with higher risk of incident frailty in community-dwelling older women in England. j am GERIATR SOC. [Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't]. 2022 2022/9/1;70(9):2602-9. Lee Y, Kim S, Kim M, Kim BS, Jeong E, Shim H, et al. A later menopausal age is associated with a lower prevalence of physical frailty in community-dwelling older adults: The Korean Frailty and Aging Cohort Study (KFACS). older adults: The Korean Frailty and Aging Cohort Study (KFACS). ARCH GERONTOL GERIAT. [Journal Article]. 2020 2020/11/1;91:104243. Najar J, Hällström T, Zettergren A, Johansson L, Joas E, Fässberg MM, et al. Reproductive period and preclinical cerebrospinal fluid markers for Alzheimer disease: a 25-year study. MENOPAUSE. [Journal Article; Research Support, Non-U.S. Gov't]. 2021 2021/7/2;28(10):1099-107. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Published Journal Publication published 27 Oct, 2025 Read the published version in Journal of Health, Population and Nutrition → Version 1 posted Editorial decision: Revision requested 12 Jul, 2025 Reviews received at journal 12 Jul, 2025 Reviewers agreed at journal 04 Jul, 2025 Reviews received at journal 04 Jul, 2025 Reviewers agreed at journal 04 Jul, 2025 Reviewers agreed at journal 12 May, 2025 Reviewers agreed at journal 09 May, 2025 Reviewers invited by journal 09 May, 2025 Editor assigned by journal 25 Mar, 2025 Submission checks completed at journal 25 Mar, 2025 First submitted to journal 20 Mar, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6270693","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455611581,"identity":"81c1b6a8-3fb9-4915-8c4c-5c8a4a72f045","order_by":0,"name":"Xiaobing Xian","email":"","orcid":"","institution":"The Thirteenth People’s Hospital of Chongqing","correspondingAuthor":false,"prefix":"","firstName":"Xiaobing","middleName":"","lastName":"Xian","suffix":""},{"id":455611582,"identity":"5414cc46-9d79-4ac4-9b00-5aa0fd3d7b2b","order_by":1,"name":"Jie Xiang","email":"","orcid":"","institution":"Chongqing Medical 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participants\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6270693/v1/98cef62333228b1010b27b48.jpg"},{"id":82716461,"identity":"4fbd765d-6c76-45be-a10b-f8e62c628fbb","added_by":"auto","created_at":"2025-05-14 12:17:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47120,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDose-response association between reproductive factors and frailty\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6270693/v1/76e3eeedc7d11a00e074ab60.jpg"},{"id":95040687,"identity":"cfb8b9e7-e5c4-4a84-b1d1-0aa50a577022","added_by":"auto","created_at":"2025-11-03 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Introduction","content":"\u003cp\u003eFrailty is a growing concern in addressing the challenges of global aging(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Frailty is a clinical condition characterized by a reduction in the body's physiological reserves, a decrease in stress resistance, and an increase in physical vulnerability and disease susceptibility(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).In people over the age of 65 in China, the prevalence of frailty ranges from 4\u0026ndash;17%, and in the older age group of 75 and above, it climbs from 10\u0026ndash;45%(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Frailty can significantly increase the risk of adverse health outcomes, including hospitalization, falls, physical disability and even death. This chain reaction not only poses a serious threat to an individual's quality of life but also places a heavy financial burden on the healthcare system and care services(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Therefore, timely identification and adaptation of variable risk factors for frailty is essential for developing effective prevention strategies(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt has been demonstrated by previous studies that the prevalence of frailty is significantly higher in women compared to men(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). This may be due to the generally weaker muscular strength of women compared to men, as well as women's greater responsibility for household tasks and greater symptom tolerance(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In addition, female reproductive characteristics may have a significant gender-specific effect on frailty. Reproductive history, as a uniquely female life experience, can influence hormone levels, overall health, and lifestyle choices. Among the many reproductive factors, the number of biological children plays a key role and may have a significant impact on the health status of older women(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Extensive literature reviews the correlation between reproductive behaviour and health in later life and reveals the potential adverse effects of having more children and having them earlier on physical and mental health in later life(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Research conducted on middle-aged and elderly Chinese women has indicated that having more children substantially raises the likelihood of developing co-morbidities and chronic illnesses(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). During the investigation into the association between the number of children a woman has and cardiovascular disease, it was revealed that the cardiovascular health of women with five or more children is relatively inferior(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and has a greater likelihood of developing cardiovascular disease(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), while women with no children had a lower prevalence of cardiovascular disease(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The results of a study of women in the UK showed an upward trend in frailty as the number of biological children increased. This correlation was not only demonstrated in British women but also validated in studies of other populations, further supporting the conclusion that there is a positive association between the number of biological children and frailty(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Menopause is a key transitional stage in the female life cycle. Changes in levels of estrogen luteinizing, and follicle-stimulating hormones due to ovarian failure during menopause can lead to a range of problems such as mental, physical, and endocrine disorders(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Regarding the health effects of menopause in women, a study reveals that postmenopausal women show more severe conditions in terms of experiencing negative emotions, musculoskeletal pain, and symptoms such as sleep and memory disorders compared to premenopausal women(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This provides some evidence that menopause, a reproductive factor, may lead to an increased risk of developing female frailty. The timing of menarche is affected by various contributing elements, including nutritional status(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), physical activity(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), prepubertal growth trajectory(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), stress exposure, and early life events(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Therefore, the age at which a woman experiences menarche, serving as a comprehensive measure of her early-life environmental influences, could be linked to a higher likelihood of frailty in later years. Although the association between past abortions and frailty continues to be debated, research involving elderly Greek women demonstrated that abortion could elevate the likelihood of developing frailty. In another study using a two-sample Mendelian randomization method, no significant correlation was found between pregnancy abortion and frailty in old age(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA long-term study conducted in the United Kingdom uncovered a notable association between women's reproductive factors and their likelihood of experiencing frailty in older age(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The results of this study found that first menarche\u0026thinsp;\u0026lt;\u0026thinsp;13 years, age at menopause\u0026thinsp;\u0026lt;\u0026thinsp;45 years, surgical menopause, and shorter reproductive cycles\u0026thinsp;\u0026lt;\u0026thinsp;35 years were associated with an increased risk of frailty and an increased prevalence of combined frailty, whereas having two or three children and having the first child later in life reduced the risk of frailty. A significant portion of studies exploring the connection between female reproductive factors and frailty has been carried out in Western nations, with research data from Asian populations remaining scarce. This is crucial due to notable racial and ethnic variations in reproductive characteristics(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). For example, Chinese women typically have a later age of menarche and tend to have a comparatively younger age of menopause compared to Caucasian women(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). To our knowledge, the relationship between reproductive factors and frailty risk in Chinese women has not been reported.\u003c/p\u003e \u003cp\u003eBased on data from the China Health and Retirement Longitudinal Study (CHARLS), this study aimed to investigate the possible association between reproductive factors (age at first livebirth, reproductive period, age at menopause, number of biological children, age at menarche, and history of abortion) and frailty in women.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Population\u003c/h2\u003e \u003cp\u003eThe database for this study is derived from the China Health and Retirement Longitudinal Study (CHARLS)(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This comprehensive longitudinal survey is designed for residents aged 45 and above in mainland China.CHARLS aims to comprehensively collect key information on participants' socio-economic status, health status, etc.CHARLS employs a stratified multi-stage probability proportional sampling (PPS) approach to achieve a comprehensive and representative sample. The study encompasses 150 districts and 450 rural/urban communities across the country, with a total participation of 17,708 individuals from 10,257 households. To date, CHARLS has successfully implemented four surveys, conducted in 2011, 2013, 2015, and 2018, and detailed survey reports and updates can be accessed through its official website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.charls.pku.edu.cn/en\u003c/span\u003e\u003cspan address=\"http://www.charls.pku.edu.cn/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) at(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). It is important to highlight that this survey adhered to rigorous ethical standards and received approval from the Institutional Review Board of Peking University (Approval No. IRB00001052-11015). Additionally, informed consent was obtained from all participants before their involvement.\u003c/p\u003e \u003cp\u003eIn this study, we specifically used the dataset published by CHARLS in 2018. The follow-up survey in that year had a participation count of 19,816 individuals. Excluding male respondents, participants with missing frailty information, and 1,891 participants with missing information on covariates and lack of information on reproductive factors, a final total of 2,948 participants were included in this analysis. See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for details of the selection process.\u003c/p\u003e \u003cp\u003eThe minimum sample size of the study was determined according to the formula for calculating sample size in cross-sectional studies [N=(Z\u0026sup2;\u003csub\u003e(α/2\u003c/sub\u003e)-p-q)/δ\u003csup\u003e2\u003c/sup\u003e]: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) N denotes the sample size needed for the study; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) p denotes the prevalence rate of frailty among middle-aged and elderly women in China; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) q=(1-p); (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Z\u003csub\u003eα/2\u003c/sub\u003e was set to 1.96, and α was set to 0.05 for the two-sided test; (5 ) δ denotes the permissible error, calculated at 0.1p. According to a previous study, the prevalence of frailty among middle-aged and elderly women in China was 16.57%(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), which was calculated to mean that at least 1,934 participants were needed for this study to achieve the required sample size. Therefore, the sample in this study met the minimum sample size requirement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Assessment of reproductive factors\u003c/h2\u003e \u003cp\u003eThe study extracted information on female reproductive factors, including number of biological children, age at menopause, age at menarche, and history of abortion, and calculated age at first livebirth and reproductive period from cross-sectional survey data published in CHARLS. Information on the number of biological children was extracted from the cross-sectional survey data published in CHARLS 2018 (Wave 4). Age at menopause was collected from Wave 1 to Wave 4 data, whichever was recorded first. Age at menarche and history of abortion were extracted from Wave 4 data. Age at first live births was calculated from the biological age and age of the oldest child. The duration reproductive period was calculated as age at menarche to age at menopause.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Assessment of frailty\u003c/h2\u003e \u003cp\u003eThe Frailty Index (FI), a quantitative assessment tool designed to measure an individual's debilitating condition, is calculated by accumulating age-related health deficits, and its indices can be programmed and freely constructed as needed based on following the principles of health deficit selection, but it must contain at least 30 health deficit items(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In this study, 50 indicators were selected for the construction of the FI assessment scale (eTable1) based on the criteria for constructing the FI: 18 items for physical limitations, 10 items for depressive symptoms, chronic diseases (hypertension, dyslipidemia, diabetes mellitus or hyperglycemia, cancer or malignant tumor, chronic lung disease, liver disease, heart disease, stroke, kidney disease, gastric or other digestive disorders,, memory-related disorders, arthritis or rheumatic disease, asthma) 13 items, trauma history 2 items, cognitive impairment 4 items, and others including hearing hearing impairment, visual impairment, self-reported health and self-reported pain 4 items. The evaluation of the above dimensions assigns values to the health variables on a scale of 0\u0026ndash;1. The process of calculating the FI involves aggregating the individual's scores on the various health deficits and dividing them by the total number of indicators involved in the assessment (in the present study this total number was set at 50 items). The results obtained take values in the range of 0 to 1, where the closer the value converges to 1, the more severe the individual's frailty is.FI has been used in several CHARLS studies(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Based on previous research experience, individuals are classified as frail when the Frailty Index (FI) value is greater than 0.25(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Assessment of Covariates\u003c/h2\u003e \u003cp\u003eThe covariates were divided into four categories, including basic social characteristics (age, education, marital status, place of residence), health status (hypertension, diabetes, heart disease, BMI), lifestyle (social activities, physical labor, smoking, alcohol consumption), and mental status (self-rated health, life satisfaction, depression) (eTable 2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eCategorical variables were expressed using frequency counts and frequencies (n (%)). Continuous variables were subjected to the Kolmogorov-Smirnov test, and if p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, they were expressed as mean plus or minus standard deviation (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). Comparisons between groups were made using two independent samples t-test and χ2 test. To investigate the relationship between reproductive factors and frailty, multivariate logistic regression models were used to estimate odds risk (OR) and 95% confidence intervals (CI). Model 1 was unadjusted. Model 2 was adjusted for age, marriage, residence, education level, social activity, physical activity, hypertension, diabetes, heart disease, health self-assessment, smoking, alcohol use, life satisfaction, BMI, and depressive symptoms. The dose-response relationship between reproductive factors and frailty was further examined using restricted cubic spline analysis, with the most appropriate number of nodes determined by the Akaike Information Criterion (AIC). Subgroup analyses were also conducted based on age (\u0026lt;\u0026thinsp;60 years, \u0026ge;\u0026thinsp;60 years), area of residence (rural, urban), education (illiterate, literate), and BMI (normal, paradoxical), respectively. Effect size estimates are expressed as OR and 95% CI.\u003c/p\u003e \u003cp\u003eAll analyses were performed using R 4.3.0. Two-sided p-values less than 0.05 were considered statistically significant\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Basic Characteristics of the Study Population\u003c/h2\u003e \u003cp\u003eA total of 2,948 women were enrolled in the study, out of which 595 (20.18%) participants were adjudged to be frail (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), The chi-square test results revealed significant variations in frailty prevalence across different age groups, educational backgrounds, marital statuses, residential locations, and the presence or absence of depression. Additionally, notable differences in frailty prevalence were observed based on the number of biological children and the age at menarche.\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\u003eBasic Characteristics of the Study Population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;2,948)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-frailty (n\u0026thinsp;=\u0026thinsp;2,353)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrailty (n\u0026thinsp;=\u0026thinsp;595)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003estatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=34.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e553 (18.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e491 (88.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62 (11.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,395 (81.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,862 (77.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e533 (22.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=39.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliteracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,084 (36.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e813 (75.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e271 (25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary school and below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,680 (56.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,368 (81.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e312 (18.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003e184 (6.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172 (93.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (6.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=20.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidow/divorced/unmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e625 (21.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e459 (73.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e166 (26.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003e2,323 (78.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,894 (81.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e429 (18.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=26.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \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\u003e679 (23.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e589 (86.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90 (13.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003e2,269 (76.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,764 (77.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e505 (22.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial activities, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.189\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\u003e1,349 (45.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,091 (80.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e258 (19.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003e1,599 (54.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,262 (78.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e337 (21.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activities, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.428\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\u003e257 (8.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e210 (81.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47 (18.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003e2,691 (91.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,143 (79.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e548 (20.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.616\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\u003e2,594 (87.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,074 (79.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e520 (20.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003e354 (12.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e279 (78.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75 (21.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.345\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\u003e2,802 (95.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,232 (79.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e570 (20.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003e146 (4.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121 (82.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (17.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart attack, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.692\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\u003e2,749 (93.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,192 (79.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e557 (20.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003e199 (6.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161 (80.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38 (19.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-rated health, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,214 (75.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,779 (80.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e435 (19.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e734 (24.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e574 (78.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160 (21.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.231\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\u003e2,845 (96.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,266 (79.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e579 (20.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003e103 (3.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87 (84.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (15.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.204\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\u003e1,894 (64.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,525 (80.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e369 (19.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003e1,054 (35.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e828 (78.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e226 (21.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLife Satisfaction, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,563 (86.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,060 (80.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e503 (19.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e385 (13.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e293 (76.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92 (23.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e161 (5.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128 (79.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33 (20.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,394 (47.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,109 (79.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e285 (20.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u0026ndash;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,010 (34.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e821 (81.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e189 (18.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e383 (12.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e295 (77.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88 (22.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=709.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\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\u003e1,677 (56.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,626 (96.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51 (3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003e1,271 (43.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e727 (57.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e544 (42.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of abortion, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.694\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\u003e2,228 (75.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,782 (79.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e446 (20.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003e720 (24.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e571 (79.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e149 (20.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of children, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=25.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e761 (25.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e633 (83.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e128 (16.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e352 (11.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e306 (86.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46 (13.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,835 (62.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,414 (77.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e421 (22.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menarche, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=14.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,135 (38.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e935 (82.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e200 (17.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,306 (44.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,042 (79.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e264 (20.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e507 (17.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e376 (74.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e131 (25.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menopause, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,178 (73.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,748 (80.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e430 (19.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e451 (15.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e359 (79.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92 (20.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e319 (10.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e246 (77.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73 (22.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at first livebirth, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,022 (34.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e811 (79.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e211 (20.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,766 (59.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,418 (80.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e348 (19.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e160 (5.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124 (77.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36 (22.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of reproductive period, n(%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,421 (48.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,123 (79.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e298 (20.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u0026ndash;43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,450 (49.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,168 (80.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e282 (19.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77 (2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62 (80.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (19.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: χ\u0026sup2;: Chi-square test; BMI: body mass index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association between reproductive factors and frailty\u003c/h2\u003e \u003cp\u003eThe results of the associations between the six reproductive factors and frailty are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. After adjusting for all covariates, we found that: participants with \u0026ge;\u0026thinsp;3 children had a 26.6% increased risk of frailty compared to participants with two children; participants with \u0026ge;\u0026thinsp;18 years of age at menarche had a 30.1% increased prevalence of frailty compared to participants with age at menarche\u0026thinsp;\u0026le;\u0026thinsp;15 years; and participants who experienced menopause at the age of 45\u0026ndash;55 years showed a 57.4% higher prevalence of frailty compared to those who reached menopause at or after 55 years of age. Women with a history of miscarriage were found to have a greater association with frailty. In addition, having a reproductive cycle\u0026thinsp;\u0026le;\u0026thinsp;33 was a protective factor compared to 33\u0026ndash;43. No significant association was found between the age at first livebirth and the likelihood of frailty.\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\u003eAssociation between reproductive factors and frailty\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of children\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\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.743 (0.527,1.069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.970 (0.667,1.410)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.472 (1.183,1.833)***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.266 (1.008,1.589)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menarche\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\u003e\u0026le;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.184 (0.966,1.452)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.071 (0.869,1.319)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.629 (1.268,2.093)***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.301 (1.002,1.688)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of abortion\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.043 (0.847,1.284)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.271 (1.022,1.580)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menopause\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\u003e\u0026le;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.042 (0.810,1.341)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.816(0.609,1.093)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.206 (0.910,1.599)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.574 (1.109, 2.233)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at first livebirth\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\u003e\u0026le;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.943 (0.779,1.142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.875 (0.696,1.099)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.116 (0.747,1.666)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.787 (0.491,1.260)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of reproductive period\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\u003e\u0026le;33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.910(0.758,1.092)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.720 (0.580,0.895)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u0026ndash;43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.912 (0.511,1.626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.013 (0.504,2.037)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Ref.:reference group;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e* :P\u0026thinsp;\u0026lt;\u0026thinsp;0.05,*** :P\u0026thinsp;\u0026lt;\u0026thinsp;0.00\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Subgroup analysis of reproductive factors and frailty\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the association between various reproductive factors and frailty, categorized by age, residence, education level, and BMI status. The findings indicate that women aged 60 years or older who had three or more children exhibited a significantly higher prevalence of frailty. A history of abortion was connected to a greater chance of frailty, particularly in individuals with a normal BMI. On the other hand, experiencing menopause at an earlier age (\u0026le;\u0026thinsp;45 years) reduced the risk of frailty in women residing in urban areas, whereas menopause at a later age (55 years and older) increased the risk of frailty in older age, especially among those who were 60 years or older, educated, and had a normal BMI. In addition, shorter reproductive cycles (\u0026le;\u0026thinsp;33 years) were associated with a reduced risk of frailty, particularly among those\u0026thinsp;\u0026ge;\u0026thinsp;60 years of age, urban-dwelling, and illiterate. These findings highlight the potential influence of reproductive factors on frailty risk in specific populations.\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\u003eSubgroup analysis of reproductive factors and frailty\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eReproductive factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLiterate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAbnormal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003eOR (95 percent CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of children\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.094 (0.472,2.536)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.062 (0.642,1.758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.866 (0.535,1.426)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.874 (0.535,1.426)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.089 (0.485,2.445)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.980 (0.593,1.617)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.257 (0.688,2.296)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.837 (0.462,1.520)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\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\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.683 (0.337,1.384)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.444(1.084,1.924)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.410 (0.715,2.784)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.226 (0.923,1.628)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.447 (0.952,2.201)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.196 (0.857,1.670)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.309 (0.895,1.915)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.233 (0.862,1.762)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menarche\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\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\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.224 (0.626,2.393)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.893 (0.687,1.160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.972 (0.563,1.762)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.975 (0.747,1.273)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.905 (0.617,1.327)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.005(0.735,1.374)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.065 (0.754,1.505)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.865 (0.617,1.212)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.197 (0.727,6.644)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.016 (0.741,1.395)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.469 (0.652,3.310)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.051 (0.757,1.460)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.947 (0.611,1.468)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.371 (0.896,2.099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.316 (0.845,2.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.999(0.659,1.513)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of abortion\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\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\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.154 (0.569,2.340)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.175 (0.896,1.539)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.922 (0.528,1.611)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.792 (0.596,1.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.082(0.720,1.625)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.197 (0.869,1.649)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.445 (1.007,2.075)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.919 (0.647,1.305)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menopause\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.539 (0.214,1.361)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.880 (0.645,1.201)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.335 (0.601,2.967)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.715(0.520,0.982)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.702 (0.449,1.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.915 (0.620,1.352)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.816(0.535,1.244)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.848 (0.564,1.273)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\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\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.850 (0.290,2.495)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.690 (1.158,2.466)**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.916 (0.744,4.744)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.412 (0.962,2.072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.163 (0.689,1.961)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.028 (1.261,3.259)**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.976 (1.202,3.250)**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.206 (0.732,1.988)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at first livebirth\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.011 (0.492,2.078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.855 (0.671,1.089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.053 (0.589,1.881)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.842 (0.654,1.083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.816(0.575,1.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.928(0.685,1.256)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.892 (0.640,1.242)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.892 (0.649,1.224)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\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\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.927 (0.569,1.511)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.286 (0.388,4.270)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.672 (0.400,1.127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.934 (0.465,1.876)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.662 (0.345,1.264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.955 (0.463,1.969)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.735 (0.395,1.367)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of reproductive period\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.802 (0.430,1.498)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.721(0.572,0.910)**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.990 (0.572,1.714)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.656(0.517,0.833)**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.651(0.468,0.905)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.792 (0.592,1.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.575(0.419,0.789)**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.921 (0.682,1.244)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u0026ndash;43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\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\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.293 (0.033,2.592)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,437 (0.641,3.226)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,536 (0.150, 15.747)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.813 (0.384,1.722)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.859 (0.275,2.689)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.163 (0.479,2.822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.760 (0.255,2.264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.317 (0.529,3.276)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote:Ref.:reference group;NA:data not available;BMI:body mass index\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e* :P\u0026thinsp;\u0026lt;\u0026thinsp;0.05,** :P\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Dose-response association between reproductive factors and frailty\u003c/h2\u003e \u003cp\u003eFigure 2 illustrates the non-linear association of the four continuous reproductive factor variables with frailty. The findings indicate that the number of biological children and the duration of the reproductive period are nonlinearly associated with frailty, while age at menopause and age at first livebirth are linearly associated with frailty.\u003c/p\u003e "},{"header":"4. Discussion","content":"\u003cp\u003eIt is demonstrated by our findings that middle-aged and elderly women who have three or more biological children face a greater risk of frailty than those with two biological children. Moreover, in comparison to the group with menarche occurring at an earlier age (15 years old and below), no history of abortion, and normal menopausal age (45\u0026ndash;55), the subgroup of individuals who had their menarche at 18 years or older, had a history of abortion, and experienced a later menopause demonstrated a significantly elevated risk of frailty development. In contrast, a shorter reproductive period (\u0026le;\u0026thinsp;33) reduces the risk of frailty, using the 33\u0026ndash;43 reproductive period as a reference.\u003c/p\u003e \u003cp\u003eNotably, our study found that in the group of middle-aged and older women, those with more biological children (\u0026ge;\u0026thinsp;3) faced a higher risk of developing frailty compared with those with 2 biological children. This finding maintained statistical significance after adjusting for all relevant covariates, thus reinforcing its reliability. This outcome corroborates earlier research findings and reinforces the association between the number of biological children and the risk of frailty in middle-aged and elderly women(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). A large number of children means a greater number of pregnancies. The mechanisms involved in why women with more pregnancies are more likely to acquire frailty in old age are not fully understood. It has been shown that a higher number of pregnancies triggers a series of pathophysiological changes in the form of increased insulin resistance, weight gain, and decreased glucose tolerance(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In addition, a high number of children is strongly linked to a higher likelihood of developing diabetes, specific cancers, and stroke(\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Furthermore, during pregnancy, some women encounter complications like gestational diabetes, gestational hypertension, pre-eclampsia, and mental health issues. The occurrence of these complications indicates an elevated risk of future cardiovascular and metabolic diseases(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In addition, women experience fluctuations in blood lipid and blood pressure levels during pregnancy, and these dynamics often indicate an increased risk of coronary heart disease in later life, which may further exacerbate frailty.\u003c/p\u003e \u003cp\u003eIn addition, several socio-economic and lifestyle factors merit particular attention. Mothers with a large number of children are inclined to be from rural households characterized by low incomes, low educational attainment, and inadequate health insurance coverage(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Most of them are more likely to be engaged in manual labor and to have experienced unemployment and malnutrition, which may go some way to explaining the association between the number of biological children and the risk of debilitating illnesses found in this study. In addition, the fact that having more children implies greater responsibility and financial burdens may make mothers more prone to anxiety and less sleep, negative effects that may also contribute to increased frailty in old age.\u003c/p\u003e \u003cp\u003eAnother important finding of this study is that women with a menarche age of \u0026ge;\u0026thinsp;18 years have a higher risk of developing frailty relative to women with a menarche age of \u0026le;\u0026thinsp;15 years. This compares with previous findings in both British women and Chinese women living in Singapore, where younger age at menarche was associated with a higher risk of frailty in later life(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In addition, a study conducted on British women revealed a J-shaped correlation between the age of menarche and the incidence of frailty, with both precocious and delayed menarche being linked to an increased prevalence of frailty(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). According to the energetics theory, girls who are continually subjected to environments characterized by food and nutrient deprivation will demonstrate reduced growth rates, their pubertal development (including first menstruation) will be later, and they will be smaller as adults than children who are exposed to better food conditions(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Thus, delayed age at menarche suggests a delay in female development associated with early nutritional deficiencies(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), which may lead to an elevated risk of frailty later in life. In general, menarche, the onset of the first menstrual period, is considered to be the central event of female puberty(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), and the onset of menarche is complex and controlled by multiple genetic and environmental factors. Explaining the mechanisms linking age at menarche and the prevalence of frailty is beyond the scope of this study, and we believe more research is necessary to explore this.\u003c/p\u003e \u003cp\u003eIn addition, we determined a significant effect of a history of abortion on Chinese women's frailty. Inducing an abortion can prove to be a psychologically traumatic experience for women, often resulting in emotional distress. Studies have indicated that pregnancy loss is associated with anxiety, depression, and post-traumatic stress disorder(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). The enduring nature of these psychological issues can have long-term consequences for physical and mental well-being, potentially leading to an increased risk of frailty.\u003c/p\u003e \u003cp\u003eEvidence on the relationship between age at natural menopause and frailty has historically been divided(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Nonetheless, research conducted on British women and Chinese females residing in Singapore both revealed a correlation between an earlier onset of menopause and a heightened vulnerability to frailty(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). To our understanding, this research marks the initial exploration of the association between menopause age and frailty in middle-aged and elderly Chinese women. The results demonstrate that a later age at menopause is associated with an increased risk of frailty. The reasons behind this phenomenon are quite complex with potentially contradictory findings across geographic, cultural, and living environments. We speculate that differences in genetic background between populations may influence the timing of menopause and its long-term effects on health. Some genetic traits may make a certain group more likely to experience early or late menopause, while at the same time, these traits may be associated with other health indicators. In addition, there are significant differences in dietary habits, physical activity, living environment, and healthcare between Chinese women living in China compared to those living in Singapore. In some parts of China, due to historical healthcare constraints, women may face a higher risk of childbearing and disease burden, which in turn leads to frailty in the elderly age, which may be the reason why our results present a later age of menopause associated with a higher risk of frailty prevalence. In addition, environmental factors such as pollution and stress can affect the endocrine system and immune function, indirectly influencing the association between age at menopause and health. Taken together, the association between age at menopause and the risk of frailty is not a simple linear correspondence but is subject to the interaction of many variables. We believe that future studies need to examine the social, economic, and cultural contexts of specific populations in greater detail in order to reveal deeper mechanisms.\u003c/p\u003e \u003cp\u003eIn addition, unlike findings in other national populations, Our analysis demonstrated that a reduced reproductive period was associated with a protective effect against frailty development among middle-aged and elderly Chinese women. In a separate study conducted on British individuals, it was observed that shorter reproductive periods (\u0026lt;30 years) were associated with a heightened risk of frailty(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Research on Korean women revealed that a one-year extension in the reproductive period was associated with a 4% reduction in frailty risk(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). It has been shown that the risk of breast cancer increases with age, but the rate of increase in risk is significantly lower with loss of ovarian function, suggesting that the hormones associated with ovarian production are a key risk factor for human breast cancer. The reproductive period reflects the time of exposure to endogenous estrogen(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). A shorter reproductive cycle means that estrogen exposure is relatively limited, which may reduce the incidence of breast cancer as well as other hormone-related diseases, and thus reduce the risk of debilitating disease.\u003c/p\u003e"},{"header":"5. Conclusion","content":" \u003cp\u003eBased on data from the China Health and Retirement Longitudinal Study (CHARLS), this study examined the association between reproductive factors and frailty in middle-aged and older women. The analysis revealed that middle-aged and elderly women exhibiting three or more children, delayed menarche onset, a history of abortion, or later menopause timing showed elevated frailty risks, whereas a compressed reproductive period demonstrated protective effects against frailty development. These findings highlight the importance of targeting health surveillance and attention to groups of women with specific reproductive factors and provide a scientific basis for the development of future prevention strategies.\u003c/p\u003e"},{"header":"6. Limitation","content":"\u003cp\u003eThe present study has some limitations in exploring the association between reproductive factors and frailty in middle-aged and older women. Firstly, the study relied on cross-sectional data, which did not allow causality to be established, only correlations were observed. Second, the data on reproductive factors relied on participants' self-reports, which may be subject to recall bias. In addition, the study failed to cover all reproductive factors that may influence frailty, such as birth spacing and breastfeeding. Finally, the characteristics of specific populations might have exerted an influence on the study results, thereby restricting their generalizability. Future studies need to use longitudinal designs and collect more comprehensive data to further validate and delve deeper into these relationships.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThis survey adhered to rigorous ethical standards and received approval from the Institutional Review Board of Peking University (Approval No. IRB00001052-11015).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests\u0026quot; in this section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e Xiaobing Xian was responsible for Writing-Original Draft, analyzing the experimental data, and visualizing the results of the data. Jie Xiang contribution to the paper was the same as Xiaobing Xian. Shiwei Cao organizes and manages data and visualizes data. Jiaxia Li and Shiqin Ren are responsible for designing the research methodology. Yuanyuan Wang organizes and visualizes data. Kun Shen presented ideas, validated and verified results, and reviewed and revised papers. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFan J, Yu C, Guo Y, Bian Z, Sun Z, Yang L, et al. Frailty index and all-cause and cause-specific mortality in Chinese adults: a prospective cohort study. LANCET PUBLIC HEALTH. 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[Journal Article; Research Support, Non-U.S. Gov\u0026apos;t]. 2021 2021/7/2;28(10):1099-107.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"frailty, reproductive factors, CHARLS, women","lastPublishedDoi":"10.21203/rs.3.rs-6270693/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6270693/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBACKGROUND\u003c/h2\u003e \u003cp\u003eThe study was based on data from CHARLS, a national longitudinal health survey of residents aged 45 years and older in mainland China.\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e \u003cp\u003eFrailty is assessed by the Frailty Index (FI), constructed based on 50 indicators of health deficits. Reproductive factors were extracted from the CHARLS survey. They included the number of biological children, age at menopause, age at menarche, age at first birth, reproductive period, and history of abortion.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eWe found that participants with \u0026ge;\u0026thinsp;3 children had a 26.6% increased risk of frailty compared with participants with 2 children, and those with menarche aged 18 years or older had a 30.1% increased probability of frailty compared with participants with menarche aged 15 years or younger. Participants with menopause at age 55 or older were 57.4 percent more likely to be frail compared to those with menopause between 45 and 55 years of age. In addition, women with a history of abortion were significantly associated with an increased prevalence of frailty, with reproductive period\u0026thinsp;\u0026le;\u0026thinsp;33 being a protective factor. The age of the first livebirth did not exhibit a significant association with frailty.\u003c/p\u003e\u003ch2\u003eCONCLUSIONS\u003c/h2\u003e \u003cp\u003eThe findings suggest that the risk of developing frailty among middle-aged and older Chinese women is strongly associated with specific reproductive factors, such as the number of biological children, age at menarche, history of abortion, and age at menopause. These findings provide potential reproductive health indicators for early identification and prevention of frailty and highlight the importance of further research into these relationships.\u003c/p\u003e","manuscriptTitle":"Associations between reproductive factors and frailty in middle-aged and older women: Evidence from the China Health and Retirement Longitudinal Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-14 12:09:24","doi":"10.21203/rs.3.rs-6270693/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-12T07:52:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-12T04:32:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132708085728751942116868434327216894329","date":"2025-07-04T17:28:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-04T16:03:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100107099284504621520785465159087393526","date":"2025-07-04T15:27:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129112664259844822029621951080491593684","date":"2025-05-12T04:25:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49612552028028717867444575334841891001","date":"2025-05-09T15:06:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-09T08:24:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-25T09:17:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-25T09:12:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Health, Population and Nutrition","date":"2025-03-20T14:39:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"433dc09f-ea1f-469e-96cd-fba672797966","owner":[],"postedDate":"May 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:07:46+00:00","versionOfRecord":{"articleIdentity":"rs-6270693","link":"https://doi.org/10.1186/s41043-025-01077-w","journal":{"identity":"journal-of-health-population-and-nutrition","isVorOnly":false,"title":"Journal of Health, Population and Nutrition"},"publishedOn":"2025-10-27 15:57:23","publishedOnDateReadable":"October 27th, 2025"},"versionCreatedAt":"2025-05-14 12:09:24","video":"","vorDoi":"10.1186/s41043-025-01077-w","vorDoiUrl":"https://doi.org/10.1186/s41043-025-01077-w","workflowStages":[]},"version":"v1","identity":"rs-6270693","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6270693","identity":"rs-6270693","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}