Abstract
Background
Hysterectomy prevalence varies from 4 to 41% across populations, but the rates in China and the risk factors remain unclear. The study aimed to estimate the prevalence of hysterectomy in Chinese and explore the potential risk factors.
Methods
A multicenter cross-sectional study was conducted with Meinian health screening center chain across 31 provinces of China between January 2017 and December 2018. Data from 9,013,462 participants aged ≥ 18 years were extracted for the current study. The geographic variation of hysterectomy prevalence was illustrated with different colors on the national map of China. Relative risk (RR) and 95% confidence intervals (CIs) from log-binomial regression were used to estimate the associations between hysterectomy and metabolic disorders.
Results
The age-standardized prevalence of hysterectomy in China was 2.36% (95% CIs, 2.35–2.37), with the highest in the Jiangsu Province (3.26%) and Northeast region (2.67%). Women aged 55–59 years had the highest prevalence of hysterectomy (7.61%). Hysterectomy was positively associated with obesity [RR, 1.31 (95% CIs, 1.29–1.32)]; hypertension [1.22 (1.21–1.23)]; diabetes [1.26 (1.24–1.28)]; hyperglycemia [1.22 (1.20–1.23)]; dyslipidemia [1.18 (1.16–1.19)]; metabolic associated fatty liver disease [1.25 (1.24–1.26)]; and metabolic syndrome [1.18 (1.16–1.21)]. In the 18–34 years age group, the positive associations of hysterectomy with diabetes and hypertension were 6.09 (4.48–8.26) and 6.08 (5.18–7.14).
Conclusions
In this large-scale study, the prevalence of hysterectomy was higher among menopausal women or those living in the East and Northeast regions. Hysterectomy was strongly associated with metabolic disorders, especially in women of childbearing age. Further studies were warranted to elucidate the underlying mechanisms and develop public health policies.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12916-025-04595-w.
Keywords: Hysterectomy, Diabetes, Dyslipidemia, Hypertension, Metabolic syndrome, Obesity
Background
Hysterectomy is one of the most common surgical procedures (second only to cesarean section) for women patients worldwide [1]. It was usually performed for benign gynecological diseases, gynecological cancers, and complications of pregnancy (mostly placental complications or uncontrolled hemorrhage) [1–4]. The prevalence of hysterectomy has been reported to vary widely among different populations, such as the USA, ranging from 4 to 41% [5, 6]. Understanding the epidemiology of hysterectomy might help to obtain precise estimates of uterine cancer incidence rates [1]. In addition, understanding the risk factors for hysterectomy in women of childbearing age may help preserve the uterus and subsequently improve the continuing decline in fertility worldwide [7]. Besides, it has been reported that the risk of developing cardiovascular disease (CVD) was elevated following a hysterectomy [5, 8]. However, the prevalence rate of hysterectomy in Chinese has rarely been reported.
A few studies have reported the associations between metabolic factors and hysterectomy, including obesity, hypertension, and metabolic syndrome [9–11], suggesting metabolic disorders and indicators might be risk factors of hysterectomy. Considering China’s population size, the number of individuals with metabolic disorders is substantial [12, 13], making it worthwhile to investigate the potential associations between hysterectomy and metabolic disorders. In addition, most studies have focused on menopausal women [5, 14, 15], but the epidemiology and risk factors for women of childbearing age should also be studied, as these may directly affect fertility outcomes. Therefore, we conducted a multicenter population-based study to estimate the prevalence of hysterectomy and to explore the potential metabolic factors.
Methods
Study design
This cross-sectional, multicenter, population-based study was based on the database of the Meinian health screening centers, the largest health screening center chain in China, providing general physical examination services for residents from 31 provinces of mainland China [16]. For the current study, 10,098,830 women’s records of information on physical examination and data on hysterectomy status from January 2017 to December 2018, prior to the COVID-19 epidemic, were extracted from the database. After the removal of duplicates for the same individual by keeping the most recent records (n = 1,077,607) and then excluding participants aged < 18 years (n = 7761), a total of 9,013,462 participants were included in the current study (Fig. 1).
Fig. 1.
Study design flowchart
This dataset-based study has been approved by the Institutional Review Board of Peking University Health Science Center (ID of the approval: IRB00001052-19077), in which individual informed consent was waived as the analyses only used anonymous data.
Hysterectomy status was determined by B-scan ultrasonography. Body height, weight, and blood pressure (BP) were evaluated by trained physicians. BP was measured with a medical electronic sphygmomanometer on the right arm while seated after resting for at least five minutes. Venous blood samples were collected from all participants after at least 10 h of fasting overnight. Serum total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), fasting blood glucose (FBG), triglycerides (TG), and uric acid (UA) were measured using an automatic biochemical analyzer (all approved by authority) [17, 18]. Hemoglobin A1c (HbA1c) was measured with high-performance liquid chromatography (HPLC) [18]. All the laboratories in the health screening centers had completed a standardization and certification program.
Body mass index (BMI) was calculated as weight in kilograms divided by height squared in meters. According to the Chinese BMI classification [19], underweight was defined as < 18.5 kg/m2, normal weight as 18.5–23.9 kg/m2, overweight as 24.0–27.9 kg/m2, and obesity as ≥ 28.0 kg/m2.
Dyslipidemia was defined as any of the following abnormalities: TC ≥ 6.22 mmol/L; TG ≥ 2.26 mmol/L; LDL-C ≥ 4.14 mmol/L; HDL-C < 1.04 mmol/L; or a previous history of dyslipidemia reported [20]. Elevated TC was defined as TC ≥ 6.22 mmol/L. Elevated TG was defined as TG ≥ 2.26 mmol/L.
Hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg, and/or previous history of hypertension reported [21]. Elevated blood pressure was defined as SBP > 130 mmHg and/or DBP > 85 mmHg [22].
Diabetes was defined as FBG ≥ 7.0 mmol/L, and/or a previous history of diabetes was reported [23]. Hyperglycemia was defined as FBG ≥ 6.1 mmol/L and/or a prior history of diabetes reported [23]. Elevated FBG was defined as FBG > 5.6 mmol/L [24]. Elevated HbA1c was defined as HbA1c ≥ 5.7% [23].
Metabolic dysfunction-associated fatty liver disease (MAFLD) was defined as one of the following three criteria, namely overweight/obesity, presence of diabetes, or the presence of at least two metabolic risk abnormalities: waist circumference ≥ 80 cm; blood pressure ≥ 130/85 mmHg; TG ≥ 1.70 mmol/L; HDL-C < 1.3 mmol/L; FBG 5.6–6.9 mmol/L or HbA1c 5.7%–6.4% [25].
Metabolic syndrome was defined as BMI ≥ 28.0 kg/m2 and meeting any two of the following criteria: TG ≥ 1.7 mmol/L; female HDL-C < 1.3 mmol/L; SBP > 130 mmHg, and/or DBP > 85 mmHg and/or have been confirmed as hypertension; FBG > 5.6 mmol/L, and/or have been confirmed as diabetes [22].
Thirty-one provinces of mainland China were divided into six geographic regions: North (Beijing, Tianjin, Hebei, Shanxi, and Inner Mongolia Autonomous Region); Northeast (Liaoning, Jilin, and Heilongjiang); East (Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, and Shandong); South-central (Henan, Hubei, Hunan, Guangdong, Guangxi Zhuang Autonomous Region, and Hainan); Northwest (Shaanxi, Gansu, Qinghai, Ningxia Hui Autonomous Region, and Xinjiang Uygur Autonomous Region); and Southwest (Chongqing, Sichuan, Guizhou, Yunnan, and Tibet Autonomous Region). Different geographical regions have different climates, representing different lifestyles and dietary habits. Cities were classified into medium/smaller-sized cities (< 1 million population), large cities (1 million to 3 million population), and mega/larger-sized cities (> 3 million population) to represent the socioeconomic status.
Statistical analysis
Frequencies and percentages for categorical variables and mean ± standard deviation (SD) for continuous variables were used to present the data. The prevalence of hysterectomy with 95% confidence intervals (CIs) was estimated in the overall population. Population distribution of the 2010 Chinese census data, the most recent census data available, was used to estimate age-standardized prevalence. Geographical differences for the age-standardized prevalence rates of hysterectomy were shown in different colors on a national map of China. The associations between metabolic disorders and hysterectomy were formally tested with log-binomial regression, presented as relative risk (RR) and 95% CIs. RR was used to estimate the prevalence ratio. The associations were conducted by three models: Model 1 was adjusted for age (≤ 29, 30–39, 40–49, 50–59, 60–69, ≥ 70 years) and BMI (underweight, normal weight, overweight, obesity); Model 2 was adjusted for age, city size (medium/smaller, large, mega/larger), and geographic region (north, east, south-central, northeast, northwest, southwest); and Model 3 was adjusted for age, BMI, city size, and geographic region. Prevalence and relative risk were also estimated by age groups: the normal age childbearing group (18–34 years), the high age childbearing group (35–44 years), the perimenopausal group (45–59 years), and the postmenopausal group (≥ 60 years) [26]. Data analyses were performed using SAS (version 9.4; SAS Institute Inc.); maps were constructed using R (version 4.1.3; R Core Team 2022) and R Studio (version 2022.2.1.461; R Studio Team, 2022). A RR of 1 indicated no difference between groups, with values < 1 suggesting a reduced risk and > 1 indicating an elevated risk. The results were considered statistically significant if the 95% CI did not include 1.
Results
Population characteristics
The basic characteristics of 9,013,462 women participants are shown in Table 1 and Additional file 1: Table S1. The mean age of the whole population was 42 and the mean age of those who had undergone hysterectomy was 55. Women who underwent hysterectomy were more likely to have a higher BMI and a higher proportion of metabolic disorders.
Table 1.
Characteristics of the study participants
| Total, n = 9,013,462 | Non-hysterectomy, n = 8,802,669 (97.7%) | Hysterectomy, n = 210,793 (2.34%) | |
|---|---|---|---|
| Age (years) | 42.0 ± 12.7 | 41.7 ± 12.6 | 55.2 ± 7.36 |
| 18–34 | 3,111,762 (34.5%) | 3,110,515 (35.3%) | 1247 (0.59%) |
| 35–44 | 2,179,260 (24.2%) | 2,169,398 (24.6%) | 9862 (4.68%) |
| 45–59 | 2,781,878 (30.9%) | 2,638,408 (30.0%) | 143,470 (68.1%) |
| ≥ 60 | 940,562 (10.4%) | 884,348 (10.0%) | 56,214 (26.7%) |
| BMI (kg/m2) | 23.1 ± 3.44 | 23.1 ± 3.43 | 24.9 ± 3.32 |
| < 18.5 (underweight) | 502,166 (6.24%) | 499,756 (6.36%) | 2410 (1.24%) |
| 18.5–23.9 (normal weight) | 4,666,188 (58.0%) | 4,587,216 (58.4%) | 78,972 (40.6%) |
| 24.0–27.9 (overweight) | 2,182,004 (27.1%) | 2,100,953 (26.8%) | 81,051 (41.6%) |
| ≥ 28.0 (obesity) | 698,596 (8.68%) | 666,292 (8.48%) | 32,304 (16.6%) |
| Geographical region | |||
| North | 1,039,080 (11.5%) | 1,012,743 (11.5%) | 26,337 (12.5%) |
| East | 2,727,272 (30.3%) | 2,662,132 (30.2%) | 65,140 (30.9%) |
| South-central | 2,522,831 (28.0%) | 2,473,763 (28.1%) | 49,068 (23.3%) |
| Northeast | 860,085 (9.54%) | 831,127 (9.44%) | 28,958 (13.7%) |
| Northwest | 521,461 (5.79%) | 510,166 (5.80%) | 11,295 (5.36%) |
| Southwest | 1,342,733 (14.9%) | 1,312,738 (14.9%) | 29,995 (14.2%) |
| Metabolic disorders | |||
| Hypertension | 1,407,506 (16.9%) | 1,327,387 (16.4%) | 80,119 (40.0%) |
| Diabetes | 300,922 (3.54%) | 280,833 (3.38%) | 20,089 (9.88%) |
| Hyperglycemia | 601,880 (7.08%) | 566,211 (6.82%) | 35,669 (17.5%) |
| Dyslipidemia | 1,656,529 (19.4%) | 1,583,706 (19.0%) | 72,823 (35.5%) |
| MAFLD | 2,032,191 (23.5%) | 1,937,998 (23.0%) | 94,193 (46.0%) |
| Metabolic syndrome | 372,881 (4.63%) | 351,349 (4.47%) | 21,532 (11.1%) |
| Metabolic indicators | |||
| SBP (mm/Hg) | 119.1 ± 18.7 | 118.8 ± 18.6 | 131.6 ± 20.1 |
| DBP (mm/Hg) | 71.9 ± 11.3 | 71.7 ± 11.2 | 77.6 ± 11.9 |
| FBG (mmol/L) | 5.09 ± 1.05 | 5.08 ± 1.04 | 5.54 ± 1.45 |
| HbA1c (%) | 5.37 ± 0.84 | 5.36 ± 0.83 | 5.68 ± 1.01 |
| TG (mmol/L) | 1.25 ± 0.92 | 1.24 ± 0.91 | 1.68 ± 1.17 |
| TC (mmol/L) | 4.79 ± 0.98 | 4.78 ± 0.97 | 5.31 ± 1.01 |
| HDL-C (mmol/L) | 1.47 ± 0.34 | 1.47 ± 0.34 | 1.45 ± 0.33 |
| LDL-C (mmol/L) | 2.64 ± 0.82 | 2.63 ± 0.82 | 3.01 ± 0.87 |
| Uric acid (μmol/L) | 274.0 ± 66.9 | 273.7 ± 66.7 | 287.0 ± 70.6 |
Data are presented as n (%) or mean ± SD. BMI body mass index, DBP diastolic blood pressure, FBG fasting blood glucose, HbA1c hemoglobin A1c, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, MAFLD metabolic associated fatty liver disease, SBP systolic blood pressure, SD standard deviation, TC total cholesterol, TG triglyceride. Number of missing values: BMI n = 964,508; hypertension, n = 700,446; diabetes, n = 508,959; hyperglycemia, n = 508,959; dyslipidemia, n = 454,860; MAFLD, n = 372,966; metabolic syndrome, n = 964,508
Prevalence of hysterectomy
The age-standardized prevalence rate of hysterectomy in the total population was 2.36% (95% CI, 2.35–2.37) (Fig. 2). The crude prevalence of hysterectomy was less than 1% among participants younger than 45 years old and increased sharply after the age of 40, reaching a peak of 7.61% among participants aged 55–59 years (Fig. 2A). The highest age-standardized prevalence rates of hysterectomy were observed in Jiangsu (3.26%; 95% CI, 3.18–3.34) and Heilongjiang (3.21%; 95% CI, 3.07–3.36) provinces, as well as in the Northeast region (2.67%; 95% CI, 2.64–2.71) (Fig. 2B, Table 2). Additionally, the age-standardized prevalence of hysterectomy increased from 1.64% to 3.08% as BMI increased (Table 2).
Fig. 2.
Crude prevalence of hysterectomy by age group and age-standardized prevalence across geographical regions, 2017–2018. A Crude prevalence rates of hysterectomy by age group; B Age-standardized prevalence rates of hysterectomy across geographical region. Prevalence rates were presented as percentages
Table 2.
Age-standardized prevalence of hysterectomy among women in different age groups
| Total | 18–34 years | 35–44 years | 45–59 years | ≥ 60 years | |
|---|---|---|---|---|---|
| BMI | |||||
| Underweight | 1.64 (1.57–1.71) | 0.020 (0.015–0.026) | 0.33 (0.28–0.38) | 3.20 (3.03–3.37) | 3.71 (3.41–4.02) |
| Normal weight | 2.11 (2.09–2.13) | 0.031 (0.029–0.034) | 0.42 (0.40–0.43) | 3.87 (3.83–3.90) | 4.96 (4.87–5.05) |
| Overweight | 2.62 (2.59–2.64) | 0.076 (0.067–0.084) | 0.62 (0.60–0.64) | 4.95 (4.90–5.00) | 5.59 (5.50–5.68) |
| Obesity | 3.08 (3.04–3.12) | 0.10 (0.085–0.12) | 0.86 (0.81–0.91) | 6.10 (6.00–6.19) | 6.06 (5.91–6.20) |
| Geographical region | |||||
| North | 2.39 (2.36–2.43) | 0.041 (0.034–0.048) | 0.48 (0.45–0.51) | 4.49 (4.42–4.56) | 5.39 (5.23–5.54) |
| East | 2.52 (2.49–2.54) | 0.030 (0.027–0.034) | 0.42 (0.40–0.43) | 4.58 (4.53–4.62) | 6.03 (5.92–6.13) |
| South-central | 2.09 (2.07–2.11) | 0.043 (0.039–0.047) | 0.48 (0.46–0.50) | 4.02 (3.97–4.06) | 4.51 (4.41–4.60) |
| Northeast | 2.67 (2.64–2.71) | 0.070 (0.058–0.081) | 0.60 (0.57–0.64) | 5.09 (5.01–5.17) | 5.76 (5.61–5.92) |
| Northwest | 2.18 (2.13–2.23) | 0.055 (0.044–0.066) | 0.49 (0.45–0.53) | 3.88 (3.79–3.97) | 5.17 (4.95–5.40) |
| Southwest | 2.31 (2.28–2.34) | 0.032 (0.027–0.037) | 0.57 (0.55–0.60) | 4.47 (4.40–4.53) | 4.94 (4.81–5.07) |
| City size | |||||
| Medium/smaller | 2.28 (2.26–2.30) | 0.075 (0.068–0.081) | 0.58 (0.56–0.60) | 4.39 (4.35–4.43) | 4.76 (4.68–4.85) |
| Large | 2.40 (2.39–2.42) | 0.038 (0.035–0.042) | 0.47 (0.46–0.49) | 4.45 (4.41–4.48) | 5.53 (5.44–5.62) |
| Mega/larger | 2.41 (2.38–2.43) | 0.019 (0.017–0.022) | 0.41 (0.39–0.43) | 4.41 (4.36–4.46) | 5.76 (5.65–5.87) |
| Metabolic disorders | |||||
| Obesity | |||||
| No | 2.31 (2.29–2.32) | 0.037 (0.034–0.039) | 0.47 (0.46–0.48) | 4.31 (4.28–4.33) | 5.24 (5.18–5.30) |
| Yes | 3.08 (3.04–3.12) | 0.10 (0.085–0.12) | 0.86 (0.81–0.91) | 6.10 (6.00–6.19) | 6.06 (5.91–6.20) |
| Hypertension | |||||
| No | 2.20 (2.18–2.22) | 0.033 (0.031–0.035) | 0.46 (0.45–0.47) | 4.12 (4.09–4.15) | 5.04 (4.95–5.13) |
| Yes | 2.99 (2.96–3.02) | 0.29 (0.25–0.33) | 0.89 (0.84–0.94) | 5.65 (5.58–5.71) | 5.63 (5.56–5.70) |
| Diabetes | |||||
| No | 2.32 (2.30–2.33) | 0.038 (0.036–0.041) | 0.49 (0.48–0.50) | 4.37 (4.35–4.40) | 5.18 (5.12–5.24) |
| Yes | 3.49 (3.41–3.57) | 0.54 (0.38–0.69) | 1.04 (0.92–1.16) | 6.52 (6.35–6.68) | 6.29 (6.14–6.44) |
| Hyperglycemia | |||||
| No | 2.29 (2.27–2.30) | 0.037 (0.035–0.040) | 0.48 (0.47–0.49) | 4.31 (4.28–4.33) | 5.14 (5.08–5.20) |
| Yes | 3.13 (3.08–3.17) | 0.26 (0.20–0.31) | 0.85 (0.78–0.91) | 5.96 (5.86–6.07) | 5.98 (5.87–6.10) |
| Dyslipidemia | |||||
| No | 2.24 (2.23–2.26) | 0.034 (0.032–0.036) | 0.45 (0.44–0.46) | 4.17 (4.14–4.19) | 5.17 (5.10–5.24) |
| Yes | 2.77 (2.75–2.80) | 0.099 (0.087–0.11) | 0.75 (0.72–0.78) | 5.36 (5.30–5.41) | 5.65 (5.56–5.74) |
| MAFLD | |||||
| No | 2.09 (2.08–2.11) | 0.032 (0.030–0.034) | 0.43 (0.42–0.44) | 3.91 (3.88–3.94) | 4.78 (4.71–4.85) |
| Yes | 2.92 (2.90–2.94) | 0.11 (0.094–0.12) | 0.74 (0.72–0.77) | 5.61 (5.55–5.66) | 6.05 (5.97–6.14) |
| Metabolic syndrome | |||||
| No | 2.33 (2.31–2.34) | 0.037 (0.035–0.040) | 0.48 (0.47–0.50) | 4.36 (4.34–4.39) | 5.26 (5.20–5.32) |
| Yes | 3.36 (3.31–3.41) | 0.19 (0.14–0.23) | 1.04 (0.95–1.12) | 6.69 (6.55–6.82) | 6.23 (6.06–6.40) |
The age-standardized prevalence rates are presented as % (95% confidence intervals) and used the population distribution of the 2010 Chinese census data
BMI body mass index, MAFLD metabolic associated fatty liver disease
Associations between metabolic disorders and hysterectomy
After adjusting for age, BMI, city size, and geographic region, hysterectomy was positively associated with all metabolic disorders and across all age groups, including obesity (RR = 1.31, 95% CI = 1.29–1.32); hypertension (RR = 1.22, 95% CI = 1.21–1.23); diabetes (RR = 1.26, 95% CI = 1.24–1.28); hyperglycemia (RR = 1.22, 95% CI = 1.20–1.23); dyslipidemia (RR = 1.18, 95% CI = 1.16–1.19); MAFLD (RR = 1.25, 95% CI = 1.24–1.26); and metabolic syndrome (RR = 1.18, 95% CI = 1.16–1.21) (Table 3). Interestingly, in the 18–34 years age group, the positive associations of hysterectomy with diabetes (RR = 6.09, 95% CI = 4.48–8.26) and hypertension (RR = 6.08, 95% CI = 5.18–7.14) were higher than those in older age groups (Table 3). The relative risks of hysterectomy with elevated blood pressure, TC, TG, and HbA1c were also ranked highest in the 18–34 age group (Additional file 1: Fig. S1). The risk differences (RD) for both conditions were smallest in the 18–34 age group (diabetes RD = 0.502%; hypertension RD = 0.257%) and largest in the 45–59 age group (diabetes RD = 2.15%; hypertension RD = 1.53%) (Table 2 and Additional file 1: Fig. S1).
Table 3.
Relative risk (95% CI) for associations between metabolic disorders and hysterectomy
| Metabolic disorders | Total | 18–34 years | 35–44 years | 45–59 years | ≥ 60 years |
|---|---|---|---|---|---|
| N (hysterectomy/non-hysterectomy) | 210,793/8,802,669 | 1247/3,110,515 | 9862/2,169,398 | 143,470/2,638,408 | 56,214/884,348 |
| Obesitya | |||||
| Model 1 | 1.32 (1.30–1.33) | 2.74 (2.29–3.28) | 1.82 (1.71–1.93) | 1.35 (1.34–1.37) | 1.15 (1.13–1.18) |
| Model 2 | 1.31 (1.29–1.32) | 2.54 (2.12–3.04) | 1.80 (1.70–1.92) | 1.35 (1.33–1.37) | 1.15 (1.12–1.17) |
| Hypertension | |||||
| Model 1 | 1.22 (1.20–1.23) | 6.38 (5.43–7.49) | 1.62 (1.53–1.72) | 1.24 (1.22–1.25) | 1.08 (1.06–1.10) |
| Model 2 | 1.29 (1.28–1.30) | 7.54 (6.49–8.75) | 1.87 (1.76–1.97) | 1.31 (1.30–1.32) | 1.11 (1.09–1.13) |
| Model 3 | 1.22 (1.21–1.23) | 6.08 (5.18–7.14) | 1.60 (1.51–1.70) | 1.23 (1.22–1.25) | 1.08 (1.06–1.10) |
| Diabetes | |||||
| Model 1 | 1.26 (1.24–1.28) | 6.55 (4.82–8.90) | 1.67 (1.48–1.88) | 1.31 (1.28–1.33) | 1.17 (1.15–1.20) |
| Model 2 | 1.33 (1.32–1.35) | 9.65 (7.34–12.7) | 2.06 (1.84–2.31) | 1.40 (1.37–1.42) | 1.21 (1.18–1.24) |
| Model 3 | 1.26 (1.24–1.28) | 6.09 (4.48–8.26) | 1.65 (1.46–1.86) | 1.30 (1.28–1.33) | 1.18 (1.15–1.21) |
| Hyperglycemia | |||||
| Model 1 | 1.22 (1.20–1.23) | 3.92 (3.08–4.98) | 1.43 (1.32–1.56) | 1.25 (1.23–1.27) | 1.13 (1.10–1.15) |
| Model 2 | 1.29 (1.27–1.30) | 5.04 (4.07–6.25) | 1.69 (1.56–1.83) | 1.33 (1.32–1.35) | 1.17 (1.14–1.19) |
| Model 3 | 1.22 (1.20–1.23) | 3.48 (2.74–4.42) | 1.40 (1.29–1.52) | 1.25 (1.23–1.27) | 1.14 (1.11–1.16) |
| Dyslipidemia | |||||
| Model 1 | 1.18 (1.17–1.19) | 2.19 (1.89–2.53) | 1.50 (1.42–1.57) | 1.19 (1.17–1.20) | 1.09 (1.07–1.11) |
| Model 2 | 1.22 (1.21–1.23) | 2.62 (2.29–2.99) | 1.65 (1.57–1.73) | 1.23 (1.21–1.24) | 1.09 (1.08–1.11) |
| Model 3 | 1.18 (1.16–1.19) | 2.07 (1.79–2.40) | 1.47 (1.40–1.55) | 1.18 (1.17–1.19) | 1.08 (1.06–1.10) |
| MAFLD | |||||
| Model 1 | 1.26 (1.24–1.27) | 1.94 (1.64–2.30) | 1.41 (1.33–1.48) | 1.25 (1.24–1.27) | 1.20 (1.17–1.22) |
| Model 2 | 1.37 (1.35–1.38) | 2.95 (2.60–3.35) | 1.75 (1.67–1.83) | 1.37 (1.35–1.38) | 1.23 (1.21–1.25) |
| Model 3 | 1.25 (1.24–1.26) | 1.97 (1.67–2.34) | 1.42 (1.34–1.50) | 1.25 (1.23–1.26) | 1.18 (1.15–1.20) |
| Metabolic syndrome | |||||
| Model 1 | 1.19 (1.16–1.21) | 2.87 (2.04–4.04) | 1.44 (1.28–1.62) | 1.22 (1.19–1.26) | 1.13 (1.08–1.18) |
| Model 2 | 1.37 (1.35–1.39) | 4.16 (3.32–5.22) | 2.10 (1.93–2.28) | 1.44 (1.41–1.46) | 1.18 (1.16–1.21) |
| Model 3 | 1.18 (1.16–1.21) | 2.68 (1.90–3.78) | 1.41 (1.26–1.59) | 1.22 (1.19–1.26) | 1.13 (1.08–1.18) |
Relative risk estimation by log-binomial regression
BMI body mass index, CI confidence interval, MAFLD metabolic associated fatty liver disease, RR relative risk
Model 1: adjusted for age (≤ 29, 30–39, 40–49, 50–59, 60–69, ≥ 70 years) and BMI (underweight, normal weight, overweight, obesity)
Model 2: adjusted for age (≤ 29, 30–39, 40–49, 50–59, 60–69, ≥ 70 years), city size (medium/smaller, large, mega/larger), and geographic region (north, east, south-central, northeast, northwest, southwest)
Model 3: adjusted for age (≤ 29, 30–39, 40–49, 50–59, 60–69, ≥ 70 years), BMI (underweight, normal weight, overweight, obesity), city size (medium /smaller, large, mega/larger), and geographic region (north, east, south-central, northeast, northwest, southwest)
aNot adjusted for BMI
Discussion
Based on 9,013,462 women participants, the age-standardized prevalence of hysterectomy in urban China was 2.36%, with the highest rate observed in the Northeast region. The highest crude prevalence was observed among women in the 55–59 age group. Obesity, dyslipidemia, diabetes, hypertension, hyperglycemia, MAFLD, and metabolic syndrome were positively associated with hysterectomy risk. The strongest positive association between metabolic disorders and hysterectomy was observed in women of childbearing age (18–44 years).
The prevalence of hysterectomy in the current population was lower than that in other populations, ranging from 3.3% to 41% for American (4%–41%), European (10.4%–22.4%), Australian (21.9%), and Asian (3.3%–11.4%) (Table 4). The variation in prevalence could be attributed to differences in age groups used to calculate prevalence (the menopausal status of the population), ethnic diversity, and socioeconomic status.
Table 4.
Prevalence rates of hysterectomy in previous studies
| Authors (year) | Country | Study period | N | Age | Study population | Hysterectomy determination | Data source | Prevalence |
|---|---|---|---|---|---|---|---|---|
| North America | ||||||||
| Howard BV (2005) [5] | USA | 1993.9–1998.12 | 89,914 | 50–79 | Non severe medical condition | Self-report | WHI | 41.0% |
| Powell LH (2005) [14] | USA | 1995.11–1997.8 | 15,160 | 40–55 | Reproductive cancer free | Self-report | SWAN | 18.8% |
| Bower JK (2009) [6] | USA | 2000–2002 | 1863 | 33–45 | Black and white subjects; long-term disease or disability-free | Self-report and transvaginal ultrasonography confirmed | CARDIA | 4% White, 12% Black |
| Harvey SV (2022) [27] | USA | 2006 | 211,659 | 18–80 | General population | Self-report | BRFSS | 21.4% |
| USA | 2016 | 213,561 | 18–80 | General population | Self-report | BRFSS | 21.1% | |
| Gopalani SV (2023) [28] | USA | 2012 | 79,607 | ≥ 18 | General population | Self-report | BRFSS | 18.9% |
| USA | 2014 | 75,260 | ≥ 18 | General population | Self-report | BRFSS | 18.6% | |
| USA | 2016 | 63,620 | ≥ 18 | General population | Self-report | BRFSS | 18.0% | |
| USA | 2018 | 62,020 | ≥ 18 | General population | Self-report | BRFSS | 17.3% | |
| USA | 2020 | 52,855 | ≥ 18 | General population | Self-report | BRFSS | 17.0% | |
| Scime NV (2021) [26] | Canada | 2012 | 30,170 | ≥ 20 | General population | Self-report | CCHS | 15.4% |
| Europe | ||||||||
| Settnes A (1996) [29] | Denmark | 1982–1990 | 1765 | 30, 40, 50, 60 | General population | Self-report and medical records confirmed | National Civil Registration System | 10.4% |
| Ong S (2000) [30] | Ireland | 1989 | 17,735 | 50–65 | General population | Self-report | Eccles Breast Screening Program | 22.2% |
| Cooper R (2008) [9] | Britain | 1989–1999 | 1797 | 57 | General population | Self-report | NSHD | 22.4% |
| Progetto Menopausa Italia Study G. (2000) [31] | Italy | 1997–1999 | 25,644 | 40–78 | Menopausal symptoms counselling | Gynecological examination and self-report | First-level outpatient menopause clinics | 18.4% |
| Tanaka LF (2023) [32] | Germany | 2005–2007 | 4719 | 30–65 | General population | Self-report | MARZY study | 20.4% |
| Prütz F (2013) [33] | Germany | 2008–2011 | 3500 | 18–79 | General population | Self-report | DEGS1 | 17.5% |
| Australia | ||||||||
| Byles JE (2000) [34] | Australia | 1996 | 13,928 | 45–50 | Basic health insurance participants | Self-report | HIC | 21.9% |
| Latin America | ||||||||
| Escobar DA (2016) [35] | Argentina | 1999–2000 | 656 | ≥ 60 | General population | Self-report | SABE | 12.8% |
| Mexico | 1999–2000 | 740 | ≥ 60 | General population | Self-report | SABE | 18.2% | |
| Chile | 1999–2000 | 854 | ≥ 60 | General population | Self-report | SABE | 13.1% | |
| Uruguay | 1999–2000 | 916 | ≥ 60 | General population | Self-report | SABE | 15.6% | |
| Barbados | 1999–2000 | 924 | ≥ 60 | General population | Self-report | SABE | 30.4% | |
| Cuba | 1999–2000 | 1197 | ≥ 60 | General population | Self-report | SABE | 16.5% | |
| Brazil | 1999–2000 | 1262 | ≥ 60 | General population | Self-report | SABE | 15.1% | |
| Asia | ||||||||
| Shekhar C (2019) [36] | India | 2015–2016 | 340,127 | 30–49 | General population | Self-report | NFHS-4 | 6.0% |
| Desai S (2019) [37] | India | 2015–2016 | 699,686 | 15–49 | General population | Self-report | NFHS-4 | 0.36% (15–29y), 3.59% (30–39y), 9.20% (40–49y) |
| Rout D (2023) [38] | India | 2017–2018 | 38,154 | > 18 | General population | Self-report | LASI | 11.4% |
| Kumari P (2022) [39] | India | 2019–2021 | 724,115 | 15–49 | General population | Self-report | NFHS-5 | 3.3% |
| Liu F (2017) [40] | China | 2009–2011 | 3328 | 25–69 | Oesophageal cancer screening; cancer and virus-free | Gynecological examination | Oesophageal cancer cohort study in rural Anyang | 3.3% |
| Current study | China | 2017–2018 | 9,021,223 | ≥ 18 | Health screening participants | B-scan ultrasonography | Menian health-screening center chain | 2.4% |
BRFSS Behavioral Risk Factor Surveillance System, CARDIA Coronary Artery Risk Development in Young Adults, CCHS Canadian Community Health Survey, DEGS1 German Health Interview and Examination Survey for Adults, HIC Health Insurance Commission, LASI Longitudinal Aging Study in India, NFHS National Family and Health Survey, NSHD National Survey of Health and Development, SABE Health, Well-Being and Aging in Latin America and the Caribbean Study, SWAN Study of Women’s Health Across the Nation
Age
The relatively lower prevalence of hysterectomy in our study can be attributed, in part, to the age of the participants, as our study included a higher proportion of individuals of childbearing age (58.7%) with a lower prevalence of hysterectomy. In the current study, the prevalence of hysterectomy in the 35–44 years age group was nearly ten times higher than that of the 18–34 years group, while the rate among those aged 45–59 was in turn almost ten times higher than that of the 35–44 years age group. The highest prevalence of hysterectomy in our study was observed in the 50–64 years age group (5.96%–7.61%). The pattern of prevalence rate changing with age was comparable with that reported in the USA (50–64 years, 28.3%–40.5%) [14, 41]; Switzerland (50–54 years with highest incidence rate ratio) [3]; Germany (50–65 years, 19%–29%) [42]; and Australia (45–54 years, 90 per 10,000 women) [43]. The age variation of hysterectomy may reflect the difference of uterus diseases at different ages. Previous studies have reported that uterine leiomyoma was one of the most common indications for hysterectomy, with the highest prevalence in women aged 50–54 years [44]. As reported by Whiteman MK et al. [4], approximately 78% of hysterectomies in women aged 50–54 years were performed concomitantly with bilateral oophorectomy. For women aged 55 years and older, uterine prolapse has been identified as the primary indication for hysterectomy [45]. In addition, data from the National Central Cancer Registry of China [46] suggested the age-specific incidence of uterine cancer, which might lead to hysterectomy, reached the highest level at the age of 55 years. From this point of view, our results are reasonable. As for the decline in the prevalence of hysterectomy when women were over 60 years old, survival bias may be an explanation.
Ethnicity
Several studies have reported that black women were more likely to undergo hysterectomy than white women after controlling for socioeconomic factors [5, 6, 14, 27, 47, 48]. A study in the USA also reported that the prevalence of hysterectomy had varied by ethnicity, with the highest rate in Black (29.0%), followed by White (25.5%), Hispanic (22.7%), and Asian (16.0%) [47]. Although our study was not designed to examine ethnic differences, the finding of a lower hysterectomy prevalence among the Chinese population was consistent with the hypothesis of a correlation between ethnicity and hysterectomy.
Socioeconomic status
Studies have suggested that the variation in hysterectomy risk could be partially explained by differences in socioeconomic status [41, 49–52]. The geographic variation of hysterectomy prevalence observed in our study may also be linked to socioeconomic differences between geographic regions or city size. Education, a marker of socioeconomic conditions [53], has been reported to be inversely related to hysterectomy [31, 41, 49, 51, 54, 55]. Women with less education were more likely to lead unhealthy lifestyles and delay seeking help for gynecological problems, and eventually, hysterectomy became their last treatment option [55]. Because our population was mostly from urban areas, with a relatively higher education level [56], it may be one of the reasons explaining the lower prevalence of hysterectomy in our study than that in Anyang (3.31%) [40], a rural area of China, where 66.7% of participants were illiterate (< 1 year) or had only primary school (1–6 years) education.
Metabolic disorders
Our study found that women of reproductive age (18–44 years) exhibited a higher relative risk of hysterectomy associated with metabolic disorders compared to women of other ages. Notably, in women of optimal reproductive age (18–34 years), the relative risks of hysterectomy linked to hypertension and diabetes were about five times higher than those observed in postmenopausal women. These findings were consistent with results from the study of Laughlin-Tommaso et al., where an elevated risk of cardiovascular diseases was associated with hysterectomy in women under 35 years of age, while no significant association was observed in women over 50 [57]. The stronger association observed in younger women may be explained, in part, by the fact that older women with long-standing and increasing metabolic abnormalities or uncontrolled hypertension [57]. The young women were less likely to undergo hysterectomy due to increased surgical risks. The large relative risk in the reproductive age group suggested a strong association between hysterectomy and metabolic disorders in young women, potentially of biological and etiological significance, whereas the small risk difference suggested limited excess cases due to low baseline prevalence in this age. Further research is needed to elucidate the mechanisms in reproductive-age women and to raise awareness of their clinical and public health implications.
The prevalence of metabolic disorders increased sharply starting at age 45 [58, 59]. In the current study, hysterectomy rates also rose significantly after age 45, suggesting a plausible positive association between metabolic disorders and hysterectomy. Studies have reported that metabolic disorders, including obesity [60–62], diabetes [63–66], hypertension [10, 67–71], and dyslipidemia [72] may contribute to the development of uterine leiomyoma. Given that uterine leiomyoma accounts for approximately one-third to half of hysterectomy cases [73], these metabolic disorders may be indirect factors for hysterectomy. Several studies suggested that obesity and diabetes may facilitate the conversion of adrenal androgens to estrone and contribute to the production of estrogen and progesterone, thereby promoting the development of uterine leiomyomas [62, 63, 74]. The antihypertensive drug was associated with a reduced incidence of uterine leiomyoma, suggesting a potential role for the renin-angiotensin system in the pathophysiological link between hypertension and uterine leiomyoma [71]. The risk of uterine leiomyomas can be effectively reduced by the use of statins, a cholesterol-lowering drug, which was hypothesized to be mediated by the inhibition of growth factor signaling and the activation of calcium-dependent apoptotic pathways [72]. Notably, we observed a strong association between metabolic disorders and hysterectomy among women of childbearing age. This might emphasize the importance of controlling metabolic disorders to improve the situation of continuous birthrate decline worldwide. In the world, there were 504 million obese women in 2022 [75] and about 268.3 million diabetes women in 2021 [12], which might be a large pool for future hysterectomy. Controlling metabolic disorders might be important to decrease the hysterectomy rates and further contribute to fertility maintenance.
On the other hand, studies have reported that women who underwent hysterectomy had a significantly higher risk of developing obesity [57], diabetes [15], hypertension [57, 76], hyperlipidemia [57, 77, 78], MAFLD [79, 80], metabolic syndrome [11], and cardiovascular disease [5, 8, 57] later on, compared to women without hysterectomy. In a cohort of 4188 women followed for a median of 21.9 years, hysterectomy was associated with increased risks of incident obesity (HR 1.18, 95% CI 1.04–1.35); hypertension (HR 1.13, 95% CI 1.03–1.25); hyperlipidemia (HR 1.14, 95% CI 1.05–1.25); cardiac arrhythmias (HR 1.17, 95% CI 1.05–1.32); and coronary artery disease (HR 1.33, 95% CI 1.12–1.58) [57]. In a large cohort with 67,130 women followed for a mean of 13.4 years, hysterectomy was associated with a 13% higher risk of diabetes (HR 1.13, 95% CI 1.06–1.21) [15]. Over a median follow-up of 10.5 years among 1355 participants, hysterectomy was associated with an increased risk of incident metabolic syndrome (HR 1.32, 95% CI 1.01–1.73) [11]. The authors speculated that the reason might be due to the surgical menopause caused by hysterectomy. Surgical menopause, especially with bilateral oophorectomy, causes an abrupt drop in estrogen and testosterone due to immediate ovarian removal [81]. After the suddenly menopause transition, which might disturb normal lipid metabolism [8], including synthesis of excess fatty acids, adipocytokines, proinflammatory cytokines, and reactive oxygen species; the latter could cause lipid peroxidation, and subsequently insulin resistance, abdominal adiposity, and dyslipidemia [57, 82]. Additionally, we speculated that post-hysterectomy inactivity and stress may drive adiposity, hypercaloric intake, and metabolic dysregulation. If these associations are confirmed, effective management of metabolic disorders may benefit from strategies aimed at reducing unnecessary hysterectomies.
Strengths and limitations
This study has some strengths. Our study is the first to investigate the prevalence of hysterectomy in the general population of China. With over 9 million women recruited in the study, we had enough power to explore the risk factors for women of childbearing age, as well as the potential link between metabolic disorders and hysterectomy rates. In addition, hysterectomy status was determined by B-scan ultrasonography, which was more objective and precise.
This study also has some limitations. The population undergoing physical examination in health screening centers was from urban areas, which restricted our power to explore the role of socioeconomic status in hysterectomy etiology, and it also restricted the results extrapolated to the whole nation. Lacking of data on indication for hysterectomy, age at hysterectomy, menstrual-reproductive history, and other unknown factors limited our exploration of their roles in hysterectomy. The cross-sectional design limits causal inference, and further studies are needed to examine the temporal relationship between hysterectomy and metabolic disorders.
Conclusions
Hysterectomy was more prevalent among menopausal individuals, those from the East and Northeast regions of China, and those with higher BMI. Our study observed that hysterectomy was positively associated with metabolic disorders, particularly among women of childbearing age, suggesting a potential link between hysterectomy and metabolism. Further investigation is warranted to validate the findings and elucidate the underlying mechanisms.
Supplementary Information
Additional file 1: Table S1 and Fig. S1. Table S1–Number of participants (hysterectomy/non–hysterectomy) stratified by age. Fig. S1–Relative risk (95% CI) for associations of hysterectomy with clinical indicators and socioeconomic factors by age.
Acknowledgements
We acknowledge the expert assistance of Prof. Kaijun Niu in manuscript preparation. We express our sincere appreciation to all study participants and research staff in the project.
Abbreviations
- BMI
Body mass index
- BP
Blood pressure
- CIs
Confidence intervals
- CVD
Cardiovascular disease
- DBP
Diastolic blood pressure
- FBG
Fasting blood glucose
- HbA1c
Hemoglobin a1c
- HDL-C
High-density lipoprotein cholesterol
- HPLC
High-performance liquid chromatography
- LDL-C
Low-density lipoprotein cholesterol
- MAFLD
Metabolic dysfunction-associated fatty liver disease
- RR
Relative risk
- SBP
Systolic blood pressure
- SD
Standard deviation
- TC
Total cholesterol
- TG
Triglycerides
- UA
Uric acid
Authors' contributions
Y.B.: Formal analysis, Writing - Original Draft; Y.M.: Formal analysis, Data Curation; J.Z.: Writing - Review & Editing; S.W.: Writing - Review & Editing; Q.Z.: Writing - Review & Editing; Y.X.G.: Methodology; R.H.: Investigation; N.L.: Writing - Review & Editing; H.W.: Writing - Review & Editing; B.W.: Resources, Writing - Review & Editing, Supervision; Y.N.: Conceptualization, Resources, Supervision; Y.G.: Conceptualization, Resources, Supervision, Writing - Review & Editing, Funding acquisition. All authors reviewed and revised the manuscript. All authors had full access to all the data in the study and accepted responsibility for submitting it for publication.
Funding
This work was supported by grant 2022YFA0807300 from the National Key R&D Program of China, grant XDA26040304 from Chinese Academy of Sciences Strategic Priority Research Program, and grant 231100110300 from the Major Science and Technology Projects in Henan Province. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, writing, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Data availability
The de-identified datasets used during the current study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This dataset-based study has been approved by the Institutional Review Board of Peking University Health Science Center (ID of the approval: IRB00001052-19077), in which individual informed consent was waived as the analyses only used anonymous data.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yanrui Bi and Yuan Ma contributed equally to this work.
Contributor Information
Bo Wang, Email: paul@meinianresearch.com.
Yi Ning, Email: ningyi@vip.163.com.
Ying Gao, Email: yinggao@sinh.ac.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Additional file 1: Table S1 and Fig. S1. Table S1–Number of participants (hysterectomy/non–hysterectomy) stratified by age. Fig. S1–Relative risk (95% CI) for associations of hysterectomy with clinical indicators and socioeconomic factors by age.
Data Availability Statement
The de-identified datasets used during the current study are available from the corresponding author upon reasonable request.