Unplanned hysterectomy: a comprehensive analysis of race, ethnicity, sociodemographic factors, pregnancy complications, and cardiovascular disease risk factors.

We used the 2005 to 2014 population level Florida vital records data obtained from the Florida Department of Health’s Bureau of Vital Statistics containing sociodemographic and health information on over 2 million women. Subjects with missing or unknown data in any of the variables of interest were excluded, resulting in a total of 2,181,402 subjects included in the analysis. The data contained birth-related information about mothers and newborns. The dependent variable of the study, unplanned hysterectomy, was obtained by the question “Did an unplanned hysterectomy occur?” with answers being yes, no, or unknown. The covariates in the study were chosen based on prior literature of adverse maternal outcomes and consisted of maternal sociodemographic and medical risk factors. The sociodemographic variables include age (classified as <20 years, 20-34 years, and ≥35 years), race (White, Black, other [includes Asian, Pacific Islander, American Indian, and other minority races]), ethnicity (non-Hispanic, Hispanic), education (“What was the highest educational diploma/degree achieved by the mother?”; less than high school degree[<HS], high school degree or GED, some college, and college degree or higher), and insurance type used (none, Medicaid, private insurance, other). The medical risk factors include smoking during pregnancy (yes, no), diabetes (yes, no), history of hypertension (yes, no), previous cesarean delivery (yes, no), preterm birth (defined as <37 weeks; yes, no), preeclampsia (yes, no), method of delivery (vaginal, cesarean), premature rupture of membranes (PRoM) at 12 hours or more before labor (yes, no), and uterine rupture during labor/delivery (yes, no). R statistical package version 4.0.2 was used for analysis. Chi-square test was used to examine the association between the occurrence of UH with categorical covariates and t-test to compare the equality of continuous variables between groups with UH and those without. Univariate and multivariable logistic regression analyses were performed to examine the association between UH and variables of interest that included two-way interaction terms between maternal sociodemographic characteristics and pregnancy complications. The independent variables were assessed for correlation with one another using a correlation matrix and variance inflation factor calculations (data not shown). The final variables and interaction terms included in the adjusted model were determined using forward variable selection. Adjusted Odds Ratio (aOR) and 95% confidence intervals were obtained from the multiple logistic regression analysis. Statistical significance was set at α <0.05.

Table 1 shows the maternal sociodemographic characteristics of the study population as well as the percentage of those with or without UH within each factor. Of the 2,181,402 women included in the analysis, 641 (0.03%) had UH. About 62% of the women studied were White, 67% non-Hispanic, and 88% were less than 35 years of age. The most common insurance types used were Medicaid (46%) and private insurance (41%). Table 1 also shows significant differences between women who had UH and those who did not in relation to the maternal medical history and pregnancy complications. Significant differences were seen in the occurrence of UH for a mother’s age, race, premature birth, smoking during pregnancy, diabetes, history of hypertension, preeclampsia, history of preterm birth, previous cesarian section, method of delivery, ProM, and uterine rupture during labor/delivery. No significant difference was seen based on ethnicity, education level, or insurance type. Table 2 shows the crude odds ratios (OR) of UH. Maternal age greater than or equal to 35 years (OR = 2.89, 95% CI: 2.45-3.40, p<0.001), preterm birth (OR = 3.14, 95% CI: 2.67- 3.70, p<0.001), history of hypertension (OR = 2.89, 95% CI: 1.94- 4.31, p<0.001), history of preterm birth (OR = 2.95, 95% CI: 2.00- 4.38, p<0.001), previous cesarean (OR = 4.19, 95% CI: 3.58- 4.91, p<0.001), cesarean delivery (OR = 4.81, 95% CI: 4.03- 5.74, p<0.001), ProM (OR = 2.97, 95% CI: 2.20-4.00, p<0.001), and uterine rupture (OR = 448.95, 95% CI: 314.65- 640.58, p<0.001) had the largest increase in risk of UH compared to their respective references in the crude analysis. All other covariates, except for ethnicity, education level, and insurance type, were also associated with significant difference in crude odds of UH. Table 3 shows the adjusted odds ratios (aOR) for the occurrence of UH after accounting for maternal sociodemographic characteristics, medical history, and pregnancy complications. Several interaction terms were identified in the adjusted model. Race had significant interactions with ethnicity (Black:Hispanic aOR= 1.67, 95% CI: 1.08-2.57, p=0.021; Other:Hispanic aOR=2.25, 95% CI: 1.26-4.02, p=0.006), age (Black:<20 years aOR= 0.24, 95% CI: 0.07-0.87, p=0.029), education (Other:< HS aOR= 0.44, 95% CI: 0.24-0.82, p=0.009; Other:≥College degree aOR=0.40, 95% CI: 0.22-0.74, p=0.003), and ProM (Other:ProM aOR=2.17, 95% CI: 1.004-4.70, p=0.049). Preterm birth had significant interactions with smoking during pregnancy (aOR= 0.54, 95% CI: 0.29-.99, p=0.048), diabetes (aOR=0.38, 95% CI: 0.20-0.76, p=0.006), previous cesarean (aOR=3.11, 95% CI: 2.20-4.40, p<0.001), PRoM (aOR=2.93, 95% CI: 1.19-7.23, p=0.019), and uterine rupture during birth (aOR=0.24, 95% CI: 0.10-0.57, p=0.001). Method of delivery had significant interactions with preeclampsia (aOR=0.46, 95% CI: 0.24-0.87, p=0.017), ProM (aOR=3.63, 95% CI: 1.08-12.17, p=0.037), and uterine rupture during birth (aOR=0.17, 95% CI: 0.07-0.39, p<0.001). Maternal age had significant interactions with preeclampsia (≥35 years:Preeclampsia aOR=0.45, 95% CI: 0.23-0.88, p=0.019) and diabetes (<20 years: Diabetes aOR=5.19, 95% CI: 1.10-24.45, p=0.037). While this highlights the significance seen in the interactions of these variables, the contribution of the individual terms must also be included to see the relative adjusted odds ( Figure 1 ). This is achieved by multiplying the aOR estimates of the interaction term and the individual variables that make up the interaction term (i.e., Black/Hispanic x Black x Hispanic). When compared to White non-Hispanic women, Black Hispanic women and women of other races who are Hispanic have 1.33 times and 1.58 times the odds of having UH respectively. Black women less than 20 years old are 81% less likely to have UH compared to White women 20-34 years of age, and women of other race whose education is less than a high school degree or who have college education are also less likely to have UH compared to White high school educated counterparts by 40% and 59% respectively. Compared to White women who do not have ProM, women of other races who have ProM have a 50% decrease in risk of having UH. Women who had a preterm birth and diabetes have an 26% lower risk of UH than women delivered to term and no diabetes. However, higher odds were seen in women who had a preterm birth combined with smoking during pregnancy (1.51 times the odds), a previous cesarean section (6.45 times the odds), PRoM (1.33 times the odds), or uterine rupture during birth (403.43 times the odds) compared to women who delivered to term and did not smoke, had no previous cesarean section, PRoM, or had no uterine rupture, respectively. Similarly, higher odds of UH were seen in women who had a cesarean delivery coupled with preeclampsia (4.36 times the odds), ProM (2.74 times the odds), or uterine rupture during birth (833.36 times the odds) compared to women who delivered vaginally and had no preeclampsia, ProM, or uterine rupture respectively. Women aged 35 years or older who also had preeclampsia and women under the age of 20 years who had diabetes also had higher odds (1.68 and 4.53 times the odds respectively) of UH compared to women aged 20-34 years who had no preeclampsia or diabetes. Women with history of hypertension had 1.52 times the odds of having an UH than women without a history of hypertension (aOR = 1.51, 95% CI: 1.002 - 2.29, p=0.049). While a significant difference in UH was seen in the crude odds analysis for history of preterm births, there was no significant difference for this factor in the final adjusted model. Insurance type used also had no significance even after adjusting for other sociodemographic factors.

Findings from this study indicate that there are several significant interactions between maternal sociodemographic characteristics, medical history, and pregnancy complications in association with UH. While the current study was not the first study to look at pregnancy complications as risk factors for hysterectomy ( Wei et al. 2014 ), to our knowledge, it is the first study to look at multiple co-occurring pregnancy complications to determine risk of UH. This allows us to control potential confounding variables and their interactions, which helps provide a better representation of the role these variables play in the risk of UH. It is also the first study to look at the interaction of these maternal characteristics and medical risk factors. It is important to discuss the relationships seen in this study regarding race as it interacts with ethnicity, age, education, and premature rupture of membranes. The interaction between race and ethnicity indicates an increased risk for Black women or women of other races who are Hispanic compared to White, non-Hispanic women. Additionally, the interaction between race and ProM indicates a significantly lower risk of an UH occurring for women of other races who have ProM compared to White women with no ProM. Similarly, the interaction between race and age indicates that young Black women also have a reduced risk of UH, and women of other races whose education was either less than a high school degree or a college degree or higher were also less likely to have UH. Differences seen in the Hispanic population may represent a language barrier or other cultural factors. When a language barrier exists, patients may struggle to express their symptoms, medical history, or concerns accurately, and this can lead to delayed diagnosis or misdiagnosis of medical conditions ( Timmins 2002 ). Healthcare providers may find it difficult to explain diagnoses, treatment options, and preventive measures clearly. Thus, patients might not fully comprehend their conditions, prescribed treatments, or follow-up instructions, leading to non-adherence and increased risk of adverse health outcomes. Information about the primary language spoken could help assess this and should be explored in future studies. Historically, racial and ethnic minorities have faced various forms of discrimination and social disadvantages including socioeconomic status, physical and built environment, access to healthcare, educational opportunities, discrimination, and racism, which are thought to drive health disparities ( Phelan and Link 2015 ). Racial and ethnic minorities often reside in communities with limited access to healthy food options, safe and affordable housing, recreational facilities, and quality healthcare facilities. Living in disadvantaged neighborhoods with higher crime rates and limited resources can contribute to chronic stress and negatively impact health outcomes ( Patterson, Becker, and Baluran 2022 ). Solely focusing on reproductive care will not bridge the maternal mortality gap between minority and white women in the United States. While many proposed solutions involve treating target risk factors, they only scratch the surface of a more complex issue. Addressing the physical symptoms resulting from social conditions does not effectively counter the impact on maternal health. Therefore, better solutions lie in taking steps towards dismantling racism and sexism. These measures might entail implementing additional cultural humility programs and mindfulness interventions within healthcare professions ( Saluja and Bryant 2021 ). These initiatives aim to reduce and ultimately eliminate implicit bias while also fostering empathy and establishing stronger connections with patients. Other measures may involve outreach programs and patient empowerment to provide health education and social support for pregnant women in minority communities and help raise awareness of risk factors for adverse pregnancy outcomes ( Baffour, Jones, and Contreras 2006 ). There were several other interactions seen in the current study among pregnancy complications and maternal medical history that illustrate the complexity of determining risk of UH. As previously discussed, these pregnancy complications have already been individually associated with hysterectomy. However, the results seen here indicate that it is important to assess the risk of hysterectomy while taking multiple co-occurring pregnancy complications into consideration. Previous studies have shown that maternal smoking is associated with increased risk of premature births ( Ion and Bernal 2015 ) and PRoM ( England, Benjamin, and Abenhaim 2013 ) but decreased risk of preeclampsia ( Karumanchi and Levine 2010 ), which highlights the potential for these variables to be confounders. In the current study, the interaction of premature birth and smoking during pregnancy resulted in an overall increased risk of UH. Conversely, premature birth interacting with diabetes resulted in an overall decreased risk of UH, which was unexpected because the crude odds indicated an increased risk of UH for these conditions individually. Previous research has established that pregestational and gestational diabetic women have a greater risk of delivering prematurely (<37 weeks) ( Seah et al. 2021 ; Kong et al. 2019 ). Additionally, mothers diagnosed with gestational diabetes are also more likely to give birth to larger babies with higher birth weight. As a result, these mothers are more likely to deliver via cesarean section to mitigate the risk of harm to both the mother and the baby during childbirth ( Al-Hakeem 2006 ). However, there is scarce literature describing the role of premature birth in hysterectomy, and most studies on hysterectomy involving diabetes focus only on gestational diabetes ( Jou et al. 2008 ). It is well-known that diabetic patients have impaired wound healing and are more prone to infection ( Greenhalgh 2003 ; Sharp and Clark 2011 ; Anderson and Hamm 2012 ), so an invasive surgery such as a hysterectomy could present a big challenge. It is also important to note that our data did not differentiate between the types of diabetes. Being able to differentiate types of diabetes in future studies may further clarify the interaction seen in this study. We also saw differences in the role of premature birth on previous cesarean, uterine rupture, and PRoM as it relates to UH. Previous studies have shown that women who had a cesarean section during their first delivery were more likely to have a premature birth in their second pregnancy ( Williams et al. 2018 ), and mothers who experience uterine rupture during labor or PRoM are more likely to have a preterm delivery ( Ronel et al. 2012 ; Steer and Flint 1999 ). In the current study, premature birth co-occurring with previous cesarean, uterine rupture, or PRoM resulted in an overall increased risk of UH. While the overall risk is still very high for these women, it is of a lower magnitude than the aOR seen for women who only experience uterine rupture as a pregnancy complication. This may reflect a difference in preparedness and planning of delivery for women with pregnancy complications other than uterine rupture. If no other pregnancy complications were present during gestation, a hysterectomy might not have been planned or discussed. However, if other complications arise, and adverse outcomes are anticipated as a result, a hysterectomy might be part of the delivery plan. We also saw cesarean delivery interact with preeclampsia, ProM, and uterine rupture when determining UH risk, all of which resulted in an overall increased risk. Previous studies have demonstrated relationships between these pregnancy complications such that women who experience preeclampsia or ProM are more likely to deliver via cesarean ( Tavassoli et al. 2010 ). The most common reasons for cesarean delivery were placenta previa, fetal distress, and non-response to induction of labor ( Tavassoli et al. 2010 ; Mylonas and Friese 2015 ). These complications could not be included in the current study because they were not in the original data set. A seemingly new relationship was seen with the interaction between cesarean delivery and uterine rupture. Most of the uterine rupture literature focuses on the relationship with previous rather than current cesarean delivery. It is well established that previous cesarean delivery greatly increases the risk of experiencing uterine rupture in subsequent pregnancies ( Ronel et al. 2012 ). However, to our knowledge, no studies have demonstrated a relationship between uterine rupture and current cesarean delivery. In the present study, the interaction of uterine rupture and cesarean delivery resulted in an overall increased risk of UH. The last set of interactions were seen when maternal age was 35 years or greater and mothers experienced preeclampsia and when maternal age was less than 20 years and mothers had diabetes. Previous studies have indicated that advanced maternal age increases risk of mothers experiencing preeclampsia ( Frick 2021 ; Kenny et al. 2013 ). The present study indicated an interactive relationship between maternal age and preeclampsia that resulted in an overall increased risk of UH, which was also true for the interaction between maternal age and diabetes. As previously mentioned, mothers with diabetes are more likely to have cesarean deliveries due to birthing larger babies with higher birth weight. This complication, particularly among younger mothers, may lead to a planned rather than unplanned hysterectomy. As previously discussed, many factors have been individually associated with increased risk of having a hysterectomy ( Downes, Grantz, and Shenassa 2017 ; Knight et al. 2008 ; Jakobsson et al. 2015 ) , and the results of the current study found only a few factors that did not interact with other variables. We also observed that history of hypertension was associated with higher odds of having an UH, but history of preterm birth and insurance used were not significant predictors of UH when accounting for other demographic and health factors. Risk factors for cardiovascular disease encompass various elements such as lack of physical activity, obesity, high blood pressure, high cholesterol, diabetes, smoking, and a familial history of CVD ( Bays 2020 ). Moreover, unfavorable outcomes during pregnancy, including preeclampsia, hypertension, gestational diabetes, preterm births, underweight newborns, and stillbirths, have been associated with an elevated likelihood of developing CVD and experiencing CVD-related mortality later in life ( Cirillo and Cohn 2015 ; Täufer Cederlöf et al. 2022 ; Ingelsson et al. 2011 ; Bellamy et al. 2007 ; Harskamp and Zeeman 2007 ). The present study highlights the significant influence of the CVD risk factors of diabetes, smoking, preeclampsia, and a history of hypertension as risk factors for unplanned hysterectomies. Incorporating assessments of cardiovascular health and family history of CVD into the evaluation of pregnant women may enhance the identification of hysterectomy risk. However, this study did not address cardiovascular health or family history of CVD due to their absence from the original dataset. Furthermore, physical activity levels and obesity were not addressed due to insufficient data on physical activity and BMI, which was solely based on pre-pregnancy information. One of the strengths of this study is the sample size of over 2 million women. However, removing subjects with missing or unknown data may introduce bias. Having the data collected from across the entire state of Florida is both a strength and a limitation. While having population-based data is a strength, it is also a limitation because the population of Florida does not adequately represent the population of the United States and prevents drawing conclusions about pregnant women across the United States. The study could be strengthened by including more information to differentiate between having no hysterectomy, planned hysterectomy, and unplanned hysterectomy, though the data used here lacked such an indicator. Another limitation of the study is the lack of information about income or type of medical facility in which the labor/delivery occurred (i.e., teaching vs. non-teaching hospital). Future studies would benefit from having more socioeconomic, neighborhood, built environment, and hospital-related variables included. Despite these limitations, the current study contributes to the understanding of how maternal sociodemographic characteristics, health history, CVD risk factors, and pregnancy complications affect risk of UH. Additionally, this study provides a more encompassing assessment of the risk of UH, and therefore helps identify high-risk groups of pregnant women who experience co-occurring CVD risk factors and pregnancy complications. Identification of such groups allows physicians, obstetricians, and labor and delivery teams to anticipate and plan for pregnancies that are more likely to require emergent hysterectomies.

With nearly 600,000 procedures performed annually, hysterectomies are one of the most performed gynecologic surgeries in the United States, and an estimated 25% of females over the age of 18 have had a hysterectomy ( Prevention 2020 ; Merrill 2008 ; Wu et al. 2007 ). Hysterectomy, a known cardiovascular disease (CVD) risk factor, is a surgical procedure performed to remove the uterus. There are three types of hysterectomies: supracervical, total, and radical ( Mettler, Ahmed-Ebbiary, and Schollmeyer 2005 ). A supracervical hysterectomy removes only the upper part of the uterus, a total hysterectomy involves removal of the uterus and cervix, and a radical hysterectomy includes the removal of the uterus, cervix, and surrounding structures ( Thakar and Sultan 2005 ). While some hysterectomies are planned, many hysterectomies are unplanned, life-saving procedures performed due to severe pregnancy complications ( Shellhaas et al. 2009 ). Additionally, unplanned hysterectomies (UH) present a much greater risk of morbidity and mortality to mothers compared to planned ones ( Machado 2011 ; Reforma et al. 2022 ). Pregnancy complications associated with increased risk of hysterectomy include cesarean delivery, preterm delivery, placenta accreta spectrum, rupture of the placenta, and uterine rupture ( Downes, Grantz, and Shenassa 2017 ; Knight et al. 2008 ; Jakobsson et al. 2015 ; Givens et al. 2022 ). Other common indications for hysterectomy include, but are not limited to, uterine fibroids, endometriosis, dysfunctional uterine bleeding, chronic pelvic pain, abnormal uterine pathology, and gynecologic cancer ( Wu et al. 2007 ; Clarke et al. 1995 ; van den Akker et al. 2016 ; Basnet et al. 2017 ; Shaheen and Shaheen 2015 ; Hernandez et al. 2012 ). A woman’s race, ethnicity, and education level also influence the risk of having a hysterectomy. African American women and women with less than college education are more likely to undergo hysterectomy ( Meilahn et al. 1989 ; Powell et al. 2005 ). On the other hand, Hispanic White women have a lower risk of having a hysterectomy than non-Hispanic White women ( Brett and Higgins 2003 ). Racial and ethnic minorities often face systemic barriers to economic opportunities, leading to lower income levels, limited access to quality education, and restricted employment prospects, which can adversely affect their access to healthcare and, consequently, their health ( Phelan and Link 2015 ). Racial and ethnic differences seen in maternal outcomes may reflect these barriers as well as racial/ethnic biases in healthcare diagnoses and treatments ( Hoffman et al. 2016 ). Together these issues affect the ability of minority women to receive timely, appropriate, and effective treatments. While these pregnancy complications have been individually identified as hysterectomy risk factors, few studies have investigated the combination of pregnancy complications associated with high risk, and whether they predict unplanned hysterectomies. The current study aims to present a more comprehensive investigation of the co-occurrence of multiple pregnancy complications and maternal sociodemographic factors in relation to UH, and therefore refine the risk spectrum to identify very-high-risk target groups for early preparation and intervention. We are interested in understanding the synergic impact race, ethnicity, and co-occurring multiple pregnancy complications and cardiovascular disease risk factors have on the risk of UH.