Abstract
Background:
We examined the association of patient factors, gynecologic diagnoses, and hospital characteristics with utilization of the robotic approach for benign hysterectomy.
Methods:
We performed cross-sectional study of women (n = 725,050) undergoing hysterectomies in the 2012-2014 National Inpatient Sample
Results:
725,050 women underwent inpatient hysterectomy for benign indications: 70,345 (10%) were performed robotically. Women were more likely to receive robotic hysterectomy at teaching hospitals (RR 1.60 (95%CI 1.54-1.66) after adjustment for other patient factors, gynecologic diagnoses, and hospital characteristics. They were more likely to undergo robotic hysterectomy at large (RR 1.34 (95%CI 1.29-1.39)) and for-profit hospitals (RR 1.16 (95%CI 1.11-1.22)). Women were less likely to undergo robotic hysterectomy if they were rural (RR 0.68 (95%CI 0.64-0.72)), African American (RR 0.78 (95%CI 0.74-0.82)), or publicly-insured or uninsured (RR 0.55 (95%CI 0.53-0.57)) women.
Conclusion:
Significant geographic and hospital-level disparities exist in access to robotic hysterectomy in the United States.
Keywords: Benign hysterectomy, Minimally invasive, Racial disparity, Robotic surgery
Introduction
In the United States, hysterectomy is the second most common surgical procedure for women with one in ten women ages 4044 in 2011-2015 undergoing hysterectomy.1 Despite advances in minimally invasive surgery (MIS) via laparoscopy and robotics, the majority of hysterectomies are still performed abdominally. MIS hysterectomy is associated with decreased pain, less blood loss, faster return to normal activities, and fewer wound complications.2,3 The American College of Obstetricians and Gynecologists (ACOG) thus recommends MIS approaches for benign hysterectomies whenever feasible.4
There are significant sociodemographic disparities in access to MIS hysterectomy. African American females were half as likely to receive MIS hysterectomy in one recent study.5–10 Geographic and hospital factors, however, may mediate these disparities. Rural Americans, for example, are more likely to reside further away from large or teaching hospitals where MIS is more frequently practiced.11,12 Similarly, counties with more African-American, low-income, and publicly-insured residents are less likely to have advancely-equipped hospitals and surgeons with MIS expertise.13
Adoption of robotic hysterectomy may abate the barriers to MIS uptake and potentially improve disparities in access to MIS. For example, with large fibroids or adhesive disease, the dexterity of robotic arms and improved visualization can allow cases to be performed minimally-invasively that would otherwise be performed open. The learning curve for robotics may also be less than for complex laparoscopy allowing adoption outside of academic centers.8,14 Robotic platforms have thus been marketed to smaller and non-teaching hospitals.15,16 Prior studies on MIS and sociodemographic disparities have focused on laparoscopic or vaginal approaches rather than robotics. They frequently have not accounted for hospital factors that mediate access to care.
Accurately delineating sources of disparity will help clinicians address barriers to care and facilitate improvement in MIS utilization. Therefore, this study aims to examine which patient factors, gynecologic diagnoses, and hospital characteristics correlate with robotic hysterectomy utilization for benign indications.
Materials and Methods
The Johns Hopkins School of Medicine Institutional Review Board reviewed this study and determined to be exempt as data were de-identified and publicly available. A cross-sectional study was performed using hospital-reported data from the 2012-2014 National Inpatient Sample (NIS). The NIS is the largest publicly-available all-payer database of hospitalizations in the United States, including over 7 million hospitalizations from more than 4000 hospitals annually.17 It includes surgical procedures such as hysterectomy in which patients were admitted or observed one or more nights post-procedure.
Our primary outcome was the likelihood (relative risk) of undergoing robotic hysterectomy compared to other approaches to hysterectomy (laparoscopic, vaginal, or abdominal). We included women age 18 years or older who underwent hysterectomy. We identified hysterectomy approach using International Classification of Disease (ICD)-9 codes. Eligible procedures included robotic-assisted procedures (17.42, 17.44), laparoscopic hysterectomy (68.31, 68.41, 68.51), vaginal hysterectomy (68.5, 68.59) and abdominal hysterectomy (68.4, 68.49). Women who had a code for robotic assisted procedure were classified as having undergone robotic hysterectomy (i.e., women who had ICD-9 codes for laparoscopic hysterectomy and robotic hysterectomy were coded as robotic). Cases with hysterectomy approach not specified (68.9) or conversions to open (V64.4, V64.41) were excluded. Women who underwent surgery for gynecologic malignancies (180-184.9) and/or radical hysterectomies (68.61, 68.69, 68.71, 68.79) were excluded as well.
Patient demographics included age, race, insurance status, median household income, and rural residence. Age was classified into 4 categories (18-39 years, 40-49 years, 50-59 years, 60 years and older). Insurance status was categorized as privately insured, publicly insured, and uninsured. Uninsured and publicly insured categories were grouped together in the final analysis, given the small number of uninsured women (less than 3,000 women per year). Median household income was defined as the median income in the patient’s zip code at the time of diagnosis and was divided into quartiles by year (2014 quartiles: less than $40,000, $40,000–50,999, $51,000–65,999, $66,000 or more). Although the NIS lacks patient-level income data, the zip code-based variables appear to highly correlate with actual patient income.18 Rural residence was defined using National Center for Health Statistics classifications as residence in a county with a population of less than 50,000.
Gynecologic diagnoses included in analysis were endometriosis, fibroids, abnormal uterine bleeding, and pelvic organ prolapse. They were defined using the NIS’s Clinical Classification System codes, which condense corresponding ICD-9 codes: 46 for fibroids, 169 for endometriosis, 170 for pelvic organ prolapse, and 171 for abnormal uterine bleeding. Gynecologic diagnoses were not mutually exclusive categories, i.e., procedures could be coded as both for fibroids and abnormal uterine bleeding.
Hospital factors included bed size, teaching and for-profit status, Census region, and hysterectomy volume. We used NIS definitions for hospital factors. A teaching hospital is labelled as such if it had an accredited residency program, was a member of the Council of Teaching hospitals, or had full-time equivalent ratio of 0.25 residents to hospital beds. For-profit status was categorized using the American Hospital Association Annual Survey of Hospitals; government-owned and private, not-for-profit hospitals were the comparator. Hospital Census region was categorized as Northeast, South, Midwest, and West. Large hospital bed size was defined as 45 or more beds with some NIS-defined variance in size categories by Census region and teaching status. We defined hysterectomy volume as the number of cases of benign hysterectomies per year averaged over the 3 years of study. We divided hysterectomy volume into low, medium, and high based on approximate tertiles. Low volume was defined as 1-100 hysterectomies per year, medium volume as 101-1000 hysterectomies per year, and high-volume as 1001 or more hysterectomies per year. Cases missing any of the patient demographics, hospital factors, or gynecologic diagnoses were excluded from our analysis (~3000 or 2% inpatient hysterectomies per year). The year of surgery was also included as a co-variate. We added a hospital variable to account for hospital-level clustering.
Analyses were conducted using Stata 11 (StataCorp, Texas). Two-sided p-values <.05 were considered significant. We examined the frequency of patient demographics, gynecologic diagnosis, and hospital factors using chi-squared statics. We used a multivariate log-binomial regression model to compare the likelihood of undergoing robotic hysterectomy compared to other approaches in univariate and then multivariate analyses. We used NIS weighting by hospital discharges to obtain population-level estimates.
We conducted a sensitivity analysis examing the likelihood of undergoing robotic hysterectomy only at hospitals where 1 or more robotic hysterectomy for benign indications was performed during the study period.
Results
A total of 146,564 women were identified to have undergone inpatient hysterectomy in 4,804 hospitals. After NIS weighting, approximately 725,050 women underwent hysterectomy for benign indications from 2012-2014 nationwide. Of those, 70,345 (9.6%) women underwent robotic hysterectomy, 99,720 (13.6%) underwent laparoscopic hysterectomy, 86,215 (11.8%) underwent vaginal hysterectomy, while the majority (467,925 (63.9%)) underwent abdominal hysterectomy.
Sixty-nine percent (95% CI 69.0-69.5) of our study population were white, 15.0% (95% CI 14.8-15.2) were African American, 10.0% (95% CI 9.9-10.2) were Hispanic, and 5.7% (95% CI 5.6-5.8) were of other races (Table 1). Twenty-one percent (95% CI 20.6-21.0) were in the lowest income quartile, and 14.9% (95% CI 14.7-15.1) resided in a rural area. As regards the gynecologic diagnosis, 36.6% of patients were operated for abnormal uterine bleeding, 40% for fibroids, 35.1% had endometriosis, while 14.4% had pelvic organ prolapse. Thirty-seven percent (95% CI 36.9-37.4) were operated at a large hospital, 43.3% (95% CI 43.1-43.6) at a teaching hospital, and 13.4% (95% CI 13.2-13.6) at a for-profit hospital.
Table 1.
Baseline Characteristics of Women Undergoing Hysterectomy for Benign Conditions, 2012-2014
| All hysterectomies % (95% CI) | Robotic hysterectomy % (95% CI) | Laparoscopic hysterectomy % (95% CI) | Vaginal hysterectomy % (95% CI) | Open hysterectomy % (95% CI) | |
|---|---|---|---|---|---|
| Year | |||||
| - 2012 | 36.9 (36.7-37.2) | 49.5 (48.7-50.4) | 50.9 (50.2-51.6) | 50.8 (50.1-51.6) | 29.5 (29.2-29.8) |
| - 2013 | 30.6 (30.3-30.8) | 37.8 (37.0-38.6) | 37.9 (37.3-38.6) | 37.4 (36.7-38.2) | 26.6 (26.3-26.9) |
| - 2014 | 32.5 (32.3-32.7) | 12.6 (12.1-13.2) | 11.2 (10.8-11.6) | 11.8 (11.3-12.2) | 43.9 (43.6-44.2) |
| Patient Characteristics | |||||
| Age | |||||
| - 18-39 years | 17.0 (16.8-17.2) | 18.7 (18.1-19.4) | 24.7 (24.1-25.3) | 16.6 (16.0-17.1) | 15.4 (15.1-15.6) |
| - 40-49 years | 54.8 (54.5-55.0) | 42.9 (42.1-43.7) | 46.4 (45.7-47.1) | 29.1 (28.4-29.8) | 64.0 (63.7-64.3) |
| - 50-59 years | 15.6 (15.4-15.8) | 21.9 (21.2-22.6) | 18.6 (18.1-19.2) | 19.6 (19.0-20.2) | 13.5 (13.3-13.8) |
| - 60 years and older | 12.7 (12.5-12.8) | 16.5 (15.9-17.1) | 10.3 (9.8-10.7) | 34.7 (34.0-35.4) | 7.1 (6.9-7.2) |
| Race | |||||
| - White | 69.2 (69.0-69.5) | 66.3 (65.5-67.1) | 66.8 (66.2-67.5) | 69.9 (69.2-70.7) | 70.1 (69.8-70.4) |
| - African-American | 15.0 (14.8-15.2) | 14.3 (13.7-14.9) | 13.3 (12.8-13.8) | 7.9 (7.5-8.4) | 16.7 (16.4-16.9) |
| - Hispanic | 10.0 (9.9-10.2) | 12.0 (11.4-12.5) | 13.0 (12.5-13.5) | 15.6 (15.0-16.2) | 8.2 (8.0-8.4) |
| - Other race(s) | 5.7 (5.6-5.8) | 7.4 (6.9-7.8) | 6.9 (6.5-7.3) | 6.5 (6.1-6.9) | 5.1 (5.0-5.2) |
| Insurance type | |||||
| - Privately-insured | 51.0 (50.7-51.2) | 72.3 (71.5-73.0) | 69.1 (68.5-69.7) | 56.6 (55.9-57.4) | 42.8 (42.5-43.2) |
| - Publicly-insured | 45.3 (45.0-45.6) | 25.6 (24.9-26.3) | 27.3 (26.7-27.9) | 40.2 (39.4-40.9) | 53.0 (52.7-53.4) |
| - Uninsured | 3.8 (3.7-3.8) | 2.1 (1.9-2.4) | 3.6 (3.3-3.8) | 3.2 (3.0-3.5) | 4.1 (4.0-4.3) |
| Household income | |||||
| - Lowest quartile | 20.8 (20.6-21.0) | 19.7 (19.0-20.4) | 22.9 (22.3-23.5) | 23.5 (22.9-24.2) | 20.0 (19.8-20.3) |
| - 2nd quartile | 20.1 (19.9-20.3) | 22.2 (21.5-22.9) | 25.3 (24.6-25.9) | 26.9 (26.2-27.5) | 17.5 (17.2-17.7) |
| - 3rd quartile | 19.8 (19.6-20.1) | 28.2 (27.4-28.9) | 25.9 (25.3-26.5) | 26.6 (26.0-27.3) | 16.0 (15.8-16.3) |
| - Highest quartile | 39.2 (39.0-39.5) | 29.9 (29.1-30.6) | 25.9 (25.3-26.5) | 22.9 (22.3-23.6) | 46.5 (46.1-46.8) |
| Living in a rural area | 14.9 (14.7-15.1) | 11.5 (11.0-12.0) | 19.2 (18.6-19.7) | 21.3 (20.7-21.9) | 13.1 (12.8-13.3) |
| Gynecologic Diagnoses * | |||||
| - Abnormal uterine bleeding | 36.6 (36.3-36.8) | 47.1 (46.3-48.0) | 56.1 (55.4-56.8) | 33.7 (33.0-34.4) | 31.9 (31.6-32.2) |
| - Fibroids | 40.0 (39.7-40.2) | 46.7 (45.9-47.6) | 47.5 (46.8-48.2) | 24.0 (23.3-24.6) | 41.0 (40.7-41.3) |
| - Endometriosis | 35.1 (34.9-35.3) | 20.8 (20.2-21.5) | 23.1 (22.5-23.7) | 9.4 (9.0-9.9) | 45.2 (44.8-45.5) |
| - Pelvic organ prolapse | 14.4 (14.2-14.6) | 18.3 (17.7-19.0) | 17.4 (16.8-17.9) | 67.8 (67.1-68.5) | 3.6 (3.5-3.7) |
| Hospital Characteristics | |||||
| Large hospital | 37.1 (36.9-37.4) | 50.4 (49.6-51.2) | 44.1 (43.4-44.8) | 45.8 (45.1-46.6) | 32.2 (31.9-32.5) |
| Teaching hospital | 43.3 (43.1-43.6) | 62.2 (61.4-63.0) | 50.8 (50.1-51.5) | 51.6 (50.9-52.4) | 37.6 (37.3-37.9) |
| For-profit hospital | 13.4 (13.2-13.6) | 17.2 (16.6-17.8) | 17.6 (17.1-18.1) | 16.7 (16.1-17.2) | 11.0 (10.8-11.2) |
| Region | |||||
| - North | 13.4 (13.2-13.6) | 18.1 (17.4-18.7) | 16.7 (16.2-17.2) | 15.2 (14.7-15.7) | 11.9 (11.7-12.1) |
| - South | 57.1 (56.8-57.3) | 43.5 (42.9-44.3) | 43.9 (43.2-44.6) | 40.9 (40.1-41.6) | 65.0 (64.7-65.3) |
| - Midwest | 16.6 (16.5-16.8) | 22.1(21.4-22.2) | 18.1 (17.6-18.7) | 23.0 (22.3-23.6) | 14.6 (14.4-14.8) |
| - West | 12.9 (12.7-13.1) | 16.3 (15.7-17.0) | 21.2 (20.7-21.8) | 21.0 (20.4-21.6) | 8.5 (8.4-8.7) |
| Hospital volume | |||||
| - 1-100 hysterectomies per year | 27.8 (27.5-28.0) | 27.2 (26.5-28.0) | 34.3 (33.7-35.0) | 34.7 (33.9-35.4) | 25.6 (25.3-25.9) |
| - 101-1000 hysterectomies per year | 32.7 (32.5-33.0) | 44.4 (43.6-45.2) | 40.3 (39.7-41.0) | 41.8 (41.0-42.5) | 26.6 (26.3-26.9) |
| - 1001 or more hysterectomies per year | 39.5 (39.2-39.7) | 28.4 (27.6-29.1) | 25.3 (24.7-25.9) | 23.6 (22.9-24.2) | 47.8 (47.5-48.1)1 |
Categories for gynecologic diagnoses are not mutually exclusive. 2014 income quartiles: less than $40,00, $40,000–50,999, $51,000–65,999, $66,000 or more
Women were more likely to receive robotic hysterectomy at teaching hospitals (RR 1.60 (95% CI 1.54-1.66), adjusted for patient demographics, gynecologic diagnoses, and other hospital characteristics (Figure 1). They were also more likely to undergo robotic hysterectomy at large (RR 1.34 (95% CI 1.29-1.39) or for-profit hospitals (RR 1.16 (95% CI 1.11-1.22)). Rural residence correlated with a lower likelihood to undergo robotic hysterectomy (RR 0.68 (95% CI 0.64-0.72). Similarly, women operated in the South (RR 0.86 (95% CI 0.77-0.96)) were less likely to undergo robotic hysterectomy compared to other approaches to hysterectomy than women in the Northeast. Women operated at medium-volume and high-volume hospitals were less likely to receive robotic hysterectomy (RR 0.92 (95% CI 0.88-0.96) and RR 0.82 (95% CI 0.78-0.86) for high-volume) than women at low-volume hospitals (Table 2).
Figure 1.
Relative Risk of Undergoing Robotic Hysterectomy Compared to Other Approach to Hysterectomy for Benign Conditions
Table 2.
Association of Robotic Hysterectomy with Patient, Hospital, and Gynecologic Characteristics in all hospitals during study time window (n=725,050 hysterectomies)
| Univariate Analysis Relative risk (95% CI) |
p-value | Multivariate Analysis Relative risk (95% CI) |
P-value | |
|---|---|---|---|---|
| Year | ||||
| - 2012 | 1.70 (1.65-1.76) | <.001 | Reference | |
| - 2013 | 1.40 (1.36-1.45) | <.001 | 0.92 (0.89-0.96) | <.001 |
| - 2014 | 0.30 (0.29-0.32) | <.001 | 0.46 (0.43-0.49) | <.001 |
| Patient Characteristics | ||||
| Age | ||||
| - 18-39 years | 1.13 (1.08-1.17) | <.001 | Reference | |
| - 40-49 years | 0.62 (0.60-0.64) | <.001 | 0.93 (0.89-0.98) | .004 |
| - 50-59 years | 1.52 (1.46-1.57) | <.001 | 1.09 (1.04-1.15) | .001 |
| - 60 years and older | 1.37 (1.31-1.42) | 1.42 (1.34-1.50) | <.001 | |
| Race | ||||
| - White | 0.88 (0.85-0.91) | <.001 | Reference | |
| - African-American | 0.95 (0.90-0.99) | .02 | 0.78 (0.74-0.82) | <.001 |
| - Hispanic | 1.22 (1.16-1.28) | <.001 | 0.96 (0.91-1.02) | .16 |
| - Other race(s) | 1.31 (1.23-1.40) | <.001 | 0.97 (0.91-1.03) | .31 |
| Insurance type | ||||
| - Privately-insured | 2.51 (2.42-2.60) | <.001 | Reference | |
| - Publicly-insured or uninsured | 0.40 (0.38-0.41) | <.001 | 0.55 (0.53-0.57) | <.001 |
| Household income | ||||
| - Lowest quartile | 0.93 (0.90-0.97) | .001 | Reference | |
| - 2nd quartile | 1.13 (1.09-1.18) | <.001 | 1.06 (1.00-1.11) | .04 |
| - 3rd quartile | 1.59 (1.53-1.64) | <.001 | 1.20 (1.15-1.27) | <.001 |
| - Highest quartile | 0.66 (0.64-0.68) | <.001 | 1.07 (1.02-1.13) | .01 |
| Living in a rural area | 0.74 (0.71-0.78) | <.001 | 0.68 (0.64-0.72) | <.001 |
| Gynecologic Diagnoses | ||||
| - Abnormal uterine bleeding | 1.55 (1.50-1.60) | <.001 | Reference | |
| - Fibroids | 1.32 (1.28-1.36) | <.001 | 0.95 (0.92-0.98) | .006 |
| - Endometriosis | 0.49 (0.47-0.51) | <.001 | 0.92 (0.88-0.96) | <.001 |
| - Pelvic organ prolapse | 1.33 (1.28-1.39) | <.001 | 0.89 (0.85-0.94) | <.001 |
| Hospital Characteristics | ||||
| Large hospital | 1.72 (1.67-1.77) | <.001 | 1.34 (1.29-1.39) | <.001 |
| Teaching hospital | 2.15 (2.08-2.22) | <.001 | 1.60 (1.54-1.66) | <.001 |
| For-profit hospital | 1.34 (1.29-1.40) | <.001 | 1.16 (1.11-1.22) | <.001 |
| Region | ||||
| - North | 1.43 (1.37-1.48) | <.001 | Reference | |
| - South | 0.58 (0.56-0.60) | <.001 | 0.86 (0.77-0.96) | .007 |
| - Midwest | 1.42 (1.37-1.47) | <.001 | 1.05 (0.99-1.12) | .13 |
| - West | 1.32 (1.27-1.38) | <.001 | 0.95 (0.80-1.14) | .60 |
| Hospital volume | ||||
| - 1-100 hysterectomies per year | 0.97 (0.94-1.01) | .14 | Reference | |
| - 101-1000 hysterectomies per year | 1.64 (1.59-1.69) | <.001 | 0.92 (0.88-0.96) | <.001 |
| - 1001+ hysterectomies per year | 0.61 (0.59-0.63) | <.001 | 0.82 (0.78-0.86) | <.001 |
Unweighted n is 146,564 hysterectomies in 4,804 hospitals.
Multivariate analysis weighted and adjusted for all patient, gynecologic diagnosis, and hospital characteristics.
Large hospital size was defined as 45 or more beds with variance in size categories by Census region and teaching status.
2014 income quartiles: less than $40,00, $40,000–50,999, $51,000–65,999, $66,000 or more
When addressing gynecologic diagnoses, women were less likely to undergo robotic hysterectomy for fibroids, endometriosis, and/or prolapse than for abnormal uterine bleeding.
After controlling for the aforementioned hospital variables and gynecologic diagnoses, African American women were less likely to undergo robotic hysterectomy compared to white women (RR 0.78 (95%CI 0.74-0.82)). There was no significant association between robotic hysterectomy and Hispanic or other races. Publicly insured or uninsured women were also less likely to undergo robotic hysterectomy (RR 0.55 (95% CI 0.53-0.57)). Higher income was associated with higher likelihood of having a robotic hysterectomy.
When limited to the 1,232 hospitals where 1 or more robotic hysterectomy was performed for benign indications during the study period, African American (RR 0.79 (95% CI 0.75-0.83)) and rural residing (RR 0.53 (95% CI 0.51-0.55)) women were less likely to have a robotic hysterectomy (Table 3). Similarly, lower-income women were less likely to be operated robotically as were publicly-insured women (RR 0.55 (95% CI 0.53-0.57)). On the contrary, women at a large, teaching, or for-profit hospital were more likely to receive robotic hysterectomy (RR 1.23 (95% CI 1.19-1.28), 1.41 (95% CI 1.35-1.46), and 1.22 (95% CI 1.16-1.28), respectively). Even among hospitals with robotic capacity, women in the South were less likely to receive a robotic hysterectomy (RR 0.77 (95% CI 0.68-0.87)).
Table 3.
Association of Robotic Hysterectomy with Patient, Hospital, and Gynecologic Characteristics in Hospitals Performing Robotic Hysterectomies (n=651,920 hysterectomies)
| Univariate Analysis Relative risk (95% CI) |
p-value | Multivariate Analysis Relative risk (95% CI) |
P-value | |
|---|---|---|---|---|
| Year | ||||
| - 2012 | 1.63 (1.58-1.68) | <.001 | Reference | |
| - 2013 | 1.36 (1.32-1.40) | <.001 | 0.96 (0.93-1.00) | .04 |
| - 2014 | 0.31 (0.30-0.33) | <.001 | 0.58 (0.54-0.62) | <.001 |
| Patient Characteristics | ||||
| Age | ||||
| - 18-39 years | 1.19 (1.14-1.24) | <.001 | Reference | |
| - 40-49 years | 0.57 (0.56-0.59) | <.001 | 0.89 (0.85-0.93) | <.001 |
| - 50-59 years | 1.53 (1.47-1.59) | <.001 | 1.06 (1.01-1.12) | .03 |
| - 60 years and older | 1.51 (1.45-1.57) | <.001 | 1.39 (1.31-1.47) | <.001 |
| Race | ||||
| - White | 0.88 (0.84-0.90) | <.001 | Reference | |
| - African-American | 0.95 (0.91-1.00) | .05 | 0.79 (0.75-0.83) | <.001 |
| - Hispanic | 1.21 (1.15-1.27) | <.001 | 0.96 (0.91-1.02) | .17 |
| - Other race(s) | 1.32 (1.25-1.41) | <.001 | 0.99 (0.93-1.05) | .71 |
| Insurance type | ||||
| - Privately-insured | 2.61 (2.52-2.70) | <.001 | Reference | |
| - Publicly-insured or uninsured | 0.38 (0.37-0.40) | <.001 | 0.53 (0.51-0.55) | <.001 |
| Household income | ||||
| - Lowest quartile | 0.99 (0.96-1.03) | .77 | Reference | |
| - 2nd quartile | 1.24 (1.19-1.29) | <.001 | 1.06 (1.00-1.11) | .04 |
| - 3rd quartile | 1.61 (1.56-1.67) | <.001 | 1.18 (1.12-1.24) | <.001 |
| - Highest quartile | 0.59 (0.57-0.61) | <.001 | 0.99 (0.94-1.05) | .85 |
| Living in a rural area | 0.96 (0.91-1.01) | .09 | 0.80 (0.75-0.84) | <.001 |
| Gynecologic Diagnoses | ||||
| - Abnormal uterine bleeding | 1.62 (1.57-1.67) | <.001 | Reference | |
| - Fibroids | 1.35 (1.31-1.39) | <.001 | 0.98 (0.94-1.02) | .26 |
| - Endometriosis | 0.44 (0.43-0.46) | <.001 | 0.86 (0.82-0.91) | <.001 |
| - Pelvic organ prolapse | 1.35 (1.30-1.40) | <.001 | 0.90 (0.86-0.94) | <.001 |
| Hospital Characteristics | ||||
| Large hospital | 1.66 (1.61-1.71) | <.001 | 1.23 (1.19-1.28) | <.001 |
| Teaching hospital | 1.94 (1.88-2.00) | <.001 | 1.41 (1.35-1.46) | <.001 |
| For-profit hospital | 1.37 (1.32-1.43) | <.001 | 1.22 (1.16-1.28) | <.001 |
| Region | <.001 | |||
| - North | 1.41 (1.36-1.47) | <.001 | Reference | |
| - South | 0.53 (0.51-0.55) | <.001 | 0.77 (0.68-0.87) | <.001 |
| - Midwest | 1.60 (1.55-1.66) | <.001 | 1.08 (1.01-1.15) | .02 |
| - West | 1.40 (1.34-1.46) | <.001 | 0.96 (0.79-1.17) | .70 |
Unweighted n is 130,384 hysterectomies in 1232 hospitals performing one or more robotic hysterectomy during the study period.
Multivariate analysis is weight and adjusted for all patient, gynecologic diagnosis, and hospital characteristics.
2014 income quartiles: less than $40,00, $40,000–50,999, $51,000–65,999, $66,000 or more.
Large hospital size was defined as 45 or more beds with variance in size categories by Census region and teaching status.
Hospital volume removed from this analysis as occurrence of any robotic hysterectomies highly correlated with larger hospital volume (r=0.49).
Discussion
In our analysis of more than 725,000 women receiving benign hysterectomy in the United States, we found significant sociodemographic and hospital-level disparities in access to robotic hysterectomy. Publicly-insured women were half as likely to undergo robotic hysterectomy, and women in rural areas were one-third as likely to undergo robotic hysterectomy.
By including detailed hospital and diagnosis variables, our study expands on prior studies finding substantial health disparities exist in MIS (laparoscopic and robotic) hysterectomy. 5 It has long been known that African American women are at increased risk of developing fibroids and increased uterine volume compared to white women, possibly attributing for their decreased access to minimally invasive approaches.19 In our study, racial disparities in robotic hysterectomy use persisted despite adjusting for surgical indications, including fibroids, implying a potential inherent socioeconomic component to these disparities.
Within a single hospital system, Price et al. similarly demonstrated racial, income, and insurance disparities in robotic hysterectomy.7 Persistence of disparities in robotic hysterectomy utilization within hospitals performing robotic surgery imply a potential room for improvement in MIS access. Addressing use of robotic and laparoscopic surgery at the hospital level or through referrals could be one approach to reducing disparities. In the area of gynecologic cancer, for example, regionalization of care is increasingly implemented to minimize disparities in MIS utilization and outcomes.13,20,21 In our study, women cared for at teaching and/or large hospitals had the highest odds of robotic hysterectomy, regardless of race. In addition, incorporating algorithms in clinical decision-making and adopting surgical mentorships have been shown to increase the uptake of MIS approaches, including in rural areas and smaller hospitals.22–24 Decision-making algorithms, in particular, may aid in reducing racial and income disparities that can inadvertently bias the surgeon approach.25
On the other hand, at the patient level, direct patient marketing has been a significant contributor to robotic surgery uptake, although clinical outcomes appear similar with robotic and laparoscopic approaches.15,16,26 Moreover, the regional competition among hospitals has been noted to drive robotic surgery use.27 Much of this marketing appears to reach privileged populations, which may further exacerbate disparities in access. Broader marketing campaigns may increase patient awareness of these readily available, less invasive approaches, and, eventually, patient interest in robotic procedures and thus the surgeon uptake.
Of note, the NIS does not capture outpatient hysterectomies, and, the transition of hysterectomies from an inpatient to outpatient setting may potentially underestimate the MIS hysterectomies throughout our study period. In addition, the Food and Drug Administration (FDA) issued a warning against use of mechanical morcellators for fibroids in 2014, a technology that had facilitated MIS approaches for large fibroid uteruses. We, therefore, added the surgery year in our analysis and included fibroids as a clinical variable to account for possible impacts of this warning on hysterectomy approach selection. After the warning, other studies observed decreased MIS utilization.28,29
We recognized some limitations in our study, including those inherent to a cross-sectional retrospective study design. The NIS database contains a sample of hospital discharges and, while weighted to reflect geographic differences, may miss some regional and hospital-level variations. In addition, the uncaptured outpatient procedures may underestimate laparoscopic and robotic hysterectomies: between 10-50% of MIS hysterectomies were performed in the outpatient setting during the study period.30,31 Nevertheless, as white and privately insured women appear to more likely receive outpatient hysterectomy, this limitation is more likely to bias our findings towards the null.31 The NIS does not document some clinical information, such as uterine size and patient BMI, which surgeons use to decide the hysterectomy route.5 Robotic hysterectomy costs, on average, $2000 more than laparoscopic hysterectomy, and clinical outcomes are similar between robotic and laparoscopic approaches.8
Our findings demonstrate significant disparities in access to robotic hysterectomy in the United States. In particular, race and insurance disparities persisted, after adjusting for other factors and within hospitals with robotic access. Efforts to reduce disparities in access to robotic surgery should include attention to sociodemographic factors and within hospital initiatives.
Acknowledgments:
The authors thank the Johns Hopkins Department of Gynecology and Obstetrics’ Kelly Society grant for financial support. In addition, this work was supported in part by NIH grant 1R01HD094380-01 to Dr. Mostafa A. Borahay.
Abbreviations
- CI
Confidence Interval
- FDA
Food and Drug Administration
- MIS
Minimally-Invasive Surgery
- NIS
National Inpatient Sample
- RR
Relative risk
Footnotes
Financial Disclosure
The authors did not report any potential conflicts of interest. Each author has indicated that he or she has met the journal’s requirements for authorship.
Presentation at Meetings: A previous version of this paper was presented at the 2018 AAGL Global Congress in Las Vegas, NV on November 12, 2018.
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