Abstract
Background:
The Affordable Care Act implemented optional Medicaid expansion starting in 2014, but the association between Medicaid expansion and gynecologic cancer survival is unknown.
Objective:
To evaluate the impact of Medicaid expansion by comparing 2-year survival among gynecologic cancers before and after 2014 in states that did and did not expand Medicaid using a difference-in-difference analysis.
Study Design:
We searched the National Cancer Database for women ages 40-64 diagnosed with a primary gynecologic malignancy (endometrial, ovarian, cervical, vulvar, and vaginal) between 2010-2016. We used a quasi-experimental difference-in-difference multivariable Cox regression analysis to compare 2-year survival between states that expanded Medicaid in January 2014 to states that did not expand Medicaid as of 2016. We performed univariable subgroup difference-in-difference Cox regression analyses based on stage, income, race, ethnicity, and geographic location. Adjusted linear difference-in-difference regressions evaluated the proportion of uninsured patients based on expansion status after 2014. We evaluated adjusted Kaplan-Meier curves to examine differences based on study period and expansion status.
Results:
Our sample included 169,731 women, including 78,669 (46.3%) in expansion states and 91,062 (53.7%) in non-expansion states. There was improved 2-year survival on adjusted difference-in-difference Cox regressions for women with ovarian cancer in expansion compared to non-expansion states after 2014 (HR 0.88, 95% CI 0.82-0.94, p<0.001) with no differences in endometrial, cervical, vaginal, vulvar, or combined gynecologic cancer sites based on expansion status. On univariable subgroup difference-in-difference Cox analyses, women with ovarian cancer with stage III-IV disease (p=0.008), Non-Hispanic ethnicity (p=0.042), and those in the South (p=0.016), and women with vulvar cancer in the Northeast (p=0.022), had improved 2-year survival in expansion compared to non-expansion states after 2014. In contrast, women with cervical cancer in the South (p=0.018) had worse 2-year survival in expansion compared to non-expansion states after 2014. All cancer sites had lower proportions of uninsured patients in expansion compared to non-expansion states after 2014.
Conclusion:
There was a significant association between Medicaid expansion and improved 2-year survival for women with ovarian cancer in states that expanded Medicaid after 2014. Despite improved insurance coverage, racial, ethnic, and regional survival differences exist between expansion and non-expansion states.
Keywords: Affordable Care Act, cervical cancer, endometrial cancer, epidemiology, health disparities, health insurance, health policy, ovarian cancer, vaginal cancer, vulvar cancer
CONDENSATION
Medicaid expansion was associated with improved 2-year survival for women with ovarian cancer in a difference-in-difference analysis of the National Cancer Database.
INTRODUCTION
The Affordable Care Act (ACA) became law in 2010 with the goal of expanding affordable health insurance to more Americans1. In 2014 the Affordable Care Act allowed states to expand Medicaid to provide healthcare coverage to adults with incomes below 138% of the federal poverty level, which has been unequally adopted by different states within the US2. As a result of the ACA, there has been a substantial increase in access to and utilization of healthcare services for low-income and young adults, especially in primary care and preventative services3,4.
Multiple studies have demonstrated increased cancer screening5 and insurance coverage for patients with cancer6-9 since the advent of the ACA, which has translated into more early-stage cancer diagnoses and improved cancer care in a variety of cancer sites6,9-14. In the field of gynecologic oncology, the ACA has been associated with improved early-stage cancer diagnoses9,13, the receipt of fertility-sparing treatment15, improved time to treatment after diagnosis9,13 and improved survival in some patients with endometrial cancer16. Medicaid expansion specifically has demonstrated improved insurance coverage, early stage diagnosis, and timely treatment of women with gynecologic malignancies9,17 and has also demonstrated an association with improved survival in lung, breast, and colorectal cancers18. Despite these encouraging studies, the ultimate impact of the ACA and its Medicaid expansion mandate on survival in women with gynecologic cancer is unknown.
The primary objective of our study was to use the National Cancer Database to compare 2-year survival among gynecologic cancers before and after the advent of Medicaid expansion in 2014 in states that did and did not expand Medicaid using a difference-in-difference (DID) analysis; our secondary objective was to identify subgroup differences in survival pre and post-2014 based on expansion status.
MATERIALS AND METHODS
We used a retrospective cohort study design with DID analysis of women diagnosed with a primary gynecologic malignancy within the National Cancer Database (NCDB), which captures hospital-reported patient data from programs accredited by the American College of Surgeons and covers approximately 70% of new cancer diagnoses19,20. Our primary outcome was 2-year all-cause survival after a new diagnosis of gynecologic cancer. We censored patients who survived >24 months to ensure the same median follow-up for all groups. Cancer specific survival is not available in the NCDB database19..
Within the NCDB, we identified patients using International Classification of Diseases for Oncology codes and included those diagnosed with primary uterine corpus (C54.0-C54.9), ovarian (C56.9), peritoneal (C48.1-48.2), fallopian tube (C57.0), cervix (C53.0-53.9), vulvar (C51.0-51.9), and vaginal (C52.9) cancers. Ovarian, peritoneal, and fallopian tubes were grouped together for analysis. For uterine corpus diagnosis codes, we excluded sarcoma, lipoma, and complex missed stromal neoplasm histologies; for ovarian and fallopian tube diagnosis codes, we excluded sex cord stromal, germ cell, and trophoblastic neoplasm histologies. Although we included outcomes data through 2017, we only included patients diagnosed with a new gynecologic malignancy between 2010-2016 after the implementation of the Affordable Care in 2010 and excluded those diagnosed in 2017 due to lack of mortality information. We also excluded patients with missing data between their diagnosis and last contact or death and with unknown stage. We excluded women <40 years old as data on Medicaid expansion status was suppressed for this group in the NCDB database, and excluded women >64 years old due to universal Medicare coverage starting at age 65 (Table 1).
Table 1:
Database population selection
| Step | Selection Criteria | Included |
|---|---|---|
| 1 | Primary uterine corpus (C54.0-C54.9), ovarian (C56.9), peritoneal (C48.1-48.2), fallopian tube (C57.0), cervix (C53.0-53.9), vulvar (C51.0-51.9), or vaginal (C52.9) cancer | 1,047,376 |
| 2 | Exclude histology codes 859-958 for fallopian and uterine corpus sites | 981,712 |
| 3 | Age 40-64 | 572,061 |
| 4 | Exclude patients diagnosed in 2017 due to lack of mortality information | 527,389 |
| 5 | Exclude patients with missing months from diagnosis to last contact or death | 527,344 |
| 6 | Keep stage I-IV disease only | 464,626 |
| 7 | Keep January 2014 Expansion (KY, NV, CO, OR, NM, WV, AR, RI, AZ, MD, MA, ND, OH, IA, IL, VT, HI, NY, DE) and Non-expansion (TN, NC, ID, GA, FL, MO, AL, MS, KS, TX, WI, UT, SC, SD, VA, OK, NE, WY, ME) states only | 284,132 |
| 8 | Exclude patients diagnosed before 2010 | 169,731 |
Next, we selected patients based on their state’s Medicaid expansion status. Medicaid expansion states in our study included 19 states (KY, NV, CO, OR, NM, WV, AR, RI, AZ, MD, MA, ND, OH, IA, IL, VT, HI, NY, DE) that expanded their Medicaid program in January 2014. Non-expansion states included the 19 states that had not expanded Medicaid (TN, NC, ID, GA, FL, MO, AL, MS, KS, TX, WI, UT, SC, SD, VA, OK, NE, WY, ME) as of 2016. We did not include states that participated in “early” Medicaid expansion prior to 2014 or “late” Medicaid expansion between January 2014 and December 2016 to minimize bias due to unequal timing of Medicaid expansion.
DID analyses provide a quasi-experimental method that attempts to account for both measured and unmeasured confounding to examine outcomes over time in response to a change that affects one group but not the other21,22. In our analysis, differences in survival outcomes before and after 2014 were compared between expansion and non-expansion states, allowing us to examine the change in outcomes related to Medicaid expansion beyond background trends in cancer survival occurring in all states. To ensure that the trends in outcomes between expansion and non-expansion groups were the same prior to Medicaid expansion in January 2014, we tested for the parallel trends assumption by assessing the significance of the interaction term between time period and Medicaid expansion in a regression model adjusted for all examined covariates. There was no significant interaction term between time period and Medicaid expansion on our analysis, that our parallel trends assumption was met21,22 (S1).
We assessed baseline characteristics including cancer site, cancer stage using American Joint Committee on Cancer Stage Group, age group, race, ethnicity, Charlson-Deyo score23, health insurance (Medicaid, Medicare, uninsured, other government, private, and unknown), geographic location, median household income (categorized as quartiles) and educational attainment (categorized as quartiles based on high school graduation rates) according to zip code, and urbanicity (metro, urban non-metro, rural, and unknown). Among January 2014 expansion and non-expansion states included in our analysis, geographic location was grouped into Northeast (MA, ME, RI, VT, NY), Midwest (IL, OH, WI, IA, KS, MO, ND, NE, SD), South (DE, FL, GA, MD, NC, SC, VA, WV, AL, KY, MS, TN, AR, OK, TX), and West (AZ, CO, ID, NM, NV, UT, WY, HI, OR). We compared baseline characteristics differences between Medicaid expansion groups using chi-square tests for categorical and t-tests for continuous variables. We assessed two-year adjusted survival probability by study period and expansion status (S2).
The pre/post Cox proportional hazards model compared the change in 2-year survival between pre and post-2014 study periods for expansion and non-expansion states adjusted for cancer type, cancer stage, age, race, ethnicity, Charlson-Deyo score, health insurance, geographic location, median income, education level, and urbanicity. We reported hazard ratios and 95% confidence intervals (CI) and computed the adjusted 2-year survival rate based on the pre/post multivariable Cox regression models. A HR less than 1 indicates an improvement in 2-year survival in the post-2014 study period compared to the pre-2014 study period. We then built a multivariable DID Cox regression model adjusted for the above patient covariates to evaluate the significance of the difference-in-difference (DID) interaction term18. We reported hazard ratios and 95% CIs, with a DID HR less than 1 indicating a greater improvement in 2-year survival in expansion states compared to non-expansion states after 2014.
We performed exploratory univariable subgroup Cox regressions to estimate DID hazard ratios based on covariates of interest including stage (early I/II vs advanced III/IV), income (lowest vs highest quartile), race (Black vs White), ethnicity (Hispanic vs non-Hispanic), and geographic location (Northeast vs Midwest vs South vs West). As above, we performed both a pre/post 2014 Cox regression analysis and a DID Cox regression analysis. To assess whether insurance coverage changed after Medicaid expansion, we also performed DID analyses for the outcome variable of insurance status (insured vs uninsured) adjusted for time period of diagnosis, patient age, race, ethnicity, and primary cancer site. The models treated the binary outcome variable as a continuous variable, with mean estimates representing the proportion of uninsured patients and results presented as adjusted DID in percentage points with associated 95% CIs.
Finally, Kaplan-Meier survival curves adjusted for patient age, stage, race, and ethnicity were generated and stratified according to expansion status and year group for combined gynecologic malignancies and each primary cancer site. A significant p-value indicates a difference between any of the generated Kaplan-Meier curves.
Comparisons were considered statistically significant using a 2-sided alpha level of 0.05. This study received IRB approval at MD Anderson Cancer Center. Data analysis was done using SAS enterprise guide 7.1. (SAS Institute, Cary, NC). Strengthening the Reporting of Observational Studies in Epidemiology guidelines were used in the preparation of this manuscript.
RESULTS
We identified 169,731 patients diagnosed with a gynecologic malignancy after 2010 who met inclusion criteria, including 78,669 (46.3%) in expansion states and 91,062 (53.7%) in non-expansion states (Table 1). Non-expansion states had significant differences in all examined covariates in the pre-2014 compared to post-2014 study period (Table 2). Expansion states had significant differences in all examined covariates except primary cancer site (p=0.08) and geographic location (p=0.25) in the post-2014 compared to the pre-2014 study period (Table 2).
Table 2:
Baseline characteristics of study population by expansion status and pre- and post-2014 study periods
| Covariates | January 2014 Expansion states (N=78,669) |
Non-expansion states (N=91,062) |
||||
|---|---|---|---|---|---|---|
| Pre-2014 (2010-13) N(%) |
Post-2014 (2014-16) N(%) |
P Value |
Pre-2014 (2010-13) N(%) |
Post-2014 (2014-16) N(%) |
P Value |
|
| Primary cancer site | 0.079 | <0.001 | ||||
| Cervical | 6262 (14.2) | 4883 (14.1) | 8553 (17.1) | 6943 (16.9) | ||
| Endometrial | 24499 (55.6) | 19566 (56.5) | 25694 (51.3) | 21868 (53.3) | ||
| Ovary/peritoneal | 11038 (25.1) | 8460 (24.4) | 12960 (25.9) | 10002 (24.4) | ||
| Vaginal | 427 (1.0) | 299 (0.9) | 549 (1.1) | 397 (1.0) | ||
| Vulvar | 1816 (4.1) | 1419 (4.1) | 2290 (4.6) | 1806 (4.4) | ||
| Age group | <0.001 | <0.001 | ||||
| 40-44 | 3804 (8.6) | 2812 (8.1) | 4887 (9.8) | 4031 (9.8) | ||
| 45-49 | 5727 (13.0) | 4201 (12.1) | 6859 (13.7) | 5218 (12.7) | ||
| 50-54 | 9100 (20.7) | 6829 (19.7) | 10179 (20.3) | 7976 (19.4) | ||
| 55-59 | 12247 (27.8) | 9796 (28.3) | 13286 (26.5) | 11374 (27.7) | ||
| 60-64 | 13164 (29.9) | 10989 (31.7) | 14835 (29.6) | 12417 (30.3) | ||
| Race | <0.001 | <0.001 | ||||
| American Indian/Alaskan | 238 (0.5) | 160 (0.5) | 208 (0.4) | 237 (0.6) | ||
| Asian/Pacific Islander | 1769 (4.0) | 1667 (4.8) | 940 (1.9) | 931 (2.3) | ||
| Black | 3900 (8.9) | 3336 (9.6) | 6632 (13.3) | 5776 (14.1) | ||
| Other | 485 (1.1) | 543 (1.6) | 504 (1.0) | 569 (1.4) | ||
| Unknown | 578 (1.3) | 422 (1.2) | 326 (0.7) | 364 (0.9) | ||
| White | 37072 (84.2) | 28499 (82.3) | 41436 (82.8) | 33139 (80.8) | ||
| Ethnicity | <0.001 | <0.001 | ||||
| Hispanic | 2359 (5.4) | 2197 (6.3) | 4111 (8.2) | 3689 (9.0) | ||
| Non-Hispanic | 40084 (91.0) | 31753 (91.7) | 44611 (89.1) | 36494 (89.0) | ||
| Unknown | 1599 (3.6) | 677 (2.0) | 1324 (2.6) | 833 (2.0) | ||
| Charlson-Deyo score | <0.001 | <0.001 | ||||
| 0 | 34200 (77.7) | 26988 (77.9) | 38805 (77.5) | 31571 (77.0) | ||
| 1 | 7772 (17.6) | 5797 (16.7) | 9105 (18.2) | 7249 (17.7) | ||
| >=2 | 2070 (4.7) | 1842 (5.3) | 2136 (4.3) | 2196 (5.4) | ||
| Health insurance | <0.001 | <0.001 | ||||
| Unknown | 849 (1.9) | 440 (1.3) | 1423 (2.8) | 957 (2.3) | ||
| Medicaid | 5039 (11.4) | 5839 (16.9) | 4855 (9.7) | 3551 (8.7) | ||
| Medicare | 3785 (8.6) | 3139 (9.1) | 4447 (8.9) | 3998 (9.7) | ||
| Not Insured | 2658 (6.0) | 779 (2.2) | 5618 (11.2) | 3686 (9.0) | ||
| Other Government | 477 (1.1) | 393 (1.1) | 1155 (2.3) | 990 (2.4) | ||
| Private Insurance | 31234 (70.9) | 24037 (69.4) | 32548 (65.0) | 27834 (67.9) | ||
| Geographic location | 0.249 | 0.006 | ||||
| Midwest | 16726 (38.0) | 12959 (37.4) | 8541 (17.1) | 6676 (16.3) | ||
| Northeast | 12713 (28.9) | 10070 (29.1) | 1057 (2.1) | 829 (2.0) | ||
| South | 8116 (18.4) | 6356 (18.4) | 38755 (77.4) | 32056 (78.2) | ||
| West | 6487 (14.7) | 5242 (15.1) | 1693 (3.4) | 1455 (3.5) | ||
| Median income | <0.001 | <0.001 | ||||
| 1st quartile | 6014 (13.7) | 4832 (14.0) | 11834 (23.6) | 9520 (23.2) | ||
| 2nd quartile | 8072 (18.3) | 6063 (17.5) | 12575 (25.1) | 9701 (23.7) | ||
| 3rd quartile | 9739 (22.1) | 7003 (20.2) | 11087 (22.2) | 8093 (19.7) | ||
| 4th quartile (richest) | 15668 (35.6) | 11694 (33.8) | 10771 (21.5) | 8846 (21.6) | ||
| Not Available | 4549 (10.3) | 5035 (14.5) | 3779 (7.6) | 4856 (11.8) | ||
| Educational attainment | <0.001 | <0.001 | ||||
| 1st quartile | 6525 (14.8) | 5756 (16.6) | 11759 (23.5) | 10215 (24.9) | ||
| 2nd quartile | 10230 (23.2) | 7688 (22.2) | 13364 (26.7) | 10402 (25.4) | ||
| 3rd quartile | 12362 (28.1) | 8452 (24.4) | 12397 (24.8) | 9040 (22.0) | ||
| 4th quartile (most educated) | 10427 (23.7) | 7762 (22.4) | 8792 (17.6) | 6550 (16.0) | ||
| Not Available | 4498 (10.2) | 4969 (14.4) | 3734 (7.5) | 4809 (11.7) | ||
| Locale | 0.002 | <0.001 | ||||
| Metro | 35301 (80.2) | 28125 (81.2) | 38619 (77.2) | 32468 (79.2) | ||
| Rural | 627 (1.4) | 474 (1.4) | 1307 (2.6) | 909 (2.2) | ||
| Unknown | 1502 (3.4) | 1138 (3.3) | 1423 (2.8) | 1204 (2.9) | ||
| Urban non-metro | 6612 (15.0) | 4890 (14.1) | 8697 (17.4) | 6435 (15.7) | ||
| Stage at diagnosis | <0.001 | <0.001 | ||||
| Stage I | 26772 (60.8) | 20828 (60.1) | 28945 (57.8) | 23570 (57.5) | ||
| Stage II | 3557 (8.1) | 2611 (7.5) | 4396 (8.8) | 3341 (8.1) | ||
| Stage III | 9109 (20.7) | 7249 (20.9) | 11086 (22.2) | 9128 (22.3) | ||
| Stage IV | 4604 (10.5) | 3939 (11.4) | 5619 (11.2) | 4977 (12.1) | ||
Our pre/post adjusted Cox regression model demonstrated that within non-expansion states, 2-year survival significantly improved in the post-2014 compared to the pre-2014 study period for combined cancer sites with a 4% decreased hazard of death (HR 0.96, 95% CI 0.92-0.99) and for cervical cancer with a 13% decreased hazard of death (HR 0.87, 95% CI 0.81-0.93) (Table 3). Within expansion states, 2-year survival significantly improved for combined cancer sites with a 10% decreased hazard of death (HR 0.90, 95% CI 0.87-0.94), endometrial cancer with a 9% decreased hazard of death (HR 0.91, 95% CI 0.85-0.99), ovarian cancer with a 12% decreased hazard of death (HR 0.88, 95% CI 0.82-0.94), and cervical cancer with an 11% decreased hazard of death (HR 0.89, 95% CI 0.81-0.97) in the post-2014 compared to the pre-2014 study period (Table 3). Vaginal and vulvar cancers had no significant survival differences after 2014 in either expansion or non-expansion states.
Table 3:
Adjusted Cox regression analyses for 2-year survival by study period and expansion status
| Cancer site | Non-expansion states | January 2014 expansion states | Difference-in- difference HR† |
P value |
||
|---|---|---|---|---|---|---|
| Post-2014 vs pre-2014 HR (95% CI)* |
P value |
Post-2014 vs pre-2014 HR (95% CI)* |
P value |
|||
| Combined cancer sites | 0.96 (0.92-0.99) | 0.015 | 0.90 (0.87 – 0.94) | <0.001 | 0.95 (0.90-1.00) | 0.05 |
| Endometrial | 0.95 (0.89-1.02) | 0.16 | 0.91 (0.85-0.99) | 0.011 | 0.95 (0.86-1.06) | 0.35 |
| Ovarian | 0.99 (0.93-1.05) | 0.67 | 0.88 (0.82-0.94) | <0.001 | 0.90 (0.82-0.98) | 0.016 |
| Cervical | 0.87 (0.81-0.93) | <0.001 | 0.89 (0.81-0.97) | 0.007 | 1.03 (0.92-1.15) | 0.67 |
| Vaginal | 1.01 (0.77-1.32) | 0.96 | 1.01 (0.73-1.41) | 0.93 | 1.08 (0.71-1.63) | 0.73 |
| Vulvar | 1.13 (0.94-1.35) | 0.20 | 1.14 (0.92-1.41) | 0.22 | 0.99 (0.75-1.30) | 0.92 |
Adjusted for cancer type, cancer stage, age, race, ethnicity, Charlson-Deyo score, health insurance, geographic location, median income, education level, and urbanicity
Bolded text indicates significant p-values.
The pre/post Cox proportional hazards model compared the change in 2-year OS in post-2014 compared to pre-2014 (the reference group). A HR < 1 indicates an improvement in 2-year OS in the post-2014 study period compared to the pre-2014 study period
The DID Cox regression model compares the change in 2-year OS in expansion states compared to non-expansion states (the reference group). A DID HR <1 indicates a greater improvement in expansion states compared to non-expansion states in the post-2014 study period compared to the pre-2014 study period
On our DID adjusted Cox regression model, 2-year survival for ovarian cancer was significantly improved in expansion states compared to non-expansion states after 2014, with a 10% decreased hazard of death at 2 years (DID HR 0.90, 95% CI 0.82-0.98) (Table 3). The remaining gynecologic cancer sites had no differences based on expansion status in DID analysis.
On univariable subgroup Cox regression analyses (Table 4), there were no survival differences for combined cancer sites on our DID analysis based on expansion status at 2 years. Ovarian cancer had improved 2-year survival in expansion states compared to non-expansion states on DID analysis for women with stage III/IV disease (p=0.008), non-Hispanic ethnicity (p=0.042), and those living in the South (p=0.016). However, compared to non-expansion states on DID analysis, women with cervical cancer living in expansion states in the South (p=0.018) had a worse 2-year survival after 2014. Compared to non-expansion states on DID analysis, 2-year survival for vulvar cancer was improved for women in expansion states in the Northeast post-2014 (p=0.022). There were no differences on subgroup analysis for endometrial or vaginal cancers.
Table 4:
Univariable Cox regression subgroup analysis for 2-year survival by study period and expansion status
| Cancer site | Non-expansion states | January 2014 expansion states | Difference-in- difference HR† |
P value |
||
|---|---|---|---|---|---|---|
| Post-2014 vs pre-2014 HR (95% CI)* |
P value |
Post-2014 vs pre-2014 HR (95% CI)* |
P value |
|||
| Combined cancer sites | ||||||
| Cancer stage | ||||||
| I-II | 0.91 (0.84-0.98) | 0.017 | 0.91 (0.83-1.00) | 0.06 | 1.003 (0.88-1.14) | 0.97 |
| III-IV | 0.94 (0.90-0.98) | 0.002 | 0.89 (0.85-0.94) | <0.001 | 0.95 (0.90-1.01) | 0.12 |
| Income | ||||||
| Lowest quartile | 0.93 (0.87-1.00) | 0.039 | 0.90 (0.82-0.99) | 0.031 | 0.97 (0.86-1.09) | 0.55 |
| Highest quartile | 0.99 (0.91-1.08) | 0.85 | 0.95 (0.89-1.03) | 0.23 | 0.96 (0.86-1.08) | 0.51 |
| Race | ||||||
| Black | 0.89 (0.83-0.97) | 0.005 | 0.94 (0.84-1.05) | 0.25 | 1.05 (0.92-1.20) | 0.49 |
| White | 0.97 (0.93-1.01) | 0.18 | 0.94 (0.90-0.99) | 0.013 | 0.97 (0.91-1.03) | 0.31 |
| Ethnicity | ||||||
| Hispanic | 0.80 (0.70-0.92) | 0.002 | 0.85 (0.70-1.03) | 0.10 | 1.06 (0.83-1.34) | 0.64 |
| Non-Hispanic | 0.98 (0.94-1.02) | 0.27 | 0.94 (0.9-0.98) | 0.006 | 0.96 (0.91-1.02) | 0.17 |
| Geographic location | ||||||
| Northeast | 1.17 (0.88-1.57) | 0.28 | 0.91 (0.84-0.99) | 0.032 | 0.77 (0.57-1.05) | 0.10 |
| Midwest | 1.00 (0.91-1.10) | 0.99 | 0.97 (0.91-1.04) | 0.35 | 0.97 (0.86-1.09) | 0.60 |
| South | 0.96 (0.92-1.00) | 0.027 | 0.94 (0.85-1.03) | 0.18 | 0.98 (0.89-1.09) | 0.73 |
| West | 0.89 (0.73-1.10) | 0.28 | 0.89 (0.80-0.99) | 0.035 | 1.00 (0.79-1.25) | 0.97 |
| Endometrial | ||||||
| Cancer stage | ||||||
| I-II | 0.94 (0.82-1.06) | 0.30 | 0.91 (0.79-1.06) | 0.22 | 0.97 (0.80-1.18) | 0.79 |
| III-IV | 0.94 (0.87-1.02) | 0.13 | 0.94 (0.86-1.03) | 0.16 | 1.00 (0.88-1.12) | 0.95 |
| Income | ||||||
| Lowest quartile | 0.97 (0.85-1.10) | 0.59 | 0.86 (0.72-1.04) | 0.12 | 0.90 (0.72-1.12) | 0.34 |
| Highest quartile | 1.07 (0.91-1.26) | 0.40 | 1.05 (0.92-1.20) | 0.50 | 0.98 (0.79-1.20) | 0.83 |
| Race | ||||||
| Black | 0.90 (0.78-1.03) | 0.12 | 0.91 (0.76-1.09) | 0.32 | 1.02 (0.81-1.28) | 0.88 |
| White | 1.05 (0.97-1.13) | 0.27 | 1.03 (0.94-1.13) | 0.51 | 0.98 (0.88-1.11) | 0.78 |
| Ethnicity | ||||||
| Hispanic | 0.88 (0.67-1.15) | 0.35 | 1.28 (0.90-1.80) | 0.17 | 1.46 (0.94-2.26) | 0.09 |
| Non-Hispanic | 1.06 (0.98-1.13) | 0.14 | 1.01 (0.93-1.09) | 0.85 | 0.96 (0.86-1.06) | 0.39 |
| Geographic location | ||||||
| Northeast | 1.20 (0.75-2.28) | 0.35 | 0.97 (0.83-1.13) | 0.68 | 0.74 (0.41-1.32) | 0.31 |
| Midwest | 1.09 (0.92-1.30) | 0.31 | 1.07 (0.95-1.21) | 0.28 | 0.98 (0.79-1.21) | 0.83 |
| South | 1.01 (0.94-1.09) | 0.72 | 0.93 (0.78-1.10) | 0.40 | 0.92 (0.76-1.11) | 0.38 |
| West | 1.15 (0.80-1.65) | 0.46 | 1.08 (0.89-1.32) | 0.44 | 0.94 (0.62-1.42) | 0.76 |
| Ovarian | ||||||
| Cancer stage | ||||||
| I-II | 0.87 (0.73-1.04) | 0.12 | 0.96 (0.78-1.18) | 0.70 | 1.10 (0.84-1.45) | 0.50 |
| III-IV | 0.97 (0.91-1.03) | 0.34 | 0.85 (0.79-0.92) | <0.001 | 0.88 (0.80-0.97) | 0.008 |
| Income | ||||||
| Lowest quartile | 0.96 (0.86-1.08) | 0.53 | 0.93 (0.79-1.11) | 0.42 | 0.97 (0.79-1.19) | 0.76 |
| Highest quartile | 1.00 (0.88-1.13) | 0.95 | 0.94 (0.84-1.05) | 0.26 | 0.94 (0.80-1.12) | 0.50 |
| Race | ||||||
| Black | 0.93 (0.81-1.07) | 0.29 | 0.87 (0.71-1.08) | 0.21 | 0.95 (0.74-1.22) | 0.67 |
| White | 0.98 (0.91-1.04) | 0.47 | 0.90 (0.83-0.96) | 0.002 | 0.91 (0.82-1.01) | 0.06 |
| Ethnicity | ||||||
| Hispanic | 0.82 (0.65-1.03) | 0.08 | 0.77 (0.55-1.08) | 0.13 | 0.95 (0.63-1.42) | 0.79 |
| Non-Hispanic | 0.98 (0.92-1.04) | 0.50 | 0.90 (0.83-0.95) | <0.001 | 0.91 (0.83-1.00) | 0.042 |
| Geographic location | ||||||
| Northeast | 1.11 (0.72-1.71) | 0.63 | 0.90 (0.78-1.02) | 0.10 | 0.80 (0.51-1.26) | 0.34 |
| Midwest | 1.05 (0.90-1.23) | 0.55 | 0.96 (0.86-1.07) | 0.44 | 0.92 (0.76-1.11) | 0.36 |
| South | 0.96 (0.90-1.02) | 0.21 | 0.78 (0.66-0.91) | 0.002 | 0.81 (0.68-0.96) | 0.016 |
| West | 0.81 (0.60-1.11) | 0.19 | 0.80 (0.68-0.94) | 0.008 | 0.99 (0.70-1.40) | 0.94 |
| Cervical | ||||||
| Cancer stage | ||||||
| I-II | 0.81 (0.69-0.94) | 0.007 | 0.84 (0.69-1.03) | 0.09 | 1.04 (0.81-1.34) | 0.75 |
| III-IV | 0.88 (0.82-0.95) | 0.001 | 0.86 (0.79-0.95) | 0.003 | 0.98 (0.87-1.11) | 0.76 |
| Income | ||||||
| Lowest quartile | 0.91 (0.81-1.03) | 0.12 | 0.92 (0.78-1.09) | 0.33 | 1.01 (0.82-1.24) | 0.93 |
| Highest quartile | 0.89 (0.74-1.08) | 0.23 | 0.82 (0.69-0.98) | 0.030 | 0.92 (0.71-1.19) | 0.53 |
| Race | ||||||
| Black | 0.95 (0.82-1.09) | 0.43 | 1.11 (0.90-1.37) | 0.34 | 1.18 (0.91-1.52) | 0.21 |
| White | 0.91 (0.84-1.09) | 0.032 | 0.90 (0.81-0.98) | 0.019 | 0.97 (0.86-1.11) | 0.66 |
| Ethnicity | ||||||
| Hispanic | 0.73 (0.58-0.92) | 0.008 | 0.70 (0.49-1.01) | 0.05 | 0.96 (0.62-1.47) | 0.84 |
| Non-Hispanic | 0.94 (0.87-1.01) | 0.10 | 0.91 (0.83-1.00) | 0.049 | 0.97 (0.86-1.09) | 0.63 |
| Geographic location | ||||||
| Northeast | 1.06 (0.52-2.18) | 0.87 | 0.81 (0.67-0.97) | 0.02 | 0.75 (0.36-1.58) | 0.45 |
| Midwest | 0.92 (0.77-1.11) | 0.39 | 0.88 (0.77-1.01) | 0.08 | 0.96 (0.76-1.21) | 0.71 |
| South | 0.92 (0.86-1.00) | 0.040 | 1.16 (0.98-1.38) | 0.09 | 1.26 (1.04-1.52) | 0.018 |
| West | 0.70 (0.44-1.13) | 0.15 | 0.81 (0.64-1.03) | 0.08 | 1.15 (0.68-1.95) | 0.60 |
| Vulvar | ||||||
| Cancer stage | ||||||
| I-II | 1.41 (1.07-1.87) | 0.016 | 1.14 (0.81-1.61) | 0.46 | 0.81 (0.52-1.27) | 0.36 |
| III-IV | 0.90 (0.72-1.13) | 0.38 | 1.16 (0.90-1.50) | 0.26 | 1.28 (0.91-1.81) | 0.16 |
| Income | ||||||
| Lowest quartile | 1.08 (0.79-1.47) | 0.65 | 1.31 (0.82-2.09) | 0.25 | 1.23 (0.70-2.15) | 0.48 |
| Highest quartile | 1.23 (0.80-1.87) | 0.34 | 1.14 (0.76-1.70) | 0.52 | 0.92 (0.52-1.65) | 0.79 |
| Race | ||||||
| Black | 0.89 (0.53-1.48) | 0.64 | 0.96 (0.50-1.87) | 0.91 | 1.08 (0.47-2.50) | 0.85 |
| White | 1.18 (0.98-1.42) | 0.09 | 1.19 (0.96-1.49) | 0.11 | 1.02 (0.76-1.36) | 0.90 |
| Ethnicity | ||||||
| Hispanic | 1.19 (0.47-3.01) | 0.72 | 0.74 (0.13-4.03) | 0.72 | 0.62 (0.09-4.29) | 0.63 |
| Non-Hispanic | 1.12 (0.94-1.34) | 0.22 | 1.17 (0.94-1.44) | 0.15 | 1.04 (0.79-1.38) | 0.77 |
| Geographic location | ||||||
| Northeast | 6.40 (1.38-29.68) | 0.018 | 0.97 (0.65-1.45) | 0.90 | 0.16 (0.03-0.76) | 0.022 |
| Midwest | 0.82 (0.51-1.30) | 0.40 | 0.99 (0.70-1.41) | 0.96 | 1.23 (0.69-2.19) | 0.49 |
| South | 1.15 (0.95-1.40) | 0.15 | 1.47 (0.97-2.23) | 0.07 | 1.28 (0.80-2.02) | 0.30 |
| West | 0.75 (0.18-4.16) | 0.70 | 1.66 (0.92-3.01) | 0.09 | 2.22 (0.47-10.46) | 0.31 |
| Vaginal | ||||||
| Cancer stage | ||||||
| I-II | 1.13 (0.72-1.76) | 0.60 | 0.88 (0.49-1.56) | 0.66 | 0.77 (0.37-1.60) | 0.48 |
| III-IV | 0.78 (0.57-1.06) | 0.11 | 1.14 (0.78-1.65) | 0.50 | 1.47 (0.90-2.39) | 0.12 |
| Income | ||||||
| Lowest quartile | 0.78 (0.50-1.23) | 0.29 | 1.05 (0.55-2.00) | 0.88 | 1.34 (0.61-2.94) | 0.47 |
| Highest quartile | 0.82 (0.41-1.63) | 0.57 | 1.05 (0.53-2.10) | 0.88 | 1.29 (0.49-3.42) | 0.61 |
| Race | ||||||
| Black | 0.87 (0.52-1.45) | 0.59 | 1.66 (0.77-3.58) | 1.20 | 1.90 (0.75-4.80) | 0.18 |
| White | 0.88 (0.65-1.20) | 0.42 | 1.03 (0.72-1.46) | 0.89 | 1.16 (0.73-1.84) | 0.53 |
| Ethnicity | ||||||
| Hispanic | 2.51 (0.65-9.73) | 0.18 | 0.33 (0.04-2.80) | 0.31 | 0.13 (0.01-1.69) | 0.12 |
| Non-Hispanic | 0.86 (0.66-1.12) | 0.26 | 1.09 (0.79-1.50) | 0.61 | 1.27 (0.83-1.92) | 0.27 |
| Geographic location | ||||||
| Northeast | 0.35 (0.04-2.98) | 0.33 | 1.40 (0.63-3.08) | 0.41 | 3.95 (0.40-38.90) | 0.24 |
| Midwest | 0.54 (0.24-1.22) | 0.14 | 1.21 (0.77-1.90) | 0.41 | 2.23 (0.88-5.66) | 0.09 |
| South | 0.94 (0.71-1.25) | 0.67 | 0.98 (0.52-1.83) | 0.94 | 1.03 (0.52-2.04) | 0.93 |
| West | 2.18 (0.66-7.16) | 0.20 | 1.09 (0.41-2.94) | 0.86 | 0.54 (0.12-2.55) | 0.44 |
Bolded text indicates significant p-values.
The pre/post Cox proportional hazards model compared the change in 2-year OS in post-2014 compared to pre-2014 (the reference group). A HR < 1 indicates an improvement in 2-year OS in the post-2014 study period compared to the pre-2014 study period
The DID Cox regression model compares the change in 2-year OS in expansion states compared to non-expansion states (the reference group). A DID HR <1 indicates a greater improvement in expansion states compared to non-expansion states in the post-2014 study period compared to the pre-2014 study period
Our linear DID regression models assessing for a change in insurance status demonstrated a significantly lower proportion of uninsured patients in expansion compared to non-expansion states after 2014 for all cancer sites (Table 5).
Table 5:
Adjusted percentage of uninsured patients by study period and expansion status
| Cancer Sites | Study period |
Percentage (%) |
Difference, Percentage Points* |
Percentage (%) |
Difference, Percentage Points* |
DID (95% CI), Percentage Points† |
P- value |
|---|---|---|---|---|---|---|---|
| Combined cancer sites | Pre-2014 | 15.6 | 11.1 | ||||
| Post-2014 | 13.2 | −2.4 | 7.1 | −4 | −1.60 (−2.09, −1.1) | <0.001 | |
| Endometrial | Pre-2014 | 13.1 | 9.1 | ||||
| Post-2014 | 10.1 | −3 | 5.7 | −3.4 | −0.96 (−1.6, −0.34) | 0.003 | |
| Ovarian | Pre-2014 | 15.7 | 11.1 | ||||
| Post-2014 | 13.3 | −2.4 | 7.3 | −3.8 | −1.4 (−2.4, −0.46) | 0.004 | |
| Cervical | Pre-2014 | 19.6 | 13.5 | ||||
| Post-2014 | 17.3 | −2.3 | 7.6 | −5.9 | −3.6 (−5.1, −2.0) | <0.001 | |
| Vaginal | Pre-2014 | 12.3 | 14.3 | ||||
| Post-2014 | 13.3 | 1 | 7.7 | −6.6 | −7.5 (−12.7, −2.25) | 0.005 | |
| Vulvar | Pre-2014 | 12.3 | 6.7 | ||||
| Post-2014 | 10 | −2.3 | 1.3 | −5.4 | −3.0 (−5.6, −0.3) | 0.03 |
Adjusted for year of diagnosis, cancer type, cancer stage, age, race, and ethnicity
Bolded text indicates significant p-values.
The pre/post linear regression model compares the change in the proportion of uninsured patients in the post-2014 study period compared to the pre-2014 study period (the reference group) in percentage points
The DID linear regression model compares the change in the proportion of uninsured patients in expansion states compared to non-expansion states (the reference group) after 2014 in percentage points
On adjusted Kaplan-Meier survival curves, there was significantly different 2-year survival for combined gynecologic malignancies (p<0.001), endometrial cancer (p<0.001), ovarian cancer (p<0.001), and cervical cancer (p<0.001) based on expansion status and time period with no difference for vulvar or vaginal cancer (Figure 1, S3).
Figure 1: Adjusted Kaplan-Meier survival curves by cancer site based on expansion status and pre/post Medicaid expansion time period.
• Adjusted for patient age, stage, race, and ethnicity
COMMENT
PRINCIPAL FINDINGS
In this cancer registry database study of patients with gynecologic malignancies using a quasi-experimental difference-in-difference analysis, Medicaid expansion was associated with significantly improved 2-year survival in ovarian cancer in expansion states compared to non-expansion states. There were also improvements in 2-year survival due to Medicaid expansion in subgroup analyses, most notably for non-Hispanic women with advanced ovarian cancer in the South of the US, although racial, ethnic, and regional survival disparities persisted beyond 2014.
RESULTS IN THE CONTEXT OF WHAT IS KNOWN
Our study expands on previous literature examining the association between survival and Medicaid expansion among women with gynecologic malignancies by looking at an expanded set of cancers and over a longer time period. Our analysis is also the first we know of which specifically examines the association between Medicaid expansion and survival among ovarian, vulvar, and vaginal cancers. Previous studies utilizing the NCDB and DID analyses to examine Medicaid expansion found associations with improved early-stage diagnosis in ovarian, cervical, and endometrial malignancies, as well as an increase in timely treatment of ovarian cancer9,13,16,17. While our study examines a longer survival timeframe, it is consistent with Lee et al’s DID analysis that found no significant association between Medicaid expansion and cervical cancer survival among women diagnosed between 2011-2015 in the NCDB17. Likewise, our study results echo those of Barrington et al’s DID analysis, which found no significant association between Medicaid expansion and endometrial cancer survival among women diagnosed between 2004-2015 in the NCDB, although they reported a trend towards improved survival which was significant in a subgroup analysis of women ages 53-5716.. In addition, our findings are consistent with other studies demonstrating an association with improved survival for all-cause mortality24,25 and in lung, breast, and colorectal cancers18 after statewide Medicaid expansion. Our findings of improved insurance status after the implementation of Medicaid expansion in 2014 is also consistent with previous studies9,16,17 and lends credence to the idea that Medicaid expansion is driving improved insurance coverage as an explanation for our survival findings instead of merely inferring this association.
CLINICAL IMPLICATIONS
Our finding of improved survival among women with ovarian cancer due to Medicaid expansion translates into a real difference in lives saved. Given that roughly 20,000 women are diagnosed with ovarian cancer in the US annually26, a 10% decreased hazard ratio of death over 2 years would translate to over 1000 deaths from ovarian cancer averted each year. If data continues to demonstrate improved insurance coverage and improved survival outcomes due to Medicaid expansion, it provides a strong rationale for improved insurance coverage throughout the US, especially in states that have not yet expanded Medicaid.
While the link between insurance coverage and screening is well-established across cancers with recommended screening modalities including breast, colorectal, and cervical cancers27-30, ovarian cancer does not have any recommended screening31. However, this was the only gynecologic malignancy with improved survival in expansion states in our DID analysis, and women with ovarian cancer also had improved survival in advanced disease in expansion compared to non-expansion states on subgroup analysis despite the overall poor prognosis of ovarian cancer26. Plausible explanations for these findings could be that the most important impact of improved insurance coverage is not higher screening rates, but instead timelier workup of concerning symptoms or timelier treatment of cancer9,13. Access to insurance has also been associated with higher rates of guideline concordant care32, although women with Medicaid coverage may be less likely than women who are privately insured to receive guideline concordant care32,33 and may also face relative difficulties in finding a primary care physician or gynecologic oncologist33. .
Surprisingly, those living in the South with cervical cancer had worse OS in expansion states on univariable analysis. While the reason for this is unclear, women with Medicaid may not be receiving screening at the same rates as commercially insured women34, and previous studies have shown no association between Medicaid expansion and the stage at diagnosis or timely treatment of cervical cancer17. It is also important to point out that our study population only included women ages 40-64, and thus may not be well-representative of the overall cervical cancer population. In particular, our study population excludes younger, healthier women with cervical cancer who may have relatively higher rates of survival at baseline35,36 and are also more likely to have received the HPV vaccine after its initial approval in 201037.
While not significant on our DID analysis, expansion states had improved survival for White, non-Hispanic women in the Northeast and West for combined cancer sites, ovarian cancer, and cervical cancer after 2014. These findings are potentially troubling, as they point to continued regional, racial, and ethnic disparities in cancer care despite improved insurance coverage. Previous studies have indicated that there are racial, socioeconomic, and insurance-based disparities in receipt of guideline-recommended ovarian38-40, endometrial41, and cervical42 treatments. While we did not analyze insurance coverage by subgroups, prior studies have demonstrated the association between Medicaid expansion and improved insurance coverage among racial and ethnic minorites9,13. However, even if Medicaid expansion improves insurance rates and timely treatment for historically disadvantaged groups9,13 but women with Medicaid continue to have lower quality oncologic care, we have not achieved the goal of improved health outcomes. Thus, we must be vigilant as providers to identify continued systemic barriers to care that women may have and develop interventions that appropriately address these health inequities.
RESEARCH IMPLICATIONS
While we limited our analysis to states that expanded Medicaid in January 2014, multiple states have expanded Medicaid since that time through ballot and legislative methods. As of 2021, 38 states and the District of Columbia having adopted Medicaid expansion2, and it is possible that Medicaid may be further expanded on a federal level as upcoming congressional proposals are considered. Future studies should examine how the timing of Medicaid expansion impacts survival and what role other laws and regulations related to access to or reimbursement of oncologic care plays in cancer survival.
We limited the timeframe of our survival analysis to 2 years so that our pre- and post-2014 groups had the same median follow-up time of 24 months to avoid bias due to differential follow-up time. Because ovarian cancer has higher mortality than other gynecologic malignancies26, the 2-year time point we examined may have been too short to identify survival differences within other primary cancer sites. We plan to examine longer time frames as NCDB data continues to mature.
STRENGTHS AND LIMITATIONS
Strengths of our study include the large study size due to our use of the NCDB database19. Our study examines the association between gynecologic cancer survival and Medicaid expansion over a longer time frame and with more gynecologic cancer types than previous studies, which improves our knowledge of the ultimate outcome of Medicaid expansion. We also examined insurance coverage after Medicaid expansion to assess for an association instead of implying improved coverage after Medicaid expansion. The NCDB captures patient insurance status and state expansion status, and our use of a difference-in-difference analysis allowed us to control for differences between expansion and non-expansion states over time to identify changes due to Medicaid expansion policy in a quasi-experimental model21,22. Because we only compared January 2014 Medicaid expansion to non-expansion states, we limited potential bias from differential timing of Medicaid expansion policy implementation.
Limitations of our study include those inherent to large database studies. While the NCDB covers a large percentage of the US cancer population, it does contain missing data that can affect survival outcomes 43. In addition, it does not collect cancer-specific mortality19. It also does not identify the state of residence of a patient, only the state of treatment receipt19, so there may be discrepancies between the expansion status of where a patient lives compared to where she receives cancer care. We excluded both women <40 years old as well as women >65 years old, which limits the generalizability of our results and may differentially affect our findings by cancer site. Because we only had outcome data through 2017 and were only able to evaluate 2-year survival data, our analysis does not capture longer-term oncologic outcomes. While we adjusted for multiple patient covariates, we could not take into account differences in treatment or changes in gynecologic cancer care over the time period examined. Finally, conclusions from our subgroup analyses are exploratory and should be interpreted with caution.
CONCLUSIONS
We found a significant association between improved survival and Medicaid expansion for women with ovarian cancer in a difference-in-difference analysis. While ongoing racial, ethnic, and regional differences in survival despite improved insurance coverage may speak to differences in treatment, our findings overall demonstrate the benefits of Medicaid expansion for women with gynecologic malignancies.
Supplementary Material
AJOG AT A GLANCE:
A. Why was this study conducted?
Medicaid expansion has been associated with improved insurance coverage, early-stage diagnosis, and timely treatment of gynecologic cancers, but its impact on survival is unknown.
B. What are the key findings?
Women with ovarian cancer had improved 2-year survival in expansion compared to non-expansion states in a difference-in-difference analysis. Racial, ethnic, and geographic differences in survival exist between expansion and non-expansion states.
C. What does this study add to what is already known?
Our findings demonstrate an association between Medicaid expansion and gynecologic cancer survival and add to existing evidence in support of improved insurance coverage throughout the US.
Funding Support:
This work was supported in part by the MD Anderson Cancer Center Support Grant from the National Cancer Institute of the National Institutes of Health (NIH/NCI P30 CA016672, CA217685) and the T32 training grant CA101642. LAM is supported by a NIH-NCIK07-CA201013 grant. SHG is supported by CPRIT RP160674 and Komen SAC150061.
Footnotes
Disclosures: LAM reports consulting fees from Bristol Meyers Squibb, advisory board participation for GlaxoSmithKline, and stocks in Crisper, Invitae, and Bristol-Myers Squibb. CCS reports partial research funding from AstraZeneca and stock in Inform Genomics. The remaining authors report no conflicts of interest.
Presentation information: Not previously presented.
CRediT author statement
Sarah Huepenbecker: Conceptualization, Methodology, Investigation, Writing – original draft. Shuangshuang Fu: Methodology, Investigation, Formal analysis, Writing – review and editing. Charlotte Sun: Supervision, Writing – review and editing. Hui Zhao: Validation, Writing – review and editing. Kristin Primm: Conceptualization, Methodology, Writing – review and editing. Sharon Giordano: Supervision, Writing – review and editing. Larissa Meyer: Conceptualization, Methodology, Supervision, Writing – review and editing.
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