Impact of Medicaid expansion on women with gynecologic cancer: a difference-in-difference analysis

Abstract

Background: Women with gynecologic cancer face socioeconomic disparities in care that affect survival outcomes. The Affordable Care Act offered states the option to expand Medicaid enrollment eligibility criteria as a means of improving timely and affordable access to care for the most vulnerable. The variable uptake of expansion by states created a natural experiment, allowing for quasi-experimental methods that offer more unbiased estimates of treatment effects from retrospective data than the traditional regression adjustment.

Objective: To use a quasi-experimental, difference-in-difference framework to create unbiased estimates of impact of Medicaid expansion on women with gynecologic cancer.

Study design: We performed a quasi-experimental retrospective cohort study from the National Cancer Database files for women with invasive cancers of the uterus, ovary and fallopian tube, cervix, vagina, and vulva diagnosed from 2008 to 2016. Using a marker for state Medicaid expansion status, we created difference-in-difference models to assess the impact of Medicaid expansion on the outcomes of access to and timeliness of care. We excluded women aged <40 years owing to the suppression of the state Medicaid expansions status in the data and women aged ≥65 years owing to the universal Medicare coverage availability. Our primary outcome was the rate of uninsurance at diagnosis. Secondary outcomes included Medicaid coverage, early-stage diagnosis, treatment at an academic facility, and any treatment or surgery within 30 days of diagnosis. Models were run within multiple subgroups and on a propensity-matched cohort to assess the robustness of the treatment estimates. The assumption of parallel trends was assessed with event study time plots.

Results: Our sample included 335,063 women. Among this cohort, 121,449 were from nonexpansion states and 213,614 were from expansion states, with 79,886 posttreatment cases diagnosed after the expansion took full effect in expansion states. The groups had minor differences in demographics, and we found occasional preperiod event study coefficients diverging from the mean, but the outcome trends were generally similar between the expansion and nonexpansion states in the preperiod, satisfying the necessary assumption for the difference-in-difference analysis. In a basic difference-in-difference model, the Medicaid expansion in January 2014 was associated with significant increases in insurance at diagnosis, treatment at an academic facility, and treatment within 30 days of diagnosis (P<.001 for all). In an adjusted model including all states and accounting for variable expansion implementation time, there was a significant treatment effect of Medicaid expansion on the reduction in uninsurance at diagnosis (-2.00%; 95% confidence interval, -2.3 to -1.7; P<.001), increases in early-stage diagnosis (0.80%; 95% confidence interval, 0.2-1.4; P=.02), treatment at an academic facility (0.83%; 95% confidence interval, 0.1-1.5; P=.02), treatment within 30 days (1.62%; 95% confidence interval, 1.0-2.3; P<.001), and surgery within 30 days (1.54%; 95% confidence interval, 0.8-2.3; P<.001). In particular, large gains were estimated for women living in low-income zip codes, Hispanic women, and women with cervical cancer. Estimates from the subgroup and propensity-matched cohorts were generally consistent for all outcomes besides early-stage diagnosis and treatment within 30 days.

Conclusion: Medicaid expansion was significantly associated with gains in the access and timeliness of treatment for nonelderly women with gynecologic cancer. The implementation of Medicaid expansion could greatly benefit women in nonexpansion states. Gynecologists and gynecologic oncologists should advocate for Medicaid expansion as a means of improving outcomes and reducing socioeconomic and racial disparities.

Keywords: Affordable Care Act; cervical cancer; epidemiology; health disparities; ovarian cancer; quasi-experimental methods; uterine cancer; vaginal cancer; vulvar cancer.

Figure 1.. Map and timeline diagram depicting Medicaid expansion status and timing by state.

Color-coded map depicting state Medicaid expansion status (top), and with timeline depicting exact timing of expansion implementation for expansion states and corresponding definitions of full or partial treatment by state Medicaid expansions status and year as used for complex difference-in-difference models. Map created via MapChart.net.

Figure 2.. Time-plot of (A) uninsurance and (B) Medicaid rates for women ages 40–64 with gynecologic cancer, by state Medicaid expansion status.

Time-plots for outcomes of access to care for visual assessment of parallel trends in the pre-expansion time period (2008–2009 for early expansion; 2008–2013 for January 2014 and late expansion states). No gross violations of the parallel trends assumption were identified.

Figure 3.. Time-plot of (A) early stage diagnosis, (B) treatment at academic facility, and (C) treatment within 30 days rates for women ages 40–64 with gynecologic cancer, by state Medicaid expansion status.

Time-plots for outcomes of secondary outcomes of timeliness of care for visual assessment of parallel trends in the pre-expansion time period (2008–2009 for early expansion; 2008–2013 for January 2014 and late expansion states). No gross violations of the parallel trends assumption were identified.

Figure 3.. Time-plot of (A) early stage diagnosis, (B) treatment at academic facility, and (C) treatment within 30 days rates for women ages 40–64 with gynecologic cancer, by state Medicaid expansion status.

Time-plots for outcomes of secondary outcomes of timeliness of care for visual assessment of parallel trends in the pre-expansion time period (2008–2009 for early expansion; 2008–2013 for January 2014 and late expansion states). No gross violations of the parallel trends assumption were identified.

Figure 4.. Event study time-plot of association of Medicaid expansion with uninsurance at diagnosis for women ages 40–64 with gynecologic cancer for (A) original cohort, (B) propensity-matched cohort.

Displayed according to time t relative to expansion year in order to accommodate variable implementation time. All coefficients estimated relative to group-level differences at t-2. Pre-treatment coefficients are centered by the overall pre-treatment mean so that the level differences in the post-period are presented relative to the relevant pre-period used in the main regression analysis.