Affordable Care Act and cancer stage at diagnosis in an underserved population

Determining the impact of Medicaid expansion on cancer burden

“…The analysis by Kim et al 8 has several methodological limitations that are worth discussing. Similar to other studies published to date on this topic, 11 Kim et al were unable to completely isolate the effects of Medicaid expansion 8 from the health care marketplace and Medicaid cost sharing. In addition, excluding elderly adults from their analysis was a serious limitation because policy evaluations that impact a population are more informative if performed on a population level.…”

Interrupted Time-Series for the Evaluation of Affordable Care Act Impact on Stereotactic Radiosurgery for the Treatment of Brain Metastases

“…There was, however, only weak evidence for ACA impact on increased rates of cancer screening (20). Lu and colleagues performed ITS analyses using JPS Center for Cancer Care institutional registry data on 4808 urban, underserved, adult patients diagnosed with a rst primary invasive solid tumor between 2008 and 2015, to examine the impact of ACA implementation on stage at diagnosis in Texas, a Medicaid nonexpansion state (15). Their results suggested that ACA implementation decreased the prevalence of uninsured cancer patients but had little effect on cancer stage at diagnosis (15).…”

“…Lu and colleagues performed ITS analyses using JPS Center for Cancer Care institutional registry data on 4808 urban, underserved, adult patients diagnosed with a rst primary invasive solid tumor between 2008 and 2015, to examine the impact of ACA implementation on stage at diagnosis in Texas, a Medicaid nonexpansion state (15). Their results suggested that ACA implementation decreased the prevalence of uninsured cancer patients but had little effect on cancer stage at diagnosis (15). Moss and colleagues examined the impact of the ACA Medicaid expansion (2008-2010 vs. 2011-2014 and 2014 vs. 2011-2013) on insurance rates among 181866 women diagnosed with cervical, uterine or ovarian cancer using 2008-2014 SEER data (19).…”

“…We used time point-speci c prevalence rates to perform comparative ITS analyses by examining differences in levels and slopes over time between periods prepost ACA implementation, before and after stratifying by patient or hospital characteristics (20). ITS analysis is a robust quasi-experimental method for evaluating the impact of population-level interventions through estimation of change in outcome around the time of an intervention while avoiding individual-level unmeasured confounding and controlling for pre-existing time trends prior to that intervention (10,15,20). All ITS models included indicator variables for time point (year and quarter coded as 51,…,144), post-intervention status and an interaction term between time point and post-intervention status (18).…”

“…All ITS models included indicator variables for time point (year and quarter coded as 51,…,144), post-intervention status and an interaction term between time point and post-intervention status (18). We applied post-estimation to calculate change in intercept and slope for pre-post ACA prevalence rates (11,21), and calculated Newey-West standard errors to adjust for autocorrelation (10,15,20). We also constructed Poisson regression models whereby each patient and hospital characteristic, post-ACA status and their interactions were examined as predictors of prevalent radiation therapy and SRS.…”