Skip to main content
PLOS One logoLink to PLOS One
. 2020 Sep 16;15(9):e0238813. doi: 10.1371/journal.pone.0238813

Insurance coverage, stage at diagnosis, and time to treatment following dependent coverage and Medicaid expansion for men with testicular cancer

Adam B Weiner 1, Stephen Jan 2, Ketan Jain-Poster 1, Oliver S Ko 1, Anuj S Desai 1, Shilajit D Kundu 1,*
Editor: Ernest K Amankwah3
PMCID: PMC7494102  PMID: 32936794

Abstract

Introduction

We sought to assess the impact of Affordable Care Act Dependent Care Expansion (ACA-DCE), which allowed dependent coverage for adults aged 19–25, and Medicaid expansion on outcomes for men with testicular cancer.

Methods

Using a US-based cancer registry, we performed adjusted difference-in-difference (DID) analyses comparing outcomes between men aged 19–25 (n = 8,026) and 26–64 (n = 33,303) pre- (2007–2009) and post-ACA-DCE (2011–2016) and between men in states that expanded Medicaid (n = 2,296) to men in those that did not (n = 2,265)pre- (2011–2013) and post-Medicaid expansion (2015–2016).

Results

In ACA-DCE analysis, rates of uninsurance decreased (DID -5.64, 95% confidence interval [CI] -7.23 to -4.04%, p<0.001) among patients aged 19–25 relative to older patients aged 26–64. There was no significant DID in advanced stage at diagnosis (stage≥II; p = 0.6) or orchiectomy more than 14 days after diagnosis (p = 0.6). For patients who received chemotherapy or radiotherapy as their first course of treatment, treatment greater than 60 days after diagnosis decreased (DID -4.84%, 95% CI -8.22 to -1.45%, p = 0.005) among patients aged 19–25 relative to patients aged 26–64. In Medicaid expansion states, rates of uninsurance decreased (DID -4.20%, 95% CI -7.67 to -0.73%, p = 0.018) while patients receiving chemotherapy or radiotherapy greater than 60 days after diagnosis decreased (DID -8.76, 95% CI -17.13 to -0.38%, p = 0.040) compared to rates in non-expansion states. No significant DIDs were seen for stage (p = 0.8) or time to orchiectomy (p = 0.1).

Conclusions

Men with testicular cancer had lower uninsurance rates and decreased time to delivery of chemotherapy or radiotherapy following ACA-DCE and Medicaid expansions. Time to orchiectomy and stage at diagnosis did not change following either insurance expansion.

Introduction

Testicular cancer is the most common cancer among adolescent and young adult men [1]. Young adults have historically also had the highest rates of no health insurance in the US [2]. Among many factors contributing to long-term outcomes, being under-insured has been linked to worse cancer outcomes, in particular among young men [3]. Uninsured men with testicular cancer are more likely to present at later stages of disease and have worse mortality outcomes [4]. Additionally, insurance coverage impacts type of treatment received for testicular cancer [5].

Recently, changes in the Affordable Care Act Dependent Care Expansion (ACA-DCE) have significantly increased coverage and access to care for young adults between the ages of 19 and 25 [68]. Once these changes took effect in 2010, young adults were allowed to remain covered under their parents’ plans until the age of 26. Revisions to the ACA also allowed for several states to expand Medicaid eligibility to include U.S. citizens whose income falls below 133% of the federal poverty level and several did in January 2014. As of the 2017 fiscal year, over 12.6 million Americans were newly eligible and received coverage due to state expansion of Medicaid coverage [9]. Accordingly, rates of cancer patients without insurance decreased in states that expanded Medicaid relative to those in states that did not expand [3].

Previous works have assessed the associations between insurance expansion and outcomes specific to certain tumor types such as prostate and breast cancer [10, 11], which have shown differential benefits of insurance expansion for young patients with cancer depending on the cancer type. Thus, assessment of insurance expansion on outcomes for young patients with testicular cancer warrant investigations particularly since these patients present at young ages. To that end, we hypothesized the ACA-DCE in 2010 and widespread Medicaid expansion in 2014 impacted men presenting with testicular cancer by decreasing the percentage of those with no insurance and late stage disease (≥II). We also hypothesized insurance expansion was associated with decreases in the percentage of men receiving delayed treatment for testicular cancer. We were able to answer these questions using a large national dataset in the US to compare each outcome before and after each expansion.

Materials and methods

Patients

Institutional Review Board exemption was granted for this study from Northwestern University (STU00210266). Data were obtained and accessed on May 6, 2019 and these data were fully anonymized before access was available. Because the data were without any patient identifiers, no consents were obtained. The National Cancer Database is a hospital-based cancer registry in the United States organized by the American Cancer Society and the American College of Surgeons [12]. It captures data on over 70% of all new cancer in the United States. For the analysis of the ACA-DCE, we included all male patients (n = 49,221, 100%) diagnosed with testicular cancer ages 19–64 from 2007 to 2009 and 2011 to 2016. Patients were excluded if they had missing data on regional income or high school attainment (n = 666, 1.4%), insurance type (n = 959, 1.9%), or stage at diagnosis (n = 6,267, 12.7%). For the Medicaid expansion analysis, we included all male patients (n = 5,601, 100%) diagnosed with testicular cancer ages 40–64 from 2011 to 2013 and 2015 to 2016 residing in states that either expanded Medicaid on January 1, 2014 or never expanded Medicaid. In this analysis, there was only data available in the NCDB on Medicaid expansion for patients age 40 years or older. Patients were excluded if they had missing data on regional income or high school attainment (n = 71, 1.3%), insurance type (n = 88, 1.6%), or stage at diagnosis (n = 881, 15.7%).

Independent variables

All independent variables were between-subjects. The main exposure of interest was year of diagnosis in both analyses: pre- (2007–2009) and post-ACA-DCE (2011–2013) and pre- (2011–2013) and post-Medicaid expansion (2015–2016). The years 2010 and 2014 were excluded as washout years for their respective expansions. Other covariates included in all adjusted regressions included race/ethnicity (Non-Hispanic White, non-Hispanic Black, Hispanic, or other/unknown), and Charlson/Deyo comorbidity index (0, 1, >1) [13]. Each regression was adjusted for zip code, median household income, and rate of adult high school attainment. These values were based on the 2012–2016 American Community Survey and were categorized based on quartiles relative to the entire United States. (www.census.gov/programs-surveys/acs)

Outcomes

Our outcomes included proportion of patients without insurance coverage, proportion of patients with advanced stage at diagnosis (American Joint Committee on Cancer edition 7; ≥II [14]) in those with staging information, days from diagnosis to orchiectomy in patients whose first treatment was orchiectomy (<14 versus ≥14 days), and days from diagnosis to chemotherapy or radiotherapy in patients whose first treatment was either chemotherapy or radiotherapy (<60 versus ≥60 days). In the absence of specific guidelines on treatment timing, the cutoffs for timing of treatment were chosen a priori as generally acceptable timeframes as reflections of favorable (earlier treatment) versus unfavorable (delayed treatment) access to healthcare.

Statistical analysis

A simple pre- and post-exposure comparison of our outcomes of interest would not account for any factors external to insurance expansion. It would also not account for trends that were already present prior to expansion. Thus, we performed difference-in-difference (DID) analyses based on the exposure to insurance expansion [15]. This method addresses the issues of external factors that could affect outcomes by using a comparison group that experiences the same external factors but does not experience the exposure (insurance expansion). In the analysis of the ACA-DCE, patients were considered to be exposed to the “intervention” if they were age 19–25 at the time of diagnosis. Controls were those aged 26–64 as this age group would not have been affected by the ACA-DCE and would have been too young to receive Medicare [8]. In the Medicaid expansion analysis, patients were considered to be in the “intervention” group if they resided in a state that expanded Medicaid on January 1, 2014. Controls were those patients who resided in states that never expanded Medicaid. Using multivariable linear regression for each outcome with an interaction term between the intervention/controls and pre- and post-exposure years of diagnosis, we calculated the DID of the percentage for each outcome to assess how each outcome in the exposed groups changed relative the non-exposed groups before and after insurance expansion. A separate dummy variable was created for year of diagnosis for the pre-exposure years and the individual years following exposure for the ACA-DCE analysis (2015 alone and 2016 alone). This was not done for the Medicaid expansion analysis given the low numbers of patients. Subgroup analyses were performed limiting our analysis to patients living in zip code regions of low income (<$40,277 annual median household income). All statistical tests were performed using Stata 13 (College Station, TX) and p<0.05 was considered statistically significant.

Results

ACA-DCE analysis

Patient characteristics

In total, 8,026 patients age 19–25 years and 33,303 patients age 26–64 years were included in the final analysis (S1 Table). The median age at diagnosis for the patients age 19–25 years was 23 years vs 36 for patients age 26–64 years (p<0.001). In the 19–25 age group, fewer patients were white (70% vs 79%, p<0.001), more had zero comorbidities (95% vs 93%, p<0.001), and more resided in areas of low income and high non-high school attainment (both <0.001). Among those with information on treatment type and timing of treatment (n = 33,594), 18,996 (57%) received orchiectomy as their first treatment while 14,598 (43%) received chemotherapy or radiotherapy as their first form of treatment.

Insurance

Over the entire study period, 15% of patients age 19–25 years and 10% of those age 26–64 years had no insurance coverage (S1 Table and Fig 1A). The adjusted DID indicated rates of uninsurance decreased -5.64% (95% confidence interval [CI] -7.23 to -4.04%, p<0.001: Table 1 and S2 Table) relative to older patients. Comparing the pre-ACA-DCE era (2007–2009) to the year 2016 revealed a DID of -7.43% (95% CI -10.12 to -4.73%, p = <0.001; S3 Table). When limiting the analysis to patients living in regions of low income, there was no statistically significant change in rates of uninsurance (p = 0.080).

Fig 1. Unadjusted temporal trends before and after ACA-DCE.

Fig 1

Unadjusted temporal trends comparing (a) patients without insurance coverage, (b) patients with advanced stage at diagnosis, (c) patients whose first treatment was orchiectomy who received treatment greater than 14 days after diagnosis, and (d) patients whose first treatment was chemotherapy or radiotherapy who received treatment greater than 60 days after diagnosis, between those who qualified for ACA-DCE coverage (ages 19–25) and those who didn’t (ages 26–64), pre- (2007–2009) and post-ACA-DCE (2011–2016). Vertical lines demarcate initiation of the ACA-DCE. Abbreviation: ACA-DCE, Affordable Care Act Dependent Care Expansion.

Table 1. Difference-in-difference analyses on outcomes for men with testicular cancer following ACA-DCE and Medicaid expansion.
All patients Regional low-income
Pre-expansion vs. post-expansion Difference in difference (95% CI) p Difference in difference (95% CI) p
% Patients without insurance coverage
ACA-DCE -5.64 (-7.23 to -4.04) <0.001 -4.64 (-9.84 to 0.56) 0.080
Medicaid Expansion -4.20 (-7.67 to -0.73) 0.018 -11.80 (-23.85 to 0.24) 0.055
% Patients with advanced stage at diagnosis
ACA-DCE -0.57 (-2.92 to 1.77) 0.6 -0.38 (-6.55 to 5.78) 0.9
Medicaid Expansion -0.79 (-6.32 to 4.73) 0.8 -6.55 (-21.56 to 8.45) 0.4
% Patients whose first treatment was orchiectomy who received treatment greater than 14 days after diagnosis
ACA-DCE -0.70 (-3.05 to 1.64) 0.6 2.57 (-3.77 to 8.91) 0.4
Medicaid Expansion -4.59 (-10.19 to 1.02) 0.1 -23.35 (-39.50 to -7.20) 0.005
% Patients whose first treatment was chemotherapy or radiotherapy who received treatment greater than 60 days after diagnosis
ACA-DCE -4.84 (-8.22 to -1.45) 0.005 -3.80 (-12.35 to 4.75) 0.4
Medicaid Expansion -8.76 (-17.13 to -0.38) 0.040 -14.74 (-36.02 to 6.53) 0.2

Multivariable linear regression analyses were used to evaluate difference-in-differences for each outcome between intervention and controls, and pre- (2007–2009) and post-exposure years (2011–2016) for the ACA-DCE, and pre- (2011–2013) and post-exposure years (2015–2016) for Medicaid expansion. Covariates included in the adjusted analysis included patient age, race/ethnicity, Charlson-comorbidity index, regional income, and regional high school attainment. Bolded p values are statistically significant (p < 0.05); Abbreviation: ACA-DCE, Affordable Care Act Dependent Care Expansion; CI, confidence interval.

Stage at diagnosis

In total, 36% of patients age 19–25 and 27% of those age 26–64 presented with advanced disease (Stage ≥II) at diagnosis (S1 Table and Fig 1B). No significant changes in advanced disease was seen among all patients (p = 0.6) or those living in low income areas (p = 0.9: Table 1 and S2 Table).

Days from diagnosis to orchiectomy

Among patients who received orchiectomy as their first form of treatment, treatment was received 14 or more days after diagnosis for 9% of those age 19–25 and 10% of those age 26–64 (S1 Table and Fig 1C). No significant changes in days to orchiectomy was seen among all patients (p = 0.6) or those living in low income areas (p = 0.4: Table 1 and S2 Table).

Days from diagnosis to chemotherapy or radiotherapy

Among patients who received chemotherapy or radiotherapy as their first form of treatment, treatment was received 60 or more days after diagnosis for 21% of patients age 19–25 and 21% of those age 26–64 (S1 Table and Fig 1D). Adjusted DID analysis showed a decrease in this figure of -4.84% (95% CI -8.22 to -1.45%, p = 0.005; Table 1 and S2 Table). Comparing the pre-ACA-DCE era to the year 2016 alone showed an adjusted DID of -5.38% (95% CI -10.78 to 0.02%, p = 0.051; S3 Table). There was no change in time to chemotherapy or radiotherapy when the analysis was limited to patients living in regions of low income (p = 0.4).

Medicaid expansion analysis

Patient characteristics

In total, 2,296 patients in Medicaid expansion states and 2,265 patients in non-expansion states were included in the final analysis (S4 Table). In the expansion group, more patients were white (88% vs 82%, p<0.001), and fewer resided in areas of low income and high non-high school attainment (both <0.001). Among those with information on treatment type and timing of treatment (n = 3,790), 2,217 (58%) received orchiectomy as their first treatment while 1,573 (42%) received chemotherapy or radiotherapy as their first form of treatment.

Insurance

Over the entire study period, 6% of patients in expansion states and 13% of those in non-expansion states had no insurance coverage (S4 Table and Fig 2A). Adjusted DID showed rates of uninsurance decreased -4.20% (95% CI -7.67 to -0.73%, p = 0.018; Table 1 and S5 Table). When limiting the analysis to patients living in regions of low income, there was no change in rates of uninsurance (p = 0.055).

Fig 2. Unadjusted temporal trends before and after Medicaid expansion.

Fig 2

Unadjusted temporal trends comparing (a) patients without insurance coverage, (b) patients with advanced stage at diagnosis, (c) patients whose first treatment was orchiectomy who received treatment greater than 14 days after diagnosis, and (d) patients whose first treatment was chemotherapy or radiotherapy who received treatment greater than 60 days after diagnosis, between those residing in Medicaid expansion and non-expansion states, pre- (2011–2013) and post-Medicaid expansion (2015–2016). Vertical lines demarcate when state-dependent participation in Medicaid Expansion began.

Stage at diagnosis

A total of 28% of patients in the expansion states and 31% of those in non-expansion states presented with advanced disease (Stage ≥II) at diagnosis (S4 Table and Fig 2B). No significant change in stage at diagnosis was seen among all patients (p = 0.8) or those living in low income areas (p = 0.4: Table 1 and S5 Table).

Days from diagnosis to orchiectomy

Among patients who received orchiectomy as their first form of treatment, treatment was received 14 or more days after diagnosis for 13% of patients in expansion states and 11% of those in non-expansion states (S4 Table and Fig 2C). There was no significant change in overall time to orchiectomy following expansion (p = 0.1; Table 1 and S5 Table). Adjusted DID analysis showed patients living in regions of low income were less likely to receive orchiectomy after 14 days following diagnosis (-23.35%, 95% CI -39.50 to -7.20%, p = 0.005).

Days from diagnosis to chemotherapy or radiotherapy

Among patients who received chemotherapy or radiotherapy as their first form of treatment, treatment was received 60 or more days after diagnosis for 22% of patients in expansion states and 20% of those in non-expansion states. (S4 Table and Fig 2D). This figure decreased -8.76 (95% CI -17.13 to -0.38, p = 0.040) among all patients (Table 1 and S5 Table) but did not change among those living in low income areas (p = 0.2).

Discussion

In this retrospective study, we used data from a national cancer registry to identify changes in the rate of insurance coverage, stage at diagnosis, and time to treatment in male patients diagnosed with testicular cancer following both the ACA-DCE and Medicaid expansion. Compared to the control group, patients age 19–25 and those in Medicaid expansion states experienced a significant decrease in rates of uninsurance following ACA-DCE in 2010 and Medicaid expansion in 2014, respectively. They both, likewise, experienced decreases in time to treatment for those whose first treatment was chemotherapy or radiotherapy relative to controls. There were no decreases noted in stage at diagnosis or time to treatment for those whose first treatment was orchiectomy.

The ACA-DCE and Medicaid expansions have prompted an examination of the effects of increased insurance coverage on cancer outcomes. There has been a significant decrease in the percentage of uninsured, non-elderly patients with newly diagnosed cancer following implementation of the ACA in both Medicaid expansion and non-expansion states, but with greater magnitude in Medicaid expansion states [3, 16]. A more recent study showed higher insurance rates did not result in changes in time to treatment for young women with breast cancer [10]. Notably, though, among patients with newly diagnosed prostate cancer, Medicaid expansion was associated with decreases in the proportion of patients presenting with high-risk disease [11]. Thus, recent insurance expansions may differentially impact patients with different cancer.

Because testicular cancer is ordinarily diagnosed at a relatively young age [1], insurance expansion via the ACA-DCE and Medicaid expansion had substantial potential to positively impact patients with newly diagnosed testicular cancer. According to recent reports, young men aged 20–34 have the highest incidence of being both uninsured and diagnosed with testicular cancer [1719]. Additionally, previous work that analyzed data collected from the Surveillance, Epidemiology and End Reports database demonstrated lack of insurance in patients with testicular cancer increases risk of presenting with advanced stage disease, which is associated with worse mortality [4, 20]. This makes young men with testicular cancer a vulnerable population, yet likely to benefit from recent insurance expansions. Our analysis indicates decreases in the rates of uninsured patients with testicular cancer with following both ACA-DCE and Medicaid expansion. However, our findings also indicate neither policy change was associated with earlier staging at diagnosis. This is in contrast to prior studies indicating reductions in uninsured rates and late stage disease for all young patients with cancer following the ACA-DCE [16, 21, 22]. These inconsistencies may be in part due to relatively short term follow up data following each policy change. Longer-term follow-up may continue to show improvements in insurance coverage and outcomes for patients with testicular cancer including survival.

Notably, the rates of uninsurance or stage at diagnosis did not measurably change among men from regions of low annual income. However, the number of men in these analyses were small for both expansions (S1 and S4 Tables). Lack of data from patients age 19–40 in the Medicaid expansion dataset likely reduced the power of the Medicaid expansion analysis. Additionally, men from backgrounds of higher socioeconomic status and previous insurance coverage in childhood were more likely to take advantage of the ACA-DCE and parental coverage [23]. Further work is needed to understand the benefits of insurance expansion on men of low socioeconomic status.

In addition to staging, earlier treatment has also been linked to better survival and disease outcomes [24]. Prior studies have demonstrated insurance expansion is associated with improvements in access and timeliness of treatment for pancreatic, thyroid, and colorectal cancer [25, 26]. Mirroring these results, our findings noted ACA-DCE and Medicaid expansion were associated with a decrease in time to treatment in men whose first treatment was either chemotherapy or radiotherapy. Additionally, men from regions of low annual income experienced decreases in time to treatment if their first treatment was orchiectomy. These results are encouraging and may reflect ongoing positive trends in more timely treatment for men with testicular cancer.

Several limitations should be taken into consideration when reviewing this study’s findings. First, NCDB only includes data from Commission on Cancer-accredited US hospitals. Accredited hospitals are more likely to be larger and have more cancer-related services available to patients, thus results may not be generalizable to the overall population [27]. Second, only information for men age 40 years or older was available for the Medicaid expansion analysis. Given the highest incidence of testicular cancer occurs in men aged 20–44 with a median of 33 years old, this limits the generalizability of these findings to all young men diagnosed with testicular cancer [17]. Third, relatively short-term follow-up data in conjunction with inclusion of the years of implementation for each policy change may have biased the results in ways that are difficult to ascertain. There were likely inevitable logistical delays in transitioning in new policy, which may have delayed changes in patients’ access to care. Fourth, our outcomes are all still proxies for more meaningful oncologic outcomes such as cancer-specific survival and morbidities related to advanced stage cancer diagnosis. Additionally, unbalanced age at diagnosis between in the comparison groups in the ACA-DCE analysis may create inherent bias given all patients age 19 to 25 years would have benefited from ACA-DCE, thus an older “control” group was required. Several unmeasured relevant variables not included in the NCDB that may have affected our outcomes warrant mentioning including individual-level measurements of socioeconomic status, smoking history and body-mass index among others. The current study was also limited by relatively small sample sizes and short follow-up leading to reduced patient numbers for subgroup analyses. For this reason, ongoing work should continue to evaluate the differential effects of insurance expansion for vulnerable patient groups based on race and ethnicity. Finally, we acknowledge a large percentage of patients who were described as receiving chemotherapy or radiotherapy as their first form of treatment. This finding likely reflects either a selection bias based on recorded treatments or timing to treatments in the NCDB. It may also reflect a lack of recording orchiectomy if, for instance, the orchiectomy was performed prior to presentation to the NCDB facility. Thus, some of the patients recorded as receiving chemotherapy or radiotherapy as their first treatment may have received those treatments as adjuvant treatments.

In summary, data from the NCDB revealed ACA-DCE and Medicaid expansion were associated with decreases in the percentage of uninsured men with testicular cancer. Despite this, no association was found between either expansion and the staging of disease at diagnosis. However, both expansions were associated with decreases in time to treatment for men whose first treatment was chemotherapy or radiotherapy. Moving forward, future research should attempt to identify additional variables affecting testicular cancer outcomes such as race and education. Longer-term follow up should continue to assess outcomes for men with testicular cancer related to insurance expansions. Additionally, examination of the impact of insurance expansion on survival outcomes would offer a more comprehensive analysis on oncologic outcomes.

Conclusion

In a national cohort of men with testicular cancer, ACA-DCE and Medicaid expansion were each independently associated with decreases in rates of uninsurance, but no association with stage at diagnosis was found. Both expansions were associated with improvements in time to treatment in patients whose first treatment was chemotherapy or radiotherapy. Further understanding of the impact insurance expansions have had on healthcare for men with testicular cancer warrants longer-term follow up, inclusion of more patients aged 19–40 for Medicaid expansion analysis, and addition of cancer-specific survival outcomes.

Supporting information

S1 Table. Patient characteristics for Affordable Care Act Dependent Care Expansion analysis.

a Comparisons based on Pearson’s Chi-squared analyses for discrete covariates and Mann-Whitney U test for age.

(DOCX)

S2 Table. Raw data for difference-in-difference analyses for ACA-DCE.

These data were used to generate Table 1 and Fig 1.

(DOCX)

S3 Table. Difference-in-difference analyses on outcomes for men with testicular cancer following ACA-DCE.

Multivariable linear regression analyses were used to evaluate difference-in-differences for each outcome between intervention and controls, and pre- (2007–2009) and individual post-exposure years (2011–2016). Bolded p values are statistically significant (p < 0.05). Abbreviation: ACA-DCE, Affordable Care Act Dependent Care Expansion; CI, confidence interval.

(DOCX)

S4 Table. Patient characteristics for Medicaid expansion analysis.

a Comparisons based on Pearson’s Chi-squared analyses for discrete covariates and Mann-Whitney U test for age.

(DOCX)

S5 Table. Raw data for difference-in-difference analyses for Medicaid expansion.

These data were used to generate Table 1 and Fig 2.

(DOCX)

Acknowledgments

Disclaimer: The NCDB is a joint project of the Commission on Cancer of the American College of Surgeons and the American Cancer Society. The data used in the study are derived from a de-identified NCDB file. The American College of Surgeons and the Commission on Cancer have not verified and are not responsible for the analytic or statistical methodology employed, or the conclusions drawn from these data by the investigators.

Data Availability

The authors do not have the right share the third-party data used in this study. The data underlying this study are available from the National Cancer Database upon request and application by investigators by emailing the NCDB at NCDB_PUF@facs.org. The data for this analysis was obtained from the “Testis(Testis) special set(0->90+)” Participant User File, application ID 2016.797.

Funding Statement

This work was supported in part by the 2019 Urology Care Foundation Residency Research Award Program and the Russell Scott, Jr., MD Urology Research Fund (ABW). https://www.auanet.org/research/research-funding/aua-funding/residency-research-awards/residency-research-awards-fellows The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Bleyer A, Barr R, Hayes-Lattin B, Thomas D, Ellis C, Anderson B, et al. The distinctive biology of cancer in adolescents and young adults. Nat Rev Cancer. 2008;8(4):288–98. Epub 2008/03/21. 10.1038/nrc2349 . [DOI] [PubMed] [Google Scholar]
  • 2.Adolescent and Young Adult Oncology Progress Review Group. Closing the Gap: Research and Care Imperatives for Adolescents and Young Adults with Cancer Bethesda, MD: 2006.
  • 3.Agarwal A, Katz AJ, Chen RC. The Impact of the Affordable Care Act on Disparities in Private and Medicaid Insurance Coverage Among Patients Under 65 With Newly Diagnosed Cancer. Int J Radiat Oncol Biol Phys. 2019;105(1):25–30. 10.1016/j.ijrobp.2019.05.033 . [DOI] [PubMed] [Google Scholar]
  • 4.Kamel MH, Elfaramawi M, Jadhav S, Saafan A, Raheem OA, Davis R. Insurance Status and Differences in Treatment and Survival of Testicular Cancer Patients. Urology. 2016;87:140–5. Epub 2015/10/20. 10.1016/j.urology.2015.06.059 . [DOI] [PubMed] [Google Scholar]
  • 5.Gray PJ, Lin CC, Sineshaw H, Paly JJ, Jemal A, Efstathiou JA. Management trends in stage I testicular seminoma: Impact of race, insurance status, and treatment facility. Cancer. 2015;121(5):681–7. 10.1002/cncr.29094 . [DOI] [PubMed] [Google Scholar]
  • 6.Parsons HM, Schmidt S, Tenner LL, Bang H, Keegan TH. Early impact of the Patient Protection and Affordable Care Act on insurance among young adults with cancer: Analysis of the dependent insurance provision. Cancer. 2016;122(11):1766–73. Epub 2016/03/22. 10.1002/cncr.29982 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Nogueira LM, Chawla N, Han X, Jemal A, Yabroff KR. Patterns of Coverage Gains Among Young Adult Cancer Patients Following the Affordable Care Act. JNCI Cancer Spectr. 2019;3(1):pkz001 10.1093/jncics/pkz001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Sommers BD, Buchmueller T, Decker SL, Carey C, Kronick R. The Affordable Care Act has led to significant gains in health insurance and access to care for young adults. Health Affairs. 2013;32(1):165–74. 10.1377/hlthaff.2012.0552 [DOI] [PubMed] [Google Scholar]
  • 9.Miller S, Wherry LR. Health and Access to Care during the First 2 Years of the ACA Medicaid Expansions. The New England journal of medicine. 2017;376(10):947–56. Epub 2017/03/09. 10.1056/NEJMsa1612890 . [DOI] [PubMed] [Google Scholar]
  • 10.Han X, Zhao J, Ruddy KJ, Lin CC, Sineshaw HM, Jemal A. The impact of dependent coverage expansion under the Affordable Care Act on time to breast cancer treatment among young women. PLoS ONE. 2018;13(6):e0198771 10.1371/journal.pone.0198771 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Weiner AB, Vo AX, Desai AS, Hu JC, Spratt DE, Schaeffer EM. Changes in prostate-specific antigen at the time of prostate cancer diagnosis after Medicaid expansion in young men. Cancer. 2020;126(14):3229–36. 10.1002/cncr.32930 . [DOI] [PubMed] [Google Scholar]
  • 12.Boffa DJ, Rosen JE, Mallin K, Loomis A, Gay G, Palis B, et al. Using the National Cancer Database for Outcomes Research. Jama Oncol. 2017;3(12):1722–8. 10.1001/jamaoncol.2016.6905 WOS:000418029200022. [DOI] [PubMed] [Google Scholar]
  • 13.Deyo RA, Cherkin DC, Ciol MA. Adapting a Clinical Comorbidity Index for Use with Icd-9-Cm Administrative Databases. Journal of Clinical Epidemiology. 1992;45(6):613–9. 10.1016/0895-4356(92)90133-8 WOS:A1992JA22600006. [DOI] [PubMed] [Google Scholar]
  • 14.Edge S, Byrd D, Compton C, April F, Green F, Trotti A. AJCC Cancer Staging Manual. 7 ed New York, NY: Springer; 2010. [Google Scholar]
  • 15.Dimick JB, Ryan AM. Methods for evaluating changes in health care policy: the difference-in-differences approach. JAMA. 2014;312(22):2401–2. 10.1001/jama.2014.16153 . [DOI] [PubMed] [Google Scholar]
  • 16.Han X, Zang Xiong K, Kramer MR, Jemal A. The Affordable Care Act and Cancer Stage at Diagnosis Among Young Adults. J Natl Cancer Inst. 2016;108(9). Epub 2016/05/04. 10.1093/jnci/djw058 . [DOI] [PubMed] [Google Scholar]
  • 17.Howlader N NA, Krapcho M, Miller D, Brestt A, Yu M. SEER Cancer Statistics Review (CSR) 1975–2016. Bethesda, MD: National Cancer Institute; 2019. [Google Scholar]
  • 18.Berchick E. Who Are the Uninsured? Most Uninsured Were Working-Age Adults [Internet]. 2018 July 10, 2019. Available from: https://www.census.gov/library/stories/2018/09/who-are-the-uninsured.html.
  • 19.Cantor JC, Monheit AC, DeLia D, Lloyd K. Early impact of the Affordable Care Act on health insurance coverage of young adults. Health Serv Res. 2012;47(5):1773–90. Epub 2012/08/29. 10.1111/j.1475-6773.2012.01458.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Robbins AS, Lerro CC, Barr RD. Insurance status and distant-stage disease at diagnosis among adolescent and young adult patients with cancer aged 15 to 39 years: National Cancer Data Base, 2004 through 2010. Cancer. 2014;120(8):1212–9. Epub 2014/01/30. 10.1002/cncr.28568 . [DOI] [PubMed] [Google Scholar]
  • 21.Rosenberg AR, Kroon L, Chen L, Li CI, Jones B. Insurance status and risk of cancer mortality among adolescents and young adults. Cancer. 2015;121(8):1279–86. Epub 2014/12/11. 10.1002/cncr.29187 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Mesquita-Neto JWB, Cmorej P, Mouzaihem H, Weaver D, Kim S, Macedo FI. Disparities in access to cancer surgery after Medicaid expansion. Am J Surg. 2019. Epub 2019/07/04. 10.1016/j.amjsurg.2019.06.023 . [DOI] [PubMed] [Google Scholar]
  • 23.Han X, Zhu S, Jemal A. Characteristics of Young Adults Enrolled Through the Affordable Care Act-Dependent Coverage Expansion. J Adolesc Health. 2016;59(6):648–53. Epub 2016/10/12. 10.1016/j.jadohealth.2016.07.027 . [DOI] [PubMed] [Google Scholar]
  • 24.Dong W, Gang W, Liu M, Zhang H. Analysis of the prognosis of patients with testicular seminoma. Oncol Lett. 2016;11(2):1361–6. Epub 2016/02/20. 10.3892/ol.2015.4065 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Loehrer AP, Chang DC, Hutter MM, Song Z, Lillemoe KD, Warshaw AL, et al. Health Insurance Expansion and Treatment of Pancreatic Cancer: Does Increased Access Lead to Improved Care? J Am Coll Surg. 2015;221(6):1015–22. Epub 2015/11/28. 10.1016/j.jamcollsurg.2015.09.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Loehrer AP, Murthy SS, Song Z, Lubitz CC, James BC. Association of Insurance Expansion With Surgical Management of Thyroid Cancer. JAMA Surg. 2017;152(8):734–40. Epub 2017/04/07. 10.1001/jamasurg.2017.0461 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Bilimoria KY, Bentrem DJ, Stewart AK, Winchester DP, Ko CY. Comparison of commission on cancer-approved and -nonapproved hospitals in the United States: implications for studies that use the National Cancer Data Base. J Clin Oncol. 2009;27(25):4177–81. Epub 2009/07/29. 10.1200/JCO.2008.21.7018 . [DOI] [PubMed] [Google Scholar]

Decision Letter 0

Ernest K Amankwah

14 Jul 2020

PONE-D-20-10591

Insurance coverage, stage at diagnosis, and time to treatment following dependent coverage and Medicaid expansion for men with testicular cancer.

PLOS ONE

Dear Dr. Kundu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR:

Address the following comments in addition to the reviewers’ comments

Abstract

  • The n’s in the methods of the abstract are a bit confusing. For example, it is not clear if n=41,329 refers to only the post-population. Revise for clarity

  • In the results of the abstract, clarify when results are for all men and when they are comparing 19-25 and 26+

  • Be consistent with the decimal places for the non-significant p-values in the abstract. I will suggest you change p=0.109 to p=0.1

Methods

  • Line 120: Move n=49221,100% to line 119 after “all men”

  • Line 126: Move n=5601,100% to line 123 after “all men”

  • Lines 126-127 somehow conflicts with line 124. Revise one of these sentences as you cannot include 19-64 years if the database has information for men aged 40+ only

  • Provide a rationale for selecting 14 days and 60 days for orchiectomy and chemotherapy or radiation respectively

  • Provide a rationale for comparing the two different age groups

Results

  • Lines 180-183: Clarify the comparison groups. If you are comparing 19-25 to 26-64 then it is obvious that they will be younger. You could instead indicate that “the median age for 19-24 group was …”

  • Provide the percentage of men that received orchiectomy as first line of therapy

  • Provide the pre and post percentages for each age group in Table 1 in addition to the DID or in a different table

Discussion

  • I will not refer to this pre and post study as a cohort study

==============================

Please submit your revised manuscript by Aug 28 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Ernest K. Amankwah, PhD

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2.  In the ethics statement in the manuscript and in the online submission form, please provide additional information about the patient records used in your retrospective study, including: a) whether all data were fully anonymized before you accessed them and b) the date range (month and year) during which patients' medical records were accessed."

3. To comply with PLOS ONE submission guidelines, in your Methods section, please provide additional information regarding your statistical analyses, including the specific statistical tests performed in your analysis. For more information on PLOS ONE's expectations for statistical reporting, please see https://journals.plos.org/plosone/s/submission-guidelines.#loc-statistical-reporting

4. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

5. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: PONE-D-20-10591

Insurance coverage, stage at diagnosis, and time to treatment following dependent coverage and Medicaid expansion for men with testicular cancer

Summary: The authors conducted a retrospective cohort study that investigated changes in rates of insurance coverage, cancer stage at diagnosis, and time to treatment among men with testicular cancer following introduction of the Affordable Care Act’s Dependent Care Expansion (ACA-DCE) in 2010 and the Medicare expansion program in 2014. Data were obtained from the National Cancer Database (NCDB). Because the ACA-DCE permits dependent coverage for adults between the ages 19 to 25 years, participants were considered exposed to the “intervention” if they were between 19 to 25 years at the time of diagnosis, while those between the ages 26 to 64 years at diagnosis were used as controls. For the Medicaid expansion analysis, patients were considered exposed to the “intervention” if they resided in a state that expanded Medicaid on January 1, 2014, while those who resided in states that did not adopt the Medicare expansion coverage were used as controls. For statistical analysis, multivariable-adjusted linear regression was used. The results show that compared to the control group, ACA-DCE beneficiaries of age 19 to 25 and patients in Medicaid expansion states experienced significant increases in medical insurance rates following ACA-DCE in 2010 and Medicaid expansion in 2014. These two groups also experienced a decrease in time to treatment for those whose first-line treatment was chemotherapy or radiotherapy as compared with controls, but no differences were observed in stage at diagnosis or time to treatment for those whose first-line therapy was orchiectomy. Overall, this study adds to the literature on the impact of ACA-DCE and the Medicare expansion program on health outcomes. Below are few suggestions for improvement.

1. For the ACA-DCE analysis, patients in the control group are older than those in the control group. This raises the concern of whether the observed differences could have been driven by age and not necessary the introduction of ACA-DCE. I wonder why the authors didn’t choose a control group of similar age range as the intervention group. Could the authors have used testicular cancer patients between age 19 to 25 years who did not benefit from ACA-DCE as controls?

2. Abstract: Please make explicitly clear which patients belonged to the “intervention” group versus the comparison groups. It is quite confusing to understand until I read the entire manuscript.

3. Abstract: In the concluding statement, please indicate that two of the hypotheses tested produced null results; i.e., no association between ACA-DCE coverage and advanced state at diagnosis or orchiectomy.

4. Methods: Stratified analysis by race/ethnicity would be of great interest as it will show whether the findings are consistent across racial minority groups or not.

5. The repeated use of “men” in the results section presupposes that women may have been included. Since testicular cancer occurs only in men, I suggest stating briefly in the first paragraph of results section that only men were included in the study and then reduce the use of “men” throughout this section.

6. Study limitations: Please include residual confounding by poorly measured factors (e.g., neighborhood income level and neighborhood education level, as opposed to individual level data) and confounding by unmeasured factors, such as smoking history, BMI, etc.

Reviewer #2: Thank you for the opportunity to review this manuscript. The purpose of the paper was to assess the impact of ACA-DCE on adults aged 19-25 and Medicaid expansion on outcomes for men with testicular cancer. The authors found rates of uninsurance decreased among men aged 19-25 relative to older men, and in Medicaid expansion states, rates of uninsurance also decreased. The authors provided valuable information that is significant to oncology regarding the relationship between expanded coverage and outcomes in testicular cancer in adolescent and young males. Overall, the manuscript is well organized, and the authors' data and analyses fully supported their hypotheses and provided statistical measures that captured the exposure to insurance expansion. I recommend the manuscript for publication with the following minor recommendations:

Introduction – The authors provided appropriate evidence to support their claim for their study. However, in the introduction (line 103), it is mentioned that this study is the first to be studied in adolescent and young adult males. Are there any prior studies in other areas of oncology and young adults to boost the importance of expanded coverage and delayed treatment in this population?

Results – The results are nicely presented in tables and sub-headings. Line 198, figure 1 is not visible, and the figure in line 250 is not listed here.

Discussion - Overall, the authors provided substantial evidence regarding the context of previous literature in comparison to their results. However, I would like to see more focus in the second paragraph regarding the impact of ACA-DCE and Medicaid expansions on increased insurance coverage on cancer outcomes. Was there only one study regarding increased insurance coverage on cancer outcomes? If so, was there any additional information on why higher insurance rates did not change outcomes for the study presented?

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Samuel O. Antwi

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Decision Letter 1

Ernest K Amankwah

25 Aug 2020

Insurance coverage, stage at diagnosis, and time to treatment following dependent coverage and Medicaid expansion for men with testicular cancer.

PONE-D-20-10591R1

Dear Dr. Kundu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Ernest K. Amankwah, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have mostly addressed the concerns cited in the initial review. There is one concern related to differential age range between the intervention groups. As explained by the authors, it is an inherent limitation in the administrative data used for the analyses and they have discussed this as part of the study limitations.

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Acceptance letter

Ernest K Amankwah

26 Aug 2020

PONE-D-20-10591R1

Insurance coverage, stage at diagnosis, and time to treatment following dependent coverage and Medicaid expansion for men with testicular cancer.

Dear Dr. Kundu:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Ernest K. Amankwah

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Patient characteristics for Affordable Care Act Dependent Care Expansion analysis.

    a Comparisons based on Pearson’s Chi-squared analyses for discrete covariates and Mann-Whitney U test for age.

    (DOCX)

    S2 Table. Raw data for difference-in-difference analyses for ACA-DCE.

    These data were used to generate Table 1 and Fig 1.

    (DOCX)

    S3 Table. Difference-in-difference analyses on outcomes for men with testicular cancer following ACA-DCE.

    Multivariable linear regression analyses were used to evaluate difference-in-differences for each outcome between intervention and controls, and pre- (2007–2009) and individual post-exposure years (2011–2016). Bolded p values are statistically significant (p < 0.05). Abbreviation: ACA-DCE, Affordable Care Act Dependent Care Expansion; CI, confidence interval.

    (DOCX)

    S4 Table. Patient characteristics for Medicaid expansion analysis.

    a Comparisons based on Pearson’s Chi-squared analyses for discrete covariates and Mann-Whitney U test for age.

    (DOCX)

    S5 Table. Raw data for difference-in-difference analyses for Medicaid expansion.

    These data were used to generate Table 1 and Fig 2.

    (DOCX)

    Attachment

    Submitted filename: Response to Reviewers.docx

    Data Availability Statement

    The authors do not have the right share the third-party data used in this study. The data underlying this study are available from the National Cancer Database upon request and application by investigators by emailing the NCDB at NCDB_PUF@facs.org. The data for this analysis was obtained from the “Testis(Testis) special set(0->90+)” Participant User File, application ID 2016.797.


    Articles from PLoS ONE are provided here courtesy of PLOS

    RESOURCES