Advertisement

Impact of the affordable care act dependent coverage provision on young adult cancer patient insurance coverage by sociodemographic and economic characteristics

  • Justin M. Barnes
  • Derek S. Brown
  • Jenine K. Harris
  • Allison A. King
  • Kimberly J. JohnsonEmail author
Original Paper

Abstract

Purpose

To evaluate the impact of the Affordable Care Act Dependent Care Provision by sociodemographic and economic characteristics in young adult cancer patients.

Methods

The National Cancer Database (NCDB) and the Surveillance, Epidemiology, and End Results (SEER) 18 database were queried for young adult cancer cases diagnosed during 2007–2014. Using a difference-in-differences approach, we examined insurance coverage in different subgroups of policy-eligible 19–25 year-olds versus policy-ineligible 27–29 year-olds from the pre- (2007–2009) to post- (2011–2014) Dependent Care Provision period.

Results

Across subgroups and study populations, insurance coverage increased significantly following the Provision enactment in the policy-eligible versus policy-ineligible group across most subgroups (range in NCDB: 1.83 to 6.38% for low and mid-low education areas, respectively; range in SEER: 1.43 to 6.18 for Non-Hispanic Others and Hispanics, respectively). Heterogenous impacts were observed by sex with a larger impact in males (NCDB: 5.14%, 95% CI 3.59–6.69; SEER: 4.46, 2.12–6.8) than females (NCDB: 2.51%, 95% CI 1.39–3.62; SEER: 2.50, 0.82–4.18). We observed no other statistical evidence for Dependent Care Provision subgroup heterogeneity except for a smaller impact in individuals from low education areas in NCDB.

Conclusions

Our results indicate a positive Dependent Care Provision impact on insurance coverage in young adults with cancer across subgroups, with evidence for a smaller impact in females relative to males and in low relative to high education areas.

Keywords

Affordable care act Dependent coverage Cancer Young adult 

Notes

Acknowledgments

Per the NCDB data use agreement, we acknowledge that 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.

Funding

This work was supported by the Center for Health Economics and Policy Pilot Funding Program at the Institute for Public Health at Washington University (PI: Kimberly Johnson, MPH, PhD) and the Siteman Cancer Institute Leah Menshouse Summer Fellowship (Justin Barnes, MS).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10552_2019_1246_MOESM1_ESM.pdf (43 kb)
Supplementary file1 (PDF 44 kb)
10552_2019_1246_MOESM2_ESM.pdf (46 kb)
Supplementary file2 (PDF 47 kb)
10552_2019_1246_MOESM3_ESM.pdf (44 kb)
Supplementary file3 (PDF 44 kb)
10552_2019_1246_MOESM4_ESM.pdf (47 kb)
Supplementary file4 (PDF 48 kb)
10552_2019_1246_MOESM5_ESM.xlsx (20 kb)
Supplementary file5 (XLSX 20 kb)
10552_2019_1246_MOESM6_ESM.xlsx (16 kb)
Supplementary file6 (XLSX 15 kb)
10552_2019_1246_MOESM7_ESM.xlsx (16 kb)
Supplementary file7 (XLSX 16 kb)
10552_2019_1246_MOESM8_ESM.xlsx (16 kb)
Supplementary file8 (XLSX 16 kb)
10552_2019_1246_MOESM9_ESM.xlsx (11 kb)
Supplementary file9 (XLSX 12 kb)

References

  1. 1.
    United States Cancer Statistics: 1999–2014 Incidence, WONDER Online Database. United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute; 2016. https://wonder.cdc.gov/cancer-v2014.HTML. Accessed 24 Aug 2018
  2. 2.
    United States Cancer Statistics: 1999–2014 Mortality, WONDER Online Database. United States Department of Health and Human Services, Centers for Disease Control and Prevention; 2016. https://wonder.cdc.gov/controller/datarequest/D137. Accessed 24 Aug 2018
  3. 3.
    Rosenberg AR, Kroon L, Chen L et al (2015) Insurance status and risk of cancer mortality among adolescents and young adults. Cancer 121:1279–1286.  https://doi.org/10.1002/cncr.29187 CrossRefPubMedGoogle Scholar
  4. 4.
    Moke DJ, Tsai K, Hamilton AS et al (2019) Emerging cancer survival trends, disparities, and priorities in adolescents and young adults: a california cancer registry-based study. JNCI cancer Spectr 3:pkz31.  https://doi.org/10.1093/jncics/pkz031 CrossRefGoogle Scholar
  5. 5.
    Bownes LV, Stafman LL, Maizlin II et al (2018) Socioeconomic disparities affect survival in malignant ovarian germ cell tumors in AYA population. J Surg Res 222:180–186.  https://doi.org/10.1016/j.jss.2017.09.013 (e3)CrossRefPubMedGoogle Scholar
  6. 6.
    Isenalumhe LL, Fridgen O, Beaupin LK et al (2016) Disparities in adolescents and young adults with cancer. Cancer Control 23:424–433CrossRefGoogle Scholar
  7. 7.
    Keegan THM, Ries LAG, Barr RD et al (2016) Comparison of cancer survival trends in the United States of adolescents and young adults with those in children and older adults. Cancer 122:1009–1016.  https://doi.org/10.1002/cncr.29869 CrossRefPubMedGoogle Scholar
  8. 8.
    Barr RD, Ferrari A, Ries L et al (2016) Cancer in adolescents and young adults. JAMA Pediatr 170:495.  https://doi.org/10.1001/jamapediatrics.2015.4689 CrossRefPubMedGoogle Scholar
  9. 9.
    Bleyer WA (2010) Potential favorable impact of the Affordable Care Act of 2010 on cancer in young adults in the United States. Cancer J 16:563–571.  https://doi.org/10.1097/PPO.0b013e3181ff6509 CrossRefPubMedGoogle Scholar
  10. 10.
    McMorrow S, Kenney GM, Long SK, Anderson N (2015) Uninsurance among young adults continues To decline, particularly in medicaid expansion states. Health Aff 34:616–620.  https://doi.org/10.1377/hlthaff.2015.0044 CrossRefGoogle Scholar
  11. 11.
    Akosa Antwi Y, Moriya AS, Simon KI (2015) Access to health insurance and the use of inpatient medical care: Evidence from the Affordable Care Act young adult mandate. J Health Econ 39:171–187.  https://doi.org/10.1016/J.JHEALECO.2014.11.007 CrossRefPubMedGoogle Scholar
  12. 12.
    Sommers BD, Buchmueller T, Decker SL et al (2013) The affordable care act has led to significant gains in health insurance and access to care for young adults. Health Aff 32:165–174.  https://doi.org/10.1377/hlthaff.2012.0552 CrossRefGoogle Scholar
  13. 13.
    Busch SH, Golberstein E, Meara E (2014) ACA dependent coverage provision reduced high out-of-pocket health care spending for young adults. Health Aff 33:1361–1366.  https://doi.org/10.1377/hlthaff.2014.0155 CrossRefGoogle Scholar
  14. 14.
    Barnes JM, Harris JK, Brown DS et al (2019) Impacts of the affordable care act dependent coverage provision on young adults with cancer. Am J Prev Med 56:716–726.  https://doi.org/10.1016/j.amepre.2018.12.011 CrossRefPubMedGoogle Scholar
  15. 15.
    Parsons HM, Schmidt S, Tenner LL et al (2016) Early impact of the patient protection and affordable care act on insurance among young adults with cancer: analysis of the dependent insurance provision. Cancer 122:1766–1773.  https://doi.org/10.1002/cncr.29982 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Alvarez EM, Keegan TH, Johnston EE et al (2018) The patient protection and affordable care act dependent coverage expansion: disparities in impact among young adult oncology patients. Cancer 124:110–117.  https://doi.org/10.1002/cncr.30978 CrossRefPubMedGoogle Scholar
  17. 17.
    American College of Surgeons (2017) National Cancer Data base, 2004–2015 Participant Use Data Files.Google Scholar
  18. 18.
    Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov) SEER*Stat Database: Incidence—SEER 9 Regs Research Data, Nov 2017 Sub (1975–2015) %3cKatrina/Rita Population Adjustment%3e - Linked To County Attributes—Total U.S., 1969–2015
  19. 19.
    Slusky DJ (2017) Significant placebo results in difference-in-differences analysis: the case of the ACA’s parental mandate. East Econ J 43:580–603.  https://doi.org/10.1057/eej.2015.49 CrossRefGoogle Scholar
  20. 20.
    Mohanty S, Bilimoria KY (2014) Comparing national cancer registries: The National Cancer Data Base (NCDB) and the surveillance, epidemiology, and end results (SEER) program. J Surg Oncol 109:629–630.  https://doi.org/10.1002/jso.23568 CrossRefPubMedGoogle Scholar
  21. 21.
    Insurance Recode Variable—SEER Documentation. https://seer.cancer.gov/seerstat/variables/seer/insurance-recode/. Accessed 20 Dec 2018
  22. 22.
    Sherman RL, Williamson L, Andrews P, Kahn A (2016) Primary Payer at DX: issues with collection and assessment of data quality. J Registry Manag 43:99–100PubMedGoogle Scholar
  23. 23.
    Stuber J, Bradley E (2005) Barriers to Medicaid enrollment: who is at risk? Am J Public Health 95:292–298.  https://doi.org/10.2105/AJPH.2002.006254 CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Bradley CJ, Gardiner J, Given CW, Roberts C (2005) Cancer, Medicaid enrollment, and survival disparities. Cancer 103:1712–1718.  https://doi.org/10.1002/cncr.20954 CrossRefPubMedGoogle Scholar
  25. 25.
    Rural-Urban Continuum Code—SEER Datasets. https://seer.cancer.gov/seerstat/variables/countyattribs/ruralurban.html#definitions.2003. Accessed 29 Oct 2018
  26. 26.
    Han X, Zang Xiong K, Kramer MR, Jemal A (2016) The affordable care act and cancer stage at diagnosis among young adults. J Natl Cancer Inst 108:djw58.  https://doi.org/10.1093/jnci/djw058 CrossRefGoogle Scholar
  27. 27.
    Dimick JB, Ryan AM (2014) Methods for evaluating changes in health care policy: the difference-in-differences approach. JAMA 312:2401–2402.  https://doi.org/10.1001/jama.2014 CrossRefPubMedGoogle Scholar
  28. 28.
    White IR, Royston P (2009) Imputing missing covariate values for the Cox model. Stat Med 28:1982–1998.  https://doi.org/10.1002/sim.3618 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    White IR, Royston P, Wood AM (2011) Multiple imputation using chained equations: issues and guidance for practice. Stat Med 30:377–399.  https://doi.org/10.1002/sim.4067 CrossRefPubMedGoogle Scholar
  30. 30.
    Sterne JAC, White IR, Carlin JB et al (2009) Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ 338:b2393.  https://doi.org/10.1136/BMJ.B2393 CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Rubin DB (1987) Multiple imputation for nonresponse in surveys. Wiley, HobokenGoogle Scholar
  32. 32.
    Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov) SEER*Stat Database: Incidence - SEER 18 Regs Research Data, Nov 2018 Sub (1973–2016) %3cKatrina/Rita Population Adjustment%3e - Linked To County Attributes—Total U.S., 1969–201
  33. 33.
    Cantor JC, Monheit AC, DeLia D, Lloyd K (2012) Early impact of the affordable care act on health insurance coverage of young adults. Health Serv Res 47:1773–1790.  https://doi.org/10.1111/j.1475-6773.2012.01458.x CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Shane DM, Ayyagari P (2014) Will health care reform reduce disparities in insurance coverage? Med Care 52:528–534.  https://doi.org/10.1097/MLR.0000000000000134 CrossRefPubMedGoogle Scholar
  35. 35.
    Barbaresco S, Courtemanche CJ, Qi Y (2015) Impacts of the affordable care act dependent coverage provision on health-related outcomes of young adults. J Health Econ 40:54–68.  https://doi.org/10.1016/j.jhealeco.2014.12.004 CrossRefPubMedGoogle Scholar
  36. 36.
    O’Hara B, Brault MW (2013) The disparate impact of the ACA-dependent expansion across population subgroups. Health Serv Res.  https://doi.org/10.1111/1475-6773.12067 CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Scott JW, Salim A, Sommers BD et al (2015) Racial and regional disparities in the effect of the affordable care act’s dependent coverage provision on young adult trauma patients. J Am Coll Surg 221:495–501.  https://doi.org/10.1016/j.jamcollsurg.2015.03.032 (e1)CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Breslau J, Han B, Stein BD et al (2018) Did the affordable care act’s dependent coverage expansion affect race/ethnic disparities in health insurance coverage? Health Serv Res 53:1286–1298.  https://doi.org/10.1111/1475-6773.12728 CrossRefPubMedGoogle Scholar
  39. 39.
    Ryan AM, Burgess JF, Dimick JB (2015) Why we should not be indifferent to specification choices for difference-in-differences. Health Serv Res 50:1211–1235.  https://doi.org/10.1111/1475-6773.12270 CrossRefPubMedGoogle Scholar
  40. 40.
    Longford NT (1993) Random coefficient models. Clarendon Press, LondonGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Justin M. Barnes
    • 1
    • 2
  • Derek S. Brown
    • 2
    • 3
  • Jenine K. Harris
    • 3
  • Allison A. King
    • 2
    • 4
    • 5
    • 6
  • Kimberly J. Johnson
    • 2
    • 3
    Email author
  1. 1.Saint Louis University School of MedicineSt. LouisUSA
  2. 2.Siteman Cancer Center, Washington University in St. LouisSt. LouisUSA
  3. 3.Brown SchoolWashington University in St. LouisSt. LouisUSA
  4. 4.Program in Occupational TherapyWashington University School of MedicineSt. LouisUSA
  5. 5.Division of Public Health Sciences, Department of SurgeryWashington University School of MedicineSt. LouisUSA
  6. 6.Department of Pediatrics, Division of Hematology/OncologySt. Louis Children’s Hospital, Washington University School of MedicineSt. LouisUSA

Personalised recommendations