Implications of Evolving Delivery System Reforms for Prostate Cancer Care

September 20, 2016

Healthcare integration was associated with small declines in treatment, but no change in overtreatment of prostate cancer. Integrated care delivery alone may be insufficient to curtail overtreatment.


Objectives: Prostate cancer treatment is a significant source of morbidity and healthcare spending. Evolving clinical data have supported expanding surveillance as a means to “right-size” treatment. Integrated delivery systems afford the possibility of hastening this objective.

Study Design: Retrospective cohort study of Medicare beneficiaries.

Methods: We used a 20% sample of national Medicare claims to assess the impact of healthcare integration on rates of treatment and potential overtreatment in men newly diagnosed with prostate cancer between 2007 and 2011. Rates were measured according to the extent of integration within a market (ie, none, low, intermediate, and high). Generalized estimating equations were used to assess the relationship between integration and utilization, adjusting for confounders.

Results: Rates of treatment declined across all markets (P <.01 for overall time trend), but the rate of decline was similar for the 4 market types (P = .27). In the most integrated markets, the rate decreased by 28.8%, or from 55.5 per 10,000 population in 2007 to 39.5 per 10,000 in 2011. After adjusting for confounders, men residing in the most integrated markets were 2.1% less likely to be treated with curative intent compared with those living in areas without integrated delivery systems (P = .04). However, rates of potential overtreatment were similar across all markets regardless of the level of integration (P = .21).

Conclusions: Healthcare integration was associated with small declines in prostate cancer treatment in newly diagnosed men, but not with potential overtreatment. Integrated care alone may be insufficient to curtail potential overtreatment of prostate cancer.

Am J Manag Care. 2016;22(9):569-575

Take-Away Points

  • Integration was associated with a small decrease in the treatment of prostate cancer.
  • Integration was not associated with treatment among men least likely to benefit (ie, those with a high risk of noncancer mortality).
  • Integration alone may be insufficient to promote optimal care for prostate cancer.
  • Financial risk, implied by evolving delivery system reforms, may help to promote better stewardship of preference-sensitive diseases, such as prostate cancer.

Prostate cancer is among the most common malignancies in men in the United States.1 Ongoing uncertainties about how best to treat the disease, coupled with the availability of multiple options, have led to wide variations in both the quantity and quality of care.2,3 Prostate cancer spending has increased by 11% annually over the last decade, outpacing rates for other common conditions (such as cardiovascular and pulmonary diseases) and resulting in $12 billion in yearly expenditures.4,5 Although the merits of screening are a subject of ongoing debate in the field, consensus is growing that some newly diagnosed men with prostate cancer stand to gain little from treatment.6-8

Improving the efficiency of the delivery system and eliminating wasteful spending have long been priorities for payers and policy makers, and many hope that accountable care organizations (ACOs) and related components of healthcare reform will do just that. By encouraging closer alignment between hospitals and caregivers, ACOs aim to focus on improving quality and cutting costs—both of which may affect prostate cancer care. To a large extent, ACOs are extensions of integrated delivery systems that, due to their emphasis on evidence-based medicine and minimizing unnecessary healthcare, are associated with providing higher quality.9-12 Thus, understanding the implications of integrated delivery systems for prostate cancer care will help us to anticipate the likely effect of evolving reforms of the Affordable Care Act.

For this reason, we performed a national study to determine the impact of healthcare integration on the management of prostate cancer. We hypothesized that the most integrated markets would be more selective in the use of curative treatment for prostate cancer, particularly among those unlikely to benefit from intervention.


Using a 20% sample of Medicare claims, we performed a retrospective cohort study of fee-for-service beneficiaries newly diagnosed with prostate cancer between January 1, 2007, and December 31, 2011. We limited our study to men continuously enrolled in Parts A and B for at least the 12-month periods prior to and after the prostate cancer diagnosis. To ensure that we had complete claims on all patients, we excluded patients in risk-bearing Medicare managed care plans. Patients were followed through December 31, 2012.

To identify incident prostate cancer cases in national Medicare claims, we developed an algorithm and validated it using Surveillance Epidemiology and End Results (SEER) cancer registry data. Briefly, we used a 5% sample of Medicare beneficiaries residing in a catchment area of a SEER registry in the years 2003 to 2005. We selected men with at least 2 “Evaluation and Management” visit codes in which the line diagnosis International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code was 185 for prostate cancer. We further required that all incident cases underwent prostate biopsy within 180 days of the first visit code associated with a prostate cancer diagnosis. Men with any claim in the preceding 12-month period that was associated with an ICD-9-CM diagnosis code of 185 (prostate cancer) or V10.46 (personal history of malignant neoplasm of prostate) were excluded. Finally, we validated this approach against the Patient Entitlement Denominator Summary File, which identifies all incident cases in SEER regions, and found our algorithm to have specificity and positive predictive values of 99.8% and 88.7%, respectively. We then implemented this algorithm in our 20% national sample of Medicare claims to identify incident cases, which compose our study population.

We used hospital referral regions (HRRs), as described by the Dartmouth Atlas,13 to reflect distinct healthcare markets. There are 306 HRRs in the United States, each of which represents a collection of zip codes in which Medicare patients receive the bulk of their healthcare. We determined each market’s level of integration by measuring the proportion of hospital discharges occurring from an integrated delivery system, which were identified from public reports based on data from IMS Health14 and have been used in a similar context.15 These data provide information on delivery system relationships, including affiliations between hospitals and physician practices. Each health system is rated for 33 attributes in 8 domains: overall integration, integrated technology, hospital utilization, financial stability, services, access, contract capabilities, and physicians. Domain-specific scores are summed to yield an overall score for the delivery system, with higher scores reflecting a greater degree of integration.

All HRRs were characterized by the proportion of hospital discharges occurring from a top-100 integrated delivery system.16 Of these, 127 HRRs had no discharges from a top integrated delivery system. The remaining 179 HRRs were sorted into 3 equal groups (ie, terciles) ranging from least integrated (a mean of 14% of discharges from an integrated delivery system) to most (a mean of 71% of discharges from an integrated delivery system). For the purpose of analysis, our market level of integration exposure was treated as a categorical variable with 4 levels: none, low, intermediate, and high.

Because our measure of integration relies on public reports of integrated delivery systems and does not necessarily distinguish between clinical and/or financial integration, it is possible that it may be insensitive to real differences in quality. For this reason, we assessed whether this measure was able to identify differences in publicly available measures17 of quality—including patients with diabetes undergoing glycated hemoglobin (A1C) testing, eye examinations, and blood lipid testing—between 2008 and 2011. Generally, we observed a monotonic relationship between the level of integration and adherence to each measure. In all instances, the most integrated markets performed better than those without integration. For instance, beneficiaries in highly integrated markets were 21% more likely to undergo A1C testing than were those residing in markets without a top-100 integrated delivery system. These data support that our exposure can detect measurable differences in quality in circumstances in which clinical integration is important.


Our primary outcome was the rate of curative treatment (ie, surgery, external beam radiation therapy, brachytherapy, and cryotherapy) within 12 months of diagnosis, measured at the HRR level. For this calculation, the numerator was determined by the annual count of newly diagnosed patients undergoing any of the aforementioned treatments in a given HRR; the denominator was determined by the eligible male Medicare population residing in an HRR in a given year.

We also measured potential overtreatment. Because of their emphasis on quality and cost containment, integrated delivery systems and, presumably, the markets in which they dominate may be more selective in the services they provide. In accordance with clinical guidelines, we would expect more-integrated markets to have lower rates of treatment among those patients with a high risk of noncancer mortality within 10 years of diagnosis. These patients typically have a low probability of dying from the disease, even absent treatment.18,19 We assessed treatment among this population of patients (ie, the quartile of men with the highest risk of dying from noncancer causes within 10 years) by implementing methods developed by Gross and colleagues.20 Using a 5% sample of Medicare beneficiaries without cancer, we built a robust patient-level model to predict mortality (C-index = 0.91). This enabled us to estimate the 10-year mortality risk of patients in our prostate cancer cohort absent their cancer diagnosis. Those patients in the top quartile had a 78% risk of non—prostate cancer mortality within 10 years of their diagnosis. Potential overtreatment was assessed at the patient level, and population-based rates were calculated as described above (ie, the numerator was the number of potentially overtreated newly diagnosed patients with prostate cancer, and the denominator was the number of eligible Medicare beneficiaries).



We first contrasted patient and regional characteristics according to the healthcare integration exposure (ie, none, low, intermediate, and high). Statistical inference was made using the 2 for categorical data and t tests for continuous data. We then fit a Poisson model with an offset term for the population denominator to assess a trend in population-based rates of curative treatment over time across the integration groups.

To test the independent effect of healthcare integration on rates of prostate cancer treatment, we fit a multivariable logistic regression model using patient-level treatment as the outcome. Our healthcare integration exposure was incorporated into the model at the HRR level. To account for the nested nature of the data (ie, patients within HRRs), generalized estimating equations were used. We adjusted the model for patient-level differences, including age, race, comorbidity, and socioeconomic class. Comorbidity was calculated with patient claims for the 12-month window prior to diagnosis using established methods.21 Socioeconomic class was estimated using a composite measure developed at the zip code level, as described by Diez-Roux and colleagues.22 We used the Area Resource File to derive several market-level variables to include in the model, including measures of social capital (% female head-of-household) and education (% with a high school degree or more), and 2 supply side variables (urologists and hospital beds per capita). We computed adjusted percentages for the use of treatment for each level of our market integration exposure by back-transforming the predicted use from the model. Using similar methods, a separate model was then fit to derive adjusted percentages of treatment among those least likely to benefit. Further, we assessed trends in rates by both curative (ie, surgery, external beam radiation therapy, brachytherapy, cryotherapy) and noncurative (ie, hormone therapy, no treatment) modalities in the population least likely to benefit from treatment.

All analyses were carried out using computerized software SAS version 9.4 (SAS Institute, Cary, North Carolina). All tests were 2-tailed and the probability of type 1 error was set at 0.05. The study protocol was judged to be exempt by the Institutional Review Board at the University of Michigan.


Patient and regional characteristics were contrasted according to the market level of integration (Table). Although statistically significant differences among market types were evident for some variables (eg, age), the magnitudes of the absolute differences were small, with one exception. Among markets with at least some discharges from integrated delivery systems, patients in markets with the lowest level of integration were more affluent than those in more integrated ones. For instance, 28.8% of patients in the least integrated markets resided in the highest quartile of socioeconomic class compared with 21.9% of patients in the most integrated ones (P <.01).

Between 2007 and 2011, population-based rates of curative treatment among men newly diagnosed with prostate cancer declined across all markets (Figure 1), regardless of the level of integration (P <.01 for overall time trend). For example, in the most integrated markets, rates of treatment decreased from 55.5 per 10,000 male beneficiaries in 2007 to 39.5 per 10,000 in 2011, a relative decrease of 28.8%. In markets without integrated delivery systems (ie, no discharges from these facilities), rates of treatment declined by 30.1%, or from 57.2 per 10,000 to 40.0 per 10,000 over the same period. The rate of decline in treatment over time was similar for the 4 market types (P = .27).

As illustrated in Figure 2, rates of potential overtreatment—treatment of those patients with the highest probability of death from noncancer causes—also decreased, albeit to a lesser extent (P <.01 for overall time trend). In the most integrated markets, curative treatment in this population decreased by 19%, from 10.0 per 10,000 to 8.1 per 10,000. Rates of decline were similar for the less integrated markets (P = .98). We next explored changes in treatment, by modality, among those least likely to benefit from treatment (Figure 3). Rates of radical prostatectomy in these patients were low but remained stable over time (P = .99). Conversely, rates of brachytherapy, cryotherapy, and external beam radiation therapy decreased during the course of the study (each P <.01 for overall time trend), but these declines did not vary with respect to the market level of integration (each P >.20). Similar trends were observed for both noncurative modalities.

Finally, we used multivariable modeling to account for the observed subtle differences between patients and healthcare markets (Figure 4). Among beneficiaries with newly diagnosed prostate cancer, we found that men residing in the most integrated markets were 2.1% less likely to be treated with curative intent compared with those living in areas without integrated delivery systems (P = .04). However, rates of potential overtreatment (ie, treatment of men with a high probability of death from noncancer causes) were similar across all markets regardless of the level of integration (P = .21).


Rates of treatment for prostate cancer among Medicare beneficiaries declined significantly between 2007 and 2011. Similarly, treatment among men with a high risk of noncancer mortality (ie, potential overtreatment) decreased over the same period, albeit to a lesser extent. The use of nonsurgical approaches decreased significantly in men with the highest probability of noncancer death within 10 years, while rates of surgery remained stable in this population. Rates of observation (ie, no treatment) and hormone therapy in this population declined similarly. This supports the theory that secular declines in screening and diagnosis, which are well established,23 underlie the observed trends, as opposed to more selective curative treatment by physicians. Although patients residing in markets with the highest level of integration were less likely to undergo curative treatment for their cancer, the difference in magnitude relative to less integrated markets was small and likely of limited clinical significance. Notwithstanding the advantages of highly integrated markets to deliver better evidence-driven healthcare, we found that the use of treatment among those least likely to benefit did not vary significantly according to the degree of integration in a market.

Although prostate cancer remains a common cause of cancer-related death in the United States, consensus is growing about the potential pitfalls of screening and the indolent nature of some cancers.6-8 Approaches to management of prostate cancer have evolved considerably over the last decade, and surveillance has increasingly been recognized as a strategy in some men to prevent overtreatment. Despite this recognition, there is little evidence that the use of treatment in men unlikely to benefit is decreasing; rather, population-based data suggest that it is increasing.24 In our study of national Medicare beneficiaries, we noted a significant decline in potential overtreatment across all markets regardless of their level of integration. However, these declines were relatively modest given the nature of the population (ie, having a very high risk of noncancer mortality within 10 years of diagnosis).

That healthcare integration was associated with small declines in treatment, but not with potential overtreatment, is surprising. Indeed, integrated delivery systems are more apt to follow evidence-based practice guidelines, adopt electronic health information systems, and implement strategies for performance improvement; ultimately, they are associated with providing higher-quality healthcare.9-12 Because of these qualities, we would expect healthcare markets dominated by these systems to be more selective in whom they treat for prostate cancer, particularly among those who are unlikely to benefit.

With recent health reforms toward more accountable care, collections of providers and, in some cases, hospitals, are evolving to become better stewards of population health. To some extent, ACOs are the result of the natural evolution of high-performing integrated delivery systems. However, in addition to sharing a focus on care coordination and other aspects of integrated care, ACOs imply a level of financial risk likely surpassing that which was assumed by integrated delivery systems in the era prior to health reform. Perhaps it is this risk, added to the pressure to realize savings at the beneficiary level, that will promote better stewardship of preference-sensitive conditions in which there are clear tradeoffs with treatment, such as prostate cancer.


One potential limitation of our findings is the absence of measures of disease severity (ie, cancer grade, stage, and prostate-specific antigen levels) in national Medicare claims. Although we classify potential overtreatment using the probability of noncancer death, our findings may underestimate the scope of treatment in the population, as patients with low-risk prostate cancer, regardless of life expectancy, are well-accepted candidates for surveillance.6 Further, the absence of disease severity measures has little implication for our comparison of treatment rates across healthcare markets, as the distribution of cancer grade and stage tends to be similar across geographic regions.25


Our findings have important implications for evolving reforms aimed at improving the efficiency of healthcare delivery. We observed a small, albeit significant, association between higher levels of integration and more constrained use of prostate cancer treatment. However, rates of potential overtreatment of men with prostate cancer were similar, regardless of the extent of market integration. Collectively, these findings suggest that integration alone may be insufficient for optimizing the management of conditions such as prostate cancer, in which considerable uncertainty exists about the tradeoffs between treatment and observation. Future research should explore how the added financial risk associated with ACOs is able to modulate the management of preference-sensitive diseases such as prostate cancer.

Author Affiliations: Dow Division of Health Services Research, Department of Urology (BKH, MJB, SRK, LH, TAS, DCM) and the Kidney Epidemiology Cost Center (VBS), University of Michigan, Ann Arbor, MI.

Source of Funding: This work was supported by a Research Scholar Grant RSGI-13-323-01-CPHPS to BKH from the American Cancer Society. VBS is supported by funding from the National Cancer Institute (R01 CA168691). DCM is supported by funding from the National Cancer Institute (R01 CA174768).

Author Disclosures: Dr Hollenbeck received a grant from the American Cancer Society. Dr Miller received a grant from the National Cancer Institute. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (BKH, DCM, VBS); acquisition of data (BKH); analysis and interpretation of data (MJB, BKH, LH, SRK, TAS, VBS); drafting of the manuscript (BKH, LH, DCM, TAS); critical revision of the manuscript for important intellectual content (MJB, BKH, LH, SRK, DCM, TAS, VBS); statistical analysis (MJB, SRK); obtaining funding (BKH, DCM, VBS); and supervision (SRK).

Address Correspondence to: Brent K. Hollenbeck, MD, MS, Dow Division of Health Services Research, Department of Urology, University of Michigan, 2800 Plymouth Rd, NCRC Bldg 16, Ann Arbor, MI 48109-2800. E-mail:


1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin. 2015;65(1):5-29. doi: 10.3322/caac.21254.

2. Cooperberg MR, Broering JM, Carroll PR. Time trends and local variation in primary treatment of localized prostate cancer. J Clin Oncol. 2010;28(7):1117-1123. doi: 10.1200/JCO.2009.26.0133.

3. Spencer BA, Miller DC, Litwin MS, et al. Variations in quality of care for men with early-stage prostate cancer. J Clin Oncol. 2008;26(22):3735-3742. doi: 10.1200/JCO.2007.13.2555.

4. Roehrig C, Miller G, Lake C, Bryant J. National health spending by medical condition, 1996-2005. Health Aff (Millwood). 2009;28(2):w358-w367. doi: 10.1377/hlthaff.28.2.w358.

5. Mariotto AB, Yabroff KR, Shao Y, Feuer EJ, Brown ML. Projections of the cost of cancer care in the United States: 2010-2020. J Natl Cancer Inst. 2011;103(2):117-128. doi: 10.1093/jnci/djq495.

6. Klotz L, Vesprini D, Sethukavalan P, et al. Long-term follow-up of a large active surveillance cohort of patients with prostate cancer. J Clin Oncol. 2015;33(3):272-277. doi: 10.1200/JCO.2014.55.1192.

7. Godtman RA, Holmberg E, Khatami A, Stranne J, Hugosson J. Outcome following active surveillance of men with screen-detected prostate cancer. results from the Göteborg randomised population-based prostate cancer screening trial. Eur Urol. 2013;63(1):101-107. doi: 10.1016/j.eururo.2012.08.066.

8. Bul M, Zhu X, Valdagni R, et al. Active surveillance for low-risk prostate cancer worldwide: the PRIAS study. Eur Urol. 2013;63(4):597-603. doi: 10.1016/j.eururo.2012.11.005.

9. Sterns JB. Quality, efficiency, and organizational structure. J Health Care Finance. 2007;34(1):100-107.

10. Casalino LP. Which type of medical group provides higher-quality care? Ann Intern Med. 2006;145(11):860-861.

11. Tollen L. Physician organization in relation to quality and efficiency of care: a synthesis of recent literature. The Commonwealth Fund website. Published April 1, 2008. Accessed December 18, 2015.

12. Solberg LI, Asche SE, Shortell SM, et al. Is integration in large medical groups associated with quality? Am J Manag Care. 2009;15(6):e34-e41.

13. Wennberg JE. The Dartmouth Atlas of Health Care in the United States: A Report on the Medicare Program. Chicago, IL: AHA Press, 1999.

14. 2012 IMS top 100 IHNs. Mercy website. Published 2012. Accessed December 18, 2015.

15. Miller DC, Ye Z, Gust C, Birkmeyer JD. Anticipating the effects of accountable care organizations for inpatient surgery. JAMA Surg. 2013;148(6):549-554. doi: 10.1001/jamasurg.2013.1699.

16. Rodak S. 100 integrated health systems to know. Becker’s Hospital Review website. Published May 15, 2013. Accessed December 18, 2015.

17. The Dartmouth Atlas of Health Care [download tool]. The Dartmouth Atlas of Health Care website. Accessed December 18. 2015.

18. Mohler J, Bahnson RR, Boston B, et al. NCCN clinical practice guidelines in oncology: prostate cancer. J Natl Compr Canc Netw. 2010;8(2):162-200.

19. Heidenreich A, Bellmunt J, Bolla M, et al; European Association of Urology. EAU guidelines on prostate cancer. part 1: screening, diagnosis, and treatment of clinically localised disease. Eur Urol. 2011;59(1):61-71. doi: 10.1016/j.eururo.2010.10.039.

20. Gross CP, McAvay GJ, Krumholz HM, Paltiel AD, Bhasin D, Tinetti ME. The effect of age and chronic illness on life expectancy after a diagnosis of colorectal cancer: implications for screening. Ann Intern Med. 2006;145(9):646-653.

21. Klabunde CN, Potosky AL, Legler JM, Warren JL. Development of a comorbidity index using physician claims data. J Clin Epidemiol. 2000;53(12):1258-1267.

22. Diez Roux AV, Merkin SS, Arnett D, et al. Neighborhood of residence and incidence of coronary heart disease. N Engl J Med. 2001;345(2):99-106.

23. Jemal A, Fedewa SA, Ma J, et al. Prostate cancer incidence and PSA testing patterns in relation to USPSTF screening recommendations. JAMA. 2015;314(19):2054-2061. doi: 10.1001/jama.2015.14905.

24. Jacobs BL, Zhang Y, Schroeck FR, et al. Use of advanced treatment technologies among men at low risk of dying from prostate cancer. JAMA. 2013;309(24):2587-2595. doi: 10.1001/jama.2013.6882.

25. Wang SY, Wang R, Yu JB, et al. Understanding regional variation in Medicare expenditures for initial episodes of prostate cancer care. Med Care. 2014;52(8):680-687. doi: 10.1097/MLR.0000000000000158.