Insurance Network Status and 1-Year Health Care Utilization and Outpatient Costs Among Commercially Insured Patients With Newly Diagnosed and Non-Definitively Treated Prostate Cancer, 2010-2021.

Insurance Network Status and 1‐Year Health Care Utilization and Outpatient Costs Among Commercially Insured Patients With Newly Diagnosed and Non‐Definitively Treated Prostate Cancer, 2010–2021 - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Cancer Med . 2026 May 20;15(5):e71983. doi: 10.1002/cam4.71983 Insurance Network Status and 1‐Year Health Care Utilization and Outpatient Costs Among Commercially Insured Patients With Newly Diagnosed and Non‐Definitively Treated Prostate Cancer, 2010–2021 Aurora J Grutman 1, 2, ✉ , Mark N Alshak 2 , Michelle I Higgins 2 , Christian P Pavlovich 2 , Derek K Ng 1 Author information Article notes Copyright and License information 1 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA 2 James Buchanan Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, Maryland, USA * Correspondence: Aurora J. Grutman ( agrutma1@jh.edu ) ✉ Corresponding author. Revised 2026 May 2; Received 2025 Oct 2; Accepted 2026 May 16; Collection date 2026 May. © 2026 The Author(s). Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. PMC Copyright notice PMCID: PMC13240012  PMID: 42163058 ABSTRACT Introduction Cancer care in the U.S. exceeds $200B, with patients paying over $16B out‐of‐pocket. We examined whether insurance network status (in‐network [IN] vs. out‐of‐network [OON]) at prostate cancer diagnosis was associated with subsequent OON care use and patient spending within 1 year of diagnosis. Methods Using the MarketScan database (2010–2021), we identified patients aged 40–64 with a new outpatient prostate cancer diagnosis. Patients who received definitive local treatment (radical prostatectomy or radiotherapy) were excluded. The exposure was network status at diagnosis. Outcomes were the proportion of OON claims and patient spending for prostate cancer‐related care and/or non‐definitive treatments during 1 year of follow‐up. Results were stratified by insurance type. Results Among 67,271 patients, 5% were diagnosed OON. Compared with IN diagnosis, OON diagnosis was associated with greater OON follow‐up claims (aIRR 29.0, 95% CI 25.0, 33.5), lower odds of prostate cancer‐related spending (aOR 0.55, 95% CI 0.49, 0.61), and lower outpatient costs (% difference = −8.6, 95% CI −13.4, −3.6). In stratified analyses, patients with HMO (aOR 0.54, 95% CI 0.44, 0.67) and PPO (aOR 0.47, 95% CI 0.41, 0.54) plans had lower odds of any spending. Patients with HMO (% difference = −22.3, 95% CI −30.9, −12.5) and CDHP (% difference = −26.5, 95% CI −43.6, −4.3) plans had lower costs. Conclusions OON diagnosis was associated with continued OON care. Some patients diagnosed OON had lower liability, likely reflecting differences in plan design and adjudicated claims. Claims‐based estimates may underestimate the financial impact of OON non‐definitive prostate cancer care. Keywords: health care costs, insurance, prostate cancer 1. Introduction The United States spends more on healthcare than any other country [ 1 ]. Healthcare spending in the U.S. rose to $4.9T in 2023 from $4.5T in 2022. Each year, over $200B is allocated toward cancer care [ 2 , 3 ]. Although costs are shared among insurance companies, employers, and government programs, cancer patients pay approximately $16B out‐of‐pocket for healthcare each year [ 4 ]. The negative impacts of cancer care extend beyond the physical. Financial toxicity describes out‐of‐pocket expenses cancer patients face [ 5 ]. An estimated 12%–62% of cancer patients experience medical debt and nearly 50% feel financially stressed by cancer care [ 6 ]. Financial burdens may be particularly high for prostate cancer patients who undergo extensive testing at diagnosis and attend routine outpatient visits during the continued phase of care [ 7 ]. Prostate cancer is most commonly diagnosed in men over 65 years old, but it is increasingly common among younger individuals [ 8 , 9 , 10 ]. Younger patients may experience greater financial hardship due to varied healthcare coverage under private insurance plans and less long‐term savings [ 11 ]. In many cases, a prostate cancer diagnosis does not necessitate radical (definitive) treatment. In fact, guidelines support active surveillance for men with low to favorable intermediate risk prostate cancer [ 12 ]. While prior research has evaluated costs associated with treatment type and disease severity, less is known about how insurance coverage at the time of diagnosis may influence follow‐up care and care costs, especially in patients who defer definitive therapy and elect surveillance or alternative therapies (e.g., focal ablation) [ 13 , 14 ]. Diagnosis represents a pivotal moment in the prostate cancer continuum, often initiating referral cascades and shaping continuity within or outside insurer networks [ 15 ]. Network status at diagnosis may therefore influence subsequent out‐of‐network care use and patient financial exposure. However, this association has not been well characterized, especially in patients whose disease did not warrant expeditious radical therapy with the standard of care definitive options of surgery or radiation therapy. We therefore investigated whether diagnostic network status (in‐ vs. out‐of‐network) at prostate cancer diagnosis among those not definitively treated was associated with out‐of‐network care use and patient out‐of‐pocket outpatient costs in the year following diagnosis. We hypothesized patients diagnosed out‐of‐network would have greater out‐of‐network follow‐up care compared to patients diagnosed in‐network and incur greater overall prostate‐cancer related outpatient costs as a result. 2. Methods 2.1. Data Source This retrospective cohort study used data from the IBM MarketScan Research Databases which contain insurance claims from 20 billion service encounters, approximately 350 payers, and nearly 230 million patients in the United States [ 16 ]. We used MarketScan's Commercial Claims and Encounters (CCAE) database, which includes information from patients under 65 who are employed, early retirees, dependents on employer‐sponsored insurance plans, or covered under the Consolidated Omnibus Budget Reconciliation Act [ 16 ]. Inpatient and outpatient service claims, pharmaceutical claims, enrollment information, geographic region, insurance plan type, and detailed payment fields (insurer payment, adjudicated patient cost‐sharing) are available. Data analyzed in this study spanned 2010 to 2021. This study was approved by our Institutional Review Board in October 2024. 2.2. Study Population We identified patients aged 18–64 who received a prostate cancer diagnosis (ICD‐9 185, 233.4, V10.46; ICD‐10 C61, D07.5, Z85.46) between January 2010 and December 2021. Current American Urological Association guidelines recommend prostate cancer screening begin at age 40 for select patients, so we restricted the cohort to patients aged 40–64 [ 17 ]. Patients were required to have at least one outpatient prostate cancer claim, one follow‐up visit, and continuous enrollment in a single insurance type for 6 months prior to and 1 year following the first outpatient diagnosis. The index diagnosis date was defined as the first outpatient claim containing a prostate cancer diagnosis code. Because the index date reflected the first observed prostate cancer diagnosis and continuous enrollment during the 6‐month baseline period was required, no prostate cancer diagnosis codes were observed in the baseline window. However, diagnoses prior to enrollment are not captured by MarketScan. We identified treatment types within a year of diagnosis using inpatient and outpatient claims. Patients who received radiotherapy or radical prostatectomy were excluded to reduce heterogeneity in cost and care patterns driven by treatment type. We instead focused on outpatient prostate cancer care associated with active surveillance, focal therapies, and other non‐definitive treatments. However, because MarketScan claims lack stage and grade information, disease severity could not be directly assessed. The following insurance types were included: healthcare maintenance organization (HMO), preferred provider organization (PPO), exclusive provider organization (EPO), consumer‐driven health plans (CDHPs), and high‐deductible health plans (HDHPs) due to varied cost‐sharing mechanisms [ 18 ]. Patients were excluded if they had missing/negative payment data, unknown diagnostic network status, mixed network payments at diagnosis, or missing demographic variables. Codes used to identify treatment types were compiled and validated from prior studies (Table S1 ) [ 19 , 20 , 21 , 22 ]. Inclusion criteria are presented in the (Figure S1 ). 2.3. Exposure The exposure was whether a patient received an in‐ or out‐of‐network prostate cancer diagnosis. Prostate cancer diagnosis was defined as the first instance of a prostate cancer claim. Patients with only out‐of‐network claims at the diagnostic visit were classified as out‐of‐network. Patients with only in‐network claims at the diagnostic visit were classified as in‐network. Patients with both in‐network and out‐of‐network claims on the index diagnosis date were excluded to avoid exposure misclassification. 2.4. Outcomes The primary outcomes of interest were the proportion of out‐of‐network prostate cancer‐related outpatient claims and patient out‐of‐pocket spending for prostate cancer‐related outpatient claims in the year after diagnosis. Outpatient prostate cancer claims, referred to as “prostate cancer‐related care,” were defined as claims containing a prostate cancer diagnosis code in any diagnosis position. Follow‐up claims were those occurring in the year after the index diagnosis date, excluding claims from the index diagnostic visit. Out‐of‐network prostate cancer care was modeled as a yearly rate defined as the number of out‐of‐network claims over the total number of claims in the year after diagnosis, excluding claims from the initial diagnostic visit. Cost analyses were restricted to outpatient medical claims and did not include pharmacy claims. For cost analyses, the net patient cost per claim was calculated as the sum of coinsurance, copayment, and deductible payments. Costs were inflated to 2021 US dollars using the U.S. Bureau of Labor Statistics Consumer Price Index and log‐transformed to reduce skewness [ 23 ]. 2.5. Covariates We constructed a directed acyclic graph (Figure S2 ) and selected age at diagnosis and geographic region as covariates. Results were stratified by insurance type. Age was modeled as a continuous variable. Geographic region (South, Northeast, North Central, West, as defined by MarketScan) was handled categorically. Comorbidities were conceptualized as influencing follow‐up care utilization but not diagnostic network status, so they were not included as covariates in the primary models. 2.6. Statistical Analysis Differences in baseline characteristics among patients diagnosed out‐of‐network versus in‐network were compared using the Wilcoxon rank‐sum test for continuous variables and Pearson's chi‐squared test for categorical variables. To assess out‐of‐network outpatient care in the year following diagnosis, a multivariable negative binomial regression was constructed. We reported the incidence ratios (IRRs) to compare out‐of‐network care use in patients diagnosed out‐of‐network versus in‐network. Because 8%–15% of patients reported no OOP spending and patient OOP spending was right skewed among those with positive values, we used a two‐part modeling approach. First, logistic regression was used to determine the odds of any spending. Second, linear regression on log‐transformed spending (costs) was performed for patients with non‐zero costs to estimate the relative difference in costs associated with the exposure. For cost analyses, the number of non‐diagnostic claims, defined as prostate cancer‐related outpatient claims in the year follow‐up period after diagnosis, excluding index date claims, was included as a covariate to account for variation in follow‐up healthcare utilization. Statistical significance was defined as p ≤ 0.05. Analyses were conducted in STATA 18.0/BE (StataCorp, College Station, TX). 3. Results 3.1. Cohort Characteristics A total of 67,271 patients met the inclusion criteria. There were 64,131 (95%) patients diagnosed in‐network and 3140 (5%) diagnosed out‐of‐network. A greater proportion of out‐of‐network patients had an HMO insurance plan, but fewer had a CDHP or HDHP plan. Most out‐of‐network patients resided in the West while most in‐network patients lived in the South. In‐network patients paid more than out‐of‐network patients at the diagnostic visit ($37.92 vs. $33.95). Among patients with a non‐zero cost at diagnosis, in‐network patients paid less than out‐of‐network patients ($60.15 vs. $114.34). Baseline characteristics and longitudinal data are displayed in Table 1 . TABLE 1. Baseline characteristics and description of longitudinal data for cohort of males aged 40–64 who were diagnosed with prostate cancer between 2010 and 2021, as determined from a large U.S. insurance claims database. Continuous variables are displayed as median [Q1, Q3] and the number of patients by category is listed as count (%). Characteristic Total In‐network diagnosis Out‐of‐network diagnosis p No. of males n = 67,271 n = 64,131 (95%) n = 3140 (5%) Age at diagnosis (years) 59 [56, 62] 59 [56, 62] 59 [56, 62] 0.06 Year of diagnosis 2014 [2012, 2018] 2014 [2012, 2018] 2012 [2011, 2014] < 0.001 Insurance type < 0.001 PPO 47,230 (70%) 45,021 (70%) 2209 (70%) EPO 1423 (2%) 1321 (2%) 102 (3%) HMO 8548 (13%) 8045 (13%) 503 (16%) CDHP 5302 (8%) 5174 (8%) 128 (4%) HDHP 4768 (7%) 4570 (7%) 198 (6%) Region a < 0.001 South 28,849 (43%) 28,121 (44%) 728 (23%) Northeast 12,362 (18%) 11,618 (18%) 744 (24%) North Central 13,998 (21%) 13,490 (21%) 508 (16%) West 12,062 (18%) 10,902 (17%) 1160 (37%) No. claims at diagnosis 2 [1, 4] 2 [1, 4] 2 [1, 3] < 0.001 Cost to patient at diagnosis $37.83 [$2.16, $143.10] $37.92 [$3.13, $140.67] $33.95 [$0, $217.03] 0.03 No. patients with no cost at diagnosis 16,363 (24%) 15,315 (24%) 1048 (33%) Cost at diagnosis for patients with costs > 0 b $61.09 [$28.21, $219.08] $60.15 [$28.07, $211.96] $114.34 [$34.00, $414.41] < 0.001 No. claims after diagnosis 12 [6, 41] 13 [6, 41] 11 [5, 35] < 0.001 No. visits after diagnosis 4 [2, 10] 4 [2, 10] 3 [1, 9] < 0.001 Open in a new tab Abbreviations: CDHP, Consumer‐Driven Health Plan; EPO, Exclusive Provider Organization; HDHP, High‐Deductible Health Plan; HMO, Healthcare Maintenance Organization; PPO, Preferred Provider Organization. a South includes Washington, DC, Delaware, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, West Virginia, Alabama, Kentucky, Mississippi, Tennessee, Arkansas, Louisiana, Oklahoma, and Texas. Northeast includes Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont, New Jersey, New York, Pennsylvania. North Central includes Illinois, Indiana, Michigan, Ohio, Wisconsin, Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota. West includes Arizona, Colorado, Idaho, Montana, Nevado, New Mexico, Utah, Wyoming, Alaska, California, Hawaii, Oregon, Washington, and Puerto Rico. b Given high proportion of patients diagnosed out‐of‐network with no cost at diagnosis, likely reflective of denied or unsubmitted claims, costs were also calculated for individuals with non‐zero payments at diagnosis. 3.2. Yearly Out‐Of‐Network Healthcare Use In‐network patients had more claims over more visits than out‐of‐network patients in the year following prostate cancer diagnosis. The median [IQR] number of non‐diagnostic claims was 13 [6, 41] for in‐network patients and 11 [5, 35] for out‐of‐network patients. In the overall model, out‐of‐network diagnosis was associated with significantly greater out‐of‐network claims in the year following diagnosis (aIRR 29.0, 95% CI 25.0, 33.5; p < 0.001). By insurance type, patients with HMO (aIRR 67.8, 95% CI 47.6, 96.6; p < 0.001) and EPO (aIRR 153.6, 95% CI 88.2, 267.8; p < 0.001) plans had the greatest proportion of out‐of‐network claims (Table 2 ). TABLE 2. Adjusted incidence rates (IR) per 100 claims and incidence rate ratios (IRR) for patients diagnosed in‐network (IN) versus out‐of‐network (OON), overall and by insurance type. Overall and by insurance type IN OON aIRR (OON vs. IN) 95% CI p n IR (%) n IR (%) Overall 64,131 1.2 3140 33.6 29.0 25.0, 33.5 < 0.001 PPO 45,021 1.1 2209 27.4 25.9 21.8, 30.7 < 0.001 EPO 1321 0.4 102 60.8 153.6 88.2, 267.8 < 0.001 HMO 8045 1.4 503 95.7 67.8 47.6, 96.6 < 0.001 CDHP 5174 1.5 128 40.5 26.9 12.9, 56.1 < 0.001 HDHP 4570 1.3 198 35.3 28.2 16.2, 49.2 < 0.001 Open in a new tab Note: The overall model was adjusted for insurance type, age at diagnosis, and geographic region. Models stratified by insurance type were adjusted for age at diagnosis and geographic region. Adjusted IRRs were calculated for patients from the South who were diagnosed at age 59. Abbreviations: CDHP, Consumer‐Driven Health Plan; EPO, Exclusive Provider Organization; HDHP, High‐Deductible Health Plan; HMO, Healthcare Maintenance Organization; PPO, Preferred Provider Organization. 3.3. Yearly Outpatient Prostate Cancer Care Costs A greater proportion of patients diagnosed in‐network incurred any fees after the diagnostic visit compared to out‐of‐network patients (92% vs. 85%, respectively). In the overall model, out‐of‐network diagnosis was associated with lower odds of any outpatient prostate cancer‐related cost (aOR 0.55, 95% CI 0.49, 0.61) and lower outpatient costs overall (% difference = −8.6, 95% CI −13.4, −3.6). By insurance type, out‐of‐network diagnosis was associated with decreased odds of any outpatient costs for patients with PPO or HMO plans (Table 3 ). Out‐of‐network diagnosis was associated with significantly lower yearly outpatient costs for patients with HMO or CDHP plans (Table 4 ). TABLE 3. Adjusted odds ratio (aOR) of any prostate cancer‐related spending following in‐network (IN) versus out‐of‐network (OON) diagnosis, overall and by insurance type. Overall and by insurance type IN OON aOR (OON vs. IN) 95% CI p IN ( n ) OON ( n ) Overall 64,131 3140 0.55 0.49, 0.61 < 0.001 PPO 45,021 2209 0.47 0.41, 0.54 < 0.001 EPO 1321 102 1.08 0.48, 2.43 0.9 HMO 8045 503 0.54 0.44, 0.67 < 0.001 CDHP 5174 128 1.77 0.86, 3.66 0.1 HDHP 4570 198 0.88 0.53, 1.46 0.6 Open in a new tab Note: The overall model was adjusted for insurance type, age at diagnosis, geographic region, and number of claims. Covariates in the adjusted model by insurance type included age, region, and number of claims. The adjusted OR was calculated for patients from the South who were diagnosed in‐network at age 59 and had 12 subsequent non‐diagnostic claims for prostate cancer‐related care. Values are presented with associated 95% CI. Abbreviations: CDHP, Consumer‐Driven Health Plan; EPO, Exclusive Provider Organization; HDHP, High‐Deductible Health Plan; HMO, Healthcare Maintenance Organization; PPO, Preferred Provider Organization. TABLE 4. Adjusted total outpatient cost for prostate cancer‐related care in the year after prostate cancer diagnosis for patients diagnosed in‐network (IN) versus out‐of‐network (OON), overall and by insurance type. Overall and by insurance type IN OON % difference 95% CI p n Cost n Cost Overall 58,936 $189.77 2665 $173.40 −8.6 −13.4, −3.6 0.001 PPO 42,055 $187.25 1906 $178.82 −4.5 −10.5, 1.9 0.2 EPO 1204 $132.01 93 $102.84 −22.1 −40.6, 2.2 0.07 HMO 6818 $152.78 365 $118.77 −22.3 −30.9, −12.5 < 0.001 CDHP 4665 $247.04 210 $181.45 −26.5 −43.6, −4.3 0.02 HDHP 4194 $243.63 181 $276.08 13.3 −6.8, 37.7 0.2 Open in a new tab Note: Linear regression of log‐transformed yearly non‐diagnostic cost on diagnostic network status was performed for individuals with a non‐zero yearly cost. The overall model was adjusted for insurance type, age at diagnosis, region, and number of non‐diagnostic claims. The stratified model was adjusted for age at diagnosis, region, and number of non‐diagnostic claims. Adjusted costs were calculated for patients from the South who were diagnosed at age 59 and had 12 subsequent non‐diagnostic claims for prostate cancer‐related care. Values are presented with associated 95% confidence intervals (CI). Abbreviations: CDHP, Consumer‐Driven Health Plan; EPO, Exclusive Provider Organization; HDHP, High‐Deductible Health Plan; HMO, Healthcare Maintenance Organization; PPO, Preferred Provider Organization. The geometric mean was $249.80 and reasonably close to the median ($216.89), demonstrating log‐transformed costs were appropriate as the dependent outcome in a linear model. The maximum observed yearly cost following diagnosis was $47,092.92, likely related to receipt of non‐definitive procedural therapy. 4. Discussion In this retrospective cohort study of privately insured U.S. men aged 40–64 with prostate cancer, out‐of‐network diagnosis was strongly associated with subsequent patterns of healthcare utilization in the year following diagnosis. Patients diagnosed out‐of‐network had 26–154 times greater out‐of‐network follow‐up claims compared to patients diagnosed in‐network. Despite this, we observed modest differences in outpatient costs. In stratified analyses, out‐of‐network diagnosis was associated with lower odds of any recorded prostate cancer‐related spending among patients with PPO and HMO plans and lower outpatient costs for patients with HMO and CDHP plans. The large observed difference in out‐of‐network care use between patients diagnosed in‐ vs. out‐of‐network suggests that network status at diagnosis influences downstream care. Patients undergoing evaluation for prostate cancer often rely on primary care physicians for a urology referral [ 24 ]. This referral cascade may unintentionally channel patients diagnosed out‐of‐network into continued out‐of‐network care. Such patterns may persist even when patients have financial incentives to seek in‐network care. Structural and behavioral factors may also contribute. Patients diagnosed out‐of‐network may continue care with the diagnosing provider because of perceived expertise, convenience, or a desire for consistency following an emotionally challenging diagnosis [ 24 , 25 ]. Geographic variation in provider networks and hospital consolidation patterns may also increase the likelihood of out‐of‐network encounters. Among patients in the studied cohort, more out‐of‐network diagnoses occurred in the West, where large‐scale hospital consolidation may increase negotiating power with insurers and the likelihood of out‐of‐network services, even at in‐network hospitals [ 26 , 27 ]. Although out‐of‐network diagnosis was strongly associated with out‐of‐network care use, patients diagnosed out‐of‐network did not incur greater outpatient costs. In fact, out‐of‐network diagnosis was associated with lower odds of any recorded prostate cancer‐related spending among certain insurance types. Several factors may explain this apparent paradox. Patients diagnosed out‐of‐network had fewer recorded follow‐up claims, suggesting possible care discontinuity, delayed care, or receipt of services outside the insurance system. Insurance claims capture only payments processed through insurers. Payments outside the claims adjudication process (e.g., balance billing, denied claims, direct‐pay arrangements) are not observable in claims databases such as MarketScan [ 18 , 28 , 29 ]. Research shows out‐of‐network care often results in balance billing or denied claims [ 18 , 28 ]. Claims‐based estimates may therefore underestimate financial burdens experienced by patients receiving out‐of‐network care. Differences across insurance plan types may reflect variation in network design and cost‐sharing structures. For example, HMO plans typically restrict care to provider networks and rely on fixed copayments for covered services. In contrast, PPO and HDHP generally allow greater flexibility in provider choice but may expose patients to different cost‐sharing structures. However, because claims data do not capture balance billing, denied claims, or direct‐pay arrangements, the observed spending patterns should be interpreted cautiously. Findings highlight a critical limitation of claims‐based analyses when evaluating financial burden associated with out‐of‐network care. Observed spending in claims may not fully reflect the financial risks faced by patients. Improving transparency in healthcare payment data by including information on denied claims and balance billing, for example, would enable more accurate assessments of patient financial burden. To our knowledge, this is the first study to examine how network status at diagnosis influences out‐of‐network care use and healthcare costs for prostate cancer patients managed in the outpatient setting. The study is strengthened by its use of a large national cohort derived from a validated insurance claims research dataset [ 30 ]. Stratified analyses allowed evaluation of how insurance plan influences care and spending patterns. Several limitations should be considered. First, the CCAE database includes only individuals under the age of 65. It does not reflect care patterns among older individuals who represent most prostate cancer cases. Second, we excluded patients who received surgery or radiation to focus on outpatient care claims. We therefore likely selected for patients with lower disease severity amenable to surveillance, deferred treatment, or alternative outpatient treatments such as focal therapy. However, variation in disease severity likely remains because tumor grade information was not available. It is possible that a mixture of patients with low‐risk disease on active surveillance, patients treated with focal therapy, patients who declined treatment, and even some higher risk patients who received systemic therapy were included. Moreover, the first observed prostate cancer diagnosis may not represent the incident diagnosis. In addition, the requirement for continuous enrollment during follow‐up may have introduced potential survivorship bias. Lastly, MarketScan does not provide data on race, ethnicity, and income, which shape healthcare access [ 30 ]. Findings should therefore be understood only as associations and not as causal relationships. We acknowledge the possibility of unmeasured confounding, given patients may have pursued out‐of‐network care for various reasons. This study used data prior to the implementation of the No Surprises Act, a federal law designed to protect patients from unexpected out‐of‐network medical bills [ 29 ]. Future research should examine whether the legislation altered patterns of out‐of‐network cancer care. Additional research is needed to evaluate how network structures and insurance plan design influence care pathways and financial outcomes for patients with prostate cancer. 5. Conclusion An out‐of‐network prostate cancer diagnosis was associated with greater out‐of‐network care use. However, it was not consistently associated with greater outpatient costs. Differences in spending may reflect plan design and limitations of claims‐based financial measures. Transparent data on unprocessed claims are needed to accurately assess financial burden and improve care for prostate cancer patients whose disease does not mandate immediate definitive treatment. Author Contributions Mark N. Alshak: writing – review and editing. Michelle I. Higgins: writing – review and editing. Aurora J. Grutman: conceptualization, methodology, formal analysis, writing – original draft, writing – review and editing. Christian P. Pavlovich: methodology, writing – review and editing, supervision. Derek K. Ng: methodology, conceptualization, formal analysis, visualization, writing – review and editing, supervision. Funding A.J.G. was supported by the Johns Hopkins Institute for Clinical and Translational Research which is funded in part by Grant Number T32 TR004928 from the National Center for Advancing Translational Sciences, a component of the National Institutes of Health. Ethics Statement The study was approved by our Institutional Review Board in October 2024. Conflicts of Interest The authors declare no conflicts of interest. Supporting information Table S1: Diagnostic codes used to identify treatment types. Figure S1: Flowchart for sample inclusion criteria. Figure S2: Directed acyclic graph illustrating the conceptual framework used to identify covariates for adjusted analyses. The exposure of interest was in‐network versus out‐of‐network prostate cancer diagnosis. The outcome was whether subsequent prostate cancer care visits were in‐network or out‐of‐network during the year after diagnosis. Variables displayed below represent potential covariates used in model adjustment. Referral ( t 0 ) is defined as the referral for the diagnostic visit. Referral ( t i ) represents referral patterns at any time in the year after prostate cancer diagnosis. CAM4-15-e71983-s001.docx (388.8KB, docx) Data Availability Statement The dataset generated and analyzed during this study was obtained from the Johns Hopkins Surgery Center for Outcomes Research (JSCOR) and are available contingent upon JSCOR approval. References 1. Chow R. D., Bradley E. H., and Gross C. P., “Comparison of Cancer‐Related Spending and Mortality Rates in the US vs 21 High‐Income Countries,” JAMA Health Forum 3, no. 5 (2022): e221229, 10.1001/jamahealthforum.2022.1229. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Mariotto A. B., Enewold L., Zhao J., Zeruto C. A., and Yabroff K. R., “Medical Care Costs Associated With Cancer Survivorship in the United States,” Cancer Epidemiology, Biomarkers & Prevention 29, no. 7 (2020): 1304–1312, 10.1158/1055-9965.EPI-19-1534. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. Mariotto A. B., Robin Yabroff K., Shao Y., Feuer E. J., and Brown M. L., “Projections of the Cost of Cancer Care in the United States: 2010–2020,” JNCI Journal of the National Cancer Institute 103, no. 2 (2011): 117–128, 10.1093/jnci/djq495. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Yabroff K. R., Mariotto A., Tangka F., et al., “Annual Report to the Nation on the Status of Cancer, Part 2: Patient Economic Burden Associated With Cancer Care,” JNCI Journal of the National Cancer Institute 113, no. 12 (2021): 1670–1682, 10.1093/jnci/djab192. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Zafar S. Y. and Abernethy A. P., “Financial Toxicity, Part I: A New Name for a Growing Problem,” Oncology (Williston Park, N.Y.) 27, no. 2 (2013): 80–81. [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Altice C. K., Banegas M. P., Tucker‐Seeley R. D., and Yabroff K. R., “Financial Hardships Experienced by Cancer Survivors: A Systematic Review,” Journal of the National Cancer Institute 109, no. 2 (2017): djw205, 10.1093/jnci/djw205. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 7. Zaorsky N. G., Khunsriraksakul C., Acri S. L., et al., “Medical Service Use and Charges for Cancer Care in 2018 for Privately Insured Patients Younger Than 65 Years in the US,” JAMA Network Open 4, no. 10 (2021): e2127784, 10.1001/jamanetworkopen.2021.27784. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 8. Abudoubari S., Bu K., Mei Y., Maimaitiyiming A., An H., and Tao N., “Prostate Cancer Epidemiology and Prognostic Factors in the United States,” Frontiers in Oncology 13 (2023): 1142976, 10.3389/fonc.2023.1142976. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Bleyer A., Spreafico F., and Barr R., “Prostate Cancer in Young Men: An Emerging Young Adult and Older Adolescent Challenge,” Cancer 126, no. 1 (2020): 46–57, 10.1002/cncr.32498. [ DOI ] [ PubMed ] [ Google Scholar ] 10. Mali B., Mali A., Mali A., Abdulrazzak M., and Jobran A. W. M., “Exploring the Epidemiological Characteristics and Survival Analysis Among Prostate Cancer Patients Under 50: A Seer‐Based Population Study,” Health Science Reports 8, no. 1 (2025): e70414, 10.1002/hsr2.70414. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 11. Yabroff K. R., Dowling E. C., Guy G. P., et al., “Financial Hardship Associated With Cancer in the United States: Findings From a Population‐Based Sample of Adult Cancer Survivors,” Journal of Clinical Oncology 34, no. 3 (2016): 259–267, 10.1200/JCO.2015.62.0468. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 12. Eastham J. A., Auffenberg G. B., Barocas D. A., et al., “Clinically Localized Prostate Cancer: AUA/ASTRO Guideline, Part II: Principles of Active Surveillance, Principles of Surgery, and Follow‐Up,” Journal of Urology 208, no. 1 (2022): 19–25, 10.1097/JU.0000000000002758. [ DOI ] [ PubMed ] [ Google Scholar ] 13. Wallis C. J. D., Joyce D. D., Klaassen Z., et al., “Out‐of‐Pocket Costs for Commercially Insured Patients With Localized Prostate Cancer,” Urologic Oncology: Seminars and Original Investigations 39, no. 12 (2021): 797–805, 10.1016/j.urolonc.2021.08.026. [ DOI ] [ PubMed ] [ Google Scholar ] 14. Horný M., Yabroff K. R., Filson C. P., et al., “The Cost Burden of Metastatic Prostate Cancer in the US Populations Covered by Employer‐Sponsored Health Insurance,” Cancer 129, no. 20 (2023): 3252–3262, 10.1002/cncr.34905. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Wray C. M., Khare M., and Keyhani S., “Access to Care, Cost of Care, and Satisfaction With Care Among Adults With Private and Public Health Insurance in the US,” JAMA Network Open 4, no. 6 (2021): e2110275, 10.1001/jamanetworkopen.2021.10275. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. “Merative MarketScan Research Databases,” accessed February 2, 2025, https://www.merative.com/documents/merative‐marketscan‐research‐databases . 17. Wei J. T., Barocas D., Carlsson S., et al., “Early Detection of Prostate Cancer: AUA/SUO Guideline Part I: Prostate Cancer Screening,” Journal of Urology 210, no. 1 (2023): 46–53, 10.1097/JU.0000000000003491. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Sen A. P., Meiselbach M. K., Wang Y., Eisenberg M. D., and Anderson G. F., “Frequency and Costs of Out‐of‐Network Bills for Outpatient Laboratory Services Among Privately Insured Patients,” JAMA Internal Medicine 181, no. 6 (2021): 834–841, 10.1001/jamainternmed.2021.1422. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. Hebert K. J., Matta R., Fendereski K., et al., “Genitourinary Radiation Injury Following Prostate Cancer Treatment: Assessment of Cost and Health Care System Burden,” Urology 179 (2023): 166–173, 10.1016/j.urology.2023.03.056. [ DOI ] [ PubMed ] [ Google Scholar ] 20. Housten A. J., Rice H. E., Chang S. H., et al., “Financial Burden of Men With Localized Prostate Cancer: A Process Paper,” Frontiers in Psychology 14 (2023): 1176843, 10.3389/fpsyg.2023.1176843. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 21. Prasad M. M., Prasad S. M., Hevelone N. D., et al., “REVIEW: Utilization of Pharmacotherapy for Erectile Dysfunction Following Treatment for Prostate Cancer,” Journal of Sexual Medicine 7, no. 3 (2010): 1062–1073, 10.1111/j.1743-6109.2009.01644.x. [ DOI ] [ PubMed ] [ Google Scholar ] 22. Pan H. Y., Jiang J., Hoffman K. E., et al., “Comparative Toxicities and Cost of Intensity‐Modulated Radiotherapy, Proton Radiation, and Stereotactic Body Radiotherapy Among Younger Men With Prostate Cancer,” Journal of Clinical Oncology 36, no. 18 (2018): 1823–1830, 10.1200/JCO.2017.75.5371. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 23. U.S. Bureau of Labor Statistics , “Consumer Price Index,” accessed February 24, 2025, https://www.bls.gov/cpi/ . 24. Jiang T., Stillson C. H., Pollack C. E., et al., “How Men With Prostate Cancer Choose Specialists: A Qualitative Study,” Journal of American Board of Family Medicine 30, no. 2 (2017): 220–229, 10.3122/jabfm.2017.02.160163. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Salvy S. J., “Psychological Interventions in Prostate Cancer: A Farewell to Mind–Body Dualism,” Prostate Cancer and Prostatic Diseases 24, no. 3 (2021): 587–588, 10.1038/s41391-021-00350-3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Robinson J. C. and Miller K., “Total Expenditures Per Patient in Hospital‐Owned and Physician‐Owned Physician Organizations in California,” Journal of the American Medical Association 312, no. 16 (2014): 1663–1669, 10.1001/jama.2014.14072. [ DOI ] [ PubMed ] [ Google Scholar ] 27. Furukawa M. F., Kimmey L., Jones D. J., Machta R. M., Guo J., and Rich E. C., “Consolidation of Providers Into Health Systems Increased Substantially, 2016–18: Study Examines Provider Consolidation Into Vertically‐Integrated Health Systems,” Health Affairs 39, no. 8 (2020): 1321–1325, 10.1377/hlthaff.2020.00017. [ DOI ] [ PubMed ] [ Google Scholar ] 28. Song Z., Johnson W., Kennedy K., Biniek J. F., and Wallace J., “Out‐of‐Network Spending Mostly Declined in Privately Insured Populations With A Few Notable Exceptions From 2008 to 2016,” Health Affairs 39, no. 6 (2020): 1032–1041, 10.1377/hlthaff.2019.01776. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. Lieneck C., Gallegos M., Ebner M., Drake H., Mole E., and Lucio K., “Rapid Review of ‘No Surprise’ Medical Billing in the United States: Stakeholder Perceptions and Challenges,” Healthcare (Basel) 11, no. 5 (2023): 761, 10.3390/healthcare11050761. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. Greenup R. A., Chu D., and Pawlik T. M., “Practical Guide to Administrative and Billing Big Data Sources,” JAMA Surgery 160, no. 3 (2025): 342–343, 10.1001/jamasurg.2024.6003. [ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Table S1: Diagnostic codes used to identify treatment types. Figure S1: Flowchart for sample inclusion criteria. Figure S2: Directed acyclic graph illustrating the conceptual framework used to identify covariates for adjusted analyses. The exposure of interest was in‐network versus out‐of‐network prostate cancer diagnosis. The outcome was whether subsequent prostate cancer care visits were in‐network or out‐of‐network during the year after diagnosis. Variables displayed below represent potential covariates used in model adjustment. Referral ( t 0 ) is defined as the referral for the diagnostic visit. Referral ( t i ) represents referral patterns at any time in the year after prostate cancer diagnosis. CAM4-15-e71983-s001.docx (388.8KB, docx) Data Availability Statement The dataset generated and analyzed during this study was obtained from the Johns Hopkins Surgery Center for Outcomes Research (JSCOR) and are available contingent upon JSCOR approval. Articles from Cancer Medicine are provided here courtesy of Wiley ACTIONS View on publisher site PDF (401.6 KB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

Related documents

• Quality of Life, Employment, and Informal Care Costs in Women Who Are Receiving Chemotherapy for Breast Cancer#875215
• Screening for Financial Toxicity and Health-Related Social Risks in Patients With GI Cancer: Results From a Large Cancer Center.#360826
• Impact of Physician Patient Load on Imaging Use in the Emergency Department#1106754
• A Study on the Impact of Intelligent Nutrition Management on Clinical Outcomes in Chemotherapy Patients With Gastrointestinal Malignancies#877758
• Medical Spending Differences in the United States and Canada: The Role of Prices, Procedures, and Administrative Expenses#485757