Abstract
Introduction
Treatment intensification with androgen receptor signaling inhibitors and/or chemotherapy is guideline recommended for patients with de novo metastatic hormone‐sensitive prostate cancer (mHSPC). However, most patients only receive androgen deprivation therapy monotherapy. The aim was to identify physician‐, patient‐, and tumor‐related factors associated with the receipt of treatment intensification.
Methods
A population‐based cohort study was conducted in Ontario, Canada, which included men ≥66 years newly diagnosed with de novo mHSPC between January 2014 and December 2022. Hierarchical regression modeling was used to examine the association of physician, patient, and tumor characteristics with the receipt of treatment intensification, defined as the initiation of an androgen receptor signaling inhibitor, docetaxel, or both within six months of diagnosis. Darlington’s method was used to assess predictor importance via standardized regression coefficients (SRC).
Results
Among 6099 eligible older men newly diagnosed with de novo mHSPC, 1475 (24.2%) received treatment intensification. In multivariable modeling, patients initiated on androgen deprivation therapy by radiation oncologists were less likely to receive treatment intensification (odds ratio [OR]. 0.48; 95% CI, 0.37–0.61; p < .01; SRC: 19.46; p < .01) whereas those by medical oncologists were more likely to receive treatment intensification (OR, 1.64; 95% CI, 1.21–2.22; p < .01; SRC: 9.56; p < .01), each compared to urologists. Older patients were significantly less likely to receive treatment intensification (OR 0.94 per year over age 66; 95% CI, 0.93–0.95; p < .01; SRC: –36.21; p < .01).
Conclusion
Patient and physician characteristics significantly influence variation in the use of treatment intensification for de novo mHSPC. These findings inform targeted interventions and policies to enhance the delivery of life‐prolonging mHSPC care.
Keywords: prostatic neoplasms, androgen antagonists, medical oncology, radiation oncology, urology, physicians, practice patterns, physicians, health services research
Short abstract
In this population‐based study of older men with de novo metastatic hormone‐sensitive prostate cancer in Ontario, Canada, treatment intensification varied substantially by physician specialty. Patients initiated on androgen‐deprivation therapy by medical oncologists were more likely to receive intensification compared to those treated by urologists or radiation oncologists.
INTRODUCTION
Androgen deprivation therapy (ADT) remains a cornerstone in the treatment of advanced prostate cancer. 1 However, its effectiveness for patients diagnosed with de novo metastatic hormone‐sensitive prostate cancer (mHSPC) is modest. 2 Over the past decade, the addition of abiraterone acetate, second‐generation androgen receptor signaling inhibitors (ARSIs) (apalutamide, enzalutamide, and darolutamide), and/or docetaxel chemotherapy has revolutionized the treatment landscape. 3 , 4 , 5 , 6 , 7 , 8 These intensified regimens have been shown to significantly improve overall survival (OS) in patients with mHSPC, leading to their endorsement as standard‐of‐care options for patients with mHSPC in clinical practice guidelines. 9 , 10 , 11 , 12 , 13
Despite the compelling evidence, the adoption of treatment intensification in real‐world clinical practice remains suboptimal. In studies of real‐world practice, treatment intensification rates ranged from 9.3% to 38.1% in nearly all health systems, 14 , 15 , 16 , 17 , 18 , 19 , 20 underscoring that system‐level factors may not fully explain this efficacy‐effectiveness gap. For example, in a US study, <5% of patients did not receive treatment intensification because of lack of coverage. 21
There is a need to understand the factors driving this variation in care. In particular, the role of the treating physician—alongside patient‐specific and disease‐specific factors such as age, comorbidities, sociodemographic characteristics, and tumor biology—in initiating treatment intensification must be explored to inform equitable and optimal delivery of these life‐extending therapies to patients with mHSPC.
METHODS
Study design
We performed a retrospective population‐based cohort study using province‐wide linked administrative data in Ontario, Canada. Ethics approval was obtained from the Mount Sinai Hospital Research Ethics Board (#22‐0072‐C). We report our findings according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. 22
Care setting
Canada has a universal, publicly funded health care system that ensures coverage for all legal and permanent residents. Ontario, the most populous province, is home to >16 million people, representing approximately 40% of the Canadian population. The provincial health care system provides covered residents with access to hospital and physician services free of charge. Public coverage for outpatient prescription medications is available to individuals aged 65 and older and advanced oncology agents are covered for all insured individuals.
Data source
The patient‐level dataset was constructed using linked health administrative databases maintained at ICES (www.ices.on.ca), an independent nonprofit research organization funded by the Ministry of Health and Long‐Term Care. These databases include records of publicly funded administrative health services for Ontario residents eligible for health coverage. Following section 45(1) of the Personal Health Information Protection Act, 2004, ICES is authorized to conduct health services research using anonymized administrative data without requiring patient consent; thus, individual patient consent was not obtained.
Study population
Our study cohort consisted of individuals aged 66 or older with newly diagnosed mHSPC (defined as AJCC stage 4 prostate cancer at the time of diagnosis) between January 1, 2014, and December 31, 2022. The start date for the primary analysis was chosen to allow for sufficient baseline and follow‐up data, and to capture early real‐world adoption of treatment intensification following the presentation of the CHAARTED trial results in 2014. 23 A more contemporary period, reflecting broader availability of treatment intensification options, was explored in sensitivity analyses.
We excluded patients if they were female, younger than age 66, had a missing or invalid identification number, died before their diagnosis, were ineligible for the Ontario Health Insurance Plan coverage in the 2 years before diagnosis, did not receive any ADT within 6 months of diagnosis, had missing data for key exposures, or had localized disease. We included patients aged ≥66 years to ensure complete capture of systemic therapy data through the Ontario Drug Benefit program, which provides prescription drug coverage to all residents aged ≥65 years. This age cutoff allowed for at least 1 year of baseline drug data. We further restricted the cohort to individuals with de novo mHSPC to reduce heterogeneity in disease trajectory and prior treatment exposure in patients with metachronous disease. This population is more clinically homogeneous and is the group for whom treatment intensification is generally indicated.
Exposure
The primary exposure was the prescribing physician’s characteristics. The prescribing physician was defined as the prescriber for initial ADT, not for any intensified therapy. This approach enabled attribution of treatment intensification in cases referred to other providers. For example, if a urologist initiated the ADT and then referred the patient to medical oncology for the addition of an ARSI and/or chemotherapy, treatment intensification would be attributed to the urologist who initiated the ADT. This reflects our intention to capture also the early role of the initiating physician in facilitating multidisciplinary care, leading to treatment intensification. Physician characteristics included primary specialty, age, sex, years in practice, and annualized physician volume of patients with prostate cancer at the index date obtained from provincial health administrative physician registration databases. When comparing specialties, we used urologists as the referent because they represented the most common initial prescribers of ADT in our cohort. This choice provided a clinically meaningful and statistically stable baseline for comparison across other, smaller physician specialty subgroups.
Secondary exposures included several patient characteristics. These included patient age at diagnosis, overall comorbidity burden (characterized using the Charlson comorbidity score, CCI), and presence of specific comorbidities (with a focus on those that may be anticipated to affect mHSPC treatment decision making). Specific comorbidities that were captured included diabetes, myocardial infarction, cerebrovascular accident, congestive heart failure, chronic obstructive pulmonary disease, hypertension, arrhythmia, dementia, liver disease, and renal disease. Additionally, we captured tumor factors including Gleason score (sourced from the Ontario Cancer Registry).
Outcome
The primary outcome was the prostate cancer treatment intensification within 6 months of diagnosis, defined as the use of (1) a luteinizing hormone‐releasing hormone agonist/antagonist ± first‐generation nonsteroidal antiandrogen (e.g., bicalutamide, flutamide, nilutamide), or surgical orchiectomy combined with (2) a second‐generation ARSI (abiraterone acetate, apalutamide, enzalutamide, darolutamide), and/or (3) docetaxel chemotherapy. All other patients were considered as not receiving treatment intensification.
During our study period, abiraterone acetate was not publicly reimbursed for mHSPC. Patients accessed it through compassionate use programs. Because abiraterone acetate is coprescribed with prednisone, which is publicly funded, we employed a previously reported and validated proxy to identify patients receiving both medications. 19 This proxy included patients prescribed continuous prednisone (5–10 mg daily) for at least three months, with no more than a 14‐day gap between overlapping prescriptions. To ensure prednisone use was indicated for the treatment of mHSPC, we excluded patients who had consulted with a rheumatologist and those with prednisone prescriptions exceeding three months in the year before cohort entry. Although enzalutamide and apalutamide became publicly available during the study period, darolutamide did not.
Covariates
Baseline characteristics were captured by linking health administrative data. In addition to patient‐level characteristics highlighted above, we captured area‐level socioeconomic factors including rurality, health care region, socioeconomic status (using a proxy measure), marginalization, and income quintile and adjusted the models for these characteristics.
Statistical analysis
We examined the association between patient sociodemographic and clinical characteristics, tumor factors, and physician‐level variation on the receipt of treatment intensification using generalized estimating equations with physicians as clustering units and an exchangeable correlation structure. To quantify the extent of physician‐level variation and to generate standardized regression coefficients (SRCs), we also fit hierarchical mixed‐effects logistic regression models with random intercepts. Darlington’s method was applied to assess the relative importance of predictors.
In sensitivity analysis, we restricted the cohort to patients diagnosed after 2018 to reflect a more contemporary treatment context following the broader dissemination of treatment intensification.
All analyses were performed using SAS Enterprise Guide v8.3. Statistical significance was determined at a two‐tailed p < .05.
RESULTS
Baseline patient demographics
After exclusions, the cohort comprised 6099 Ontario residents aged 66 years and older diagnosed with de novo mHSPC between January 1, 2014, and December 31, 2022 (Figure 1). The mean patient age was 77.5 years (±7.5). The median Charlson Comorbidity Index score was 1 (interquartile range [IQR] 0–2), and the median prostate‐specific antigen (PSA) level at diagnosis was 74 ng/mL (IQR 0–331; maximum value 16,312 ng/mL). Urologists were the initial ADT prescribers for most patients (4,191, 68.7%).
FIGURE 1.
Patient selection flow chart.
During the study period, 1,475 (24.2%) received treatment intensification. The proportion of patients receiving treatment intensification increased over the study period, reaching 32.0% among those diagnosed after 2018 (Supplementary Figure 1). Among patients who received treatment intensification, 933 (63.3%) received ADT with ARSI only, 483 (32.7%) received ADT with docetaxel only, and 59 (4.0%) received ADT with ARSI and docetaxel.
Additional demographic and clinical characteristics are provided in Table 1.
TABLE 1.
Baseline patient demographics, stratified by the prescribing physician’s specialty.
| Characteristic | Family physician (n = 315) | Medical oncologist (n = 114) | Other specialty (n = 204) | Radiation oncologist (n = 141) | Urologist (n = 345) | Overall | p value |
|---|---|---|---|---|---|---|---|
| Patient age at index date | |||||||
| Median (IQR) | 80 (73–85) | 77 (72–84) | 78 (72–84) | 74 (70–81) | 77 (71–83) | 77 (71–83) | <.001 |
| Year of prostate cancer diagnosis | |||||||
| 2014 | 44 (12.4%) | 25 (6.2%) | 20 (7.5%) | 83 (9.4%) | 320 (7.6%) | 492 (8.1%) | <.001 |
| 2015 | 36 (10.2%) | 26 (6.4%) | 32 (12.1%) | 83 (9.4%) | 410 (9.8%) | 587 (9.6%) | |
| 2016 | 33 (9.3%) | 37 (9.1%) | 42 (15.8%) | 84 (9.5%) | 409 (9.8%) | 605 (9.9%) | |
| 2017 | 41 (11.6%) | 35 (8.6%) | 18 (6.8%) | 89 (10.1%) | 458 (10.9%) | 641 (10.5%) | |
| 2018 | 36 (10.2%) | 58 (14.3%) | 29 (10.9%) | 103 (11.7%) | 572 (13.6%) | 798 (13.1%) | |
| 2019 | 54 (15.3%) | 49 (12.1%) | 44 (16.6%) | 136 (15.4%) | 591 (14.1%) | 874 (14.3%) | |
| 2020 | 52 (14.7%) | 74 (18.3%) | 33 (12.5%) | 92 (10.4%) | 559 (13.3%) | 810 (13.3%) | |
| 2021 | 42 (11.9%) | 69 (17.0%) | 29 (10.9%) | 123 (13.9%) | 567 (13.5%) | 830 (13.6%) | |
| 2022 | 16 (4.5%) | 32 (7.9%) | 18 (6.8%) | 91 (10.3%) | 305 (7.3%) | 462 (7.6%) | |
| Gleason score at diagnosis | |||||||
| ≤6 | 1–5 a | 1–5 a | 1–5 a | 1–5 a | 16 (0.4%) | 23 (0.4%) | <.001 |
| 7 | 105 (29.7%) | 161 (39.8%) | 76 (28.7%) | 242 (27.4%) | 870 (20.8%) | 1454 (23.8%) | |
| 8 | 34 (9.6%) | 30 (7.4%) | 23 (8.7%) | 160 (18.1%) | 639 (15.2%) | 886 (14.5%) | |
| 9 | 92 (26.0%) | 70 (17.3%) | 69 (26.0%) | 251 (28.4%) | 1594 (38.0%) | 2076 (34.0%) | |
| 10 | 12–16 a | 17–21 a | 11–15 a | 22–26 a | 273 (6.5%) | 348 (5.7%) | |
| Missing | 106 (29.9%) | 122 (30.1%) | 81 (30.6%) | 204 (23.1%) | 799 (19.1%) | 1312 (21.5%) | |
| Prostate specific antigen at diagnosis | |||||||
| Median (IQR) | 172 (49–647) | 120 (23–500) | 169 (47–772) | 28 (12–120) | 75 (23–260) | 74 (21–278) | <.001 |
| Geographic region (local health integration network) | |||||||
| Missing | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 1–5 a | 1–5 a | <.001 |
| Central | 28 (7.9%) | 68 (16.8%) | 26 (9.8%) | 55 (6.2%) | 484 (11.5%) | 661 (10.8%) | |
| Central East | 35 (9.9%) | 45 (11.1%) | 31 (11.7%) | 90 (10.2%) | 368 (8.8%) | 569 (9.3%) | |
| Central West | 20 (5.6%) | 28 (6.9%) | 12 (4.5%) | 35 (4.0%) | 202 (4.8%) | 297 (4.9%) | |
| Champlain | 31 (8.8%) | 21 (5.2%) | 18 (6.8%) | 138 (15.6%) | 329 (7.9%) | 537 (8.8%) | |
| Erie St. Clair | 16 (4.5%) | 13 (3.2%) | 10 (3.8%) | 71 (8.0%) | 240 (5.7%) | 350 (5.7%) | |
| Hamilton Niagara Haldimand Brant | 26 (7.3%) | 42 (10.4%) | 30 (11.3%) | 90 (10.2%) | 577 (13.8%) | 765 (12.5%) | |
| Mississauga Halton | 27 (7.6%) | 18 (4.4%) | 20 (7.5%) | 42 (4.8%) | 270 (6.4%) | 377 (6.2%) | |
| North East | 32 (9.0%) | 17 (4.2%) | 29 (10.9%) | 88 (10.0%) | 222 (5.3%) | 388 (6.4%) | |
| North Simcoe Muskoka | 12 (3.4%) | 6 (1.5%) | 1–5 a | 32 (3.6%) | 173–177 a | 228 (3.7%) | |
| North West | 27 (7.6%) | 16 (4.0%) | 2–6 a | 66 (7.5%) | 72 (1.7%) | 185–189 a | |
| South East | 28 (7.9%) | 29 (7.2%) | 16 (6.0%) | 38 (4.3%) | 244 (5.8%) | 355 (5.8%) | |
| South West | 35 (9.9%) | 29 (7.2%) | 14 (5.3%) | 64 (7.2%) | 379 (9.0%) | 521 (8.5%) | |
| Toronto Central | 13 (3.7%) | 54 (13.3%) | 45 (17.0%) | 48 (5.4%) | 321 (7.7%) | 481 (7.9%) | |
| Waterloo Wellington | 24 (6.8%) | 19 (4.7%) | 2–6 a | 27 (3.1%) | 307–311 a | 383 (6.3%) | |
| Rurality | |||||||
| Missing | 0 (0.0%) | 1–5 a | 1–5 a | 0 (0.0%) | 8–12 a | 14 (0.2%) | .06 |
| No | 287 (81.1%) | 358 (88.4%) | 232 (87.5%) | 740 (83.7%) | 3561 (85.0%) | 5178 (84.9%) | |
| Yes | 67 (18.9%) | 42–46 a | 28–32 a | 144 (16.3%) | 618–622 a | 907 (14.9%) | |
| Neighborhood Income Quintile | |||||||
| Missing | 0 (0.0%) | 1–5 a | 1–5 a | 1–5 a | 12–16 a | 19 (0.3%) | .01 |
| 1 | 90 (25.4%) | 96 (23.7%) | 67 (25.3%) | 164 (18.6%) | 805 (19.2%) | 1222 (20.0%) | |
| 2 | 75 (21.2%) | 89 (22.0%) | 52 (19.6%) | 178 (20.1%) | 830 (19.8%) | 1224 (20.1%) | |
| 3 | 63 (17.8%) | 78 (19.3%) | 54 (20.4%) | 201 (22.7%) | 805 (19.2%) | 1201 (19.7%) | |
| 4 | 56 (15.8%) | 66–70 a | 46–50 a | 145–149 a | 818–822 a | 1143 (18.7%) | |
| 5 | 70 (19.8%) | 71 (17.5%) | 41 (15.5%) | 191 (21.6%) | 917 (21.9%) | 1290 (21.2%) | |
| Charlson Comorbidity Index score | |||||||
| Mean ± SD | 1.54 ± 1.77 | 2.09 ± 2.22 | 1.20 ± 1.43 | 1.08 ± 1.30 | 1.17 ± 1.51 | 1.26 ± 1.59 | <.001 |
| Specific comorbidities | |||||||
| Diabetes | 144 (40.7%) | 145 (35.8%) | 82 (30.9%) | 270 (30.5%) | 1302 (31.1%) | 1943 (31.9%) | .001 |
| CHF | 62 (17.5%) | 65 (16.0%) | 45 (17.0%) | 87 (9.8%) | 580 (13.8%) | 839 (13.8%) | <.001 |
| COPD | 82 (23.2%) | 103 (25.4%) | 65 (24.5%) | 176 (19.9%) | 962 (23.0%) | 1388 (22.8%) | .18 |
| Hypertension | 261 (73.7%) | 282 (69.6%) | 189 (71.3%) | 636 (71.9%) | 3055 (72.9%) | 4423 (72.5%) | .63 |
| Dementia (5 years prior) | 41 (11.6%) | 24 (5.9%) | 17 (6.4%) | 18 (2.0%) | 140 (3.3%) | 240 (3.9%) | <.001 |
| Myocardial infarction (5 years prior) | 10–14 a | 14 (3.5%) | 1–5 a | 22 (2.5%) | 97 (2.3%) | 148 (2.4%) | .45 |
| Cerebrovascular accident (5 years prior) | 13 (3.7%) | 10 (2.5%) | 6 (2.3%) | 16 (1.8%) | 75 (1.8%) | 120 (2.0%) | .15 |
| Arrhythmia (1 year prior) | 9–13 a | 10 (2.5%) | 1–5 a | 15 (1.7%) | 46 (1.1%) | 85 (1.4%) | .03 |
| Liver disease (5 years prior) | 1–5 a | 1–5 a | 1–5 a | 6 (0.7%) | 28 (0.7%) | 48 (0.8%) | .24 |
| Renal disease (5 years prior) | 33 (9.3%) | 22 (5.4%) | 20 (7.5%) | 48 (5.4%) | 236 (5.6%) | 359 (5.9%) | .04 |
Abbreviations: CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; IQR, interquartile range.
Number suppressed for privacy reasons in keeping with ICES regulations.
Baseline physician demographics
A total of 1119 unique physicians were identified as the initiating ADT prescribers. Most were male (n = 828, 74.0%), with a mean age of 45.0 years (±12.9) and an average of 17.9 years (±12.4) in practice.
Table 2 summarizes the characteristics of these physicians, stratified by specialty.
TABLE 2.
Baseline physician characteristics.
| Medical oncology | Radiation oncologist | Urologist | Family physician | Other specialty | Total | p value | |
|---|---|---|---|---|---|---|---|
| n = 114 | n = 141 | n = 345 | n = 315 | n = 204 | n = 1119 | ||
| Age | |||||||
| Median (IQR) | 41 (36–51) | 42 (36–50) | 41 (33–53) | 49 (36–60) | 38 (33–51) | 43 (34–55) | <.001 |
| Sex | |||||||
| Female | 42 (36.8%) | 32 (22.7%) | 32 (9.3%) | 101 (32.1%) | 84 (41.2%) | 291 (26.0%) | <.001 |
| Male | 72 (63.2%) | 109 (77.3%) | 313 (90.7%) | 214 (67.9%) | 120 (58.8%) | 828 (74.0%) | |
| Physician years in practice | |||||||
| Median (IQR) | 13 (8–25) | 13 (9–24) | 15 (7–26) | 19 (7–32) | 11 (6–24) | 15 (7–28) | .001 |
| Physician volume | |||||||
| Median (IQR) | 52 (22–105) | 195 (40–357) | 175 (20–321) | 24 (14–36) | 24 (0–55) | 40 (13–158) | <.001 |
Abbreviation: IQR, interquartile range.
Physician characteristic predictors of treatment intensification
Compared to urologists, in adjusted analyses, de novo patients with mHSPC were more likely to receive treatment intensification when the first‐ADT prescribing physician was a medical oncologist (odds ratio [OR], 1.64; 95% CI, 1.21–2.22; SRC, 9.56; p < .01), but less likely to receive treatment intensification if it was a radiation oncologist (OR, 0.48; 95% CI, 0.37–0.61; SRC, 19.46; p < .01). Physician sex (OR, 1.15; 95% CI, 0.89–1.48; p = .29), age (OR, 0.99; 95% CI, 0.96–1.03; p = .63), and years in practice (OR, 1.00; 95% CI, 0.97–1.04; p = .79) were not associated with receipt of treatment intensification.
Additional physician characteristics and their association with treatment intensification are presented in Table 3.
TABLE 3.
Multivariable model for the primary logistic regression model examining the association between tumor, patient, and physician characteristics associated with treatment intensification. a , b
| Label | Value | OR | 95% CI | p value | |
|---|---|---|---|---|---|
| Physician characteristics | |||||
| Physician main specialty group (reference: urology) | General practice/family physician | 1.02 | 0.66 | 1.56 | .94 |
| Medical oncology | 1.64 | 1.21 | 2.22 | <.01 | |
| Other specialty | 1.21 | 0.80 | 1.85 | .37 | |
| Radiation oncology | 0.48 | 0.37 | 0.61 | <.001 | |
| Physician age (per year) | 0.99 | 0.96 | 1.03 | .63 | |
| Physician years in practice (per year) | 1.00 | 0.97 | 1.04 | .79 | |
| Physician sex (female vs male) | 1.15 | 0.89 | 1.48 | .29 | |
| Physician volume group | 0–49 | Referent | |||
| 50–99 | 1.12 | 0.78 | 1.62 | .54 | |
| 100–299 | 1.13 | 0.81 | 1.56 | .48 | |
| 300+ | 1.16 | 0.82 | 1.64 | .40 | |
| Patient characteristics | |||||
| Age at index date (per year) | 0.94 | 0.93 | 0.95 | <.001 | |
| Charlson category | 0 | Referent | |||
| 1 | 1.22 | 0.82 | 1.81 | .33 | |
| 2 | 1.13 | 0.74 | 1.73 | .57 | |
| 3+ | 1.12 | 0.71 | 1.75 | .63 | |
| Missing | 1.26 | 1.00 | 1.59 | .05 | |
| Congestive heart failure (yes vs no) | 0.75 | 0.59 | 0.94 | .01 | |
| Chronic obstructive pulmonary disease (yes vs no) | 0.94 | 0.79 | 1.10 | .43 | |
| Hypertension (yes vs no) | 0.95 | 0.82 | 1.11 | .53 | |
| Diabetes (yes vs no) | 1.11 | 0.95 | 1.29 | .18 | |
| Dementia (yes vs no) | 0.46 | 0.29 | 0.73 | <.01 | |
| Myocardial infarction (yes vs no) | 1.19 | 0.76 | 1.87 | .44 | |
| Cerebrovascular accident (yes vs no) | 0.34 | 0.17 | 0.67 | <.01 | |
| Arrhythmia (yes vs no) | 0.97 | 0.52 | 1.83 | .93 | |
| Liver disease (yes vs no) | 1.31 | 0.61 | 2.79 | .49 | |
| Renal disease (yes vs no) | 0.81 | 0.59 | 1.10 | .18 | |
| Gleason score at diagnosis | ≤6 | 1.55 | 0.53 | 4.56 | .42 |
| 7 | Referent | ||||
| 8 | 1.02 | 0.81 | 1.29 | .86 | |
| 9 | 1.42 | 1.18 | 1.72 | <.001 | |
| 10 | 1.64 | 1.21 | 2.22 | <.01 | |
| Missing | 1.37 | 1.05 | 1.78 | .02 | |
Abbreviations: ADT, androgen deprivation therapy; OR, odds ratio.
The model adjusted for the area itself as well as area‐level income, marginalization, and rurality and year of diagnosis.
The model was clustered on the first ADT‐treating physician.
Patient and tumor characteristic predictors of treatment intensification
Among patient characteristics, increasing age was associated with decreased odds of receiving treatment intensification (OR, 0.94; 95% CI, 0.93–0.95; SRC, −36.21; p < .01). Based on the SRC, age was the single most influential factor distinguishing those who received vs. those who did not receive treatment intensification. Overall comorbidity, as measured by the Charlson Comorbidity Index, was not significantly associated with treatment intensification (3+ vs 0: OR, 1.12; 95% CI, 0.71–1.75; p = .63). However, a history of congestive heart failure (OR, 0.75; 95% CI, 0.59–0.94; SRC, −7.62; p = .01), dementia in the 5 years before the index date (OR, 0.46; 95% CI, 0.29–0.73; SRC, −11.52; p < .001), and cerebrovascular accident in the 5 years before the index date (OR, 0.34; 95% CI, 0.17–0.67; SRC, −11.52; p = .002) were significantly associated with lower odds of treatment intensification compared to a lack of these diagnoses. Regarding tumor characteristics, higher Gleason scores at diagnosis were associated with increased odds of receiving treatment intensification (Gleason 10 vs. Gleason 7: OR, 1.64; 95% CI, 1.21–2.22; SRC, 8.75; p < .01).
Additional patient and tumor characteristics and their associations with treatment intensification are presented in Table 3 and 4.
TABLE 4.
Standard regression coefficients for the primary logistic regression model examining the association between tumor, patient, and physician characteristics associated with treatment intensification.
| Label | Value | Estimate | Lower | Upper | p value |
|---|---|---|---|---|---|
| Physician main specialty group (reference: urology) | General practice/family physician | 0.28 | −7.20 | 7.76 | .94 |
| Medical oncology | 9.56 | 3.70 | 15.41 | <.01 | |
| Other specialty | 2.79 | −3.25 | 8.82 | .37 | |
| Radiation oncology | ‐19.46 | −26.05 | −12.88 | <.001 | |
| Physician age (per year) | ‐6.80 | −34.64 | 21.03 | .63 | |
| Physician years in practice (per year) | 3.74 | −24.07 | 31.54 | .79 | |
| Physician sex (female vs male) | 3.30 | −2.76 | 9.36 | .29 | |
| Physician volume group | 0‒49 | Referent | |||
| 50‒99 | 2.02 | −4.47 | 8.51 | .54 | |
| 100‒299 | 4.11 | −7.29 | 15.53 | .48 | |
| 300+ | 5.64 | −7.39 | 18.68 | .40 | |
| Age at index date (per year) | −36.21 | −41.96 | −30.47 | <.001 | |
| Charlson category | 0 | Referent | |||
| 1 | 3.23 | −3.29 | 9.74 | .33 | |
| 2 | 1.82 | −4.39 | 8.02 | .57 | |
| 3+ | 1.67 | −5.15 | 8.49 | .63 | |
| Missing | 7.40 | −0.11 | 14.91 | .05 | |
| Congestive heart failure (yes vs no) | −7.62 | −13.56 | −1.68 | .01 | |
| Chronic obstructive pulmonary disease (yes vs no) | −2.09 | −7.32 | 3.15 | .43 | |
| Hypertension (yes vs no) | −1.66 | −6.81 | 3.49 | .53 | |
| Diabetes (yes vs no) | 3.63 | −1.65 | 8.91 | .18 | |
| Dementia (yes vs no) | −11.52 | −18.34 | −4.71 | .001 | |
| Myocardial infarction (yes vs no) | 2.07 | −3.20 | 7.33 | .44 | |
| Cerebrovascular accident (yes vs no) | −11.52 | −18.86 | −4.18 | <.01 | |
| Arrhythmia (yes vs no) | −0.25 | −5.91 | 5.40 | .93 | |
| Liver disease (yes vs no) | 1.76 | −3.26 | 6.78 | .49 | |
| Renal disease (yes vs no) | −3.81 | −9.37 | 1.74 | .18 | |
| Gleason score at diagnosis | ≤6 | 2.02 | −2.90 | 6.94 | .42 |
| 7 | Referent | ||||
| 8 | 0.56 | −5.75 | 6.86 | .86 | |
| 9 | 12.67 | 5.97 | 19.38 | <.001 | |
| 10 | 8.75 | 3.39 | 14.10 | <.01 | |
| Missing | 9.70 | 1.47 | 17.93 | .02 | |
Sensitivity analyses
Our findings were robust in sensitivity analyses restricted to patients after 2018, with statistically significant predictors and their directionality of association remaining consistent with those observed in the primary analysis (Supplementary Tables 1 and 2).
DISCUSSION
In a large, population‐based cohort of older patients diagnosed with de novo mHSPC in a single‐payer health care system, we found that physician and patient characteristics influence the receipt of treatment intensification in patients with de novo mHSPC, beyond tumor characteristics. Our analysis identified key factors, such as practice variations by specialty and patient characteristics, which may be targeted by initiatives seeking to enhance equitable and efficient delivery of treatment intensification to de novo patients with mHSPC.
Physician specialty emerged as a key factor influencing treatment intensification. Patients managed by medical oncologists were significantly more likely to receive treatment intensification than those initially prescribed ADT by a urologist or a radiation oncologist. This may reflect differences in referral pathways, with patients initially managed by medical oncologists potentially systematically differing from those initiated on ADT by urologists or radiation oncologists. For example, the median PSA level at diagnosis was four‐fold higher among patients initiated on ADT by medical oncologists compared to radiation oncologists, which may reflect differences in metastatic disease volume, which was an indication for treatment intensification during much of the study period. However, beyond referral biases, this disparity may reflect differences in clinical training, expertise in systemic therapies, or diversity of clinical practice. Notably, physician age, sex, and years in practice were not significant predictors, suggesting that variations in care are driven more by specialty‐specific practices than individual provider characteristics. These findings highlight the importance of interdisciplinary collaboration and the implementation of standardized treatment protocols to ensure equitable access to treatment intensification. Additionally, targeted continuing medical education and standardized referral pathways could help mitigate between‐specialty variations and promote consistent delivery of evidence‐based cancer care. Supporting this, a survey conducted by Agarwal et al. between 2018 and 2022 found that more than half of the patients who did not receive treatment intensification despite being indicated for it were managed under the inaccurate belief that the prescribed approach adhered to guidelines. 21
We identified patient characteristics associated with the lack of treatment intensification, suggesting disparities in clinical care. Older patients in our cohort appeared to be disproportionately undertreated, raising concerns about age‐related inequities in care delivery. Although administrative and large registry studies often lack the granular clinical information needed to definitively characterize undertreatment, 24 , 25 it is likely that a significant proportion of older adults are not receiving the indicated care they require. Considering that older men had similar clinical outcomes compared to younger men when receiving docetaxel in addition to ADT in CHAARTED 26 and relative tolerability of secondary hormonal agents in trials where the proportion of men who were 75 years or older ranged from 20% to 25%, 4 , 5 , 27 few older adults with mHSPC will be ineligible to treatment intensification. 28 Furthermore, socioeconomic disparities were evident despite the universal health care system setting of our study, which provides comprehensive access to care and metastatic treatment. This underscores the persistence of geographic disparities in health care resources and systemic barriers that impede equitable access to guideline‐concordant care, even within publicly funded systems.
Concerning tumor characteristics, a higher Gleason score was associated with more treatment intensification. This aligns with previous research suggesting that worse disease prompts more aggressive systemic treatment. 29 However, updated clinical guidelines, informed by recent clinical trials, emphasize that treatment intensification offers significant benefits for all patients with mHSPC who can tolerate systemic therapies. 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 Therefore, its use should not be limited solely to individuals with the poorest prognosis. Future research should focus on accurately stratifying patients with de novo mHSPC to balance the risks of overtreatment and undertreatment, ensuring that therapeutic interventions are appropriately matched to tumor aggressiveness.
Our study has limitations that merit discussion. First, the absence of detailed clinical data, such as imaging information, restricted our ability to analyze outcomes based on critical factors like disease volume and metastatic sites, which are known to significantly influence treatment decision‐making in metastatic prostate cancer. 21 This lack of granularity limits further analyses to characterize treatment patterns and their alignment with patient and disease characteristics. For example, differences in PSA at diagnosis by specialty suggest different referral patterns. In addition, because imaging modality is not captured in administrative data, we were unable to determine whether metastatic disease was identified using conventional imaging or novel modalities such as prostate‐specific membrane antigen positron emission tomography (PSMA PET). Although PSMA PET was not routinely available in Ontario during the study period, a small number of patients may have been diagnosed with mHSPC based on PSMA PET findings alone. This could have influenced decisions to initiate treatment intensification. 30 Second, although we used a validated proxy to identify patients receiving abiraterone acetate and prednisone, 19 this method is susceptible to misclassification. False positives may occur because of other indications for low‐dose prednisone, whereas false negatives may result from treatment interruptions or dosing outside predefined thresholds. This misclassification may bias our findings. Third, our cohort was older, with a median age of 77 years, reflecting the inclusion of patients aged ≥66 years to ensure complete capture of prescription drug data. As such, our findings may not be generalizable to younger patients with de novo mHSPC, and observed treatment intensification rates may be lower because of age‐related factors influencing treatment selection. Last, our analysis did not capture patients receiving mHSPC therapies that were either not approved or not publicly funded in Canada during the study period. These limitations again contribute to an underestimation of the true prevalence of treatment intensification in the studied population. However, our finding that only 25% of patients with de novo mHSPC received treatment intensification is in keeping with findings from other jurisdictions. 20 , 31 Additionally, given the low rates of treatment intensification observed, this underestimation is unlikely to significantly affect the overall interpretation of our findings. Although treatment intensification may not be appropriate for all patients, and our data cannot fully capture the factors guiding appropriate nonintensification, such as patient preference, the finding that only 24% of patients with de novo mHSPC receive intensification points to a potential fall in quality of care. Despite its limitations, our work provides critical population‐based insights into variations and drivers of treatment intensification for de novo mHSPC.
CONCLUSION
There are important variations and disparities in the delivery of treatment intensification for patients with de novo mHSPC, even within a universal health care system. Our findings suggest that treatment decisions for de novo mHSPC are heavily influenced by the involvement—or absence—of specific oncology specialties. Older patients and those in rural areas were less likely to receive guideline‐concordant care, highlighting systematic barriers that persist despite universal access. Addressing these issues requires a concerted effort to improve interdisciplinary collaboration, enhance education on evidence‐based therapies, and promulgate policies to reduce variations in care. By bridging the gap between clinical guidelines and real‐world practice, we can ensure that treatment decisions are driven by patient needs and best practices rather than unwarranted variations.
AUTHOR CONTRIBUTIONS
David‐Dan Nguyen: Conceptualization; formal analysis; writing — original draft; writing — review & editing; visualization. Raj Satkunasivam: Methodology and Writing — review & editing. Khatereh Aminoltejari: Methodology and Writing — review & editing. Amanda Hird: Methodology and Writing — review & editing. Soumyajit Roy: Methodology and Writing — review & editing. Scott C. Morgan: Methodology and Writing — review & editing. Shawn Malone: Methodology and Writing — review & editing. Michael Ong: Methodology and Writing — review & editing. Di Maria Jiang: Methodology and Writing — review & editing. Geoffrey T. Gotto: Methodology and Writing — review & editing. Bobby Shayegan: Methodology and Writing — review & editing. Girish S. Kulkarni: Methodology and Writing — review & editing. Rodney H. Breau: Methodology and Writing — review & editing. Aly‐Khan A. Lalani: Methodology and Writing — review & editing. Christopher J. D. Wallis: Conceptualization; formal analysis; Writing — original draft; writing — review & editing; visualization; supervision; funding acquisition.
CONFLICT OF INTEREST STATEMENT
Christopher Wallis is supported by the Hold’em for Life Early Career Professor in Cancer Research, a university limited‐term named professorship at the University of Toronto. and he reports receiving personal fees from Janssen Oncology, Nanostics Inc, Precision Point Specialty Inc, Sesen Bio, AbbVie, Astellas, Bayer, EMD Serono, Haymarket Media, Healing and Cancer Foundation, Knight Therapeutics, Intuitive Surgical, Merck, Science & Medicine Canada, TerSera Canada, and Tolmar Pharmaceuticals Canada as well as grants from Knight Therapeutics, Bayer, and Tolmar Pharmaceuticals Canada, all outside the submitted work. David‐Dan Nguyen is supported by a Canadian Institutes of Health Research (CIHR) Vanier Canada Graduate Scholarship, the CMCC/Atrium Hold’em for Life Oncology Fellowship, and the Ontario Ministry of Health Clinician‐Investigator Program. Raj Satkunasivam reports research funding and institutional support from Pfizer, BMS, Anchiano Therapeutics, QED Therapeutics, Merck, CoImmune, UroGen, enGene, Photocure, and Janssen as well as receiving consulting fees from Pfizer (2022‐2024), Intuitive Surgical (Proctor, 2019; 2023), and GNE/Roche (2017). Soumyajit Roy is supported by Prostate Cancer Foundation young investigator award; he reports honorariums from Varian Medical Systems and stocks in Merck and Pfizer Pharmaceuticals. Scott Morgan reports institutional research funding from Knight Therapeutics. Shawn Malone reports personal/consulting fees from Bayer and Janssen Biotech. Michael Ong reports receiving personal fees from Janssen, AstraZeneca, Bristol‐Myers Squibb, Merck, Pfizer, EMD‐Serono, and Novartis/AAA, and institutional research funding from AstraZeneca and Bristol‐Myers Squibb, all outside the submitted work; he holds research funding grants from the Canadian Institutes of Health Research. Di Maria Jiang received honorariums from Janssen, Bayer, Amgen, Astra Zeneca, Astellas, Novartis AAA, Bayer, Pfizer, McKesson, Astra Zeneca/Merck, Janssen, Novartis AAA and research funding from Astellas, Amgen, Tersera, Bayer, Pfizer. Geoffrey T. Gotto reports participating as a Principle Investigator in clinical trials with Astellas, Astra Zeneca, Bayer, Ferring, Janssen, Merck, and Pfizer, and having received research support form Janssen and has also received honoraria from Astellas, Astra Zeneca, Bayer, EMD Serono, BMS, Ferring, Janssen, Merck, Pfizer, and Tolmar. Bobby Shayegan has been an advisory board member for AbbVie, Astellas, Bayer, Janssen, Knight, Novartis, TerSera, Tolmar, and Verity. Girish S. Kulkarni has been an advisory board member for AstraZeneca, Astellas, Bayer, Biosyent, BMS, Janssen, Merck, Roche, Knight Therapeutics, Verity, Pfizer, EMD Serono, Ferring, Photocure, Biosyent, Bayer, Teresa, Theralase and has participated in clinical trials supported by Seagen, Merck, Janssen, BMS, Theralase, Verity. Rodney Breau reports receiving personal fees from Knight, Tolmar, Astellas, CG Oncology, AbbVie, Merck, and EMD Serono; he received a research grant from Tomar. Aly‐Khan Lalani reports research grants from Bristol Myers Squibb, BioCanRx, Novartis, Roche, Ipsen, and EMD Serono; and speaker's honoraria from AbbVie, Astellas, AstraZeneca, Bayer, Bristol Myers Squibb, Eisai, EMD Serona, Ipsen, Janssen, McKesson Corporation, Merck, Novartis, Pfizer, Roche, and TerSera Therapeutics.
Supporting information
Supplementary Material S1
Supplementary Material S2
Figure S1
ACKNOWLEDGMENTS
This study directly received funding from Bayer (C.J.D.W.); also received funding from the CUASF Early Investigator Research Scholarship and the University of Toronto Urologic Oncology Research and Innovation Fund for the conduct of this study. The funders had no role in the design, analysis, or reporting of results.
Nguyen D‐D, Satkunasivam R, Aminoltejari K, et al. Association of patient and physician characteristics with androgen‐deprivation‐therapy intensification in patients with de novo hormone‐sensitive metastatic prostate cancer: a population‐based study. Cancer. 2025;e70070. doi: 10.1002/cncr.70070
DATA AVAILABILITY STATEMENT
Research data are not shared.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Material S1
Supplementary Material S2
Figure S1
Data Availability Statement
Research data are not shared.