Europe PMC open access CC BY 4.0

Ethnic/Racial Variation in Prostate Cancer Net Survival: A Systematic Review

Masangwi D et al. · europe_pmc

Europe PMC · Papers · License: CC BY 4.0

Conceptio Open Archive

Analyze & explore related research in Conceptio

Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.

Explore in Search →Connect via MCP / API ↗

Ethnic/Racial Variation in Prostate Cancer Net Survival: A Systematic Review | Research Square Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Article Ethnic/Racial Variation in Prostate Cancer Net Survival: A Systematic Review Didjier Masangwi, Steven AZIZI, Kenrick Ng, Luke Mounce, Helena Carreira, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8734466/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Prostate cancer (PC) is the second most common cancer affecting men worldwide, with incidence and survival varying across ethnic/racial groups. Most evidence on ethnic PC survival in population studies is based on overall, progression-free and cause-specific approaches. No reviews examined ethnic disparities in PC net survival (NS) when assessed within relative survival (RS) framework . Methods We systematically reviewed studies examining PC net survival by ethnicity/race. We searched MEDLINE, Web of Science and Embase databases for articles published from 01-January-2010 to 28-February-2025. Five-year net survival differences (NSD) were summarized using forest plots and narrative synthesis. Results Twenty-six studies met inclusion criteria, twenty of which were conducted in the United States (US). White men had higher net survival than Black (13/18 studies: Range: NSD: 1% to 6% ), Asian/Pacific Islander (5/5 studies: Range: NSD: 2.8% to 6% ), Indian/Alaska Natives (6/6 studies, Range: NSD: 2% to 7% ), and Hispanic men (5/5 studies: Range: NSD: 2.6% to 6%). Conclusions White men have higher PC net survival than minority ethnic groups in US and effort should focus on addressing the inequalities. Studies in equal-access healthcare settings are needed to guide international policy. PROSPERO ID: CRD420251042423 Health sciences/Diseases/Cancer/Cancer epidemiology Biological sciences/Cancer/Urological cancer/Prostate cancer Figures Figure 1 Figure 2 Figure 3 Background Prostate cancer (PC) is the second most common cancer in men worldwide, and outcomes differ by ethnicity/race with Black men as twice as likely to be diagnosed and die from the disease than other ethnic groups ( 1 , 2 ). As healthcare systems aim to narrow ethnic/racial inequalities in cancer care and outcomes, measures of survival that reflect real-world clinical care experiences are essential. Increasingly, researchers have turned to population-based PC datasets collected through national or regional health registries such as National Cancer Registration and Analysis Services (NCRAS, United Kingdom (UK)) and Surveillance, Epidemiology, and End Results (SEER) Program (US) or surveys to estimate PC outcomes and make comparisons across ethnic groups in routine clinical settings ( 3 – 5 ). These datasets give valuable insights into the effectiveness of health systems in the management and delivery of PC care, examination of real-world impact of therapeutic PC interventions and help highlight differences in outcomes across population subgroups defined by ethnicity, and inform policy interventions aimed at addressing avoidable inequalities ( 6 – 8 ). Most population level studies examining ethnic disparities in PC outcomes have predominantly relied on overall and progression-free survival metrics, and often reporting poorer outcomes for Black men when compared to other men ( 9 – 12 ). A key focus of recent studies has been on examining ethnic/racial disparities in “net survival,” the survival due to prostate cancer, independent of the other causes of death ( 13 , 14 ). Two main settings are used to estimate net survival in population studies: cause-specific survival (CSS) and relative survival (RS) ( 15 ). CSS approach requires cause-of-death information and previous reviews had addressed ethnic/racial differences in PC survival in this setting ( 10 , 12 ). In RS setting, net survival is estimated by comparing the observed survival of men with cancer to the expected survival/background mortality data of similar cancer-free men in the general population ( 13 ). The expected survival is estimated using population life tables produced by national statistics departments ( 16 ). The approach does not require cause-of-death information hence more robust - especially when cause-of-death information is not reliable ( 17 ). This approach has allowed clinicians, commissioners, and policymakers to better understand PC outcomes in cancer surveillance programs and make international cancer survival comparisons ( 15 , 18 – 20 ). No prior reviews examined ethnic differences in PC net survival, and the extent of disparities when assessed within RS framework remains unclear. To address this knowledge gap, we conducted a systematic review to examine PC net survival by ethnicity in population studies. In this study, three estimators of net survival namely: relative survival ratio, non-parametric Pohar Perme and regression-based estimators are reported. By focusing on studies that incorporate background mortality data, this review provides the most recent synthesis of evidence on ethnic/racial inequalities on the survival impact of PC. METHODS Our systematic review was conducted in accordance with the Preferred Reporting Items for Systematic review and Meta-analysis (PRISMA) ( 21 ). The study protocol was registered on PROSPERO database (ID: CRD420251042423). Studies meeting the following criteria were included : (i) Population : Men with prostate cancer; (ii) Exposure : Race or ethnicity ( as described in the original studies, including White, Black, Asian/Pacific Islanders, Indians/Alaska Natives and others); (ii) Study design : Prospective/retrospective population-based studies (iv) Outcomes : Studies comparing 5-year PC net survival by ethnicity and (vi) Time : Articles published between 1-January-2010 and 28-February-2025. We excluded studies that were clinical trials, editorials, written in languages other than English, conference papers, reviews, commentaries, abstracts and letters to editors. Literature search and screening We searched articles in three databases; MEDLINE via PubMed, Embase, and Web of Science from 2010 to the search date (28-February-2025). Search terms included key words and Medical Subject Headings (MeSH) terms across two domains: Prostate cancer (PC) and Net/Relative Survival. The exposure “ethnicity” was not included in the search expression to avoid missing studies that only reported ethnicity in sub-analyses. The search expression was developed in MEDLINE and adapted for Web of Science and Embase databases. Reference list of identified relevant articles were examined to identify additional potentially relevant studies. The following search expressions were used in PubMed: Prostat* AND ("Neoplasms"[Mesh] OR "cancer" OR "tumo*" OR "carcinoma*" OR "malignan*”) AND ("Relative survival" OR "Net survival" OR "Excess Hazard" OR "Excess death" OR "Excess mortality" OR "Flexible parametric survival model*"). Retrieved studies were screened in Covidence, a web-based software for streamlining and facilitating the conduct of systematic reviews ( 22 ). Covidence was used to flag and remove duplicates and screen titles and abstracts for eligibility. Two independent reviewers (DDM, SCA) screened abstracts and titles against the selection criteria. Full text review of eligible articles was done by the same reviewers. Disagreements were resolved through consensus. For articles published by the same authors using the same population but updated periodically, the latest available eligible article was included. Data extraction and synthesis Two independent reviewers (DDM, SCA) extracted data against a predefined and standardized data extraction tool from the eligible articles. The data extraction tool and process were informed by study objectives and the Critical Appraisal Skills Program (CASP) checklist for systematic reviews with meta-analysis of observational studies ( 23 ). The following data were extracted: Author, study design, study location, cancer database used, sample size, study outcomes (survival estimates and standard errors), period of diagnosis, study population characteristics, estimator used and stage at diagnosis information. We summarised characteristics of the eligible studies in a tabular form. 5-year differences in PC net survival were derived for White vs Black, White vs Indians/Alaska Natives, White vs Asians/Pacific Islander, White vs Hispanics and standard errors of the differences were computed by treating each ethnic/racial group independent. The same approach was used for studies reporting other ethnic/racial groups. A narrative synthesis approach was used. Due to heterogeneity in study designs, locations, and significant overlap in patients’ population between studies, we did not perform a meta-analysis. Forest plots were used to graphically display the differences without pooled estimates. Risk of bias assessment Risk of bias was assessed using the Cochrane Risk Of Bias (RoB) In Non-randomized Studies - of Exposure (ROBINS-E) tool ( 24 ). The tool considers risk of bias in 7 domains (confounding, measure of exposure, participant selection, intervention post exposure, missing data, outcome measurement and reporting of results). Risk of bias in each domain ranges from low, some concern to high risk. Two reviewers (DDM, SCA) independently assessed risk of bias of all eligible articles and disagreements were resolved through consensus. RESULTS We identified 1346 records from database searches. After removing duplicates and initial assessment of abstracts and titles, 48 full text manuscripts were assessed for eligibility. Of these, 26 studies met the eligibility criteria and were included in the review (Fig. 1 ). Figure 1 : Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) Study characteristics All 26 studies included were retrospective cohort studies ( 5 , 13 , 25 – 48 ). Most studies (22/26) reported data on net survival by White or Hispanics or Black, Asians/Pacific Islanders or Indians/Alaska Natives( 25 – 34 , 36 – 38 , 40 – 48 ) and four studies reported net survival by First nationals or Aboriginals or Metis or Asians or Non-Asians ( 5 , 13 , 35 , 39 ). Twenty studies were from the US ( 25 , 26 , 28 – 34 , 36 – 38 , 40 – 47 ), two from the UK ( 27 , 39 ), three from Cannada ( 5 , 13 , 48 ) and one from Australia ( 35 ). SEER (Surveillance, Epidemiology, and End Results) was the main source of cancer data for studies from the US, NCRAS (National Cancer Registration Analysis Services) for UK studies, Canadian Cancer registry and National Census cohort for studies from Canada and National Cancer registry for Australian study. The majority of the studies (23/26) reported survival for all PC stages (combined) ( 5 , 13 , 25 – 29 , 32 – 39 , 41 – 48 ), and three studies reported NS for specific cancer stages by ethnicity ( 30 , 31 , 40 ). Study sizes varied with most studies reporting number of participants by ethnicity and others reporting total number of participants. Study cohort diagnosis periods markedly varied between individual studies with earliest diagnosis period from 1988 to 1999 and latest from 2015 to 2019. Twenty studies estimated net survival using relative survival ratio ( 25 – 28 , 30 , 32 – 34 , 36 , 37 , 40 – 44 , 46 , 47 , 49 , 50 ), four studies used flexible parametric regression-based estimators ( 5 , 13 , 35 , 48 ), and two studies used non-parametric Pohar Perme estimator ( 38 , 39 ). Study characteristics were summarized in Table 1 . Table 1 Baseline Study Characteristics Study Design Location Cancer Database Database for other data Sample size Ethnicity Age Cancer-stage Diagnosis period Estimator Yang_2020 ( 25 ) RC US SEER National Centre for health statistics 87951 17745 7776 White Black Other \(\:\le\:\) 99 years All 2010–2016 Relative survival ratio using life-tables stratified by age, ethnicity and sex. Siegel_2024 ( 26 ) RC US SEER National Centre for health statistics, National Program of Cancer Registries, North American Association of Central Cancer Registries 299010* White Black NA All 2013–2019 Relative survival ratio using life-tables stratified by race, geography and socioeconomics. Redaniel_2015 ( 27 ) RC UK NCRAS Hospital Episode Statistics and Office for National Statistics 6671 446 2621 White Non-white Unknown 15 + years All 1996–2009 Relative survival ratio using UK life-tables stratified by age, region and calendar year and Ederer II method. Malago’n_2025 ( 48 ) RC Canada Canadian cancer registry Canadian vital Statistics death database, National household survey 111480* White, Indigenous South East Asian, South Asian, East Asian, Middle Easten, Black, Latin America, Other 15–99 years All 2006–2011 Relative survival ratio using life tables stratified by age, sex, race, immigration status, income and education through Poisson regression. Nash_2023 ( 28 ) RC US North American Association of Central Cancer Registries NA 523983 7882 Non-Hispanic white American Indians & Alaska Natives 20 + years All 2000–2017 Relative survival ratio using life-tables stratified by age, race, sex, geography and socioeconomic status Howlader_2010 ( 29 ) RC US SEER National Centre for Health Statistics NA White Black American Indian and Alaska Natives < 65 and \(\:\ge\:\) 65 years All 1992–2004 Relative survival ratio using actuarial method and life-tables stratified by Age, sex, year of diagnosis and race Mazereeuw_2018 ( 5 ) RC Canada Canadian Census Health and Environment Cohort Canadian Mortality database, Canadian cancer registry NA Nonaboriginal Metis 45–90 years All 1992–2009 Net survival using flexible parametric survival models for excess hazard. Cetin_2010 ( 30 ) RC US SEER NA 8040 1424 562 White Black Other/Unknown 35 + years Stage IV 1998–2003 Relative survival ratio using actuarial method and life-tables specific for tumor, year of diagnosis and treatment. Krokschoen_2018 ( 50 ) RC US SEER NA 142401* Black White 65 + years Localized, regional and distant 2006–2013 Relative survival ratio Powell_2014 ( 32 ) RC US SEER NA NA Black White NA All 1998–2005 Relative survival ratio using Age specific life-tables. Sung_2023 ( 33 ) RC US SEER NA 20059 2702 1530 823 White Black Hispanic Asian/Pacific Islander 20 + years All 2000–2013 Relative survival ratio using life tables stratified by age, sex and race. De Lacerdaa_2019 ( 34 ) RC US SEER NA 198139 102609 2332 32642 Non-Hispanic White Non-Hispanic Black Indian/Alaska Natives Asian/Pacific Islander 20 + years All 2000–2013 Relative survival ratio using life tables stratified by geography, socio-economic, race/ ethnicity and Ederer II method. Dasgupta_2022 ( 35 ) RC Australia National Cancer registry Australian National Death Index 1233 131646 Aboriginal or Torres Strait Islander Other Australians 18–84 years All 2005–2016 Relative survival ratio using life-tables stratified by age, sex and state and flexible parametric survival models. Pulte_2012 ( 36 ) RC US SEER NA 67052 11581 3899 Non-Hispanic White African Americans Hispanic 15 + years All 2002–2006 Relative survival ratio using life tables stratified by age, sex and race & Ederer II method. Desantis_2016 ( 37 ) RC US SEER North American Association of Central Cancer Registries Cancer, Census Bureau NA Black White NA All 2005–2011 Relative survival ratio using age-specific life-tables. Steele_2017 ( 38 ) RC US SEER NA 1,241,940 285,662 White Black 15–99 years All 2001–2009 Net survival using non-parametric Pohar Perme estimator and life tables stratified by age, sex, calendar year and race. Maringe_2015 ( 39 ) RC UK National Cancer registry Office for National Statistics 1363 108,476 South-Asian Non-South Asian 15–69 years All 1996–2004 Net survival using non-parametric Pohar Perme estimotor and life tables stratified by age, calendar year, sex and deprivation and . Devasia_2023 ( 40 ) RC US SEER National Centre for Health Statistics, US Census Bureau 14781 3542 White Black 15 + years Metastatic 2012–2017 Relative survival ratio using life-tables stratified by age, race and period of diagnosis. Merrill_2011 ( 41 ) RC US SEER NA NA White Black 0–80 + years All 2000–2007 Relative survival ratio using life-tables stratified by age, race and sex. Withrow_2017 ( 13 ) RC Canada Canadian Cancer registry Census Mortality cohort, Canadian Mortality database 455 41820 First Nationals Non-aboriginal 45–90 years All 2001–2019 Net survival using flexible parametric models for excess hazard adjusted for age, ethnicity, sex and calendar time. Stroup_2014 ( 42 ) RC US SEER Centre for Disease Control's Behaviour Risk Factor Surveillance NA White Black < 65 and \(\:>=\) 65 years Localized 2000–2009 Relative survival ratio using US and state-specific life-tables stratified by age, sex, race and year. Hu_2013 ( 43 ) RC US SEER NA 14292 2855 1271 White Black Other 19–99 years All 1988–1999 Relative survival ratio using US-specific life tables stratified by sex, race, calendar & Ederer I method. Chen_2024 ( 44 ) RC US SEER NA 153668 37852 974 13235 Non-Hispanic white Non-Hispanic Black Non-Hispanic Indian/Alaska Natives Non-Hispanic Asian/Pacific Islanders NA All 2015–2019 Relative survival ratio using age-standardized life-tables and Ederer II method. D_Siegel_2020 ( 45 ) RC US Cancer Statistics NA 2,323,828 459,665 11,983 55,310 193,770 White Black American Indian/Alaska Natives Asian/Pacific Islanders Hispanics NA All 2001–2016 Relative survival ratio using life tables stratified by age, sex, race/ethnicity, geographical location, socioeconomic status, calendar year. Islami_2022 ( 46 ) RC US SEER National Cancer registry, US Census Bureau, National Health Interview Survey NA White Black American Indian/Alaska Natives Asian/Pacific Islanders Hispanics 50 + years All 2011–2018 Relative survival ratio using age-standardized life-tables. Jemal_2017 ( 47 ) RC US SEER Cancer Registry, North American cancer registry NA White Black < 99 years All 2006–2013 Relative survival ratio using age, sex, year, state, race, county socio-economic specific life-tables. Risk of bias The major source of bias of potential arose from inconsistent reporting of study results e.g. no point estimates, and where point estimates were reported, no reported standard errors (Fig. 2 ). Lack of information on stratifiers in the life tables when estimating expected survival was another source of bias. As a result, six studies were judged “high risk”. Four studies were judged low risk of bias. Most of the studies had some concerns mainly due to unclear reporting of handling missing data, with some studies not clearly reporting measurement of the outcome “net survival. Figure 2 : Risk of bias assessment: Yellow= “some concerns”, Green= “low-risk”, Red= “high risk”. Difference in 5-year net survival by ethnicity/race in US White Vs Black differences Of the 20 studies from the US, 18 reported 5-year NS by White and Black men ( 25 , 26 , 29 – 31 , 33 , 34 , 36 – 38 , 40 – 47 ). Of these studies, 15 reported NS for all age-groups combined ( 25 , 26 , 30 , 33 , 34 , 36 – 38 , 40 , 41 , 43 – 47 ), two by age group ( 29 , 42 ), and one by age group and stage ( 31 ). Out of 18 studies, 17 studies reported relative survival ratio as an estimator of NS( 25 , 26 , 29 – 31 , 33 , 34 , 36 , 37 , 40 – 47 ) and one study used Pohar Perme estimator ( 38 ). Seven studies using relative survival ratio reported significantly higher 5 year NS for White men than Black men (NSD range: 1% to 6%) ( 25 , 26 , 31 , 33 , 34 , 37 , 41 , 43 – 47 ), and the other three studies did not find differences in NS( 30 , 36 , 40 ) (Fig. 3 (A)). Studies that only reported NS point estimates consistently showed marked higher 5-year NS in White men than Black men ( 37 , 41 , 43 ). Similar 5-year NS were reported in both ethnic groups for younger men with regional and localized PC ( 31 ). In one study, 5-year NS estimates were over 100% and similar in younger men in both ethnic groups ( 29 ), suggesting higher observed survival in the cancer cohort than the general population used in the study. Another study comparing state-specific life tables with general US life tables found that when using general US life-tables, younger White men (< 65 years) had higher 5-year NS than their Black counterparts (NSD: 3%) ( 42 ). The difference reduced to 1% when using state-specific life-tables. In older men, similar trends were observed when using state-specific life-tables with White men having a 5% NS advantage. One study that used Pohar Perne estimator reported higher NS in White men than Black men with (NSD: 4.2%, 95% CI: 3.6 % to 4.8%) ( 38 ). Data on corresponding actual NS estimates is in the Supplementary Table 1 . White Vs Asians/Pacific Islanders differences There were 5 studies that reported NS by White and Asians/Pacific Islanders using relative survival ratio. When compared to Asians/Pacific Islanders, significantly higher 5-year NS were reported for White men in 4 studies with NSD range 2.8% to 6% ( 33 , 34 , 44 , 45 ) (Fig. 3 (B)). White men had a 3% higher NS than Asian/Pacific Islanders men as reported in one study ( 46 ). Data on actual reported NS are in Supplementary Table 1. White Vs Indians/Alaska Natives differences Six studies reported 5-year NS by White and Indians/Alaska Natives. Four studies found significant differences in NS between the two ethnicities, with higher NS in White than Indians/Alaska Natives (NSD range: 2% ; 7%) ( 28 , 34 , 44 , 45 ) (Fig. 3 (C)). Notable higher NS were observed in White than Indians/Alaska Natives in the other two studies ( 29 , 46 ), that only reported point estimates. White-Hispanics differences Out of five studies that reported White-Hispanics comparison in 5-year NS, 3 had found statistically significant differences between the groups ( 33 , 34 , 36 , 45 ). White men had higher 5-year NS when compared to their Hispanic counterparts (NSD range: 2% to 6%) (Fig. 3 (D)).The other study showed higher survival in White than Hispanics (NSD: 4%) ( 46 ). Figure 3 : Forest plots ( A, B, C, D) of 5-year differences in NS by ethnicity/race in the US. NS is based on relative survival ratio estimator. Canadian studies In Canada, one recent study comparing NS between White and other ethnic groups estimated using flexible parametric regression estimator found that White men (NS: 94.6%) versus (vs) Black (NS: 95.1%) (NSD: -0.5%: 95% CI: -2.4% to 1.4%), White versus North South Asians (NS: 94.8%) (NSD: -0.2% : 95% CI: -2% to 1.6%), White versus South-East Asians (NS: 95.9%) (NSD: -1.3%: 95% CI: -4.2% to 1.6%) and White versus Latin Americans (NS: 97.9%) (NSD: -3.3%, 95% CI: -10% to 3.4% ) had comparable 5-year survival ( 48 ). Higher NS were reported in White men when compared to Middle Eastern Asian men (NS: 90.5%) and Indigenous group men (NS: 90.4%) (NSD: 4.1% : 95% CI: 0.03 % t 8.2%, NSDs: 4.2%: 95% CI: 3.7% to 4.7%) respectively in the same study. The same study also found statistically significant lower NS in White men when compared to Easten Asians men (NS: 97.3%) and Other ethnic groups (100%) (NSD: -2.7% : 95% CI: -4.1% to -1.3%, NSD: -5.4%: 95% CI: -5.8% to -5.04%) respectively. Another study using the same estimator found that Aboriginals (NS: 93.9%) had higher 5-year NS ( NSD: 6.9%: 95% CI: 1.4% to 12.4%) when compared to first nationals (NS: 87%) ( 13 ). Further, a higher NS (27.8%, 95% CI: 13.8% to 41.8% ) was observed among Non-aboriginal (NS: 93.2%) than Metis (NS: 65.4%) with a wide confidence interval suggesting imprecision in the estimates ( 5 ). Australia For the single Australian study that reported NS using flexible parametric regression-based estimator for NS ( 35 ), other Australians ( NS: 87.5%) had a significantly higher NS ( NSD: 10%, 95% CI: 8.2% to 12.0%) than Aboriginal/Torres Islanders (NS: 77.4%). United Kingdom (UK) In the UK, a study comparing NS (Pohar Perme estimator) between South Asians (NS: 78.4%) and Non-South Asians (NS: 81.1%) showed similar NS (NSD: 2.7%, 95% CI: -5.6% to 11% ) ( 39 ). In another small area population study reporting relative survival ratio estimator of NS ( 27 ), similar 5-year survival were observed among White (106%), Non-White (107%) and those with unknown ethnicity (107%) (NSDs: 95% CI; -1% ; -4.4% to 2.4%, -1%, -2.8% to 0.79%) respectively. NS estimates beyond 100% indicated that cancer patients in the cohort were heathier than the general population. DISCUSSION Summary of the findings We systematically identified and summarised current evidence on PC net survival by ethnicity. We found that White men have significantly higher PC net survival than Black, Indians/Alaska Natives, Asians/Pacific Islanders and Hispanics in the US. Evidence on ethnic/racial disparities in PC survival predominantly comes from the US, with few studies performed in other ethnically diverse countries. These findings extend the long-established evidence of poorer PC outcomes among non-White groups and show that such disparities persist even when accounting for background mortality data in RS framework. The results underscore the need for concerted efforts in eliminating these inequalities. The limited research outside the USA highlights the need for international studies from different healthcare settings including those with universal healthcare systems. To our knowledge, this is the first study to systematically summarize evidence on ethnic differences in PC net survival in population studies. Our analysis approach offers a more comprehensive overview of existing ethnic inequalities in PC net survival. The results improve our understanding of true contribution of PC to survival in men of various ethnic backgrounds and allow reliable comparisons between ethnic groups. Our results align with substantial body of prior research demonstrating that White men have higher overall survival and PC-specific survival than Black, Hispanics, Asians/Pacific Islanders and Indians/Alaska Natives men in the US (51–54). Population level studies of 5-year PC-specific survival similarly show lower survival in non-White men (53,55). State-specific estimates of net PC survival concur with these results, with higher survival observed among White men than Black men (17,47). A recent systematic review on metastatic PC overall and cause-specific survival by ethnicity in the US reported that out of 54 studies, 51 studies were from the US with three studies from other countries, consistent with predominance of US data in our review (12). However, in the same study, Asian and Black men on certain PC therapies reported lower PC-specific mortality than White, and similar outcomes were reported in Hispanics and Indians. These findings differ from our study results which may reflect the fundamentally different study conditions such as stringent conditions in treatment-based studies, confounding control, and selection of patient populations. In Non-US countries, Canadian studies using CSS approach have reported comparable PC survival between White, Black and South-East Asians, and in some analyses, South Asians men had higher survival than White men (56,57). These results are broadly in concordance with our findings in the Canadian study included in this review. In contrast, the limited studies in the UK show more variability. A regional study reported higher overall survival in Black men than White men with metastatic PC treated with hormonal therapy (58). This differs from comparable survival estimates reported between White and Non-White men in the study included this review. Differences in disease stage, and greater therapeutic responsiveness sometimes reported in Black men for androgen-targeted therapies may explain these inconsistencies. While the general US studies, where access to healthcare may differ substantially between patients demonstrate ethnic disparities in PC net survival, some studies, and a systematic review conducted in an equal access to healthcare settings (Veterans Affairs healthcare), suggest favourable and comparable overall survival and PC-specific outcomes by ethnicity (59,60). This suggests that in equal access to healthcare systems, the effect of ethnicity /race on PC survival may be reversed. Possible reasons for differences in PC net survival by ethnicity Observed ethnic disparities in PC net survival are likely to be multi-factorial, with several clinical, social and behavioural drivers implicated as confounders or mediators of the association between ethnicity and PC survival outcomes (9,61,62). Socioeconomic deprivation is well recognised as a contributor to poorer PC survival, and minority ethnic groups are disproportionately represented in lower socioeconomic strata in countries such as the US and UK (63,64). Consistent with this, a study that stratified results by socioeconomic status found wider differences in net PC survival, with White men having higher survival probabilities than Hispanics and Black men (65). Differentials in access to treatment, timeliness of care, and continuity throughout PC treatment pathway in clinical settings may also contribute to the observed differences. Studies in the US have shown that Black and Asian men are more likely to decline recommended treatment for PC than White men, while Hispanic men are less likely to receive treatment even when eligible (66,67). Delayed time to PC treatment have been reported among Black and Hispanics men than White men (67,68), and both Black and Asian men have been reported to have higher rates of treatment incompletion than White men (68,69). Similarly, non-completion of certain PC therapies has been reported more frequently in Black men than White men (70). These differences in treatment uptake, timeline, and adherence may contribute to the observed disparities. Underrepresentation of minority ethnic groups in PC clinical trials may further exacerbate inequalities in outcomes. Evidence suggests that Black, Asian, and other minority ethnic groups are less willing or less likely to participate in PC trials (71,72), but this reluctance maybe shaped by structural than individual factors. Studies have documented unequal opportunity in PC trial enrolment, limited community-focused awareness programmes, mistrust of research institution, and communication barriers, all of which may reduce participation of minority ethnic groups (73,74). Limited trial representation restricts the generalisability of treatment evidence and may contribute to poorer outcomes in real-world settings for some groups. Strengths and limitations Our review synthesised evidence from population-based studies that accounted for background mortality data and therefore provided NS estimates that are less affected by inaccuracies in cause-of-death reporting among men with PC. The use of population-based registry data also meant that included studies were generally large and well powered, thus reducing the likelihood of underrepresentation of minority ethnic groups to which clinical trials are prone (31,75). Consequently, the review offers findings with broader contextual generalisability than single studies or trials. The reliance on comprehensive registry datasets also supported the use of more precise survival estimates and enhanced the robustness of observations across ethnic groups. This study is not without limitations. First, most studies included did not stratify with important prognostic variables such as socio-economic status, tumour stage and treatment data when estimating survival, which may have contributed to residual confounding. Secondly, studies comparing White-Asians/Pacific Islanders/Hispanics were sparse and this limits generalisability of results in these population subgroups. Thirdly, unclear and limited reporting of data on geography/state, age groups and time periods prevented integration of spatial, and time trends analyses in this review. Further, studies used overlapping data in the analyses, which constrained our ability to compare results across studies independently. We excluded studies published in languages other than English which might have led to omission of some relevant articles. Finally, as with all systematic reviews, our findings are limited by the quality and completeness of the underlying primary studies, including variations in ethnic classification (self-reported, medical records), life table construction and estimation method in RS framework, and follow-up procedures. Implication of the findings The findings of this review have several implications for clinical practice, health-care policy, and future research. For clinicians and policymakers, efforts to reduce inequalities in PC outcomes must address modifiable determinants of survival. This includes developing targeted policies and clinical guidelines designed to increase equitable access to timely diagnosis and high-quality treatment, ensuring that evidence-based interventions benefit men from all ethnic groups. Greater participation of minority ethnic groups in PC clinical trials is essential to improving understanding of ethnic variation in tumour biology, treatment effectiveness, and therapeutic response (31). Enhancing trial accessibility through community-engaged recruitment strategies and reducing structural barriers to participation may improve representation (76,77). Strengthening trust and communication between health-care providers and patients may reduce disparities arising from mistrust and perceived discrimination. Increasing ethnic diversity within the health-care workforce and among PC research teams may support more culturally responsive care and mitigate communication barriers (78,79). For researchers, qualitative research is needed to explore behavioural, cultural, and system-level factors underlying delayed treatment, non-completion of therapy, and treatment refusal among minority ethnic groups. Investment in high-quality, ethnicity-specific life tables and improved ethnic classification in registries would enhance the accuracy of net survival estimates across diverse populations. Given that most evidence on ethnic disparities in PC net survival is derived from the United States, an unequal-access to healthcare setting (80), research is needed to examine whether inequalities in survival exist when studies are performed in countries with equal access or universal healthcare systems. In conclusion, our study has demonstrated that White men have significantly higher PC net survival than minority ethnic groups even after accounting for background mortality data, with most evidence derived from the US. Coordinated clinical, policy, and research interventions are needed to achieve equity in PC outcomes. Studies in equal-access settings are essential to clarify whether similar disparities exist. Declarations ADDITIONAL INFORMATION Acknowledgements We are grateful to Queen Mary Library staff and collaborators for their support during the review process. Authors’ contribution DDM*: Conceived and designed the analysis; collected the data; performed the analysis; and wrote the paper. SCA: Collected data, performed analysis, revised the paper. KN: Conceived, designed analysis, revised manuscript. HC: Conceived, designed analysis, revised manuscript. MQ: Designed analysis, revised manuscript. JW : Conceived, designed analysis, revised manuscript and supervision, GF: Conceived, designed analysis, revised manuscript and supervision. Ethic Approval and consent to participate The study did not require ethnic approval and consent to participate. Consent for publication All authors consented to publication of the article. Conflict of Interest The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Data availability Data that support our study findings is available upon reasonable request to corresponding author (DDM). Funding DDM * is funded by Wellcome Trust Health Data in Practice Programme at Queen Mary University of London. References Cancer Research UK. Prostate Cancer Incidence Statistics [Internet]. [cited 2024 Aug 5]. Available from: https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/prostate-cancer/incidence Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. National Cancer Institute. No Title [Internet]. [cited 2025 Dec 29]. Available from: https://seer.cancer.gov/ Services NCR and A. No Title [Internet]. Available from: https://www.gov.uk/guidance/national-cancer-registration-and-analysis-service-ncras Mazereeuw M V., Withrow DR, Nishri ED, Tjepkema M, Vides E, Marrett LD. Cancer incidence and survival among Métis adults in Canada: Results from the Canadian census follow-up cohort (1992-2009). Cmaj. 2018;190(11):E320–6. ESTEVE, J; BENHAMOU, E;CROASDALE, M;RAYMOND L. Relative Survival and estimation of net survival - elements for further discussion.pdf. Stat Med. 1990;529–38. Coleman MP. Cancer survival: Global surveillance will stimulate health policy and improve equity. The Lancet [Internet]. 2014;383(9916):564–73. Available from: http://dx.doi.org/10.1016/S0140-6736(13)62225-4 OECD/The World Bank. OECD Publishing, Paris. 2020 [cited 2025 Aug 27]. Health at a Glance: Latin America and the Caribbean 2020. Available from: https://doi.org/10.1787/6089164f-en Yang J, Xiong X, Zheng W, Liao X, Xu H, Yang L, et al. Evaluation of Survival Outcomes Among Black and White Patients with Metastatic Castration-resistant Prostate Cancer: A Systematic Review and Meta-analysis. Eur Urol Open Sci [Internet]. 2024;61:10–7. Available from: https://doi.org/10.1016/j.euros.2024.01.004 Evans S, Metcalfe C, Ibrahim F, Persad R, Ben-Shlomo Y. Investigating Black-White differences in prostate cancer prognosis: A systematic review and meta-analysis. Int J Cancer. 2008;123(2):430–5. Mahal BA, Gerke T, Awasthi S, Soule HR, Simons JW, Miyahira A, et al. Prostate Cancer Racial Disparities: A Systematic Review by the Prostate Cancer Foundation Panel. Eur Urol Oncol [Internet]. 2022;5(1):18–29. Available from: https://doi.org/10.1016/j.euo.2021.07.006 Freedland SJ, Samjoo IA, Rosta E, Lansing A, Worthington E, Niyazov A, et al. The impact of race on survival in metastatic prostate cancer: a systematic literature review. Prostate Cancer Prostatic Dis. 2023;26(3):461–74. Withrow DR, Pole JD, Nishri ED, Tjepkema M, Marrett LD. Choice of relative or cause-specific approach to cancer survival analysis impacts estimates differentially by cancer type, population, and application: Evidence from a Canadian population-based cohort study. Popul Health Metr. 2017;15(1):1–9. Dickman PW, Coviello E. Estimating and modeling relative survival. Stata Journal. 2015;15(1):186–215. Sarfati D, Blakely T, Pearce N. Measuring cancer survival in populations: Relative survival vs cancer-specific survival. Int J Epidemiol. 2010;39(2):598–610. Mariotto AB, Zou Z, Johnson CJ, Scoppa S, Weir HK, Huang B. Geographical, racial and socio-economic variation in life expectancy in the US and their impact on cancer relative survival. PLoS One. 2018;13(7):1–18. Konermann S. 乳鼠心肌提取 HHS Public Access. Journal of Clinical Sleep Medicine. 2016;12(2):263–6. Dinmohamed AG, Lemmens VEPP, De Hingh IHJT, Visser O. Relative survival in early-stage cancers in the Netherlands: A population-based study. J Hematol Oncol. 2020;13(1):1–4. Bright CJ, Brentnall AR, Wooldrage K, Myles J, Sasieni P, Duffy SW. Errors in determination of net survival: cause-specific and relative survival settings. Br J Cancer [Internet]. 2020;122(7):1094–101. Available from: http://dx.doi.org/10.1038/s41416-020-0739-4 Morrell CH, Kattan MW, MacKintosh FR, Eeden S Van Den, Neville TB. Estimating Prostate Cancer Relative Survival and Cancer-Specific Death. Adv Appl Stat. 2015;46(3):197–223. Page MJ, Moher D, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews. The BMJ. 2021;372. Covidence [Internet]. [cited 2025 Feb 3]. Available from: https://www.covidence.org/ CASP. Critical Apprasal Skills Programme [Internet]. 2024 [cited 2025 Feb 3]. Available from: https://casp-uk.net/casp-checklists/CASP-checklist-systematic-reviews-observational-studies-checklist-2024.pdf Higgins JPT, Morgan RL, Rooney AA, Taylor KW, Thayer KA, Silva RA, Lemeris C, Akl EA BT, Berkman ND, Glenn BS, Hróbjartsson A, LaKind JS, McAleenan A, Meerpohl JJ, Nachman RM OJ, O’Connor A, Radke EG, Savović J, Schünemann HJ, Shea B, Tilling K, Verbeek J, Viswanathan M S, JAC. A tool to assess risk of bias in non-randomized follow-up studies of exposure effects (ROBINS-E). Environment International [Internet]. 2024 [cited 2025 Feb 3]. p. 186. Available from: https://www.riskofbias.info/welcome/robins-e-tool Yang R, Zhou Y, Wang Y, Du C, Wu Y. Trends in cancer incidence and mortality rates in the United States from 1975 to 2016. Ann Transl Med. 2020;8(24):1671–1671. Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):12–49. Redaniel MT, Martin RM, Ridd MJ, Wade J, Jeffreys M. Diagnostic intervals and its association with breast, prostate, lung and colorectal cancer survival in England: Historical cohort study using the Clinical Practice Research Datalink. PLoS One. 2015;10(5):1–17. Nash SH, Wahlen MM, Meisner ALW, Morawski BM. Choice of Survival Metric and Its Impacts on Cancer Survival Estimates for American Indian and Alaska Native People. Cancer Epidemiology Biomarkers and Prevention. 2023;32(3):398–405. Howlader N, Ries LAG, Mariotto AB, Reichman ME, Ruhl J, Cronin KA. Improved estimates of cancer-specific survival rates from population-based data. J Natl Cancer Inst. 2010;102(20):1584–98. Cetin K, Beebe-Dimmer JL, Fryzek JP, Markus R, Carducci MA. Recent Time Trends in the Epidemiology of Stage IV Prostate Cancer in the United States: Analysis of Data From the Surveillance, Epidemiology, and End Results Program*. Urology [Internet]. 2010;75(6):1396–404. Available from: http://dx.doi.org/10.1016/j.urology.2009.07.1360 Krok-Schoen JL, Fisher JL, Baltic RD, Paskett ED. White-black differences in cancer incidence, stage at diagnosis, and survival among adults aged 85 years and older in the United States. Cancer Epidemiology Biomarkers and Prevention. 2016;25(11):1517–23. Isaac J. Powell, Fawn D. Vigneau, Cathryn Bock,Julie Ruterbusch and LK, Heilbrun. Reducing Prostate Cancer Racial Disparity: Evidence for Aggressive Early Prostate Cancer PSA Testing of African American Men. Cancer Epidemiol Biomarkers Prev [Internet]. 2014;8(1):1505–1511. Available from: https://doi.org/10.1158/1055-9965.EPI-13-1328 Sung H, Nisotel L, Sedeta E, Islami F, Jemal A. Racial and Ethnic Disparities in Survival among People with Second Primary Cancer in the US. JAMA Netw Open. 2023;6(8):E2327429. De Lacerda GF, Howlader N, Mariotto AB. Differences in cancer survival with relative versus cause-specific approaches: An update using more accurate life tables. Cancer Epidemiology Biomarkers and Prevention. 2019;28(9):1544–51. Dasgupta P, Andersson TML, Garvey G, Baade PD. Quantifying Differences in Remaining Life Expectancy after Cancer Diagnosis, Aboriginal and Torres Strait Islanders, and Other Australians, 2005-2016. Cancer Epidemiology Biomarkers and Prevention. 2022;31(6):1168–75. Pulte D, Redaniel MT, Brenner H, Jeffreys M. Changes in survival by ethnicity of patients with cancer between 1992-1996 and 2002-2006: Is the discrepancy decreasing? Annals of Oncology [Internet]. 2012;23(9):2428–34. Available from: https://doi.org/10.1093/annonc/mds023 DeSantis C, Naishadham D, Jemal A. Cancer statistics for African Americans, 2013. CA Cancer J Clin. 2013;63(3):151–66. Steele CB, Li J, Huang B, Weir HK. Prostate cancer survival in the United States by race and stage (2001-2009): Findings from the CONCORD-2 study. Cancer. 2017;123(i):5160–77. Maringe C, Li R, Mangtani P, Coleman MP, Rachet B. Cancer survival differences between South Asians and non-South Asians of England in 1986-2004, accounting for age at diagnosis and deprivation. Br J Cancer. 2015;113(1):173–81. Devasia TP, Mariotto AB, Nyame YA, Etzioni R. Estimating the Number of Men Living with Metastatic Prostate Cancer in the United States. Cancer Epidemiology Biomarkers and Prevention. 2023;32(5):659–65. Merrill RM, Sloan A, Anderson AE, Ryker K. Unstaged cancer in the United States: A population-based study. BMC Cancer. 2011;11. Stroup AM, Cho H, Scoppa SM, Weir HK, Mariotto AB. The impact of state-specific life tables on relative survival. J Natl Cancer Inst Monogr. 2014;2014(49):218–27. Hu CY, Xing Y, Cormier JN, Chang GJ. Assessing the utility of cancer-registry-processed cause of death in calculating cancer-specific survival. Cancer. 2013;119(10):1900–7. Chen C, Yin L, Lu C, Wang G, Li Z, Sun F, et al. Trends in 5-year cancer survival disparities by race and ethnicity in the US between 2002–2006 and 2015–2019. Sci Rep. 2024;14(1):1–9. Siegel DA, O’Neil ME, Richards TB, Dowling NF, Weir HK. Prostate cancer relative survival by stage and race/ethnicity, United States, 2001 to 2015. Journal of Clinical Oncology. 2020;38(15_suppl):5509–5509. Islami F, Guerra CE, Minihan A, Yabroff KR, Fedewa SA, Sloan K, et al. American Cancer Society’s report on the status of cancer disparities in the United States, 2021. CA Cancer J Clin. 2022;72(2):112–43. Jemal A, Ward EM, Johnson CJ, Cronin KA, Ma J, Ryerson AB, et al. Annual Report to the Nation on the Status of Cancer, 1975-2014, Featuring Survival. J Natl Cancer Inst. 2017;109(9):1–22. Malagón T, Botting-Provost S, Moore A, El-Zein M, Franco EL. Inequalities in relative cancer survival by race, immigration status, income, and education for 22 cancer sites in Canada, a cohort study. Int J Cancer. 2025;157(1):41–54. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72(1):7–33. Krok-Schoen JL, Fisher JL, Baltic RD, Paskett ED. White–Black Differences in Cancer Incidence, Stage at Diagnosis, and Survival Among Older Adults. J Aging Health. 2018;30(6):863–81. Frego N, Labban M, Stone B V, Koelker M, Alkhatib K, Lughezzani G, et al. Effect of type of definitive treatment on race ‐ based differences in prostate cancer ‐ specific survival. 2023;(April):1099–111. Ghali F, Kane CJ. Re: Association of Black Race with Prostate Cancer–specific and Other-cause Mortality. Eur Urol [Internet]. 2021;80(6):758–9. Available from: https://doi.org/10.1016/j.eururo.2021.09.004 White A, Coker AL, Du XL, Eggleston KS, Williams M. Racial/ethnic disparities in survival among men diagnosed with prostate cancer in Texas. Cancer. 2011;117(5):1080–8. Chinea FM, Patel VN, Kwon D, Lamichhane N. Ethnic heterogeneity and prostate cancer mortality in Hispanic / Latino men : a population-based study. 2017;8(41):69709–21. Lewis-Thames MW, Langston ME, Khan S, Han Y, Fuzzell L, Xu S, et al. Racial and Ethnic Differences in Rural-Urban Trends in 5-Year Survival of Patients with Lung, Prostate, Breast, and Colorectal Cancers: 1975-2011 Surveillance, Epidemiology, and End Results (SEER). JAMA Netw Open. 2022;5(5):E2212246. Albers P, Bashir S, Mookerji N, Broomfield S, Martín AM, Ghosh S, et al. Prostate Cancer Among Black Men in Canada. 2024;7(6):1–9. Stern N, Ly TL, Welk B, Chin J, Ballucci D, Haan M, et al. Association of Race and Ethnicity With Prostate Cancer – Specific Mortality in Canada. 2021;4(12):10–3. Ng K, Wilson P, Mutsvangwa K, Hounsome L, Shamash J. Overall survival of black and white men with metastatic castration-resistant prostate cancer (mCRPC): a 20-year retrospective analysis in the largest healthcare trust in England. Prostate Cancer Prostatic Dis. 2021 Sep 1;24(3):718–24. Rasmussen KM, Patil V, Li C, Yong C, Appukkutan S, Grossman JP, et al. Survival Outcomes by Race and Ethnicity in Veterans with Nonmetastatic Castration-Resistant Prostate Cancer. JAMA Netw Open. 2023;6(10):E2337272. Riviere P, Luterstein E, Kumar A, Vitzthum LK, Deka R, Sarkar RR, et al. Survival of African American and non-Hispanic white men with prostate cancer in an equal-access health care system. Cancer. 2020;126(8):1683–90. Rapiti E, Fioretta G, Schaffar R, Neyroud-Caspar I, Verkooijen HM, Schmidlin F, et al. Impact of socioeconomic status on prostate cancer diagnosis, treatment, and prognosis. Cancer. 2009;115(23):5556–65. Stokes WA, Hendrix LH, Royce TJ, Allen IM, Godley PA, Wang AZ, et al. Racial differences in time from prostate cancer diagnosis to treatment initiation: A Population-Based Study. Cancer. 2013;119(13):2486–93. Eylert MF, Bahl A, Hounsome L, Verne J, Jefferies ER, Persad RA. The impact of socio-economic deprivation on incidence, treatment and mortality from prostate cancer in England, 1990–2010. J Clin Urol. 2016;9(2):93–101. Shafique K, Morrison DS. Socio-Economic Inequalities in Survival of Patients with Prostate Cancer: Role of Age and Gleason Grade at Diagnosis. PLoS One. 2013;8(2):1–8. Kish JK, Yu M, Percy-Laurry A, Altekruse SF. Racial and ethnic disparities in cancer survival by neighborhood socioeconomic status in Surveillance, Epidemiology, and End Results (SEER) registries. J Natl Cancer Inst Monogr. 2014;2014(49):236–43. Dee EC, Arega MA, Yang DD, Butler SS, Mahal BA, Sanford NN, et al. Disparities in Refusal of Locoregional Treatment for Prostate Adenocarcinoma. JCO Oncol Pract. 2021;17(10):e1489–501. Hougen HY, Swami N, Dee EC, Alshalalfa M, Meiyappan K, Florez N, et al. Disparities in Diagnosis, Treatment Access, and Time to Treatment Among Hispanic Men With Metastatic Prostate Cancer. JCO Oncol Pract. 2023;19(8):645–53. Dee EC, Todd R, Ng K, Aidoo-Micah G, Amen TB, Moon Z, et al. Racial disparities in prostate cancer in the UK and the USA: similarities, differences and steps forwards. Nat Rev Urol [Internet]. 2025;22(4):223–34. Available from: http://dx.doi.org/10.1038/s41585-024-00948-x Lavrakas P. Completion Rate. Encyclopedia of Survey Research Methods. 2013. Dee EC, Taunk NK, Chino FL, Deville C, McClelland S, Muralidhar V, et al. Shorter Radiation Regimens and Treatment Noncompletion Among Patients With Breast and Prostate Cancer in the United States: An Analysis of Racial Disparities in Access and Quality. JCO Oncol Pract. 2023;19(2):e197–212. Senft N, Hamel LM, Manning MA, Kim S, Penner LA, Moore TF, et al. Willingness to Discuss Clinical Trials among Black vs White Men with Prostate Cancer. JAMA Oncol. 2020;6(11):1773–7. Riaz I Bin, Islam M, Ikram W, Naqvi SAA, Maqsood H, Saleem Y, et al. Disparities in the Inclusion of Racial and Ethnic Minority Groups and Older Adults in Prostate Cancer Clinical Trials: A Meta-analysis. JAMA Oncol. 2023;9(2):180–7. Leger P, Frencher S, Nauseef JT, Jones B, Bilen MA, Brown A, et al. A multi-perspective study assessing Black and African American participation barriers in prostate cancer clinical trials. Future Oncology [Internet]. 2025;21(8):967–73. Available from: https://doi.org/10.1080/14796694.2025.2467519 Eggly S, Senft N, Kim S, Heath EI, Jang H, Moore TF, et al. Addressing multilevel barriers to clinical trial participation among Black and White men with prostate cancer through the PACCT study. Cancer Med. 2023;12(7):8604–13. Gundrum JD, Go RS. Cancer in the oldest old in the United States: Current statistics and projections. J Geriatr Oncol. 2012;3(4):299–306. Kale S, Hirani S, Vardhan S, Mishra A, Ghode DB, Prasad R, et al. Addressing Cancer Disparities Through Community Engagement: Lessons and Best Practices. Cureus. 2023;15(8). Wright D, Foster R, Miles P, Duffield N, Rickard S, Frankland J, et al. Communities against cancer: a qualitative study assessing the effectiveness of a community engagement initiative in improving cancer awareness for marginalised communities. BMC Public Health [Internet]. 2025;25(1). Available from: https://doi.org/10.1186/s12889-025-23179-0 McKay RR, Gold T, Zarif JC, Chowdhury-Paulino IM, Friedant A, Gerke T, et al. Tackling Diversity in Prostate Cancer Clinical Trials: A Report From the Diversity Working Group of the IRONMAN Registry. JCO Glob Oncol. 2021;(7):495–505. Shore ND, Morgans AK, El-Haddad G, Srinivas S, Abramowitz M. Addressing Challenges and Controversies in the Management of Prostate Cancer with Multidisciplinary Teams. Target Oncol [Internet]. 2022;17(6):709–25. Available from: https://doi.org/10.1007/s11523-022-00925-7 Dickman SL, Himmelstein DU, Woolhandler S. Inequality and the health-care system in the USA. The Lancet [Internet]. 2017;389(10077):1431–41. Available from: http://dx.doi.org/10.1016/S0140-6736(17)30398-7 Additional Declarations There is NO conflict of interest to disclose. Supplementary Files SupplementaryMaterialsDM.docx Supplementary Tables Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information

Ethnic/Racial Variation in Prostate Cancer Net Survival: A Systematic Review

Analyze & explore related research in Conceptio

Related documents