Abstract
Background:
Prostate cancer is one of the most frequently diagnosed malignancies among men worldwide and represents a growing public health concern in low-and-middle-income countries. In sub-Saharan Africa, including South Africa, the disease is often diagnosed at advanced stages due to limited screening services, delayed referrals, and structural health barriers. Understanding the socio-demographic patterns, point prevalence, and geographic origins of patients presenting with prostate cancer is critical for informing targeted cancer control interventions.
Objective:
This study aimed to describe the socio-demographic characteristics, population point prevalence, geographic distribution, and referral patterns of patients diagnosed with prostate cancer at Nelson Mandela Academic Hospital in South Africa.
Methods:
A quantitative cross-sectional study was conducted using secondary data from medical records of men diagnosed with prostate cancer between March 2020 and December 2021. A total of 224 patient records were reviewed. Variables collected included age, district, and sub-district residence, and referral facility. Patients were categorised into low, intermediate, and high-risk clinical groups. Descriptive statistics and chi-square tests were used to assess associations between geographic variables and disease risk. Geographic distribution was visualised using heat maps.
Results:
The mean age of participants was 72.6 years (SD ± 8.8). Most patients (61.2%; 95% CI: 54.4%–67.6%) presented with high-risk disease at diagnosis. High-risk disease was most prevalent among patients aged 80 years and older (87.8%; 95% CI: 75.2%–95.4%). The majority of patients originated from the OR Tambo District (75.4%;95% CI: 69.3%–80.9%), particularly the King Sabata Dalindyebo sub-district, followed by Peripheral sub-districts; Amathole District (9.4%; 95% CI: 5.9%–14.0%), Chris Hani District (6.7%; 95% CI: 3.8%–10.8%), Alfred Nzo District (4.9%; 95% CI: 2.5%–8.6%), and Joe Gqabi District (3.6%; 95% CI: 1.6%–6.9 There was no statistically significant association between geographic district and risk category (p = 0.932).
Conclusion:
Most prostate cancer patients presenting to the teaching hospital were diagnosed with advanced disease, irrespective of geographic location. These findings highlight the need for strengthened early detection strategies, decentralised screening services, improved referral pathways, and targeted community awareness programmes in rural provinces of South Africa.
1 Introduction
Prostate cancer is the most frequently diagnosed cancer among men globally and remains a major contributor to cancer-related morbidity and mortality (). According to the global cancer database International Agency for Research on Cancer, approximately 1.4 million new prostate cancer cases were diagnosed worldwide in 2020, accounting for more than 375,000 deaths (). The burden of prostate cancer is increasing rapidly in low-and-middle-income countries (LMICs) due to population ageing, epidemiological transitions, and limited early detection programs.
The disease burden is particularly concerning in Africa, where prostate cancer represents one of the most common cancers among men. Incidence estimates suggest approximately 173,000 new cases annually across the continent, with mortality exceeding 88,000 deaths per year (). Despite these high numbers, prostate cancer incidence in Africa is believed to be underestimated due to incomplete cancer registries, limited diagnostic infrastructure, and underreporting.
Several studies indicate that prostate cancer among African men tends to present with more aggressive disease and poorer clinical outcomes compared with populations in high-income countries (). Multiple factors contribute to this pattern, including genetic susceptibility, environmental influences, limited awareness, and barriers to healthcare access.
In South Africa, prostate cancer is the most commonly diagnosed cancer among men and represents a major contributor to cancer mortality (). Rural provinces such as the Eastern Cape face additional challenges, including long travel distances to specialised hospitals, shortages of specialised oncology services, and weak referral systems. These systemic barriers often result in delayed diagnosis and advanced disease at presentation.
Socio-demographic characteristics play an important role in understanding patterns of cancer diagnosis and healthcare utilisation. Research has shown that factors such as age, geographic location, and socio-economic status influence healthcare-seeking behaviour, screening uptake, and diagnostic delays (–). Understanding these characteristics is therefore essential for designing targeted cancer control strategies and improving early detection.
The Eastern Cape Province has one of the highest levels of poverty and rurality in South Africa. Many communities rely on primary care clinics and district hospitals with limited diagnostic capacity (). Nelson Mandela Academic Hospital serves as the main quaternary referral centre for oncology services in the host district (OR Tambo District) and surrounding districts.
This study aimed to describe the socio-demographic characteristics, population point prevalence, geographic distribution, and referral patterns of patients diagnosed with prostate cancer at Nelson Mandela Academic Hospital. By identifying patterns of disease presentation and referral pathways, the study seeks to inform future cancer control interventions and improve early detection efforts in rural South Africa.
2 Methods
2.1 Study design
A quantitative cross-sectional study design was used and is described in detail in a protocol (). Briefly, secondary data were extracted from patient medical records of men diagnosed with prostate cancer at the Urology and Oncology Clinic of Nelson Mandela Academic Hospital between March 2020 and December 2021.
2.2 Study setting
The study was conducted at Nelson Mandela Academic Hospital, located in Mthatha in the OR Tambo District of the Eastern Cape Province, South Africa. The hospital is a quaternary (Central) referral centre providing specialised services to a largely rural population across several districts. The province has eight health districts. The OR Tambo district has a population exceeding 1.5 million people and is characterised by high levels of poverty, limited infrastructure, and predominantly rural settlements. Only a small proportion of the population resides in urban areas, with most communities relying on primary care facilities and district hospitals for medical services ().
Healthcare services in the district include one quaternary/Central hospital, one level 2/secondary (regional) hospital, 45 district hospitals, community health centres, clinics, and mobile health units. All oncology cases requiring specialised treatment are referred to Nelson Mandela Academic Hospital.
2.3 Study population and sampling
The study population consisted of men diagnosed with prostate cancer and receiving care at Nelson Mandela Academic Hospital during the study period.
A total of 527 patient files were reviewed. Of the 527 records reviewed, 303 records were excluded, including those with incomplete data and those not meeting the inclusion criteria, resulting in 224 records included in the final analysis.
2.3.1 Inclusion criteria
Confirmed diagnosis of prostate cancer
Diagnosis between March 2020 and December 2021
Available socio-demographic and referral data
2.3.2 Exclusion criteria
Unconfirmed diagnosis of prostate cancer
2.4 Data collection
Data were extracted from patient records using a structured data extraction form.
The variables collected included:
Age at diagnosis
District and sub-district of residence
Referral facility
Clinical risk category
Patients were categorised into three risk groups; low-, intermediate-, and high-risk groups based on Gleason score categories, consistent with standard clinical classification systems (e.g., Gleason score ≤6 = low risk; Gleason 7 = intermediate risk; Gleason ≥8 = high risk):
Low risk
Intermediate risk
High risk
2.5 Data analysis
Statistical analysis was conducted using STATA version 19.5. Descriptive statistics were used to summarise demographic characteristics. The Shapiro-Wilk test was used to test the normality of the continuous age variable. Since age was normally distributed, it was summarised using the mean, standard deviation, the minimum and maximum value. Categorical variables were summarised using frequencies, percentages, and graphs.
To estimate population, point prevalence ratios per 100,000 population () by district, sub-district and the province, denominators were sourced from Statistics South Africa's 2022 census data (). Representing the most recent and comprehensive population estimates available at district and sub-district levels. This approach is consistent with standard epidemiological practice in settings with limited intercensal data and allows for comparability with existing literature. Population denominators were restricted to males aged 40 years and older, consistent with established epidemiological thresholds for prostate cancer risk, and to enable comparability with existing literature.
The One-Way Analysis of Variance (ANOVA) test was used to compare the mean ages of the three clinician-determined Gleasen score risk groups (high, intermediate and low). Depending on values of the expected frequencies, the Fisher's or Chi-square tests were used to examine associations between geographic location, referral site and disease risk category. Statistical significance was set at p < 0.05. The 95% confidence interval (95% CI) has been used for the precision of estimates.
The geographic distribution of patients was visualised using heat maps illustrating district-level and sub-district-level referral patterns.
2.6 Ethical considerations
Ethical approval was obtained from the Human Research Ethics Committee of the Faculty of Medicine and Health Sciences at Walter Sisulu University (Reference: 014/2025). Site access approval was obtained from the Eastern Cape Provincial Health Research Committee and Nelson Mandela Academic Hospital management.
Patient confidentiality was maintained throughout the study. No identifying patient information was collected or reported.
3 Results
3.1 Patient characteristics
A total of 224 patient records of men diagnosed with prostate cancer at Nelson Mandela Academic Hospital between March 2020 and December 2021 were included in the analysis. Patients originated from five of eight (62.5%) districts within the Eastern Cape Province. The majority of patients were from the OR Tambo District, accounting for 169 cases (75.4%; 95% CI: 69.3%–80.9%), followed by Amathole District (n = 21; 9.4%: 95% CI: 5.9%–14.0%), Chris Hani District (n = 15; 6.7%; 95% CI: 3.8%–10.8%), Alfred Nzo District (n = 11; 4.9%; 95% CI: 2.5%–8.6%), and Joe Gqabi District (n = 8; 3.6%; 95% CI: 1.6%–6.9%).
The mean age of patients at diagnosis was 72.6 years (standard deviation ± 8.9), with ages ranging from 50 to 99 years. The largest proportion of patients fell within the 70–79-year age group (n = 92 or 41.1%; 95% CI: 34.6%–47.8%), followed by those aged 50–69 years (n = 83 or 37.1%; 95% CI: 30.7%–43.7%).
Overall, most patients presented with advanced disease at diagnosis. High-risk prostate cancer accounted for 137 cases (61.2%; 95% CI: 54.4%–67.6%), while intermediate-risk disease was observed in 69 patients (30.8%; 95% CI: 24.8%–37.3%).
3.2 Age distribution and disease severity
Patients aged between 80 and 99 years demonstrated the highest proportion of high-risk disease at diagnosis, with 43 out of 49 patients (87.8%; 95% CI: 75.2%–95.4%) classified in the high-risk category.
Among patients aged 70–79 years, high-risk disease was also common, accounting for 54 out of 92 patients (58.7%; 95% CI: 47.9%–68.9%), while intermediate-risk disease was observed in 35 patients (38.0%; 95% CI: 28.1%–48.8%).
In the younger age category of 50–69 years, disease severity appeared slightly less pronounced but still substantial. High-risk disease accounted for 40/83 cases or 48.2% (95% CI: 25.9%–47.4%) of cases in this group, while intermediate-risk disease accounted for 30/83 or 36.1% (95% CI: 25.9%–47.4%). Further shown in Table 1 is that mean ages of patients between the three risk groups were statistically different (p-value = 0.001).
Table 1
| Characteristics | High | Intermediate | Low | Overall | p-value | ||||
|---|---|---|---|---|---|---|---|---|---|
| n (%) | 137 | (61.2) | 69 | (30.8) | 18 | (8.0) | 224 | (100.0) | <0.001a |
| Age, years; mean ± sd (min-max) | 74.6 ± 9.1 | (50–99) | 71.0 ± 6.0 | (60–85) | 63.6 ± 10.3 | (50–85) | 72.6 ± 8.9 | (50–99) | 0.001b |
| Age categories, years; n (%) | |||||||||
| 50–69 | 40 | (48.2) | 30 | (36.1) | 13 | (15.7) | 83 | (100.0) | <0.001c |
| 70–79 | 54 | (58.7) | 35 | (38.0) | 3 | (3.3) | 92 | (100.0) | |
| 80–99 | 43 | (87.8) | 4 | (8.2) | 2 | (4.1) | 49 | (100.0) | |
| District; n (%) | |||||||||
| OR Tambo | 102 | (60.4) | 52 | (30.8) | 15 | (8.9) | 169 | (100.0) | 0.932 |
| Alfred Nzo | 7 | (63.6) | 3 | (27.3) | 1 | (9.1) | 11 | (100.0) | |
| Chris Hani | 10 | (66.7) | 5 | (33.3) | 0 | (0.0) | 15 | (100.0) | |
| Joe Gqabi | 4 | (50.0) | 4 | (50.0) | 0 | (0.0) | 8 | (100.0) | |
| Amathole | 14 | (66.7) | 5 | (23.8) | 2 | (9.5) | 21 | (100.0) | |
| Sub-district | |||||||||
| KSD | 62 | (61.4) | 29 | (28.7) | 10 | (9.9) | 101 | (100.0) | – |
| Nyandeni | 16 | (59.3) | 10 | (37.0) | 1 | (3.7) | 27 | 100.0) | |
| Mhlontlo | 13 | (65.0) | 6 | (30.0) | 1 | (5.0) | 20 | (100.0) | |
| Ingquza Hill | 9 | (47.4) | 7 | (36.8) | 3 | (15.8) | 19 | (100.0) | |
| Port St Johns | 2 | (100.0) | 0 | (0.0) | 0 | (0.0) | 2 | (100.0) | |
| AB Xuma | 10 | (66.7) | 5 | (33.3) | 0 | (0.0) | 15 | (100.0) | |
| Elundini | 4 | (50.0) | 4 | (50.0) | 0 | (0.0) | 8 | (100.0) | |
| Mbashe | 11 | (64.7) | 4 | (23.5) | 2 | (11.8) | 17 | (100.0) | |
| Umzimvubu | 4 | (80.0) | 1 | (20.0) | 0 | (0.0) | 5 | (100.0) | |
| Winnie Madikizela Mandela | 2 | (66.7) | 1 | (33.3) | 0 | (0.0) | 3 | (100.0) | |
| Matatiele | 0 | (0.0) | 1 | (50.0) | 1 | (50.0) | 2 | (100.0) | |
| Mnquma | 3 | (75.0) | 1 | (25.0) | 0 | (0.0) | 4 | (100.0) | |
| Ntabankulu | 1 | (100.0) | 0 | (0.0) | 0 | (0.0) | 1 | (100.0) | |
Patient characteristics by prostate cancer risk group.
Min, minimum; max, maximum; sd, standard deviation; KSD, King Sabata Dalindyebo.
Two-sample test of proportions was used.
One-way analysis of variance test (ANOVA) was used.
Chi-squared test used; Fisher's exact test used.
3.3 Geographic distribution of patients
Patients included in the study originated from several districts across the Eastern Cape Province, although the distribution was uneven. The majority of patients were residents of the OR Tambo district.
Within the OR Tambo District, the King Sabata Dalindyebo (KSD) sub-district contributed to the largest number of patients, with 101/224 cases or 45.1% (95% CI: 38.5%–51.9%). Other subdistricts within OR Tambo included Nyandeni (27/224 cases or 12.1%; 95% CI: 8.1%–17.1%), Mhlontlo (20 cases), Ingquza Hill (19 cases), and Port St Johns (2 cases). Patients from outside the OR Tambo District were also represented, although in smaller numbers. Amathole District contributed 21 patients, while Alfred Nzo District contributed 11 patients. Chris Hani and Joe Gqabi Districts contributed 15 and 8 patients, respectively, as can be seen in Figure 1.
Figure 1
Despite the variation in the number of cases originating from each district, statistical analysis revealed no significant association between geographic district and clinical risk category (p = 0.932). High-risk disease was consistently observed across all districts.
Heat map analysis was used to visually explore geographic patterns in the distribution of prostate cancer cases across the Eastern Cape Province. District-level health maps demonstrated a concentration of cases within the OR Tambo District, particularly around the district where the study site is located.
Analysis of patient distribution at the sub-district level revealed further geographic patterns. The KSD sub-district, where the study site (quaternary hospital) is located, accounted for the highest number of cases across all risk categories. Within this sub-district, 61.4% of patients were classified as high risk, 28.7% as intermediate risk, and 9.9% as low risk. In the Nyandeni sub-district, high-risk disease accounted for 59.3% of cases, while intermediate-risk disease represented 37.0%. Only one patient (3.75%) presented with low-risk disease.
Similarly, in the Mhlontlo sub-district, 65.0% (n = 13) of patients were classified as high risk, while 30.0% (n = 6) were intermediate risk and 5.0% (n = 1) were low risk. Peripheral sub-districts demonstrated smaller patient numbers but often exhibited higher proportions of advanced disease. For example, in Port St Johns, both recorded patients presented with high-risk disease. In Umzimvubu, 80.0% (n = 4) of patients were classified as high risk.
Sub-district heat maps further illustrated a dense cluster of cases in the KSD sub-district, with progressively lower-case densities observed as distance from the study site increased. Peripheral sub-districts such as Ntabankulu and Elundini appeared underrepresented in the number of referrals but contributed disproportionately high-risk cases when referrals occurred (Figure 2).
Figure 2
3.4 Population prevalence per 100,000 male population older than 40 years
Table 2 shows marked variation in prostate cancer prevalence across districts and sub-districts in the Eastern Cape. At the provincial level, the overall prevalence is 43 cases per 100,000 males who are 40 years and older, with OR Tambo district recording the highest burden at 119 per 100,000 population, compared to much lower rates in Alfred Nzo and Chris Hani (both 13 per 100,000). Within OR Tambo, the King Sabata Dalindyebo (KSD) sub-district stands out with a prevalence of 202 per 100,000, followed by Nyandeni and Mhlontlo (both 98 per 100,000). By contrast, districts such as Ntabankulu and Matatiele report very low prevalence levels (around 8 per 100,000).
Table 2
| Unit | Population | Crude Cases | Overall, per 100,000 | Crude High risk | High risk per 100,000 | Crude Intermediate risk | Intermediate risk per 100,000 | Crude low risk | Low risk per 100,000 |
|---|---|---|---|---|---|---|---|---|---|
| District | |||||||||
| OR Tambo | 142,407 | 169 | 119 | 102 | 72 | 52 | 37 | 15 | 11 |
| Alfred Nzo | 86,651 | 11 | 13 | 7 | 8 | 3 | 3 | 1 | 1 |
| Chris Hani | 116,138 | 15 | 13 | 10 | 9 | 5 | 4 | 0 | 0 |
| Joe Gqabi | 51,683 | 8 | 15 | 4 | 8 | 4 | 8 | 0 | 0 |
| Amathole | 124,871 | 21 | 17 | 14 | 11 | 5 | 4 | 2 | 2 |
| Provincial Totala | 521,750 | 224 | 43 | 137 | 26 | 69 | 13 | 18 | 3 |
| Sub-district | |||||||||
| KSD | 50,096 | 101 | 202 | 62 | 124 | 29 | 58 | 10 | 20 |
| Nyandeni | 27,532 | 27 | 98 | 16 | 58 | 10 | 36 | 1 | 4 |
| Mhlontlo | 20,495 | 20 | 98 | 13 | 63 | 6 | 29 | 1 | 5 |
| Ingquza Hill | 29,291 | 19 | 65 | 9 | 31 | 7 | 24 | 3 | 10 |
| Port St Johns | 14,994 | 2 | 13 | 2 | 13 | 0 | 0 | 0 | 0 |
| AB Xuma | 17,066 | 15 | 88 | 10 | 59 | 5 | 29 | 0 | 0 |
| Elundini | 18,456 | 8 | 43 | 4 | 22 | 4 | 22 | 0 | 0 |
| Mbhashe | 27,885 | 17 | 61 | 11 | 39 | 4 | 14 | 2 | 7 |
| Mzimvubu | 22,062 | 5 | 23 | 4 | 18 | 1 | 5 | 0 | 0 |
| Winnie Madikizela Mandela | 27,584 | 3 | 11 | 2 | 7 | 1 | 4 | 0 | 0 |
| Matatiele | 24,543 | 2 | 8 | 0 | 0 | 1 | 4 | 1 | 4 |
| Mnquma | 32,813 | 4 | 12 | 3 | 9 | 1 | 3 | 0 | 0 |
| Ntabankulu | 12,461 | 1 | 8 | 1 | 8 | 0 | 0 | 0 | 0 |
Prostate cancer prevalence per 100,000 population of males 40 years and older.
KSD, King Sabata Dalindyebo sub-district.
Provincial population is limited only to the five referring districts of males 40 years and older.
3.5 Referral hospital distribution
Referral patterns varied across healthcare facilities within the province. Major referral facilities closer to the study site, such as Mthatha Regional Hospital and Nessie Knight Hospital, referred the largest numbers of patients. Patients referred from these facilities included cases across all risk categories, including low-risk and intermediate-risk disease. This may indicate relatively stronger screening, diagnostic, and referral processes within facilities that are geographically closer to specialised services.
In contrast, remote facilities such as St Elizabeth Hospital, Bambisana Hospital, and Mqanduli Clinic referred significantly fewer patients. However, patients referred from these facilities predominantly presented with high-risk disease. For example, referrals from Port St Johns and Ntabankulu were exclusively high-risk cases (Figure 3).
Figure 3
Mapping of referring healthcare facilities revealed distinct geographic clusters within the Eastern Cape Province. Facilities were not evenly distributed across districts but instead concentrated around urban centres and areas with stronger healthcare infrastructure. Districts with higher referral activity contained larger clusters of healthcare facilities referring patients to the study site. These districts also demonstrated higher patient volumes, suggesting stronger integration within the provincial referral system.
Conversely, districts with lower referral activity had fewer referring facilities and reduced referral intensity. These areas may represent regions where referral systems are weaker or where access to diagnostic services is limited. The spatial analysis highlights potential gaps in healthcare access and referral pathways across the province. Areas with low referral activity may benefit from targeted interventions aimed at strengthening diagnostic capacity and improving referral coordination within the health system, as shown in.
4 Discussion
This study provides important insights into the socio-demographic characteristics and referral patterns of prostate cancer survivors presenting to a teaching hospital in a predominantly rural region of South Africa. This study site is the main referral site for an estimated 55% (521750/950126) of males who are 40 years and older in South Africa's Eastern Cape province (). To the research team's knowledge, it is the first study which assesses and maps the epidemiology of prostate cancer in the Eastern Cape province in South Africa.
The findings demonstrate a high burden of advanced disease at diagnosis, with more than sixty percent (60%) of patients classified as high-risk. This grade is associated with aggressive, non-localised and late presenting prostate cancer (). This pattern reflects persistent structural challenges in early cancer detection within resource-constrained health systems (). Furthermore, population prevalence figures highlight a concentrated burden in specific rural sub-districts, underscoring the need for targeted service delivery and training interventions in high-risk areas.
The predominance of high-risk disease observed in this study is consistent with findings from several African countries where prostate cancer is frequently diagnosed at advanced stages (1–3) (, –). For example, studies across sub-Saharan Africa report that approximately 70%–80% of men present with stage III or IV disease, with one multi-country analysis showing 76% diagnosed at advanced stages. Similarly, registry data indicate that over 75% of patients present with stage III/IV disease, with 45% already at stage IV at diagnosis. Comparable patterns are observed in other LMICs, including the Middle East and North Africa, where more than half of patients (54%) present with stage IV disease and up to 78% are diagnosed following symptomatic presentation rather than screening (). Late presentation has been widely attributed to limited access to screening services, inadequate health literacy, sociocultural perceptions surrounding male health, delayed diagnosis and referral, poor management of benign prostatic hyperplasia and systemic barriers to healthcare access (). In many rural settings, prostate-specific antigen (PSA) testing and digital rectal examinations are not routinely available in primary care facilities and level 1 hospitals, thereby delaying early identification of disease. Furthermore, limited access to diagnostic radiological and/or laboratory services, health workforce shortages and limited competence of staff in the healthcare continuum have also been found to weaken the health system's ability to detect early disease (). Stigma associated with patients' attitudes towards therapeutic or prophylactic orchidectomy has also been associated with more advanced disease in some populations (–). However, these latter aspects will be subjects of future studies in this study context.
Delayed access to diagnostic services has important clinical implications for prostate cancer progression. In the absence of routine PSA testing and timely diagnostic evaluation at the primary care level, early-stage and localised prostate cancer may remain undetected. Over time, this allows disease progression to higher-grade and more advanced stages, often with metastasis by the time patients present at tertiary care facilities. This progression highlights the critical importance of early detection pathways in reducing advanced-stage presentation (, , ).
With the youngest patient being 50 and the oldest 99 years, this is consistent with established epidemiological patterns of prostate cancer where the risk increases above 40 (). When categorised by age, the proportion of high-risk cases increased with age with the highest proportion of high-risk cases occurring among men aged 80 years and older. This finding is consistent with global epidemiological trends (, ) indicating that prostate cancer incidence increases substantially with age (). However, the high proportion of advanced disease at diagnosis likely reflects delays in healthcare utilisation and reduced participation in opportunistic screening programmes.
Established GLOBOCAN 2020/2021 statistics indicate that prostate cancer prevalence remains lower across Africa and other low- and middle-income countries (approximately 27–28 per 100,000 males) compared with South Africa, where prevalence is substantially higher (approximately 100 per 100,000 males) owing to improved case ascertainment and diagnostic capacity. In comparison, the provincial prevalence observed in this study (43 per 100,000 males aged ≥40 years) is lower than national estimates but higher than continental averages, with markedly higher sub-district prevalence near the quaternary referral hospital, suggesting geographic disparities in detection rather than true differences in disease burden (). Geographic analysis revealed important spatial disparities in referral patterns. Most patients originated from OR Tambo district, particularly the King Sabata Dalindyebo sub-district, where the specialised hospital is located. This pattern likely reflects both population density and proximity to specialised services. Conversely, peripheral sub-districts contributed fewer referrals but exhibited disproportionately high-risk disease at presentation. Similar patterns have been observed in other rural African settings, where distance from specialised care facilities has been associated with delayed diagnosis and poorer cancer outcomes (, ).
The study found no statistically significant association between geographic district and disease risk category. This suggests that late presentations are a widespread challenge across the region rather than being confined solely to remote areas. Even patients residing relatively close to the study site frequently presented with advanced disease. This finding highlights the broader systemic gaps in early detection strategies and suggests that proximity to specialised healthcare alone does not guarantee timely diagnosis.
Similar patterns have been reported in other African and South African studies, where late-stage prostate cancer presentation remains common irrespective of proximity to specialised and/or teaching health facilities. These findings have been attributed to systemic limitations in primary care detection, delayed referrals, and limited availability of PSA testing, rather than geographic remoteness alone. Together, this evidence supports the interpretation that late presentation reflects broader health-system and patient constraints rather than distance to specialised care (, , ).
Several structural barriers within the health system may contribute to these delays. Primary care facilities in Rural South Africa often face shortages of trained personnel, diagnostic equipment, and laboratory services (, ). As a result, early symptoms of prostate cancer may not be adequately investigated at the first point of contact within the health system. Additionally, referral pathways between primary care facilities and teaching hospitals are often fragmented, leading to prolonged delays before specialist consultation ().
The spatial patterns identified through heat map analysis further highlight the unequal distribution of healthcare access across the region. Peripheral areas such as Ntabankulu and Elundini appeared underrepresented in referral numbers but demonstrated high-risk disease when patients eventually reached the teaching hospital. These findings reinforce the concept of “geographic vulnerability”, whereby individuals residing in remote areas face multiple layers of disadvantage, including transportation challenges, financial barriers, and limited healthcare infrastructure (, , ).
From a cancer control perspective, these findings have several important implications. First, strengthening early detection strategies at the primary care level is essential. Training healthcare workers in prostate cancer risk assessment and integrating opportunistic PSA screening into routine primary care and level 1 hospital services could facilitate earlier diagnosis. Second, mobile screening programmes targeting rural communities may help reduce geographic disparities in access to diagnostic services. Third, adequate resourcing of health facilities at all levels is also important to ensure timely diagnosis and reduce waiting time. Such initiatives have been shown to improve cancer detection in underserved populations. Public health education campaigns targeting men may also play a critical role in improving early healthcare-seeking behaviour. Cultural norms and stigma surrounding men's health issues often contribute to delays in seeking medical attention. Community-based interventions designed to improve awareness of prostate cancer symptoms and risk factors could therefore support earlier presentations.
Finally, strengthening referral systems between primary, secondary, and specialised healthcare facilities is essential to improving cancer outcomes in rural provinces. Establishing decentralised oncology outreach programmes, where specialists periodically visit level 1 hospitals, may help reduce delays in diagnosis and treatment initiation.
5 Conclusion
The findings of this study reveal a clear pattern of late-stage prostate cancer presentation among patients referred to Nelson Mandela Academic Hospital from surrounding districts in the Eastern Cape Province. The geographic spread of referrals and the consistent presence of high-risk disease across districts suggest systemic gaps in early detection and referral processes within the regional healthcare system.
Rather than reflecting isolated facility-level challenges, these patterns point to broader structural limitations affecting cancer detection in rural settings, including uneven diagnostic capacity and barriers to timely specialist care. The spatial referral patterns further highlight disparities in access to services, with peripheral areas contributing fewer referrals despite a high burden of advanced disease. Strengthening cancer control in rural provinces will require health system interventions that prioritise earlier management with at-risk populations and more efficient referral pathways. Enhancing diagnostic capacity at primary and district levels, alongside improved coordination within the referral network, may facilitate earlier identification and management of prostate cancer.
Addressing these systemic gaps is essential to reducing delays in diagnosis and improving prostate cancer outcomes in underserved populations. The results of this study provide evidence to inform regional cancer control strategies aimed at improving equity in access to cancer detection and care.
6 Study strengths and limitations
This study provides valuable evidence on prostate cancer presentation patterns in a rural South African context, a setting that remains underrepresented in global cancer research. The use of geographic mapping to visualise referral patterns represents a key strength, offering important insights into spatial inequities in healthcare access. Nelson Mandela Academic Hospital serves as the principal quaternary oncology centre for the province and therefore captures a substantial proportion of advanced prostate cancer cases within the regional referral pathway.
However, several limitations should be considered when interpreting the findings. The study relied on retrospective review of routinely collected medical records, and some records contained incomplete demographic, referral, or clinical information. Of the 527 patient records initially reviewed, 303 records (57.5%) were excluded because they did not meet the inclusion criteria or had incomplete data required for analysis. While this exclusion rate was substantial, the final sample still included 224 confirmed prostate cancer cases drawn from multiple districts and referral facilities across the Eastern Cape Province, thereby providing important regional insights into disease presentation and referral patterns.
Variability in record completeness across healthcare facilities may have introduced some degree of selection bias. In resource-constrained rural settings, incomplete documentation may reflect broader health system challenges, including fragmented referral processes, variability in routine recordkeeping practices, and differences in diagnostic capacity between facilities. Consequently, excluded patients may have differed from included patients with respect to factors such as age, disease severity, geographic origin, or referral pathway. For example, patients referred from more remote facilities or those with complex clinical pathways may have been more likely to have incomplete records.
These factors may have influenced the observed prevalence estimates and distribution of clinical risk categories. Nevertheless, the consistent pattern predominantly high-risk disease across districts and referral sources suggests that the overall finding of late presentation remains robust within this setting. The findings should therefore be interpreted as reflective of patients successfully captured within the provincial quaternary referral system, rather than as definitive province-wide estimates.
In addition, the use of routinely collected clinical data limited the ability to explore other potentially important determinants of delayed presentation, including healthcare seeking behaviour, socio-cultural influences, referral decision-making processes, and health workforce factors at primary and district healthcare levels. Data were obtained from a single quaternary teaching hospital and did not include the other two teaching hospitals in the Eastern Cape Province. Although Nelson Mandela Academic Hospital serves a large rural catchment population, the findings may not fully represent all prostate cancer referral patterns across the province. Population estimates were based on the most recent available census data.
Statements
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
The studies involving humans were approved by Human Research Ethics Committee of the Faculty of Medicine and Health Sciences at Walter Sisulu University (Reference: 014/2025). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants' legal guardians/next of kin in accordance with the national legislation and institutional requirements.
Author contributions
XN: Conceptualization, Investigation, Methodology, Project administration, Writing – original draft. SS: Data curation, Writing – review & editing. SN: Supervision, Writing – review & editing. WC: Resources, Supervision, Writing – review & editing. AL: Data curation, Writing – review & editing. TS: Data curation, Formal analysis, Writing – review & editing. SM: Conceptualization, Methodology, Supervision, Validation, Writing – original draft.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This research was funded by the South African Medical Research Council (SAMRC), grant number 57035. The article processing charge (APC) was funded by the Walter Sisulu University School of Public Health. The South African Medical Research Council provides financial support for the programme; however, the content of this is solely the responsibility of the authors and does not necessarily represent the official views of the funder.
Acknowledgments
The authors acknowledge the support of the South African Medical Research Council through its Division of Research Capacity Development, with funding received from the National Treasury of South Africa. The content of this publication is the sole responsibility of the authors and does not necessarily represent the official views of the funders. The authors also express their sincere gratitude to the Health Sciences Research Ethics Scientific Review Committee at Walter Sisulu University for their guidance and oversight during the research process. We further acknowledge the support provided by the School of Public Health within the Faculty of Medicine and Health Sciences at Walter Sisulu University.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Summary
Keywords
cancer control, prostate cancer, referral systems, rural health, socio-demographic characteristics, South Africa
Citation
Ntlongweni X, Sibulawa S, Nomatshila SC, Chitha WW, Limaphi A, Sithole T and Mabunda SA (2026) Socio-demographic characteristics, point prevalence and referral patterns of prostate cancer patients in a South African rural teaching hospital: a cross-sectional study. Front. Cancer Control Soc. 4:1858841. doi: 10.3389/fcacs.2026.1858841
Received
17 April 2026
Revised
10 June 2026
Accepted
16 June 2026
Published
03 July 2026
Volume
4 - 2026
Edited by
Moses Gitonga, Dedan Kimathi University of Technology, Kenya
Reviewed by
Florence Mbuthia, Dedan Kimathi University of Technology, Kenya
Joko Wibowo Sentoso, Sebelas Maret University, Indonesia
Updates
Copyright
© 2026 Ntlongweni, Sibulawa, Nomatshila, Chitha, Limaphi, Sithole and Mabunda.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Xolelwa Ntlongweni xntlongweni@wsu.ac.za
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.