Prevalence and Factors Associated with Treatment Delay Among Colorectal Cancer Patients at Mulago National Referral Hospital and the Uganda Cancer Institute: A Cross-Sectional Study

Prevalence and Factors Associated with Treatment Delay Among Colorectal Cancer Patients at Mulago National Referral Hospital and the Uganda Cancer Institute: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); 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 Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prevalence and Factors Associated with Treatment Delay Among Colorectal Cancer Patients at Mulago National Referral Hospital and the Uganda Cancer Institute: A Cross-Sectional Study Brian Kasagga, Paul Ssempebwa, Godfrey Kikuba, Flavius E. Egbe, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7734788/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 Colorectal cancer (CRC) is an important cause of morbidity and mortality in Uganda. Timely treatment initiation is critical for outcomes, yet delays are common. This study assessed treatment delays and associated factors among CRC patients at Mulago National Referral Hospital (MNRH) and the Uganda Cancer Institute (UCI). Objective To determine the diagnosis to treatment interval (DTI), prevalence of treatment delay, and the associated patient and clinicopathologic factors among CRC patients. Methods A hospital-based cross-sectional study was conducted among 67 patients with histologically confirmed CRC between December 2024 and May 2025. Treatment delay was defined as > 31 days between histological diagnosis and first oncologic treatment. Data were collected through interviews and record review. Descriptive statistics summarized demographics and clinical characteristics. Bivariate Poisson regression with robust variance estimation identified factors associated with delay; variables with p < 0.20 entered a multivariable model. Prevalence ratios (PRs) with 95% confidence intervals (CIs) were reported. IRB approval was obtained (Ref: Mak-SOMREC-2024-1048). Results The mean age was 50.5 years (SD: 15.1); 55.2% were female, and 71.6% (n = 48) had advanced-stage disease (Stage III/IV). The median DTI was 53 days (IQR: 25–95), with 70.1% (n = 47) experiencing delays. Median DTI by treatment: chemotherapy 53 days, radiotherapy 79 days, surgery 14 days. While late-stage disease, comorbidities, and long travel distances showed trends toward delay, only socioeconomic status (SES) was significant. Patients with high SES vulnerability (score ≥ 4) had 34% higher prevalence of delay (PR = 1.34, 95% CI: 1.01–1.78, p = 0.042). Conclusion Most CRC patients experienced treatment delays which were widespread and occurred across all categories; regardless of distance to the treatment facility, clinical status, or disease severity. Socioeconomic disadvantage was the only independent predictor, underscoring the role of structural and financial barriers in timely care. Targeted, context-specific interventions are urgently needed to reduce delays and improve outcomes. Trial registration Not applicable Colorectal cancer Treatment delay Diagnosis-to-treatment interval Figures Figure 1 BACKGROUND Colorectal cancer (CRC) represents a major global public health burden, ranking as the third most commonly diagnosed malignancy and the second leading cause of cancer-related mortality worldwide( 1 ). In 2020, an estimated 1.9 million new CRC cases and 930,000 deaths were reported, with projections suggesting a 63% increase in incidence (3.2 million cases annually) and a 73% rise in mortality (1.6 million deaths annually) by 2040 ( 2 ). Although the incidence of CRC has historically been lower in Africa compared to high-income regions, there is growing concern over a rising trend, particularly in Sub-Saharan Africa (SSA). Lifestyle changes, increasing urbanization, dietary shifts towards processed foods, and low screening coverage have contributed to a growing incidence( 3 , 4 ). In 2019, SSA recorded approximately 58,000 new CRC cases, 49,000 deaths, and over 1.3 million disability-adjusted life years (DALYs), underscoring both the growing burden and the limitations in health system capacity to manage it( 5 ). The opposite demographic trend has been observed in high income countries due to robust screening programs and therapeutic advances ( 3 , 4 ). In Uganda, colorectal cancer (CRC) accounts for an increasing share of the national cancer burden. According to the Kampala Cancer Registry, incidence rates have nearly tripled between 2008 and 2021( 6 , 7 ). CRC now ranks as the eighth most commonly diagnosed cancer in Uganda, with an estimated 1,394 new cases and 1,013 deaths annually. The 5-year prevalence stands at approximately 3,000 cases, corresponding to 6.2 per 100,000 population ( 8 , 9 ). However, data on survival outcomes remain limited due to challenges in cancer surveillance, late diagnosis, and underreporting.( 7 ) Health system constraints such as limited endoscopy services, pathology capacity, surgical expertise, and oncology infrastructure further exacerbate delays in diagnosis and treatment( 10 ). These systemic barriers, common across many low- and middle-income countries (LMICs), result in significantly prolonged intervals from symptom onset to definitive treatment—often 1.5 to 4 times longer than in high-income countries (HICs) ( 11 ). Evidence consistently shows that treatment delays are associated with poorer clinical outcomes in colorectal cancer (CRC) patients( 12 – 15 ). This is because delays contribute to tumor progression, increased risk of micro-metastatic disease, and reduced treatment efficacy( 16 ). However, treatment delay is only one of several interrelated factors that influence survival. Tumor biology, including genetic and molecular characteristics such as microsatellite instability and KRAS/BRAF mutations;- also plays a major role in prognosis and therapeutic response( 17 ). Additionally, patient-related factors like comorbidities, performance status (e.g., ECOG score), and socioeconomic conditions—including access to care, education, and financial capacity—can significantly affect treatment outcomes( 18 , 19 ). Psychological distress and prolonged uncertainty may further compromise a patient's ability to manage their illness( 20 ). Treatment delays necessitate more extensive interventions due to disease progression, compromises quality of life, and increases direct healthcare costs. Although multiple factors influence outcomes, this delay remains a critical and potentially modifiable contributor—especially in low-resource settings like Uganda. Yet, few studies in Uganda have quantified treatment delays or examined their underlying causes ( 21 ). Understanding these delays and their context-specific drivers is essential for improving timely access to care and, ultimately, enhancing patient survival. This study therefore aims to determine the interval from diagnosis to treatment initiation and to identify factors associated with treatment delays among colorectal cancer patients in MNRH and UCI. METHODS Study Design and Setting We conducted a hospital-based cross-sectional study at Mulago National Referral Hospital (MNRH) and the Uganda Cancer Institute (UCI) in Kampala, Uganda, between December 2024 and May 2025. These two public tertiary care facilities are Uganda’s principal centers for surgical and oncological care, respectively, and manage colorectal cancer (CRC) patients through an integrated multidisciplinary pathway. The study was approved by the Makerere University School of Medicine Research and Ethics Committee (Ref: Mak-SOMREC-2024-1048) and administrative clearance was obtained from both hospitals. Study Population and Sampling The study population consisted of adult patients (≥ 18 years) with a histologically confirmed diagnosis of colorectal cancer who were initiating oncological treatment (surgery, chemotherapy, or radiotherapy) at MNRH or UCI during the study period. Patients unable to provide informed consent or those being treated for recurrent disease were excluded. A consecutive sampling method was used to enroll participants. The sample size was calculated for a primary objective of estimating prevalence. Using the Kish-Leslie formula for a finite population, a prevalence of 43% for treatment delay based on prior study by Wassie et.al ( 31 ), a 95% confidence level, and a 5% margin of error, a minimum sample size of 67 participants was required. Data Management and Statistical Analysis Data were collected through patient interviews and medical record reviews using a pre-tested, structured questionnaire. The primary outcome was treatment delay, defined as an interval of > 31 days from histopathological confirmation to the first oncologic treatment ( 31 ). Data were analyzed using SPSS version 20. Descriptive statistics were used to summarize participant characteristics. The prevalence of delay was reported as a proportion with a 95% confidence interval (CI). Bivariate and Multivariable Regression: Bivariate analysis was performed using Poisson regression with robust variance to estimate crude Prevalence Ratios (PR) for factors associated with treatment delay. Variables with a p-value < 0.2 were included in a multivariable Poisson regression model to generate adjusted Prevalence Ratios (aPR) with 95% CIs. A p-value < 0.05 was considered statistically significant. Development of Composite Scores: Recognizing the multidimensional nature of barriers to care, we developed three composite scores post-hoc to better capture complex constructs: Socioeconomic Status (SES) Score (Range: 0–7): A cumulative score of disadvantage, incorporating employment status (unemployed = 2, previously employed = 1, employed = 0), education level (none = 2, primary = 1, secondary + = 0), marital status (widowed/divorced = 2, single = 1, married = 0), and health behaviors (smoking = 1, alternative medicine use = 1). A higher score indicates greater socioeconomic vulnerability. Disease Severity Score (Range: 2–8): Sum of tumor stage (I = 1, II = 2, III = 3, IV = 4) and tumor grade (1 = 1, 2 = 2, 3 = 3, 4 = 4). Clinical Burden Score (Range: 0–6): Sum of the presence of comorbidities (yes = 1), ECOG performance status (0–4), and low BMI < 18.5 kg/m² (yes = 1). For analysis, the total scores for SES and Clinical Burden were dichotomized into 'Low' and 'High' burden based on the distribution of the data. This is further illustrated in Table 1 of the Supplementary file. The association between these composite scores and treatment delay was then analyzed using the same regression framework described above. RESULTS A total of 67 patients with histologically confirmed colorectal cancer were enrolled in the study. The median age was 56 years (interquartile range [IQR]: 41–62), and 55.2% (n = 37) were female. The cohort had a mean BMI of 22.8 ± 4.5 kg/m². The majority of patients (71.6%, n = 48) presented with advanced-stage (Stage III or IV) disease, and rectal cancer was the most common tumor location (62.7%, n = 42). The first oncologic treatment was systemic chemotherapy for most patients (82.1%, n = 55), followed by radiotherapy (10.4%, n = 7) and surgical resection (7.5%, n = 5). Most participants had an ECOG performance status of 0 or 1 (85.1%, n = 57), and 25.4% (n = 17) had documented comorbidities. The median distance from a patient's residence to the treatment facility was 137.0 km (IQR: 52.0–327.7). Detailed demographic and clinical characteristics are presented in Table 1 Table 1 Baseline characteristics of participants Characteristic Category n (%) or Median (IQR) Age (years) - 56 (41–62) BMI (kg/m²) - 21.6 (19.4–24.3) Distance to facility (km) - 137.0 (52.0–327.7) Sex Male 30 (44.8%) Female 37 (55.2%) Education level No formal education 5 (7.5%) Primary 30 (44.8%) Secondary 18 (26.9%) College/University 12 (17.9%) Postgraduate 2 (3.0%) Employment status Unemployed 16 (23.9%) Employed 11 (16.4%) Private work 34 (50.7%) Employed but not currently working 6 (9.0%) Marital status Married 47 (70.1%) Single 6 (9.0%) Divorced 4 (6.0%) Widowed 10 (14.9%) Religion Protestant 27 (40.3%) Catholic 19 (28.4%) Muslim 7 (10.4%) Baptist 13 (19.4%) Born again 1 (1.5%) Smoking status No 59 (88.1%) Yes 8 (11.9%) Tumor location Colon 21 (31.3%) Rectal 42 (62.7%) Rectosigmoid 4 (6.0%) Tumor grade Grade 1 26 (38.8%) Grade 2 26 (38.8%) Grade 3 13 (19.4%) Grade 4 2 (3.0%) Clinical stage Stage I 4 (6.0%) Stage II 12 (17.9%) Stage III 32 (47.8%) Stage IV 16 (23.9%) Missing data 3 (4.5%) Comorbidities No 50 (74.6%) Yes 17 (25.4%) ECOG status 0 16 (23.9%) 1 41 (61.2%) 2 10 (14.9%) First oncologic treatment Surgery 5 (7.5%) Chemotherapy 55 (82.1%) Radiotherapy 7 (10.4%) Pretreatment CEA Normal 29 (43.3%) Elevated 17 (25.4%) High 21 (31.3%) Treatment Interval and Prevalence of Delay The median time from histological diagnosis to initiation of the first oncologic treatment was 53 days (IQR: 25–95 days), with a wide range from 3 to 1,133 days. The treatment interval for surgery was (median: 14 days; IQR: 6.5–36.0), chemotherapy (median: 53 days; IQR: 27.0–96.0), and radiotherapy (median: 79 days; IQR: 46.0–110.0). A detailed breakdown of the treatment interval across all patient subgroups is provided in Supplementary Table 2. Applying the pre-defined threshold of > 31 days, the prevalence of treatment delay among colorectal cancer patients at MNRH and UCI was 70.1% (47/67, 95% CI: 58.5% − 81.7%). Only 29.9% (20/67) of patients initiated treatment within 31 days (Fig. 1 ). Factors Associated with Treatment Delay Bivariate Analysis of Individual Factors Bivariate analysis using Poisson regression with robust variance was performed to assess the association between various patient, clinical, and pathological factors and treatment delay. The complete results are presented in Table 2 . Table 2 Bivariate analysis of factors associated with treatment delay (N = 67). Variable Category PR 95% CI p-value Sex Male (Ref: Female) 0.91 0.66–1.26 0.580 Age < 50 yrs (Ref: ≥50 yrs) 0.84 0.59–1.20 0.337 Distance to facility ≥ 200 km (Ref: <200 km) 0.88 0.65–1.19 0.403 Comorbidities Yes (Ref: No) 0.80 0.60–1.08 0.144 Education Level No formal education 0.80 0.52–1.24 0.318 Primary 0.77 0.63–0.93 0.008 Secondary 0.50 0.32–0.79 0.003 College/University 0.75 0.54–1.04 0.084 (Ref: Postgraduate) 1.00 - - Marital Status Married 0.85 0.59–1.23 0.388 Single 0.83 0.44–1.59 0.580 Divorced 0.94 0.49–1.79 0.845 (Ref: Widowed) 1.00 - - Smoking History Non-smoker (Ref: Smoker) 0.88 0.50–1.54 0.649 Alternative Therapy No (Ref: Yes) 0.94 0.66–1.35 0.751 Tumor Location Colon 1.33 0.48–3.72 0.582 Rectal 1.48 0.55–4.00 0.444 (Ref: Rectosigmoid) 1.00 - - Tumor Grade Grade 1 1.31 0.32–5.38 0.710 Grade 2 1.54 0.38–6.25 0.547 Grade 3 1.38 0.33–5.80 0.656 (Ref: Grade 4) 1.00 - - Clinical Stage Stage I 1.13 0.59–2.16 0.724 Stage II 0.88 0.49–1.56 0.651 Stage III 1.14 0.78–1.66 0.509 (Ref: Stage IV) 1.00 - - ECOG Status 0 1.07 0.65–1.76 0.785 1 0.98 0.62–1.54 0.916 (Ref: 2) 1.00 - - PR: Prevalence Ratio; CI: Confidence Interval; Ref: Reference category As shown in Table 2 , the factors primary education (PR = 0.77, 95% CI: 0.63–0.93, p = 0.008) and secondary education (PR = 0.50, 95% CI: 0.32–0.79, p = 0.003) demonstrated a statistically significant association with a lower prevalence of treatment delay compared to the postgraduate reference category. However, it is critical to note that the reference group (postgraduate) contained only 2 participants, making this comparison unstable and the results likely artefactual. The presence of comorbidities showed a trend towards association with delay but was not statistically significant (PR = 0.80, 95% CI: 0.60–1.08, p = 0.144). All other demographic, clinical, and pathological factors showed no significant association with treatment delay. Analysis Using Composite Scores Given the limitations of interpreting the education variable in isolation and to better capture the multidimensional nature of barriers to care, we developed and employed three composite scores. (See Supplementary Table 2 for full scoring details.) In bivariate analysis, a high Socioeconomic Status (SES) composite score, which incorporated education, employment, marital status, and health behaviors, was associated with a 38% higher prevalence of treatment delay (PR = 1.38, 95% CI: 1.06–1.79, p = 0.020). The Disease Severity and Clinical Burden composite scores were not significantly associated with delay in bivariate analysis (Table 3 ). Table 3 Bivariate analysis of composite scores associated with treatment delay. Variable Category PR 95% CI p-value SES Composite Score High (Ref: Low) 1.38 1.06–1.79 0.020 Disease Severity Score High (Ref: Low) 1.13 0.83–1.53 0.454 Clinical Burden Score High (Ref: Low) 0.84 0.62–1.13 0.247 Final Adjusted Model The final multivariable model included the SES composite score, adjusted for the a priori confounders of age, sex, and distance to the treatment facility. In this adjusted model, a high SES score remained a significant independent predictor of treatment delay. Patients with high socioeconomic vulnerability had a 34% higher prevalence of delay compared to those with lower vulnerability (aPR = 1.34, 95% CI: 1.01–1.78, p = 0.042). No significant associations were observed for age, sex, or distance in the final model (Table 4 ). Table 4 Final multivariable model of factors associated with treatment delay. Variable Category aPR 95% CI p-value SES Composite Score High (Ref: Low) 1.34 1.01–1.78 0.042 Age Group < 50 yrs (Ref: ≥50) 0.89 0.64–1.26 0.518 Sex Male (Ref: Female) 0.98 0.71–1.35 0.888 Distance to facility (per km) 1.00 0.999–1.001 0.970 aPR: Adjusted Prevalence Ratio Discussion This study set out to determine the extent of treatment delays and associated factors among patients diagnosed with colorectal cancer at MNRH and UCI. The median duration from diagnosis to treatment was 53 days (IQR: 25–95), with 70.1% of patients experiencing delays exceeding 31 days. Although most individual demographic, clinical and pathologic variables were not statistically significant, socioeconomic vulnerability emerged as a crucial factor, with patients exhibiting higher SES composite scores having 34% greater prevalence of treatment delays (PR = 1.34; 95% CI: 1.01–1.78; p = 0.042). When compared to previous studies and against international benchmarks, our findings reveal substantial disparities in timely oncologic CRC cancer care. Compared to recommended treatment intervals of 2–4 weeks for most common cancers Uganda's median delay of 53 days represents a significant deviation from global comparisons ( 11 , 22 ). This delay is considerably longer than intervals reported in other settings; for instance, Bouter et al. in South Africa reported a median diagnosis-to-treatment interval of 29 days among colorectal cancer patients( 23 ), while studies from Poland and Italy report averages of 38 days and median of 28 days respectively( 24 , 25 ). These discrepancies likely reflect variations in healthcare infrastructure and access to timely care between different resource settings ( 26 ). Within the Ugandan context, our findings both align and contrast with previous research. Kibudde et al., examining treatment intervals across various cancers, reported a median waiting time of 33 days (range: 16–416) ( 27 ), which was numerically higher, but within a comparable range. The overall median therefore reflected shorter treatment times associated with other malignancies, such as cervical, head and neck, sarcoma, and esophageal cancers, which were more prevalent in that cohort. Furthermore, CRC was underrepresented in their study, with only one patient included( 27 ). In contrast, our study focused exclusively on colorectal cancer, which may follow different treatment pathways. However, our findings regarding modality-specific delays show concerning consistency with local patterns; similar to Kibudde et al., we found substantially longer turnaround times for radiotherapy (median 79 days) and chemotherapy (median 53 days). This disparity reflects the additional complexities of radiotherapy planning and systemic challenges including limited equipment availability and fragmented care coordination( 28 – 30 ). The prevalence of treatment delay in our study (70.1%) exceeds rates reported in other African settings, including Ethiopia (43%) and Botswana (50.4%), though methodological differences in defining delay thresholds must be acknowledged ( 31 , 32 ). This high prevalence underscores the profound systemic challenges within Uganda's oncology infrastructure, characterized by limited radiotherapy capacity, diagnostic bottlenecks, and centralized care concentrated in Kampala ( 30 , 33 ). The biological implications of these delays are particularly concerning given tumor doubling time in colorectal cancer ranges from 92 to over 1032 days( 34 ); our mean interval of 100 days represents a period sufficient for meaningful tumor progression, potentially compromising curative outcomes, especially for the 71.6% of patients who already present with advanced-stage disease ( 35 ). Our analysis of associated factors revealed that socioeconomic vulnerability, captured through a multidimensional composite score, was the only independent predictor of treatment delay. The counterintuitive finding regarding education level where lower educational attainment at first appeared protective against delay - highlights the limitations of analyzing individual socioeconomic indicators in isolation. When educational status was incorporated into the composite SES measure alongside employment, marital status, and health behaviors, its effect was subsumed within a broader pattern of socioeconomic disadvantage that more accurately predicted treatment delays. This finding emphasizes that in resource-constrained settings like Uganda, the cumulative burden of disadvantage creates structural barriers that outweigh individual factors in determining access to timely care ( 36 , 37 ). The consistent relationship between socioeconomic disadvantage and treatment delay aligns with findings from other settings across sub-Saharan Africa. Buckle et al. in Kenya reported longer waiting times among patients from rural and lower-income backgrounds( 38 ) while Wassie et al. in Ethiopia linked delays to financial hardship and lack of awareness ( 31 ). Even in high-income settings, composite SES indices have predicted disparities in cancer care timelines( 18 , 39 ), highlighting the universal influence of structural inequality on health outcomes. Our study contributes to this literature by demonstrating the particular utility of multidimensional SES measures in low-resource settings where traditional income-based metrics may fail to capture the complexity of socioeconomic vulnerability. Several limitations should be considered when interpreting our findings. The sample size being small restricted sub analyses. Our composite score, while conceptually robust, was developed post-hoc and requires validation in larger studies. Finally, unmeasured factors such as system factors, cultural beliefs, fear of treatment, and health-seeking behaviour may contribute to delays but were not captured in our assessment. In conclusion, this study demonstrates that treatment delays are pervasive among colorectal cancer patients in Uganda's main referral hospitals, with socioeconomic vulnerability emerging as the primary predictor of delayed care. The median interval of 53 days substantially exceeds international benchmarks and likely contributes to the poor outcomes observed among Ugandan CRC patients. These findings highlight the need for multi-level interventions addressing both structural barriers (through financial protection schemes and infrastructure investment) and systemic inefficiencies (through improved care coordination and wait-time monitoring). Future efforts should prioritize vulnerable patient populations and establish time-to-treatment as a key quality metric in Uganda's evolving oncology care system. Abbreviations CRC Colorectal Cancer DTI Diagnosis to Treatment Interval MDT Multidisciplinary team MNRH Mulago National Referral Hospital PI Principal Investigator SOMREC School of Medicine Research and Ethics Committee TI Treatment interval TNM Tumor, Nodal, Metastasis UCI Uganda Cancer Institute Declarations Ethical Approval and Consent This study was approved with waiver of consent by Makerere University School of Medicine Research and Ethics Committee (Mak-SOMREC-2024-1048) and administrative clearance was obtained from MNRH and UCI. Consent for publication Not applicable Availability of data and materials The primary dataset supporting this study may be obtained from the corresponding author on request Competing interests The authors declare that they have no competing financial or non-financial interests Funding No external funding was obtained in this study Authors’ contributions B.K. conceived the study, collected and analyzed the data, and wrote the first draft of the manuscript. P.S., G.K., assisted with data cleaning. F.E.E. helped with the statistical analysis. P.C.N., P.S., and G.K. provided critical review of the manuscript. E.A.E., J.K., and P.O. supervised the study design, data interpretation, and critically revised the manuscript for important intellectual content. All authors approved the final manuscript. Acknowledgements The authors extend their sincere gratitude to the patients who participated in this study. We also thank the administration and staff of Mulago National Referral Hospital and the Uganda Cancer Institute for their support and cooperation. We are deeply indebted to our data collection team;-Odoki, Arnold, and Sister Frieda for their dedication and invaluable assistance. Authors’ Information Brian Kasagga, MBChB General Surgery Resident, Department of Surgery, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda. E-mail: [email protected] Paul Ssempebwa, MBChB General Surgery Resident, Department of Surgery, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda. E-mail: [email protected] Godfrey Kikuba, MBChB General Surgery Resident, Department of Surgery, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda. E-mail: [email protected] Flavius E. Egbe, B.M.L.S, M.D, M Med (Surgery) Department of Surgery, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda. General Surgeon, Kumba Regional Hospital Annex, Cameroon. E-mail: [email protected] Peace Caroline Nsodi, MBChB Intern Doctor, Mulago National Referral Hospital, Kampala, Uganda. E-mail: [email protected] Joanne Kayaga, MBChB, M.Med Oncologist, Uganda Cancer Institute, Kampala, Uganda. E-mail: [email protected] Paul Okeny, MBChB, M.Med, FCS(ECSA), PhD Lecturer, Department of Surgery, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda. E-mail: [email protected] Emmanuel Alex Elobu, MBChB, M.Med, FCS(ECSA), MBA Colorectal Surgeon, Department of Surgery, Mulago National Referral Hospital, Kampala, Uganda. E-mail: [email protected] References Klimeck L, Heisser T, Hoffmeister M, Brenner H. Colorectal cancer: A health and economic problem. Best Pract Res Clin Gastroenterol. 2023;66:101839. Xi Y, Xu P. 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Kibudde S, Namisango E, Nakaganda A, Atieno M, Bbaale J, Nabwana M, et al. Turnaround time and barriers to treatment of newly diagnosed cancer in Uganda: a mixed-methods longitudinal study. Afr Health Sci. 2022;22(1):327–37. Berardi R, Morgese F, Rinaldi S, Torniai M, Mentrasti G, Scortichini L, et al. Benefits and Limitations of a Multidisciplinary Approach in Cancer Patient Management. Cancer Manag Res. 2020;12:9363–74. Li H, Yu L, Anastasio MA, Chen HC, Tan J, Gay H, et al. Automatic CT simulation optimization for radiation therapy: A general strategy. Med Phys. 2014;41(3):031913. Nakaganda A, Solt K, Kwagonza L, Driscoll D, Kampi R, Orem J. Challenges faced by cancer patients in Uganda: Implications for health systems strengthening in resource limited settings. J Cancer Policy. 2021;27:100263. Abebaw Wassie L, Simie Tsega S, Sharew Melaku M, Aemro A. Delayed treatment initiation and its associated factors among cancer patients at Northwest Amhara referral hospital oncology units: A cross-sectional study. Int J Afr Nurs Sci. 2023;18:100568. Bhatia RK, Rayne S, Rate W, Bakwenabatsile L, Monare B, Anakwenze C et al. Patient Factors Associated With Delays in Obtaining Cancer Care in Botswana. J Global Oncol. 2018(4):JGO1800088. Omotoso O, Teibo JO, Atiba FA, Oladimeji T, Paimo OK, Ataya FS, et al. Addressing cancer care inequities in sub-Saharan Africa: current challenges and proposed solutions. Int J Equity Health. 2023;22(1):189. Bolin S, Nilsson E, Sjödahl R. Carcinoma of the colon and rectum–growth rate. Ann Surg. 1983;198(2):151–8. Lee Y-H, Kung P-T, Wang Y-H, Kuo W-Y, Kao S-L, Tsai W-C. Effect of length of time from diagnosis to treatment on colorectal cancer survival: A population-based study. PLoS ONE. 2019;14(1):e0210465. Ssemata AS, Smythe T, Sande S, Menya A, Hameed S, Waiswa P, et al. Exploring the barriers to healthcare access among persons with disabilities: a qualitative study in rural Luuka district, Uganda. BMJ Open. 2024;14(11):e086194. Kasagga B, Takoutsing BD, Balumuka D, Ambangira F, Kasozi D, Namiiro MA, et al. Protocol for scoping review to identify and characterise surgery, obstetric, trauma and anaesthesia care in Ugandan health policy databases. BMJ Open. 2023;13(7):e070944. Buckle GC, Collins JP, Sumba PO, Nakalema B, Omenah D, Stiffler K, et al. Factors influencing time to diagnosis and initiation of treatment of endemic Burkitt Lymphoma among children in Uganda and western Kenya: a cross-sectional survey. Infect Agent Cancer. 2013;8(1):36. Bourgeois A, Horrill T, Mollison A, Stringer E, Lambert LK, Stajduhar K. Barriers to cancer treatment for people experiencing socioeconomic disadvantage in high-income countries: a scoping review. BMC Health Serv Res. 2024;24(1):670. Additional Declarations No competing interests reported. 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Egbe","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Flavius","middleName":"E.","lastName":"Egbe","suffix":""},{"id":521767008,"identity":"d532e811-f8c1-4b13-b3c9-e6b69691a15c","order_by":4,"name":"Peace Caroline Nsodi","email":"","orcid":"","institution":"Mulago Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peace","middleName":"Caroline","lastName":"Nsodi","suffix":""},{"id":521767009,"identity":"9357f94b-dff8-4ffb-bc4d-5e008594d6fd","order_by":5,"name":"Joanne Kayaga","email":"","orcid":"","institution":"Uganda Cancer Institute","correspondingAuthor":false,"prefix":"","firstName":"Joanne","middleName":"","lastName":"Kayaga","suffix":""},{"id":521767010,"identity":"91f7c77a-b613-4bfd-af42-67f6b39bca65","order_by":6,"name":"Paul Okeny","email":"","orcid":"","institution":"Makerere 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1","display":"","copyAsset":false,"role":"figure","size":11714,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eProportion of patients with treatment delay.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7734788/v1/cc2336aa1c2ddbb84bb9c8db.png"},{"id":93044864,"identity":"da447dd7-30f1-48d9-afdc-d33950c90df4","added_by":"auto","created_at":"2025-10-08 13:02:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1094126,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7734788/v1/90b9917b-0ab5-4bfd-a6d1-d1eb994aa29a.pdf"},{"id":92480584,"identity":"b51c7b81-538b-48b7-bd9f-b5799dcd35f8","added_by":"auto","created_at":"2025-09-30 07:41:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":35218,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7734788/v1/67227e13c45653cfbc029e95.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and Factors Associated with Treatment Delay Among Colorectal Cancer Patients at Mulago National Referral Hospital and the Uganda Cancer Institute: A Cross-Sectional Study","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eColorectal cancer (CRC) represents a major global public health burden, ranking as the third most commonly diagnosed malignancy and the second leading cause of cancer-related mortality worldwide(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In 2020, an estimated 1.9\u0026nbsp;million new CRC cases and 930,000 deaths were reported, with projections suggesting a 63% increase in incidence (3.2\u0026nbsp;million cases annually) and a 73% rise in mortality (1.6\u0026nbsp;million deaths annually) by 2040 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough the incidence of CRC has historically been lower in Africa compared to high-income regions, there is growing concern over a rising trend, particularly in Sub-Saharan Africa (SSA). Lifestyle changes, increasing urbanization, dietary shifts towards processed foods, and low screening coverage have contributed to a growing incidence(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In 2019, SSA recorded approximately 58,000 new CRC cases, 49,000 deaths, and over 1.3\u0026nbsp;million disability-adjusted life years (DALYs), underscoring both the growing burden and the limitations in health system capacity to manage it(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The opposite demographic trend has been observed in high income countries due to robust screening programs and therapeutic advances (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Uganda, colorectal cancer (CRC) accounts for an increasing share of the national cancer burden. According to the Kampala Cancer Registry, incidence rates have nearly tripled between 2008 and 2021(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). CRC now ranks as the eighth most commonly diagnosed cancer in Uganda, with an estimated 1,394 new cases and 1,013 deaths annually. The 5-year prevalence stands at approximately 3,000 cases, corresponding to 6.2 per 100,000 population (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, data on survival outcomes remain limited due to challenges in cancer surveillance, late diagnosis, and underreporting.(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) Health system constraints such as limited endoscopy services, pathology capacity, surgical expertise, and oncology infrastructure further exacerbate delays in diagnosis and treatment(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). These systemic barriers, common across many low- and middle-income countries (LMICs), result in significantly prolonged intervals from symptom onset to definitive treatment\u0026mdash;often 1.5 to 4 times longer than in high-income countries (HICs) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEvidence consistently shows that treatment delays are associated with poorer clinical outcomes in colorectal cancer (CRC) patients(\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This is because delays contribute to tumor progression, increased risk of micro-metastatic disease, and reduced treatment efficacy(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, treatment delay is only one of several interrelated factors that influence survival. Tumor biology, including genetic and molecular characteristics such as microsatellite instability and KRAS/BRAF mutations;- also plays a major role in prognosis and therapeutic response(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Additionally, patient-related factors like comorbidities, performance status (e.g., ECOG score), and socioeconomic conditions\u0026mdash;including access to care, education, and financial capacity\u0026mdash;can significantly affect treatment outcomes(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Psychological distress and prolonged uncertainty may further compromise a patient's ability to manage their illness(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTreatment delays necessitate more extensive interventions due to disease progression, compromises quality of life, and increases direct healthcare costs. Although multiple factors influence outcomes, this delay remains a critical and potentially modifiable contributor\u0026mdash;especially in low-resource settings like Uganda. Yet, few studies in Uganda have quantified treatment delays or examined their underlying causes (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Understanding these delays and their context-specific drivers is essential for improving timely access to care and, ultimately, enhancing patient survival. This study therefore aims to determine the interval from diagnosis to treatment initiation and to identify factors associated with treatment delays among colorectal cancer patients in MNRH and UCI.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Setting\u003c/h2\u003e\u003cp\u003eWe conducted a hospital-based cross-sectional study at Mulago National Referral Hospital (MNRH) and the Uganda Cancer Institute (UCI) in Kampala, Uganda, between December 2024 and May 2025. These two public tertiary care facilities are Uganda\u0026rsquo;s principal centers for surgical and oncological care, respectively, and manage colorectal cancer (CRC) patients through an integrated multidisciplinary pathway. The study was approved by the Makerere University School of Medicine Research and Ethics Committee (Ref: Mak-SOMREC-2024-1048) and administrative clearance was obtained from both hospitals.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Population and Sampling\u003c/h3\u003e\n\u003cp\u003eThe study population consisted of adult patients (\u0026ge;\u0026thinsp;18 years) with a histologically confirmed diagnosis of colorectal cancer who were initiating oncological treatment (surgery, chemotherapy, or radiotherapy) at MNRH or UCI during the study period. Patients unable to provide informed consent or those being treated for recurrent disease were excluded.\u003c/p\u003e\u003cp\u003eA consecutive sampling method was used to enroll participants. The sample size was calculated for a primary objective of estimating prevalence. Using the Kish-Leslie formula for a finite population, a prevalence of 43% for treatment delay based on prior study by Wassie et.al (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), a 95% confidence level, and a 5% margin of error, a minimum sample size of 67 participants was required.\u003c/p\u003e\n\u003ch3\u003eData Management and Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eData were collected through patient interviews and medical record reviews using a pre-tested, structured questionnaire. The primary outcome was treatment delay, defined as an interval of \u0026gt;\u0026thinsp;31 days from histopathological confirmation to the first oncologic treatment (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eData were analyzed using SPSS version 20. Descriptive statistics were used to summarize participant characteristics. The prevalence of delay was reported as a proportion with a 95% confidence interval (CI).\u003c/p\u003e\u003cp\u003eBivariate and Multivariable Regression:\u003c/p\u003e\u003cp\u003eBivariate analysis was performed using Poisson regression with robust variance to estimate crude Prevalence Ratios (PR) for factors associated with treatment delay. Variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.2 were included in a multivariable Poisson regression model to generate adjusted Prevalence Ratios (aPR) with 95% CIs. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003eDevelopment of Composite Scores:\u003c/p\u003e\u003cp\u003eRecognizing the multidimensional nature of barriers to care, we developed three composite scores post-hoc to better capture complex constructs:\u003c/p\u003e\u003cp\u003eSocioeconomic Status (SES) Score (Range: 0\u0026ndash;7): A cumulative score of disadvantage, incorporating employment status (unemployed\u0026thinsp;=\u0026thinsp;2, previously employed\u0026thinsp;=\u0026thinsp;1, employed\u0026thinsp;=\u0026thinsp;0), education level (none\u0026thinsp;=\u0026thinsp;2, primary\u0026thinsp;=\u0026thinsp;1, secondary\u0026thinsp;+\u0026thinsp;=\u0026thinsp;0), marital status (widowed/divorced\u0026thinsp;=\u0026thinsp;2, single\u0026thinsp;=\u0026thinsp;1, married\u0026thinsp;=\u0026thinsp;0), and health behaviors (smoking\u0026thinsp;=\u0026thinsp;1, alternative medicine use\u0026thinsp;=\u0026thinsp;1). A higher score indicates greater socioeconomic vulnerability.\u003c/p\u003e\u003cp\u003eDisease Severity Score (Range: 2\u0026ndash;8): Sum of tumor stage (I\u0026thinsp;=\u0026thinsp;1, II\u0026thinsp;=\u0026thinsp;2, III\u0026thinsp;=\u0026thinsp;3, IV\u0026thinsp;=\u0026thinsp;4) and tumor grade (1\u0026thinsp;=\u0026thinsp;1, 2\u0026thinsp;=\u0026thinsp;2, 3\u0026thinsp;=\u0026thinsp;3, 4\u0026thinsp;=\u0026thinsp;4).\u003c/p\u003e\u003cp\u003eClinical Burden Score (Range: 0\u0026ndash;6): Sum of the presence of comorbidities (yes\u0026thinsp;=\u0026thinsp;1), ECOG performance status (0\u0026ndash;4), and low BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2; (yes\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e\u003cp\u003eFor analysis, the total scores for SES and Clinical Burden were dichotomized into 'Low' and 'High' burden based on the distribution of the data. This is further illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e of the Supplementary file. The association between these composite scores and treatment delay was then analyzed using the same regression framework described above.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 67 patients with histologically confirmed colorectal cancer were enrolled in the study. The median age was 56 years (interquartile range [IQR]: 41\u0026ndash;62), and 55.2% (n\u0026thinsp;=\u0026thinsp;37) were female. The cohort had a mean BMI of 22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5 kg/m\u0026sup2;. The majority of patients (71.6%, n\u0026thinsp;=\u0026thinsp;48) presented with advanced-stage (Stage III or IV) disease, and rectal cancer was the most common tumor location (62.7%, n\u0026thinsp;=\u0026thinsp;42). The first oncologic treatment was systemic chemotherapy for most patients (82.1%, n\u0026thinsp;=\u0026thinsp;55), followed by radiotherapy (10.4%, n\u0026thinsp;=\u0026thinsp;7) and surgical resection (7.5%, n\u0026thinsp;=\u0026thinsp;5). Most participants had an ECOG performance status of 0 or 1 (85.1%, n\u0026thinsp;=\u0026thinsp;57), and 25.4% (n\u0026thinsp;=\u0026thinsp;17) had documented comorbidities. The median distance from a patient's residence to the treatment facility was 137.0 km (IQR: 52.0\u0026ndash;327.7). Detailed demographic and clinical characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%) or Median (IQR)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (41\u0026ndash;62)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.6 (19.4\u0026ndash;24.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to facility (km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137.0 (52.0\u0026ndash;327.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (44.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 (55.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (44.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (26.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCollege/University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (17.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePostgraduate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (3.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (23.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (16.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrivate work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (50.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployed but not currently working\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (9.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (70.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (9.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (14.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReligion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProtestant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (40.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCatholic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (28.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMuslim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (10.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBaptist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (19.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBorn again\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (1.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (88.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (11.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eColon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (31.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRectal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (62.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRectosigmoid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrade 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (38.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrade 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (38.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrade 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (19.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrade 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (3.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStage I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStage II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (17.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStage III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (47.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStage IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (23.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMissing data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (4.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (74.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (25.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECOG status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (23.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (61.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (14.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirst oncologic treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChemotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (82.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRadiotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (10.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePretreatment CEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (43.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElevated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (25.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (31.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eTreatment Interval and Prevalence of Delay\u003c/h3\u003e\n\u003cp\u003eThe median time from histological diagnosis to initiation of the first oncologic treatment was 53 days (IQR: 25\u0026ndash;95 days), with a wide range from 3 to 1,133 days. The treatment interval for surgery was (median: 14 days; IQR: 6.5\u0026ndash;36.0), chemotherapy (median: 53 days; IQR: 27.0\u0026ndash;96.0), and radiotherapy (median: 79 days; IQR: 46.0\u0026ndash;110.0). A detailed breakdown of the treatment interval across all patient subgroups is provided in Supplementary Table\u0026nbsp;2. Applying the pre-defined threshold of \u0026gt;\u0026thinsp;31 days, the prevalence of treatment delay among colorectal cancer patients at MNRH and UCI was \u003cb\u003e70.1%\u003c/b\u003e (47/67, 95% CI: 58.5% \u0026minus;\u0026thinsp;81.7%). Only 29.9% (20/67) of patients initiated treatment within 31 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFactors Associated with Treatment Delay\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBivariate Analysis of Individual Factors\u003c/h2\u003e\u003cp\u003eBivariate analysis using Poisson regression with robust variance was performed to assess the association between various patient, clinical, and pathological factors and treatment delay. The complete results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBivariate analysis of factors associated with treatment delay (N\u0026thinsp;=\u0026thinsp;67).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale (Ref: Female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.66\u0026ndash;1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;50 yrs (Ref: \u0026ge;50 yrs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59\u0026ndash;1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.337\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistance to facility\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;200 km (Ref: \u0026lt;200 km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.65\u0026ndash;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.403\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes (Ref: No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.60\u0026ndash;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.52\u0026ndash;1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.318\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.63\u0026ndash;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.32\u0026ndash;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCollege/University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.54\u0026ndash;1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e(Ref: Postgraduate)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59\u0026ndash;1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.388\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.44\u0026ndash;1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u0026ndash;1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e(Ref: Widowed)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking History\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-smoker (Ref: Smoker)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.50\u0026ndash;1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.649\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlternative Therapy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo (Ref: Yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.66\u0026ndash;1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTumor Location\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eColon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.48\u0026ndash;3.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.582\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRectal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.55\u0026ndash;4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e(Ref: Rectosigmoid)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTumor Grade\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrade 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.32\u0026ndash;5.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.710\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrade 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.38\u0026ndash;6.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.547\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrade 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.33\u0026ndash;5.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e(Ref: Grade 4)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eClinical Stage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStage I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59\u0026ndash;2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.724\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStage II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u0026ndash;1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStage III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.78\u0026ndash;1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.509\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e(Ref: Stage IV)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eECOG Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.65\u0026ndash;1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.62\u0026ndash;1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.916\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e(Ref: 2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePR: Prevalence Ratio; CI: Confidence Interval; Ref: Reference category\u003c/h3\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the factors primary education (PR\u0026thinsp;=\u0026thinsp;0.77, 95% CI: 0.63\u0026ndash;0.93, p\u0026thinsp;=\u0026thinsp;0.008) and secondary education (PR\u0026thinsp;=\u0026thinsp;0.50, 95% CI: 0.32\u0026ndash;0.79, p\u0026thinsp;=\u0026thinsp;0.003) demonstrated a statistically significant association with a lower prevalence of treatment delay compared to the postgraduate reference category. However, it is critical to note that the reference group (postgraduate) contained only 2 participants, making this comparison unstable and the results likely artefactual. The presence of comorbidities showed a trend towards association with delay but was not statistically significant (PR\u0026thinsp;=\u0026thinsp;0.80, 95% CI: 0.60\u0026ndash;1.08, p\u0026thinsp;=\u0026thinsp;0.144). All other demographic, clinical, and pathological factors showed no significant association with treatment delay.\u003c/p\u003e\n\u003ch3\u003eAnalysis Using Composite Scores\u003c/h3\u003e\n\u003cp\u003e Given the limitations of interpreting the education variable in isolation and to better capture the multidimensional nature of barriers to care, we developed and employed three composite scores. (See Supplementary Table\u0026nbsp;2 for full scoring details.)\u003c/p\u003e\u003cp\u003eIn bivariate analysis, a high Socioeconomic Status (SES) composite score, which incorporated education, employment, marital status, and health behaviors, was associated with a 38% higher prevalence of treatment delay (PR\u0026thinsp;=\u0026thinsp;1.38, 95% CI: 1.06\u0026ndash;1.79, p\u0026thinsp;=\u0026thinsp;0.020). The Disease Severity and Clinical Burden composite scores were not significantly associated with delay in bivariate analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBivariate analysis of composite scores associated with treatment delay.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSES Composite Score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh (Ref: Low)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.06\u0026ndash;1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDisease Severity Score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh (Ref: Low)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.83\u0026ndash;1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.454\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eClinical Burden Score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh (Ref: Low)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.62\u0026ndash;1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eFinal Adjusted Model\u003c/h2\u003e\u003cp\u003eThe final multivariable model included the SES composite score, adjusted for the a priori confounders of age, sex, and distance to the treatment facility. In this adjusted model, a high SES score remained a significant independent predictor of treatment delay. Patients with high socioeconomic vulnerability had a 34% higher prevalence of delay compared to those with lower vulnerability (aPR\u0026thinsp;=\u0026thinsp;1.34, 95% CI: 1.01\u0026ndash;1.78, p\u0026thinsp;=\u0026thinsp;0.042). No significant associations were observed for age, sex, or distance in the final model (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFinal multivariable model of factors associated with treatment delay.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eaPR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSES Composite Score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh (Ref: Low)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.01\u0026ndash;1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge Group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;50 yrs (Ref: \u0026ge;50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.64\u0026ndash;1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.518\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale (Ref: Female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.71\u0026ndash;1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistance to facility\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(per km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.999\u0026ndash;1.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.970\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003cp\u003eaPR: Adjusted Prevalence Ratio\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study set out to determine the extent of treatment delays and associated factors among patients diagnosed with colorectal cancer at MNRH and UCI. The median duration from diagnosis to treatment was 53 days (IQR: 25\u0026ndash;95), with 70.1% of patients experiencing delays exceeding 31 days. Although most individual demographic, clinical and pathologic variables were not statistically significant, socioeconomic vulnerability emerged as a crucial factor, with patients exhibiting higher SES composite scores having 34% greater prevalence of treatment delays (PR\u0026thinsp;=\u0026thinsp;1.34; 95% CI: 1.01\u0026ndash;1.78; p\u0026thinsp;=\u0026thinsp;0.042).\u003c/p\u003e\u003cp\u003eWhen compared to previous studies and against international benchmarks, our findings reveal substantial disparities in timely oncologic CRC cancer care. Compared to recommended treatment intervals of 2\u0026ndash;4 weeks for most common cancers Uganda's median delay of 53 days represents a significant deviation from global comparisons (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This delay is considerably longer than intervals reported in other settings; for instance, Bouter et al. in South Africa reported a median diagnosis-to-treatment interval of 29 days among colorectal cancer patients(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), while studies from Poland and Italy report averages of 38 days and median of 28 days respectively(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). These discrepancies likely reflect variations in healthcare infrastructure and access to timely care between different resource settings (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWithin the Ugandan context, our findings both align and contrast with previous research. Kibudde et al., examining treatment intervals across various cancers, reported a median waiting time of 33 days (range: 16\u0026ndash;416) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), which was numerically higher, but within a comparable range. The overall median therefore reflected shorter treatment times associated with other malignancies, such as cervical, head and neck, sarcoma, and esophageal cancers, which were more prevalent in that cohort. Furthermore, CRC was underrepresented in their study, with only one patient included(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). In contrast, our study focused exclusively on colorectal cancer, which may follow different treatment pathways. However, our findings regarding modality-specific delays show concerning consistency with local patterns; similar to Kibudde et al., we found substantially longer turnaround times for radiotherapy (median 79 days) and chemotherapy (median 53 days). This disparity reflects the additional complexities of radiotherapy planning and systemic challenges including limited equipment availability and fragmented care coordination(\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe prevalence of treatment delay in our study (70.1%) exceeds rates reported in other African settings, including Ethiopia (43%) and Botswana (50.4%), though methodological differences in defining delay thresholds must be acknowledged (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). This high prevalence underscores the profound systemic challenges within Uganda's oncology infrastructure, characterized by limited radiotherapy capacity, diagnostic bottlenecks, and centralized care concentrated in Kampala (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The biological implications of these delays are particularly concerning given tumor doubling time in colorectal cancer ranges from 92 to over 1032 days(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e); our mean interval of 100 days represents a period sufficient for meaningful tumor progression, potentially compromising curative outcomes, especially for the 71.6% of patients who already present with advanced-stage disease (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur analysis of associated factors revealed that socioeconomic vulnerability, captured through a multidimensional composite score, was the only independent predictor of treatment delay. The counterintuitive finding regarding education level where lower educational attainment at first appeared protective against delay - highlights the limitations of analyzing individual socioeconomic indicators in isolation. When educational status was incorporated into the composite SES measure alongside employment, marital status, and health behaviors, its effect was subsumed within a broader pattern of socioeconomic disadvantage that more accurately predicted treatment delays. This finding emphasizes that in resource-constrained settings like Uganda, the cumulative burden of disadvantage creates structural barriers that outweigh individual factors in determining access to timely care (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe consistent relationship between socioeconomic disadvantage and treatment delay aligns with findings from other settings across sub-Saharan Africa. Buckle et al. in Kenya reported longer waiting times among patients from rural and lower-income backgrounds(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) while Wassie et al. in Ethiopia linked delays to financial hardship and lack of awareness (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Even in high-income settings, composite SES indices have predicted disparities in cancer care timelines(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), highlighting the universal influence of structural inequality on health outcomes. Our study contributes to this literature by demonstrating the particular utility of multidimensional SES measures in low-resource settings where traditional income-based metrics may fail to capture the complexity of socioeconomic vulnerability.\u003c/p\u003e\u003cp\u003eSeveral limitations should be considered when interpreting our findings. The sample size being small restricted sub analyses. Our composite score, while conceptually robust, was developed post-hoc and requires validation in larger studies. Finally, unmeasured factors such as system factors, cultural beliefs, fear of treatment, and health-seeking behaviour may contribute to delays but were not captured in our assessment.\u003c/p\u003e\u003cp\u003eIn conclusion, this study demonstrates that treatment delays are pervasive among colorectal cancer patients in Uganda's main referral hospitals, with socioeconomic vulnerability emerging as the primary predictor of delayed care. The median interval of 53 days substantially exceeds international benchmarks and likely contributes to the poor outcomes observed among Ugandan CRC patients. These findings highlight the need for multi-level interventions addressing both structural barriers (through financial protection schemes and infrastructure investment) and systemic inefficiencies (through improved care coordination and wait-time monitoring). Future efforts should prioritize vulnerable patient populations and establish time-to-treatment as a key quality metric in Uganda's evolving oncology care system.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCRC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eColorectal Cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDTI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiagnosis to Treatment Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMDT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMultidisciplinary team\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMNRH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMulago National Referral Hospital\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal Investigator\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSOMREC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSchool of Medicine Research and Ethics Committee\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTreatment interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTNM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTumor, Nodal, Metastasis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUganda Cancer Institute\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthical\u0026nbsp;Approval and Consent\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved with waiver of consent by Makerere University School of Medicine Research and Ethics Committee (Mak-SOMREC-2024-1048) and administrative clearance was obtained from MNRH and UCI.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent\u0026nbsp;for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability\u0026nbsp;of data\u0026nbsp;and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe primary dataset supporting this study may be obtained from the corresponding author on request\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting\u0026nbsp;interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;authors declare\u0026nbsp;that\u0026nbsp;they\u0026nbsp;have\u0026nbsp;no\u0026nbsp;competing\u0026nbsp;financial or\u0026nbsp;non-financial interests\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNo\u0026nbsp;external\u0026nbsp;funding\u0026nbsp;was\u0026nbsp;obtained\u0026nbsp;in\u0026nbsp;this study\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eB.K. conceived the study, collected and analyzed the data, and wrote the first draft of the manuscript. P.S., G.K., assisted with data cleaning. F.E.E. helped with the statistical analysis. P.C.N., P.S., and G.K. provided critical review of the manuscript. E.A.E., J.K., and P.O. supervised the study design, data interpretation, and critically revised the manuscript for important intellectual content. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their sincere gratitude to the patients who participated in this study. We also thank the administration and staff of Mulago National Referral Hospital and the Uganda Cancer Institute for their support and cooperation. We are deeply indebted to our data collection team;-Odoki, Arnold, and Sister Frieda for their dedication and invaluable assistance.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026rsquo; Information\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBrian Kasagga, MBChB\u003c/p\u003e\n\u003cp\u003eGeneral Surgery Resident, Department of Surgery, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda.\u003c/p\u003e\n\u003cp\u003eE-mail: [email protected]\u003c/p\u003e\n\u003cp\u003ePaul Ssempebwa, MBChB\u003c/p\u003e\n\u003cp\u003eGeneral Surgery Resident, Department of Surgery, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda.\u003c/p\u003e\n\u003cp\u003eE-mail: [email protected]\u003c/p\u003e\n\u003cp\u003eGodfrey Kikuba, MBChB\u003c/p\u003e\n\u003cp\u003eGeneral Surgery Resident, Department of Surgery, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda.\u003c/p\u003e\n\u003cp\u003eE-mail: [email protected]\u003c/p\u003e\n\u003cp\u003eFlavius E. Egbe, B.M.L.S, M.D, M Med (Surgery)\u003c/p\u003e\n\u003cp\u003eDepartment of Surgery, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda.\u003c/p\u003e\n\u003cp\u003eGeneral Surgeon, Kumba Regional Hospital Annex, Cameroon.\u003c/p\u003e\n\u003cp\u003eE-mail: [email protected]\u003c/p\u003e\n\u003cp\u003ePeace Caroline Nsodi, MBChB\u003c/p\u003e\n\u003cp\u003eIntern Doctor, Mulago National Referral Hospital, Kampala, Uganda.\u003c/p\u003e\n\u003cp\u003eE-mail: [email protected]\u003c/p\u003e\n\u003cp\u003eJoanne Kayaga, MBChB, M.Med\u003c/p\u003e\n\u003cp\u003eOncologist, Uganda Cancer Institute, Kampala, Uganda.\u003c/p\u003e\n\u003cp\u003eE-mail: [email protected]\u003c/p\u003e\n\u003cp\u003ePaul Okeny, MBChB, M.Med, FCS(ECSA), PhD\u003c/p\u003e\n\u003cp\u003eLecturer, Department of Surgery, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda.\u003c/p\u003e\n\u003cp\u003eE-mail: [email protected]\u003c/p\u003e\n\u003cp\u003eEmmanuel Alex Elobu, MBChB, M.Med, FCS(ECSA), MBA\u003c/p\u003e\n\u003cp\u003eColorectal Surgeon, Department of Surgery, Mulago National Referral Hospital, Kampala, Uganda.\u003c/p\u003e\n\u003cp\u003eE-mail: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKlimeck L, Heisser T, Hoffmeister M, Brenner H. 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J Global Oncol. 2018(4):JGO1800088.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOmotoso O, Teibo JO, Atiba FA, Oladimeji T, Paimo OK, Ataya FS, et al. Addressing cancer care inequities in sub-Saharan Africa: current challenges and proposed solutions. Int J Equity Health. 2023;22(1):189.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBolin S, Nilsson E, Sj\u0026ouml;dahl R. Carcinoma of the colon and rectum\u0026ndash;growth rate. Ann Surg. 1983;198(2):151\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee Y-H, Kung P-T, Wang Y-H, Kuo W-Y, Kao S-L, Tsai W-C. Effect of length of time from diagnosis to treatment on colorectal cancer survival: A population-based study. PLoS ONE. 2019;14(1):e0210465.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSsemata AS, Smythe T, Sande S, Menya A, Hameed S, Waiswa P, et al. Exploring the barriers to healthcare access among persons with disabilities: a qualitative study in rural Luuka district, Uganda. BMJ Open. 2024;14(11):e086194.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKasagga B, Takoutsing BD, Balumuka D, Ambangira F, Kasozi D, Namiiro MA, et al. Protocol for scoping review to identify and characterise surgery, obstetric, trauma and anaesthesia care in Ugandan health policy databases. BMJ Open. 2023;13(7):e070944.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuckle GC, Collins JP, Sumba PO, Nakalema B, Omenah D, Stiffler K, et al. Factors influencing time to diagnosis and initiation of treatment of endemic Burkitt Lymphoma among children in Uganda and western Kenya: a cross-sectional survey. Infect Agent Cancer. 2013;8(1):36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBourgeois A, Horrill T, Mollison A, Stringer E, Lambert LK, Stajduhar K. 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BMC Health Serv Res. 2024;24(1):670.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Colorectal cancer, Treatment delay, Diagnosis-to-treatment interval","lastPublishedDoi":"10.21203/rs.3.rs-7734788/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7734788/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eColorectal cancer (CRC) is an important cause of morbidity and mortality in Uganda. Timely treatment initiation is critical for outcomes, yet delays are common. This study assessed treatment delays and associated factors among CRC patients at Mulago National Referral Hospital (MNRH) and the Uganda Cancer Institute (UCI).\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo determine the diagnosis to treatment interval (DTI), prevalence of treatment delay, and the associated patient and clinicopathologic factors among CRC patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA hospital-based cross-sectional study was conducted among 67 patients with histologically confirmed CRC between December 2024 and May 2025. Treatment delay was defined as \u0026gt;\u0026thinsp;31 days between histological diagnosis and first oncologic treatment. Data were collected through interviews and record review. Descriptive statistics summarized demographics and clinical characteristics. Bivariate Poisson regression with robust variance estimation identified factors associated with delay; variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 entered a multivariable model. Prevalence ratios (PRs) with 95% confidence intervals (CIs) were reported. IRB approval was obtained (Ref: Mak-SOMREC-2024-1048).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe mean age was 50.5 years (SD: 15.1); 55.2% were female, and 71.6% (n\u0026thinsp;=\u0026thinsp;48) had advanced-stage disease (Stage III/IV). The median DTI was 53 days (IQR: 25\u0026ndash;95), with 70.1% (n\u0026thinsp;=\u0026thinsp;47) experiencing delays. Median DTI by treatment: chemotherapy 53 days, radiotherapy 79 days, surgery 14 days. While late-stage disease, comorbidities, and long travel distances showed trends toward delay, only socioeconomic status (SES) was significant. Patients with high SES vulnerability (score\u0026thinsp;\u0026ge;\u0026thinsp;4) had 34% higher prevalence of delay (PR\u0026thinsp;=\u0026thinsp;1.34, 95% CI: 1.01\u0026ndash;1.78, p\u0026thinsp;=\u0026thinsp;0.042).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eMost CRC patients experienced treatment delays which were widespread and occurred across all categories; regardless of distance to the treatment facility, clinical status, or disease severity. Socioeconomic disadvantage was the only independent predictor, underscoring the role of structural and financial barriers in timely care. Targeted, context-specific interventions are urgently needed to reduce delays and improve outcomes.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e","manuscriptTitle":"Prevalence and Factors Associated with Treatment Delay Among Colorectal Cancer Patients at Mulago National Referral Hospital and the Uganda Cancer Institute: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 07:41:37","doi":"10.21203/rs.3.rs-7734788/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"33069afa-237c-4092-b281-43ff7914c2bc","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-08T12:53:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 07:41:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7734788","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7734788","identity":"rs-7734788","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}