Determinants of Survival among Cervical Cancer Patients: A Hospital-Based Retrospective Study

Determinants of Survival among Cervical Cancer Patients: A Hospital-Based Retrospective 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 Determinants of Survival among Cervical Cancer Patients: A Hospital-Based Retrospective Study Bajarang Bahadur, Tej Bali Singh, Sunil Chaudhari, Jagriti Annu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6835791/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 Cervical cancer remains a major public health challenge, particularly in low- and middle-income countries like India. Despite being preventable and treatable, it contributes significantly to female cancer mortality due to late diagnosis and limited access to comprehensive treatment. This study aims to identify key demographic, clinical, and treatment-related prognostic indicators influencing survival outcomes among cervical cancer patients in a tertiary care hospital in Varanasi, India. Data and Methods: A retrospective cohort study was conducted using data from 615 cervical cancer patients diagnosed between 2011 and 2021 at Sir Sunder Lal Hospital. Kaplan-Meier survival analysis and Cox proportional hazards regression models were employed to estimate survival probabilities and assess the association of various factors with overall survival. Variables included age group, marital status, education, place of residence, stage at diagnosis, type of radiotherapy, and treatment combinations. Results The five-year survival rate was highest among patients diagnosed at an early stage (74.6%) and those receiving combined chemotherapy, radiotherapy, and brachytherapy (71.5%). Illiterate women had significantly lower survival rates (59.8%) compared to literate women (71.5%). Urban residents faced a higher risk of mortality than rural women, and advanced age (> 60 years) was associated with poorer survival outcomes. Cox regression confirmed that late-stage diagnosis (AHR = 1.62), illiteracy (AHR = 1.55), and urban residence (AHR = 1.53) were independent predictors of mortality. Conclusion This study highlights the critical role of early diagnosis, comprehensive treatment, and health literacy in improving cervical cancer survival. Addressing sociodemographic disparities and strengthening screening and awareness programs are essential for reducing cervical cancer mortality in India. Cervical cancer Survival analysis Prognostic factors Kaplan-Meier Cox regression Health disparities Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cervical cancer is a common cancer among women worldwide. It especially affects those in low- and middle-income countries, in 2022, there were about 661,021 new cases. Most of these cases were in low- and middle-income countries. [ 1 ] The standardized incidence rates were 13.3 cases per 100,000 women-years and 7.3 deaths per 100,000 women-years. It is the most common cancer in 23 of 185 countries. In 67 countries, it is the second most common. Cervical cancer leads to the most cancer deaths in 36 countries. It is the second most common cause of death in 49 countries [ 2 ]. Cervical cancer remains the most common cancer in women in Eastern and Middle Africa [ 3 , 5 ]. In 2020, around 348,189 women died from cervical cancer worldwide [ 1 ]. This made up 7.7% of all female cancer deaths [ 4 , 6 ]. Around nine out of ten (90%) cervical cancer deaths occur in developing regions [ 5 , 7 ]. Mortality rates differ greatly around the world. They range from under 2 per 100,000 in Western Asia, Western Europe, and Australia/New Zealand. In contrast, rates exceed 20 per 100,000 in Southern Africa (20.6), Middle Africa (22.7), and Eastern Africa (28.6) [ 6 , 8 ]. In India, there were 123,907 cases of cervical cancer in 2020. The disease caused an estimated 77,348 deaths, making up 9.1% of all cancer deaths that year [ 7 ]. In 2020, India had about one-fifth of all new cervical cancer cases. It also accounted for nearly one-fourth of related deaths worldwide. This highlights India's significant role in the global cervical cancer burden. Cervical cancer is the second most common cancer in women nationally. It makes up 18.3% of new cases and 18.7% of cancer deaths. The five-year prevalence rate is 18.8% [ 8 ]. According to the National Cancer Institute, the 5-year survival rate of cervical cancer patients is 92% when diagnosed at an early stage, and when diagnosed after it has spread to nearby tissues, organs, or regional lymph nodes, the 5-year survival rate is 59%. Patients affected by cervical cancer survive longer than 5 years in less than 50% of women in less-developed countries [ 9 ]. The survival of cervical cancer patients in economically advanced regions is about 66% at 5 years [ 10 ]. Results showed that age, marital status, high school education, and where people live linked to cervical cancer risk. Also, using oral contraceptives, having HIV, and not knowing about Pap smear screening and vaccination were connected to this risk [ 11 ]. Cervical cancer is more common in rural areas. Limited healthcare access plays a big role. Poor awareness of reproductive health is another factor. Also, sociocultural barriers can delay diagnosis and treatment. Well-known risk factors for cervical cancer include a long-lasting infection with high-risk Human Papillomavirus (HPV). Types 16 and 18 are the main causes of this cancer. Other significant risk factors include early sexual activity, multiple sexual partners, smoking, a weakened immune system, long-term use of oral contraceptives, high parity (multiple full-term pregnancies), and a family history of cervical cancer [ 13 ]. This study focuses on understanding the demographic, clinical, and treatment aspects of cervical cancer patients. We will explore how these aspects connect to survival outcomes. Specifically, the study seeks to identify key prognostic factors influencing survival outcomes. The data is sourced from records held by the Department of Radiotherapy and Radiation Medicine. Methods and Materials Study area and period A retrospective study was conducted at Sir Sunder Lal Hospital, Banaras Hindu University in Varanasi, Uttar Pradesh, India. The patients included in this study are those who were diagnosed with cervical cancer from 2011 to 2021. Therefore, the study includes 615 cervical cancer patients. Risk factors The variables including age (categorized in three part ≤ 45, 46–60 and 60+), FIGO stage (the stage based on the International Federation of Gynecology and Obstetrics system), religion (Hindu and Muslim), marital status (married and widowed), place of residence (rural and urban), treatment type (surgery, chemotherapy (CT), radiotherapy (RT), and brachytherapy (BT)) categorized in the three Parts (Surgery + CT + RT + BT, CT + RT + BT, and others(only RT, Surgery + RT, CT + RT, RT + BT, etc) and type of radiotherapy (radical and postop), were included to figure out the risk factors affecting the survival. To discover their significance to the survival time, the accessible variables have been used. The FIGO stages are categorized in two parts first one is the early stage, who include stage I and II, and the second is the late stage, who include stage III and IV. Certain stages are unknown, and postoperative categories have been combined into a single category named as others. Statistical Analysis The recorded data were entered in MS Excel and analyzed using SPSS 28 and R software. The results are presented in frequencies and percentages. The Kaplan-Meier survival curves were used to estimate and visualize the survival probability over time for patients with cervical cancer. The curve represents the probability of survival at different time points. The survival probability at time ‘t’ is given by: $$\:S\left({t}_{i}\text{}\right)=\:\prod\:_{i:{t}_{i}\le\:t}(1-\:\frac{{d}_{i}}{{n}_{i}}\:)$$ Where: t i ​ is the time at which the event occurs, d i ​ is the number of events (e.g., deaths) at time t i ​, n i ​ is the number of individuals at risk just before time t i ​. The Log-Rank test is used to compare the survival distributions between two or more groups. χ2 = \(\:\sum\:\:\:\frac{{({O}_{i}-\:{E}_{i})}^{2}}{{E}_{i}}\:\:\) Where: O i ​ is the observed number of events in the group at time t i ​, E i ​ is the expected number of events in the group at time t i . And also, to identify the factors associated with overall survival among cervical cancer patients, we employed the Cox Proportional Hazards (PH) model, a semi-parametric regression method widely used in survival analysis. The model estimates the hazard (or risk) of the event occurring at a given time, conditional on the values of explanatory covariates. h(t∣X i ) = h 0 ​(t) exp (β 1 ​X 1 ​+β 2 ​X 2 ​+⋯+β p ​X p ​) Where: h(t∣X i ) is the hazard at time t for an individual with covariates X 1 , X 2 ..., X p ​, h 0 (t) is the baseline hazard function, β 1 , β 2,… , β p ​ are the regression coefficients estimating the effect of each covariate. Results Table 1 Baseline Characteristics of Cervical Cancer Patients: Number (%) Age Group (years) Frequency Percentage ≤ 45 185 30.1 46–60 328 53.3 > 60 102 16.6 Total 615 100.0 Marital Status Married 453 87.3 Widowed 66 12.7 Total 519 100.0 Educational Status Illiterate 345 58.1 Literate 249 41.9 Total 594 100.0 Place of Residence Rural 445 72.4 Urban 170 27.6 Total 615 100.0 Religion Hindu 593 96.6 Muslim 21 3.4 Total 614 100.0 Stage Early 189 30.7 Late 266 43.3 Other (Unknown and PO) 160 26.0 Total 615 100.0 Radiotherapy Type Radical 437 71.1 Postop 178 28.9 Total 615 100.0 Type of Treatment Surgery + CT + RT + BT 149 24.2 CT + RT + BT 334 54.3 Others 132 21.5 Total 615 100.0 Table 1 shows that the majority of cervical cancer patients were in the age group of 46–60 years (53.3%), followed by those below 46 years (30.1%) and above 60 years (16.6%), indicating that middle-aged women are the most affected. Regarding marital status, 87.3% of the patients were currently married, and 12.7% were widowed. A substantial proportion of patients (72.4%) resided in rural areas, reflecting the higher burden of cervical cancer in underserved populations. The majority were Hindu (96.6%), and a small proportion were Muslim (3.4%). Data revealed that 30.7% of patients were diagnosed at an early stage, 43.3% at a late stage, while 26.0% had other (unknown and postoperative). This suggests a nearly 13% difference between early and late detection, with a significant portion lacking complete staging information. Regarding radiotherapy, 71.1% received radical radiotherapy, while 28.9% underwent postoperative radiotherapy. In terms of treatment modalities, 54.3% of patients received a combination of chemotherapy, radiotherapy, and brachytherapy (CT + RT + BT), 24.2% received surgery + CT + RT + BT, and the remaining 21.5% were treated with other combinations. Table 2 Kaplan-Meier Estimated Survival Rates at 1, 3, and 5 years among Cervical Cancer. Factor Survival Rate Log rank test value p-value 1 Year 3 Years 5 Years Age Group (years) 60 92.5 64.1 52.7 Marital Status Married 95.5 77.5 65.4 1.048 0.306 Widowed 93.1 72.5 56.7 Educational Status Illiterate 94.2 75.0 59.8 6.790 0.009 Literate 96.6 79.7 71.5 Place of Residence Rural 95.2 76.7 65.4 1.097 0.295 Urban 96.6 75.6 61.1 Religion Hindu 95.4 76.7 64.7 0.796 0.372 Muslim 100.0 85.6 57.0 Stage Early 97.8 82.5 74.6 11.534 0.003 Late 94.4 70.6 55.3 Other (Unknown and PO) 94.8 77.5 63.5 Type of Radiotherapy Radical 95.9 76.2 64.9 0.243 0.622 Postop 94.6 76.9 62.1 Type of Treatment Surgery + CT + RT + BT 95.4 79.1 61.7 21.961 0.000 CT + RT + BT 97.7 81.3 71.5 Others 89.2 58.7 44.2 Table 2 shows that the Kaplan-Meier survival analysis revealed significant differences in cervical cancer survival rates across various demographic and clinical characteristics. Age was found to be an important predictor of survival, with patients under the age of 46 years exhibiting the highest five-year survival rate of 73.3%. This survival rate declined to 63.3% in the 46–60 years age group and further decreased to 52.7% in those aged above 60 years. The observed difference in survival by age group was statistically significant ( p = 0.008), indicating that older age is associated with poorer survival outcomes. Educational status also showed a significant association with survival. Literate women had a five-year survival rate of 71.5%, compared to 59.8% among illiterate women ( p = 0.009). In contrast, marital status, place of residence, and religion did not show statistically significant differences in survival. Married women had a slightly higher five-year survival rate (65.4%) compared to widowed women (56.7%) ( p = 0.306). Similarly, urban residents had a marginally lower five-year survival rate (61.1%) than rural residents (65.4%) ( p = 0.295), and Hindu women had a five-year survival of 64.7%, slightly higher than Muslim women (57.0%) ( p = 0.372). However, these differences were not statistically significant. The stage of cancer at diagnosis had a significant impact on survival outcomes ( p = 0.003). Women diagnosed at an early stage had a five-year survival rate of 74.6%, compared to just 55.3% among those diagnosed at a late stage. Type of radiotherapy (radical vs. postoperative) was not significantly associated with survival ( p = 0.622), though those receiving radical radiotherapy had a slightly better five-year survival rate (64.9%) compared to those receiving postoperative therapy (62.1%). Notably, the type of treatment received showed a highly significant association with survival outcomes ( p < 0.001). Patients treated with a combination of chemotherapy, radiotherapy, and brachytherapy (CT + RT + BT) had the highest five-year survival rate (71.5%), followed by those who received surgery + CT + RT + BT (61.7%). In contrast, patients who received other combinations of treatment had the poorest five-year survival rate (44.2%). Table 3 Hazard ratio of Death for Cervical Cancer Patients Factor UHR 95.0% CI for HR p-value AHR 95.0% CI for HR p-value Lower Upper Lower Upper Age Group (years) 60 1.904 1.237 2.931 0.003 1.542 0.939 2.533 0.087 Religion Hindu Ref. Muslim 1.410 0.660 3.012 0.375 Educational Status Literate Ref. Illiterate 1.549 1.112 2.158 0.010 1.550 1.061 2.264 0.016 Marital Status Married Ref. Widowed 1.272 0.801 2.021 0.307 Place of Residence Rural Ref. Urban 1.380 1.007 1.892 0.045 1.535 1.083 2.176 0.016 Stage Early Ref. Late 1.737 1.212 2.488 0.003 1.620 1.108 2.368 0.013 Others (Unknown and PO) 1.400 0.919 2.135 0.117 1.435 0.896 2.298 0.133 Type of Treatment Surgery + CT + RT + BT Ref. CT + RT + BT 0.733 0.493 1.091 0.126 0.754 0.491 1.158 0.197 Other combinations 1.675 1.085 2.588 0.020 1.465 0.919 2.336 0.109 Table 3 displays the results of the Cox proportional hazards regression analysis, conducted to explore the influence of demographic and clinical variables on the mortality risk among cervical cancer patients. Both unadjusted and adjusted hazard ratios were calculated to evaluate the independent effect of each factor. Age was a notable determinant, with women over the age of 60 years exhibiting a significantly higher mortality risk in the unadjusted model (UHR = 1.904; 95% CI: 1.237–2.931; p = 0.003) compared to those under 46 years. However, after adjusting for other covariates, the risk remained elevated but was no longer statistically significant (AHR = 1.542; 95% CI: 0.939–2.533; p = 0.087). Women aged 46–60 years did not show a significant risk in either the unadjusted or adjusted models. Educational attainment emerged as a significant predictor of survival. Illiterate women faced a 55% higher risk of death compared to literate women, with this association remaining significant in both the unadjusted (UHR = 1.549; p = 0.010) and adjusted models (AHR = 1.550; p = 0.016). This highlights the critical role of education in enhancing health literacy, awareness, and timely access to care. Place of residence also showed a significant association with mortality. Patients residing in urban areas had a higher risk of death compared to those in rural settings (AHR = 1.535; 95% CI: 1.083–2.176; p = 0.016), possibly reflecting disparities in healthcare access, environmental exposures, or delays in diagnosis despite the perceived advantages of urban healthcare infrastructure. The stage at diagnosis was a strong and statistically significant factor affecting survival. Women diagnosed at a late stage faced a substantially higher risk of death compared to those diagnosed at an early stage (AHR = 1.620; 95% CI: 1.108–2.368; p = 0.013), underscoring the importance of early detection and timely intervention in reducing mortality. In contrast, variables such as marital status and religion did not show statistically significant associations with survival. Although widowed and Muslim patients had higher hazard ratios, the lack of statistical significance suggests these factors had a limited impact on mortality in this cohort. When examining treatment modalities, women who received other combinations of treatment had a significantly increased risk of death in the unadjusted model (UHR = 1.675; p = 0.020). However, this association was attenuated and became non-significant after adjustment (AHR = 1.465; p = 0.109). Discussion This study involved a five-year survival analysis of cervical cancer patients to identify significant demographic, clinical, and treatment-related predictive factors affecting survival outcomes. The primary objective was to examine the association between various patient characteristics and overall survival using Kaplan-Meier and Cox regression analyses. Our findings revealed that age at diagnosis, educational status, stage of cancer, and type of treatment significantly impacted survival rates. Patients diagnosed at an early stage and those receiving comprehensive treatment modalities, including chemotherapy, radiotherapy, and brachytherapy, had notably higher survival rates. Illiterate women and those residing in urban areas faced a higher risk of mortality, highlighting the role of socioeconomic and healthcare access disparities in survival outcomes. In this study, the mean age of patients in our study was slightly higher than that reported by other researchers in Western literature [ 14 ]. This higher age at diagnosis may reflect a relative lack of awareness and limited availability of cervical cancer screening services in our country [ 15 , 16 ]. The predominant age group among patients was 46–60 years, followed by individuals under 46 years. A similar age-wise distribution has been reported in previous studies [ 17 – 21 ]. In our study, the majority of the study subjects were Hindu (96.6%). These findings were comparable to findings of most of the Indian studies [ 22 – 25 ]. In the present study, the majority of women (87.3%) were currently married, while 12.7% were widows. These results are similar to the results of other studies [ 26 – 27 ]. In our study, 58.1% of patients were illiterate. This finding aligns with many studies which has found illiteracy as a risk factor for cervical cancer [ 28 – 30 ]. Likewise, insufficient education has been associated with early marriage and high parity, both recognized as risk factors for cervical cancer; therefore, enhancing the educational standing of women in our country is a crucial element of a comprehensive strategy for cervical cancer prevention in India [ 28 ]. A study conducted by Patil et al. in Nagpur indicated a substantial association between illiteracy and the incidence of cervical cancer [ 29 ]. A study conducted by Sankaranarayanan et al. indicated that the 5-year survival rate among illiterates was 45.2% [ 31 ], which aligns with the findings of the current study. In this study, treatment was categorized into three groups: receiving all treatment (Surgery, chemotherapy, radiotherapy, and brachytherapy), a combination of chemotherapy, radiotherapy brachytherapy, and other combinations. The findings indicate that chemotherapy, radiotherapy and brachytherapy treatment modalities, with other treatments, were independently associated with reduced survival and lower 5-year survival rates compared to patients who received all treatments. The selection of treatment for cervical cancer is determined by various aspects, including the type of cancer, the stage upon diagnosis, and possible adverse effects. Surgery is typically preferred as a standalone treatment for early-stage cervical cancer, whereas a combination of radiation and/or chemotherapy, with or without surgery, is recommended for more advanced stages [ 32 ] This study suggested that the cancer stage at diagnosis is the primary predictor of CCP survival. The advanced stage of cancer is strongly correlated with reduced survival rates among cervical cancer patients [ 33 – 34 ]. Patients with advanced cancer stages may have a greater mortality rate due to treatment problems, concomitant illness development, and the rate of metastases [ 33 – 34 ]. Moreover, this may be attributed to the fact that patients in advanced stages are less likely to respond to treatment compared to those in earlier stages [ 35 ]. This study identified advanced age as a predictor of survival in cervical cancer patients (CCP) (AHR: 1.542; 95% CI: 0.939–2.533). Women over 60 years old were roughly 1.5 times more likely to die within five years after diagnosis compared to those aged 46 or younger. Previous studies have revealed analogous findings, indicating that a later age at diagnosis correlates with reduced survival rates among cervical cancer patients [ 36 – 38 ]. A comparable study was conducted in Khon Kaen, Thailand, by Sriamporn et al. [ 39 ], which reported that patients under 40 years of age had the highest survival rates, while those over 60 years had the lowest. This demonstrated an inverse relationship between age and survival, findings that align with the present study, where patients below 46 years showed a more favorable prognosis compared to those aged 60 and above. A study from Japan further highlighted that an advanced stage at diagnosis was the primary determinant of poor survival outcomes in older cervical cancer patients [ 38 ]. Moreover, numerous studies show a poorer prognosis and elevated mortality rates in older women diagnosed with cervical cancer [ 40 – 42 ]. This may be attributed to more advanced disease at the time of diagnosis in older women [ 43 ], as well as a tendency for them to receive less aggressive treatment compared to their younger counterparts [ 41 ]. However, in contrast, a study from China found no significant difference in prognosis between older and younger women with cervical cancer [ 44 ], which could be due to differences in study design and age group classification. Cervical cancer remains a significant public health concern in India. Studies have shown that awareness of key risk factors is lacking even among educated young women, suggesting an even greater knowledge gap among less educated populations [ 45 ]. Engaging male partners and elder male family members in awareness initiatives is also crucial [ 46 ]. However, formal education alone may not be sufficient, especially when individuals lack the financial means to maintain proper hygiene. Effectively reduces the incidence of cervical cancer; it is crucial to complement educational efforts with improvements in living conditions. Engaging Accredited Social Health Activists (ASHAs), community health workers, and local panchayats in collaboration with State Health Societies (SHS) can enhance outreach and awareness programs. These stakeholders can play a vital role in implementing women-to-women awareness campaigns, ensuring that critical information on prevention, screening, and hygiene practices reaches even the most marginalized communities can significantly contribute to reducing the incidence of cervical cancer. These measures also promote greater participation in early detection programs and acceptance of HPV vaccination. Moreover, it is essential to account for the sociodemographic determinants of cervical cancer when developing and implementing public health policies and cervical cancer control strategies. The results of this investigation align with the existing body of evidence emphasizing the multifactorial nature of survival in cervical cancer patients. Timely diagnosis, access to comprehensive treatment, and socio-demographic factors like age, education, and stage at presentation are crucial in determining prognosis. These insights are essential for healthcare providers and policymakers aiming to improve cervical cancer outcomes through targeted interventions. Limitations and Strengths It is important to acknowledge that this study has a number of limitations. Notably, we were unable to capture certain essential variables, such as patients’ socioeconomic status and family income. Additionally, the timing of the onset of comorbid conditions could not be assessed. These limitations stem from the use of secondary data, restricting the analysis to information documented in medical records. Despite these constraints, the study’s strength lies in its contribution to the limited body of Indian research that comprehensively examines patients’ sociodemographic and clinical profiles, along with the associations between patient characteristics and tumor-related factors. Conclusion This study highlights the substantial impact of socio-demographic and clinical factors on the survival outcomes of cervical cancer patients. Age, education, stage of cancer at diagnosis, and type of treatment emerged as key determinants of survival. Younger women, particularly those below the age of 46, exhibited the highest five-year survival rates, while survival declined considerably among women over 60 years. Literate women had significantly better survival outcomes compared to illiterate women, highlighting the critical role of education in timely health-seeking and adherence to treatment. Late-stage diagnosis was consistently associated with poorer survival, reinforcing the importance of early detection and timely intervention. Women diagnosed at an early stage had a markedly higher probability of survival, while those with late-stage disease faced significantly increased mortality risk. The type and completeness of treatment also influenced outcomes, with women receiving all modalities of standard treatment generally experiencing better survival. Cox PH model confirmed that illiteracy, urban residence, and late-stage diagnosis were independently associated with a higher risk of death. These findings indicate that improving health literacy, strengthening cervical cancer screening programs, and ensuring access to comprehensive and timely treatment, particularly for disadvantaged and urban populations, are crucial for reducing cervical cancer mortality. Focused public health interventions addressing these disparities are essential to enhance survival outcomes and overall quality of care for women with cervical cancer. Recommendation Based on this study, several key recommendations can be made to enhance cervical cancer outcomes in India. A primary focus should be on improving early detection and screening efforts, as the study indicates that survival rates are significantly higher among patients diagnosed at an early stage. This highlights the need to expand screening programs, particularly targeting women over the age of 45, who were shown to be at increased risk. Health education is another crucial factor in improving survival. The analysis found that literate women had better outcomes than illiterate ones, suggesting that awareness and timely access to healthcare play a critical role. Therefore, public health initiatives should prioritize educational campaigns, especially in rural and marginalized communities, to raise awareness about cervical cancer symptoms, prevention, and the value of early diagnosis and treatment. Focused attention is needed for vulnerable groups such as older women, those with low literacy levels, and patients diagnosed at advanced stages. Customized approaches, such as community outreach and patient support services, can improve treatment adherence and outcomes in these populations. In addition, integrating supportive and palliative care into cancer management is critical for enhancing the well-being of patients, especially those undergoing less effective treatment regimens. Finally, there is a pressing need for policy reforms and systemic improvements in healthcare delivery. This includes investing in the training of medical personnel, upgrading diagnostic facilities, and decentralizing cancer care to ensure accessibility across regions. Establishing robust hospital-based cancer registries and improving data collection will be instrumental for tracking survival trends and informing policy decisions. Further research into healthcare access and social determinants of health is essential to design effective strategies and reduce cervical cancer-related deaths in India. Abbreviations AHR Adjusted Hazard Ratio BT Brachytherapy CCP Cervical Cancer Patients CI Confidence Interval CT Chemotherapy FIGO International Federation of Gynecology and Obstetrics HR Hazard Ratio IEC Institutional Ethics Committee IMS Institute of Medical Sciences IRB Institutional Review Board KM Kaplan–Meier NCRP National Cancer Registry Programme PH Proportional Hazards PO Postoperative RT Radiotherapy SHS State Health Society SPSS Statistical Package for the Social Sciences UHR Unadjusted Hazard Ratio WHO World Health Organization Declarations Authors' contributions: BB, TBS and SC contributed to conceptualizing the study. TBS, BB and SC are responsible for the analysis. BB, TBS, SC, JA, LA and SB contributed to the interpretation of the data, critically revised all versions of the manuscript, and approved the final version. Ethics approval and consent to participate This study was approved by the Institutional Ethics Committee (IEC), Institute of Medical Sciences, Banaras Hindu University, Varanasi, India (Approval No. Dean/2023/EC/684, dated 04.12.2023). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committee and with the Declaration of Helsinki (2013 revision). As the study was retrospective in nature and used anonymized data from hospital medical records without direct patient contact, the requirement for informed consent was waived by the Institutional Ethics Committee. Consent for publication Not applicable. Availability of data and materials The data underlying the findings presented in this study are not publicly available due to ethical and institutional restrictions. The data contain sensitive patient information, and access is limited to authorized researchers within the institution for confidentiality and compliance purposes. Requests for access to the data should be directed to the corresponding author or the Institutional Ethics Committee of Banaras Hindu University. Competing Interests The authors declare that they have no competing interests. Funding No funding was received by the authors for this study. Acknowledgements Not applicable. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin. 2024;74(3):229–63. Global Cancer Observatory. Cancer Today. 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Sociodemographic risk factors for cervical cancer in Jammu region of J and K state of India first ever report from Jammu. Indian J Sci Res. 2014;9:105 – 10. 23. Ertem G. Awareness of cervical cancer risk factors and screening behaviour among nurses in a rural region of turkey. Asian Pac J Cancer Prev 2009;10:735-8. Thakur A, Gupta B, Gupta A, et al. Risk factors for cancer cervix among rural women of a hilly state: a case-control study. Indian J Public Health. 2015;59:45–8. Biswas LN, Manna B, Maiti PK, et al. Sexual risk factors for cervical cancer among rural Indian women: a case-control study. Int J Epidemiol. 1997;26(3):491–5. Fotra R, Gupta S, Gupta S. Sociodemographic risk factors for cervical cancer in Jammu region of J and K state of India first ever report from Jammu. Indian J Sci Res. 2014;9(1):105–10. Rajarao P, Hemanth Kumar B. Study of socio-demographic profile of cancer cervix patients in tertiary care hospital, Karimnagar (Andhra Pradesh). Int J Biol Med Res. 2012;3(4):2306–10. Berraho M, Bendahhou K, Obtel M, et al. Cervical cancer in Morocco: epidemiological profile from two main oncological centers. Asian Pac J Cancer Prev APJCP. 2012;13(7):3153–7. Franceschi S, Rajkumar T, Vaccarella S, et al. Human papillomavirus and risk factors for cervical cancer in Chennai, India: a case-control study. Int J Cancer J Int Cancer. 2003;107(1):127–33. Thakur A, Gupta B, Gupta A, Chauhan R. Risk factors for cancer cervix among rural women of a hilly state: A casecontrol study. Indian J Public Health. 2015;59:45–8. Patil V, Wahab SN, Zodpey S, Vasudeo ND. Development and validation of risk scoring system for prediction of cancer cervix. Indian J Public Health. 2006;50:38–42. Kaverappa VB, Boralingaiah P, Kulkarni P, Manjunath R. Determinants of survival among patients with cervical cancer: A hospital based study. Natl J Community Med. 2015;6:4–9. Sankaranarayanan R, Nair MK, Jayaprakash PG, Stanley G, Varghese C, Ramadas V, et al. Cervical cancer in Kerala: A hospital registrybased study on survival and prognostic factors. Br J Cancer. 1995;72:1039–42. Sadalla JC, Andrade JMd, Genta MLND, et al. Cervical cancer: what’s new? Rev Assoc Med Bras. 2015;61:536–42. WHO. Comprehensive Cervical Cancer Control A guide to essential practice a guide to essential practice– 2nd ed. 2014. Cunningham FG, et al. Williams Obstetrics 24th edition. McGraw-Hill Education; 2014. Mitchell DG, Snyder B, Coakley F, Reinhold C, Thomas G, Amendola M, Schwartz LH, Woodward P, Pannu H, Hricak H. Early invasive cervical cancer: tumor delineation by magnetic resonance imaging, computed tomography, and clinical examination, verified by pathologic results, in the ACRIN 6651/GOG 183 Intergroup Study. J Clin Oncol. 2006;24(36):5687–94. Yesuf T. Survival and Associated Factors among Cervical Cancer Patients in Black Lion Hospital, Addis Ababa, Ethiopia, 2008–2012, a Retrospective Longitudinal Study (Doctoral dissertation, Addis Ababa University). Gurmu SE. Assessing survival time of women with cervical cancer using various parametric frailty models: a case study at Tikur anbessa specialized hospital, Addis Ababa, Ethiopia. Annals Data Sci. 2018;5(4):513–27. Ioka A, Ito Y, Tsukuma H. Factors relating to poor survival rates of aged cervical cancer patients: a population-based study with the relative survival model in Osaka, Japan. Asian Pac J Cancer Prev. 2009;10(3):457–62. Sriamporn S, Swaminathan R, Parkin DM, Kamsaard S, Hakama M. Lossadjusted survival of cervix cancer in Khon Kaen, Northeast Thailand. Br J Cancer. 2004;91:106–10. SK O, PREDICTIVE FACTORS ASSOCIATED WITH SURVIVAL RATE OF CERVICAL, CANCER PATIENTS IN BRUNEI DARUSSALAM. Brunei Int Med J (BIMJ). 2019;15. Quinn BA, Deng X, Colton A, Bandyopadhyay D, Carter JS, Fields EC. Increasing age predicts poor cervical cancer prognosis with subsequent effect on treatment and overall survival. Brachytherapy. 2019;18(1):29–37. Hammer A, Kahlert J, Gravitt PE, Rositch AF. Hysterectomy-corrected cervical cancer mortality rates in Denmark during 2002‐2015: a registry‐based cohort study. Acta Obstet Gynecol Scand. 2019;98(8):1063–9. Ioka A, Tsukuma H, Ajiki W, Oshima A. Influence of age on cervical cancer survival in Japan. Jpn J Clin Oncol. 2005;35(8):464–9. Gao Y, Ma JL, Gao F, Song LP. The evaluation of older patients with cervical cancer. Clinical interventions in aging. Jun. 2013;25:783–8. Saha A, Nag Chaudhary A, Bhowmik P, Chatterjee R. Awareness of cervical cancer among female students of premier colleges in Kolkata, India. Asian Pac J Cancer Prev. 2010;11:1085–90. Giftson S, Umadevi P, Kannika PS. The present scenario of cervical cancer control and HPV epidemiology in India: an outline. Asian Pac J Cancer Prev. 2011;12:1107–15. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6835791","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477714660,"identity":"994bf80e-8363-42ed-9345-f7dd20e8a7e2","order_by":0,"name":"Bajarang Bahadur","email":"","orcid":"","institution":"Banaras Hindu University","correspondingAuthor":false,"prefix":"","firstName":"Bajarang","middleName":"","lastName":"Bahadur","suffix":""},{"id":477714661,"identity":"9d2c9889-8631-4a8d-b6ff-9bf8fb88936d","order_by":1,"name":"Tej Bali Singh","email":"","orcid":"","institution":"Banaras Hindu University","correspondingAuthor":false,"prefix":"","firstName":"Tej","middleName":"Bali","lastName":"Singh","suffix":""},{"id":477714662,"identity":"e99b8224-dcc5-4281-b4c7-8ec4337b7b17","order_by":2,"name":"Sunil Chaudhari","email":"","orcid":"","institution":"Banaras Hindu University","correspondingAuthor":false,"prefix":"","firstName":"Sunil","middleName":"","lastName":"Chaudhari","suffix":""},{"id":477714663,"identity":"63ed96c8-871d-4da1-9965-89c80ddddf6c","order_by":3,"name":"Jagriti Annu","email":"","orcid":"","institution":"Banaras Hindu University","correspondingAuthor":false,"prefix":"","firstName":"Jagriti","middleName":"","lastName":"Annu","suffix":""},{"id":477714664,"identity":"1a33eea8-b9b5-4b60-95d9-02b948eb317e","order_by":4,"name":"Lavanya Anuranjani","email":"","orcid":"","institution":"Banaras Hindu 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09:38:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6835791/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6835791/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85846977,"identity":"44e65449-93d6-4846-b342-c64798b02782","added_by":"auto","created_at":"2025-07-02 09:49:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35335,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curve of age group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6835791/v1/5f0b351df9f48ef9a1acffbe.png"},{"id":85846978,"identity":"03730057-bf56-45e9-9023-2a0458e76943","added_by":"auto","created_at":"2025-07-02 09:49:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36975,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curve of Stage\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6835791/v1/ee4db61529c5a03c861eacb0.png"},{"id":85846983,"identity":"98bba24a-0aab-4f86-9be2-bb60906ca1e0","added_by":"auto","created_at":"2025-07-02 09:49:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31350,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curve of Education\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6835791/v1/265e33c5dece0cf458032a35.png"},{"id":85848420,"identity":"7ba5f28c-027e-4c36-831a-e2f4334c27c8","added_by":"auto","created_at":"2025-07-02 09:57:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73933,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curve of the Type of radiotherapy\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6835791/v1/938b4a50504237b9e6b63380.png"},{"id":85846985,"identity":"c1ea08a6-5df6-4dfd-9419-b5742a32c8f9","added_by":"auto","created_at":"2025-07-02 09:49:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":84610,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curve of type of treatment\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6835791/v1/815721b1581ceb34bff4d2ca.png"},{"id":87718999,"identity":"f9863c35-4bb1-408a-a89f-0b31bd016ea9","added_by":"auto","created_at":"2025-07-28 09:32:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1444522,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6835791/v1/b72d446e-c869-46a1-b51f-a8b2d531c9b6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determinants of Survival among Cervical Cancer Patients: A Hospital-Based Retrospective Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer is a common cancer among women worldwide. It especially affects those in low- and middle-income countries, in 2022, there were about 661,021 new cases. Most of these cases were in low- and middle-income countries. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] The standardized incidence rates were 13.3 cases per 100,000 women-years and 7.3 deaths per 100,000 women-years. It is the most common cancer in 23 of 185 countries. In 67 countries, it is the second most common. Cervical cancer leads to the most cancer deaths in 36 countries. It is the second most common cause of death in 49 countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCervical cancer remains the most common cancer in women in Eastern and Middle Africa [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In 2020, around 348,189 women died from cervical cancer worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This made up 7.7% of all female cancer deaths [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Around nine out of ten (90%) cervical cancer deaths occur in developing regions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Mortality rates differ greatly around the world. They range from under 2 per 100,000 in Western Asia, Western Europe, and Australia/New Zealand. In contrast, rates exceed 20 per 100,000 in Southern Africa (20.6), Middle Africa (22.7), and Eastern Africa (28.6) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn India, there were 123,907 cases of cervical cancer in 2020. The disease caused an estimated 77,348 deaths, making up 9.1% of all cancer deaths that year [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In 2020, India had about one-fifth of all new cervical cancer cases. It also accounted for nearly one-fourth of related deaths worldwide. This highlights India's significant role in the global cervical cancer burden. Cervical cancer is the second most common cancer in women nationally. It makes up 18.3% of new cases and 18.7% of cancer deaths. The five-year prevalence rate is 18.8% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to the National Cancer Institute, the 5-year survival rate of cervical cancer patients is 92% when diagnosed at an early stage, and when diagnosed after it has spread to nearby tissues, organs, or regional lymph nodes, the 5-year survival rate is 59%. Patients affected by cervical cancer survive longer than 5 years in less than 50% of women in less-developed countries [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The survival of cervical cancer patients in economically advanced regions is about 66% at 5 years [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResults showed that age, marital status, high school education, and where people live linked to cervical cancer risk. Also, using oral contraceptives, having HIV, and not knowing about Pap smear screening and vaccination were connected to this risk [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCervical cancer is more common in rural areas. Limited healthcare access plays a big role. Poor awareness of reproductive health is another factor. Also, sociocultural barriers can delay diagnosis and treatment. Well-known risk factors for cervical cancer include a long-lasting infection with high-risk Human Papillomavirus (HPV). Types 16 and 18 are the main causes of this cancer. Other significant risk factors include early sexual activity, multiple sexual partners, smoking, a weakened immune system, long-term use of oral contraceptives, high parity (multiple full-term pregnancies), and a family history of cervical cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study focuses on understanding the demographic, clinical, and treatment aspects of cervical cancer patients. We will explore how these aspects connect to survival outcomes. Specifically, the study seeks to identify key prognostic factors influencing survival outcomes. The data is sourced from records held by the Department of Radiotherapy and Radiation Medicine.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area and period\u003c/h2\u003e \u003cp\u003eA retrospective study was conducted at Sir Sunder Lal Hospital, Banaras Hindu University in Varanasi, Uttar Pradesh, India. The patients included in this study are those who were diagnosed with cervical cancer from 2011 to 2021. Therefore, the study includes 615 cervical cancer patients.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRisk factors\u003c/h3\u003e\n\u003cp\u003eThe variables including age (categorized in three part\u0026thinsp;\u0026le;\u0026thinsp;45, 46\u0026ndash;60 and 60+), FIGO stage (the stage based on the International Federation of Gynecology and Obstetrics system), religion (Hindu and Muslim), marital status (married and widowed), place of residence (rural and urban), treatment type (surgery, chemotherapy (CT), radiotherapy (RT), and brachytherapy (BT)) categorized in the three Parts (Surgery\u0026thinsp;+\u0026thinsp;CT\u0026thinsp;+\u0026thinsp;RT\u0026thinsp;+\u0026thinsp;BT, CT\u0026thinsp;+\u0026thinsp;RT\u0026thinsp;+\u0026thinsp;BT, and others(only RT, Surgery\u0026thinsp;+\u0026thinsp;RT, CT\u0026thinsp;+\u0026thinsp;RT, RT\u0026thinsp;+\u0026thinsp;BT, etc) and type of radiotherapy (radical and postop), were included to figure out the risk factors affecting the survival. To discover their significance to the survival time, the accessible variables have been used. The FIGO stages are categorized in two parts first one is the early stage, who include stage I and II, and the second is the late stage, who include stage III and IV. Certain stages are unknown, and postoperative categories have been combined into a single category named as others.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe recorded data were entered in MS Excel and analyzed using SPSS 28 and R software. The results are presented in frequencies and percentages. The Kaplan-Meier survival curves were used to estimate and visualize the survival probability over time for patients with cervical cancer. The curve represents the probability of survival at different time points. The survival probability at time \u0026lsquo;t\u0026rsquo; is given by:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:S\\left({t}_{i}\\text{}\\right)=\\:\\prod\\:_{i:{t}_{i}\\le\\:t}(1-\\:\\frac{{d}_{i}}{{n}_{i}}\\:)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003et\u003csub\u003ei\u003c/sub\u003e​ is the time at which the event occurs,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ed\u003csub\u003ei\u003c/sub\u003e​ is the number of events (e.g., deaths) at time t\u003csub\u003ei\u003c/sub\u003e​,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003en\u003csub\u003ei\u003c/sub\u003e​ is the number of individuals at risk just before time t\u003csub\u003ei\u003c/sub\u003e​.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe Log-Rank test is used to compare the survival distributions between two or more groups.\u003c/p\u003e \u003cp\u003eχ2 = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:\\:\\:\\frac{{({O}_{i}-\\:{E}_{i})}^{2}}{{E}_{i}}\\:\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eO\u003csub\u003ei\u003c/sub\u003e​ is the observed number of events in the group at time t\u003csub\u003ei\u003c/sub\u003e​,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eE\u003csub\u003ei\u003c/sub\u003e​ is the expected number of events in the group at time t\u003csub\u003ei\u003c/sub\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAnd also, to identify the factors associated with overall survival among cervical cancer patients, we employed the Cox Proportional Hazards (PH) model, a semi-parametric regression method widely used in survival analysis. The model estimates the hazard (or risk) of the event occurring at a given time, conditional on the values of explanatory covariates.\u003c/p\u003e \u003cp\u003eh(t∣X\u003csub\u003ei\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;h\u003csub\u003e0\u003c/sub\u003e​(t) exp (β\u003csub\u003e1\u003c/sub\u003e​X\u003csub\u003e1\u003c/sub\u003e​+β\u003csub\u003e2\u003c/sub\u003e​X\u003csub\u003e2\u003c/sub\u003e​+⋯+β\u003csub\u003ep\u003c/sub\u003e​X\u003csub\u003ep\u003c/sub\u003e​)\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eh(t∣X\u003csub\u003ei\u003c/sub\u003e) is the hazard at time t for an individual with covariates X\u003csub\u003e1\u003c/sub\u003e, X\u003csub\u003e2\u003c/sub\u003e..., X\u003csub\u003ep\u003c/sub\u003e​,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eh\u003csub\u003e0\u003c/sub\u003e(t) is the baseline hazard function,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eβ\u003csub\u003e1\u003c/sub\u003e, β\u003csub\u003e2,\u0026hellip;\u003c/sub\u003e, β\u003csub\u003ep\u003c/sub\u003e​ are the regression coefficients estimating the effect of each covariate.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\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 Cervical Cancer Patients: Number (%)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.0\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of Residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther (Unknown and PO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiotherapy Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of Treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery \u0026thinsp;+\u0026thinsp;CT\u0026thinsp;+\u0026thinsp;RT\u0026thinsp;+\u0026thinsp;BT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT\u0026thinsp;+\u0026thinsp;RT\u0026thinsp;+\u0026thinsp;BT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the majority of cervical cancer patients were in the age group of 46\u0026ndash;60 years (53.3%), followed by those below 46 years (30.1%) and above 60 years (16.6%), indicating that middle-aged women are the most affected. Regarding marital status, 87.3% of the patients were currently married, and 12.7% were widowed. A substantial proportion of patients (72.4%) resided in rural areas, reflecting the higher burden of cervical cancer in underserved populations. The majority were Hindu (96.6%), and a small proportion were Muslim (3.4%).\u003c/p\u003e \u003cp\u003eData revealed that 30.7% of patients were diagnosed at an early stage, 43.3% at a late stage, while 26.0% had other (unknown and postoperative). This suggests a nearly 13% difference between early and late detection, with a significant portion lacking complete staging information. Regarding radiotherapy, 71.1% received radical radiotherapy, while 28.9% underwent postoperative radiotherapy. In terms of treatment modalities, 54.3% of patients received a combination of chemotherapy, radiotherapy, and brachytherapy (CT\u0026thinsp;+\u0026thinsp;RT\u0026thinsp;+\u0026thinsp;BT), 24.2% received surgery\u0026thinsp;+\u0026thinsp;CT\u0026thinsp;+\u0026thinsp;RT\u0026thinsp;+\u0026thinsp;BT, and the remaining 21.5% were treated with other combinations.\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\u003eKaplan-Meier Estimated Survival Rates at 1, 3, and 5 years among Cervical Cancer.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSurvival Rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLog rank test value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 Year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 Years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 Years\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e9.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.7\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of Residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e11.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther (Unknown and PO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of Radiotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of Treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u0026thinsp;+\u0026thinsp;CT\u0026thinsp;+\u0026thinsp;RT\u0026thinsp;+\u0026thinsp;BT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e21.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT\u0026thinsp;+\u0026thinsp;RT\u0026thinsp;+\u0026thinsp;BT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the Kaplan-Meier survival analysis revealed significant differences in cervical cancer survival rates across various demographic and clinical characteristics. Age was found to be an important predictor of survival, with patients under the age of 46 years exhibiting the highest five-year survival rate of 73.3%. This survival rate declined to 63.3% in the 46\u0026ndash;60 years age group and further decreased to 52.7% in those aged above 60 years. The observed difference in survival by age group was statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), indicating that older age is associated with poorer survival outcomes. Educational status also showed a significant association with survival. Literate women had a five-year survival rate of 71.5%, compared to 59.8% among illiterate women (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e \u003cp\u003eIn contrast, marital status, place of residence, and religion did not show statistically significant differences in survival. Married women had a slightly higher five-year survival rate (65.4%) compared to widowed women (56.7%) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.306). Similarly, urban residents had a marginally lower five-year survival rate (61.1%) than rural residents (65.4%) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.295), and Hindu women had a five-year survival of 64.7%, slightly higher than Muslim women (57.0%) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.372). However, these differences were not statistically significant.\u003c/p\u003e \u003cp\u003eThe stage of cancer at diagnosis had a significant impact on survival outcomes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). Women diagnosed at an early stage had a five-year survival rate of 74.6%, compared to just 55.3% among those diagnosed at a late stage. Type of radiotherapy (radical vs. postoperative) was not significantly associated with survival (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.622), though those receiving radical radiotherapy had a slightly better five-year survival rate (64.9%) compared to those receiving postoperative therapy (62.1%).\u003c/p\u003e \u003cp\u003eNotably, the type of treatment received showed a highly significant association with survival outcomes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients treated with a combination of chemotherapy, radiotherapy, and brachytherapy (CT\u0026thinsp;+\u0026thinsp;RT\u0026thinsp;+\u0026thinsp;BT) had the highest five-year survival rate (71.5%), followed by those who received surgery\u0026thinsp;+\u0026thinsp;CT\u0026thinsp;+\u0026thinsp;RT\u0026thinsp;+\u0026thinsp;BT (61.7%). In contrast, patients who received other combinations of treatment had the poorest five-year survival rate (44.2%).\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\u003eHazard ratio of Death for Cervical Cancer Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95.0% CI for HR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95.0% CI for HR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.016\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of Residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers (Unknown and PO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of Treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u0026thinsp;+\u0026thinsp;CT\u0026thinsp;+\u0026thinsp;RT\u0026thinsp;+\u0026thinsp;BT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT\u0026thinsp;+\u0026thinsp;RT\u0026thinsp;+\u0026thinsp;BT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther combinations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the results of the Cox proportional hazards regression analysis, conducted to explore the influence of demographic and clinical variables on the mortality risk among cervical cancer patients. Both unadjusted and adjusted hazard ratios were calculated to evaluate the independent effect of each factor. Age was a notable determinant, with women over the age of 60 years exhibiting a significantly higher mortality risk in the unadjusted model (UHR\u0026thinsp;=\u0026thinsp;1.904; 95% CI: 1.237\u0026ndash;2.931; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) compared to those under 46 years. However, after adjusting for other covariates, the risk remained elevated but was no longer statistically significant (AHR\u0026thinsp;=\u0026thinsp;1.542; 95% CI: 0.939\u0026ndash;2.533; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.087). Women aged 46\u0026ndash;60 years did not show a significant risk in either the unadjusted or adjusted models.\u003c/p\u003e \u003cp\u003eEducational attainment emerged as a significant predictor of survival. Illiterate women faced a 55% higher risk of death compared to literate women, with this association remaining significant in both the unadjusted (UHR\u0026thinsp;=\u0026thinsp;1.549; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010) and adjusted models (AHR\u0026thinsp;=\u0026thinsp;1.550; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). This highlights the critical role of education in enhancing health literacy, awareness, and timely access to care.\u003c/p\u003e \u003cp\u003ePlace of residence also showed a significant association with mortality. Patients residing in urban areas had a higher risk of death compared to those in rural settings (AHR\u0026thinsp;=\u0026thinsp;1.535; 95% CI: 1.083\u0026ndash;2.176; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016), possibly reflecting disparities in healthcare access, environmental exposures, or delays in diagnosis despite the perceived advantages of urban healthcare infrastructure.\u003c/p\u003e \u003cp\u003eThe stage at diagnosis was a strong and statistically significant factor affecting survival. Women diagnosed at a late stage faced a substantially higher risk of death compared to those diagnosed at an early stage (AHR\u0026thinsp;=\u0026thinsp;1.620; 95% CI: 1.108\u0026ndash;2.368; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), underscoring the importance of early detection and timely intervention in reducing mortality.\u003c/p\u003e \u003cp\u003eIn contrast, variables such as marital status and religion did not show statistically significant associations with survival. Although widowed and Muslim patients had higher hazard ratios, the lack of statistical significance suggests these factors had a limited impact on mortality in this cohort.\u003c/p\u003e \u003cp\u003eWhen examining treatment modalities, women who received other combinations of treatment had a significantly increased risk of death in the unadjusted model (UHR\u0026thinsp;=\u0026thinsp;1.675; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020). However, this association was attenuated and became non-significant after adjustment (AHR\u0026thinsp;=\u0026thinsp;1.465; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.109).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study involved a five-year survival analysis of cervical cancer patients to identify significant demographic, clinical, and treatment-related predictive factors affecting survival outcomes. The primary objective was to examine the association between various patient characteristics and overall survival using Kaplan-Meier and Cox regression analyses. Our findings revealed that age at diagnosis, educational status, stage of cancer, and type of treatment significantly impacted survival rates. Patients diagnosed at an early stage and those receiving comprehensive treatment modalities, including chemotherapy, radiotherapy, and brachytherapy, had notably higher survival rates. Illiterate women and those residing in urban areas faced a higher risk of mortality, highlighting the role of socioeconomic and healthcare access disparities in survival outcomes.\u003c/p\u003e \u003cp\u003eIn this study, the mean age of patients in our study was slightly higher than that reported by other researchers in Western literature [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This higher age at diagnosis may reflect a relative lack of awareness and limited availability of cervical cancer screening services in our country [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The predominant age group among patients was 46\u0026ndash;60 years, followed by individuals under 46 years. A similar age-wise distribution has been reported in previous studies [\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In our study, the majority of the study subjects were Hindu (96.6%). These findings were comparable to findings of most of the Indian studies [\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In the present study, the majority of women (87.3%) were currently married, while 12.7% were widows. These results are similar to the results of other studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, 58.1% of patients were illiterate. This finding aligns with many studies which has found illiteracy as a risk factor for cervical cancer [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Likewise, insufficient education has been associated with early marriage and high parity, both recognized as risk factors for cervical cancer; therefore, enhancing the educational standing of women in our country is a crucial element of a comprehensive strategy for cervical cancer prevention in India [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. A study conducted by Patil et al. in Nagpur indicated a substantial association between illiteracy and the incidence of cervical cancer [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A study conducted by Sankaranarayanan et al. indicated that the 5-year survival rate among illiterates was 45.2% [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], which aligns with the findings of the current study.\u003c/p\u003e \u003cp\u003eIn this study, treatment was categorized into three groups: receiving all treatment (Surgery, chemotherapy, radiotherapy, and brachytherapy), a combination of chemotherapy, radiotherapy brachytherapy, and other combinations. The findings indicate that chemotherapy, radiotherapy and brachytherapy treatment modalities, with other treatments, were independently associated with reduced survival and lower 5-year survival rates compared to patients who received all treatments. The selection of treatment for cervical cancer is determined by various aspects, including the type of cancer, the stage upon diagnosis, and possible adverse effects. Surgery is typically preferred as a standalone treatment for early-stage cervical cancer, whereas a combination of radiation and/or chemotherapy, with or without surgery, is recommended for more advanced stages [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThis study suggested that the cancer stage at diagnosis is the primary predictor of CCP survival. The advanced stage of cancer is strongly correlated with reduced survival rates among cervical cancer patients [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Patients with advanced cancer stages may have a greater mortality rate due to treatment problems, concomitant illness development, and the rate of metastases [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, this may be attributed to the fact that patients in advanced stages are less likely to respond to treatment compared to those in earlier stages [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study identified advanced age as a predictor of survival in cervical cancer patients (CCP) (AHR: 1.542; 95% CI: 0.939\u0026ndash;2.533). Women over 60 years old were roughly 1.5 times more likely to die within five years after diagnosis compared to those aged 46 or younger. Previous studies have revealed analogous findings, indicating that a later age at diagnosis correlates with reduced survival rates among cervical cancer patients [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. A comparable study was conducted in Khon Kaen, Thailand, by Sriamporn et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], which reported that patients under 40 years of age had the highest survival rates, while those over 60 years had the lowest. This demonstrated an inverse relationship between age and survival, findings that align with the present study, where patients below 46 years showed a more favorable prognosis compared to those aged 60 and above. A study from Japan further highlighted that an advanced stage at diagnosis was the primary determinant of poor survival outcomes in older cervical cancer patients [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Moreover, numerous studies show a poorer prognosis and elevated mortality rates in older women diagnosed with cervical cancer [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This may be attributed to more advanced disease at the time of diagnosis in older women [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], as well as a tendency for them to receive less aggressive treatment compared to their younger counterparts [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, in contrast, a study from China found no significant difference in prognosis between older and younger women with cervical cancer [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], which could be due to differences in study design and age group classification.\u003c/p\u003e \u003cp\u003eCervical cancer remains a significant public health concern in India. Studies have shown that awareness of key risk factors is lacking even among educated young women, suggesting an even greater knowledge gap among less educated populations [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Engaging male partners and elder male family members in awareness initiatives is also crucial [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. However, formal education alone may not be sufficient, especially when individuals lack the financial means to maintain proper hygiene. Effectively reduces the incidence of cervical cancer; it is crucial to complement educational efforts with improvements in living conditions. Engaging Accredited Social Health Activists (ASHAs), community health workers, and local panchayats in collaboration with State Health Societies (SHS) can enhance outreach and awareness programs. These stakeholders can play a vital role in implementing women-to-women awareness campaigns, ensuring that critical information on prevention, screening, and hygiene practices reaches even the most marginalized communities can significantly contribute to reducing the incidence of cervical cancer. These measures also promote greater participation in early detection programs and acceptance of HPV vaccination. Moreover, it is essential to account for the sociodemographic determinants of cervical cancer when developing and implementing public health policies and cervical cancer control strategies.\u003c/p\u003e \u003cp\u003eThe results of this investigation align with the existing body of evidence emphasizing the multifactorial nature of survival in cervical cancer patients. Timely diagnosis, access to comprehensive treatment, and socio-demographic factors like age, education, and stage at presentation are crucial in determining prognosis. These insights are essential for healthcare providers and policymakers aiming to improve cervical cancer outcomes through targeted interventions.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Strengths\u003c/h2\u003e \u003cp\u003eIt is important to acknowledge that this study has a number of limitations. Notably, we were unable to capture certain essential variables, such as patients\u0026rsquo; socioeconomic status and family income. Additionally, the timing of the onset of comorbid conditions could not be assessed. These limitations stem from the use of secondary data, restricting the analysis to information documented in medical records. Despite these constraints, the study\u0026rsquo;s strength lies in its contribution to the limited body of Indian research that comprehensively examines patients\u0026rsquo; sociodemographic and clinical profiles, along with the associations between patient characteristics and tumor-related factors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the substantial impact of socio-demographic and clinical factors on the survival outcomes of cervical cancer patients. Age, education, stage of cancer at diagnosis, and type of treatment emerged as key determinants of survival. Younger women, particularly those below the age of 46, exhibited the highest five-year survival rates, while survival declined considerably among women over 60 years. Literate women had significantly better survival outcomes compared to illiterate women, highlighting the critical role of education in timely health-seeking and adherence to treatment.\u003c/p\u003e \u003cp\u003eLate-stage diagnosis was consistently associated with poorer survival, reinforcing the importance of early detection and timely intervention. Women diagnosed at an early stage had a markedly higher probability of survival, while those with late-stage disease faced significantly increased mortality risk. The type and completeness of treatment also influenced outcomes, with women receiving all modalities of standard treatment generally experiencing better survival.\u003c/p\u003e \u003cp\u003eCox PH model confirmed that illiteracy, urban residence, and late-stage diagnosis were independently associated with a higher risk of death. These findings indicate that improving health literacy, strengthening cervical cancer screening programs, and ensuring access to comprehensive and timely treatment, particularly for disadvantaged and urban populations, are crucial for reducing cervical cancer mortality. Focused public health interventions addressing these disparities are essential to enhance survival outcomes and overall quality of care for women with cervical cancer.\u003c/p\u003e"},{"header":"Recommendation","content":"\u003cp\u003eBased on this study, several key recommendations can be made to enhance cervical cancer outcomes in India. A primary focus should be on improving early detection and screening efforts, as the study indicates that survival rates are significantly higher among patients diagnosed at an early stage. This highlights the need to expand screening programs, particularly targeting women over the age of 45, who were shown to be at increased risk.\u003c/p\u003e \u003cp\u003eHealth education is another crucial factor in improving survival. The analysis found that literate women had better outcomes than illiterate ones, suggesting that awareness and timely access to healthcare play a critical role. Therefore, public health initiatives should prioritize educational campaigns, especially in rural and marginalized communities, to raise awareness about cervical cancer symptoms, prevention, and the value of early diagnosis and treatment.\u003c/p\u003e \u003cp\u003eFocused attention is needed for vulnerable groups such as older women, those with low literacy levels, and patients diagnosed at advanced stages. Customized approaches, such as community outreach and patient support services, can improve treatment adherence and outcomes in these populations.\u003c/p\u003e \u003cp\u003eIn addition, integrating supportive and palliative care into cancer management is critical for enhancing the well-being of patients, especially those undergoing less effective treatment regimens.\u003c/p\u003e \u003cp\u003eFinally, there is a pressing need for policy reforms and systemic improvements in healthcare delivery. This includes investing in the training of medical personnel, upgrading diagnostic facilities, and decentralizing cancer care to ensure accessibility across regions. Establishing robust hospital-based cancer registries and improving data collection will be instrumental for tracking survival trends and informing policy decisions. Further research into healthcare access and social determinants of health is essential to design effective strategies and reduce cervical cancer-related deaths in India.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAHR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted Hazard Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBrachytherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCCP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCervical Cancer Patients\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFIGO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Federation of Gynecology and Obstetrics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIEC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstitutional Ethics Committee\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIMS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstitute of Medical Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIRB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstitutional Review Board\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eKM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKaplan\u0026ndash;Meier\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNCRP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Cancer Registry Programme\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProportional Hazards\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePostoperative\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSHS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eState Health Society\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSPSS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUHR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnadjusted Hazard Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWHO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e BB, TBS and SC contributed to conceptualizing the study. TBS, BB and SC are responsible for the analysis. BB, TBS, SC, JA, LA and SB contributed to the interpretation of the data, critically revised all versions of the manuscript, and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Ethics Committee (IEC), Institute of Medical Sciences, Banaras Hindu University, Varanasi, India (Approval No. Dean/2023/EC/684, dated 04.12.2023). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committee and with the Declaration of Helsinki (2013 revision). As the study was retrospective in nature and used anonymized data from hospital medical records without direct patient contact, the requirement for informed consent was waived by the Institutional Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying the findings presented in this study are not publicly available due to ethical and institutional restrictions. The data contain sensitive patient information, and access is limited to authorized researchers within the institution for confidentiality and compliance purposes. Requests for access to the data should be directed to the corresponding author or the Institutional Ethics Committee of Banaras Hindu University.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Competing Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Funding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received by the authors for this study.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Int J Gynaecol Obstet. 2001;75:33\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaverappa VB, Prakash B, Kulkarni P, Renuka M. Sociodemographic profile of patients with cervical cancer in a tertiarycare cancer hospital in Mysuru, Karnataka. Int J Med Sci Public Health. 2015;4:1187\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNandakumar A, Anantha N, Venugopal TC. Incidence, mortality and survival in cancer of the cervix in Bangalore, India. Br J Cancer. 1995;71:1348\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThulaseedharan JV, Malila N, Hakama M, Esmy PO, Cheriyan M, Swaminathan R, et al. Socio demographic and reproductive risk factors for cervical cancer \u0026ndash; A large prospective cohort study from rural India. Asian Pac J Cancer Prev. 2012;13:2991\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThulaseedharan JV, Malila N, Hakama M, Esmy PO, Cheriyan M, Swaminathan R, et al. Socio demographic and reproductive risk factors for cervical cancer \u0026ndash; A large prospective cohort study from rural India. Asian Pac J Cancer Prev. 2012;13:2991\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Cancer Registry Programme (NCRP), Indian Council of Medical Research (ICMR)., An Assessment of the Burden and Care of Cancer Patients: Consolidated Report of Hospital Based Cancer Registries, 2001\u0026ndash;2003. Bangalore: NCRP, ICMR; 2007. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.icmr.nic.in/ncrp/cancer_reg\u003c/span\u003e\u003cspan address=\"http://www.icmr.nic.in/ncrp/cancer_reg\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. htm. [Last accessed on 2015 Feb 21].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajesh N, Sreelakshmi K, Ramesh K. Sociodemographic profile of patients with cancer of cervix attending tertiary care hospital. Int J Sci Res. 2014;3:331\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFotra R, Gupta S, Gupta S. Sociodemographic risk factors for cervical cancer in Jammu region of J and K state of India first ever report from Jammu. Indian J Sci Res. 2014;9:105\u0026thinsp;\u0026ndash;\u0026thinsp;10. 23. Ertem G. Awareness of cervical cancer risk factors and screening behaviour among nurses in a rural region of turkey. Asian Pac J Cancer Prev 2009;10:735-8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThakur A, Gupta B, Gupta A, et al. Risk factors for cancer cervix among rural women of a hilly state: a case-control study. Indian J Public Health. 2015;59:45\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiswas LN, Manna B, Maiti PK, et al. Sexual risk factors for cervical cancer among rural Indian women: a case-control study. Int J Epidemiol. 1997;26(3):491\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFotra R, Gupta S, Gupta S. Sociodemographic risk factors for cervical cancer in Jammu region of J and K state of India first ever report from Jammu. Indian J Sci Res. 2014;9(1):105\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajarao P, Hemanth Kumar B. Study of socio-demographic profile of cancer cervix patients in tertiary care hospital, Karimnagar (Andhra Pradesh). Int J Biol Med Res. 2012;3(4):2306\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerraho M, Bendahhou K, Obtel M, et al. Cervical cancer in Morocco: epidemiological profile from two main oncological centers. Asian Pac J Cancer Prev APJCP. 2012;13(7):3153\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranceschi S, Rajkumar T, Vaccarella S, et al. Human papillomavirus and risk factors for cervical cancer in Chennai, India: a case-control study. Int J Cancer J Int Cancer. 2003;107(1):127\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThakur A, Gupta B, Gupta A, Chauhan R. Risk factors for cancer cervix among rural women of a hilly state: A casecontrol study. Indian J Public Health. 2015;59:45\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatil V, Wahab SN, Zodpey S, Vasudeo ND. Development and validation of risk scoring system for prediction of cancer cervix. Indian J Public Health. 2006;50:38\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaverappa VB, Boralingaiah P, Kulkarni P, Manjunath R. Determinants of survival among patients with cervical cancer: A hospital based study. Natl J Community Med. 2015;6:4\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSankaranarayanan R, Nair MK, Jayaprakash PG, Stanley G, Varghese C, Ramadas V, et al. Cervical cancer in Kerala: A hospital registrybased study on survival and prognostic factors. Br J Cancer. 1995;72:1039\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadalla JC, Andrade JMd, Genta MLND, et al. Cervical cancer: what\u0026rsquo;s new? Rev Assoc Med Bras. 2015;61:536\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. Comprehensive Cervical Cancer Control A guide to essential practice a guide to essential practice\u0026ndash; 2nd ed. 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCunningham FG, et al. Williams Obstetrics 24th edition. McGraw-Hill Education; 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitchell DG, Snyder B, Coakley F, Reinhold C, Thomas G, Amendola M, Schwartz LH, Woodward P, Pannu H, Hricak H. Early invasive cervical cancer: tumor delineation by magnetic resonance imaging, computed tomography, and clinical examination, verified by pathologic results, in the ACRIN 6651/GOG 183 Intergroup Study. J Clin Oncol. 2006;24(36):5687\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYesuf T. \u003cem\u003eSurvival and Associated Factors among Cervical Cancer Patients in Black Lion Hospital, Addis Ababa, Ethiopia, 2008\u0026ndash;2012, a Retrospective Longitudinal Study\u003c/em\u003e (Doctoral dissertation, Addis Ababa University).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGurmu SE. Assessing survival time of women with cervical cancer using various parametric frailty models: a case study at Tikur anbessa specialized hospital, Addis Ababa, Ethiopia. Annals Data Sci. 2018;5(4):513\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIoka A, Ito Y, Tsukuma H. Factors relating to poor survival rates of aged cervical cancer patients: a population-based study with the relative survival model in Osaka, Japan. Asian Pac J Cancer Prev. 2009;10(3):457\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSriamporn S, Swaminathan R, Parkin DM, Kamsaard S, Hakama M. Lossadjusted survival of cervix cancer in Khon Kaen, Northeast Thailand. Br J Cancer. 2004;91:106\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSK O, PREDICTIVE FACTORS ASSOCIATED WITH SURVIVAL RATE OF CERVICAL, CANCER PATIENTS IN BRUNEI DARUSSALAM. Brunei Int Med J (BIMJ). 2019;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuinn BA, Deng X, Colton A, Bandyopadhyay D, Carter JS, Fields EC. Increasing age predicts poor cervical cancer prognosis with subsequent effect on treatment and overall survival. Brachytherapy. 2019;18(1):29\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHammer A, Kahlert J, Gravitt PE, Rositch AF. Hysterectomy-corrected cervical cancer mortality rates in Denmark during 2002‐2015: a registry‐based cohort study. Acta Obstet Gynecol Scand. 2019;98(8):1063\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIoka A, Tsukuma H, Ajiki W, Oshima A. Influence of age on cervical cancer survival in Japan. Jpn J Clin Oncol. 2005;35(8):464\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao Y, Ma JL, Gao F, Song LP. The evaluation of older patients with cervical cancer. Clinical interventions in aging. Jun. 2013;25:783\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaha A, Nag Chaudhary A, Bhowmik P, Chatterjee R. Awareness of cervical cancer among female students of premier colleges in Kolkata, India. Asian Pac J Cancer Prev. 2010;11:1085\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiftson S, Umadevi P, Kannika PS. The present scenario of cervical cancer control and HPV epidemiology in India: an outline. Asian Pac J Cancer Prev. 2011;12:1107\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Cervical cancer, Survival analysis, Prognostic factors, Kaplan-Meier, Cox regression, Health disparities","lastPublishedDoi":"10.21203/rs.3.rs-6835791/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6835791/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCervical cancer remains a major public health challenge, particularly in low- and middle-income countries like India. Despite being preventable and treatable, it contributes significantly to female cancer mortality due to late diagnosis and limited access to comprehensive treatment. This study aims to identify key demographic, clinical, and treatment-related prognostic indicators influencing survival outcomes among cervical cancer patients in a tertiary care hospital in Varanasi, India.\u003c/p\u003e\u003ch2\u003eData and Methods:\u003c/h2\u003e \u003cp\u003eA retrospective cohort study was conducted using data from 615 cervical cancer patients diagnosed between 2011 and 2021 at Sir Sunder Lal Hospital. Kaplan-Meier survival analysis and Cox proportional hazards regression models were employed to estimate survival probabilities and assess the association of various factors with overall survival. Variables included age group, marital status, education, place of residence, stage at diagnosis, type of radiotherapy, and treatment combinations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe five-year survival rate was highest among patients diagnosed at an early stage (74.6%) and those receiving combined chemotherapy, radiotherapy, and brachytherapy (71.5%). Illiterate women had significantly lower survival rates (59.8%) compared to literate women (71.5%). Urban residents faced a higher risk of mortality than rural women, and advanced age (\u0026gt;\u0026thinsp;60 years) was associated with poorer survival outcomes. Cox regression confirmed that late-stage diagnosis (AHR\u0026thinsp;=\u0026thinsp;1.62), illiteracy (AHR\u0026thinsp;=\u0026thinsp;1.55), and urban residence (AHR\u0026thinsp;=\u0026thinsp;1.53) were independent predictors of mortality.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study highlights the critical role of early diagnosis, comprehensive treatment, and health literacy in improving cervical cancer survival. Addressing sociodemographic disparities and strengthening screening and awareness programs are essential for reducing cervical cancer mortality in India.\u003c/p\u003e","manuscriptTitle":"Determinants of Survival among Cervical Cancer Patients: A Hospital-Based Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 09:48:57","doi":"10.21203/rs.3.rs-6835791/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":"b45c3c39-9d82-47f2-9d29-b06f3b3c71b1","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-28T09:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 09:48:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6835791","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6835791","identity":"rs-6835791","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}