Exploring the Intersection of HIV Status, Viral Load Levels, and Cervical Cancer Screening in Tanzania and Zimbabwe: Insights from PHIA Data

Exploring the Intersection of HIV Status, Viral Load Levels, and Cervical Cancer Screening in Tanzania and Zimbabwe: Insights from PHIA Data | 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 Exploring the Intersection of HIV Status, Viral Load Levels, and Cervical Cancer Screening in Tanzania and Zimbabwe: Insights from PHIA Data Boniface Simpoli Yohana, John Massito, Arbogast Moshi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7223468/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background: Cervical cancer remains a significant health challenge in Tanzania and Zimbabwe, particularly among populations affected by HIV. This study explored the complex relationship between HIV status, viral load, and cervical cancer screening, shedding light on the factors that drive or hinder screening uptake. As screening rates play a crucial role in early detection and improved survival, addressing disparities in access and awareness is vital. Methods: An analytical cross-sectional study was conducted in two sub-Saharan African (SSA) countries to explore the intersection of HIV status, viral load levels, and cervical cancer screening. Bivariate and multiple binary logistic regression models were used to examine the relationship between HIV status, viral load levels, and cervical cancer, in addition to other socio-economic factors. Data were extracted from the PHIA survey and analysed using STATA. Results: The study indicated a significant association between HIV status and cervical cancer screening uptake, with HIV-positive individuals having notably higher odds of screening participation (OR = 3.73). Patients with normal immune function exhibited an increased likelihood of screening (OR = 2.44, 95% CI 1.20–4.97) compared with other CD4 count categories. Age was a significant predictor, with individuals aged 36–45 years (OR = 3.59, 95% CI 1.64–7.86) and 46–55 years (OR = 4.53, 95% CI 1.97–10.41) being more likely to undergo screening than those in the 15–25 age group. Economic status also played a role, as participants in the second wealth quintile demonstrated the highest likelihood of screening (OR = 2.64, 95% CI 1.22–5.73), followed by those in the highest wealth quintile (OR = 2.31, 95% CI 0.97–5.49). Additionally, the number of sexual partners showed a marginal association with screening uptake (OR = 1.70, 95% CI 0.94–3.10), suggesting that sexual behavior may influence screening decisions. Conclusions: The study demonstrates that Cervical cancer screening rates increased significantly with HIV status, CD4 count, age, and economic status. These findings underscore the need for targeted interventions to reduce disparities and enhance screening accessibility in the future. Cervical cancer testing HIV status CD4 count and Multiple logistic analysis Figures Figure 1 1. Introduction Cervical cancer is the fourth most common cancer among women worldwide, disproportionately affecting those in low- and middle-income countries, particularly sub-Saharan Africa. (Ampofo et al., 2020 ; Belglaiaa et al., 2018 ). In this region, it ranks as the second most prevalent cancer and remains the leading cause of cancer-related mortality in women (Fentie et al., 2020 ). Women living with HIV face a significantly higher risk, up to six times greater, of developing cervical cancer compared to HIV-negative individuals (Vapiwala et al., 2021 ). This increased vulnerability stems from HIV-induced immunosuppression, which impairs the body's ability to clear human papillomavirus (HPV) infections, the primary cause of cervical cancer (Murfin et al., 2020 ). Consequently, HPV infections progress more rapidly to cervical cancer in immunocompromised women, often within 5–10 years, compared to 15–20 years in those without HIV. This heightened risk of cervical cancer among women living with HIV underscores the urgent need for comprehensive HIV management strategies in sub-Saharan Africa, where the burden of HIV remains disproportionately high. HIV continues to pose a significant public health challenge in Sub-Saharan Africa, which accounts for approximately 60% of new global infections (UNAIDS, 2018 ). Despite progress toward the UNAIDS 95-95-95 targets, which aim for 95% of people living with HIV (PLHIV) to know their status, 95% of those diagnosed to be on treatment, and 95% of those on treatment to achieve viral suppression, there remain substantial disparities to attain these goals (Van Gerwen et al., 2022 ). Viral suppression rates, a critical indicator of treatment success, vary widely across countries, with some nations reporting rates as low as 24.3% and others reporting rates as high as 99.7%. These disparities highlight the need for targeted interventions to address gaps in healthcare access and adherence to antiretroviral therapy (ART). The Population-based HIV Impact Assessment (PHIA) data from two (2) Sub-Saharan African countries, Tanzania and Zimbabwe, provide a wealth of information on the intersection of HIV and cervical cancer (Saito et al., 2017 ). For instance, the data reveal that while women generally have higher rates of viral suppression than men, the overall burden of unsuppressed viral loads remains higher among women because of their greater prevalence of HIV (UNAIDS, 2018 ). This underscores the importance of gender-sensitive approaches to address the dual burden of HIV and cervical cancer. Geographic disparities further complicate this issue. Rural areas often face significant challenges in accessing healthcare services, including HPV vaccination, cervical cancer screening, and ART (Saito et al., 2017 ). These disparities are exacerbated by socioeconomic factors, including poverty, limited access to education, and inadequate healthcare infrastructure. Addressing these inequities requires a multifaceted approach, including community-based healthcare models, mobile clinics, and increased investment in rural healthcare systems. The economic implications of this dual burden are significant. Households affected by HIV and cervical cancer often face catastrophic healthcare costs, leading to financial instability and a reduced quality of life (Habib et al., 2021 ). Moreover, the loss of productivity due to illness further exacerbates poverty, creating a vicious cycle that hinders regional economic development (Dare et al., 2021 ). Efforts to combat this dual burden must prioritize prevention and early interventions (Devarapalli et al., 2018 ). Scaling up HPV vaccination programs, particularly among adolescent girls, can significantly reduce the incidence of cervical cancer (Srivastava et al., 2018 ). Similarly, increasing access to cervical cancer screening and treatment, along with ART, can improve health outcomes for women living with HIV (S. Zhang et al., 2020 ). Achieving these goals requires robust policy frameworks, international collaboration, and sustained investment in healthcare systems. In conclusion, the intersection of HIV and cervical cancer screening in sub-Saharan Africa represents a critical public health challenge with far-reaching implications. Leveraging data from initiatives such as the PHIA can guide targeted interventions to reduce disparities, improve health outcomes, and ultimately alleviate the dual burden of these diseases in the region. 2.0 Data and Methods of Analysis 2.1 Study design This is a quantitative, cross-sectional study using secondary data from the PHIA surveys, which provide population-level information on HIV prevalence, healthcare service usage, socio-economic conditions, and cervical cancer screening. 2.2 Data Types This study used nationally representative cross-sectional data from the Population HIV Impact Survey (PHIA) to examine HIV prevalence, testing uptake, and related factors across demographic groups. The dataset includes health-related variables such as cervical cancer screening, HIV status, and CD4 count, as well as demographic, socioeconomic, and behavioral factors. 2.3 Study Variables This study focused on several key variables to investigate the intersection of HIV, viral load, and cervical cancer in Tanzania and Zimbabwe. The dependent variable was cervical cancer screening. The independent variables included HIV-related factors such as HIV status, viral load (suppressed or unsuppressed), and CD4 count. Demographic factors included age, sex, and urban versus rural residency, alongside socioeconomic factors such as education level and employment status. Behavioral aspects include the number of sexual partners. 2.4 Statistical Analysis This study employed rigorous statistical methods to examine the relationship between HIV, viral load, and cervical cancer screening in Tanzania and Zimbabwe. Descriptive statistics summarized key variables, including demographic, socioeconomic, and behavioral factors. Bivariate analysis was used to assess the associations between independent variables and cervical cancer screening uptake. Multiple binary logistic regression models were used to identify significant predictors of cervical cancer screening, adjusting for potential confounders. All analyses were conducted using Stata, which ensured robust and reliable results. 2.5 Diagnostic Test The choice of an appropriate analytical method depends on the structure of the dataset, whether it is cross-sectional, time-series, or panel data. Given the cross-sectional nature of this study, the binary response of the dependent variable, and the significant variation across countries, multiple logistic regression was selected over simple logistic regression to ensure a more robust analysis of the data. To ensure accuracy, the model must meet key assumptions, including a binary response for the dependent variable, independence of observations, minimal multicollinearity among independent variables, linearity between independent variables and log odds, and a sufficiently large sample size. Table 1 presents the results of the tests assessing binary response validity, multicollinearity, linearity, and sample size adequacy. Table 1 Diagnostic Statistical Test Variable Types Binary Test Multicollinearity Test Linearity Test Sample Size Test Heteroscedasticity Test Cervical Cancer Testing B N/A N/A 48,051 HIV status of individual B 0.82 N/A 45,048 Viral Load level B 0.97 N/A 52,653 CD4_Categories C 0.88 0.02** 1,674 Age group C 0.84 0.97 52,653 chi2(1) = 121.92 Prob > chi2 = 0.000 Marital Status B 0.87 N/A 38,457 Education Level C 0.97 0.03** 42,114 Employment status B 0.96 N/A 48,228 Individual Wealth C 0.61 0.48 52,643 Number of Partners C 0.90 0.67 52,653 Household Size C 0.99 0.96 52,653 Household Location B 0.62 N/A 52653 NB: B-Binary Variable, C- Categorical Variable The results in Table 1 revealed that the first assumption was satisfied, as the dependent variable (Cervical Cancer Testing) exhibited binary responses (YES = 1 and NO = 0). Second, the independent variables showed no multicollinearity, as the inverse of the variance inflation factor (1/VIF) for all variables exceeded the recommended threshold of 0.5. To address linearity, the study applied the Boxtid statistical test for dependent variables with more than two categories. The results indicated that both categorical variables were linearly related to cervical cancer testing, except for education level, with p-values greater than 0.05. Finally, all variables had sufficient sample sizes to support the logistic regression. Robust standard error regression was used to address nonlinearity, ensuring the best linear unbiased estimates (BLUE). 3.0 Results 3.1 Descriptive Findings Table 2 Cervical Cancer Testing by Country Name Country Not Tested for Cervical Cancer Cervical Cancer Tested p-value N = 43,590 N = 4,461 Country Name Tanzania 18,066 (41.4%) 865 (19.4%) < 0.001 Zimbabwe 25,524 (58.6%) 3,596 (80.6%) The P-value is statistically significant at 5% Table 2 highlights pronounced disparities in cervical cancer screening rates between Tanzania and Zimbabwe, underscoring significant cross-country variation. While only 19.4% of screened individuals in Tanzania underwent testing, Zimbabwe showed a significantly higher screening rate of 80.6%. The p-value (< 0.001) confirms that this disparity is statistically significant, underscoring the potential systemic factors, such as healthcare access, awareness campaigns, or policy implementation, that influence screening uptake. Given the intersection of HIV and cervical cancer risks, this disparity could have critical public health implications, particularly for populations with high viral loads. Table 3 Proportion of Cervical Cancer Testing by Explanatory Variables Not Tested for Cervical Cancer Cervical Cancer Tested N = 43,590 N = 4,461 HIV status of individual Negative (-) 35,947 (88.0%) 3,026 (75.2%) Positive (+) 4,919 (12.0%) 1,000 (24.8%) Viral Load level Unsuppressed 43,241 (99.2%) 4,426 (99.2%) Suppressed 349 (0.8%) 35 (0.8%) CD4_Categories Severe Immunosuppression 146 (9.8%) 13 (7.0%) Moderate Immunosuppression 221 (14.9%) 25 (13.4%) Mild Immunosuppression 291 (19.6%) 43 (23.1%) Normal Immune 830 (55.8%) 105 (56.5%) Age group 15–25 15,615 (35.8%) 447 (10.0%) 26–35 10,438 (23.9%) 1,203 (27.0%) 36–45 6,918 (15.9%) 1,259 (28.2%) 46–55 4,165 (9.6%) 799 (17.9%) 56–65 or more 6,454 (14.8%) 753 (16.9%) Marital status Not Married 9,280 (27.2%) 1,211 (28.9%) Married 24,876 (72.8%) 2,979 (71.1%) Education level Primary level 22,340 (59.1%) 1,639 (39.0%) Secondary level 14,263 (37.8%) 2,133 (50.7%) Tertiary level 18 (0.0%) 0 (0.0%) University level 1,157 (3.1%) 432 (10.3%) Employment status Yes 11,729 (26.9%) 1,649 (37.0%) No 31,861 (73.1%) 2,812 (63.0%) Wealth quintile Lowest 10,342 (23.7%) 464 (10.4%) Second 9,628 (22.1%) 598 (13.4%) Middle 8,845 (20.3%) 801 (18.0%) Fourth 7,225 (16.6%) 999 (22.4%) Highest 7,544 (17.3%) 1,599 (35.8%) Number of Partner Abstain 9,823 (22.5%) 1,148 (25.7%) One partner 25,320 (58.1%) 3,035 (68.0%) Multpartners 8,447 (19.4%) 278 (6.2%) Household Size Below National Average 36,033 (82.7%) 3,816 (85.5%) Within National Average 3,606 (8.3%) 321 (7.2%) Above National Average 3,951 (9.1%) 324 (7.3%) Household location Urban 13,265 (30.4%) 2,239 (50.2%) Rural 30,325 (69.6%) 2,222 (49.8%) The results in Table 3 highlight notable differences in cervical cancer screening rates based on HIV status, viral load, and sociodemographic factors. Individuals living with HIV had a higher screening rate (24.8%) than those who were HIV-negative (75.2%). This suggests that individuals aware of their HIV-positive status may engage in more frequent health check-ups, increasing their likelihood of being tested for cervical cancer screening. Regarding viral load levels, both unsuppressed and suppressed individuals showed nearly identical screening rates (99.2% vs. 0.8%), indicating that viral load suppression status did not directly influence cervical cancer testing behavior. The CD4 count categories revealed that individuals with normal immune function had the highest screening rate (56.5%), while those experiencing severe immunosuppression had the lowest (7.0%). This may reflect the challenges of healthcare accessibility for individuals with advanced HIV progression. Age appears to play a significant role in the uptake of screening. The highest proportion of screened individuals belonged to the 36–45 age group (28.2%), followed by the 26–35 age group (27.0%). In contrast, younger individuals aged 15–25 years were the least likely to be tested (10.0%), suggesting lower awareness or healthcare engagement among younger populations. Marital status showed that married individuals (71.1%) were more likely to undergo cervical cancer screening than unmarried individuals (28.9%). Education also impacted screening behavior; university-educated individuals had the highest screening rate (10.3%), while primary-level education showed lower screening uptake (39.0%). Employment status further reinforced disparities, with employed individuals (37.0%) being more likely to be screened than unemployed individuals (63.0%). Economic factors play a role in screening access, with wealthier individuals exhibiting higher screening rates. The highest wealth quintile had the highest screening rate (35.8%), whereas the lowest wealth quintile had the lowest screening rate (10.4%). In terms of sexual behavior, those with one partner (68.0%) were more likely to undergo screening than those with multiple partners (6.2%), indicating a possible link between perceived health risks and proactive medical check-ups. Finally, environmental and household factors showed that urban residents (50.2%) were more likely to be screened than their rural counterparts (49.8%). Additionally, individuals living in households below the national average size (85.5%) had higher screening rates than those living in larger families, possibly due to resource constraints affecting healthcare prioritization. Table 4 Bivariate Test Analysis of Factors Associated with Cervical Cancer Testing Variables Not Tested for Cervical Cancer Cervical Cancer Tested p-value N = 43,590 N = 4,461 HIV status of individual < 0.001 Negative (-) 35,947 (88.0%) 3,026 (75.2%) Positive (+) 4,919 (12.0%) 1,000 (24.8%) Viral Load level 0.91 Unsuppressed 43,241 (99.2%) 4,426 (99.2%) Suppressed 349 (0.8%) 35 (0.8%) CD4_Categories 0.44 Severe Immunosuppression 146 (9.8%) 13 (7.0%) Moderate Immunosuppression 221 (14.9%) 25 (13.4%) Mild Immunosuppression 291 (19.6%) 43 (23.1%) Normal Immune 830 (55.8%) 105 (56.5%) Age group < 0.001 15–25 15,615 (35.8%) 447 (10.0%) 26–35 10,438 (23.9%) 1,203 (27.0%) 36–45 6,918 (15.9%) 1,259 (28.2%) 46–55 4,165 (9.6%) 799 (17.9%) 56–65 or more 6,454 (14.8%) 753 (16.9%) Marital status < 0.018 Not Married 9,280 (27.2%) 1,211 (28.9%) Married 24,876 (72.8%) 2,979 (71.1%) Education level < 0.001 Primary level 22,340 (59.1%) 1,639 (39.0%) Secondary level 14,263 (37.8%) 2,133 (50.7%) Tertiary level 18 (0.0%) 0 (0.0%) University level 1,157 (3.1%) 432 (10.3%) Employment status < 0.001 Yes 11,729 (26.9%) 1,649 (37.0%) No 31,861 (73.1%) 2,812 (63.0%) Wealth quintile < 0.001 Lowest 10,342 (23.7%) 464 (10.4%) Second 9,628 (22.1%) 598 (13.4%) Middle 8,845 (20.3%) 801 (18.0%) Fourth 7,225 (16.6%) 999 (22.4%) Highest 7,544 (17.3%) 1,599 (35.8%) Number of Partner < 0.001 Abstain 9,823 (22.5%) 1,148 (25.7%) One partner 25,320 (58.1%) 3,035 (68.0%) Multpartners 8,447 (19.4%) 278 (6.2%) Household Size < 0.001 Below National Average 36,033 (82.7%) 3,816 (85.5%) Within National Average 3,606 (8.3%) 321 (7.2%) Above National Average 3,951 (9.1%) 324 (7.3%) Household location < 0.001 Urban 13,265 (30.4%) 2,239 (50.2%) Rural 30,325 (69.6%) 2,222 (49.8%) The P-value is statistically significant at 5% The bivariate analysis (Table 4 ) showed that only CD4 count, and viral load had no significant association with Cervical Cancer Testing. However, nine variables, namely, HIV status, age, marital status, education level, employment status, wealth level, number of partners, household size, and household location, were significantly associated with Cervical Cancer Testing (p < 0.001). 3.2 Inferential Analysis 3.2.1 All Dependent Variables Table 5 Multiple Logistic Regression Analysis of Factors Associated with Cervical Cancer Testing Robust Cervical Cancer Testing Odds Ratio Std. Err. Z [95% Conf Interval] P > z HIV status Negative (-) Ref Positive (+) 3.73 1.22 4.03 (1.97–7.07) 0.00** Viral Load Unsuppressed Ref Suppressed 0.44 0.19 -1.86 (0.19–1.05) 0.06 CD4_Categories Severe Immunosuppression Ref Moderate Immunosuppression 1.26 0.54 0.54 (0.54–2.93) 0.59 Mild Immunosuppression 1.86 0.71 1.62 (0.88–3.92) 0.10 Normal Immune 2.44 0.89 2.46 (1.19–4.97) 0.01** Age group 15–25 Ref 26–35 2.23 0.91 1.94 (0.99–4.97) 0.05 36–45 3.59 1.43 3.21 (1.64–7.87) 0.00 46–55 4.53 1.92 3.56 (1.97–10.4) 0.00** 56–65 or more 1.14 0.69 0.22 (0.35–3.73) 0.82 Marital Status Not Married Ref Married 0.82 0.17 -0.96 (0.55–1.22) 0.34 Education level Primary level Ref Secondary level 2.77 2.08 1.35 (0.63–12.07) 0.18 University level 1 (empty) Individual Wealth Lowest Ref Second 2.64 1.04 2.46 (1.22–5.72) 0.01** Middle 1.79 0.70 1.48 (0.83–3.85) 0.14 Fourth 1.85 0.78 1.45 (0.81–4.23) 0.15 Highest 2.31 1.02 1.9 (0.97–5.49) 0.06 Income Level Yes Ref No 1.11 0.21 0.56 (0.76–1.62) 0.58 Number of Partner Abstain Ref One partner 1.15 0.28 0.58 (0.71–1.87) 0.56 Multpartners 1.70 0.52 1.75 (0.94–3.10) 0.08 Household Size Below National Average Ref Within National Average 0.78 0.37 -0.53 (0.30–1.99) 0.60 Above National Average 1.78 0.64 1.6 (0.88–3.62) 0.11 Location of household Urban Ref Rural 0.67 0.17 -1.61 (0.41–1.09) 0.11 _cons 0.01 0.003 -7.25 (0.00-0.02) 0 NB **P-value statistically significant at 5% Table 5 presents the results of the multiple logistic regression analysis, which examined factors independently associated with cervical cancer screening. HIV status showed a strong association, with HIV-positive individuals (OR = 3.73, 95% CI 1.97–7.07) having significantly higher odds of undergoing cervical cancer screening than HIV-negative individuals. CD4 count categories also played a role, where individuals with normal immune function had higher odds of screening uptake (OR = 2.44, 95% CI 1.20–4.97) than those with severe immunosuppression. Age was significantly associated with screening, where individuals aged 36–45 years (OR = 3.59, 95% CI 1.64–7.86) and 46–55 years (OR = 4.53, 95% CI 1.97–10.41) demonstrated significantly higher odds of being screened than the 15–25 age group (reference category). This trend suggests an increase in health-seeking behavior among middle-aged individuals. Economic status was also significant, with individuals in the second wealth quintile (OR = 2.64, 95% CI: 1.22–5.73) having the highest screening likelihood, followed by those in the highest wealth quintile (OR = 2.31, 95% CI: 0.97–5.49). This association highlights financial accessibility as a major determinant of screening uptake. Finally, the number of sexual partners showed a marginal association (OR = 1.70, 95% CI 0.94–3.10), suggesting a potential influence of sexual behavior on screening decisions. Household size and rural residence did not exhibit statistically significant effects, although individuals in rural settings (OR = 0.67, 95% CI 0.41–1.09) had slightly lower odds of screening than urban residents. 3.2.1 Only Significant Dependent Variables The graph 1 presents the estimated coefficients from a logistic regression model examining predictors of cervical cancer testing, specifically HIV status, CD4 categories, age group, and individual wealth. Among these, HIV status shows the strongest positive association, with a notably large coefficient and wide confidence interval, suggesting individuals with a particular HIV status (likely positive) are significantly more likely to undergo cervical cancer screening. The other predictors, CD4 category, age group, and individual wealth cluster near zero, indicating relatively modest or statistically insignificant effects. The confidence intervals for these variables overlap zero, highlighting greater uncertainty about their influence. 4.0 Discussion This study aimed to examine the intersection of HIV status, viral load, CD4 count, and cervical cancer screening by identifying key health, sociodemographic, behavioral, and treatment-related factors that influence screening uptake. By analyzing these determinants, this study provides valuable insights to inform targeted interventions, ultimately enhancing healthcare accessibility and improving screening outcomes for at-risk populations. The overall findings indicate that 90.72% of individuals in the study areas have never undergone cervical cancer testing at any point in their lives. This highlights a significant gap in preventive healthcare, leaving many women at risk of a late-stage cervical cancer diagnosis. These findings are consistent with a study conducted in Ethiopia, which reported low cervical cancer screening uptake among women, with rates ranging from 6.6–17.9%, depending on the study setting and population group(Yirsaw et al., 2024 ). Similarly, a systematic review of cervical cancer screening in sub-Saharan Africa found an overall screening prevalence below 20%, emphasizing significant disparities in access to preventive care and the need for targeted interventions to improve screening rates(Yimer et al., 2021 ). In Tanzania, cervical cancer testing remains low at 19.4%, primarily due to limited awareness and health education, cultural stigma, and socioeconomic barriers such as financial constraints. Many women lack knowledge about the importance of screening, and misconceptions and social norms discourage proactive healthcare engagement(Henke et al., 2021 ). Additionally, the high cost of medical services, diagnostic tests, and transportation makes screening inaccessible to low-income and rural populations(Endalamaw et al., 2024 ). The bivariate findings indicate that biological markers, such as CD4 count and viral load, do not significantly influence cervical cancer screening, suggesting that screening decisions are primarily driven by socio-demographic and economic factors. HIV status was strongly associated with screening, likely due to increased medical interactions among HIV-positive individuals. Age and marital status play crucial roles, as older and married individuals may engage more with health care services. Education level and employment status significantly shape screening uptake, with higher education and stable employment improving access to screening. Wealth disparities highlight financial barriers, where lower-income individuals may deprioritize preventive care access. Behavioral aspects, such as the number of sexual partners, influence screening due to risk perceptions. Household size affects healthcare-seeking behavior, and location disparities emphasize urban-rural differences in access. These results underscore the need for targeted interventions to address socioeconomic and structural barriers to improve screening rates. The multiple logistic regression model found that HIV status, CD4 count, age, individual wealth, and household location were independently associated with cervical cancer testing. In terms of HIV status, HIV-positive individuals had a markedly higher likelihood of undergoing cervical cancer testing compared to their HIV-negative counterparts(Wang et al., 2022 ). This disparity can be attributed to the increase in medical engagement plays a significant role, as individuals living with HIV often have regular interactions with healthcare providers, leading to greater awareness and encouragement for screening(Stelzle et al., 2021 ). Additionally, clinical guidelines recommend more frequent cervical cancer screening for HIV-positive individuals because of their higher risk of persistent HPV infections, which can accelerate cervical cancer progression(Moshi et al., 2018 ). These results are like a study conducted by WHO report highlighted that women living with HIV face a six-fold increased risk of developing cervical cancer, leading to recommendations for more frequent screenings (every 3–5 years) using high-performance HPV DNA tests(Mahiti et al., 2025 ). Immune status was strongly associated with cervical cancer screening, with individuals with normal immune function (CD4 > 500 cells/mm³) demonstrating higher odds of being screened. Conversely, those with severe immunosuppression exhibited lower screening uptake, potentially due to poor health, mobility constraints, or lack of access to healthcare(Abila et al., 2024 ). As CD4 count declines, susceptibility to opportunistic infections increases, making cancer screening a lower priority in HIV-infected individuals(Y. Zhang et al., 2025 ). This highlights a crucial gap: those most vulnerable to cervical cancer due to HIV-induced immunosuppression may be less likely to be screened, emphasizing the need for targeted outreach to immunocompromised populations. These results are like those of a study on cervical cancer screening among women living with HIV in Ethiopia, which found that lower CD4 counts emphasize the role of immune status in screening behavior. Age was strongly associated with screening likelihood, particularly among individuals aged. Older individuals are more likely to undergo screening due to greater health-seeking behavior, awareness, and clinical recommendations advising routine cervical cancer checks beyond the age of 30 years. Conversely, younger individuals (15–25 years) had the lowest odds, possibly due to low perceived risk, limited awareness, and fewer healthcare visits related to cervical cancer. Addressing this disparity through age-specific educational campaigns and improved accessibility for younger populations could enhance early detection efforts. The study findings closely align with those of, which emphasized that younger individuals (ages 15–25) tend to have lower screening rates due to limited awareness and perceived risk. This study underscores the importance of age-stratified interventions to enhance screening uptake. Socioeconomic status significantly influenced screening uptake, with wealthier individuals more likely to be screened. Those in the second and highest wealth quintiles exhibited the highest screening rates, highlighting the role of financial stability in healthcare accessibility. Low-income individuals face financial barriers, transportation difficulties, and limited access to specialized screening centres, which reduce their likelihood of being tested. Addressing economic disparities through subsidized screening programs, mobile clinics in underserved areas, and healthcare financing strategies can improve screening rates among economically disadvantaged populations. 5.0 Conclusion The rate of cervical cancer screening among women in Tanzania and Zimbabwe remains alarmingly low, with pronounced disparities linked to socioeconomic status. 90.72% of individuals in the study areas have never undergone testing. HIV status, age, education, employment, wealth level, marital status, household size, and location significantly influenced screening rates, whereas CD4 count and viral load showed no association. HIV-positive individuals are more likely to be screened because of frequent medical engagement, whereas immunosuppressed individuals face lower uptake, highlighting the need for targeted outreach. Financial barriers limit access, with wealthier individuals having higher screening rates than poorer individuals. Rural populations encounter accessibility challenges that require improved healthcare infrastructure. Addressing cultural stigma, awareness gaps, and economic disparities is essential. Integrated HIV and cervical cancer screening programs can enhance the early detection of cervical cancer. Tailored interventions are crucial for ensuring equitable screening access. 6.0 Limitation of the Study This study faced potential limitations concerning data availability and reliability, as PHIA surveys relied on self-reported responses, which may have introduced recall bias. Variability in healthcare infrastructure across different regions could influence the applicability of the findings to diverse populations. Additionally, the use of secondary data restricts the ability to examine factors that were not initially captured in the survey. Socioeconomic and cultural differences between countries may also complicate direct cross-country comparisons, affecting broader generalizations. Furthermore, the lack of external donor funding data may pose challenges in evaluating the long-term sustainability of the healthcare programs. Abbreviations HIV Human Immune Virus ART Antiretroviral therapy PHIA Population HIV Impact Assessment AIDS Acquired Immunodeficiency Syndrome AOR Adjusted Odds Ratio SSA Sub-Saharan African Countries Declarations Ethics approval and consent to participate The study protocol underwent a thorough review process, ensuring ethical clearance was obtained from the respective Ministries of Health in each country. Additionally, informed consent was secured from all participants prior to their interviews Clinical Trial This study did not involve a clinical trial and therefore was not registered in any clinical trial registry. Consent for publication Not applicable Competing interests All authors declare no competing interests Funding No funding Author Contribution Conceptualization, B.Y. and J.M.; methodology, B.Y; software, B.Y; validation, B.Y., J.M. and A.M.; formal analysis, A.M.; investigation, B.Y.; resources, B.Y.; data curation, B.Y.; writing—original draft preparation, B.Y.; writing—review and editing J.M. and A.M.; All authors have read and agreed to the published version of the manuscript Acknowledgements- The author gratefully acknowledges the Centers for Disease Control and Prevention (CDC) for their valuable support in providing access to datasets from twelve countries upon request through their website. Data Availability Data is provided within the manuscript or supplementary information files obtained from https://phia-data.icap.columbia.edu/datasets References Abila DB, Wasukira SB, Ainembabazi P, Kiyingi EN, Chemutai B, Kyagulanyi E, Varsani J, Shindodi B, Kisuza RK, Niyonzima N. (2024). Coverage and Socioeconomic Inequalities in Cervical Cancer Screening in Low- and Middle-Income Countries Between 2010 and 2019. JCO Global Oncology , 10 . https://doi.org/10.1200/GO.23.00385 Ampofo AG, Adumatta AD, Owusu E, Awuviry-Newton K. A cross-sectional study of barriers to cervical cancer screening uptake in Ghana: An application of the health belief model. PLoS ONE. 2020;15(4):e0231459. https://doi.org/10.1371/journal.pone.0231459 . Belglaiaa E, Souho T, Badaoui L, Segondy M, Prétet J-L, Guenat D, Mougin C. Awareness of cervical cancer among women attending an HIV treatment centre: a cross-sectional study from Morocco. BMJ Open. 2018;8(8):e020343. https://doi.org/10.1136/bmjopen-2017-020343 . Dare AJ, Knapp GC, Romanoff A, Olasehinde O, Famurewa OC, Komolafe AO, Olatoke S, Katung A, Alatise OI, Kingham TP. High-burden Cancers in Middle-income Countries: A Review of Prevention and Early Detection Strategies Targeting At-risk Populations. Cancer Prev Res. 2021;14(12):1061–74. https://doi.org/10.1158/1940-6207.CAPR-20-0571 . Devarapalli P, Labani S, Nagarjuna N, Panchal P, Asthana S. Barriers affecting uptake of cervical cancer screening in low and middle income countries: A systematic review. Indian J Cancer. 2018;55(4):318. https://doi.org/10.4103/ijc.IJC_253_18 . Endalamaw A, Khatri RB, Erku D, Zewdie A, Wolka E, Nigatu F, Assefa Y. Barriers and strategies for primary health care workforce development: synthesis of evidence. BMC Prim Care. 2024;25(1):99. https://doi.org/10.1186/s12875-024-02336-1 . Fentie AM, Tadesse TB, Gebretekle GB. Factors affecting cervical cancer screening uptake, visual inspection with acetic acid positivity and its predictors among women attending cervical cancer screening service in Addis Ababa, Ethiopia. BMC Women’s Health. 2020;20(1):147. https://doi.org/10.1186/s12905-020-01008-3 . Habib SS, Jamal WZ, Zaidi SMA, Siddiqui J-U-R, Khan HM, Creswell J, Batra S, Versfeld A. Barriers to Access of Healthcare Services for Rural Women—Applying Gender Lens on TB in a Rural District of Sindh, Pakistan. Int J Environ Res Public Health. 2021;18(19):10102. https://doi.org/10.3390/ijerph181910102 . Henke A, Kluge U, Borde T, Mchome B, Serventi F, Henke O. Tanzanian women´s knowledge about Cervical Cancer and HPV and their prevalence of positive VIA cervical screening results. Data from a Prevention and Awareness Campaign in Northern Tanzania, 2017–2019. Global Health Action. 2021;14(1). https://doi.org/10.1080/16549716.2020.1852780 . Mahiti GR, Adam J, Luoga P. Prevalence and determinants of cervical cancer screening among women aged 15–49 years in Tanzania: analysis of demographic and health survey 2022. Archives Public Health. 2025;83(1):135. https://doi.org/10.1186/s13690-025-01629-w . Moshi FV, Vandervort EB, Kibusi SM. (2018). Cervical Cancer Awareness among Women in Tanzania: An Analysis of Data from the 2011-12 Tanzania HIV and Malaria Indicators Survey. International Journal of Chronic Diseases , 2018 , 1–7. https://doi.org/10.1155/2018/2458232 Murfin J, Irvine F, Meechan-Rogers R, Swift A. Education, income and occupation and their influence on the uptake of cervical cancer prevention strategies: A systematic review. J Clin Nurs. 2020;29(3–4):393–415. https://doi.org/10.1111/jocn.15094 . Saito S, Duong YT, Metz M, Lee K, Patel H, Sleeman K, Manjengwa J, Ogollah FM, Kasongo W, Mitchell R, Mugurungi O, Chimbwandira F, Moyo C, Maliwa V, Mtengo H, Nkumbula T, Ndongmo CB, Vere NS, Chipungu G, Voetsch AC. ) household surveys in three sub‐Saharan African Countries, 2015 to 2016. J Int AIDS Soc. 2017;20(S7). https://doi.org/10.1002/jia2.25004 . Returning HIV -1 viral load results to participant‐selected health facilities in national Population‐based HIV Impact Assessment ( PHIA Srivastava AN, Misra JS, Srivastava S, Das BC, Gupta S. Cervical cancer screening in rural India. Indian J Med Res. 2018;148(6):687–96. https://doi.org/10.4103/ijmr.IJMR_5_17 . Stelzle D, Tanaka LF, Lee KK, Ibrahim Khalil A, Baussano I, Shah AS, V, McAllister DA, Gottlieb SL, Klug SJ, Winkler AS, Bray F, Baggaley R, Clifford GM, Broutet N, Dalal S. Estimates of the global burden of cervical cancer associated with HIV. Lancet Global Health. 2021;9(2):e161–9. https://doi.org/10.1016/S2214-109X(20)30459-9 . UNAIDS. Miles to Go: The Response to HIV in the Context of the Sustainable Development Goals. Joint United Nations Programme on HIV/AIDS. UNAIDS; 2018. Van Gerwen OT, Muzny CA, Marrazzo JM. Sexually transmitted infections and female reproductive health. Nat Microbiol. 2022;7(8):1116–26. https://doi.org/10.1038/s41564-022-01177-x . Vapiwala N, Miller D, Laventure B, Woodhouse K, Kelly S, Avelis J, Baffic C, Goldston R, Glanz K. Stigma, beliefs and perceptions regarding prostate cancer among Black and Latino men and women. BMC Public Health. 2021;21(1):758. https://doi.org/10.1186/s12889-021-10793-x . Wang Y, Kinsler JJ, Kiwuwa-Muyingo S. Factors associated with HIV testing among youth in Tanzania based on the 2016–2017 Tanzania HIV Impact Survey (THIS). PLOS Global Public Health. 2022;2(11):e0000536. https://doi.org/10.1371/journal.pgph.0000536 . Yimer NB, Mohammed MA, Solomon K, Tadese M, Grutzmacher S, Meikena HK, Alemnew B, Sharew NT, Habtewold TD. Cervical cancer screening uptake in Sub-Saharan Africa: a systematic review and meta-analysis. Public Health. 2021;195:105–11. https://doi.org/10.1016/j.puhe.2021.04.014 . Yirsaw AN, Nigusie A, Andualem F, Getachew E, Getachew D, Tareke AA, Mihret MS, Lakew G. Cervical cancer screening utilization and associated factors among women living with HIV in Ethiopia, 2024: systematic review and meta-analysis. BMC Women’s Health. 2024;24(1):521. https://doi.org/10.1186/s12905-024-03362-y . Zhang S, Xu H, Zhang L, Qiao Y. Cervical cancer: Epidemiology, risk factors and screening. Chin J Cancer Res. 2020;32(6):720–8. https://doi.org/10.21147/j.issn.1000-9604.2020.06.05 . Zhang Y, Fan Z, Wang J, Guan B, Zhou F, Tang Z, Wu W, Huang A. HPV vaccination, screening disparities, and the shifting landscape of cervical cancer burden: a global analysis of trends, inequalities, and policy implications. BMC Women’s Health. 2025;25(1):285. https://doi.org/10.1186/s12905-025-03841-w . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Nov, 2025 Reviews received at journal 13 Oct, 2025 Reviewers agreed at journal 01 Oct, 2025 Reviewers agreed at journal 26 Sep, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviewers invited by journal 25 Sep, 2025 Editor assigned by journal 01 Aug, 2025 Submission checks completed at journal 01 Aug, 2025 First submitted to journal 26 Jul, 2025 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-7223468","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":525001574,"identity":"969670b5-a731-41d2-8291-3841823bb012","order_by":0,"name":"Boniface Simpoli Yohana","email":"data:image/png;base64,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","orcid":"","institution":"National Bureau of Statistics","correspondingAuthor":true,"prefix":"","firstName":"Boniface","middleName":"Simpoli","lastName":"Yohana","suffix":""},{"id":525001575,"identity":"0830e4fa-ead5-4b17-aa29-b0578798dea9","order_by":1,"name":"John Massito","email":"","orcid":"","institution":"University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Massito","suffix":""},{"id":525001576,"identity":"41e3e358-a04e-4fb4-b2f5-6f890d89aef7","order_by":2,"name":"Arbogast Moshi","email":"","orcid":"","institution":"University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Arbogast","middleName":"","lastName":"Moshi","suffix":""}],"badges":[],"createdAt":"2025-07-27 00:08:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7223468/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7223468/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93238434,"identity":"db19ee42-e98e-49a9-afa4-8fb72960f8d2","added_by":"auto","created_at":"2025-10-10 14:34:27","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":149415,"visible":true,"origin":"","legend":"","description":"","filename":"ExploringtheIntersectionofHIVStatusViralLoadLevelsandCervicalCancerScreeninginTanzaniaandZimbabweInsightsfromPHIAData.docx","url":"https://assets-eu.researchsquare.com/files/rs-7223468/v1/c0faef0f0db147fe2ecad54b.docx"},{"id":93238431,"identity":"32623bac-6596-4d9c-bbd1-daf536f772bd","added_by":"auto","created_at":"2025-10-10 14:34:27","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6041,"visible":true,"origin":"","legend":"","description":"","filename":"146db0a578bf48e9acae389a5fbddcef.json","url":"https://assets-eu.researchsquare.com/files/rs-7223468/v1/94ac734694d1ed3526a36932.json"},{"id":93238430,"identity":"98667117-f0ea-451e-8637-8336a5e4b140","added_by":"auto","created_at":"2025-10-10 14:34:27","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":134432,"visible":true,"origin":"","legend":"","description":"","filename":"146db0a578bf48e9acae389a5fbddcef1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7223468/v1/582495ea6bba101340c5dfc9.xml"},{"id":93241118,"identity":"b327f33b-818d-4bfb-b797-0ca63c7c774c","added_by":"auto","created_at":"2025-10-10 14:50:27","extension":"emf","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122220,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.emf","url":"https://assets-eu.researchsquare.com/files/rs-7223468/v1/edd77425b159d57f1771c5f0.emf"},{"id":93240056,"identity":"3ba0aa96-3415-4b71-9d97-f9678241038d","added_by":"auto","created_at":"2025-10-10 14:42:27","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":63868,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7223468/v1/fbf5829ffbf82884f7dfd121.png"},{"id":93238435,"identity":"dc51560f-8864-48b2-be76-e9b230cdd675","added_by":"auto","created_at":"2025-10-10 14:34:27","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136106,"visible":true,"origin":"","legend":"","description":"","filename":"146db0a578bf48e9acae389a5fbddcef1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7223468/v1/9c10064aa547b628a0ca6aa8.xml"},{"id":93238436,"identity":"377cebcc-bd4d-4c06-95f5-4e7315dc3acf","added_by":"auto","created_at":"2025-10-10 14:34:27","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":139843,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7223468/v1/1684242f74da6db53419a99e.html"},{"id":93238429,"identity":"6e4ab6e9-fafa-4708-b3f1-177ed77f4129","added_by":"auto","created_at":"2025-10-10 14:34:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38733,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph 1: Graphical representation of logistic regression coefficients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7223468/v1/913c1c9e5f67c68cb84d98bf.png"},{"id":93243368,"identity":"47dc6307-fdfa-48b5-8682-3f27ec60ca1f","added_by":"auto","created_at":"2025-10-10 14:58:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1595331,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7223468/v1/d509ca84-5c12-4191-a1fa-ce15f42147ed.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Intersection of HIV Status, Viral Load Levels, and Cervical Cancer Screening in Tanzania and Zimbabwe: Insights from PHIA Data","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCervical cancer is the fourth most common cancer among women worldwide, disproportionately affecting those in low- and middle-income countries, particularly sub-Saharan Africa. (Ampofo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Belglaiaa et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this region, it ranks as the second most prevalent cancer and remains the leading cause of cancer-related mortality in women (Fentie et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Women living with HIV face a significantly higher risk, up to six times greater, of developing cervical cancer compared to HIV-negative individuals (Vapiwala et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This increased vulnerability stems from HIV-induced immunosuppression, which impairs the body's ability to clear human papillomavirus (HPV) infections, the primary cause of cervical cancer (Murfin et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, HPV infections progress more rapidly to cervical cancer in immunocompromised women, often within 5\u0026ndash;10 years, compared to 15\u0026ndash;20 years in those without HIV. This heightened risk of cervical cancer among women living with HIV underscores the urgent need for comprehensive HIV management strategies in sub-Saharan Africa, where the burden of HIV remains disproportionately high.\u003c/p\u003e\u003cp\u003eHIV continues to pose a significant public health challenge in Sub-Saharan Africa, which accounts for approximately 60% of new global infections (UNAIDS, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Despite progress toward the UNAIDS 95-95-95 targets, which aim for 95% of people living with HIV (PLHIV) to know their status, 95% of those diagnosed to be on treatment, and 95% of those on treatment to achieve viral suppression, there remain substantial disparities to attain these goals (Van Gerwen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Viral suppression rates, a critical indicator of treatment success, vary widely across countries, with some nations reporting rates as low as 24.3% and others reporting rates as high as 99.7%. These disparities highlight the need for targeted interventions to address gaps in healthcare access and adherence to antiretroviral therapy (ART).\u003c/p\u003e\u003cp\u003eThe Population-based HIV Impact Assessment (PHIA) data from two (2) Sub-Saharan African countries, Tanzania and Zimbabwe, provide a wealth of information on the intersection of HIV and cervical cancer (Saito et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For instance, the data reveal that while women generally have higher rates of viral suppression than men, the overall burden of unsuppressed viral loads remains higher among women because of their greater prevalence of HIV (UNAIDS, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This underscores the importance of gender-sensitive approaches to address the dual burden of HIV and cervical cancer.\u003c/p\u003e\u003cp\u003eGeographic disparities further complicate this issue. Rural areas often face significant challenges in accessing healthcare services, including HPV vaccination, cervical cancer screening, and ART (Saito et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These disparities are exacerbated by socioeconomic factors, including poverty, limited access to education, and inadequate healthcare infrastructure. Addressing these inequities requires a multifaceted approach, including community-based healthcare models, mobile clinics, and increased investment in rural healthcare systems.\u003c/p\u003e\u003cp\u003eThe economic implications of this dual burden are significant. Households affected by HIV and cervical cancer often face catastrophic healthcare costs, leading to financial instability and a reduced quality of life (Habib et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, the loss of productivity due to illness further exacerbates poverty, creating a vicious cycle that hinders regional economic development (Dare et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEfforts to combat this dual burden must prioritize prevention and early interventions (Devarapalli et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Scaling up HPV vaccination programs, particularly among adolescent girls, can significantly reduce the incidence of cervical cancer (Srivastava et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, increasing access to cervical cancer screening and treatment, along with ART, can improve health outcomes for women living with HIV (S. Zhang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Achieving these goals requires robust policy frameworks, international collaboration, and sustained investment in healthcare systems.\u003c/p\u003e\u003cp\u003eIn conclusion, the intersection of HIV and cervical cancer screening in sub-Saharan Africa represents a critical public health challenge with far-reaching implications. Leveraging data from initiatives such as the PHIA can guide targeted interventions to reduce disparities, improve health outcomes, and ultimately alleviate the dual burden of these diseases in the region.\u003c/p\u003e"},{"header":"2.0 Data and Methods of Analysis","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study design\u003c/h2\u003e\u003cp\u003eThis is a quantitative, cross-sectional study using secondary data from the PHIA surveys, which provide population-level information on HIV prevalence, healthcare service usage, socio-economic conditions, and cervical cancer screening.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Types\u003c/h2\u003e\u003cp\u003eThis study used nationally representative cross-sectional data from the Population HIV Impact Survey (PHIA) to examine HIV prevalence, testing uptake, and related factors across demographic groups. The dataset includes health-related variables such as cervical cancer screening, HIV status, and CD4 count, as well as demographic, socioeconomic, and behavioral factors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Study Variables\u003c/h2\u003e\u003cp\u003eThis study focused on several key variables to investigate the intersection of HIV, viral load, and cervical cancer in Tanzania and Zimbabwe. The dependent variable was cervical cancer screening. The independent variables included HIV-related factors such as HIV status, viral load (suppressed or unsuppressed), and CD4 count. Demographic factors included age, sex, and urban versus rural residency, alongside socioeconomic factors such as education level and employment status. Behavioral aspects include the number of sexual partners.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e\u003cp\u003eThis study employed rigorous statistical methods to examine the relationship between HIV, viral load, and cervical cancer screening in Tanzania and Zimbabwe. Descriptive statistics summarized key variables, including demographic, socioeconomic, and behavioral factors. Bivariate analysis was used to assess the associations between independent variables and cervical cancer screening uptake. Multiple binary logistic regression models were used to identify significant predictors of cervical cancer screening, adjusting for potential confounders. All analyses were conducted using Stata, which ensured robust and reliable results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Diagnostic Test\u003c/h2\u003e\u003cp\u003eThe choice of an appropriate analytical method depends on the structure of the dataset, whether it is cross-sectional, time-series, or panel data. Given the cross-sectional nature of this study, the binary response of the dependent variable, and the significant variation across countries, multiple logistic regression was selected over simple logistic regression to ensure a more robust analysis of the data. To ensure accuracy, the model must meet key assumptions, including a binary response for the dependent variable, independence of observations, minimal multicollinearity among independent variables, linearity between independent variables and log odds, and a sufficiently large sample size. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the results of the tests assessing binary response validity, multicollinearity, linearity, and sample size adequacy.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiagnostic Statistical Test\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Types\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinary\u003c/p\u003e\u003cp\u003eTest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMulticollinearity Test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLinearity\u003c/p\u003e\u003cp\u003eTest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSample Size Test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHeteroscedasticity\u003c/p\u003e\u003cp\u003eTest\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCervical Cancer Testing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48,051\u003c/p\u003e\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\u003eHIV status of individual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e45,048\u003c/p\u003e\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\u003eViral Load level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52,653\u003c/p\u003e\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\u003eCD4_Categories\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,674\u003c/p\u003e\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\u003eAge group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52,653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003echi2(1) = 121.92\u003c/p\u003e\u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u0026thinsp;=\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e38,457\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e42,114\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48,228\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual Wealth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52,643\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Partners\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52,653\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52,653\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold Location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52653\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNB: B-Binary Variable, C- Categorical Variable\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e revealed that the first assumption was satisfied, as the dependent variable (Cervical Cancer Testing) exhibited binary responses (YES\u0026thinsp;=\u0026thinsp;1 and NO\u0026thinsp;=\u0026thinsp;0). Second, the independent variables showed no multicollinearity, as the inverse of the variance inflation factor (1/VIF) for all variables exceeded the recommended threshold of 0.5. To address linearity, the study applied the Boxtid statistical test for dependent variables with more than two categories. The results indicated that both categorical variables were linearly related to cervical cancer testing, except for education level, with p-values greater than 0.05. Finally, all variables had sufficient sample sizes to support the logistic regression. Robust standard error regression was used to address nonlinearity, ensuring the best linear unbiased estimates (BLUE).\u003c/p\u003e\u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Descriptive Findings\u003c/h2\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\u003eCervical Cancer Testing by Country\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eName\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot Tested for Cervical Cancer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCervical Cancer Tested\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;43,590\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;4,461\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry Name\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTanzania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18,066 (41.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e865 (19.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZimbabwe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25,524 (58.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,596 (80.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe P-value is statistically significant at 5%\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e highlights pronounced disparities in cervical cancer screening rates between Tanzania and Zimbabwe, underscoring significant cross-country variation. While only 19.4% of screened individuals in Tanzania underwent testing, Zimbabwe showed a significantly higher screening rate of 80.6%. The p-value (\u0026lt;\u0026thinsp;0.001) confirms that this disparity is statistically significant, underscoring the potential systemic factors, such as healthcare access, awareness campaigns, or policy implementation, that influence screening uptake. Given the intersection of HIV and cervical cancer risks, this disparity could have critical public health implications, particularly for populations with high viral loads.\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\u003eProportion of Cervical Cancer Testing by Explanatory\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot Tested for Cervical Cancer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCervical Cancer Tested\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;43,590\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;4,461\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eHIV status of individual\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative (-)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35,947 (88.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,026 (75.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive (+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,919 (12.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,000 (24.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eViral Load level\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\u003eUnsuppressed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43,241 (99.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,426 (99.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuppressed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e349 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD4_Categories\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\u003eSevere Immunosuppression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e146 (9.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (7.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate Immunosuppression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e221 (14.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (13.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild Immunosuppression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e291 (19.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (23.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal Immune\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e830 (55.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105 (56.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15,615 (35.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e447 (10.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26\u0026ndash;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,438 (23.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,203 (27.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e36\u0026ndash;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,918 (15.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,259 (28.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e46\u0026ndash;55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,165 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e799 (17.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e56\u0026ndash;65 or more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,454 (14.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e753 (16.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot Married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,280 (27.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,211 (28.9%)\u003c/p\u003e\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\u003e24,876 (72.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,979 (71.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22,340 (59.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,639 (39.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14,263 (37.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,133 (50.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,157 (3.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e432 (10.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEmployment status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11,729 (26.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,649 (37.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31,861 (73.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,812 (63.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWealth quintile\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\u003eLowest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,342 (23.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e464 (10.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,628 (22.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e598 (13.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,845 (20.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e801 (18.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFourth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,225 (16.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e999 (22.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHighest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,544 (17.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,599 (35.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of Partner\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbstain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,823 (22.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,148 (25.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOne partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25,320 (58.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,035 (68.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultpartners\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,447 (19.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e278 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold Size\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\u003eBelow National Average\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36,033 (82.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,816 (85.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWithin National Average\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,606 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e321 (7.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove National Average\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,951 (9.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e324 (7.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold location\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\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13,265 (30.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,239 (50.2%)\u003c/p\u003e\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\u003e30,325 (69.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,222 (49.8%)\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\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e highlight notable differences in cervical cancer screening rates based on HIV status, viral load, and sociodemographic factors. Individuals living with HIV had a higher screening rate (24.8%) than those who were HIV-negative (75.2%). This suggests that individuals aware of their HIV-positive status may engage in more frequent health check-ups, increasing their likelihood of being tested for cervical cancer screening. Regarding viral load levels, both unsuppressed and suppressed individuals showed nearly identical screening rates (99.2% vs. 0.8%), indicating that viral load suppression status did not directly influence cervical cancer testing behavior. The CD4 count categories revealed that individuals with normal immune function had the highest screening rate (56.5%), while those experiencing severe immunosuppression had the lowest (7.0%). This may reflect the challenges of healthcare accessibility for individuals with advanced HIV progression.\u003c/p\u003e\u003cp\u003eAge appears to play a significant role in the uptake of screening. The highest proportion of screened individuals belonged to the 36\u0026ndash;45 age group (28.2%), followed by the 26\u0026ndash;35 age group (27.0%). In contrast, younger individuals aged 15\u0026ndash;25 years were the least likely to be tested (10.0%), suggesting lower awareness or healthcare engagement among younger populations. Marital status showed that married individuals (71.1%) were more likely to undergo cervical cancer screening than unmarried individuals (28.9%). Education also impacted screening behavior; university-educated individuals had the highest screening rate (10.3%), while primary-level education showed lower screening uptake (39.0%). Employment status further reinforced disparities, with employed individuals (37.0%) being more likely to be screened than unemployed individuals (63.0%).\u003c/p\u003e\u003cp\u003eEconomic factors play a role in screening access, with wealthier individuals exhibiting higher screening rates. The highest wealth quintile had the highest screening rate (35.8%), whereas the lowest wealth quintile had the lowest screening rate (10.4%). In terms of sexual behavior, those with one partner (68.0%) were more likely to undergo screening than those with multiple partners (6.2%), indicating a possible link between perceived health risks and proactive medical check-ups.\u003c/p\u003e\u003cp\u003eFinally, environmental and household factors showed that urban residents (50.2%) were more likely to be screened than their rural counterparts (49.8%). Additionally, individuals living in households below the national average size (85.5%) had higher screening rates than those living in larger families, possibly due to resource constraints affecting healthcare prioritization.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBivariate Test Analysis of Factors Associated with Cervical Cancer Testing\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot Tested for Cervical Cancer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCervical Cancer Tested\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;43,590\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;4,461\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHIV status of individual\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative (-)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35,947 (88.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,026 (75.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive (+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,919 (12.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,000 (24.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eViral Load level\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\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnsuppressed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43,241 (99.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,426 (99.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuppressed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e349 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD4_Categories\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\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere Immunosuppression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e146 (9.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (7.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate Immunosuppression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e221 (14.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (13.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild Immunosuppression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e291 (19.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (23.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal Immune\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e830 (55.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105 (56.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15,615 (35.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e447 (10.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26\u0026ndash;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10,438 (23.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,203 (27.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e36\u0026ndash;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6,918 (15.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,259 (28.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e46\u0026ndash;55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,165 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e799 (17.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e56\u0026ndash;65 or more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6,454 (14.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e753 (16.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot Married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,280 (27.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,211 (28.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\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\u003e24,876 (72.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,979 (71.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22,340 (59.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,639 (39.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14,263 (37.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,133 (50.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,157 (3.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e432 (10.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEmployment status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11,729 (26.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,649 (37.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31,861 (73.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,812 (63.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWealth quintile\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLowest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10,342 (23.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e464 (10.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,628 (22.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e598 (13.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8,845 (20.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e801 (18.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFourth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,225 (16.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e999 (22.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHighest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,544 (17.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,599 (35.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of Partner\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbstain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,823 (22.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,148 (25.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOne partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25,320 (58.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,035 (68.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultpartners\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8,447 (19.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e278 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold Size\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow National Average\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36,033 (82.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,816 (85.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWithin National Average\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,606 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e321 (7.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove National Average\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,951 (9.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e324 (7.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold location\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003e13,265 (30.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,239 (50.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\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\u003e30,325 (69.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,222 (49.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe P-value is statistically significant at 5%\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe bivariate analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) showed that only CD4 count, and viral load had no significant association with Cervical Cancer Testing. However, nine variables, namely, HIV status, age, marital status, education level, employment status, wealth level, number of partners, household size, and household location, were significantly associated with Cervical Cancer Testing (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Inferential Analysis\u003c/h2\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 All Dependent Variables\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultiple Logistic Regression Analysis of Factors Associated with Cervical Cancer Testing\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRobust\u003c/p\u003e\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\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCervical Cancer Testing\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Err.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[95% Conf Interval]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;z\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIV status\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\u003eNegative (-)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive (+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(1.97\u0026ndash;7.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eViral Load\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\u003eUnsuppressed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuppressed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.19\u0026ndash;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD4_Categories\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\u003eSevere Immunosuppression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate Immunosuppression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.54\u0026ndash;2.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild Immunosuppression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.88\u0026ndash;3.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal Immune\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(1.19\u0026ndash;4.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\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\u003e15\u0026ndash;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26\u0026ndash;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.99\u0026ndash;4.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e36\u0026ndash;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(1.64\u0026ndash;7.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e46\u0026ndash;55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(1.97\u0026ndash;10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e56\u0026ndash;65 or more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.35\u0026ndash;3.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.82\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\u003eNot Married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\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\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\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.55\u0026ndash;1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\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\u003ePrimary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.63\u0026ndash;12.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(empty)\u003c/p\u003e\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\u003e\u003cb\u003eIndividual Wealth\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\u003eLowest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(1.22\u0026ndash;5.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.83\u0026ndash;3.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFourth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.81\u0026ndash;4.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHighest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.97\u0026ndash;5.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIncome Level\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.76\u0026ndash;1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of Partner\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\u003eAbstain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOne partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.71\u0026ndash;1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultpartners\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.94\u0026ndash;3.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold Size\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\u003eBelow National Average\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWithin National Average\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.30\u0026ndash;1.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove National Average\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.88\u0026ndash;3.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLocation of household\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\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\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\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\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.41\u0026ndash;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.00-0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNB **P-value statistically significant at 5%\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results of the multiple logistic regression analysis, which examined factors independently associated with cervical cancer screening. HIV status showed a strong association, with HIV-positive individuals (OR\u0026thinsp;=\u0026thinsp;3.73, 95% CI 1.97\u0026ndash;7.07) having significantly higher odds of undergoing cervical cancer screening than HIV-negative individuals. CD4 count categories also played a role, where individuals with normal immune function had higher odds of screening uptake (OR\u0026thinsp;=\u0026thinsp;2.44, 95% CI 1.20\u0026ndash;4.97) than those with severe immunosuppression.\u003c/p\u003e\u003cp\u003eAge was significantly associated with screening, where individuals aged 36\u0026ndash;45 years (OR\u0026thinsp;=\u0026thinsp;3.59, 95% CI 1.64\u0026ndash;7.86) and 46\u0026ndash;55 years (OR\u0026thinsp;=\u0026thinsp;4.53, 95% CI 1.97\u0026ndash;10.41) demonstrated significantly higher odds of being screened than the 15\u0026ndash;25 age group (reference category). This trend suggests an increase in health-seeking behavior among middle-aged individuals.\u003c/p\u003e\u003cp\u003eEconomic status was also significant, with individuals in the second wealth quintile (OR\u0026thinsp;=\u0026thinsp;2.64, 95% CI: 1.22\u0026ndash;5.73) having the highest screening likelihood, followed by those in the highest wealth quintile (OR\u0026thinsp;=\u0026thinsp;2.31, 95% CI: 0.97\u0026ndash;5.49). This association highlights financial accessibility as a major determinant of screening uptake.\u003c/p\u003e\u003cp\u003eFinally, the number of sexual partners showed a marginal association (OR\u0026thinsp;=\u0026thinsp;1.70, 95% CI 0.94\u0026ndash;3.10), suggesting a potential influence of sexual behavior on screening decisions. Household size and rural residence did not exhibit statistically significant effects, although individuals in rural settings (OR\u0026thinsp;=\u0026thinsp;0.67, 95% CI 0.41\u0026ndash;1.09) had slightly lower odds of screening than urban residents.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Only Significant Dependent Variables\u003c/h2\u003e\u003cp\u003eThe graph 1 presents the estimated coefficients from a logistic regression model examining predictors of cervical cancer testing, specifically HIV status, CD4 categories, age group, and individual wealth. Among these, HIV status shows the strongest positive association, with a notably large coefficient and wide confidence interval, suggesting individuals with a particular HIV status (likely positive) are significantly more likely to undergo cervical cancer screening. The other predictors, CD4 category, age group, and individual wealth cluster near zero, indicating relatively modest or statistically insignificant effects. The confidence intervals for these variables overlap zero, highlighting greater uncertainty about their influence.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eThis study aimed to examine the intersection of HIV status, viral load, CD4 count, and cervical cancer screening by identifying key health, sociodemographic, behavioral, and treatment-related factors that influence screening uptake. By analyzing these determinants, this study provides valuable insights to inform targeted interventions, ultimately enhancing healthcare accessibility and improving screening outcomes for at-risk populations.\u003c/p\u003e\u003cp\u003eThe overall findings indicate that 90.72% of individuals in the study areas have never undergone cervical cancer testing at any point in their lives. This highlights a significant gap in preventive healthcare, leaving many women at risk of a late-stage cervical cancer diagnosis. These findings are consistent with a study conducted in Ethiopia, which reported low cervical cancer screening uptake among women, with rates ranging from 6.6\u0026ndash;17.9%, depending on the study setting and population group(Yirsaw et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similarly, a systematic review of cervical cancer screening in sub-Saharan Africa found an overall screening prevalence below 20%, emphasizing significant disparities in access to preventive care and the need for targeted interventions to improve screening rates(Yimer et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Tanzania, cervical cancer testing remains low at 19.4%, primarily due to limited awareness and health education, cultural stigma, and socioeconomic barriers such as financial constraints. Many women lack knowledge about the importance of screening, and misconceptions and social norms discourage proactive healthcare engagement(Henke et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, the high cost of medical services, diagnostic tests, and transportation makes screening inaccessible to low-income and rural populations(Endalamaw et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe bivariate findings indicate that biological markers, such as CD4 count and viral load, do not significantly influence cervical cancer screening, suggesting that screening decisions are primarily driven by socio-demographic and economic factors. HIV status was strongly associated with screening, likely due to increased medical interactions among HIV-positive individuals. Age and marital status play crucial roles, as older and married individuals may engage more with health care services. Education level and employment status significantly shape screening uptake, with higher education and stable employment improving access to screening. Wealth disparities highlight financial barriers, where lower-income individuals may deprioritize preventive care access. Behavioral aspects, such as the number of sexual partners, influence screening due to risk perceptions. Household size affects healthcare-seeking behavior, and location disparities emphasize urban-rural differences in access. These results underscore the need for targeted interventions to address socioeconomic and structural barriers to improve screening rates.\u003c/p\u003e\u003cp\u003eThe multiple logistic regression model found that HIV status, CD4 count, age, individual wealth, and household location were independently associated with cervical cancer testing. In terms of HIV status, HIV-positive individuals had a markedly higher likelihood of undergoing cervical cancer testing compared to their HIV-negative counterparts(Wang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This disparity can be attributed to the increase in medical engagement plays a significant role, as individuals living with HIV often have regular interactions with healthcare providers, leading to greater awareness and encouragement for screening(Stelzle et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, clinical guidelines recommend more frequent cervical cancer screening for HIV-positive individuals because of their higher risk of persistent HPV infections, which can accelerate cervical cancer progression(Moshi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These results are like a study conducted by WHO report highlighted that women living with HIV face a six-fold increased risk of developing cervical cancer, leading to recommendations for more frequent screenings (every 3\u0026ndash;5 years) using high-performance HPV DNA tests(Mahiti et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eImmune status was strongly associated with cervical cancer screening, with individuals with normal immune function (CD4\u0026thinsp;\u0026gt;\u0026thinsp;500 cells/mm\u0026sup3;) demonstrating higher odds of being screened. Conversely, those with severe immunosuppression exhibited lower screening uptake, potentially due to poor health, mobility constraints, or lack of access to healthcare(Abila et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As CD4 count declines, susceptibility to opportunistic infections increases, making cancer screening a lower priority in HIV-infected individuals(Y. Zhang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This highlights a crucial gap: those most vulnerable to cervical cancer due to HIV-induced immunosuppression may be less likely to be screened, emphasizing the need for targeted outreach to immunocompromised populations. These results are like those of a study on cervical cancer screening among women living with HIV in Ethiopia, which found that lower CD4 counts emphasize the role of immune status in screening behavior.\u003c/p\u003e\u003cp\u003eAge was strongly associated with screening likelihood, particularly among individuals aged. Older individuals are more likely to undergo screening due to greater health-seeking behavior, awareness, and clinical recommendations advising routine cervical cancer checks beyond the age of 30 years. Conversely, younger individuals (15\u0026ndash;25 years) had the lowest odds, possibly due to low perceived risk, limited awareness, and fewer healthcare visits related to cervical cancer. Addressing this disparity through age-specific educational campaigns and improved accessibility for younger populations could enhance early detection efforts. The study findings closely align with those of, which emphasized that younger individuals (ages 15\u0026ndash;25) tend to have lower screening rates due to limited awareness and perceived risk. This study underscores the importance of age-stratified interventions to enhance screening uptake.\u003c/p\u003e\u003cp\u003eSocioeconomic status significantly influenced screening uptake, with wealthier individuals more likely to be screened. Those in the second and highest wealth quintiles exhibited the highest screening rates, highlighting the role of financial stability in healthcare accessibility. Low-income individuals face financial barriers, transportation difficulties, and limited access to specialized screening centres, which reduce their likelihood of being tested. Addressing economic disparities through subsidized screening programs, mobile clinics in underserved areas, and healthcare financing strategies can improve screening rates among economically disadvantaged populations.\u003c/p\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eThe rate of cervical cancer screening among women in Tanzania and Zimbabwe remains alarmingly low, with pronounced disparities linked to socioeconomic status. 90.72% of individuals in the study areas have never undergone testing. HIV status, age, education, employment, wealth level, marital status, household size, and location significantly influenced screening rates, whereas CD4 count and viral load showed no association. HIV-positive individuals are more likely to be screened because of frequent medical engagement, whereas immunosuppressed individuals face lower uptake, highlighting the need for targeted outreach. Financial barriers limit access, with wealthier individuals having higher screening rates than poorer individuals. Rural populations encounter accessibility challenges that require improved healthcare infrastructure. Addressing cultural stigma, awareness gaps, and economic disparities is essential. Integrated HIV and cervical cancer screening programs can enhance the early detection of cervical cancer. Tailored interventions are crucial for ensuring equitable screening access.\u003c/p\u003e"},{"header":"6.0 Limitation of the Study","content":"\u003cp\u003eThis study faced potential limitations concerning data availability and reliability, as PHIA surveys relied on self-reported responses, which may have introduced recall bias. Variability in healthcare infrastructure across different regions could influence the applicability of the findings to diverse populations. Additionally, the use of secondary data restricts the ability to examine factors that were not initially captured in the survey. Socioeconomic and cultural differences between countries may also complicate direct cross-country comparisons, affecting broader generalizations. Furthermore, the lack of external donor funding data may pose challenges in evaluating the long-term sustainability of the healthcare programs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHIV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHuman Immune Virus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eART\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAntiretroviral therapy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePHIA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePopulation HIV Impact Assessment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAIDS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcquired Immunodeficiency Syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSSA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSub-Saharan African Countries\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eThe study protocol underwent a thorough review process, ensuring ethical clearance was obtained from the respective Ministries of Health in each country. Additionally, informed consent was secured from all participants prior to their interviews\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eClinical Trial\u003c/h2\u003e\u003cp\u003eThis study did not involve a clinical trial and therefore was not registered in any clinical trial registry.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConsent for publication\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eAll authors declare no competing interests\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNo funding\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, B.Y. and J.M.; methodology, B.Y; software, B.Y; validation, B.Y., J.M. and A.M.; formal analysis, A.M.; investigation, B.Y.; resources, B.Y.; data curation, B.Y.; writing\u0026mdash;original draft preparation, B.Y.; writing\u0026mdash;review and editing J.M. and A.M.; All authors have read and agreed to the published version of the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgements-\u003c/h2\u003e\u003cp\u003eThe author gratefully acknowledges the Centers for Disease Control and Prevention (CDC) for their valuable support in providing access to datasets from twelve countries upon request through their website.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files obtained from https://phia-data.icap.columbia.edu/datasets\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbila DB, Wasukira SB, Ainembabazi P, Kiyingi EN, Chemutai B, Kyagulanyi E, Varsani J, Shindodi B, Kisuza RK, Niyonzima N. (2024). Coverage and Socioeconomic Inequalities in Cervical Cancer Screening in Low- and Middle-Income Countries Between 2010 and 2019. \u003cem\u003eJCO Global Oncology\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1200/GO.23.00385\u003c/span\u003e\u003cspan address=\"10.1200/GO.23.00385\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmpofo AG, Adumatta AD, Owusu E, Awuviry-Newton K. A cross-sectional study of barriers to cervical cancer screening uptake in Ghana: An application of the health belief model. PLoS ONE. 2020;15(4):e0231459. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0231459\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0231459\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBelglaiaa E, Souho T, Badaoui L, Segondy M, Pr\u0026eacute;tet J-L, Guenat D, Mougin C. Awareness of cervical cancer among women attending an HIV treatment centre: a cross-sectional study from Morocco. BMJ Open. 2018;8(8):e020343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjopen-2017-020343\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2017-020343\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDare AJ, Knapp GC, Romanoff A, Olasehinde O, Famurewa OC, Komolafe AO, Olatoke S, Katung A, Alatise OI, Kingham TP. High-burden Cancers in Middle-income Countries: A Review of Prevention and Early Detection Strategies Targeting At-risk Populations. Cancer Prev Res. 2021;14(12):1061\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/1940-6207.CAPR-20-0571\u003c/span\u003e\u003cspan address=\"10.1158/1940-6207.CAPR-20-0571\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDevarapalli P, Labani S, Nagarjuna N, Panchal P, Asthana S. Barriers affecting uptake of cervical cancer screening in low and middle income countries: A systematic review. Indian J Cancer. 2018;55(4):318. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/ijc.IJC_253_18\u003c/span\u003e\u003cspan address=\"10.4103/ijc.IJC_253_18\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEndalamaw A, Khatri RB, Erku D, Zewdie A, Wolka E, Nigatu F, Assefa Y. Barriers and strategies for primary health care workforce development: synthesis of evidence. BMC Prim Care. 2024;25(1):99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12875-024-02336-1\u003c/span\u003e\u003cspan address=\"10.1186/s12875-024-02336-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFentie AM, Tadesse TB, Gebretekle GB. Factors affecting cervical cancer screening uptake, visual inspection with acetic acid positivity and its predictors among women attending cervical cancer screening service in Addis Ababa, Ethiopia. BMC Women\u0026rsquo;s Health. 2020;20(1):147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12905-020-01008-3\u003c/span\u003e\u003cspan address=\"10.1186/s12905-020-01008-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHabib SS, Jamal WZ, Zaidi SMA, Siddiqui J-U-R, Khan HM, Creswell J, Batra S, Versfeld A. Barriers to Access of Healthcare Services for Rural Women\u0026mdash;Applying Gender Lens on TB in a Rural District of Sindh, Pakistan. Int J Environ Res Public Health. 2021;18(19):10102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph181910102\u003c/span\u003e\u003cspan address=\"10.3390/ijerph181910102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHenke A, Kluge U, Borde T, Mchome B, Serventi F, Henke O. Tanzanian women\u0026acute;s knowledge about Cervical Cancer and HPV and their prevalence of positive VIA cervical screening results. Data from a Prevention and Awareness Campaign in Northern Tanzania, 2017\u0026ndash;2019. Global Health Action. 2021;14(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/16549716.2020.1852780\u003c/span\u003e\u003cspan address=\"10.1080/16549716.2020.1852780\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMahiti GR, Adam J, Luoga P. Prevalence and determinants of cervical cancer screening among women aged 15\u0026ndash;49 years in Tanzania: analysis of demographic and health survey 2022. Archives Public Health. 2025;83(1):135. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13690-025-01629-w\u003c/span\u003e\u003cspan address=\"10.1186/s13690-025-01629-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoshi FV, Vandervort EB, Kibusi SM. (2018). Cervical Cancer Awareness among Women in Tanzania: An Analysis of Data from the 2011-12 Tanzania HIV and Malaria Indicators Survey. \u003cem\u003eInternational Journal of Chronic Diseases\u003c/em\u003e, \u003cem\u003e2018\u003c/em\u003e, 1\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2018/2458232\u003c/span\u003e\u003cspan address=\"10.1155/2018/2458232\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurfin J, Irvine F, Meechan-Rogers R, Swift A. Education, income and occupation and their influence on the uptake of cervical cancer prevention strategies: A systematic review. J Clin Nurs. 2020;29(3\u0026ndash;4):393\u0026ndash;415. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jocn.15094\u003c/span\u003e\u003cspan address=\"10.1111/jocn.15094\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaito S, Duong YT, Metz M, Lee K, Patel H, Sleeman K, Manjengwa J, Ogollah FM, Kasongo W, Mitchell R, Mugurungi O, Chimbwandira F, Moyo C, Maliwa V, Mtengo H, Nkumbula T, Ndongmo CB, Vere NS, Chipungu G, Voetsch AC. ) household surveys in three sub‐Saharan African Countries, 2015 to 2016. J Int AIDS Soc. 2017;20(S7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jia2.25004\u003c/span\u003e\u003cspan address=\"10.1002/jia2.25004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Returning\u0026thinsp;\u0026lt;\u0026thinsp;scp\u0026thinsp;\u0026gt;\u0026thinsp;HIV -1 viral load results to participant‐selected health facilities in national Population‐based\u0026thinsp;\u0026lt;\u0026thinsp;scp\u0026thinsp;\u0026gt;\u0026thinsp;HIV\u0026thinsp;Impact Assessment (\u0026lt;\u0026thinsp;scp\u0026thinsp;\u0026gt;\u0026thinsp;PHIA\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSrivastava AN, Misra JS, Srivastava S, Das BC, Gupta S. Cervical cancer screening in rural India. Indian J Med Res. 2018;148(6):687\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/ijmr.IJMR_5_17\u003c/span\u003e\u003cspan address=\"10.4103/ijmr.IJMR_5_17\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStelzle D, Tanaka LF, Lee KK, Ibrahim Khalil A, Baussano I, Shah AS, V, McAllister DA, Gottlieb SL, Klug SJ, Winkler AS, Bray F, Baggaley R, Clifford GM, Broutet N, Dalal S. Estimates of the global burden of cervical cancer associated with HIV. Lancet Global Health. 2021;9(2):e161\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2214-109X(20)30459-9\u003c/span\u003e\u003cspan address=\"10.1016/S2214-109X(20)30459-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUNAIDS. Miles to Go: The Response to HIV in the Context of the Sustainable Development Goals. Joint United Nations Programme on HIV/AIDS. UNAIDS; 2018.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Gerwen OT, Muzny CA, Marrazzo JM. Sexually transmitted infections and female reproductive health. Nat Microbiol. 2022;7(8):1116\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41564-022-01177-x\u003c/span\u003e\u003cspan address=\"10.1038/s41564-022-01177-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVapiwala N, Miller D, Laventure B, Woodhouse K, Kelly S, Avelis J, Baffic C, Goldston R, Glanz K. Stigma, beliefs and perceptions regarding prostate cancer among Black and Latino men and women. BMC Public Health. 2021;21(1):758. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-021-10793-x\u003c/span\u003e\u003cspan address=\"10.1186/s12889-021-10793-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Kinsler JJ, Kiwuwa-Muyingo S. Factors associated with HIV testing among youth in Tanzania based on the 2016\u0026ndash;2017 Tanzania HIV Impact Survey (THIS). PLOS Global Public Health. 2022;2(11):e0000536. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pgph.0000536\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgph.0000536\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYimer NB, Mohammed MA, Solomon K, Tadese M, Grutzmacher S, Meikena HK, Alemnew B, Sharew NT, Habtewold TD. Cervical cancer screening uptake in Sub-Saharan Africa: a systematic review and meta-analysis. Public Health. 2021;195:105\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.puhe.2021.04.014\u003c/span\u003e\u003cspan address=\"10.1016/j.puhe.2021.04.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYirsaw AN, Nigusie A, Andualem F, Getachew E, Getachew D, Tareke AA, Mihret MS, Lakew G. Cervical cancer screening utilization and associated factors among women living with HIV in Ethiopia, 2024: systematic review and meta-analysis. BMC Women\u0026rsquo;s Health. 2024;24(1):521. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12905-024-03362-y\u003c/span\u003e\u003cspan address=\"10.1186/s12905-024-03362-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang S, Xu H, Zhang L, Qiao Y. Cervical cancer: Epidemiology, risk factors and screening. Chin J Cancer Res. 2020;32(6):720\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21147/j.issn.1000-9604.2020.06.05\u003c/span\u003e\u003cspan address=\"10.21147/j.issn.1000-9604.2020.06.05\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Fan Z, Wang J, Guan B, Zhou F, Tang Z, Wu W, Huang A. HPV vaccination, screening disparities, and the shifting landscape of cervical cancer burden: a global analysis of trends, inequalities, and policy implications. BMC Women\u0026rsquo;s Health. 2025;25(1):285. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12905-025-03841-w\u003c/span\u003e\u003cspan address=\"10.1186/s12905-025-03841-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"infectious-agents-and-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaac","sideBox":"Learn more about [Infectious Agents and Cancer](http://infectagentscancer.biomedcentral.com/)","snPcode":"13027","submissionUrl":"https://submission.nature.com/new-submission/13027/3","title":"Infectious Agents and Cancer","twitterHandle":"@IAC_journal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cervical cancer testing; HIV status, CD4 count, and Multiple logistic analysis","lastPublishedDoi":"10.21203/rs.3.rs-7223468/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7223468/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Cervical cancer remains a significant health challenge in Tanzania and Zimbabwe, particularly among populations affected by HIV. This study explored the complex relationship between HIV status, viral load, and cervical cancer screening, shedding light on the factors that drive or hinder screening uptake. As screening rates play a crucial role in early detection and improved survival, addressing disparities in access and awareness is vital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e An analytical cross-sectional study was conducted in two sub-Saharan African (SSA) countries to explore the intersection of HIV status, viral load levels, and cervical cancer screening. Bivariate and multiple binary logistic regression models were used to examine the relationship between HIV status, viral load levels, and cervical cancer, in addition to other socio-economic factors. Data were extracted from the PHIA survey and analysed using STATA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The study indicated a significant association between HIV status and cervical cancer screening uptake, with HIV-positive individuals having notably higher odds of screening participation (OR = 3.73). Patients with normal immune function exhibited an increased likelihood of screening (OR = 2.44, 95% CI 1.20–4.97) compared with other CD4 count categories. Age was a significant predictor, with individuals aged 36–45 years (OR = 3.59, 95% CI 1.64–7.86) and 46–55 years (OR = 4.53, 95% CI 1.97–10.41) being more likely to undergo screening than those in the 15–25 age group. Economic status also played a role, as participants in the second wealth quintile demonstrated the highest likelihood of screening (OR = 2.64, 95% CI 1.22–5.73), followed by those in the highest wealth quintile (OR = 2.31, 95% CI 0.97–5.49). Additionally, the number of sexual partners showed a marginal association with screening uptake (OR = 1.70, 95% CI 0.94–3.10), suggesting that sexual behavior may influence screening decisions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e The study demonstrates that Cervical cancer screening rates increased significantly with HIV status, CD4 count, age, and economic status. These findings underscore the need for targeted interventions to reduce disparities and enhance screening accessibility in the future.\u003c/p\u003e","manuscriptTitle":"Exploring the Intersection of HIV Status, Viral Load Levels, and Cervical Cancer Screening in Tanzania and Zimbabwe: Insights from PHIA Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 14:34:22","doi":"10.21203/rs.3.rs-7223468/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-03T22:25:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T10:54:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221776311399234524702925586767908096584","date":"2025-10-01T14:31:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317687818660712405995321200443755551061","date":"2025-09-26T04:42:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16464692928806539031409639152719299032","date":"2025-09-25T21:15:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-25T20:56:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-01T12:55:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-01T12:55:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Infectious Agents and Cancer","date":"2025-07-26T23:52:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"infectious-agents-and-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaac","sideBox":"Learn more about [Infectious Agents and Cancer](http://infectagentscancer.biomedcentral.com/)","snPcode":"13027","submissionUrl":"https://submission.nature.com/new-submission/13027/3","title":"Infectious Agents and Cancer","twitterHandle":"@IAC_journal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"755e4443-02c0-4e2f-a2e4-e2aa931ce0ee","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T21:08:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 14:34:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7223468","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7223468","identity":"rs-7223468","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}