Self-Reported Prevalence and Factors Associated with Kidney Disease in Somaliland: A Secondary Analysis of the 2020 Demographic and Health Survey

Self-Reported Prevalence and Factors Associated with Kidney Disease in Somaliland: A Secondary Analysis of the 2020 Demographic and Health Survey | 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 Self-Reported Prevalence and Factors Associated with Kidney Disease in Somaliland: A Secondary Analysis of the 2020 Demographic and Health Survey Hamse Jama¹ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9304726/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Chronic kidney disease (CKD) is a critical global health priority, yet its burden remains largely uncharacterized in the Horn of Africa. This study provides the inaugural population-based assessment of kidney disease prevalence and its socio-demographic and clinical determinants in Somaliland. Methods We conducted a secondary analysis of a nationally representative, cross-sectional sample from the 2020 Somaliland Demographic and Health Survey (SLDHS) (N = 18,930). To ensure the quality and transparency of the research, this study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies. Multivariable logistic regression models—selected after comparative performance analysis with Probit and Complementary Log-Log models using Akaike and Bayesian Information Criteria (AIC/BIC)—were utilized to identify independent risk factors associated with self-reported kidney disease. Results The overall self-reported prevalence of kidney disease was 1.3% (95% CI: 1.1–1.5%). Multivariable analysis revealed that female-headed households had significantly higher odds of the condition (aOR = 1.386, p < 0.05). Pronounced regional disparities were observed, with the highest risk recorded in Sool (aOR = 2.899, p < 0.01). Advanced age and clinical comorbidities emerged as the most potent predictors: hypertension (aOR = 2.974, p < 0.01) and heart disease (aOR = 8.364, p < 0.01) were associated with substantially elevated odds. Conversely, having no formal schooling showed a marginal inverse association with the outcome (aOR = 0.750, p < 0.1). Conclusion Kidney disease in Somaliland is shaped by a complex interplay of regional disparities, gender-specific vulnerabilities, and cardiovascular comorbidities. These findings underscore an urgent need for integrated non-communicable disease (NCD) management strategies and targeted screening programs, particularly in high-prevalence regions. Strengthening diagnostic capabilities and rural healthcare infrastructure is essential for effective prevention and early intervention. Introduction Chronic kidney disease (CKD) has emerged as a formidable global public health challenge, contributing substantially to escalating morbidity, mortality, and healthcare expenditures (Bikbov et al., 2020 ; Liyanage et al., 2015 ). The global burden of CKD is disproportionately concentrated in low- and middle-income countries (LMICs), where restricted resources, inadequate healthcare infrastructure, and a rising prevalence of metabolic risk factors exacerbate the crisis (Foreman et al., 2018 ; Zhang, 2003 ). While the global prevalence of CKD is estimated to range between 10% and 13%, significant regional variations persist (Chukwuonye et al., 2018 ; Hill et al., 2016 ). Recent international evidence highlights an increasing prevalence of CKD across Africa, with estimates spanning from 5% to over 15% in diverse populations (Mareev et al., 2018 ; Tepel et al., 2003 ). These disparities are frequently attributed to socioeconomic inequities, limited healthcare access, and the high burden of traditional risk factors such as hypertension, diabetes, and infectious diseases (Bahrey et al., 2019 ; Bikbov et al., 2020 ). In African contexts, the overall prevalence of CKD remains alarmingly high and correlates strongly with the presence of comorbidities like hypertension and diabetes (Kaze et al., 2015 ). While several studies have characterized the prevalence and determinants of CKD in sub-Saharan Africa (Kaze et al., 2015 ; Warsame et al., 2016 ), the epidemiology of kidney disease in the Horn of Africa—particularly in Somaliland—remains largely uncharacterized (Akwaboah et al., 2022 ). As an autonomous region in northwestern Somalia, Somaliland faces multifaceted health challenges, including limited healthcare resources, fragile infrastructure, and pervasive poverty (Ahmed et al., 2019 ; Canton, 2021 ). Consequently, robust population-based data on non-communicable diseases (NCDs), including CKD, are critically scarce (McLeroy et al., 1988 ). Characterizing the epidemiology of CKD in Somaliland is, therefore, essential for establishing public health priorities and designing evidence-based interventions. The current study is grounded in the socio-ecological model, which posits that health outcomes are shaped by a complex interplay of individual, interpersonal, organizational, community, and policy-level factors (Coresh et al., 2007 ). At the individual level, factors such as age, biological sex, and health behaviors significantly modulate CKD risk, while interpersonal and community-level determinants—including social support systems and access to healthcare or clean water—further drive renal health outcomes (Lemos et al., 2015 )]. Utilizing data from the 2020 Somaliland Demographic and Health Survey (SLDHS), this study assesses the prevalence of kidney disease and its association with socio-demographic and economic risk factors (Foundation, 2012 ). In this context, kidney disease is defined based on self-reported diagnosis (Corsi et al., 2012 ). Given the critical paucity of data, the 2020 SLDHS provides a unique opportunity to investigate this pressing public health issue (Rutstein & Rojas, 2006 ). This study aims to determine the prevalence of kidney disease and identify independent risk factors among the Somaliland population, providing actionable insights for policymakers to develop integrated strategies for CKD prevention and management(Bikbov et al., 2020 ). Methods Study Design and Data Source Data for this analysis were sourced from the 2020 Somaliland Demographic and Health Survey (SLDHS), which represents the first nationally representative demographic and health survey conducted in Somaliland. Implemented by the Ministry of Planning and Development in collaboration with international partner organizations between 2018 and 2020 (Abdikarim et al., 2025 ; Farih et al., 2024 ), Data were accessed for research purposes on January 15, 2026 the SLDHS provides comprehensive data on fertility, maternal and child health, household demographics, water, sanitation, and hygiene (WASH) practices, as well as critical socioeconomic indicators (Mohamed et al., n.d.). To ensure the quality and transparency of the research, this study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies. To ensure the robustness and integrity of the findings, a systematic data validation and cleaning process was performed. This involved the exclusion of observations with missing or incomplete information regarding the primary outcome and key independent variables. Following the application of these inclusion criteria, the final analytical sample consisted of 18,930 participants. All statistical analyses were performed using R statistical software (version 4.3.2). To account for the complex survey design of the SLDHS 2020, including clustering and stratification, the survey package in R was utilized to apply sampling weights, ensuring that the findings are nationally representative. Descriptive statistics summarized the participants' socio-demographic and clinical characteristics. Bivariate associations were assessed using the Pearson Chi-square test. For multivariable analysis, a logistic regression model was employed to identify independent risk factors for self-reported kidney disease. Model selection was based on the lowest Akaike Information Criterion (AIC) and Bayesian Information Criteria (BIC) values. Statistical significance was set at p < 0.05. Results Prevalence of Kidney Disease Analysis of the 2020 SLDHS dataset revealed an overall self-reported prevalence of kidney disease of 1.3% (95% Confidence Interval [CI]: 1.1–1.5%) ( Table 1 ) . Initial bivariate analyses demonstrated statistically significant associations between kidney disease and several socio-demographic and clinical characteristics, including geographic region, residence type, wealth quintile, age, biological sex of the household head, and comorbidities such as diabetes and hypertension. Table 1 Prevalence of Kidney disease. Kidney disease n = 18,930 Proportion Standard error 95% Confidence interval No 0.987 0.001 0.986 0.989 Yes 0.013 0.001 0.011 0.014 Multivariable Analysis and Model Selection A multivariable logistic regression model was utilized to identify independent predictors of kidney disease. Model selection was informed by a comparative performance analysis of three binary outcome models (Logistic, Probit, and Complementary Log-Log). The logistic model was selected for its superior fit, as evidenced by the lowest Akaike Information Criterion (AIC: 2384.98) and Bayesian Information Criterion (BIC: 2581.20) values. Independent Risk Factors Adjusted multivariable analysis identified several independent risk factors for kidney disease. Specifically, female-headed households were associated with significantly higher odds of the outcome (aOR = 1.386, p < 0.05). Geographic location remained a critical predictor; compared to the reference region (Awdal), significantly elevated odds were observed in Sool (aOR = 2.899, p < 0.01), Marodijeh (aOR = 1.795, p < 0.05), and Sahil (aOR = 1.791, p < 0.05). Advanced age exhibited a strong, progressive correlation with kidney disease risk, with the highest odds recorded among individuals aged 65 years and older (aOR = 2.745, p < 0.05) relative to the reference group (under 15 years). Clinical comorbidities emerged as the most potent predictors of the condition: hypertension was associated with nearly three-fold higher odds (aOR = 2.974, p < 0.01), while pre-existing heart disease demonstrated the strongest association, resulting in an eight-fold increase in odds (aOR = 8.364, p < 0.01). Conversely, a marginal inverse association was observed for individuals with no formal education, who showed lower odds of the outcome (aOR = 0.750, p < 0.1). Table 2 Socio-demographic and health characteristics of the study population (N = 18,930) Variable Levels Frequency (n) Percentage (%) Region Awdal 2,694 14.23 Marodijeh 2,414 12.75 Sahil 2,222 11.74 Togdheer 3,284 17.35 Sool 4,007 21.17 Sanaag 4,309 22.76 Type of residence Urban 7,085 37.43 Rural 5,854 30.92 Nomadic 5,991 31.65 Wealth Quantile Lowest 7,802 41.22 Second 1,968 10.40 Middle 2,442 12.90 Fourth 3,303 17.45 Highest 3,415 18.04 Age (Years) Less than 15 3,107 16.41 15–24 6,014 31.77 25–34 3,406 17.99 35–44 2,308 12.19 45–54 1,693 8.94 55–64 1,075 5.68 Above 64 1,327 7.01 Sex Male 8,143 43.02 Female 10,787 56.98 Marital Status Married 8,721 46.07 Divorced 482 2.55 Abandoned 178 0.94 Widowed 898 4.74 Never Married 8,651 45.70 Attended School Yes 7,460 39.41 No 11,285 59.61 Don't know/Missing 185 0.98 Use of tobacco Yes 856 4.52 No 18,074 95.48 Diabetes Yes 267 1.41 No 18,663 98.59 High Blood Pressure Yes 916 4.84 No 18,014 95.16 Heart Disease Yes 140 0.74 No 18,790 99.26 Demographic and socioeconomic characteristics of respondents Demographic and Socioeconomic Characteristics of Respondents The demographic and socioeconomic profiles of the study population (N = 18,930) are detailed in Table 2 . Geographically, the largest proportion of respondents resided in the Sanaag region (22.76%), followed by Sool (21.17%) and Togdheer (17.35%). Conversely, the regions of Awdal (14.23%), Marodijeh (12.75%), and Sahil (11.74%) represented smaller segments of the sample. In terms of residential distribution, urban residents constituted the largest group (37.43%), while rural and nomadic populations accounted for 30.92% and 31.65%, respectively. Socioeconomic status, assessed via wealth quintiles, revealed that a significant plurality of participants (41.22%) belonged to the lowest wealth category. The remaining population was distributed across the second (10.40%), middle (12.90%), fourth (17.45%), and highest (18.04%) quintiles. The study population was notably young; the 15–24 age bracket was the most prominent (31.77%), followed by those under the age of 15 (16.41%) and the 25–34 age group (17.99%). Participants aged Above 64 comprised the smallest proportion (7.01%) of the sample. Regarding biological sex, females represented the majority of the study population at 56.98%. Marital Status, Education, and Clinical Characteristics Regarding marital status, 45.70% of participants had never married, while 46.07% were married. Educational attainment was notably low, with more than half of the study population (59.61%) reported never having attended school. Clinical indicators showed that tobacco use was prevalent among 4.52% of respondents, while 1.41% reported a diagnosis of diabetes. Additionally, 4.84% of the sample had hypertension, and 0.74% were affected by heart disease (Table 2 ). Associations Between Predictors and Kidney Disease (Chi-square Analysis) Table 3 presents the results of Chi-square tests examining the associations between various socio-demographic factors and kidney disease. Statistically significant associations (p < 0.05) were observed between kidney disease and the following variables: region (χ2 = 47.4081, df = 5, p < 0.001), type of residence (χ2 = 14.0914, df = 2, p = 0.001), wealth quantile (χ2 = 19.7559, df = 4, p = 0.001), age (χ2 = 86.1537, df = 6, p < 0.001), sex (χ2 = 10.4348, df = 1, p = 0.001), marital status (χ2 = 66.8983, df = 4, p < 0.001), diabetes (χ2 = 6.3977, df = 1, p = 0.011) and blood pressure (χ2 = 141.2454, df = 1, p < 0.001). Geographically, the prevalence of kidney disease varied significantly, with the highest rate observed in Sool (2.20%) and the lowest in Sanaag (0.63%). Rural residents reported a higher prevalence (1.67%) compared to urban (1.26%) and nomadic (0.90%) participants. Furthermore, prevalence showed a strong age-related trend, increasing significantly with age and peaking at 3.24% among those aged over 64 years. Females exhibited a significantly higher prevalence (1.50%) than males (0.97%). Regarding marital status, widowed individuals demonstrated the highest prevalence of kidney disease (3.34%) compared to other groups. Finally, individuals with clinical comorbidities had significantly elevated rates of kidney disease: 3.00% among those with diabetes compared to 1.25% in non-diabetics, and 17.86% among those with hypertension compared to 1.05% in those with normal blood pressure. Table 3 Bivariate analysis of factors associated with kidney disease (N = 18,930) Variable Kidney Disease (No) Kidney Disease (Yes) χ2 df P-value n (%) n (%) Region 47.41 5 < 0.001 * Awdal 2,673 (99.22) 21 (0.78) Marodijeh 2,380 (98.59) 34 (1.41) Sahil 2,191 (98.60) 31 (1.40) Togdheer 3,244 (98.78) 40 (1.22) Sool 3,919 (97.80) 88 (2.20) Sanaag 4,282 (99.37) 27 (0.63) Type of residence 14.09 2 0.001 * Urban 6,996 (98.74) 89 (1.26) Rural 5,756 (98.33) 98 (1.67) Nomadic 5,937 (99.10) 54 (0.90) Wealth Quantile 19.76 4 0.001 * Lowest 7,704 (98.74) 98 (1.26) Second 1,929 (98.02) 39 (1.98) Middle 2,400 (98.28) 42 (1.72) Fourth 3,267 (98.91) 36 (1.09) Highest 3,389 (99.24) 26 (0.76) Age (Years) 86.15 6 < 0.001 * Less than 15 3,090 (99.45) 17 (0.55) 15–24 5,966 (99.20) 48 (0.80) 25–34 3,367 (98.85) 39 (1.15) 35–44 2,274 (98.53) 34 (1.47) 45–54 1,661 (98.11) 32 (1.89) 55–64 1,047 (97.40) 28 (2.60) Above 64 1,284 (96.76) 43 (3.24) Sex 10.43 1 0.001 * Male 8,064 (99.03) 79 (0.97) Female 10,625 (98.50) 162 (1.50) Marital Status 66.90 4 < 0.001 * Married 8,579 (98.37) 142 (1.63) Divorced 477 (98.96) 5 (1.04) Abandoned 173 (97.19) 5 (2.81) Widowed 868 (96.66) 30 (3.34) Never Married 8,592 (99.32) 59 (0.68) Attended School 3.06 2 0.216 Yes 7,376 (98.87) 84 (1.13) No 11,132 (98.64) 153 (1.36) Don't know 181 (97.84) 4 (2.16) Use of tobacco 0.94 1 0.333 No 18,074 (98.74) 227 (1.26) Yes 842 (98.36) 14 (1.64) Diabetes 6.40 1 0.011 * No 18,430 (98.75) 233 (1.25) Yes 259 (97.00) 8 (3.00) High Blood Pressure 141.25 1 < 0.001 * No 17,824 (98.95) 190 (1.05) Yes 1,106 (82.14) 25 (17.86) Heart Disease 308.62 1 < 0.001 * No 18,574 (98.85) 216 (1.15) Yes 115 (82.14) 25 (17.86) *Note: χ2 = Chi-square, df = degrees of freedom. Statistically significant (p < 0.05). Use of tobacco and Attended School, however, does not show a significant association. Binary Logistic Regression on dv with significantly associated predictors Table 4 presents unadjusted, or crude, odds ratios (CORs) examining the relationship between several variables and a given outcome, with the COR serving as an estimate of the relative odds of the outcome being present in each level relative to the reference group. While these unadjusted odds ratios provide initial insights, a multivariable analysis is necessary to refine these associations and account for interdependence among variables. These odds ratios are presented along with their standard errors, p-values, and 95% confidence intervals. Regarding regional differences, when compared to residents of Awdal, those living in Marodijeh (COR = 1.82, p = 0.032) and Sahil (COR = 1.80, p = 0.038) had statistically significantly higher odds. The odds of residents from Sool (COR = 2.86, p < 0.001) were also significantly higher. Those living in Togdheer and Sanaag did not have statistically significant differences in their odds when compared to Awdal residents. Looking at the type of residence, those in rural locations had statistically significantly higher odds (COR = 1.34, p = 0.048) than those in urban locations. Being a nomadic resident showed a non-significant trend towards decreased odds (COR = 0.71, p = 0.053). There was a strong trend regarding age, with each increasing age bracket having a higher odds than the reference group (those under 15). These age brackets reached statistical significance at 25–34 (COR = 2.11, p = 0.011), 35–44 (COR = 2.72, p = 0.001), 45–54 (COR = 3.50, p < 0.001), 55–64 (COR = 4.86, p < 0.001) and above 64 (COR = 6.09, p < 0.001).Looking at wealth, only the second and highest wealth quintiles were statistically significant. The second highest quintile showed increased odds (COR = 1.59, p = 0.015), while the highest wealth quintile showed decreased odds (COR = 0.51, p = 0.022). Finally, having diabetes was associated with statistically significantly increased odds (COR = 2.44, p = 0.014), and the same was true for high blood pressure (COR = 5.53, p < 0.001). High blood pressure had the largest crude odds ratio of all the factors looked at, which may indicate a strong relationship between high blood pressure and the outcome of interest. It is important to keep in mind that this is an unadjusted analysis. Further research is needed that accounts for confounders that could impact these relationships. Table 4 Logistic regression analysis showing Crude Odds Ratios (COR) for kidney disease (N = 18,930) Variable Levels COR Std. Error P-value 95% CI Region Awdal 1(Ref.) — — — Marodijeh 1.82 0.51 0.032* (1.05–3.13) Sahil 1.80 0.51 0.038* (1.03–3.15) Togdheer 1.57 0.42 0.096 (0.92–2.66) Sool 2.86 0.70 < 0.001* (1.77–4.59) Sanaag 0.80 0.23 0.451 (0.45–1.42) Type of residence Urban 1(Ref.) — — — Rural 1.34 0.20 0.048* (1.00–1.68) Nomadic 0.71 0.12 0.053 (0.51–0.99) Age (Years) Less than 15 1(Ref.) — — — 15–24 1.46 0.41 0.179 (0.84–2.55) 25–34 2.11 0.61 0.011* (1.19–3.73) 35–44 2.72 0.81 0.001* (1.51–4.88) 45–54 3.50 1.06 < 0.001* (1.94–6.33) 55–64 4.86 1.50 < 0.001* (2.65–8.92) Above 64 6.09 1.76 < 0.001* (3.46–10.71) Wealth Quintile Lowest 1(Ref.) — — — Second 1.59 0.30 0.015* (1.09–2.31) Middle 0.87 0.17 0.086 (0.96–1.98) Fourth 0.60 0.13 0.464 (0.59–1.27) Highest 0.51 0.13 0.022* (0.39–0.93) Diabetes No 1(Ref.) — — — Yes 2.44 0.89 0.014* (1.19–5.00) Hypertension (BP) No 1 (Ref.) — — — Yes 5.53 0.89 < 0.001* (4.03–7.59) *Note: COR = Crude Odds Ratio; CI = Confidence Interval; Ref = Reference category. Statistically significant (p < 0.05). Table 5 presents adjusted odds ratio (aOR) results reveal several significant associations. Compared to being male, being a female-headed household was associated with a significantly higher odds of the outcome (aOR = 1.386, p < 0.05). Table 5 Multivariable logistic Regression Analysis of factors associated with kidney disease (N = 18,930) Variable Levels aOR Std. Err. P-value 95% CI Sig. Sex of Household Head Male 1 (Ref.) — — — Female 1.386 0.215 0.035 (1.02–1.88) ** Region Awdal 1 (Ref.) — — — Marodijeh 1.795 0.506 0.038 (1.03–3.12) ** Sahil 1.791 0.515 0.043 (1.02–3.15) ** Togdheer 1.671 0.459 0.061 (0.98–2.86) * Sool 2.899 0.725 < 0.001 (1.78–4.73) *** Sanaag 0.859 0.254 0.607 (0.48–1.53) Type of Residence Urban 1 (Ref.) — — — Rural 1.240 0.192 0.164 (0.92–1.68) Nomadic 0.814 0.156 0.283 (0.56–1.19) Age Category Less than 15 1 (Ref.) — — — 15–24 1.349 0.396 0.308 (0.76–2.40) 25–34 1.529 0.557 0.245 (0.75–3.13) 35–44 1.986 0.771 0.077 (0.93–4.25) * 45–54 2.343 0.926 0.031 (1.08–5.08) ** 55–64 3.022 1.238 0.007 (1.35–6.74) *** Above 64 2.745 1.132 0.014 (1.22–6.16) ** Attended School Yes 1 (Ref.) — — — No 0.750 0.122 0.077 (0.55–1.03) * Don't Know 1.234 0.655 0.692 (0.44–3.50) Clinical Factors Hypertension (Yes) 2.974 0.564 < 0.001 (2.05–4.31) *** Heart Disease (Yes) 8.364 2.159 < 0.001 (5.04–13.88) *** Diabetes (Yes) 0.703 0.284 0.382 (0.32–1.55) Note: N = 18,930 ; Pseudo R2 = 0.094; χ2 = 243.49 ,p < 0.001; AIC = 2388.73; BIC = 2584.94. Abbreviations: aOR, Adjusted Odds Ratio; CI, Confidence Interval; Ref, Reference category. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Interpretation of Multivariable Findings (aOR) The multivariable analysis, summarized in Table 5 , identifies several significant independent predictors of kidney disease. Households headed by females exhibited significantly higher odds of the outcome compared to those headed by males (aOR = 1.386, p < 0.05). Regional disparities were also pronounced; compared to the reference region of Awdal, significantly higher odds were observed in Sool (aOR = 2.899, p < 0.01), Marodijeh (aOR = 1.795, p < 0.05), and Sahil (aOR = 1.791, p < 0.05), while Togdheer showed a marginal increase in odds (aOR = 1.671, p < 0.1). Advanced age emerged as a robust independent risk factor. Individuals in the 45–54 (aOR = 2.343, p < 0.05), 55–64 (aOR = 3.022, p < 0.01), and over 64 (aOR = 2.745, p < 0.05) age brackets had significantly higher odds of kidney disease relative to those under 15 years. Furthermore, clinical comorbidities were the strongest predictors in the model: hypertension was associated with nearly three-fold higher odds (aOR = 2.974, p < 0.01), while heart disease exhibited the most potent association, with an eight-fold increase in odds (aOR = 8.364, p < 0.01). Conversely, a marginal inverse association was noted for individuals with no formal education, who showed lower odds of the outcome compared to those who attended school (aOR = 0.750, p < 0.1). Model Comparison (Logistic, Probit, and Clog-Log) Table 6 presents a comparative performance analysis of three binary outcome models: Logistic Regression, logistic, and Complementary Log-Log (Clog-log). The selection of the optimal model was based on the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Lower values for these information criteria indicate a superior model that optimally balances goodness-of-fit with predictive parsimony. As demonstrated in Table 6 , the logistic model exhibited the lowest values for both AIC (2384.98) and BIC (2581.20) compared to the Logistic Regression and Complementary Log-Log models. Consequently, the logistic model was selected as the most appropriate and efficient representation of the data-generating process for this study. Table 6 Model Comparison Model AIC BIC Logistic Regression 2388.731 2584.943 logistic 2384.983 2581.196 Complementary Log-Log 2389.314 2585.527 These information criteria provide a means to assess the relative performance of each model, balancing goodness-of-fit with model complexity. Lower AIC and BIC values indicate a more desirable model, reflecting a superior trade-off between predictive accuracy and parsimony. As shown in Table 6 , the logistic model exhibits the lowest AIC (2384.983) and BIC (2581.196) values in comparison to the Logistic Regression (AIC = 2388.731, BIC = 2584.943) and Complementary Log-Log (AIC = 2389.314, BIC = 2585.527) models. Therefore, based on these information criteria, the logistic model is the preferred choice, suggesting a more efficient and accurate representation of the data-generating process relative to the alternatives considered. This selection warrants careful consideration of the theoretical underpinnings of each model and their appropriateness for the specific research question and dataset. Discussion This study, utilizing the 2020 Somaliland Demographic and Health Survey (SLDHS) data, provides the inaugural population-based estimate for the prevalence of kidney disease in Somaliland. Our findings reveal a self-reported prevalence of 1.3%, a figure that provides a critical baseline for the region. While this prevalence is lower than the global estimate of 10–13% (Chukwuonye et al., 2018 ; Hill et al., 2016 ) and some African estimates ranging between 5% and 15% [(Mareev et al., 2018 ; Tepel et al., 2003 ), it likely reflects significant under-diagnosis due to limited routine screening, low health literacy, and the reliance on self-reported data in the SLDHS. The prevalence of 1.3% observed in this study is significantly lower than the global average of 10–13%. This discrepancy likely stems from the self-reported nature of the data. Since chronic kidney disease is often asymptomatic in its early stages, many individuals in Somaliland may remain undiagnosed due to limited routine screening and low health literacy. Therefore, this figure should be interpreted as the 'recognized' prevalence rather than the actual biological burden of the disease in the population Bivariate analyses identified significant associations between kidney disease and several socio-demographic factors, including geographic region, residence type, wealth quintile, age, biological sex, and marital status, alongside clinical comorbidities such as diabetes and hypertension. Multivariable analysis further refined these associations, identifying female-headed households, specific geographic regions (Marodijeh, Sahil, Togdheer, and Sool), advanced age, hypertension, and heart disease as independent risk factors for the condition. The observed regional variations suggest that community-level determinants—such as environmental exposures, water quality, or disparities in healthcare infrastructure—significantly influence kidney disease risk. These findings align with existing literature highlighting the profound impact of geographic location on epidemiological trends (Bikbov et al., 2020 ). The high prevalence in the Sool region, in particular, warrants further qualitative and environmental research to understand the localized drivers of renal health. Furthermore, consistent with global trends (Cockwell & Fisher, 2020 ), advancing age emerged as a robust risk factor, necessitating the implementation of targeted screening programs for older adults within the primary healthcare system. The association between female-headed households and higher kidney disease odds underscores a potential intersection of gender-specific vulnerabilities and socioeconomic stressors. Additionally, clinical comorbidities were the most potent predictors of kidney disease in this population. Hypertension was associated with nearly a three-fold increase in odds, while heart disease exhibited the strongest association, with an eight-fold increase. These findings emphasize the urgent need for integrated management strategies for non-communicable diseases (NCDs), as comorbidities often cluster within individuals, compounding the complexity of care (Bigna & Noubiap, 2019 ). Methodologically, the logistic model was selected for its superior fit based on AIC and BIC values, ensuring that our statistical inferences are both robust and parsimonious. Conclusion and Policy Implications In conclusion, this study, based on a nationally representative sample from the 2020 SLDHS, reveals that kidney disease is a significant public health concern in Somaliland, shaped by a complex interplay of geographic, demographic, and clinical factors. The prevalence of 1.3% likely represents the "tip of the iceberg," highlighting an urgent need for strengthened diagnostic capabilities and public health awareness. Our findings emphasize the importance of addressing regional disparities, the challenges of an aging population, and gendered vulnerabilities in kidney disease prevention and management. Based on these findings, several policy-level interventions are recommended: Targeted Regional Interventions: Public health efforts should prioritize high-prevalence regions such as Sool, Marodijeh, Sahil, and Togdheer. This includes establishing specialized renal clinics and improving access to clean water and diagnostic services. Integrated NCD Management: Given the strong association between kidney disease, hypertension, and heart disease, the Somaliland Ministry of Health should implement integrated screening and management protocols. Managing these conditions simultaneously can reduce the progression to end-stage renal disease (ESRD). Strengthening Healthcare Infrastructure: There is a critical need to enhance rural healthcare infrastructure. Mobile screening units and community health worker training can bridge the gap for nomadic and rural populations who face higher barriers to care. Gender-Sensitive Healthcare: Healthcare delivery must address the specific socioeconomic and health literacy barriers faced by female-headed households to ensure equitable access to kidney disease prevention and treatment. Evidence-Based Policy and Research: Continued data collection through future SLDHS cycles and targeted clinical research into the underlying causes of regional clusters are necessary. Further investigation is also required to explore the marginal association between education levels and disease reporting. Ultimately, these findings provide a critical evidence base for policymakers to design multi-faceted interventions. By addressing socioeconomic disparities and integrating comorbidity management, Somaliland can achieve sustainable improvements in kidney health and reduce the overall burden of non-communicable diseases. Declarations Ethics approval and consent to participate The 2020 Somaliland Demographic and Health Survey (SLDHS) was reviewed and approved by the Internal Review Board (IRB) of the Somaliland Ministry of Planning and National Development and the Ministry of Health Development. Informed consent was obtained from all individual participants included in the original survey. For this secondary analysis, the dataset was fully anonymized before access, and further ethical approval was not required as per the guidelines for secondary data analysis. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding The authors received no specific funding for this work. Author Contribution HJ conceived the study, performed the statistical analysis, and drafted the original manuscript. The author has read and approved the final version. Acknowledgements The author thanks the Somaliland Ministry of Planning and National Development for providing access to the SLDHS 2020 data. Data Availability The data used in this study are third-party data belonging to the Somaliland Ministry of Planning and National Development. The data are available upon reasonable request from the Central Statistics Department (CSD) via their official website ( [https://mopnd.govsomaliland.org/](https:/www.google.com/url?sa=E&q=https%3A%2F%2Fmopnd.govsomaliland.org%2F) ) or by contacting [ [email protected] ](https:/www.google.com/url?sa=E&q=mailto%3Ainfo%40somalilandcsd.org) . References Abdikarim H, Muse AH, Hassan MA, Muse YH. Prevalence and determinants of home delivery among pregnant women in Somaliland: Insights from SLDHS 2020 data. Aten Primaria. 2025;57(2):103082. Ahmed SH, Meyer HE, Kjøllesdal MK, Marjerrison N, Mdala I, Htet AS, Bjertness E, Madar AA. The prevalence of selected risk factors for non-communicable diseases in Hargeisa, Somaliland: a cross-sectional study. BMC Public Health. 2019;19(1):878. Akwaboah PK, Somuah AA, Odonkor ST. Prevalence and distribution of non-communicable diseases in sub-Saharan Africa: the case of hypertension, diabetes, and chronic kidney disease/acute kidney injury. Cent Eur Manage J. 2022;30(4):1310–26. Bahrey D, Gebremedhn G, Mariye T, Girmay A, Aberhe W, Hika A, Teklay G, Tasew H, Zeru T, Gerensea H. Prevalence and associated factors of chronic kidney disease among adult hypertensive patients in Tigray teaching hospitals: a cross-sectional study. BMC Res Notes. 2019;12(1):562. Bigna JJ, Noubiap JJ. The rising burden of non-communicable diseases in sub-Saharan Africa. Lancet Global Health. 2019;7(10):e1295–6. Bikbov B, Purcell C, Levey AS, Smith M, Abdoli A, Abebe M, Adebayo OM, Afarideh M, Agarwal SK, Agudelo-Botero M, Ahmadian E, Al-Aly Z, Alipour V, Almasi-Hashiani A, Al-Raddadi RM, Alvis-Guzman N, Amini S, Andrei T, Andrei CL, Vos T. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709–33. https://doi.org/10.1016/S0140-6736(20)30045-3 . Canton H. The Europa directory of international organizations 2021. (No Title; 2021. Chukwuonye II, Ogah OS, Anyabolu EN, Ohagwu KA, Nwabuko OC, Onwuchekwa U, Chukwuonye ME, Obi EC, Oviasu E. (2018). Prevalence of chronic kidney disease in Nigeria: systematic review of population-based studies. Int J Nephrol Renovascular Disease, 165–72. Cockwell P, Fisher L-A. The global burden of chronic kidney disease. Lancet. 2020;395(10225):662–4. Coresh J, Selvin E, Stevens LA, Manzi J, Kusek JW, Eggers P, Van Lente F, Levey AS. Prevalence of chronic kidney disease in the United States. JAMA. 2007;298(17):2038–47. Corsi DJ, Neuman M, Finlay JE, Subramanian SV. Demographic and health surveys: a profile. Int J Epidemiol. 2012;41(6):1602–13. Farih OA, Ali AO, Abokor AH, Ali MA, Egge AAA, Muse AH. (2024). Prevalance and determinants of hypertension among adults in Somalia using Somalia demographic health survey data, SDHS 2020. Curr Probl Cardiol, 102783. Foreman KJ, Marquez N, Dolgert A, Fukutaki K, Fullman N, McGaughey M, Pletcher MA, Smith AE, Tang K, Yuan C-W. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016–40 for 195 countries and territories. Lancet. 2018;392(10159):2052–90. Foundation NK. KDOQI clinical practice guideline for diabetes and CKD: 2012 update. Am J Kidney Dis. 2012;60(5):850–86. Hill NR, Fatoba ST, Oke JL, Hirst JA, O’Callaghan CA, Lasserson DS, Hobbs FDR. Global prevalence of chronic kidney disease–a systematic review and meta-analysis. PLoS ONE. 2016;11(7):e0158765. Kaze FF, Meto DT, Halle M-P, Ngogang J, Kengne A-P. Prevalence and determinants of chronic kidney disease in rural and urban Cameroonians: a cross-sectional study. BMC Nephrol. 2015;16(1):117. Lemos CF, Rodrigues MP, Veiga JRP. Family income is associated with quality of life in patients with chronic kidney disease in the pre-dialysis phase: a cross sectional study. Health Qual Life Outcomes. 2015;13:1–9. Liyanage T, Ninomiya T, Jha V, Neal B, Patrice HM, Okpechi I, Zhao M, Lv J, Garg AX, Knight J. Worldwide access to treatment for end-stage kidney disease: a systematic review. Lancet. 2015;385(9981):1975–82. Mareev VY, Fomin IV, Ageev FT, Begrambekova YL, Vasyuk YA, Garganeeva AA, Gendlin GE, Glezer MG, Gautier SV, Dovzhenko TV. Russian heart failure society, Russian society of cardiology. Russian scientific medical society of internal medicine guidelines for heart failure: chronic (CHF) and acute decompensated (ADHF). Diagnosis, prevention and treatment. Kardiologiia. 2018;58(6S):8–158. McLeroy KR, Bibeau D, Steckler A, Glanz K. An ecological perspective on health promotion programs. Health Educ Q. 1988;15(4):351–77. Mohamed FI, Dahir HM, Korse AH. (n.d.). Socioeconomic Determinants and Inequalities in the Prevalence of Non-Communicable Disease in Somaliland: Data from DHS2020. Available at SSRN 5569491 . Rutstein SO, Rojas G. Guide to DHS statistics. Calverton MD: ORC Macro. 2006;38:78. Tepel M, van der Giet M, Statz M, Jankowski J, Zidek W. The antioxidant acetylcysteine reduces cardiovascular events in patients with end-stage renal failure: a randomized, controlled trial. Circulation. 2003;107(7):992–5. Warsame A, Handuleh J, Patel P. Prioritization in Somali health system strengthening: a qualitative study. Int Health. 2016;8(3):204–10. Zhang GP. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 2003;50:159–75. Additional Declarations No competing interests reported. Supplementary Files STROBEChecklist.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 14 May, 2026 Reviewers invited by journal 07 May, 2026 Editor invited by journal 08 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 02 Apr, 2026 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. 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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-9304726","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":641551718,"identity":"cc146be1-025d-49a5-a6fd-d1565a925a66","order_by":0,"name":"Hamse Jama¹","email":"data:image/png;base64,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","orcid":"","institution":"Gollis University","correspondingAuthor":true,"prefix":"","firstName":"Hamse","middleName":"","lastName":"Jama¹","suffix":""}],"badges":[],"createdAt":"2026-04-02 14:57:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9304726/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9304726/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109760399,"identity":"74734a16-b084-4726-ae89-cf870e570c77","added_by":"auto","created_at":"2026-05-22 07:28:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":535053,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9304726/v1/3a393aff-187a-46ad-8dec-a1b5a49e025a.pdf"},{"id":109484762,"identity":"c3f293f3-56cb-47ba-90f0-779dfc252fe7","added_by":"auto","created_at":"2026-05-18 16:05:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14106,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEChecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-9304726/v1/445afd7f36f8172ead2e4da6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Self-Reported Prevalence and Factors Associated with Kidney Disease in Somaliland: A Secondary Analysis of the 2020 Demographic and Health Survey","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease (CKD) has emerged as a formidable global public health challenge, contributing substantially to escalating morbidity, mortality, and healthcare expenditures (Bikbov et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liyanage et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The global burden of CKD is disproportionately concentrated in low- and middle-income countries (LMICs), where restricted resources, inadequate healthcare infrastructure, and a rising prevalence of metabolic risk factors exacerbate the crisis (Foreman et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhang, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). While the global prevalence of CKD is estimated to range between 10% and 13%, significant regional variations persist (Chukwuonye et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hill et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Recent international evidence highlights an increasing prevalence of CKD across Africa, with estimates spanning from 5% to over 15% in diverse populations (Mareev et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tepel et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). These disparities are frequently attributed to socioeconomic inequities, limited healthcare access, and the high burden of traditional risk factors such as hypertension, diabetes, and infectious diseases (Bahrey et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bikbov et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn African contexts, the overall prevalence of CKD remains alarmingly high and correlates strongly with the presence of comorbidities like hypertension and diabetes (Kaze et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). While several studies have characterized the prevalence and determinants of CKD in sub-Saharan Africa (Kaze et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Warsame et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the epidemiology of kidney disease in the Horn of Africa\u0026mdash;particularly in Somaliland\u0026mdash;remains largely uncharacterized (Akwaboah et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As an autonomous region in northwestern Somalia, Somaliland faces multifaceted health challenges, including limited healthcare resources, fragile infrastructure, and pervasive poverty (Ahmed et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Canton, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, robust population-based data on non-communicable diseases (NCDs), including CKD, are critically scarce (McLeroy et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Characterizing the epidemiology of CKD in Somaliland is, therefore, essential for establishing public health priorities and designing evidence-based interventions.\u003c/p\u003e \u003cp\u003eThe current study is grounded in the socio-ecological model, which posits that health outcomes are shaped by a complex interplay of individual, interpersonal, organizational, community, and policy-level factors (Coresh et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). At the individual level, factors such as age, biological sex, and health behaviors significantly modulate CKD risk, while interpersonal and community-level determinants\u0026mdash;including social support systems and access to healthcare or clean water\u0026mdash;further drive renal health outcomes (Lemos et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)].\u003c/p\u003e \u003cp\u003eUtilizing data from the 2020 Somaliland Demographic and Health Survey (SLDHS), this study assesses the prevalence of kidney disease and its association with socio-demographic and economic risk factors (Foundation, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In this context, kidney disease is defined based on self-reported diagnosis (Corsi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Given the critical paucity of data, the 2020 SLDHS provides a unique opportunity to investigate this pressing public health issue (Rutstein \u0026amp; Rojas, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This study aims to determine the prevalence of kidney disease and identify independent risk factors among the Somaliland population, providing actionable insights for policymakers to develop integrated strategies for CKD prevention and management(Bikbov et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Data Source\u003c/h2\u003e \u003cp\u003eData for this analysis were sourced from the 2020 Somaliland Demographic and Health Survey (SLDHS), which represents the first nationally representative demographic and health survey conducted in Somaliland. Implemented by the Ministry of Planning and Development in collaboration with international partner organizations between 2018 and 2020 (Abdikarim et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Farih et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Data were accessed for research purposes on January 15, 2026 the SLDHS provides comprehensive data on fertility, maternal and child health, household demographics, water, sanitation, and hygiene (WASH) practices, as well as critical socioeconomic indicators (Mohamed et al., n.d.). To ensure the quality and transparency of the research, this study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies. To ensure the robustness and integrity of the findings, a systematic data validation and cleaning process was performed. This involved the exclusion of observations with missing or incomplete information regarding the primary outcome and key independent variables. Following the application of these inclusion criteria, the final analytical sample consisted of \u003cb\u003e18,930\u003c/b\u003e participants.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using R statistical software (version 4.3.2). To account for the complex survey design of the SLDHS 2020, including clustering and stratification, the survey package in R was utilized to apply sampling weights, ensuring that the findings are nationally representative. Descriptive statistics summarized the participants' socio-demographic and clinical characteristics. Bivariate associations were assessed using the Pearson Chi-square test. For multivariable analysis, a logistic regression model was employed to identify independent risk factors for self-reported kidney disease. Model selection was based on the lowest Akaike Information Criterion (AIC) and Bayesian Information Criteria (BIC) values. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of Kidney Disease\u003c/h2\u003e \u003cp\u003eAnalysis of the 2020 SLDHS dataset revealed an overall self-reported prevalence of kidney disease of 1.3% (95% Confidence Interval [CI]: 1.1\u0026ndash;1.5%) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Initial bivariate analyses demonstrated statistically significant associations between kidney disease and several socio-demographic and clinical characteristics, including geographic region, residence type, wealth quintile, age, biological sex of the household head, and comorbidities such as diabetes and hypertension.\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\u003ePrevalence of Kidney disease.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKidney disease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18,930\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 \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% Confidence interval\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.989\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\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultivariable Analysis and Model Selection\u003c/h3\u003e\n\u003cp\u003eA multivariable logistic regression model was utilized to identify independent predictors of kidney disease. Model selection was informed by a comparative performance analysis of three binary outcome models (Logistic, Probit, and Complementary Log-Log). The logistic model was selected for its superior fit, as evidenced by the lowest Akaike Information Criterion (AIC: 2384.98) and Bayesian Information Criterion (BIC: 2581.20) values.\u003c/p\u003e\n\u003ch3\u003eIndependent Risk Factors\u003c/h3\u003e\n\u003cp\u003eAdjusted multivariable analysis identified several independent risk factors for kidney disease. Specifically, female-headed households were associated with significantly higher odds of the outcome (aOR\u0026thinsp;=\u0026thinsp;1.386, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Geographic location remained a critical predictor; compared to the reference region (Awdal), significantly elevated odds were observed in Sool (aOR\u0026thinsp;=\u0026thinsp;2.899, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Marodijeh (aOR\u0026thinsp;=\u0026thinsp;1.795, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and Sahil (aOR\u0026thinsp;=\u0026thinsp;1.791, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAdvanced age exhibited a strong, progressive correlation with kidney disease risk, with the highest odds recorded among individuals aged 65 years and older (aOR\u0026thinsp;=\u0026thinsp;2.745, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) relative to the reference group (under 15 years). Clinical comorbidities emerged as the most potent predictors of the condition: hypertension was associated with nearly three-fold higher odds (aOR\u0026thinsp;=\u0026thinsp;2.974, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while pre-existing heart disease demonstrated the strongest association, resulting in an eight-fold increase in odds (aOR\u0026thinsp;=\u0026thinsp;8.364, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Conversely, a marginal inverse association was observed for individuals with no formal education, who showed lower odds of the outcome (aOR\u0026thinsp;=\u0026thinsp;0.750, p\u0026thinsp;\u0026lt;\u0026thinsp;0.1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic and health characteristics of the study population (N\u0026thinsp;=\u0026thinsp;18,930)\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAwdal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.23\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\u003eMarodijeh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.75\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\u003eSahil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.74\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\u003eTogdheer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.35\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\u003eSool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.17\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\u003eSanaag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.43\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\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.92\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\u003eNomadic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth Quantile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.22\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\u003eSecond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.40\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\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.90\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\u003eFourth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.45\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\u003eHighest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (Years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.41\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\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.77\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\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.99\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\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.19\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\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.94\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\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.68\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\u003eAbove 64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.55\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\u003eAbandoned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.74\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\u003eNever Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAttended School\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.41\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11,285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.61\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\u003eDon't know/Missing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUse of tobacco\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.52\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18,074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.41\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18,663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh Blood Pressure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.84\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18,014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeart Disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18,790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and socioeconomic characteristics of respondents\u003c/h2\u003e \u003cp\u003eDemographic and Socioeconomic Characteristics of Respondents\u003c/p\u003e \u003cp\u003eThe demographic and socioeconomic profiles of the study population (N\u0026thinsp;=\u0026thinsp;18,930) are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Geographically, the largest proportion of respondents resided in the Sanaag region (22.76%), followed by Sool (21.17%) and Togdheer (17.35%). Conversely, the regions of Awdal (14.23%), Marodijeh (12.75%), and Sahil (11.74%) represented smaller segments of the sample. In terms of residential distribution, urban residents constituted the largest group (37.43%), while rural and nomadic populations accounted for 30.92% and 31.65%, respectively.\u003c/p\u003e \u003cp\u003eSocioeconomic status, assessed via wealth quintiles, revealed that a significant plurality of participants (41.22%) belonged to the lowest wealth category. The remaining population was distributed across the second (10.40%), middle (12.90%), fourth (17.45%), and highest (18.04%) quintiles. The study population was notably young; the 15\u0026ndash;24 age bracket was the most prominent (31.77%), followed by those under the age of 15 (16.41%) and the 25\u0026ndash;34 age group (17.99%). Participants aged Above 64 comprised the smallest proportion (7.01%) of the sample. Regarding biological sex, females represented the majority of the study population at 56.98%.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMarital Status, Education, and Clinical Characteristics\u003c/h3\u003e\n\u003cp\u003eRegarding marital status, 45.70% of participants had never married, while 46.07% were married. Educational attainment was notably low, with more than half of the study population (59.61%) reported never having attended school. Clinical indicators showed that tobacco use was prevalent among 4.52% of respondents, while 1.41% reported a diagnosis of diabetes. Additionally, 4.84% of the sample had hypertension, and 0.74% were affected by heart disease (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAssociations Between Predictors and Kidney Disease (Chi-square Analysis)\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of Chi-square tests examining the associations between various socio-demographic factors and kidney disease. Statistically significant associations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed between kidney disease and the following variables: region (χ2\u0026thinsp;=\u0026thinsp;47.4081, df\u0026thinsp;=\u0026thinsp;5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), type of residence (χ2\u0026thinsp;=\u0026thinsp;14.0914, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;=\u0026thinsp;0.001), wealth quantile (χ2\u0026thinsp;=\u0026thinsp;19.7559, df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;=\u0026thinsp;0.001), age (χ2\u0026thinsp;=\u0026thinsp;86.1537, df\u0026thinsp;=\u0026thinsp;6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), sex (χ2\u0026thinsp;=\u0026thinsp;10.4348, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;0.001), marital status (χ2\u0026thinsp;=\u0026thinsp;66.8983, df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), diabetes (χ2\u0026thinsp;=\u0026thinsp;6.3977, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;0.011) and blood pressure (χ2\u0026thinsp;=\u0026thinsp;141.2454, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Geographically, the prevalence of kidney disease varied significantly, with the highest rate observed in Sool (2.20%) and the lowest in Sanaag (0.63%). Rural residents reported a higher prevalence (1.67%) compared to urban (1.26%) and nomadic (0.90%) participants. Furthermore, prevalence showed a strong age-related trend, increasing significantly with age and peaking at 3.24% among those aged over 64 years. Females exhibited a significantly higher prevalence (1.50%) than males (0.97%). Regarding marital status, widowed individuals demonstrated the highest prevalence of kidney disease (3.34%) compared to other groups. Finally, individuals with clinical comorbidities had significantly elevated rates of kidney disease: 3.00% among those with diabetes compared to 1.25% in non-diabetics, and 17.86% among those with hypertension compared to 1.05% in those with normal blood pressure.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate analysis of factors associated with kidney disease (N\u0026thinsp;=\u0026thinsp;18,930)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKidney Disease (No)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKidney Disease (Yes)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\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 \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwdal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,673 (99.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (0.78)\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\u003eMarodijeh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,380 (98.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (1.41)\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\u003eSahil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,191 (98.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (1.40)\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\u003eTogdheer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,244 (98.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40 (1.22)\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\u003eSool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,919 (97.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88 (2.20)\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\u003eSanaag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,282 (99.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (0.63)\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\u003eType of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e*\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\u003e6,996 (98.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89 (1.26)\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\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,756 (98.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98 (1.67)\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\u003eNomadic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,937 (99.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54 (0.90)\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\u003eWealth Quantile\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e*\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\u003e7,704 (98.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98 (1.26)\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\u003eSecond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,929 (98.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39 (1.98)\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\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,400 (98.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42 (1.72)\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\u003eFourth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,267 (98.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (1.09)\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\u003eHighest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,389 (99.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (0.76)\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\u003eAge (Years)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,090 (99.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (0.55)\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\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,966 (99.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48 (0.80)\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\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,367 (98.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39 (1.15)\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\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,274 (98.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (1.47)\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\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,661 (98.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32 (1.89)\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\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,047 (97.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (2.60)\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\u003eAbove 64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,284 (96.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43 (3.24)\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\u003eSex\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,064 (99.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79 (0.97)\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,625 (98.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162 (1.50)\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\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e*\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,579 (98.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142 (1.63)\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\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e477 (98.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (1.04)\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\u003eAbandoned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173 (97.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (2.81)\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\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e868 (96.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (3.34)\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\u003eNever Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,592 (99.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59 (0.68)\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\u003eAttended School\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.216\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\u003e7,376 (98.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84 (1.13)\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,132 (98.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e153 (1.36)\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\u003eDon't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e181 (97.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (2.16)\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\u003eUse of tobacco\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.333\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18,074 (98.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e227 (1.26)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e842 (98.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (1.64)\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\u003eDiabetes\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e*\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18,430 (98.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e233 (1.25)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259 (97.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (3.00)\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\u003eHigh Blood Pressure\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e141.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e*\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17,824 (98.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e190 (1.05)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,106 (82.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (17.86)\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\u003eHeart Disease\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e308.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e*\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18,574 (98.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e216 (1.15)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115 (82.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (17.86)\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Note:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eχ2\u0026thinsp;=\u0026thinsp;Chi-square, df\u0026thinsp;=\u0026thinsp;degrees of freedom. \u003cem\u003eStatistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/em\u003e\u003c/p\u003e \u003cp\u003eUse of tobacco and Attended School, however, does not show a significant association.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBinary Logistic Regression on dv with significantly associated predictors\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents unadjusted, or crude, odds ratios (CORs) examining the relationship between several variables and a given outcome, with the COR serving as an estimate of the relative odds of the outcome being present in each level relative to the reference group. While these unadjusted odds ratios provide initial insights, a multivariable analysis is necessary to refine these associations and account for interdependence among variables. These odds ratios are presented along with their standard errors, p-values, and 95% confidence intervals. Regarding regional differences, when compared to residents of Awdal, those living in Marodijeh (COR\u0026thinsp;=\u0026thinsp;1.82, p\u0026thinsp;=\u0026thinsp;0.032) and Sahil (COR\u0026thinsp;=\u0026thinsp;1.80, p\u0026thinsp;=\u0026thinsp;0.038) had statistically significantly higher odds. The odds of residents from Sool (COR\u0026thinsp;=\u0026thinsp;2.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also significantly higher. Those living in Togdheer and Sanaag did not have statistically significant differences in their odds when compared to Awdal residents. Looking at the type of residence, those in rural locations had statistically significantly higher odds (COR\u0026thinsp;=\u0026thinsp;1.34, p\u0026thinsp;=\u0026thinsp;0.048) than those in urban locations. Being a nomadic resident showed a non-significant trend towards decreased odds (COR\u0026thinsp;=\u0026thinsp;0.71, p\u0026thinsp;=\u0026thinsp;0.053). There was a strong trend regarding age, with each increasing age bracket having a higher odds than the reference group (those under 15). These age brackets reached statistical significance at 25\u0026ndash;34 (COR\u0026thinsp;=\u0026thinsp;2.11, p\u0026thinsp;=\u0026thinsp;0.011), 35\u0026ndash;44 (COR\u0026thinsp;=\u0026thinsp;2.72, p\u0026thinsp;=\u0026thinsp;0.001), 45\u0026ndash;54 (COR\u0026thinsp;=\u0026thinsp;3.50, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 55\u0026ndash;64 (COR\u0026thinsp;=\u0026thinsp;4.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and above 64 (COR\u0026thinsp;=\u0026thinsp;6.09, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).Looking at wealth, only the second and highest wealth quintiles were statistically significant. The second highest quintile showed increased odds (COR\u0026thinsp;=\u0026thinsp;1.59, p\u0026thinsp;=\u0026thinsp;0.015), while the highest wealth quintile showed decreased odds (COR\u0026thinsp;=\u0026thinsp;0.51, p\u0026thinsp;=\u0026thinsp;0.022).\u003c/p\u003e \u003cp\u003eFinally, having diabetes was associated with statistically significantly increased odds (COR\u0026thinsp;=\u0026thinsp;2.44, p\u0026thinsp;=\u0026thinsp;0.014), and the same was true for high blood pressure (COR\u0026thinsp;=\u0026thinsp;5.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). High blood pressure had the largest crude odds ratio of all the factors looked at, which may indicate a strong relationship between high blood pressure and the outcome of interest. It is important to keep in mind that this is an unadjusted analysis. Further research is needed that accounts for confounders that could impact these relationships.\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\u003eLogistic regression analysis showing Crude Odds Ratios (COR) for kidney disease (N\u0026thinsp;=\u0026thinsp;18,930)\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=\"left\" 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\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAwdal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\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\u003eMarodijeh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.05\u0026ndash;3.13)\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\u003eSahil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.03\u0026ndash;3.15)\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\u003eTogdheer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.92\u0026ndash;2.66)\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\u003eSool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.77\u0026ndash;4.59)\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\u003eSanaag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.45\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\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\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.048*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.00\u0026ndash;1.68)\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\u003eNomadic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.51\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (Years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\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\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.84\u0026ndash;2.55)\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\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.19\u0026ndash;3.73)\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\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.51\u0026ndash;4.88)\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\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.94\u0026ndash;6.33)\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\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.65\u0026ndash;8.92)\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\u003eAbove 64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(3.46\u0026ndash;10.71)\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 \u003cp\u003eLowest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\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\u003eSecond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.09\u0026ndash;2.31)\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\u003eMiddle\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\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.96\u0026ndash;1.98)\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\u003eFourth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.59\u0026ndash;1.27)\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\u003eHighest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.022*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.39\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.19\u0026ndash;5.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension (BP)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(4.03\u0026ndash;7.59)\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\u003e*Note: COR\u0026thinsp;=\u0026thinsp;Crude Odds Ratio; CI\u0026thinsp;=\u0026thinsp;Confidence Interval; Ref\u0026thinsp;=\u0026thinsp;Reference category. \u003cem\u003eStatistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/em\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents adjusted odds ratio (aOR) results reveal several significant associations. Compared to being male, being a female-headed household was associated with a significantly higher odds of the outcome (aOR\u0026thinsp;=\u0026thinsp;1.386, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\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\u003eMultivariable logistic Regression Analysis of factors associated with kidney disease (N\u0026thinsp;=\u0026thinsp;18,930)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex of Household Head\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.02\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAwdal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eMarodijeh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.03\u0026ndash;3.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSahil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.02\u0026ndash;3.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTogdheer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.98\u0026ndash;2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.78\u0026ndash;4.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSanaag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.48\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of Residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.92\u0026ndash;1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eNomadic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.56\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge Category\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.76\u0026ndash;2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.75\u0026ndash;3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.93\u0026ndash;4.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.08\u0026ndash;5.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.35\u0026ndash;6.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove 64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.22\u0026ndash;6.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAttended School\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.55\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDon't Know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.44\u0026ndash;3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical Factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.05\u0026ndash;4.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeart Disease (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5.04\u0026ndash;13.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetes (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.32\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNote:\u003c/h2\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;18,930 ; Pseudo R2\u0026thinsp;=\u0026thinsp;0.094; χ2\u0026thinsp;=\u0026thinsp;243.49 ,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; AIC\u0026thinsp;=\u0026thinsp;2388.73; BIC\u0026thinsp;=\u0026thinsp;2584.94. \u003cem\u003eAbbreviations: aOR, Adjusted Odds Ratio; CI, Confidence Interval; Ref, Reference category.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eSignificance levels: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation of Multivariable Findings (aOR)\u003c/h2\u003e \u003cp\u003eThe multivariable analysis, summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, identifies several significant independent predictors of kidney disease. Households headed by females exhibited significantly higher odds of the outcome compared to those headed by males (aOR\u0026thinsp;=\u0026thinsp;1.386, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Regional disparities were also pronounced; compared to the reference region of Awdal, significantly higher odds were observed in Sool (aOR\u0026thinsp;=\u0026thinsp;2.899, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Marodijeh (aOR\u0026thinsp;=\u0026thinsp;1.795, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and Sahil (aOR\u0026thinsp;=\u0026thinsp;1.791, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while Togdheer showed a marginal increase in odds (aOR\u0026thinsp;=\u0026thinsp;1.671, p\u0026thinsp;\u0026lt;\u0026thinsp;0.1).\u003c/p\u003e \u003cp\u003eAdvanced age emerged as a robust independent risk factor. Individuals in the 45\u0026ndash;54 (aOR\u0026thinsp;=\u0026thinsp;2.343, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), 55\u0026ndash;64 (aOR\u0026thinsp;=\u0026thinsp;3.022, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and over 64 (aOR\u0026thinsp;=\u0026thinsp;2.745, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) age brackets had significantly higher odds of kidney disease relative to those under 15 years. Furthermore, clinical comorbidities were the strongest predictors in the model: hypertension was associated with nearly three-fold higher odds (aOR\u0026thinsp;=\u0026thinsp;2.974, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while heart disease exhibited the most potent association, with an eight-fold increase in odds (aOR\u0026thinsp;=\u0026thinsp;8.364, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Conversely, a marginal inverse association was noted for individuals with no formal education, who showed lower odds of the outcome compared to those who attended school (aOR\u0026thinsp;=\u0026thinsp;0.750, p\u0026thinsp;\u0026lt;\u0026thinsp;0.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eModel Comparison (Logistic, Probit, and Clog-Log)\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents a comparative performance analysis of three binary outcome models: Logistic Regression, logistic, and Complementary Log-Log (Clog-log). The selection of the optimal model was based on the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC).\u003c/p\u003e \u003cp\u003eLower values for these information criteria indicate a superior model that optimally balances goodness-of-fit with predictive parsimony. As demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the \u003cb\u003elogistic model\u003c/b\u003e exhibited the lowest values for both AIC (2384.98) and BIC (2581.20) compared to the Logistic Regression and Complementary Log-Log models. Consequently, the logistic model was selected as the most appropriate and efficient representation of the data-generating process for this study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Comparison\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogistic Regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2388.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2584.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2384.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2581.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplementary Log-Log\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2389.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2585.527\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\u003eThese information criteria provide a means to assess the relative performance of each model, balancing goodness-of-fit with model complexity. Lower AIC and BIC values indicate a more desirable model, reflecting a superior trade-off between predictive accuracy and parsimony. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the logistic model exhibits the lowest AIC (2384.983) and BIC (2581.196) values in comparison to the Logistic Regression (AIC\u0026thinsp;=\u0026thinsp;2388.731, BIC\u0026thinsp;=\u0026thinsp;2584.943) and Complementary Log-Log (AIC\u0026thinsp;=\u0026thinsp;2389.314, BIC\u0026thinsp;=\u0026thinsp;2585.527) models. Therefore, based on these information criteria, the logistic model is the preferred choice, suggesting a more efficient and accurate representation of the data-generating process relative to the alternatives considered. This selection warrants careful consideration of the theoretical underpinnings of each model and their appropriateness for the specific research question and dataset.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study, utilizing the 2020 Somaliland Demographic and Health Survey (SLDHS) data, provides the inaugural population-based estimate for the prevalence of kidney disease in Somaliland. Our findings reveal a self-reported prevalence of 1.3%, a figure that provides a critical baseline for the region. While this prevalence is lower than the global estimate of 10\u0026ndash;13% (Chukwuonye et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hill et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and some African estimates ranging between 5% and 15% [(Mareev et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tepel et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), it likely reflects significant under-diagnosis due to limited routine screening, low health literacy, and the reliance on self-reported data in the SLDHS. The prevalence of 1.3% observed in this study is significantly lower than the global average of 10\u0026ndash;13%. This discrepancy likely stems from the self-reported nature of the data. Since chronic kidney disease is often asymptomatic in its early stages, many individuals in Somaliland may remain undiagnosed due to limited routine screening and low health literacy. Therefore, this figure should be interpreted as the \u0026apos;recognized\u0026apos; prevalence rather than the actual biological burden of the disease in the population\u003c/p\u003e\n\u003cp\u003eBivariate analyses identified significant associations between kidney disease and several socio-demographic factors, including geographic region, residence type, wealth quintile, age, biological sex, and marital status, alongside clinical comorbidities such as diabetes and hypertension. Multivariable analysis further refined these associations, identifying female-headed households, specific geographic regions (Marodijeh, Sahil, Togdheer, and Sool), advanced age, hypertension, and heart disease as independent risk factors for the condition.\u003c/p\u003e\n\u003cp\u003eThe observed regional variations suggest that community-level determinants\u0026mdash;such as environmental exposures, water quality, or disparities in healthcare infrastructure\u0026mdash;significantly influence kidney disease risk. These findings align with existing literature highlighting the profound impact of geographic location on epidemiological trends (Bikbov et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The high prevalence in the Sool region, in particular, warrants further qualitative and environmental research to understand the localized drivers of renal health. Furthermore, consistent with global trends (Cockwell \u0026amp; Fisher, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), advancing age emerged as a robust risk factor, necessitating the implementation of targeted screening programs for older adults within the primary healthcare system.\u003c/p\u003e\n\u003cp\u003eThe association between female-headed households and higher kidney disease odds underscores a potential intersection of gender-specific vulnerabilities and socioeconomic stressors. Additionally, clinical comorbidities were the most potent predictors of kidney disease in this population. Hypertension was associated with nearly a three-fold increase in odds, while heart disease exhibited the strongest association, with an eight-fold increase. These findings emphasize the urgent need for integrated management strategies for non-communicable diseases (NCDs), as comorbidities often cluster within individuals, compounding the complexity of care (Bigna \u0026amp; Noubiap, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Methodologically, the logistic model was selected for its superior fit based on AIC and BIC values, ensuring that our statistical inferences are both robust and parsimonious.\u003c/p\u003e"},{"header":"Conclusion and Policy Implications","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003cp\u003eIn conclusion, this study, based on a nationally representative sample from the 2020 SLDHS, reveals that kidney disease is a significant public health concern in Somaliland, shaped by a complex interplay of geographic, demographic, and clinical factors. The prevalence of 1.3% likely represents the \"tip of the iceberg,\" highlighting an urgent need for strengthened diagnostic capabilities and public health awareness. Our findings emphasize the importance of addressing regional disparities, the challenges of an aging population, and gendered vulnerabilities in kidney disease prevention and management.\u003c/p\u003e \u003cp\u003eBased on these findings, several policy-level interventions are recommended:\u003c/p\u003e \u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e Targeted Regional Interventions: Public health efforts should prioritize high-prevalence regions such as Sool, Marodijeh, Sahil, and Togdheer. This includes establishing specialized renal clinics and improving access to clean water and diagnostic services.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIntegrated NCD Management: Given the strong association between kidney disease, hypertension, and heart disease, the Somaliland Ministry of Health should implement integrated screening and management protocols. Managing these conditions simultaneously can reduce the progression to end-stage renal disease (ESRD).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eStrengthening Healthcare Infrastructure: There is a critical need to enhance rural healthcare infrastructure. Mobile screening units and community health worker training can bridge the gap for nomadic and rural populations who face higher barriers to care.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eGender-Sensitive Healthcare: Healthcare delivery must address the specific socioeconomic and health literacy barriers faced by female-headed households to ensure equitable access to kidney disease prevention and treatment.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e Evidence-Based Policy and Research: Continued data collection through future SLDHS cycles and targeted clinical research into the underlying causes of regional clusters are necessary. Further investigation is also required to explore the marginal association between education levels and disease reporting.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e \u003cp\u003eUltimately, these findings provide a critical evidence base for policymakers to design multi-faceted interventions. By addressing socioeconomic disparities and integrating comorbidity management, Somaliland can achieve sustainable improvements in kidney health and reduce the overall burden of non-communicable diseases.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The 2020 Somaliland Demographic and Health Survey (SLDHS) was reviewed and approved by the Internal Review Board (IRB) of the Somaliland Ministry of Planning and National Development and the Ministry of Health Development. Informed consent was obtained from all individual participants included in the original survey. For this secondary analysis, the dataset was fully anonymized before access, and further ethical approval was not required as per the guidelines for secondary data analysis.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors received no specific funding for this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHJ conceived the study, performed the statistical analysis, and drafted the original manuscript. The author has read and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe author thanks the Somaliland Ministry of Planning and National Development for providing access to the SLDHS 2020 data.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used in this study are third-party data belonging to the Somaliland Ministry of Planning and National Development. The data are available upon reasonable request from the Central Statistics Department (CSD) via their official website ( [https://mopnd.govsomaliland.org/](https:/www.google.com/url?sa=E\u0026amp;q=https%3A%2F%2Fmopnd.govsomaliland.org%2F) ) or by contacting [[email protected]](https:/www.google.com/url?sa=E\u0026amp;q=mailto%3Ainfo%40somalilandcsd.org) .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdikarim H, Muse AH, Hassan MA, Muse YH. 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Lancet. 2020;395(10225):709\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(20)30045-3\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(20)30045-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCanton H. The Europa directory of international organizations 2021. (No Title; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChukwuonye II, Ogah OS, Anyabolu EN, Ohagwu KA, Nwabuko OC, Onwuchekwa U, Chukwuonye ME, Obi EC, Oviasu E. (2018). Prevalence of chronic kidney disease in Nigeria: systematic review of population-based studies. Int J Nephrol Renovascular Disease, 165\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCockwell P, Fisher L-A. The global burden of chronic kidney disease. Lancet. 2020;395(10225):662\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoresh J, Selvin E, Stevens LA, Manzi J, Kusek JW, Eggers P, Van Lente F, Levey AS. Prevalence of chronic kidney disease in the United States. JAMA. 2007;298(17):2038\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorsi DJ, Neuman M, Finlay JE, Subramanian SV. Demographic and health surveys: a profile. Int J Epidemiol. 2012;41(6):1602\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarih OA, Ali AO, Abokor AH, Ali MA, Egge AAA, Muse AH. (2024). Prevalance and determinants of hypertension among adults in Somalia using Somalia demographic health survey data, SDHS 2020. Curr Probl Cardiol, 102783.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForeman KJ, Marquez N, Dolgert A, Fukutaki K, Fullman N, McGaughey M, Pletcher MA, Smith AE, Tang K, Yuan C-W. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016\u0026ndash;40 for 195 countries and territories. Lancet. 2018;392(10159):2052\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoundation NK. KDOQI clinical practice guideline for diabetes and CKD: 2012 update. Am J Kidney Dis. 2012;60(5):850\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHill NR, Fatoba ST, Oke JL, Hirst JA, O\u0026rsquo;Callaghan CA, Lasserson DS, Hobbs FDR. Global prevalence of chronic kidney disease\u0026ndash;a systematic review and meta-analysis. PLoS ONE. 2016;11(7):e0158765.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaze FF, Meto DT, Halle M-P, Ngogang J, Kengne A-P. Prevalence and determinants of chronic kidney disease in rural and urban Cameroonians: a cross-sectional study. BMC Nephrol. 2015;16(1):117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLemos CF, Rodrigues MP, Veiga JRP. Family income is associated with quality of life in patients with chronic kidney disease in the pre-dialysis phase: a cross sectional study. Health Qual Life Outcomes. 2015;13:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiyanage T, Ninomiya T, Jha V, Neal B, Patrice HM, Okpechi I, Zhao M, Lv J, Garg AX, Knight J. Worldwide access to treatment for end-stage kidney disease: a systematic review. Lancet. 2015;385(9981):1975\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMareev VY, Fomin IV, Ageev FT, Begrambekova YL, Vasyuk YA, Garganeeva AA, Gendlin GE, Glezer MG, Gautier SV, Dovzhenko TV. Russian heart failure society, Russian society of cardiology. Russian scientific medical society of internal medicine guidelines for heart failure: chronic (CHF) and acute decompensated (ADHF). Diagnosis, prevention and treatment. Kardiologiia. 2018;58(6S):8\u0026ndash;158.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcLeroy KR, Bibeau D, Steckler A, Glanz K. An ecological perspective on health promotion programs. Health Educ Q. 1988;15(4):351\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohamed FI, Dahir HM, Korse AH. (n.d.). Socioeconomic Determinants and Inequalities in the Prevalence of Non-Communicable Disease in Somaliland: Data from DHS2020. \u003cem\u003eAvailable at SSRN 5569491\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRutstein SO, Rojas G. Guide to DHS statistics. Calverton MD: ORC Macro. 2006;38:78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTepel M, van der Giet M, Statz M, Jankowski J, Zidek W. The antioxidant acetylcysteine reduces cardiovascular events in patients with end-stage renal failure: a randomized, controlled trial. Circulation. 2003;107(7):992\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarsame A, Handuleh J, Patel P. Prioritization in Somali health system strengthening: a qualitative study. Int Health. 2016;8(3):204\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang GP. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 2003;50:159\u0026ndash;75.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9304726/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9304726/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eChronic kidney disease (CKD) is a critical global health priority, yet its burden remains largely uncharacterized in the Horn of Africa. This study provides the inaugural population-based assessment of kidney disease prevalence and its socio-demographic and clinical determinants in Somaliland.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a secondary analysis of a nationally representative, cross-sectional sample from the 2020 Somaliland Demographic and Health Survey (SLDHS) (N\u0026thinsp;=\u0026thinsp;18,930). To ensure the quality and transparency of the research, this study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies. Multivariable logistic regression models\u0026mdash;selected after comparative performance analysis with Probit and Complementary Log-Log models using Akaike and Bayesian Information Criteria (AIC/BIC)\u0026mdash;were utilized to identify independent risk factors associated with self-reported kidney disease.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe overall self-reported prevalence of kidney disease was 1.3% (95% CI: 1.1\u0026ndash;1.5%). Multivariable analysis revealed that female-headed households had significantly higher odds of the condition (aOR\u0026thinsp;=\u0026thinsp;1.386, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Pronounced regional disparities were observed, with the highest risk recorded in Sool (aOR\u0026thinsp;=\u0026thinsp;2.899, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Advanced age and clinical comorbidities emerged as the most potent predictors: hypertension (aOR\u0026thinsp;=\u0026thinsp;2.974, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and heart disease (aOR\u0026thinsp;=\u0026thinsp;8.364, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were associated with substantially elevated odds. Conversely, having no formal schooling showed a marginal inverse association with the outcome (aOR\u0026thinsp;=\u0026thinsp;0.750, p\u0026thinsp;\u0026lt;\u0026thinsp;0.1).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eKidney disease in Somaliland is shaped by a complex interplay of regional disparities, gender-specific vulnerabilities, and cardiovascular comorbidities. These findings underscore an urgent need for integrated non-communicable disease (NCD) management strategies and targeted screening programs, particularly in high-prevalence regions. Strengthening diagnostic capabilities and rural healthcare infrastructure is essential for effective prevention and early intervention.\u003c/p\u003e","manuscriptTitle":"Self-Reported Prevalence and Factors Associated with Kidney Disease in Somaliland: A Secondary Analysis of the 2020 Demographic and Health Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 16:05:37","doi":"10.21203/rs.3.rs-9304726/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"222809236016675364671041276861297808513","date":"2026-05-14T15:45:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-07T14:35:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-08T16:50:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T22:14:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-07T22:13:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-04-02T14:35:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e932cd8-4364-4af6-b310-a542c0f5d172","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"222809236016675364671041276861297808513","date":"2026-05-14T15:45:54+00:00","index":64,"fulltext":""},{"type":"reviewersInvited","content":"30","date":"2026-05-07T14:35:18+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T16:05:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 16:05:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9304726","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9304726","identity":"rs-9304726","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}