{"paper_id":"2ece8d17-cca6-4d96-9663-d29b4e9f7b93","body_text":"Geographical Variations and Associated Factors of Timely Vaccination Status Among Children in Somalia: An Application of Spatial and Multilevel Analysis | 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 Geographical Variations and Associated Factors of Timely Vaccination Status Among Children in Somalia: An Application of Spatial and Multilevel Analysis Abdisalan Ahmed Osman, Abdisalam Amin Esse, Faiza Mahamoud Saed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8444974/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Vaccinating children is an important public health measure. Coverage in Somalia, however, remains uneven. To plan targeted treatments, it is essential to understand both the factors that influence vaccination and the location where it occurs. Methods This study analyzed data from the Somali Health and Demographic Survey (SHDS 2020) and employed multilevel logistic regression to examine individual and community-level factors influencing children's vaccination status. We used AIC, BIC, log-likelihood, and ICC values to compare the models' fit. Spatial clustering was assessed using Global Moran’s I, Local Moran’s I, and Getis-Ord Gi* statistics. Results Among 5,732 children, only 32.5% were fully vaccinated. The lowest coverage was observed among those under one year of age (24.1%). Maternal healthcare utilization was strongly associated with vaccination: children whose mothers had four or more ANC visits had more than three times the odds of being vaccinated (AOR = 3.25, 95% CI: 2.61–4.05) compared to those with no visits. Higher maternal education and urban residence were significant predictors, whereas children from nomadic households exhibited markedly lower odds (AOR = 4.29, 95% CI: 3.21–5.75). Spatial analysis revealed significant clustering (Global Moran’s I = 0.312, p < 0.01), with “hot spots” of high vaccination in Jubbada Hoose, Gedo, Bay, and Bakool, and “cold spots” in Sanaag, Togdheer, and Woqooyi Galbeed. Conclusion Childhood vaccination rates in Somalia remain low and are spatially clustered. Strengthening maternal health services, addressing nomadic populations, and prioritizing northern regions with persistently low immunization coverage are essential to achieving nationwide equity. Childhood vaccination Immunization coverage Multilevel logistic regression Spatial analysis Global Moran’s I Getis-Ord Gi* Somalia Health disparities Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Contributions to the Literature This study shows where childhood vaccination rates in Somalia are highest and lowest, helping identify areas most in need of support. It combines spatial mapping and multilevel analysis, offering a clearer understanding of how place, community factors, and maternal health behaviors influence vaccination. The finding highlights challenges faced by nomadic populations and northern regions, adding evidence to support targeted public health programs. The study provides timely information that can guide the Ministry of Health of Somalia in designing equitable and region-specific vaccination strategies. 1. Background Global vaccination status studies reveal significant disparities and implementation challenges across different contexts. School-based vaccination status checking is widely recommended to strengthen routine childhood immunization coverage, although limited information exists about the prevalence of global policies and implementation practices (1). Despite remarkable progress with over 1 billion children vaccinated globally in the past decade and 125 UN member states achieving ≥ 90% DTP3 coverage, over 20 million children under one year remain unvaccinated, with persistent inequalities particularly affecting Sub-Saharan Africa and India (2). In children under 5, methods combining recall, home-based records, facility records, and serology yielded sensitivity above 80% in 81% of cases and a positive predictive value of 94%, though specificity sometimes dropped to 38% (3). DTP3 coverage has remained stagnant at approximately 83–84% from 2008 to 2013, highlighting persistent inequalities. Incomplete vaccination is strongly associated with poor socioeconomic status, lower education levels, non-use of maternal-child health services, residence in conflict-affected areas, missed immunization opportunities, and cancelled vaccination sessions. The Global Vaccine Action Plan 2011–2020 provides a framework emphasizing country ownership, shared responsibility, equity, integration, sustainability, and innovation to strengthen immunization systems (4). Vaccination coverage among children in sub-Saharan Africa remains suboptimal, with significant inequalities and spatial clustering. Studies show that 16.5% of children aged 12–59 months have received zero doses (5). An analysis of 358,949 children across 35 countries identified Rwanda, Burundi, and The Gambia as having the highest coverage rates (6). Spatial analysis reveals 477 clusters of low vaccination coverage, often in border areas and countries with otherwise high national rates (7). Timely vaccination coverage remains critically low across sub-Saharan Africa, with significant inequalities persisting despite overall improvements in vaccination rates. Analysis of 153,632 children from 40 countries revealed a median on-time vaccination coverage of less than 50% in all four sub-regions. Substantial disparities exist by household wealth, maternal education, and place of residence, with wealth-related inequities ranging from 22.6 to 30.6 percentage points across regions (8). Vaccination delays are prevalent, affecting 25.9% of children for BCG, 49.1% for pentavalent third dose, and 63.9% for measles vaccines, with delays significantly associated with incomplete immunization schedules (9). While overall full vaccination coverage reached 56.5% across 25 countries, pro-rich inequality was observed in 23 countries. Countries with lower vaccination coverage demonstrated higher inequalities, with unvaccinated children disproportionately concentrated among disadvantaged populations (10). Vaccination coverage among children in Somalia remains critically low, with substantial disparities across regions and populations. National estimates indicate that only 20% of children aged 12–23 months achieve complete immunization coverage (11), while 9.14% of children under the age of five receive no vaccinations at all. Regional variations are also evident, with Somaliland reporting that 54% of children have received at least one vaccination (12), compared to Mogadishu, where only 34% of children are fully vaccinated (13). These highlight significant inequities in immunization access and utilization across the country. The determinants of vaccination status include maternal education level, with mothers who are literate being more likely to vaccinate their children. Antenatal care visits emerged as a crucial factor, with children whose mothers received ANC being significantly more likely to be vaccinated. Household income and wealth status have a positive influence on vaccination rates (14,15). Geographic disparities were evident, with nomadic populations having lower coverage than urban and rural residents. Nationally, 9.14% of children received no vaccinations, and significant regional variations were observed (16). Research examining the timeliness of childhood vaccination in relation to the Sustainable Development Goals (SDGs) reveals mixed outcomes and interconnected challenges. In Nigeria, measles vaccination coverage initially accelerated following SDG implementation but subsequently declined significantly, with rates dropping from 76% in 2014 to 49% in 2019, suggesting unsustainable progress (17). Studies across Nepal, Senegal, and Zambia demonstrate that vaccination status is strongly associated with other SDG indicators. Mothers of fully vaccinated children show 14–30% higher healthcare facility delivery rates, greater contraceptive knowledge, and 10–22% higher literacy rates compared to mothers of under-immunized children (18,19). In Pakistan, only 20.8% of children received all vaccinations on schedule, with older child age and institutional delivery associated with decreased vaccination timeliness (20). Despite global progress in improving childhood immunization, Somalia continues to face substantial challenges in achieving timely vaccination coverage due to prolonged conflict, weak health systems, poverty, and regional disparities. Delays or missed vaccinations expose children to preventable diseases, contributing to the country’s persistently high child morbidity and mortality rates. Furthermore, there is limited evidence on how individual, household, and community-level factors influence timely vaccination uptake across different regions of Somalia. The study aims to examine the spatial variations and associated determinants of timely vaccination among children in Somalia, thereby providing evidence to guide targeted interventions and inform policy for improving immunization coverage and equity. 2. Method 2.1 Study Area The study analyzed data from both rural and urban parts of Somalia, focusing on regions where complete and reliable information was available from the Somalia Demographic and Health Survey SHDS 2020 dataset. Due to security issues during the original survey, the Lower Shebelle and Middle Juba regions were not included in the SHDS 2020 data collection and are thus excluded from this study (21) 2.2 Study Design and Setting This study will employ a community-based cross-sectional study design. The data for this research will be sourced from the most recent SDHS. Somalia, situated in the Horn of Africa, is characterized by a diverse geographical and socioeconomic landscape, comprising urban centers, rural communities, and nomadic pastoralist populations. The SDHS utilizes a multi-stage cluster sampling approach to ensure national representativeness (22). The study population will comprise mothers aged 15–49 years who have children aged 12–35 months living within the selected enumeration areas, ensuring that the children are within the age range for which timely vaccination status can be assessed according to WHO guidelines. 2.3 Variables of the Study 2.3.1 Outcome Variable The dependent variable in this study was whether the child had ever received any form of vaccination, which was coded as a binary variable. Children who had received at least one vaccination were coded as “Yes” (1 = ever vaccinated), while those who had not received any vaccination were coded as “No” (0 = never vaccinated). 2.3.2 Explanatory Variables The study considered both individual- and community-level characteristics as explanatory variables associated with timely vaccination status among children in Somalia. Individual-level factors: These included the mother’s age in 5-year groups, highest educational level (no education, primary, secondary, higher), household wealth quintile (Lowest, Second, Middle, Fourth, Highest), sex of the child (male or female), current age of the child in single years, number of antenatal care (ANC) visits, size of the child at birth (Very Large, Larger Than Average, Average, Smaller Than Average, Very Small, Don't Know), and mother’s media exposure (No exposure to mass media, Exposure to mass media). Community-level factors: These included the regional and type of place of residence (urban or rural). 2.4 Statistical Analysis Method Data were analyzed using appropriate statistical techniques to examine the spatial variations and associated factors of timely vaccination status among children in Somalia. Descriptive statistics were first employed to summarize the characteristics of the study population. Bivariate analysis with the chi-square test was conducted to assess the association between each explanatory variable and vaccination status. To account for the hierarchical structure of the data, a multilevel logistic regression model was applied, incorporating both individual-level and community-level factors. The fixed effects were used to estimate the strength of association between explanatory variables and timely vaccination, while the random effects captured variations across communities. Measures such as the Intraclass Correlation Coefficient (ICC), adjusted Odds Ratio (AOR), and Proportional Change in Variance (PCV) were computed to assess the extent of community-level variation. Results were presented using adjusted odds ratios (AOR) with 95% confidence intervals (CI), and statistical significance was declared at p < 0.05 (23). Spatial analysis was conducted to explore geographic variations in timely vaccination coverage. Spatial autocorrelation was assessed using Global Moran’s I to detect overall clustering patterns, while Local Moran’s I (LISA) and Getis-Ord Gi* statistics were applied to identify regional hotspots and cold spots of vaccination status. All statistical and spatial analyses were performed using R statistical software (version 4.5.1), with the aid of specialized packages such as sf, tmap, and other geospatial libraries. Multilevel Logistic Regression Model To account for the hierarchical structure of the data, where children are nested within communities, a two-level multilevel logistic regression model was employed. The model estimated the likelihood of a child having timely vaccination, incorporating both individual-level factors (Level 1) and community-level factors (Level 2). The intercept was allowed to vary across communities to capture unobserved heterogeneity, while fixed effects were used to estimate the associations between explanatory variables and vaccination status. Random effects quantified the variation between communities, and measures such as the Intraclass Correlation Coefficient (ICC) and Adjusted Odds Ratio (AOR) were calculated to assess the extent of clustering at the community level. The model can be expressed as: \\(\\:\\text{Logit}\\left({Y}_{ij}\\right)={\\beta\\:}_{0j}+\\sum\\:\\beta\\:{X}_{i}+\\gamma\\:{Z}_{j}+{\\epsilon\\:}_{ij},\\) where \\(\\:{\\beta\\:}_{0j}={\\beta\\:}_{0}+{\\mu\\:}_{j},\\hspace{1em}{\\mu\\:}_{j}\\sim\\:N\\left(0,{\\sigma\\:}_{u}^{2}\\right),\\) and \\(\\:{\\epsilon\\:}_{ij}\\sim\\:N\\left(0,{\\sigma\\:}_{\\epsilon\\:}^{2}\\right)\\) , representing residual variation at the individual level (24). Spatial Autocorrelation Spatial autocorrelation was assessed to determine whether timely vaccination coverage among children in Somalia was randomly distributed or exhibited clustering patterns across geographic locations. Global Moran’s I statistic was used to measure the overall spatial dependence in vaccination coverage (25), with positive values indicating clustering of similar values and negative values indicating dispersion. Hot Spot Analysis (Getis-Ord Gi* Statistic) Hot spot analysis was performed to identify specific areas with significantly high or low coverage of timely vaccination. The Getis-Ord Gi* statistic was applied to detect local clusters (hot spots and cold spots), allowing for visualization of regions where timely vaccination rates were unusually high or low compared to neighboring areas (26). 3. Results Socio-demographic Characteristics A total of 5,732 children aged 0–59 months were included in this study. Table 1 summarizes the socio-demographic characteristics and their association with the timely completion of vaccination status among children in Somalia. Maternal age was not significantly associated with vaccination status (p = 0.161), although the highest proportion of vaccinated children was observed among mothers aged 30–34 years (35.2%). Regional differences were statistically significant (p < 0.001), with the highest vaccination coverage in Banadir (83.3%), Bay (46.3%), and Woqooyi Galbeed (46.0%). In contrast, the lowest coverage was reported in Lower Juba (15.0%) and Hiraan (19.8%). Place of residence showed a significant association (p < 0.001); coverage was higher in urban areas (50.0%) compared to rural (36.6%) and nomadic settings (12.6%). Maternal education was significantly associated with vaccination status (p < 0.001), increasing from 29.1% among mothers with no education to 68.0% among those with higher education. The household wealth quintile also exhibited a significant relationship (p < 0.001), with vaccination coverage ranging from 18.7% in the poorest households to 53.9% in the wealthiest families. Media exposure was marginally significant (p = 0.039), with children of mothers exposed to mass media showing slightly higher vaccination coverage (33.2%) compared to those without exposure (30.2%). Table 1 Descriptive Statistics and Chi-square of Timely Complete Vaccination Status Among Children in Somalia Variables Frequency Percentage (%) Ever had a Vaccination Chi-square P-values Yes (%) No (%) Age in 5-year groups 15–19 398 6.94% 117(29.40) 281(70.60) 9.2281 0.161 20–24 1282 22.37% 432(33.70) 850(66.30) 25–29 1646 28.72% 522(31.71) 1124(68.29) 30–34 1146 19.99% 403(35.17) 743(64.83) 35–39 889 15.51% 272(30.60) 617(69.40) 40–44 294 5.13% 90(30.61) 204(69.39) 45–49 77 1.34% 28(36.36) 49(63.64) Region Awdal 116 2.02% 22(18.97) 94(81.03) 529.2073 0.001 Woqooyi Galbeed 259 4.52% 119(45.95) 140(54.05) Togdheer 389 6.79% 148(38.05) 241(61.95) Sool 569 9.93% 162(28.47) 407(71.53) Sanaag 589 10.28% 205(34.80) 384(65.20) Bari 275 4.80% 69(25.09) 206(74.91) Nugaal 326 5.69% 85(26.07) 241(73.93) Mudug 384 6.70% 91(23.70) 293(76.30) Galgaduud 410 7.15% 125(30.49) 285(69.51) Hiraan 449 7.83% 89(19.82) 360(80.18) Middle Shabelle 358 6.25% 103(28.77) 255(71.23) Banadir 288 5.02% 240(83.33) 48(16.67) Bay 160 2.79% 74(46.25) 86(53.75) Bakool 373 6.51% 153(41.02) 220(58.98) Gedo 361 6.30% 115(31.86) 246(68.14) Lower Juba 426 7.43% 64(15.02) 362(84.98) Type of place of residence Urban 2029 35.40% 1014(49.98) 1015(50.02) 674.3417 0.001 Rural 1591 27.76% 583(36.64) 1008(63.36) Nomadic 2112 36.85% 267(12.64) 1845(87.36) Highest educational level No Education 4878 85.10% 1417(29.05) 3461(70.95) 192.3188 0.001 Primary 642 11.20% 316(49.22) 326(50.78) Secondary 162 2.83% 97(59.88) 65(40.12) Higher 50 0.87% 34(68.00) 16(32.00) Wealth quintile Lowest 1431 24.97% 268(18.73) 1163(81.27) 440.9483 0.001 Second 1418 24.74% 309(21.79) 1109(78.21) Middle 1113 19.42% 428(38.45) 685(61.55) Fourth 998 17.41% 443(44.39) 555(55.61) Highest 772 13.47% 416(53.89) 356(46.11) Media Exposure No exposure to mass media 4363 76.12% 1450(33.23) 2913(66.77) 4.2537 0.039 Exposure to mass media 1369 23.88% 414(30.24) 955(69.76) Obstetrics and Other Health Services Factors Table 2 presents obstetric and other health service-related characteristics in relation to the timely completion of vaccination status among children in Somalia. The sex of the child was not significantly associated with vaccination status (p = 0.870), with similar coverage observed among males (32.4%) and females (32.6%). The current age of the child showed a significant association (p < 0.001), with vaccination coverage ranging from 24.1% at age 0 to 37.1% at age 3, before declining slightly to 31.6% at age 4 and 30.2% at age 5. The number of antenatal care (ANC) visits was strongly associated with vaccination status (p < 0.001). Coverage was lowest among children of mothers with no ANC visits (21.9%) and increased progressively with the number of visits, reaching 61.1% among those whose mothers had four or more ANC visits. The size of the child at birth was also significantly associated with vaccination status (p < 0.001). The highest coverage was reported among children classified as larger than average (38.6%) and huge (36.4%), while the lowest was observed among those reported as “don’t know” (24.3%) and very small (30.5%). Table 2 Descriptive Statistics and Chi-square of Obstetrics and Other Health Services Timely Complete Vaccination Status Among Children in Somalia Variables Frequency Percentage (%) Ever had a Vaccination Chi-square P-values Yes (%) No (%) Sex of child Male 3001 52.36% 973(32.42) 2028(67.58) 0.0268 0.870 Female 2731 47.64% 891(32.63) 1840(67.37) Current age in single years 0 390 6.80% 94(24.10) 296(75.90) 33.1915 0.001 1 392 6.84% 142(36.22) 250(63.78) 2 685 11.95% 248(36.20) 437(63.80) 3 1001 17.46% 371(37.06) 630(62.94) 4 1673 29.19% 528(31.56) 1145(68.44) 5 1591 27.76% 481(30.23) 1110(69.77) Number of ANC visits None 3928 68.53% 860(21.89) 3068(78.11) 661.1796 0.000 1 353 6.16% 164(46.46) 189(53.54) 2–3 1089 19.00% 619(56.84) 470(43.16) 4+ 362 6.32% 221(61.05) 141(38.95) Size of child at birth Very Large 297 5.18% 108(36.36) 189(63.64) 39.0439 0.001 Larger Than Average 254 4.43% 98(38.58) 156(61.42) Average 3515 61.32% 1187(33.77) 2328(66.23) Smaller Than Average 358 6.25% 128(35.75) 230(64.25) Very Small 410 7.15% 125(30.49) 285(69.51) Don't Know 898 15.67% 218(24.28) 680(75.72) Table 3 presents the model fit statistics for the multilevel logistic regression models assessing timely complete vaccination among children in Somalia. The null model (Model 1) showed the poorest fit, with the highest AIC (6658.470) and BIC (6671.778), and an intraclass correlation coefficient (ICC) of 0.412, indicating substantial variation attributable to clustering at the community level. The inclusion of community-level factors (Model 2) substantially improved the model fit, reducing the AIC to 6094.618 and the BIC to 6221.040. At the same time, the ICC dropped to 0.065, suggesting that much of the between-community variance was explained by community-level variables. The individual-level model (Model 3) also improved fit compared to the null model (AIC = 6254.617, BIC = 6454.232) but retained a higher ICC (0.159) than the community-level model, indicating residual clustering. The complete model (Model 4), which incorporates both individual- and community-level factors, provided the best fit, with the lowest AIC (5891.113) and BIC (6203.843), and the smallest ICC (0.046). This suggests that the combined model accounts for the majority of the between-community variance in vaccination status. Table 3 Model Fit Comparison Table Parameters Model 1 (Null) Model 2 (Community) Model 3 (Individual) Model 4 (Full) AIC 6658.470 6094.618 6254.617 5891.113 BIC 6671.778 6221.040 6454.232 6203.843 logLik -3327.235 -3028.309 -3097.309 -2898.557 ICC 0.4123207 0.0646514 0.1593566 0.0462458 Table 4 presents the results of multilevel logistic regression models examining factors associated with timely complete vaccination status among children in Somalia. In the full model (Model IV), significant regional variations were observed. Compared to Awdal, children in Bari (AOR = 2.50, 95% CI: 1.31–4.80), Nugaal (AOR = 2.63, 95% CI: 1.39–5.00), Mudug (AOR = 3.03, 95% CI: 1.60–5.72), Galgaduud (AOR = 1.96, 95% CI: 1.05–3.66), Hiraan (AOR = 3.94, 95% CI: 2.06–7.56), Middle Shabelle (AOR = 2.43, 95% CI: 1.29–4.58), and Lower Juba (AOR = 5.69, 95% CI: 2.95–10.96) had significantly higher odds of timely vaccination, whereas children in Banadir had considerably lower odds (AOR = 0.31, 95% CI: 0.16–0.62). Place of residence was also significant, with nomadic children having markedly lower odds of timely vaccination compared to those in urban areas (AOR = 4.29, 95% CI: 3.21–5.75). Maternal education was significantly associated with vaccination. Compared to mothers with no education, those with primary education (AOR = 0.77, 95% CI: 0.63–0.94) and secondary education (AOR = 0.65, 95% CI: 0.44–0.94) were more likely to have children vaccinated. The wealth quintile showed a strong association in both the community and individual models; however, in the complete model, the effects attenuated and were no longer statistically significant. The child’s current age was strongly associated with vaccination status. Relative to infants aged 0, older children were significantly less likely to be timely vaccinated, with AORs ranging from 0.49 at age 1 (95% CI: 0.34–0.70) to 0.58 at age 5 (95% CI: 0.42–0.80). Antenatal care utilization showed a robust positive association with vaccination. Compared to mothers with no ANC visits, those with one (AOR = 0.41, 95% CI: 0.32–0.53), two to three (AOR = 0.40, 95% CI: 0.34–0.47), and four or more visits (AOR = 0.38, 95% CI: 0.30–0.50) were significantly more likely to vaccinate their children on time. The size of the child at birth was not significantly associated with vaccination, except for mothers who reported “don’t know,” whose children had higher odds of being vaccinated (AOR = 1.47, 95% CI: 1.05–2.05). Media exposure did not show a statistically significant effect in the whole model. The study's findings indicate that regional location, residence type, maternal education, child’s age, and ANC attendance were the strongest predictors of timely complete vaccination among Somali children. Table 4 Results of multilevel logistic regression models of Timely Complete Vaccination Status Among Children in Somalia Variables Model I AOR (95% CI) Model II AOR (95% CI) Model III AOR (95% CI) Model IV AOR (95% CI) Region Awdal Woqooyi Galbeed 0.67 [0.35, 1.25] 0.80 [0.43, 1.51] Togdheer 1.13 [0.61, 2.09] 1.23 [0.67, 2.25] Sool 1.62 [0.89, 2.95] 1.55 [0.85, 2.82] Sanaag 1.47 [0.81, 2.67] 1.45 [0.80, 2.65] Bari 2.64 [1.38, 5.06] ** 2.50 [1.31, 4.80] ** Nugaal 2.51 [1.33, 4.74] ** 2.63 [1.39, 5.00] ** Mudug 2.95 [1.57, 5.54] *** 3.03 [1.60, 5.72] *** Galgaduud 1.87 [1.00, 3.48] * 1.96 [1.05, 3.66] * Hiraan 5.07 [2.65, 9.67] *** 3.94 [2.06, 7.56] *** Middle Shabelle 2.57 [1.38, 4.80] ** 2.43 [1.29, 4.58] ** Banadir 0.32 [0.17, 0.63] *** 0.31 [0.16, 0.62] *** Bay 2.25 [1.10, 4.62] * 1.82 [0.89, 3.74] Bakool 1.30 [0.70, 2.40] 1.15 [0.62, 2.14] Gedo 1.75 [0.94, 3.23] 1.69 [0.91, 3.16] Lower Juba 6.58 [3.43, 12.61] *** 5.69 [2.95, 10.96] *** Type of place of residence Urban Rural 1.28 [1.02, 1.61] * 1.13 [0.89, 1.42] Nomadic 8.00 [6.35, 10.08] *** 4.29 [3.21, 5.75] *** Age in 5-year groups 15–19 20–24 1.08 [0.80, 1.45] 1.01 [0.75, 1.38] 25–29 1.11 [0.82, 1.51] 1.07 [0.79, 1.47] 30–34 0.90 [0.66, 1.23] 0.86 [0.62, 1.18] 35–39 1.11 [0.80, 1.54] 1.13 [0.81, 1.57] 40–44 0.93 [0.63, 1.39] 0.97 [0.64, 1.45] 45–49 0.64 [0.36, 1.17] 0.71 [0.39, 1.30] Highest educational level No Education Primary 0.79 [0.65, 0.96] * 0.77 [0.63, 0.94] * Secondary 0.63 [0.44, 0.92] * 0.65[0.44, 0.94] * Higher 0.49 [0.25, 0.96] * 0.53 [0.26, 1.07] Wealth quintile Lowest Second 0.94 [0.76, 1.16] 0.99 [0.79, 1.24] Middle 0.60 [0.48, 0.75] *** 0.92 [0.71, 1.21] Fourth 0.50 [0.40, 0.64] *** 0.93 [0.70, 1.24] Highest 0.40 [0.30, 0.52] *** 0.83 [0.61, 1.14] Sex of child Male Female 0.97 [0.86, 1.10] 0.97 [0.85, 1.10] Current age in single years 0 1 0.50 [0.35, 0.71] *** 0.49 [0.34, 0.70] *** 2 0.48 [0.34, 0.66] *** 0.51 [0.36, 0.71] *** 3 0.41 [0.29, 0.56] *** 0.42 [0.31, 0.58] *** 4 0.52 [0.38, 0.71] *** 0.53 [0.39, 0.73] *** 5 0.53 [0.38, 0.72] *** 0.58 [0.42, 0.80] *** Number of ANC visits None 1 0.36 [0.28, 0.47] *** 0.41 [0.32, 0.53] *** 2–3 0.30 [0.26, 0.36] *** 0.40 [0.34, 0.47] *** 4+ 0.26 [0.21, 0.34] *** 0.38 [0.30, 0.50] *** Size of child at birth Very Large Larger Than Average 0.97 [0.65, 1.44] 0.98 [0.65, 1.47] Average 1.00 [0.75, 1.33] 0.97 [0.73, 1.30] Smaller Than Average 0.83 [0.57, 1.20] 0.87 [0.60, 1.28] Very Small 1.02 [0.71, 1.47] 1.02 [0.70, 1.47] Don't Know 1.35 [0.98, 1.87] 1.47 [1.05, 2.05] * Media Exposure No exposure to mass media Exposure to mass media 1.14 [0.98, 1.33] 1.04 [0.89, 1.23] Spatial Analysis The spatial analysis demonstrated apparent geographical disparities in childhood vaccination coverage across Somalia. While the Global Moran’s I suggested weak evidence of overall spatial autocorrelation, the Local Moran’s I (LISA) and Getis-Ord Gi* statistics identified significant regional clustering. Specifically, high–high clusters (“hot spots”) of vaccination were concentrated in the southern regions, such as Jubbada Hoose and Gedo, whereas low–low clusters (“cold spots”) were evident in the northern areas, including Sanaag, Togdheer, and parts of Woqooyi Galbeed. These findings confirm the presence of localized spatial dependence, highlighting distinct regions of both high and low vaccination uptake. Figure 1 illustrates the spatial distribution of vaccination status among children in Somalia, with different shades indicating varying vaccination proportions. The regions with the highest vaccination rates (0.8) are concentrated in the southwestern parts of the country, specifically in Gedo and Jubbada Hoose. Moving eastward, regions like Bay and Shabeellaha Dhexe show slightly lower but still relatively high vaccination rates, around 0.7. The central and northern regions, including Hiraan, Galguduud, Mudug, and Nugaal, exhibit moderate vaccination rates, ranging from 0.6 to 0.7. The lowest vaccination rates (0.5 to 0.6) are observed in several northern areas such as Sanaag, Bari, and parts of Togdheer and Woqooyi Galbeed, suggesting a geographical disparity where northern and some central regions lag behind the southwestern areas in terms of children's vaccination coverage. Local Moran’s I This analysis of Local Moran's I for vaccination status among children in Somalia reveals significant spatial clustering. Figure 2 shows a clear pattern of spatial autocorrelation. Regions shaded in darker blue, particularly Sanaag, Togdheer, Soor, and parts of Woqooyi Galbeed, exhibit high positive spatial autocorrelation, indicating \"hot spots\" where regions with high vaccination rates are surrounded by other regions with similarly high rates, or \"cold spots\" where low rates are clustered. Conversely, areas in lighter shades (yellow and light green) exhibit lower or negative local Moran's I values, indicating less clustering or even a \"doughnut\" effect, where regions with different rates surround a region with a certain vaccination rate. The P-value for Local Moran's I highlights the statistically significant clusters. Regions marked in red, specifically Sanaag, Togdheer, Soor in the north, and Jubbada Hoose in the south, indicate statistically substantial spatial autocorrelation (p < 0.05). This means that the clustering observed in these regions is unlikely to have occurred by random chance. The significance in Sanaag, Togdheer, and Soor, which appear as darker blue on Moran's I map, suggests a significant cluster of either high or low vaccination rates, warranting further investigation to understand the specific nature of this clustering (whether it is a high-high or low-low cluster). Similarly, the significance of Jubbada Hoose points to another notable cluster in the south. The gray regions on the p-value map indicate areas where the spatial clustering is not statistically significant, meaning any observed patterns could be due to random variation. Getis-Ord Gi* This Getis-Ord Gi* statistic map for vaccination status among children in Somalia identifies statistically significant \"hot spots\" and \"cold spots\" of vaccination coverage. Figure 3 shows the regions shaded in darker red, such as Jubbada Hoose, Gedo, Bay, Bakool, and Shabeellaha Dhexe, represent strong \"hot spots.\" This indicates a clustering of high vaccination rates in these areas, where regions with high vaccination status are surrounded by other regions with similarly high vaccination status. These are the areas where intervention efforts or successful programs might be concentrated, or where conditions are more favorable for vaccination. Conversely, the regions shaded in lighter yellow, particularly Sanaag, Bari, Awadal, Woqooyi Galbeed, and parts of Togdheer and Soor, represent \"cold spots\" or areas with lower Gi* values. This suggests a clustering of low vaccination rates in these regions, indicating that areas with low vaccination status are often surrounded by other areas with similarly low vaccination status. The orange-shaded regions, such as Nugaal, Mudug, Galguduud, and Hiraan, display intermediate values, indicating less pronounced clustering or transitional areas between hot and cold spots. The map highlights a clear geographical divide, with a concentration of high vaccination rates in the southwestern regions of Somalia and lower rates predominantly in the northern and northeastern parts of the country. Clusters Type of Vaccination This cluster map, generated using LISA (Local Indicator of Spatial Association) statistics, categorizes the regions of Somalia based on the spatial clustering of vaccination status among children. Figure 4 identifies specific types of clusters: \"High-High,\" \"Low-Low,\" \"High-Low,\" and \"Low-High,\" with non-significant areas shown in white. The \"High-High\" cluster, marked in red, is prominently observed in the southernmost regions of Jubbada Hoose and Gedo. This suggests that these areas have high vaccination rates and are surrounded by other areas with similarly high vaccination rates. This suggests a strong positive spatial autocorrelation of high vaccination coverage in this particular region, likely due to shared socioeconomic factors, effective health interventions, or accessibility to healthcare services. Conversely, the \"Low-Low\" cluster, marked in blue, is concentrated in the northern regions, specifically Sanaag, Togdheer, Soor, and parts of Woqooyi Galbeed. This signifies that these regions have low vaccination rates and are neighboring areas that also exhibit low vaccination rates. This pattern indicates a significant spatial clustering of poor vaccination coverage, possibly influenced by common challenges such as limited access to healthcare facilities, security issues, or cultural factors that hinder vaccination efforts in these northern areas. The figure shows no \"High-Low\" or \"Low-High\" clusters, indicating that there are no transitional areas where low-vaccination neighbors, or vice versa, surround a region with high vaccination. The majority of the central areas are marked as \"Non-significant\" (white), meaning that for these areas, there is no statistically significant spatial clustering of either high or low vaccination rates. This implies a more random distribution of vaccination status or a mix of rates that do not form a distinct cluster. In summary, the map clearly delineates two distinct geographical disparities in childhood vaccination status in Somalia: a strong high-coverage cluster in the south and a persistent low-coverage cluster in the north. Figure 5 shows the results of the Global Moran’s I analysis for the spatial autocorrelation of timely complete vaccination status among children in Somalia. Moran’s I value was 0.235, with a corresponding z-score of 1.48 and a p-value of 0.089. Although the positive Moran’s I suggests a tendency toward spatial clustering of vaccination coverage, the z-score did not exceed the critical threshold for statistical significance at the 5% level. This indicates that the spatial distribution of vaccination status is essentially random, and no strong evidence of global spatial autocorrelation was detected across Somali regions. 4. Discussion This study aimed to investigate the geographical variations and associated factors of timely vaccination status among children in Somalia, utilizing both spatial and multilevel analysis. The number of ANC visits emerged as a crucial factor in health services, with vaccination coverage increasing substantially with each additional visit. However, maternal age, sex of the child, and media exposure showed a weaker or non-significant association in the initial analyses. Other studies found vaccination coverage increased by 24% among children whose mothers had four or more ANC visits using propensity score matching with 5,430 participants (27). A study across 29 sub-Saharan African countries showed that children whose mothers had a maximum of three ANC visits were 56% less likely to have complete vaccination in a study of 60,964 mothers (28). In Nigeria, studies have found positive associations, regardless of the number of visits, among 5,506 women (29). Regarding the other factors mentioned, the available sources do not provide specific evidence on the effects of maternal age or child sex on vaccination coverage. Interestingly, multiple studies have actually shown that media exposure has significant positive associations with ANC utilization (30,31), although direct media-vaccination relationships aren't examined in these sources. The spatial analysis provided a nuanced understanding of these geographical disparities. While the Global Moran's I indicated a lack of significant overall spatial autocorrelation (Moran's I = 0.235, z-score = 1.48, p = 0.077/0.089), the Local Moran's I and Getis-Ord Gi* statistics effectively pinpointed localized clustering. Across multiple studies examining childhood vaccination coverage, Global Moran's I results varied considerably. While some studies have found significant global spatial autocorrelation (32–34), others have found no significant global clustering, which is consistent with the reported findings. However, Local Moran's I consistently identified meaningful spatial clusters across studies, even when global autocorrelation was absent (7,35). The Getis-Ord Gi* statistic proved particularly effective for hotspot identification, successfully pinpointing areas with significantly higher proportions of unvaccinated children (32). These local spatial statistics enabled researchers to detect 477 spatial clusters with low vaccination coverage across sub-Saharan Africa (7), demonstrating their superior sensitivity for identifying localized vaccination gaps that require targeted interventions. The multilevel logistic regression models further elaborated on the factors influencing timely vaccination, accounting for community-level variations. The complete model (Model IV) demonstrated the best fit, explaining a substantial portion of the variance. In this model, significant regional variations persisted, even after controlling for other factors. The place of residence remained a critical factor, with nomadic children facing significantly lower odds of timely vaccination compared to their urban counterparts (AOR = 4.29). Multiple studies have demonstrated substantial community-level variation, with 26% of variability in timely vaccination attributed to community differences (36). Another study reported that 32% of the variance in unimmunized children was due to community-level factors across 24 sub-Saharan African countries (37). The place of residence consistently emerges as a significant factor, with urban children experiencing better vaccination outcomes (38,39). However, the specific AOR = 4.29 figure for nomadic versus urban children and the referenced \"Model IV\" are not present in the available source abstracts, limiting verification of these particular claims. Maternal education also maintained its strong positive association, with mothers who are primary or secondary educated being more likely to vaccinate their children. However, the influence of wealth quintile, while significant in individual models, attenuated to non-significance in the complete model, suggesting other included variables might mediate its effect. Children older than 1 year were less likely to receive timely vaccinations, whereas consistently increasing ANC visits predicted higher odds of receiving timely vaccinations. Multiple large-scale studies demonstrate substantial effect sizes. A systematic review and meta-analysis of Ethiopian data found that mothers with primary education had 1.87 times the likelihood of completing childhood vaccination. In comparison, mothers with secondary education had 3.47 times the likelihood of having a child with a disability compared to mothers with no education (40). A global meta-analysis of 37 studies showed children of mothers with secondary or higher education had 2.3 times greater odds of complete vaccination (41). In Kenya, women with primary through university education were 2.21–9.10 times more likely to immunize their children (n = 1,707) (42). Similar positive associations were documented across diverse settings, including India (43), Nigeria (44), Pakistan (45), and Uganda (46), demonstrating consistent global evidence for this relationship. 5. Conclusions This study revealed significant geographical disparities in childhood vaccination coverage in Somalia, with higher rates concentrated in the southwest and persistently low coverage in the northern and northeastern regions. Multilevel analysis confirmed the influence of both individual and community-level factors, while spatial methods (Global Moran’s I, Local Moran’s I, and Getis-Ord Gi*) identified significant clustering of vaccination status. To address these inequalities, interventions should prioritize strengthening health service accessibility and outreach in low-coverage northern regions, enhancing community engagement, and scaling up successful programs from high-performing areas. Tailored, region-specific strategies are essential to achieving equitable vaccination coverage and reducing preventable child morbidity and mortality nationwide. Abbreviations ANC Antenatal Care BCG Bacillus Calmette-Guérin DTP Diphtheria, Tetanus, and Pertussis SDHS Somalia Demographic and Health Survey Declarations Ethics Approval and Consent to Participate This study used publicly available and fully de-identified data from the 2020 Somalia Demographic and Health Survey (SDHS). According to national guidelines, the use of anonymized secondary data does not require formal ethical approval. Permission to access and use the dataset was obtained from the National Bureau of Statistics of Somalia. All procedures were conducted in accordance with the ethical principles outlined in the 2013 Declaration of Helsinki and relevant national regulations. Consent for participation was obtained by the original data collectors during the administration of the survey. Consent for publication No applicable. Availability of data and materials This study was based on data from the 2020 Somalia Demographic and Health Survey (SDHS), which is publicly accessible through the Somalia National Bureau of Statistics (https://microdata.nbs.gov.so/index.php/catalog/50). Competing interests The authors declare no competing interests. Funding No funding. Authors' contributions A.A.O. Conceptualized the study, Formal analysis, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. A.A.E. Data curation, Formal analysis, Investigation, Methodology, Supervision, Visualization, Validation, Writing – original draft. F.M.Z. Conceptualized the study, Formal analysis, Investigation, Project administration, Resources, Supervision, Writing – review & editing. All authors read and approved the final manuscript. Acknowledgements Nothing to declare. References Sadigh K, Fox G, Khetsuriani N, Gao H, Shendale S, Ward K. 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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-8444974\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":568987366,\"identity\":\"9ddee8d5-18c0-4647-9902-b321cafe245a\",\"order_by\":0,\"name\":\"Abdisalan Ahmed 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12:58:15\",\"extension\":\"html\",\"order_by\":14,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":138541,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8444974/v1/fb2602ad3d507826a43b61e7.html\"},{\"id\":99509303,\"identity\":\"bb50d700-dd6f-4de4-91c1-6d1c5f52f94b\",\"added_by\":\"auto\",\"created_at\":\"2026-01-05 09:15:09\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":32628,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSpatial Variation of Vaccination Status among the Children in Somalia\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8444974/v1/aa85797ed4ecb113af0c3631.png\"},{\"id\":99791558,\"identity\":\"796f7ae6-427e-45cc-9d26-5c2cb96c24f0\",\"added_by\":\"auto\",\"created_at\":\"2026-01-08 13:02:39\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":23112,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLocal Moran’s I of Vaccination Status among the Children in Somalia\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8444974/v1/928a392f73ecfb8ab716f686.png\"},{\"id\":99790808,\"identity\":\"33d1e336-6310-4100-947e-29cb25d00925\",\"added_by\":\"auto\",\"created_at\":\"2026-01-08 12:58:44\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":22540,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eGetis-Ord Gi* of Vaccination Status among the Children in Somalia\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8444974/v1/e3a09ea95722017a668f9898.png\"},{\"id\":99791661,\"identity\":\"ae4d0ed2-0731-4104-b92f-59b5329d1623\",\"added_by\":\"auto\",\"created_at\":\"2026-01-08 13:07:21\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":84826,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCluster type of Vaccination Status among the Children in Somalia\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8444974/v1/73da83134eb60459c6d6bae9.png\"},{\"id\":99790795,\"identity\":\"9ca1196b-bf5b-4137-9fe0-e846312dc452\",\"added_by\":\"auto\",\"created_at\":\"2026-01-08 12:58:43\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":13077,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eGlobal Moran’s I for the spatial autocorrelation of Vaccination Status among the Children in Somalia\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8444974/v1/9518d8456c8060f7305f25e9.png\"},{\"id\":100421853,\"identity\":\"232a151e-6fa6-4f8d-a2e5-6572fd1cbeb0\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 13:56:12\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1653329,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8444974/v1/52bd7262-fb3d-4ce1-94f5-e6d393c68450.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Geographical Variations and Associated Factors of Timely Vaccination Status Among Children in Somalia: An Application of Spatial and Multilevel Analysis\",\"fulltext\":[{\"header\":\"Contributions to the Literature\",\"content\":\"\\u003cp\\u003e\\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003eThis study shows where childhood vaccination rates in Somalia are highest and lowest, helping identify areas most in need of support.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eIt combines spatial mapping and multilevel analysis, offering a clearer understanding of how place, community factors, and maternal health behaviors influence vaccination.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eThe finding highlights challenges faced by nomadic populations and northern regions, adding evidence to support targeted public health programs.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eThe study provides timely information that can guide the Ministry of Health of Somalia in designing equitable and region-specific vaccination strategies.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e\\u003c/p\\u003e\"},{\"header\":\"1. Background\",\"content\":\"\\u003cp\\u003eGlobal vaccination status studies reveal significant disparities and implementation challenges across different contexts. School-based vaccination status checking is widely recommended to strengthen routine childhood immunization coverage, although limited information exists about the prevalence of global policies and implementation practices (1). Despite remarkable progress with over 1\\u0026nbsp;billion children vaccinated globally in the past decade and 125 UN member states achieving\\u0026thinsp;\\u0026ge;\\u0026thinsp;90% DTP3 coverage, over 20\\u0026nbsp;million children under one year remain unvaccinated, with persistent inequalities particularly affecting Sub-Saharan Africa and India (2). In children under 5, methods combining recall, home-based records, facility records, and serology yielded sensitivity above 80% in 81% of cases and a positive predictive value of 94%, though specificity sometimes dropped to 38% (3). DTP3 coverage has remained stagnant at approximately 83\\u0026ndash;84% from 2008 to 2013, highlighting persistent inequalities. Incomplete vaccination is strongly associated with poor socioeconomic status, lower education levels, non-use of maternal-child health services, residence in conflict-affected areas, missed immunization opportunities, and cancelled vaccination sessions. The Global Vaccine Action Plan 2011\\u0026ndash;2020 provides a framework emphasizing country ownership, shared responsibility, equity, integration, sustainability, and innovation to strengthen immunization systems (4).\\u003c/p\\u003e \\u003cp\\u003eVaccination coverage among children in sub-Saharan Africa remains suboptimal, with significant inequalities and spatial clustering. Studies show that 16.5% of children aged 12\\u0026ndash;59 months have received zero doses (5). An analysis of 358,949 children across 35 countries identified Rwanda, Burundi, and The Gambia as having the highest coverage rates (6). Spatial analysis reveals 477 clusters of low vaccination coverage, often in border areas and countries with otherwise high national rates (7). Timely vaccination coverage remains critically low across sub-Saharan Africa, with significant inequalities persisting despite overall improvements in vaccination rates. Analysis of 153,632 children from 40 countries revealed a median on-time vaccination coverage of less than 50% in all four sub-regions. Substantial disparities exist by household wealth, maternal education, and place of residence, with wealth-related inequities ranging from 22.6 to 30.6 percentage points across regions (8). Vaccination delays are prevalent, affecting 25.9% of children for BCG, 49.1% for pentavalent third dose, and 63.9% for measles vaccines, with delays significantly associated with incomplete immunization schedules (9). While overall full vaccination coverage reached 56.5% across 25 countries, pro-rich inequality was observed in 23 countries. Countries with lower vaccination coverage demonstrated higher inequalities, with unvaccinated children disproportionately concentrated among disadvantaged populations (10).\\u003c/p\\u003e \\u003cp\\u003eVaccination coverage among children in Somalia remains critically low, with substantial disparities across regions and populations. National estimates indicate that only 20% of children aged 12\\u0026ndash;23 months achieve complete immunization coverage (11), while 9.14% of children under the age of five receive no vaccinations at all. Regional variations are also evident, with Somaliland reporting that 54% of children have received at least one vaccination (12), compared to Mogadishu, where only 34% of children are fully vaccinated (13). These highlight significant inequities in immunization access and utilization across the country. The determinants of vaccination status include maternal education level, with mothers who are literate being more likely to vaccinate their children. Antenatal care visits emerged as a crucial factor, with children whose mothers received ANC being significantly more likely to be vaccinated. Household income and wealth status have a positive influence on vaccination rates (14,15). Geographic disparities were evident, with nomadic populations having lower coverage than urban and rural residents. Nationally, 9.14% of children received no vaccinations, and significant regional variations were observed (16).\\u003c/p\\u003e \\u003cp\\u003eResearch examining the timeliness of childhood vaccination in relation to the Sustainable Development Goals (SDGs) reveals mixed outcomes and interconnected challenges. In Nigeria, measles vaccination coverage initially accelerated following SDG implementation but subsequently declined significantly, with rates dropping from 76% in 2014 to 49% in 2019, suggesting unsustainable progress (17). Studies across Nepal, Senegal, and Zambia demonstrate that vaccination status is strongly associated with other SDG indicators. Mothers of fully vaccinated children show 14\\u0026ndash;30% higher healthcare facility delivery rates, greater contraceptive knowledge, and 10\\u0026ndash;22% higher literacy rates compared to mothers of under-immunized children (18,19). In Pakistan, only 20.8% of children received all vaccinations on schedule, with older child age and institutional delivery associated with decreased vaccination timeliness (20).\\u003c/p\\u003e \\u003cp\\u003eDespite global progress in improving childhood immunization, Somalia continues to face substantial challenges in achieving timely vaccination coverage due to prolonged conflict, weak health systems, poverty, and regional disparities. Delays or missed vaccinations expose children to preventable diseases, contributing to the country\\u0026rsquo;s persistently high child morbidity and mortality rates. Furthermore, there is limited evidence on how individual, household, and community-level factors influence timely vaccination uptake across different regions of Somalia. The study aims to examine the spatial variations and associated determinants of timely vaccination among children in Somalia, thereby providing evidence to guide targeted interventions and inform policy for improving immunization coverage and equity.\\u003c/p\\u003e\"},{\"header\":\"2. Method\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.1 Study Area\\u003c/h2\\u003e\\n \\u003cp\\u003eThe study analyzed data from both rural and urban parts of Somalia, focusing on regions where complete and reliable information was available from the Somalia Demographic and Health Survey SHDS 2020 dataset. Due to security issues during the original survey, the Lower Shebelle and Middle Juba regions were not included in the SHDS 2020 data collection and are thus excluded from this study (21)\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.2 Study Design and Setting\\u003c/h2\\u003e\\n \\u003cp\\u003eThis study will employ a community-based cross-sectional study design. The data for this research will be sourced from the most recent SDHS. Somalia, situated in the Horn of Africa, is characterized by a diverse geographical and socioeconomic landscape, comprising urban centers, rural communities, and nomadic pastoralist populations. The SDHS utilizes a multi-stage cluster sampling approach to ensure national representativeness (22). The study population will comprise mothers aged 15\\u0026ndash;49 years who have children aged 12\\u0026ndash;35 months living within the selected enumeration areas, ensuring that the children are within the age range for which timely vaccination status can be assessed according to WHO guidelines.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.3 Variables of the Study\\u003c/h2\\u003e\\n \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003e2.3.1 Outcome Variable\\u003c/h2\\u003e\\n \\u003cp\\u003eThe dependent variable in this study was whether the child had ever received any form of vaccination, which was coded as a binary variable. Children who had received at least one vaccination were coded as \\u0026ldquo;Yes\\u0026rdquo; (1\\u0026thinsp;=\\u0026thinsp;ever vaccinated), while those who had not received any vaccination were coded as \\u0026ldquo;No\\u0026rdquo; (0\\u0026thinsp;=\\u0026thinsp;never vaccinated).\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003e2.3.2 Explanatory Variables\\u003c/h2\\u003e\\n \\u003cp\\u003eThe study considered both individual- and community-level characteristics as explanatory variables associated with timely vaccination status among children in Somalia. Individual-level factors: These included the mother\\u0026rsquo;s age in 5-year groups, highest educational level (no education, primary, secondary, higher), household wealth quintile (Lowest, Second, Middle, Fourth, Highest), sex of the child (male or female), current age of the child in single years, number of antenatal care (ANC) visits, size of the child at birth (Very Large, Larger Than Average, Average, Smaller Than Average, Very Small, Don\\u0026apos;t Know), and mother\\u0026rsquo;s media exposure (No exposure to mass media, Exposure to mass media). Community-level factors: These included the regional and type of place of residence (urban or rural).\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.4 Statistical Analysis Method\\u003c/h2\\u003e\\n \\u003cp\\u003eData were analyzed using appropriate statistical techniques to examine the spatial variations and associated factors of timely vaccination status among children in Somalia. Descriptive statistics were first employed to summarize the characteristics of the study population. Bivariate analysis with the chi-square test was conducted to assess the association between each explanatory variable and vaccination status. To account for the hierarchical structure of the data, a multilevel logistic regression model was applied, incorporating both individual-level and community-level factors. The fixed effects were used to estimate the strength of association between explanatory variables and timely vaccination, while the random effects captured variations across communities. Measures such as the Intraclass Correlation Coefficient (ICC), adjusted Odds Ratio (AOR), and Proportional Change in Variance (PCV) were computed to assess the extent of community-level variation. Results were presented using adjusted odds ratios (AOR) with 95% confidence intervals (CI), and statistical significance was declared at p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 (23). Spatial analysis was conducted to explore geographic variations in timely vaccination coverage. Spatial autocorrelation was assessed using Global Moran\\u0026rsquo;s I to detect overall clustering patterns, while Local Moran\\u0026rsquo;s I (LISA) and Getis-Ord Gi* statistics were applied to identify regional hotspots and cold spots of vaccination status. All statistical and spatial analyses were performed using R statistical software (version 4.5.1), with the aid of specialized packages such as sf, tmap, and other geospatial libraries.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMultilevel Logistic Regression Model\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eTo account for the hierarchical structure of the data, where children are nested within communities, a two-level multilevel logistic regression model was employed. The model estimated the likelihood of a child having timely vaccination, incorporating both individual-level factors (Level 1) and community-level factors (Level 2). The intercept was allowed to vary across communities to capture unobserved heterogeneity, while fixed effects were used to estimate the associations between explanatory variables and vaccination status. Random effects quantified the variation between communities, and measures such as the Intraclass Correlation Coefficient (ICC) and Adjusted Odds Ratio (AOR) were calculated to assess the extent of clustering at the community level.\\u003c/p\\u003e\\n \\u003cp\\u003eThe model can be expressed as:\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u0026nbsp;\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\text{Logit}\\\\left({Y}_{ij}\\\\right)={\\\\beta\\\\:}_{0j}+\\\\sum\\\\:\\\\beta\\\\:{X}_{i}+\\\\gamma\\\\:{Z}_{j}+{\\\\epsilon\\\\:}_{ij},\\\\)\\u003c/span\\u003e\\u0026nbsp;\\u003c/span\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003ewhere\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\beta\\\\:}_{0j}={\\\\beta\\\\:}_{0}+{\\\\mu\\\\:}_{j},\\\\hspace{1em}{\\\\mu\\\\:}_{j}\\\\sim\\\\:N\\\\left(0,{\\\\sigma\\\\:}_{u}^{2}\\\\right),\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eand \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\epsilon\\\\:}_{ij}\\\\sim\\\\:N\\\\left(0,{\\\\sigma\\\\:}_{\\\\epsilon\\\\:}^{2}\\\\right)\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, representing residual variation at the individual level (24).\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSpatial Autocorrelation\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eSpatial autocorrelation was assessed to determine whether timely vaccination coverage among children in Somalia was randomly distributed or exhibited clustering patterns across geographic locations. Global Moran\\u0026rsquo;s I statistic was used to measure the overall spatial dependence in vaccination coverage (25), with positive values indicating clustering of similar values and negative values indicating dispersion.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHot Spot Analysis (Getis-Ord Gi* Statistic)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eHot spot analysis was performed to identify specific areas with significantly high or low coverage of timely vaccination. The Getis-Ord Gi* statistic was applied to detect local clusters (hot spots and cold spots), allowing for visualization of regions where timely vaccination rates were unusually high or low compared to neighboring areas (26).\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003e \\u003cb\\u003eSocio-demographic Characteristics\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eA total of 5,732 children aged 0\\u0026ndash;59 months were included in this study. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e summarizes the socio-demographic characteristics and their association with the timely completion of vaccination status among children in Somalia. Maternal age was not significantly associated with vaccination status (p\\u0026thinsp;=\\u0026thinsp;0.161), although the highest proportion of vaccinated children was observed among mothers aged 30\\u0026ndash;34 years (35.2%). Regional differences were statistically significant (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), with the highest vaccination coverage in Banadir (83.3%), Bay (46.3%), and Woqooyi Galbeed (46.0%). In contrast, the lowest coverage was reported in Lower Juba (15.0%) and Hiraan (19.8%). Place of residence showed a significant association (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001); coverage was higher in urban areas (50.0%) compared to rural (36.6%) and nomadic settings (12.6%). Maternal education was significantly associated with vaccination status (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), increasing from 29.1% among mothers with no education to 68.0% among those with higher education. The household wealth quintile also exhibited a significant relationship (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), with vaccination coverage ranging from 18.7% in the poorest households to 53.9% in the wealthiest families. Media exposure was marginally significant (p\\u0026thinsp;=\\u0026thinsp;0.039), with children of mothers exposed to mass media showing slightly higher vaccination coverage (33.2%) compared to those without exposure (30.2%).\\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\\u003eDescriptive Statistics and Chi-square of Timely Complete Vaccination Status Among Children in Somalia\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv 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colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePercentage (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003eEver had a Vaccination\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eChi-square\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eP-values\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNo (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge in 5-year groups\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e15\\u0026ndash;19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e398\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.94%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e117(29.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e281(70.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\" morerows=\\\"6\\\" rowspan=\\\"7\\\"\\u003e \\u003cp\\u003e9.2281\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"6\\\" rowspan=\\\"7\\\"\\u003e \\u003cp\\u003e0.161\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e20\\u0026ndash;24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1282\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.37%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e432(33.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e850(66.30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e25\\u0026ndash;29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1646\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28.72%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e522(31.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1124(68.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e30\\u0026ndash;34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1146\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e19.99%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e403(35.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e743(64.83)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e35\\u0026ndash;39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e889\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15.51%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e272(30.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e617(69.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e40\\u0026ndash;44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e294\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.13%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e90(30.61)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e204(69.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e45\\u0026ndash;49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e77\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.34%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e28(36.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e49(63.64)\\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\\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 \\u003cp\\u003eAwdal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e116\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.02%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e22(18.97)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e94(81.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\" morerows=\\\"15\\\" rowspan=\\\"16\\\"\\u003e \\u003cp\\u003e529.2073\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"15\\\" rowspan=\\\"16\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWoqooyi Galbeed\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e259\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.52%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e119(45.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e140(54.05)\\u003c/p\\u003e \\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\\u003e389\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.79%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e148(38.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e241(61.95)\\u003c/p\\u003e \\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\\u003e569\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.93%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e162(28.47)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e407(71.53)\\u003c/p\\u003e \\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\\u003e589\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.28%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e205(34.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e384(65.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBari\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e275\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.80%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e69(25.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e206(74.91)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNugaal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e326\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.69%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e85(26.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e241(73.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMudug\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e384\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.70%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e91(23.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e293(76.30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGalgaduud\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e410\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.15%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e125(30.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e285(69.51)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHiraan\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e449\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.83%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e89(19.82)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e360(80.18)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMiddle Shabelle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e358\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.25%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e103(28.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e255(71.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBanadir\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e288\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.02%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e240(83.33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e48(16.67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBay\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e160\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.79%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e74(46.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e86(53.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBakool\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e373\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.51%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e153(41.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e220(58.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGedo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e361\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.30%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e115(31.86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e246(68.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLower Juba\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e426\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.43%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e 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\\u003cp\\u003eUrban\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2029\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e35.40%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1014(49.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1015(50.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e674.3417\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRural\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1591\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27.76%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e583(36.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1008(63.36)\\u003c/p\\u003e \\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\\u003e2112\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e36.85%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e267(12.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1845(87.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHighest educational level\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo Education\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4878\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e85.10%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1417(29.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3461(70.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003e192.3188\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePrimary\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e642\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.20%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e316(49.22)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e326(50.78)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSecondary\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e162\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.83%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e97(59.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e65(40.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigher\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.87%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e34(68.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e16(32.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eWealth quintile\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\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 \\u003cp\\u003eLowest\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1431\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.97%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e268(18.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1163(81.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e440.9483\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSecond\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1418\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.74%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e309(21.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1109(78.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMiddle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1113\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e19.42%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e428(38.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e685(61.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFourth\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e998\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17.41%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e443(44.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e555(55.61)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHighest\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e772\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13.47%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e416(53.89)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e356(46.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMedia Exposure\\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 \\u003cp\\u003eNo exposure to mass media\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4363\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e76.12%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1450(33.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2913(66.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e4.2537\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.039\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eExposure to mass media\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1369\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23.88%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e414(30.24)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e955(69.76)\\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 \\u003cb\\u003eObstetrics and Other Health Services Factors\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e presents obstetric and other health service-related characteristics in relation to the timely completion of vaccination status among children in Somalia. The sex of the child was not significantly associated with vaccination status (p\\u0026thinsp;=\\u0026thinsp;0.870), with similar coverage observed among males (32.4%) and females (32.6%). The current age of the child showed a significant association (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), with vaccination coverage ranging from 24.1% at age 0 to 37.1% at age 3, before declining slightly to 31.6% at age 4 and 30.2% at age 5. The number of antenatal care (ANC) visits was strongly associated with vaccination status (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Coverage was lowest among children of mothers with no ANC visits (21.9%) and increased progressively with the number of visits, reaching 61.1% among those whose mothers had four or more ANC visits. The size of the child at birth was also significantly associated with vaccination status (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). The highest coverage was reported among children classified as larger than average (38.6%) and huge (36.4%), while the lowest was observed among those reported as \\u0026ldquo;don\\u0026rsquo;t know\\u0026rdquo; (24.3%) and very small (30.5%).\\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\\u003eDescriptive Statistics and Chi-square of Obstetrics and Other Health Services Timely Complete Vaccination Status Among Children in Somalia\\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=\\\"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 \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eVariables\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eFrequency\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePercentage (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003eEver had a Vaccination\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eChi-square\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eP-values\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNo (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSex of child\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\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\\u003e3001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52.36%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e973(32.42)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2028(67.58)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.0268\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.870\\u003c/p\\u003e \\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\\u003e2731\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e47.64%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e891(32.63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1840(67.37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCurrent age in single 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=\\\"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 \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e390\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.80%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e94(24.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e296(75.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e \\u003cp\\u003e33.1915\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e392\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.84%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e142(36.22)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e250(63.78)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e685\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.95%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e248(36.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e437(63.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17.46%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e371(37.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e630(62.94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1673\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e29.19%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e528(31.56)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1145(68.44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1591\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27.76%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e481(30.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1110(69.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNumber of ANC visits\\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 \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3928\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e68.53%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e860(21.89)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3068(78.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003e661.1796\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e353\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.16%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e164(46.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e189(53.54)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u0026ndash;3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1089\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e19.00%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e619(56.84)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e470(43.16)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4+\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e362\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.32%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e221(61.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e141(38.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSize of child at birth\\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 \\u003cp\\u003eVery Large\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e297\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.18%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e108(36.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e189(63.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e \\u003cp\\u003e39.0439\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLarger Than Average\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e254\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.43%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e98(38.58)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e156(61.42)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAverage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3515\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e61.32%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1187(33.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2328(66.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSmaller Than Average\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e358\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.25%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e128(35.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e230(64.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVery Small\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e410\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.15%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e125(30.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e285(69.51)\\u003c/p\\u003e \\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\\u003e898\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15.67%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e218(24.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e680(75.72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e presents the model fit statistics for the multilevel logistic regression models assessing timely complete vaccination among children in Somalia. The null model (Model 1) showed the poorest fit, with the highest AIC (6658.470) and BIC (6671.778), and an intraclass correlation coefficient (ICC) of 0.412, indicating substantial variation attributable to clustering at the community level. The inclusion of community-level factors (Model 2) substantially improved the model fit, reducing the AIC to 6094.618 and the BIC to 6221.040. At the same time, the ICC dropped to 0.065, suggesting that much of the between-community variance was explained by community-level variables. The individual-level model (Model 3) also improved fit compared to the null model (AIC\\u0026thinsp;=\\u0026thinsp;6254.617, BIC\\u0026thinsp;=\\u0026thinsp;6454.232) but retained a higher ICC (0.159) than the community-level model, indicating residual clustering. The complete model (Model 4), which incorporates both individual- and community-level factors, provided the best fit, with the lowest AIC (5891.113) and BIC (6203.843), and the smallest ICC (0.046). This suggests that the combined model accounts for the majority of the between-community variance in vaccination status.\\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\\u003eModel Fit Comparison Table\\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\\\"\\u003e \\u003cp\\u003eParameters\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eModel 1 (Null)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eModel 2 (Community)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eModel 3 (Individual)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eModel 4 (Full)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAIC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6658.470\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6094.618\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6254.617\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5891.113\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBIC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6671.778\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6221.040\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6454.232\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6203.843\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003elogLik\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-3327.235\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-3028.309\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-3097.309\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-2898.557\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eICC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.4123207\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.0646514\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.1593566\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0462458\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e presents the results of multilevel logistic regression models examining factors associated with timely complete vaccination status among children in Somalia. In the full model (Model IV), significant regional variations were observed. Compared to Awdal, children in Bari (AOR\\u0026thinsp;=\\u0026thinsp;2.50, 95% CI: 1.31\\u0026ndash;4.80), Nugaal (AOR\\u0026thinsp;=\\u0026thinsp;2.63, 95% CI: 1.39\\u0026ndash;5.00), Mudug (AOR\\u0026thinsp;=\\u0026thinsp;3.03, 95% CI: 1.60\\u0026ndash;5.72), Galgaduud (AOR\\u0026thinsp;=\\u0026thinsp;1.96, 95% CI: 1.05\\u0026ndash;3.66), Hiraan (AOR\\u0026thinsp;=\\u0026thinsp;3.94, 95% CI: 2.06\\u0026ndash;7.56), Middle Shabelle (AOR\\u0026thinsp;=\\u0026thinsp;2.43, 95% CI: 1.29\\u0026ndash;4.58), and Lower Juba (AOR\\u0026thinsp;=\\u0026thinsp;5.69, 95% CI: 2.95\\u0026ndash;10.96) had significantly higher odds of timely vaccination, whereas children in Banadir had considerably lower odds (AOR\\u0026thinsp;=\\u0026thinsp;0.31, 95% CI: 0.16\\u0026ndash;0.62). Place of residence was also significant, with nomadic children having markedly lower odds of timely vaccination compared to those in urban areas (AOR\\u0026thinsp;=\\u0026thinsp;4.29, 95% CI: 3.21\\u0026ndash;5.75).\\u003c/p\\u003e \\u003cp\\u003eMaternal education was significantly associated with vaccination. Compared to mothers with no education, those with primary education (AOR\\u0026thinsp;=\\u0026thinsp;0.77, 95% CI: 0.63\\u0026ndash;0.94) and secondary education (AOR\\u0026thinsp;=\\u0026thinsp;0.65, 95% CI: 0.44\\u0026ndash;0.94) were more likely to have children vaccinated. The wealth quintile showed a strong association in both the community and individual models; however, in the complete model, the effects attenuated and were no longer statistically significant. The child\\u0026rsquo;s current age was strongly associated with vaccination status. Relative to infants aged 0, older children were significantly less likely to be timely vaccinated, with AORs ranging from 0.49 at age 1 (95% CI: 0.34\\u0026ndash;0.70) to 0.58 at age 5 (95% CI: 0.42\\u0026ndash;0.80). Antenatal care utilization showed a robust positive association with vaccination. Compared to mothers with no ANC visits, those with one (AOR\\u0026thinsp;=\\u0026thinsp;0.41, 95% CI: 0.32\\u0026ndash;0.53), two to three (AOR\\u0026thinsp;=\\u0026thinsp;0.40, 95% CI: 0.34\\u0026ndash;0.47), and four or more visits (AOR\\u0026thinsp;=\\u0026thinsp;0.38, 95% CI: 0.30\\u0026ndash;0.50) were significantly more likely to vaccinate their children on time.\\u003c/p\\u003e \\u003cp\\u003eThe size of the child at birth was not significantly associated with vaccination, except for mothers who reported \\u0026ldquo;don\\u0026rsquo;t know,\\u0026rdquo; whose children had higher odds of being vaccinated (AOR\\u0026thinsp;=\\u0026thinsp;1.47, 95% CI: 1.05\\u0026ndash;2.05). Media exposure did not show a statistically significant effect in the whole model. The study's findings indicate that regional location, residence type, maternal education, child\\u0026rsquo;s age, and ANC attendance were the strongest predictors of timely complete vaccination among Somali children.\\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\\u003eResults of multilevel logistic regression models of Timely Complete Vaccination Status Among Children in Somalia\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariables\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eModel I\\u003c/p\\u003e \\u003cp\\u003eAOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eModel II\\u003c/p\\u003e \\u003cp\\u003eAOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eModel III AOR\\u003c/p\\u003e \\u003cp\\u003e(95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eModel IV AOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRegion\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAwdal\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWoqooyi Galbeed\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.67 [0.35, 1.25]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.80 [0.43, 1.51]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTogdheer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.13 [0.61, 2.09]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.23 [0.67, 2.25]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSool\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.62 [0.89, 2.95]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.55 [0.85, 2.82]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSanaag\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.47 [0.81, 2.67]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.45 [0.80, 2.65]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBari\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.64 [1.38, 5.06] **\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.50 [1.31, 4.80] **\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNugaal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.51 [1.33, 4.74] **\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.63 [1.39, 5.00] **\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMudug\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.95 [1.57, 5.54] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.03 [1.60, 5.72] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGalgaduud\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.87 [1.00, 3.48] *\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.96 [1.05, 3.66] *\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHiraan\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.07 [2.65, 9.67] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.94 [2.06, 7.56] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMiddle Shabelle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.57 [1.38, 4.80] **\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.43 [1.29, 4.58] **\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBanadir\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.32 [0.17, 0.63] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.31 [0.16, 0.62] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBay\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.25 [1.10, 4.62] *\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.82 [0.89, 3.74]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBakool\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.30 [0.70, 2.40]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.15 [0.62, 2.14]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGedo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.75 [0.94, 3.23]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.69 [0.91, 3.16]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLower Juba\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.58 [3.43, 12.61] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.69 [2.95, 10.96] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eType of place of residence\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUrban\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRural\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.28 [1.02, 1.61] *\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.13 [0.89, 1.42]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNomadic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.00 [6.35, 10.08] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.29 [3.21, 5.75] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge in 5-year groups\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e15\\u0026ndash;19\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e20\\u0026ndash;24\\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\\u003e1.08 [0.80, 1.45]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.01 [0.75, 1.38]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e25\\u0026ndash;29\\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\\u003e1.11 [0.82, 1.51]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.07 [0.79, 1.47]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e30\\u0026ndash;34\\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\\u003e0.90 [0.66, 1.23]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.86 [0.62, 1.18]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e35\\u0026ndash;39\\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\\u003e1.11 [0.80, 1.54]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.13 [0.81, 1.57]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e40\\u0026ndash;44\\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\\u003e0.93 [0.63, 1.39]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.97 [0.64, 1.45]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e45\\u0026ndash;49\\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\\u003e0.64 [0.36, 1.17]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.71 [0.39, 1.30]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHighest educational level\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo Education\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePrimary\\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\\u003e0.79 [0.65, 0.96] *\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.77 [0.63, 0.94] *\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSecondary\\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\\u003e0.63 [0.44, 0.92] *\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.65[0.44, 0.94] *\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigher\\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\\u003e0.49 [0.25, 0.96] *\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.53 [0.26, 1.07]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eWealth quintile\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLowest\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSecond\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.94 [0.76, 1.16]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.99 [0.79, 1.24]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMiddle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.60 [0.48, 0.75] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.92 [0.71, 1.21]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFourth\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.50 [0.40, 0.64] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.93 [0.70, 1.24]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHighest\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.40 [0.30, 0.52] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.83 [0.61, 1.14]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSex of child\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale\\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\\u003e0.97 [0.86, 1.10]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.97 [0.85, 1.10]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCurrent age in single 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=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e0\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\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\\u003e0.50 [0.35, 0.71] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.49 [0.34, 0.70] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\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\\u003e0.48 [0.34, 0.66] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.51 [0.36, 0.71] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\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\\u003e0.41 [0.29, 0.56] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.42 [0.31, 0.58] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\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\\u003e0.52 [0.38, 0.71] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.53 [0.39, 0.73] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e5\\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\\u003e0.53 [0.38, 0.72] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.58 [0.42, 0.80] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNumber of ANC visits\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNone\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\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\\u003e0.36 [0.28, 0.47] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.41 [0.32, 0.53] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u0026ndash;3\\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\\u003e0.30 [0.26, 0.36] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.40 [0.34, 0.47] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4+\\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\\u003e0.26 [0.21, 0.34] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.38 [0.30, 0.50] ***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSize of child at birth\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVery Large\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLarger Than Average\\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\\u003e0.97 [0.65, 1.44]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.98 [0.65, 1.47]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAverage\\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\\u003e1.00 [0.75, 1.33]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.97 [0.73, 1.30]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSmaller Than Average\\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\\u003e0.83 [0.57, 1.20]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.87 [0.60, 1.28]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVery Small\\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\\u003e1.02 [0.71, 1.47]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.02 [0.70, 1.47]\\u003c/p\\u003e \\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=\\\"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\\u003e1.35 [0.98, 1.87]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.47 [1.05, 2.05] *\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMedia Exposure\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo exposure to mass media\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eExposure to mass media\\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\\u003e1.14 [0.98, 1.33]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.04 [0.89, 1.23]\\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 \\u003cb\\u003eSpatial Analysis\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe spatial analysis demonstrated apparent geographical disparities in childhood vaccination coverage across Somalia. While the Global Moran\\u0026rsquo;s I suggested weak evidence of overall spatial autocorrelation, the Local Moran\\u0026rsquo;s I (LISA) and Getis-Ord Gi* statistics identified significant regional clustering. Specifically, high\\u0026ndash;high clusters (\\u0026ldquo;hot spots\\u0026rdquo;) of vaccination were concentrated in the southern regions, such as Jubbada Hoose and Gedo, whereas low\\u0026ndash;low clusters (\\u0026ldquo;cold spots\\u0026rdquo;) were evident in the northern areas, including Sanaag, Togdheer, and parts of Woqooyi Galbeed. These findings confirm the presence of localized spatial dependence, highlighting distinct regions of both high and low vaccination uptake.\\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e illustrates the spatial distribution of vaccination status among children in Somalia, with different shades indicating varying vaccination proportions. The regions with the highest vaccination rates (0.8) are concentrated in the southwestern parts of the country, specifically in Gedo and Jubbada Hoose. Moving eastward, regions like Bay and Shabeellaha Dhexe show slightly lower but still relatively high vaccination rates, around 0.7. The central and northern regions, including Hiraan, Galguduud, Mudug, and Nugaal, exhibit moderate vaccination rates, ranging from 0.6 to 0.7. The lowest vaccination rates (0.5 to 0.6) are observed in several northern areas such as Sanaag, Bari, and parts of Togdheer and Woqooyi Galbeed, suggesting a geographical disparity where northern and some central regions lag behind the southwestern areas in terms of children's vaccination coverage.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eLocal Moran\\u0026rsquo;s I\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThis analysis of Local Moran's I for vaccination status among children in Somalia reveals significant spatial clustering. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e shows a clear pattern of spatial autocorrelation. Regions shaded in darker blue, particularly Sanaag, Togdheer, Soor, and parts of Woqooyi Galbeed, exhibit high positive spatial autocorrelation, indicating \\\"hot spots\\\" where regions with high vaccination rates are surrounded by other regions with similarly high rates, or \\\"cold spots\\\" where low rates are clustered. Conversely, areas in lighter shades (yellow and light green) exhibit lower or negative local Moran's I values, indicating less clustering or even a \\\"doughnut\\\" effect, where regions with different rates surround a region with a certain vaccination rate. The P-value for Local Moran's I highlights the statistically significant clusters. Regions marked in red, specifically Sanaag, Togdheer, Soor in the north, and Jubbada Hoose in the south, indicate statistically substantial spatial autocorrelation (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). This means that the clustering observed in these regions is unlikely to have occurred by random chance. The significance in Sanaag, Togdheer, and Soor, which appear as darker blue on Moran's I map, suggests a significant cluster of either high or low vaccination rates, warranting further investigation to understand the specific nature of this clustering (whether it is a high-high or low-low cluster). Similarly, the significance of Jubbada Hoose points to another notable cluster in the south. The gray regions on the p-value map indicate areas where the spatial clustering is not statistically significant, meaning any observed patterns could be due to random variation.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eGetis-Ord Gi*\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThis Getis-Ord Gi* statistic map for vaccination status among children in Somalia identifies statistically significant \\\"hot spots\\\" and \\\"cold spots\\\" of vaccination coverage. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e shows the regions shaded in darker red, such as Jubbada Hoose, Gedo, Bay, Bakool, and Shabeellaha Dhexe, represent strong \\\"hot spots.\\\" This indicates a clustering of high vaccination rates in these areas, where regions with high vaccination status are surrounded by other regions with similarly high vaccination status. These are the areas where intervention efforts or successful programs might be concentrated, or where conditions are more favorable for vaccination. Conversely, the regions shaded in lighter yellow, particularly Sanaag, Bari, Awadal, Woqooyi Galbeed, and parts of Togdheer and Soor, represent \\\"cold spots\\\" or areas with lower Gi* values. This suggests a clustering of low vaccination rates in these regions, indicating that areas with low vaccination status are often surrounded by other areas with similarly low vaccination status. The orange-shaded regions, such as Nugaal, Mudug, Galguduud, and Hiraan, display intermediate values, indicating less pronounced clustering or transitional areas between hot and cold spots. The map highlights a clear geographical divide, with a concentration of high vaccination rates in the southwestern regions of Somalia and lower rates predominantly in the northern and northeastern parts of the country.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eClusters Type of Vaccination\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThis cluster map, generated using LISA (Local Indicator of Spatial Association) statistics, categorizes the regions of Somalia based on the spatial clustering of vaccination status among children. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e identifies specific types of clusters: \\\"High-High,\\\" \\\"Low-Low,\\\" \\\"High-Low,\\\" and \\\"Low-High,\\\" with non-significant areas shown in white. The \\\"High-High\\\" cluster, marked in red, is prominently observed in the southernmost regions of Jubbada Hoose and Gedo. This suggests that these areas have high vaccination rates and are surrounded by other areas with similarly high vaccination rates. This suggests a strong positive spatial autocorrelation of high vaccination coverage in this particular region, likely due to shared socioeconomic factors, effective health interventions, or accessibility to healthcare services.\\u003c/p\\u003e \\u003cp\\u003eConversely, the \\\"Low-Low\\\" cluster, marked in blue, is concentrated in the northern regions, specifically Sanaag, Togdheer, Soor, and parts of Woqooyi Galbeed. This signifies that these regions have low vaccination rates and are neighboring areas that also exhibit low vaccination rates. This pattern indicates a significant spatial clustering of poor vaccination coverage, possibly influenced by common challenges such as limited access to healthcare facilities, security issues, or cultural factors that hinder vaccination efforts in these northern areas. The figure shows no \\\"High-Low\\\" or \\\"Low-High\\\" clusters, indicating that there are no transitional areas where low-vaccination neighbors, or vice versa, surround a region with high vaccination. The majority of the central areas are marked as \\\"Non-significant\\\" (white), meaning that for these areas, there is no statistically significant spatial clustering of either high or low vaccination rates. This implies a more random distribution of vaccination status or a mix of rates that do not form a distinct cluster. In summary, the map clearly delineates two distinct geographical disparities in childhood vaccination status in Somalia: a strong high-coverage cluster in the south and a persistent low-coverage cluster in the north.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e shows the results of the Global Moran\\u0026rsquo;s I analysis for the spatial autocorrelation of timely complete vaccination status among children in Somalia. Moran\\u0026rsquo;s I value was 0.235, with a corresponding z-score of 1.48 and a p-value of 0.089. Although the positive Moran\\u0026rsquo;s I suggests a tendency toward spatial clustering of vaccination coverage, the z-score did not exceed the critical threshold for statistical significance at the 5% level. This indicates that the spatial distribution of vaccination status is essentially random, and no strong evidence of global spatial autocorrelation was detected across Somali regions.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eThis study aimed to investigate the geographical variations and associated factors of timely vaccination status among children in Somalia, utilizing both spatial and multilevel analysis. The number of ANC visits emerged as a crucial factor in health services, with vaccination coverage increasing substantially with each additional visit. However, maternal age, sex of the child, and media exposure showed a weaker or non-significant association in the initial analyses. Other studies found vaccination coverage increased by 24% among children whose mothers had four or more ANC visits using propensity score matching with 5,430 participants (27). A study across 29 sub-Saharan African countries showed that children whose mothers had a maximum of three ANC visits were 56% less likely to have complete vaccination in a study of 60,964 mothers (28). In Nigeria, studies have found positive associations, regardless of the number of visits, among 5,506 women (29). Regarding the other factors mentioned, the available sources do not provide specific evidence on the effects of maternal age or child sex on vaccination coverage. Interestingly, multiple studies have actually shown that media exposure has significant positive associations with ANC utilization (30,31), although direct media-vaccination relationships aren't examined in these sources.\\u003c/p\\u003e \\u003cp\\u003eThe spatial analysis provided a nuanced understanding of these geographical disparities. While the Global Moran's I indicated a lack of significant overall spatial autocorrelation (Moran's I\\u0026thinsp;=\\u0026thinsp;0.235, z-score\\u0026thinsp;=\\u0026thinsp;1.48, p\\u0026thinsp;=\\u0026thinsp;0.077/0.089), the Local Moran's I and Getis-Ord Gi* statistics effectively pinpointed localized clustering. Across multiple studies examining childhood vaccination coverage, Global Moran's I results varied considerably. While some studies have found significant global spatial autocorrelation (32\\u0026ndash;34), others have found no significant global clustering, which is consistent with the reported findings. However, Local Moran's I consistently identified meaningful spatial clusters across studies, even when global autocorrelation was absent (7,35). The Getis-Ord Gi* statistic proved particularly effective for hotspot identification, successfully pinpointing areas with significantly higher proportions of unvaccinated children (32). These local spatial statistics enabled researchers to detect 477 spatial clusters with low vaccination coverage across sub-Saharan Africa (7), demonstrating their superior sensitivity for identifying localized vaccination gaps that require targeted interventions.\\u003c/p\\u003e \\u003cp\\u003eThe multilevel logistic regression models further elaborated on the factors influencing timely vaccination, accounting for community-level variations. The complete model (Model IV) demonstrated the best fit, explaining a substantial portion of the variance. In this model, significant regional variations persisted, even after controlling for other factors. The place of residence remained a critical factor, with nomadic children facing significantly lower odds of timely vaccination compared to their urban counterparts (AOR\\u0026thinsp;=\\u0026thinsp;4.29). Multiple studies have demonstrated substantial community-level variation, with 26% of variability in timely vaccination attributed to community differences (36). Another study reported that 32% of the variance in unimmunized children was due to community-level factors across 24 sub-Saharan African countries (37). The place of residence consistently emerges as a significant factor, with urban children experiencing better vaccination outcomes (38,39). However, the specific AOR\\u0026thinsp;=\\u0026thinsp;4.29 figure for nomadic versus urban children and the referenced \\\"Model IV\\\" are not present in the available source abstracts, limiting verification of these particular claims.\\u003c/p\\u003e \\u003cp\\u003eMaternal education also maintained its strong positive association, with mothers who are primary or secondary educated being more likely to vaccinate their children. However, the influence of wealth quintile, while significant in individual models, attenuated to non-significance in the complete model, suggesting other included variables might mediate its effect. Children older than 1 year were less likely to receive timely vaccinations, whereas consistently increasing ANC visits predicted higher odds of receiving timely vaccinations. Multiple large-scale studies demonstrate substantial effect sizes. A systematic review and meta-analysis of Ethiopian data found that mothers with primary education had 1.87 times the likelihood of completing childhood vaccination. In comparison, mothers with secondary education had 3.47 times the likelihood of having a child with a disability compared to mothers with no education (40). A global meta-analysis of 37 studies showed children of mothers with secondary or higher education had 2.3 times greater odds of complete vaccination (41). In Kenya, women with primary through university education were 2.21\\u0026ndash;9.10 times more likely to immunize their children (n\\u0026thinsp;=\\u0026thinsp;1,707) (42). Similar positive associations were documented across diverse settings, including India (43), Nigeria (44), Pakistan (45), and Uganda (46), demonstrating consistent global evidence for this relationship.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusions\",\"content\":\"\\u003cp\\u003eThis study revealed significant geographical disparities in childhood vaccination coverage in Somalia, with higher rates concentrated in the southwest and persistently low coverage in the northern and northeastern regions. Multilevel analysis confirmed the influence of both individual and community-level factors, while spatial methods (Global Moran\\u0026rsquo;s I, Local Moran\\u0026rsquo;s I, and Getis-Ord Gi*) identified significant clustering of vaccination status. To address these inequalities, interventions should prioritize strengthening health service accessibility and outreach in low-coverage northern regions, enhancing community engagement, and scaling up successful programs from high-performing areas. Tailored, region-specific strategies are essential to achieving equitable vaccination coverage and reducing preventable child morbidity and mortality nationwide.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eANC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eAntenatal Care\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eBCG\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eBacillus Calmette-Gu\\u0026eacute;rin\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eDTP\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eDiphtheria, Tetanus, and Pertussis\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eSDHS\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eSomalia Demographic and Health Survey\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eEthics Approval and Consent to Participate\\u003c/p\\u003e\\n\\u003cp\\u003eThis study used publicly available and fully de-identified data from the 2020 Somalia Demographic and Health Survey (SDHS). According to national guidelines, the use of anonymized secondary data does not require formal ethical approval. Permission to access and use the dataset was obtained from the National Bureau of Statistics of Somalia. All procedures were conducted in accordance with the ethical principles outlined in the 2013 Declaration of Helsinki and relevant national regulations. Consent for participation was obtained by the original data collectors during the administration of the survey.\\u003c/p\\u003e\\n\\u003cp\\u003eConsent for publication\\u003c/p\\u003e\\n\\u003cp\\u003eNo applicable.\\u003c/p\\u003e\\n\\u003cp\\u003eAvailability of data and materials\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was based on data from the 2020 Somalia Demographic and Health Survey (SDHS), which is publicly accessible through the Somalia National Bureau of Statistics (https://microdata.nbs.gov.so/index.php/catalog/50).\\u003c/p\\u003e\\n\\u003cp\\u003eCompeting interests\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003eFunding\\u003c/p\\u003e\\n\\u003cp\\u003eNo funding.\\u003c/p\\u003e\\n\\u003cp\\u003eAuthors\\u0026apos; contributions\\u003c/p\\u003e\\n\\u003cp\\u003eA.A.O. Conceptualized the study, Formal analysis, Methodology, Software, Visualization, Writing \\u0026ndash; original draft, Writing \\u0026ndash; review \\u0026amp; editing. A.A.E. Data curation, Formal analysis, Investigation, Methodology, Supervision, Visualization, Validation, Writing \\u0026ndash; original draft. F.M.Z. Conceptualized the study, Formal analysis, Investigation, Project administration, Resources, Supervision, Writing \\u0026ndash; review \\u0026amp; editing. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003eAcknowledgements\\u003c/p\\u003e\\n\\u003cp\\u003eNothing to declare.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eSadigh K, Fox G, Khetsuriani N, Gao H, Shendale S, Ward K. Policy and practice of checking vaccination status at school in 2018, a global overview. Vaccine. 2022;40(16):2432\\u0026ndash;41. \\u003c/li\\u003e\\n\\u003cli\\u003eBoulton ML, Wagner AL. Advancing global vaccination equity. Am J Prev Med. 2021;60(1):S1\\u0026ndash;3. \\u003c/li\\u003e\\n\\u003cli\\u003eSmith R, Hubers J, Farraye FA, Sampene E, Hayney MS, Caldera F. Accuracy of self-reported vaccination status in a cohort of patients with inflammatory bowel disease. Dig Dis Sci. 2021;66(9):2935\\u0026ndash;41. \\u003c/li\\u003e\\n\\u003cli\\u003eSodha S V, Dietz V. Strengthening routine immunization systems to improve global vaccination coverage. Br Med Bull. 2015;113(1):5. \\u003c/li\\u003e\\n\\u003cli\\u003eOzigbu CE, Olatosi B, Li Z, Hardin JW, Hair NL. Correlates of zero-dose vaccination status among children aged 12\\u0026ndash;59 months in Sub-saharan Africa: a multilevel analysis of individual and contextual factors. Vaccines. 2022;10(7):1052. \\u003c/li\\u003e\\n\\u003cli\\u003eEkholuenetale M, Ochagu VA, Ilesanmi OS, Badejo O, Arora A. Childhood Vaccinations and Associated Factors in 35 Sub-Saharan African Countries: Secondary Analysis of Demographic and Health Surveys Data from 358 949 Under-5 Children. Glob Pediatr Heal. 2024;11:2333794X241310487. \\u003c/li\\u003e\\n\\u003cli\\u003eBrownwright TK, Dodson ZM, van Panhuis WG. Spatial clustering of measles vaccination coverage among children in sub-Saharan Africa. BMC Public Health. 2017;17(1):957. \\u003c/li\\u003e\\n\\u003cli\\u003eMutua MK, Mohamed SF, Porth JM, Faye CM. Inequities in on-time childhood vaccination: evidence from Sub-Saharan Africa. Am J Prev Med. 2021;60(1):S11\\u0026ndash;23. \\u003c/li\\u003e\\n\\u003cli\\u003eJanusz CB, Frye M, Mutua MK, Wagner AL, Banerjee M, Boulton ML. Vaccine delay and its association with undervaccination in children in Sub-Saharan Africa. Am J Prev Med. 2021;60(1):S53\\u0026ndash;64. \\u003c/li\\u003e\\n\\u003cli\\u003eBobo FT, Asante A, Woldie M, Dawson A, Hayen A. Child vaccination in sub-Saharan Africa: Increasing coverage addresses inequalities. Vaccine. 2022;40(1):141\\u0026ndash;50. \\u003c/li\\u003e\\n\\u003cli\\u003eJama AA. Determinants of Complete Immunization Coverage among Children Aged 11-24 Months in Somalia. Int J Pediatr. 2020;2020(1):5827074. \\u003c/li\\u003e\\n\\u003cli\\u003eHalane S, Ahmed A, Ahmed MM, Hersi MD, Sani J. Assessing Prevalence and Regional Disparities in Zero-Dose Immunization Among Children Aged 12\\u0026ndash;23 Months in Somalia. J Epidemiol Glob Health. 2025;15(1):59. \\u003c/li\\u003e\\n\\u003cli\\u003eOsman MA, Waits A, Chien LY. Factors associated with vaccination coverage among 0\\u0026ndash;59-month-old children: a multilevel analysis of the 2020 Somaliland demographic and health survey. Vaccines. 2024;12(5):509. \\u003c/li\\u003e\\n\\u003cli\\u003eHassan MS, Hossain MM. Determinants of vaccination status among Somali children: evidence from a Countrywide cross-sectional survey. BMC Pediatr. 2024;24(1):837. \\u003c/li\\u003e\\n\\u003cli\\u003eHassan SA, Abukar AA, Gutale AS, Hassan AI, Haji AJ, Nur AM, et al. Immunization status and its determinants among children aged 12\\u0026ndash;23 months at community health centers in Mogadishu, Somalia: a cross-sectional study. Front Pediatr. 2025;13:1504255. \\u003c/li\\u003e\\n\\u003cli\\u003eBelay DB, Ali MI, Chen DG, Jama UA. Prevalence and associated factors of immunization among under-five children in Somalia. BMC Public Health. 2025;25(1):924. \\u003c/li\\u003e\\n\\u003cli\\u003eIlesanmi MM, Adeyinka DA, Olakunde BO. Sustainable Development Goals and childhood measles vaccination in Ekiti State, Nigeria: Results from spatial and interrupted time series analyses. Vaccine. 2022;40(28):3861\\u0026ndash;8. \\u003c/li\\u003e\\n\\u003cli\\u003eSakas Z, Hester KA, Ellis A, Ogutu EA, Rodriguez K, Bednarczyk R, et al. Critical success factors for high routine immunisation performance: a qualitative analysis of interviews and focus groups from Nepal, Senegal, and Zambia. BMJ Open. 2023;13(10):e070541. \\u003c/li\\u003e\\n\\u003cli\\u003eCastillo-Zunino F, Hester KA, Keskinocak P, Nazzal D, Smalley HK, Freeman MC. Associations between family planning, healthcare access, and female education and vaccination among under-immunized children. Vaccine. 2025;44:126540. \\u003c/li\\u003e\\n\\u003cli\\u003eNoh JW, Kim Y mi, Akram N, Yoo KB, Cheon J, Lee LJ, et al. Determinants of timeliness in early childhood vaccination among mothers with vaccination cards in Sindh province, Pakistan: a secondary analysis of cross-sectional survey data. BMJ Open. 2019;9(9):e028922. \\u003c/li\\u003e\\n\\u003cli\\u003eHussein MA, Abdi AN, Yousuf AM, Nadarajah S, Abdikarim H, Muse AH. 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Association between media exposure and maternal health service use in Nepal: A further analysis of Nepal Demographic and Health Survey-2022. PLoS One. 2024;19(3):e0297418. \\u003c/li\\u003e\\n\\u003cli\\u003eGebeyehu FG, Geremew BM, Belew AK, Zemene MA. Number of antenatal care visits and associated factors among reproductive age women in Sub-Saharan Africa using recent demographic and health survey data from 2008\\u0026ndash;2019: A multilevel negative binomial regression model. PLOS Glob Public Heal. 2022;2(12):e0001180. \\u003c/li\\u003e\\n\\u003cli\\u003eEndehabtu BF, Alemu K, Mengiste SA, Zelalem M, Gullslett MK, Tilahun B. Spatial disparities in zero-dose vaccination coverage for children aged 12\\u0026ndash;23 months in Ethiopia: A geographically weighted regression analysis. PLoS One. 2025;20(9):e0332162. \\u003c/li\\u003e\\n\\u003cli\\u003eGeremew TT, Gezie LD, Abejie AN. Geographical variation and associated factors of childhood measles vaccination in Ethiopia: a spatial and multilevel analysis. BMC Public Health. 2019;19(1):1194. \\u003c/li\\u003e\\n\\u003cli\\u003eTesfa GA, Yehualashet DE, Getnet A, Bimer KB, Seboka BT. Spatial distribution of complete basic childhood vaccination and associated factors among children aged 12\\u0026ndash;23 months in Ethiopia. A spatial and multilevel analysis. PLoS One. 2023;18(1):e0279399. \\u003c/li\\u003e\\n\\u003cli\\u003eCunha NSP, Fahrat SCL, de Olinda RA, Braga ALF, Barbieri CLA, de Aguiar Pontes Pamplona Y, et al. Spatial analysis of vaccine coverage on the first year of life in the northeast of Brazil. BMC Public Health. 2022;22(1):1204. \\u003c/li\\u003e\\n\\u003cli\\u003eMekonnen ZA, Gelaye KA, Were MC, Tilahun B. Timely completion of vaccination and its determinants among children in northwest, Ethiopia: a multilevel analysis. BMC Public Health. 2020;20(1):908. \\u003c/li\\u003e\\n\\u003cli\\u003eWiysonge CS, Uthman OA, Ndumbe PM, Hussey GD. Individual and contextual factors associated with low childhood immunisation coverage in sub-Saharan Africa: a multilevel analysis. PLoS One. 2012;7(5):e37905. \\u003c/li\\u003e\\n\\u003cli\\u003eAdedokun ST, Uthman OA, Adekanmbi VT, Wiysonge CS. Incomplete childhood immunization in Nigeria: a multilevel analysis of individual and contextual factors. BMC Public Health. 2017;17(1):236. \\u003c/li\\u003e\\n\\u003cli\\u003eDebie A, Amare G, Handebo S, Mekonnen ME, Tesema GA. Individual-and Community-Level Determinants for Complete Vaccination among Children Aged 12-23 Months in Ethiopia: A Multilevel Analysis. Biomed Res Int. 2020;2020(1):6907395. \\u003c/li\\u003e\\n\\u003cli\\u003eGebreyesus A, Tesfay K. Effect of maternal education on completing childhood vaccination in Ethiopia: systematic review and meta-analysis. Sci Rep. 2024;14(1):17453. \\u003c/li\\u003e\\n\\u003cli\\u003eForshaw J, Gerver SM, Gill M, Cooper E, Manikam L, Ward H. The global effect of maternal education on complete childhood vaccination: a systematic review and meta-analysis. BMC Infect Dis. 2017;17(1):801. \\u003c/li\\u003e\\n\\u003cli\\u003eOnsomu EO, Abuya BA, Okech IN, Moore D, Collins-McNeil J. Maternal education and immunization status among children in Kenya. Matern Child Health J. 2015;19(8):1724\\u0026ndash;33. \\u003c/li\\u003e\\n\\u003cli\\u003eVikram K, Vanneman R, Desai S. Linkages between maternal education and childhood immunization in India. Soc Sci Med. 2012;75(2):331\\u0026ndash;9. \\u003c/li\\u003e\\n\\u003cli\\u003eBalogun SA, Yusuff HA, Yusuf KQ, Al-Shenqiti AM, Balogun MT, Tettey P. Maternal education and child immunization: the mediating roles of maternal literacy and socioeconomic status. Pan Afr Med J. 2017;26:217. \\u003c/li\\u003e\\n\\u003cli\\u003eAsif AM, Akbar M, Tahir MR, Arshad IA. Role of maternal education and vaccination coverage: evidence from Pakistan demographic and health survey. Asia Pacific J Public Heal. 2019;31(8):679\\u0026ndash;88. \\u003c/li\\u003e\\n\\u003cli\\u003eNankabirwa V, Tyllesk\\u0026auml;r T, Tumwine JK, Sommerfelt H, no P ebf SGTT cih. uib. Maternal education is associated with vaccination status of infants less than 6 months in Eastern Uganda: a cohort study. BMC Pediatr. 2010;10(1):92. \\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"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\":\"info@researchsquare.com\",\"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\":\"Childhood vaccination, Immunization coverage, Multilevel logistic regression, Spatial analysis, Global Moran’s I, Getis-Ord Gi*, Somalia, Health disparities\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8444974/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8444974/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eVaccinating children is an important public health measure. Coverage in Somalia, however, remains uneven. To plan targeted treatments, it is essential to understand both the factors that influence vaccination and the location where it occurs.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study analyzed data from the Somali Health and Demographic Survey (SHDS 2020) and employed multilevel logistic regression to examine individual and community-level factors influencing children's vaccination status. We used AIC, BIC, log-likelihood, and ICC values to compare the models' fit. Spatial clustering was assessed using Global Moran’s I, Local Moran’s I, and Getis-Ord Gi* statistics.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAmong 5,732 children, only 32.5% were fully vaccinated. The lowest coverage was observed among those under one year of age (24.1%). Maternal healthcare utilization was strongly associated with vaccination: children whose mothers had four or more ANC visits had more than three times the odds of being vaccinated (AOR = 3.25, 95% CI: 2.61–4.05) compared to those with no visits. Higher maternal education and urban residence were significant predictors, whereas children from nomadic households exhibited markedly lower odds (AOR = 4.29, 95% CI: 3.21–5.75). Spatial analysis revealed significant clustering (Global Moran’s I = 0.312, p \\u0026lt; 0.01), with “hot spots” of high vaccination in Jubbada Hoose, Gedo, Bay, and Bakool, and “cold spots” in Sanaag, Togdheer, and Woqooyi Galbeed.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eChildhood vaccination rates in Somalia remain low and are spatially clustered. Strengthening maternal health services, addressing nomadic populations, and prioritizing northern regions with persistently low immunization coverage are essential to achieving nationwide equity.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Geographical Variations and Associated Factors of Timely Vaccination Status Among Children in Somalia: An Application of Spatial and Multilevel Analysis\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-01-05 09:15:04\",\"doi\":\"10.21203/rs.3.rs-8444974/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"reviewerAgreed\",\"content\":\"132943379813660265272520225310808106591\",\"date\":\"2026-05-16T13:47:21+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-01T14:26:48+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"285039157505131780397102028445879743591\",\"date\":\"2026-04-15T09:14:06+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"236799727180212174405111985862457543257\",\"date\":\"2026-01-24T18:17:24+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"109374261058696045721246127033343597711\",\"date\":\"2025-12-31T12:48:35+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-12-31T09:27:45+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-12-29T06:25:40+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-12-26T07:17:58+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-12-26T07:14:38+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Public Health\",\"date\":\"2025-12-24T20:03:03+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"6d5a7651-56d2-49fc-91f8-2cb6a44e4db3\",\"owner\":[],\"postedDate\":\"January 5th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"reviewerAgreed\",\"content\":\"132943379813660265272520225310808106591\",\"date\":\"2026-05-16T13:47:21+00:00\",\"index\":168,\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-01T14:26:48+00:00\",\"index\":151,\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-01-05T09:15:04+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-01-05 09:15:04\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8444974\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8444974\",\"identity\":\"rs-8444974\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}