Determinants of Tetanus Toxoid Immunization Among Pregnant Women in Somaliland: Evidence from the 2020 Nationwide Survey Using a Zero-Inflated Negative Binomial Model

Determinants of Tetanus Toxoid Immunization Among Pregnant Women in Somaliland: Evidence from the 2020 Nationwide Survey Using a Zero-Inflated Negative Binomial Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Determinants of Tetanus Toxoid Immunization Among Pregnant Women in Somaliland: Evidence from the 2020 Nationwide Survey Using a Zero-Inflated Negative Binomial Model Hamda Jama Yousuf, Mohamed Hussein Egeh, Ahmed Abdirahman Farah, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8679360/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Tetanus Toxoid immunization is a critical public health intervention for reducing maternal and neonatal mortality. Despite global recommendations, Tetanus Toxoid coverage remains low in several low- and middle-income countries, including Somaliland. Objectives This study aims to assess the prevalence and determinants of Tetanus Toxoid immunization among pregnant women in Somaliland using the 2020 Somaliland Demographic Health Survey. Methods A cross-sectional analytic study was conducted using the 2020 Somaliland Demographic Health Survey. Data from 2,584 women aged 15–49 years. STATA 17 was used for analysis. Descriptive statistics were used to examine immunization coverage. The chi-square test was used to identify bivariate associations between TT uptake and explanatory variables. Given the nature of the count outcome and high proportion of zeros, a zero-inflated negative binomial regression model was applied. The result was interpreted using incidence rate ratios with 95% confidence intervals. The model with the lowest AIC and BIC values and the highest log likelihood was selected as the best fit. Results Only 14.24% of pregnant women received at least two Tetanus Toxoid doses, while 73.03% received none. ANC attendance (IRR = 3.93; 95% CI: 3.32–4.66), wealth index, maternal employment (IRR = 0.41; 95% CI: 0.23–0.75 for unemployed), institutional delivery, and distance to health facility (IRR = 1.17; 95% CI: 1.02–1.34) were significantly associated with Tetanus Toxoid uptake. Regional disparities and media exposure were also observed. Conclusion Tetanus Toxoid immunization among pregnant women in Somaliland is alarmingly low and shaped by both socio-demographic and reproductive health factors. Targeted interventions should focus on expanding ANC coverage, reducing geographic and economic barriers, empowering women, enhancing awareness through media, and improving physical access to healthcare facilities. These findings are aligned with SDG 3 in Somaliland. Tetanus toxoid immunization Somaliland Demographic Health Survey Zero-inflated negative binomial Somaliland Figures Figure 1 Background Tetanus is a potentially fatal disease caused by Clostridium tetani, a bacterium whose spores are found worldwide.( 1 ). Tetanus is more prevalent in areas with poor sanitation, poverty, and limited access to healthcare.( 2 ). While tetanus can affect anyone, newborns and recently pregnant women are particularly vulnerable, especially if deliveries occur in an unsanitary environment.( 3 ). Neonatal tetanus (NT) is defined as tetanus occurring within a baby's first 28 days, with symptoms usually appearing 3–14 days after birth. The condition is marked by difficulty feeding due to lockjaw (trismus), followed by stiff muscles and spasms throughout the body, potentially leading to autonomic nervous system problems and respiratory failure.( 4 ). Unhygienic childbirth practices and a lack of maternal tetanus vaccination significantly elevate an infant's risk. Despite being preventable, NT persists in many low- and middle-income countries (LMICs), where its near-certain mortality rate is due to a lack of access to specialized medical care.( 4 ). When a pregnant woman is fully vaccinated against tetanus, she transfers protective antibodies to her baby via the placenta. This shields the infant from tetanus until they are old enough to receive their own vaccinations at 6 weeks. Research indicates that adequate tetanus vaccination during pregnancy can reduce neonatal tetanus mortality by an impressive 94%( 5 ). Tetanus is often fatal in areas with limited resources, with mortality rates reaching nearly 100%. However, with proper medical treatment, this can be significantly reduced to 10–20%( 6 ). Worldwide, tetanus caused 38,000 deaths in 2017( 7 ). In 2004, tetanus was estimated to be responsible for the deaths of roughly 128,250 newborns and 30,000 mothers, primarily in Africa and Asia. ( 8 ). Maternal and neonatal tetanus can be prevented by practicing good hygiene during childbirth and ensuring mothers are vaccinated against tetanus. ( 9 , 10 ). This preventative approach is supported by research demonstrating that vaccinating women of childbearing age against tetanus lowers the risk of neonatal tetanus ( 11 ). Furthermore, a meta-analysis has revealed that vaccinating pregnant women with at least two doses of tetanus toxoid (TT2) can decrease neonatal mortality from tetanus by 94%( 5 ). Therefore, the World Health Organization (WHO) advises that women with no prior tetanus vaccination receive a total of five doses of the tetanus toxoid (TT) vaccine. The initial two doses should be administered one month apart during the first trimester, followed by another dose during a subsequent pregnancy or within a year. ( 12 ). In 2017, Sub-Saharan Africa and South Asia experienced the highest incidence of new tetanus cases, accounting for 82% of all cases globally. These regions also accounted for 29,500 tetanus-related deaths, representing 77% of the total mortality. Sub-Saharan Africa continues to have the highest rate of tetanus cases worldwide.( 13 , 14 ). Sub-Saharan African countries have alarmingly high tetanus mortality rates. For example, reports indicate mortality rates of 64% in Nigeria, 47% in Uganda, 43.1% in Tanzania, and 48.5% in Cameroon.( 15 ). Somalia, South Sudan, Afghanistan, and Kenya reported over 1,000 newborn tetanus deaths per 100,000 people, indicating the highest prevalence of the disease. Somalia, South Sudan, and Kenya also had the highest rates of tetanus mortality after the newborn period (greater than five deaths per 100,000 people)( 16 ). In Somaliland, high maternal and neonatal mortality rates persist, with the 2020 Somaliland Demographic and Health Survey (SLHDS) showing low coverage of TT immunization among pregnant women.( 17 ). The Somaliland Demographic and Health Survey (SLHDS) 2020 highlights that a significant proportion of pregnant women do not receive the recommended doses of tetanus toxoid vaccines. Research from around the world indicates that maternal age.( 18 ), education level( 19 ), marital status( 20 ), household wealth( 21 ), distance to healthcare facilities( 22 ), inadequate antenatal care( 23 ), number of previous pregnancies( 24 ), and the type of residence( 25 ) Where significantly associated with tetanus immunization before birth. The World Health Organization (WHO) has implemented maternal immunization programs, starting in 2016, recommending routine vaccination of pregnant women and women of reproductive age (15–49 years) with tetanus toxoid. However, providing at least three doses of the tetanus toxoid vaccine to women of reproductive age in high-risk areas through supplemental immunization campaigns remains a significant public health challenge in low- and middle-income countries( 26 , 27 ). While low- and middle-income countries bear the overwhelming burden of maternal and neonatal illness and death linked to tetanus, our literature review indicates a lack of research examining the specific number of tetanus injections received before birth and the factors influencing this among pregnant women in these countries. This study aims to provide reliable evidence that can inform policy and strategies to improve maternal and neonatal health outcomes in Somaliland. This research also contributes to the achievement of several Sustainable Development Goals (SDGs). It supports SDG 3, which aims to ensure healthy lives and promote well-being for all, particularly Targets 3.1 and 3.2 on reducing maternal and neonatal mortality. It also aligns with SDG 3.8 on universal health coverage. However, using the 2020 Somaliland Demographic and Health Survey (SLHDS) dataset ensures a nationally representative sample, increasing the reliability of the findings. Addressing these key factors was essential for improving maternal and neonatal health outcomes in Somaliland. Method and material Study setting The study was conducted in Somaliland, a self-declared independent country in the Horn of Africa with a population of approximately 4.5 million. Geographically, Somaliland is situated between Djibouti, Ethiopia, and Somalia, encompassing 176,119.2 km2, including an 850 km coastline, and featuring diverse terrain from coastal plains to hilly northern regions (1,800-2,100m). The climate alternates between wet and dry seasons. The population is predominantly ethnic Somali and Muslim. Hargeisa, the capital, is home to over one-third of the population. Somaliland is administratively structured into six regions and 22 districts. The economy, though expanding due to livestock exports, faces challenges as Somaliland remains a low-income country with limited foreign investment and trade opportunities. Remittances and livestock exports to the Gulf States contributed to an estimated GDP of USD 2.5 billion and a per capita GDP of USD 566 in 2018. However, the absence of international recognition significantly restricts the nation’s access to bilateral agreements and direct donor investments( 17 , 28 ). Study Design, Data Source, and Study Period The research utilized data from the 2020 Somaliland Demographic Health Survey (SLHDS), the first nationally representative health survey in the country. This cross-sectional survey was conducted from August 2018 to December 2019. This design is appropriate for exploring the Determinants of Poor Tetanus Toxoid Immunization Among Pregnant Women in Somaliland. The 2020 SLHDS aimed to strengthen data systems and promote evidence-based planning within Somaliland( 17 ). Sample Size and Sampling This study analyzed a weighted sample of 2,584 women of reproductive age (15–49 years) from the 2020 Somaliland Demographic Health Survey (SLHDS) dataset. These women had experienced a pregnancy in the five years prior to the survey. The SLHDS utilized a two-stage sampling technique. First, a Geographic Information System (GIS) was employed to map residential buildings and create enumeration areas (EAs), with each EA containing a minimum of 50 and a maximum of 149 dwellings. A total of 2,923 EAs (1,869 urban and 1,054 rural), also known as primary sampling units (PSUs), were digitized. The final sampling frame comprised 2,086 PSUs, accounting for security issues that prevented visits to some EAs. In the first stage of sampling, 35 EAs were selected from each region (Somaliland's six first-level administrative divisions) using probability proportional to size (PPS) sampling. A complete household listing was then conducted within each of the 35 selected EAs to determine the total number of households. Sample ground verification was also performed to refine the sampling frame as necessary.( 17 ). Variables of the Study Outcome variable The outcome variable in this study was the number of tetanus toxoid (TT) injections received before birth. Data were obtained from the 2020 Somaliland Demographic and Health Survey (SLHDS), which asked women whether they had received a TT injection during pregnancy. The response was recorded as a count (0, 1, 2, 3, 4, 5+) and analyzed as a count outcome variable to reflect the extent of immunization coverage. Independent variables The independent variables comprised a total of 19 explanatory factors, selected based on their theoretical relevance and prior research evidence. These consisted of both sociodemographic and reproductive health variables, socio demographic variables included sex of household head, Maternal age, Maternal education, husband’s education, Husband employment, maternal employment, place of residence, region, media exposure, wealth index, Reproductive health variables comprised Age of respondent at 1st birth, number of antenatal care visit, parity, distance of health facility, place of birth, health care decision making. Data Processing and Analysis This study used Stata version 17 for data processing and analysis. First, the data was cleaned to remove errors like missing information, unusual values, and duplicates. Important factors such as socioeconomic background, access to healthcare, and tetanus toxoid (TT) immunization status were identified and prepared for analysis. Descriptive statistics (frequencies, percentages, averages, and standard deviations) were used to describe the study population. Then, chi-square tests were used to explore how TT immunization relates to other factors. The main analysis involved a Zero-Inflated Negative Binomial (ZINB) regression model in Stata 17, which is good for data with a lot of zeros and over-dispersion than expected. This model helped us understand how factors like socioeconomic status, healthcare access, and education level are related to low immunization rates. Finally, determinants significantly associated with poor TT immunization were identified based on the Adjusted Incidence Rate Ratios (AIRR) and p-values less than 0.05 in the final multivariable model. Model fit was assessed using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), where lower values indicate better model performance and help guide model selection. Compared to other options, we'll perform a sensitivity analysis to make sure the results are reliable. Finally, interpret the findings in light of existing research and suggest policy changes to improve immunization rates among pregnant women in Somaliland. Statistical method Count data was the variable of interest in this study. Non-linear models based on non-normal distributions are suitable for describing the relationship between a set of predictor variables and the response variable when the dependent variable is a count. The Poisson, negative binomial regression, ZIP, and ZINB models are the main frameworks used to model the relationship between the outcome variable and explanatory factors for count data.( 29 ) Poisson Regression Model Poisson regression has been widely used for fitting count data. It is traditionally conceived as the basic count model upon which a variety of other count models are based. ( 30 ). The Poisson probability mass function, with rate parameter µi, is given by: $$\:\text{P}\left({Y}_{i}={y}_{i}\right)=\frac{{e}^{-{{\mu\:}}_{i}}\hspace{0.17em}{{\mu\:}}_{i}^{{y}_{i}}}{{y}_{i}!},\hspace{1em}{y}_{i}=\text{0,1},\text{2,3},\text{4,5}\dots\:..$$ The expected count µi is linked to explanatory variables through a log-linear function: $$\:\text{log}\left({\mu\:}_{i}\right)\:=\:{\beta\:}_{0}\:+\:{\beta\:}_{1}\:{x}_{1}\:+\:{\beta\:}_{2}\:{x}_{2}\:+\:{\beta\:}_{3}\:{x}_{3}\:+\:\cdots\:\:+\:{\beta\:}_{p}\:{x}_{p}\:=\:{X}_{i}^{T}\:$$ Where Xi represents the vector of covariates (such as maternal age, education, residence, antenatal care visits), and β denotes the corresponding regression coefficients. A key assumption of the Poisson model is equidispersion, meaning the variance equals the mean: $$\:E\left(Yi\right)=Var\left(Yi\right)=\mu\:i$$ Here, β is the vector of regression coefficients, and X i ​ is the vector of covariates for the i-the observation. However, in many real-world datasets, including this study, the variance exceeds the mean, a phenomenon known as overdispersion. Overdispersion can arise from omitted covariates, model misspecification, or excess zeros.( 31 ). In such cases, the Poisson model underestimates standard errors, leading to misleading inference. Negative Binomial Regression Model To address overdispersion, the Negative Binomial (NB) regression model introduces a dispersion parameter ϕ > 0. The NB probability mass function is: $$\:\text{P}\left({Y}_{i}={y}_{i}\right)=\frac{{\Gamma\:}\left({y}_{i}+{{\upvarphi\:}}^{-1}\right)}{{\Gamma\:}\left({y}_{i}+1\right)\hspace{0.17em}{\Gamma\:}\left({{\upvarphi\:}}^{-1}\right)}{\left(\frac{{{\upvarphi\:}}^{-1}}{{{\upvarphi\:}}^{-1}+{{\mu\:}}_{i}}\right)}^{{{\upvarphi\:}}^{-1}}{\left(\frac{{{\mu\:}}_{i}}{{{\upvarphi\:}}^{-1}+{{\mu\:}}_{i}}\right)}^{{y}_{i}},\hspace{1em}{y}_{i}=\text{0,1},\text{2,3},\text{4,5}..$$ The mean and variance are: $$\:\text{E}\left(Yi\right)={\mu\:}i,\text{V}\text{a}\text{r}\left(Yi\right)={\mu\:}i\left(1+{\upvarphi\:}{\mu\:}i\right)$$ The mean and variance of the negative binomial distribution are E [y|µ ∅] = µ and V [y|µ ∅] = µ (1 + ∅ µ). Where ∅ is the dispersion parameter (if ∅ > 0 and µ > 0)? Special cases of the negative binomial include the Poisson (∅ = 0) and the geometric (∅ = 1). The method of maximum likelihood is used to estimate the parameters in the negative binomial regression model( 32 ). Zero Inflated Regression Model Poisson regression and negative binomial model with many zero outcomes on the response variable. The ZIP regression model is more effective for many zero outcomes than Poisson regression. While the ZINB regression model is more effective for many zero outcomes than the negative binomial regression ( 33 ). Zero-Inflated Poisson and Zero-Inflated Negative Binomial Regression Models In ZIP regression, the counts Yi equal 0 with probability pi and follow a Poisson distribution with mean µi, with probability 1 − pi, where i = 0, 1, 2… n. The ZIP model can thus be seen as a mixture of two-component distributions, zero parts, and non-zero components, given by( 30 ). $$\:P\left({Y}_{i}=0\right)={{\pi\:}}_{i}+\left(1-{{\pi\:}}_{i}\right){e}^{-{{\mu\:}}_{i}}$$ $$\:P\left({Y}_{i}={y}_{i}\right)=\left(1-{{\pi\:}}_{i}\right)\frac{{e}^{-{{\mu\:}}_{i}}{{\mu\:}}_{i}^{{y}_{i}}}{{y}_{i}!},\hspace{1em}{y}_{i}>0$$ The ZINB distribution is a mixture distribution assigning a mass of p to ‘extra’ zeros and a mass of (1-p) to a negative binomial distribution, where 0 ≤ p ≤ 1. Based on the probability function of the zero–modified distribution, the probability mass function for ZINB is: $$\:P\left({Y}_{i}=0\right)={{\pi\:}}_{i}+\left(1-{{\pi\:}}_{i}\right){\left(\frac{{{\upvarphi\:}}^{-1}}{{{\upvarphi\:}}^{-1}+{{\mu\:}}_{i}}\right)}^{{{\upvarphi\:}}^{-1}}$$ $$\:P\left({Y}_{i}={y}_{i}\right)=\left(1-{{\pi\:}}_{i}\right)\cdot\:\frac{{\Gamma\:}\left({y}_{i}+{{\upvarphi\:}}^{-1}\right)}{{\Gamma\:}\left({y}_{i}+1\right)\hspace{0.17em}{\Gamma\:}\left({{\upvarphi\:}}^{-1}\right)}\:{\left(\frac{{{\upvarphi\:}}^{-1}}{{{\upvarphi\:}}^{-1}+{{\mu\:}}_{i}}\right)}^{{{\upvarphi\:}}^{-1}}{\left(\frac{{{\mu\:}}_{i}}{{{\upvarphi\:}}^{-1}+{{\mu\:}}_{i}}\right)}^{{y}_{i}},\hspace{1em}{y}_{i}>0$$ Given the nature of TT immunization data from the SLHDS 2020, which likely exhibits overdispersion and excess zeros, this study compares the performance of Poisson, Negative Binomial, ZIP, and ZINB models to identify the best fit for describing the determinants of TT immunization counts among pregnant women in Somaliland. Maximum likelihood estimation is used to fit these models, and model comparison is based on criteria such as Akaike Information Criterion (AIC) and likelihood ratio tests. This approach ensures robust inference on socio-demographic and healthcare factors influencing TT immunization uptake. Results Table 1 presents the distribution of tetanus toxoid (TT) injections received before birth among pregnant women in Somaliland based on the SLHDS 2020 data. The findings indicate that a substantial proportion of the respondents (73.03%) did not receive any tetanus toxoid injections during pregnancy. Only 12.73% received one dose, while a relatively small percentage of women received the recommended two or more doses required for adequate protection against neonatal tetanus. Specifically, 7.93% received two doses, 3.79% received three doses, 0.43% received four doses, and 2.09% received five or more doses. The mean number of TT injections was 0.52, with a standard deviation of 1.05, suggesting limited uptake and some variability in vaccination coverage. The number of doses ranged from 0 to 5, with the majority falling at the lower end of the scale. These results highlight a concerning gap in maternal immunization coverage, as only 14.24% of pregnant women received at least two doses, which is the minimum required for effective immunological protection. This underscores the urgent need for targeted public health interventions to enhance access to and utilization of maternal tetanus immunization services in Somaliland. The mean and the variance of the number of tetanus toxoid injections before birth was 0.52 and 1.11, respectively ( Table 1 ). Table 1 Number of tetanus toxoid injections before birth and associated factors among pregnant women in Somaliland, SLHDS2020 Number of tetanus toxoid injections before birth Frequency Percentage 0 1,887 73.03 1 329 12.73 2 205 7.93 3 98 3.79 4 11 0.43 5+ 54 2.09 Mean .5212848 SD 1.051303 Variance 1.105238 Minimum 0 Maximum 5 Total observation 2,584 Figure 1 shows the distribution of tetanus toxoid (TT) injections received by women before birth in Somalia, with 73.03% of women receiving no injections, 12.73% receiving one injection, 7.93% receiving two injections, 3.79% receiving three injections, 0.43% receiving four injections, and 2.09% receiving five or more injections. Table 2 presents the socio-demographic and reproductive health characteristics of pregnant women in Somaliland based on the SLHDS 2020 dataset. The largest proportion of respondents was aged 25–29 years (27.55%), followed by those aged 30–34 years (21.17%) and 20–24 years (17.92%). The majority of the women resided in either Sanaag (23.03%) or Sool (22.87%), with nearly equal representation from rural (49.61%) and urban (50.39%) areas. A substantial proportion of respondents were in the lowest wealth quintile (39.59%). Alarmingly, 83.55% of the women had no formal education, and only 1.04% attained higher education. Most husbands were unemployed (61.42%), and almost all women were not employed (99.69%). Additionally, 62.42% of households were headed by males. Regarding husbands’ education, 90.56% had no formal education. Media exposure was limited, with only 35.49% of women reporting access. Nearly half (47.52%) of the respondents gave birth for the first time before the age of 20. Decision-making about healthcare was most commonly made by the husband alone (40.56%), followed by joint decision-making (38.85%). A significant number (67.41%) reported that distance to health facilities was a major problem. Antenatal care utilization was low, with 63.43% not attending any visits, and only 12.93% receiving adequate care. Furthermore, 73.45% of the women gave birth at home. Regarding parity, the majority were either grand multiparous (44.78%) or multiparous (43.30%), with only 11.92% being primiparous. Table 2 Characteristics of women who participated in the study assessing Tetanus toxoid immunization among pregnant women in Somaliland (5,584) SLHDS2020 Variables Categorize Frequency Percentage Age 15–19 134 5.19 20–24 463 17.92 25–29 712 27.55 30–34 547 21.17 35–39 464 17.96 40–44 202 7.82 45–49 62 2.40 Region Awdal 347 13.43 Marodijeh 300 11.61 Sahil 312 12.07 Togdheer 439 16.99 Sool 591 22.87 Sanaag 595 23.03 place of residence Rural 1282 49.61 Urban 1302 50.39 Wealth index Lowest 1023 39.59 Second 428 16.56 Middle 266 10.29 Fourth 396 15.33 Highest 471 18.23 Maternal educational status No Education 2159 83.55 Primary 337 13.04 Secondary 61 2.36 Higher 27 1.04 Husband employment Yes 997 38.58 No 1,587 61.42 Maternal employment Yes 8 0.31 No 2576 99.69 Sex of household head Male 1613 62.42 Female 971 37.58 Husband's educational status No 2340 90.56 Yes 244 9.44 Media exposure No 1667 64.51 Yes 917 35.49 Age of respondent at 1st birth < 20 1228 47.52 20–24 963 37.27 25–30 341 13.20 30 and above 52 2.01 Health care decision making Respondent 525 20.32 Husband 1048 40.56 Respondent and Husband Jointly 1004 38,85 In Laws 3 0.12 Someone else 2 0.08 Other 2 0.08 Distance from the health facility Big problem 1742 67.41 Not a big problem 842 32.59 Number of ANC visits Not utilized 1639 63.43 Partially utilized 611 23.65 Adequately utilized 334 12.93 Place of birth Home 1898 73.45 Health institution 686 26.55 Parity Primipara 308 11.92 Multipara 1119 43.30 grand multipara 1157 44.78 Table 3 presents the comparison of four count regression models—Poisson, Negative Binomial (NB), Zero-Inflated Poisson (ZIP), and Zero-Inflated Negative Binomial (ZINB)—used to analyze the number of tetanus toxoid injections received by pregnant women in Somaliland, based on SLHDS 2020 data. Initially, the standard Poisson regression model was considered. However, this model assumes that the mean and variance of the count data are equal, an assumption that was violated in the dataset due to evident over-dispersion and excess zeros. The data characteristics suggested unobserved heterogeneity and a high frequency of women reporting zero doses of tetanus toxoid, which necessitated the use of more flexible models. To address the over-dispersion, the Negative Binomial model was tested, as it incorporates a dispersion parameter that allows the variance to exceed the mean. To further account for the large number of zero responses, the Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB) models were also evaluated. These models assume a dual process: one generating true counts (including positive counts) and another generating excess zeros, making them especially suitable for datasets with structural zeros. Model performance was assessed using standard model fit criteria: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and log-likelihood values. Lower AIC and BIC values indicate a better model fit, while a higher log-likelihood indicates a more likely model given the observed data. Among the models tested, the ZINB model exhibited the best fit, with the lowest AIC (2160.776) and BIC (2389.203), as well as the highest log-likelihood value (-1041.388). These results suggest that the ZINB model outperformed the other models in accounting for both the over-dispersion and excess zeros present in the data. Consequently, the ZINB model was selected as the most appropriate model for analyzing the number of tetanus toxoid injections received before birth among pregnant women in Somaliland. This model provided the best balance between Table 3 Model comparison for the number of tetanus injections before birth and associated factors among pregnant women in Somaliland, SLHDS2020 Models Test statistics Poisson NB ZIP ZINB AIC 4931.374 4581.706 2163.009 2160.776 BIC 5165.658 4821.847 2397.293 2389.203 Log likelihood -2425.6871 -2249.8528 -1041.505 -1041.388 Table 4 shows the Zero-Inflated Negative Binomial (ZINB) regression model identified several significant predictors of the number of tetanus toxoid (TT) injections received before birth among pregnant women in Somaliland using the SLHDS 2020 data, region, maternal employment, antenatal care utilization, household wealth index, media exposure, distance from a health facility, and place of birth showed significant associations with the frequency of tetanus injections before birth. The number of tetanus injections before birth was significantly lower among pregnant women living in Marodijeh (IRR = 0.68; 95% CI: 0.55–0.84), Sahil (IRR = 0.71; 95% CI: 0.58–0.87), Togdheer (IRR = 0.81; 95% CI: 0.68–0.98), and Sool (IRR = 0.71; 95% CI: 0.58–0.86) compared to women residing in Awdal region. Pregnant women in higher wealth categories were significantly more likely to receive tetanus injections before birth than those in the lowest wealth category, with IRRs of 1.63 (95% CI: 1.36–1.97) for the second quintile, 1.76 (95% CI: 1.43–2.16) for the middle, 1.32 (95% CI: 1.08–1.62) for the fourth, and 1.54 (95% CI: 1.26–1.88) for the highest wealth quintile. The number of tetanus injections before birth was 0.41 times lower (IRR = 0.41; 95% CI: 0.23–0.75) among unemployed women compared to employed women. Women who reported that distance to a health facility was not a big problem had a slightly higher frequency of tetanus injections (IRR = 1.17; 95% CI: 1.02–1.34). Similarly, those who were exposed to media had marginally higher uptake of tetanus injections (IRR = 0.89; 95% CI: 0.78–1.00). Antenatal care utilization was one of the strongest predictors; women who partially utilized ANC were 3.25 times (IRR = 3.25; 95% CI: 2.82–3.76) more likely, and those who adequately utilized ANC were 3.93 times (IRR = 3.93; 95% CI: 3.32–4.66) more likely to receive tetanus injections compared to those who did not utilize ANC at all. Lastly, women who gave birth in health institutions had a higher number of tetanus injections before birth (IRR = 1.15; 95% CI: 1.00–1.31) compared to those who delivered at home. Table 4 Multivariate analysis on Zero Inflated Negative binomial fitted model for number of tetanus injections before birth and associated factors among pregnant women in Somaliland, SLHDS2020 Variables Categorize ZINB P- value IRR CI 95% Age 15–19 Ref - - 20–24 1.077595 .8046417 1.443141 0.616 25–29 1.055625 .7677881 1.451371 0.739 30–34 1.079138 .7640372 1.524191 0.666 35–39 1.141878 .7863617 1.658126 0.486 40–44 1.062201 .6859778 1.644764 0.787 45–49 1.075772 .6031659 1.918686 0.805 Region Awdal Ref - - Marodijeh .67677 .5474441 .8366472 0.000 Sahil .7128344 .5841645 .8698454 0.001 Togdheer .8146551 .6778504 .9790699 0.029 Sool .7097128 .5848672 .8612079 0.001 Sanaag .9949024 .8262838 1.197931 - Place of residence Rural Ref - - Urban 1.112011 .990313 1.248664 0.073 Maternal educational status No Education Ref - - Primary .9481582 .8152577 1.102724 0.490 Secondary 1.041544 .7787875 1.392952 0.784 Higher 1.009427 .6711849 1.518124 0.964 Wealth index Lowest Ref - - Second 1.634885 1.358697 1.967216 0.000 Middle 1.757817 1.428757 2.162664 0.000 Fourth 1.320814 1.078123 1.618135 0.007 Highest 1.535132 1.25685 1.875029 0.000 Maternal employment Yes Ref - - No .4116004 .2268289 .746884 0.004 Husband employment Yes Ref - - No 1.082473 .9537884 1.228519 0.220 Husband's educational status No Ref - - Yes 1.098736 .9349306 1.291241 0.253 Sex of household head Male Ref - - Female .9649039 .8588497 1.084054 0.548 Media exposure No Ref - - Yes .8861623 .7849613 1.000411 0.050 Age of respondent at 1st birth < 20 Ref - - 20–24 .9843349 .8588045 1.128214 0.821 25–30 .8928846 .7181374 1.110154 0.308 30 and above 1.00539 .6785224 1.489722 0.979 Health care decision making Respondent Ref - - Husband .947987 .8176485 1.099102 0.479 Respondent and Husband Jointly .8656101 .7484405 1.001123 0.052 In Laws 1.16e-06 0 0.987 Someone else 1.049522 .2511069 4.386564 0.947 Other .5529279 .0749544 4.078868 0.561 Distance from the health facility Not a big problem Ref - - Big problem 1.17286 1.023021 1.344647 0.022 Number of ANC visits not utilized Ref - - partially utilized 3.251705 2.815809 3.755079 0.000 adequately utilized 3.931047 3.31883 4.656197 0.000 Place of birth Home Ref - - Health institution 1.146322 1.000178 1.31382 0.050 Parity Primipara Ref - - Multipara .8893924 .7330558 1.07907 0.235 grand multipara .9417395 .7308229 1.213527 0.643 Discussion This study examined the factors associated with the number of tetanus toxoid injections received by pregnant women in Somaliland using data from the 2020 Somaliland Demographic Health Survey. The findings revealed a significant association between TT immunization and several socio-demographic and health care access factors. Only 14.24% of the respondents received at least two doses, with 73.03% receiving none. The findings are lower compared to the studies conducted in Africa reported 62%( 26 ) And Brazil reported 59.2%( 34 ), and Turkey reported 27.8%( 35 ). The current finding also falls slightly below that of Sierra Leone, with coverage reported at 82.1%( 36 ). The disparity may be due to differences in healthcare infrastructure, maternal education levels, and antenatal care utilization. Moreover, Somaliland’s limited healthcare and financing accessibility challenges may further explain the low TT uptake. One of the strongest predictors of TT immunization was the region of residence, with women in some regions, such as Maroodijex, Togdheer, Saahil, and Sool, being significantly less likely to receive the TT injection compared to those from Awdal. These regional differences reflect variations in the availability and quality of maternal health services across Somaliland. A similar pattern was reported in a study from Nigeria and Ghana, where geographic inequalities were found to influence access to maternal immunization and Antenatal care.( 37 ). Women in remote areas often face difficulties such as a long distance to the health facilities and a lack of transportation, which limit their ability to receive essential services like TT immunization. Women from households in higher wealth quantiles were significantly more likely to receive TT immunization; for example, those in the middle wealth quantile had 1.76 times higher odds of obtaining the TT dose than women in the poorest category. These findings align with previous studies in Kenya( 23 ), South Africa( 38 ), Alexandra( 39 ), and India( 22 ), which indicate that economic status plays a crucial role in determining access to maternal health care services, including immunization. Financial constraints can hinder women’s ability to travel to health facilities or pay for services, thus reducing their likelihood of receiving recommended vaccines. ( 40 , 41 ). The study also found that maternal employment status influences TT uptake. Unemployed women were 59% less likely to receive the TT immunization compared to those employed. Employment may empower women economically and socially, enabling them to make health-related decisions and access services independently. This is supported by research suggesting that working women are more likely to utilize maternal health services due to increased autonomy and financial resources.( 23 , 42 ). Although media exposure showed only borderline significance, it demonstrated a positive association with TT uptake (IRR = 0.98, p = 0.050). This suggests that exposure to health messages via radio, television, or print may influence maternal health behaviors by increasing awareness about the benefits and timing of TT immunization. A similar association was observed in studies from Ethiopia, which highlights the role of media in improving maternal knowledge and service utilization.( 43 )Women who weren’t exposed to the media may miss this opportunity. Access to health facilities was also a key determinant; women who reported that distance was not a major problem were more likely to receive the TT immunization (IRR = 1.17, p = 0.022). Long distances, poor roads, and a lack of transportation are known barriers in low-resource settings, especially in rural Somaliland, which shares similar challenges with regions in rural Ethiopia and northern Nigeria.( 37 ). Several barriers contribute to this issue; the expense and time involved in reaching far-off immunization centers are the primary factors. Their significant home responsibility, such as caring for children and managing the household, often keep them at home, making them reluctant to travel to distant clinics. Furthermore, the need for multiple vaccination visits, such as those for TT, can be exhausting for both women and their children if these centers are not conveniently located.( 44 ). One of the most striking findings was a strong association between antenatal care (ANC) attendance and TT uptake. Compared to those who did not attend ANC, women who partially utilized ANC were 3.25 times more likely to receive TT, while those who adequately utilized ANC were 3.93times more likely. This was consistent with studies reported in Kenya.( 23 ), Sierra Leon( 36 ), Ghana( 45 ), Mali( 46 ), Malwa( 47 ), Pakistan( 48 ), and Turkey( 49 ). This confirms the critical role of ANC as an entry point for delivering maternal vaccines, as emphasized in global health recommendations. The low ANC utilization in this study (63.43% had no ANC visit) represents a significant missed opportunity for improving TT coverage and maternal health more broadly. The Place of delivery also had a modest but notable association with TT coverage. Women who gave birth in a health institution were 15% more likely to be immunized than those who delivered at home. This may be due to increased health education, follow-up visits, and postpartum counseling typically provided in a facility setting, as well as institutional promotion of immunization schedules. This study highlights that socioeconomic status, regional location, ANC utilization, maternal employment, and access to health information and services are critical determinants of TT immunization among pregnant women in Somaliland. The result emphasizes the need for multi-pronged interventions, including expanding ANC coverage to underserved regions, enhancing women’s economic empowerment through employment opportunities, strengthening community-based media campaigns to promote maternal immunization, and improving rural healthcare infrastructure to reduce physical barriers. These interventions should be integrated into broader maternal and child health programs to enhance TT coverage and contribute to the elimination of maternal and neonatal tetanus in Somaliland. Conclusion The study concludes that tetanus toxoid immunization coverage among pregnant women in Somaliland is alarmingly low and far below the recommended global targets. The number of TT doses received is strongly influenced by both individual and structural factors. Women with resided in certain regions such as Marodijeh, Sahil, Togdheer, and Sool were significantly less likely to receive TT immunization. Indicating geographic disparities in the availability and access. Similarly, women from higher wealth quantiles were more likely to be immunized, suggesting that financial capacity enhances health care utilization. Employment status was also influential, with Employed women more likely to be vaccinated, possibly due to increased autonomy and resources. Media exposure had a borderline significant effect, indicating the potential of communication channels to promote maternal immunization. Physical access to health facilities and attendance of ANC visits were among the strongest predictors, with ANC providing an essential opportunity for administering the TT vaccine. Women who gave birth in a health institution also had a higher immunization rate, showing that facility-based care contributes positively to maternal health practice. Declarations Ethics approval and consent to participate Ethical approval wasn't required for this study because I used publicly available, anonymized data from the Demographic and Health Survey (DHS). I obtained permission to download the DHS dataset from the Central Statistical Agency (CSA) through a request at https://microdata.nbs.gov.so/index.php/catalog/50 Consent for publication Not applicable Data Availability The Somaliland Demographic and Health Survey (SLHDS) 2020 datasets used in this study are publicly accessible and can be obtained through an online request at https://microdata.nbs.gov.so/index.php/catalog/50, specifying the purpose of the research. The datasets analyzed during the current study are also available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding This research received no external funding Authors contributions H.J.Y. conducted data cleaning, analysis, and interpretation. M.H.E. consulted on data analysis and publications. A.A.F. prepared the background section. A.A.H. wrote the discussion section. A.A.O wrote the conclusion and discussion section All authors reviewed and approved the final manuscript. Acknowledgements The authors would like to express their sincere gratitude to the Somaliland Ministry of Health Development and the Somaliland Demographic and Health Survey (SLDHS) 2020 team for granting access to the dataset used in this study. We also acknowledge to colleagues and reviewers who provided valuable comments and constructive feedback that helped improve the quality of this manuscript. References Organization WH. World health statistics 2024: monitoring health for the SDGs. sustainable development goals: World Health Organization; 2024. Sheffield JS, Ramin SM. Tetanus in pregnancy. Am J Perinatol. 2004;21(04):173–82. Khan R, Vandelaer J, Yakubu A, Raza AA, Zulu F. 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Tetanus is more prevalent in areas with poor sanitation, poverty, and limited access to healthcare.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). While tetanus can affect anyone, newborns and recently pregnant women are particularly vulnerable, especially if deliveries occur in an unsanitary environment.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Neonatal tetanus (NT) is defined as tetanus occurring within a baby's first 28 days, with symptoms usually appearing 3\u0026ndash;14 days after birth. The condition is marked by difficulty feeding due to lockjaw (trismus), followed by stiff muscles and spasms throughout the body, potentially leading to autonomic nervous system problems and respiratory failure.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Unhygienic childbirth practices and a lack of maternal tetanus vaccination significantly elevate an infant's risk. Despite being preventable, NT persists in many low- and middle-income countries (LMICs), where its near-certain mortality rate is due to a lack of access to specialized medical care.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). When a pregnant woman is fully vaccinated against tetanus, she transfers protective antibodies to her baby via the placenta. This shields the infant from tetanus until they are old enough to receive their own vaccinations at 6 weeks. Research indicates that adequate tetanus vaccination during pregnancy can reduce neonatal tetanus mortality by an impressive 94%(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Tetanus is often fatal in areas with limited resources, with mortality rates reaching nearly 100%. However, with proper medical treatment, this can be significantly reduced to 10\u0026ndash;20%(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWorldwide, tetanus caused 38,000 deaths in 2017(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In 2004, tetanus was estimated to be responsible for the deaths of roughly 128,250 newborns and 30,000 mothers, primarily in Africa and Asia. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Maternal and neonatal tetanus can be prevented by practicing good hygiene during childbirth and ensuring mothers are vaccinated against tetanus. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). This preventative approach is supported by research demonstrating that vaccinating women of childbearing age against tetanus lowers the risk of neonatal tetanus (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Furthermore, a meta-analysis has revealed that vaccinating pregnant women with at least two doses of tetanus toxoid (TT2) can decrease neonatal mortality from tetanus by 94%(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Therefore, the World Health Organization (WHO) advises that women with no prior tetanus vaccination receive a total of five doses of the tetanus toxoid (TT) vaccine. The initial two doses should be administered one month apart during the first trimester, followed by another dose during a subsequent pregnancy or within a year. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2017, Sub-Saharan Africa and South Asia experienced the highest incidence of new tetanus cases, accounting for 82% of all cases globally. These regions also accounted for 29,500 tetanus-related deaths, representing 77% of the total mortality. Sub-Saharan Africa continues to have the highest rate of tetanus cases worldwide.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Sub-Saharan African countries have alarmingly high tetanus mortality rates. For example, reports indicate mortality rates of 64% in Nigeria, 47% in Uganda, 43.1% in Tanzania, and 48.5% in Cameroon.(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Somalia, South Sudan, Afghanistan, and Kenya reported over 1,000 newborn tetanus deaths per 100,000 people, indicating the highest prevalence of the disease. Somalia, South Sudan, and Kenya also had the highest rates of tetanus mortality after the newborn period (greater than five deaths per 100,000 people)(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Somaliland, high maternal and neonatal mortality rates persist, with the 2020 Somaliland Demographic and Health Survey (SLHDS) showing low coverage of TT immunization among pregnant women.(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The Somaliland Demographic and Health Survey (SLHDS) 2020 highlights that a significant proportion of pregnant women do not receive the recommended doses of tetanus toxoid vaccines. Research from around the world indicates that maternal age.(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), education level(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), marital status(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), household wealth(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), distance to healthcare facilities(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), inadequate antenatal care(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), number of previous pregnancies(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), and the type of residence(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) Where significantly associated with tetanus immunization before birth. The World Health Organization (WHO) has implemented maternal immunization programs, starting in 2016, recommending routine vaccination of pregnant women and women of reproductive age (15\u0026ndash;49 years) with tetanus toxoid. However, providing at least three doses of the tetanus toxoid vaccine to women of reproductive age in high-risk areas through supplemental immunization campaigns remains a significant public health challenge in low- and middle-income countries(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). While low- and middle-income countries bear the overwhelming burden of maternal and neonatal illness and death linked to tetanus, our literature review indicates a lack of research examining the specific number of tetanus injections received before birth and the factors influencing this among pregnant women in these countries.\u003c/p\u003e \u003cp\u003eThis study aims to provide reliable evidence that can inform policy and strategies to improve maternal and neonatal health outcomes in Somaliland. This research also contributes to the achievement of several Sustainable Development Goals (SDGs). It supports SDG 3, which aims to ensure healthy lives and promote well-being for all, particularly Targets 3.1 and 3.2 on reducing maternal and neonatal mortality. It also aligns with SDG 3.8 on universal health coverage. However, using the 2020 Somaliland Demographic and Health Survey (SLHDS) dataset ensures a nationally representative sample, increasing the reliability of the findings. Addressing these key factors was essential for improving maternal and neonatal health outcomes in Somaliland.\u003c/p\u003e"},{"header":"Method and material","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting\u003c/h2\u003e \u003cp\u003eThe study was conducted in Somaliland, a self-declared independent country in the Horn of Africa with a population of approximately 4.5\u0026nbsp;million. Geographically, Somaliland is situated between Djibouti, Ethiopia, and Somalia, encompassing 176,119.2 km2, including an 850 km coastline, and featuring diverse terrain from coastal plains to hilly northern regions (1,800-2,100m). The climate alternates between wet and dry seasons. The population is predominantly ethnic Somali and Muslim. Hargeisa, the capital, is home to over one-third of the population. Somaliland is administratively structured into six regions and 22 districts. The economy, though expanding due to livestock exports, faces challenges as Somaliland remains a low-income country with limited foreign investment and trade opportunities. Remittances and livestock exports to the Gulf States contributed to an estimated GDP of USD 2.5\u0026nbsp;billion and a per capita GDP of USD 566 in 2018. However, the absence of international recognition significantly restricts the nation\u0026rsquo;s access to bilateral agreements and direct donor investments(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Design, Data Source, and Study Period\u003c/h3\u003e\n\u003cp\u003eThe research utilized data from the 2020 Somaliland Demographic Health Survey (SLHDS), the first nationally representative health survey in the country. This cross-sectional survey was conducted from August 2018 to December 2019. This design is appropriate for exploring the Determinants of Poor Tetanus Toxoid Immunization Among Pregnant Women in Somaliland. The 2020 SLHDS aimed to strengthen data systems and promote evidence-based planning within Somaliland(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSample Size and Sampling\u003c/h3\u003e\n\u003cp\u003eThis study analyzed a weighted sample of 2,584 women of reproductive age (15\u0026ndash;49 years) from the 2020 Somaliland Demographic Health Survey (SLHDS) dataset. These women had experienced a pregnancy in the five years prior to the survey. The SLHDS utilized a two-stage sampling technique. First, a Geographic Information System (GIS) was employed to map residential buildings and create enumeration areas (EAs), with each EA containing a minimum of 50 and a maximum of 149 dwellings. A total of 2,923 EAs (1,869 urban and 1,054 rural), also known as primary sampling units (PSUs), were digitized. The final sampling frame comprised 2,086 PSUs, accounting for security issues that prevented visits to some EAs. In the first stage of sampling, 35 EAs were selected from each region (Somaliland's six first-level administrative divisions) using probability proportional to size (PPS) sampling. A complete household listing was then conducted within each of the 35 selected EAs to determine the total number of households. Sample ground verification was also performed to refine the sampling frame as necessary.(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eVariables of the Study\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eOutcome variable\u003c/h2\u003e \u003cp\u003eThe outcome variable in this study was the number of tetanus toxoid (TT) injections received before birth. Data were obtained from the 2020 Somaliland Demographic and Health Survey (SLHDS), which asked women whether they had received a TT injection during pregnancy. The response was recorded as a count (0, 1, 2, 3, 4, 5+) and analyzed as a count outcome variable to reflect the extent of immunization coverage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIndependent variables\u003c/h2\u003e \u003cp\u003eThe independent variables comprised a total of 19 explanatory factors, selected based on their theoretical relevance and prior research evidence. These consisted of both sociodemographic and reproductive health variables, socio demographic variables included sex of household head, Maternal age, Maternal education, husband\u0026rsquo;s education, Husband employment, maternal employment, place of residence, region, media exposure, wealth index, Reproductive health variables comprised Age of respondent at 1st birth, number of antenatal care visit, parity, distance of health facility, place of birth, health care decision making.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Processing and Analysis\u003c/h3\u003e\n\u003cp\u003eThis study used Stata version 17 for data processing and analysis. First, the data was cleaned to remove errors like missing information, unusual values, and duplicates. Important factors such as socioeconomic background, access to healthcare, and tetanus toxoid (TT) immunization status were identified and prepared for analysis. Descriptive statistics (frequencies, percentages, averages, and standard deviations) were used to describe the study population. Then, chi-square tests were used to explore how TT immunization relates to other factors. The main analysis involved a Zero-Inflated Negative Binomial (ZINB) regression model in Stata 17, which is good for data with a lot of zeros and over-dispersion than expected. This model helped us understand how factors like socioeconomic status, healthcare access, and education level are related to low immunization rates. Finally, determinants significantly associated with poor TT immunization were identified based on the Adjusted Incidence Rate Ratios (AIRR) and p-values less than 0.05 in the final multivariable model. Model fit was assessed using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), where lower values indicate better model performance and help guide model selection. Compared to other options, we'll perform a sensitivity analysis to make sure the results are reliable. Finally, interpret the findings in light of existing research and suggest policy changes to improve immunization rates among pregnant women in Somaliland.\u003c/p\u003e\n\u003ch3\u003eStatistical method\u003c/h3\u003e\n\u003cp\u003eCount data was the variable of interest in this study. Non-linear models based on non-normal distributions are suitable for describing the relationship between a set of predictor variables and the response variable when the dependent variable is a count. The Poisson, negative binomial regression, ZIP, and ZINB models are the main frameworks used to model the relationship between the outcome variable and explanatory factors for count data.(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePoisson Regression Model\u003c/h2\u003e \u003cp\u003ePoisson regression has been widely used for fitting count data. It is traditionally conceived as the basic count model upon which a variety of other count models are based. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The Poisson probability mass function, with rate parameter \u0026micro;i, is given by:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{P}\\left({Y}_{i}={y}_{i}\\right)=\\frac{{e}^{-{{\\mu\\:}}_{i}}\\hspace{0.17em}{{\\mu\\:}}_{i}^{{y}_{i}}}{{y}_{i}!},\\hspace{1em}{y}_{i}=\\text{0,1},\\text{2,3},\\text{4,5}\\dots\\:..$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe expected count \u0026micro;i is linked to explanatory variables through a log-linear function:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{log}\\left({\\mu\\:}_{i}\\right)\\:=\\:{\\beta\\:}_{0}\\:+\\:{\\beta\\:}_{1}\\:{x}_{1}\\:+\\:{\\beta\\:}_{2}\\:{x}_{2}\\:+\\:{\\beta\\:}_{3}\\:{x}_{3}\\:+\\:\\cdots\\:\\:+\\:{\\beta\\:}_{p}\\:{x}_{p}\\:=\\:{X}_{i}^{T}\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere Xi represents the vector of covariates (such as maternal age, education, residence, antenatal care visits), and β denotes the corresponding regression coefficients. A key assumption of the Poisson model is equidispersion, meaning the variance equals the mean:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:E\\left(Yi\\right)=Var\\left(Yi\\right)=\\mu\\:i$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, β is the vector of regression coefficients, and X\u003csub\u003ei\u003c/sub\u003e ​ is the vector of covariates for the i-the observation. However, in many real-world datasets, including this study, the variance exceeds the mean, a phenomenon known as overdispersion. Overdispersion can arise from omitted covariates, model misspecification, or excess zeros.(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). In such cases, the Poisson model underestimates standard errors, leading to misleading inference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNegative Binomial Regression Model\u003c/h2\u003e \u003cp\u003eTo address overdispersion, the Negative Binomial (NB) regression model introduces a dispersion parameter ϕ\u0026thinsp;\u0026gt;\u0026thinsp;0. The NB probability mass function is:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\text{P}\\left({Y}_{i}={y}_{i}\\right)=\\frac{{\\Gamma\\:}\\left({y}_{i}+{{\\upvarphi\\:}}^{-1}\\right)}{{\\Gamma\\:}\\left({y}_{i}+1\\right)\\hspace{0.17em}{\\Gamma\\:}\\left({{\\upvarphi\\:}}^{-1}\\right)}{\\left(\\frac{{{\\upvarphi\\:}}^{-1}}{{{\\upvarphi\\:}}^{-1}+{{\\mu\\:}}_{i}}\\right)}^{{{\\upvarphi\\:}}^{-1}}{\\left(\\frac{{{\\mu\\:}}_{i}}{{{\\upvarphi\\:}}^{-1}+{{\\mu\\:}}_{i}}\\right)}^{{y}_{i}},\\hspace{1em}{y}_{i}=\\text{0,1},\\text{2,3},\\text{4,5}..$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe mean and variance are:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:\\text{E}\\left(Yi\\right)={\\mu\\:}i,\\text{V}\\text{a}\\text{r}\\left(Yi\\right)={\\mu\\:}i\\left(1+{\\upvarphi\\:}{\\mu\\:}i\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe mean and variance of the negative binomial distribution are E [y|\u0026micro; \u0026empty;] = \u0026micro; and V [y|\u0026micro; \u0026empty;] = \u0026micro; (1 + \u0026empty; \u0026micro;). Where \u0026empty; is the dispersion parameter (if \u0026empty; \u0026gt; 0 and \u0026micro;\u0026thinsp;\u0026gt;\u0026thinsp;0)? Special cases of the negative binomial include the Poisson (\u0026empty; = 0) and the geometric (\u0026empty; = 1). The method of maximum likelihood is used to estimate the parameters in the negative binomial regression model(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eZero Inflated Regression Model\u003c/h2\u003e \u003cp\u003ePoisson regression and negative binomial model with many zero outcomes on the response variable. The ZIP regression model is more effective for many zero outcomes than Poisson regression. While the ZINB regression model is more effective for many zero outcomes than the negative binomial regression (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eZero-Inflated Poisson and Zero-Inflated Negative Binomial Regression Models\u003c/h2\u003e \u003cp\u003eIn ZIP regression, the counts Yi equal 0 with probability pi and follow a Poisson distribution with mean \u0026micro;i, with probability 1\u0026thinsp;\u0026minus;\u0026thinsp;pi, where i\u0026thinsp;=\u0026thinsp;0, 1, 2\u0026hellip; n. The ZIP model can thus be seen as a mixture of two-component distributions, zero parts, and non-zero components, given by(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:P\\left({Y}_{i}=0\\right)={{\\pi\\:}}_{i}+\\left(1-{{\\pi\\:}}_{i}\\right){e}^{-{{\\mu\\:}}_{i}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:P\\left({Y}_{i}={y}_{i}\\right)=\\left(1-{{\\pi\\:}}_{i}\\right)\\frac{{e}^{-{{\\mu\\:}}_{i}}{{\\mu\\:}}_{i}^{{y}_{i}}}{{y}_{i}!},\\hspace{1em}{y}_{i}\u0026gt;0$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe ZINB distribution is a mixture distribution assigning a mass of p to \u0026lsquo;extra\u0026rsquo; zeros and a mass of (1-p) to a negative binomial distribution, where 0\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026le;\u0026thinsp;1. Based on the probability function of the zero\u0026ndash;modified distribution, the probability mass function for ZINB is:\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$$\\:P\\left({Y}_{i}=0\\right)={{\\pi\\:}}_{i}+\\left(1-{{\\pi\\:}}_{i}\\right){\\left(\\frac{{{\\upvarphi\\:}}^{-1}}{{{\\upvarphi\\:}}^{-1}+{{\\mu\\:}}_{i}}\\right)}^{{{\\upvarphi\\:}}^{-1}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equi\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\n$$\\:P\\left({Y}_{i}={y}_{i}\\right)=\\left(1-{{\\pi\\:}}_{i}\\right)\\cdot\\:\\frac{{\\Gamma\\:}\\left({y}_{i}+{{\\upvarphi\\:}}^{-1}\\right)}{{\\Gamma\\:}\\left({y}_{i}+1\\right)\\hspace{0.17em}{\\Gamma\\:}\\left({{\\upvarphi\\:}}^{-1}\\right)}\\:{\\left(\\frac{{{\\upvarphi\\:}}^{-1}}{{{\\upvarphi\\:}}^{-1}+{{\\mu\\:}}_{i}}\\right)}^{{{\\upvarphi\\:}}^{-1}}{\\left(\\frac{{{\\mu\\:}}_{i}}{{{\\upvarphi\\:}}^{-1}+{{\\mu\\:}}_{i}}\\right)}^{{y}_{i}},\\hspace{1em}{y}_{i}\u0026gt;0$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eGiven the nature of TT immunization data from the SLHDS 2020, which likely exhibits overdispersion and excess zeros, this study compares the performance of Poisson, Negative Binomial, ZIP, and ZINB models to identify the best fit for describing the determinants of TT immunization counts among pregnant women in Somaliland. Maximum likelihood estimation is used to fit these models, and model comparison is based on criteria such as Akaike Information Criterion (AIC) and likelihood ratio tests. This approach ensures robust inference on socio-demographic and healthcare factors influencing TT immunization uptake.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the distribution of tetanus toxoid (TT) injections received before birth among pregnant women in Somaliland based on the SLHDS 2020 data. The findings indicate that a substantial proportion of the respondents (73.03%) did not receive any tetanus toxoid injections during pregnancy. Only 12.73% received one dose, while a relatively small percentage of women received the recommended two or more doses required for adequate protection against neonatal tetanus. Specifically, 7.93% received two doses, 3.79% received three doses, 0.43% received four doses, and 2.09% received five or more doses. The mean number of TT injections was 0.52, with a standard deviation of 1.05, suggesting limited uptake and some variability in vaccination coverage. The number of doses ranged from 0 to 5, with the majority falling at the lower end of the scale. These results highlight a concerning gap in maternal immunization coverage, as only 14.24% of pregnant women received at least two doses, which is the minimum required for effective immunological protection. This underscores the urgent need for targeted public health interventions to enhance access to and utilization of maternal tetanus immunization services in Somaliland. The mean and the variance of the number of tetanus toxoid injections before birth was 0.52 and 1.11, respectively \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of tetanus toxoid injections before birth and associated factors among pregnant women in Somaliland, SLHDS2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of tetanus toxoid injections before birth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.03\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.73\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 \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.93\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 \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.79\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 \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\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 \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.5212848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.051303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.105238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal observation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,584\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\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the distribution of tetanus toxoid (TT) injections received by women before birth in Somalia, with 73.03% of women receiving no injections, 12.73% receiving one injection, 7.93% receiving two injections, 3.79% receiving three injections, 0.43% receiving four injections, and 2.09% receiving five or more injections.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the socio-demographic and reproductive health characteristics of pregnant women in Somaliland based on the SLHDS 2020 dataset. The largest proportion of respondents was aged 25\u0026ndash;29 years (27.55%), followed by those aged 30\u0026ndash;34 years (21.17%) and 20\u0026ndash;24 years (17.92%). The majority of the women resided in either Sanaag (23.03%) or Sool (22.87%), with nearly equal representation from rural (49.61%) and urban (50.39%) areas. A substantial proportion of respondents were in the lowest wealth quintile (39.59%). Alarmingly, 83.55% of the women had no formal education, and only 1.04% attained higher education. Most husbands were unemployed (61.42%), and almost all women were not employed (99.69%). Additionally, 62.42% of households were headed by males. Regarding husbands\u0026rsquo; education, 90.56% had no formal education. Media exposure was limited, with only 35.49% of women reporting access. Nearly half (47.52%) of the respondents gave birth for the first time before the age of 20. Decision-making about healthcare was most commonly made by the husband alone (40.56%), followed by joint decision-making (38.85%). A significant number (67.41%) reported that distance to health facilities was a major problem. Antenatal care utilization was low, with 63.43% not attending any visits, and only 12.93% receiving adequate care. Furthermore, 73.45% of the women gave birth at home. Regarding parity, the majority were either grand multiparous (44.78%) or multiparous (43.30%), with only 11.92% being primiparous.\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\u003eCharacteristics of women who participated in the study assessing Tetanus toxoid immunization among pregnant women in Somaliland (5,584) SLHDS2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategorize\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAwdal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarodijeh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSahil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTogdheer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSanaag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eplace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFourth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHighest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMaternal educational status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHusband employment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMaternal employment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHusband's educational status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedia exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge of respondent at 1st birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eHealth care decision making\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespondent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHusband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespondent and Husband Jointly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38,85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn Laws\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSomeone else\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDistance from the health facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBig problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot a big problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNumber of ANC visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot utilized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePartially utilized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdequately utilized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePlace of birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth institution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimipara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultipara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egrand multipara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.78\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 comparison of four count regression models\u0026mdash;Poisson, Negative Binomial (NB), Zero-Inflated Poisson (ZIP), and Zero-Inflated Negative Binomial (ZINB)\u0026mdash;used to analyze the number of tetanus toxoid injections received by pregnant women in Somaliland, based on SLHDS 2020 data. Initially, the standard Poisson regression model was considered. However, this model assumes that the mean and variance of the count data are equal, an assumption that was violated in the dataset due to evident over-dispersion and excess zeros. The data characteristics suggested unobserved heterogeneity and a high frequency of women reporting zero doses of tetanus toxoid, which necessitated the use of more flexible models. To address the over-dispersion, the Negative Binomial model was tested, as it incorporates a dispersion parameter that allows the variance to exceed the mean. To further account for the large number of zero responses, the Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB) models were also evaluated. These models assume a dual process: one generating true counts (including positive counts) and another generating excess zeros, making them especially suitable for datasets with structural zeros. Model performance was assessed using standard model fit criteria: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and log-likelihood values. Lower AIC and BIC values indicate a better model fit, while a higher log-likelihood indicates a more likely model given the observed data. Among the models tested, the ZINB model exhibited the best fit, with the lowest AIC (2160.776) and BIC (2389.203), as well as the highest log-likelihood value (-1041.388). These results suggest that the ZINB model outperformed the other models in accounting for both the over-dispersion and excess zeros present in the data. Consequently, the ZINB model was selected as the most appropriate model for analyzing the number of tetanus toxoid injections received before birth among pregnant women in Somaliland. This model provided the best balance between\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 comparison for the number of tetanus injections before birth and associated factors among pregnant women in Somaliland, SLHDS2020\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoisson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eZINB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4931.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4581.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2163.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2160.776\u003c/b\u003e\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e5165.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4821.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2397.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2389.203\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2425.6871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2249.8528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1041.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-1041.388\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the Zero-Inflated Negative Binomial (ZINB) regression model identified several significant predictors of the number of tetanus toxoid (TT) injections received before birth among pregnant women in Somaliland using the SLHDS 2020 data, region, maternal employment, antenatal care utilization, household wealth index, media exposure, distance from a health facility, and place of birth showed significant associations with the frequency of tetanus injections before birth. The number of tetanus injections before birth was significantly lower among pregnant women living in Marodijeh (IRR\u0026thinsp;=\u0026thinsp;0.68; 95% CI: 0.55\u0026ndash;0.84), Sahil (IRR\u0026thinsp;=\u0026thinsp;0.71; 95% CI: 0.58\u0026ndash;0.87), Togdheer (IRR\u0026thinsp;=\u0026thinsp;0.81; 95% CI: 0.68\u0026ndash;0.98), and Sool (IRR\u0026thinsp;=\u0026thinsp;0.71; 95% CI: 0.58\u0026ndash;0.86) compared to women residing in Awdal region. Pregnant women in higher wealth categories were significantly more likely to receive tetanus injections before birth than those in the lowest wealth category, with IRRs of 1.63 (95% CI: 1.36\u0026ndash;1.97) for the second quintile, 1.76 (95% CI: 1.43\u0026ndash;2.16) for the middle, 1.32 (95% CI: 1.08\u0026ndash;1.62) for the fourth, and 1.54 (95% CI: 1.26\u0026ndash;1.88) for the highest wealth quintile. The number of tetanus injections before birth was 0.41 times lower (IRR\u0026thinsp;=\u0026thinsp;0.41; 95% CI: 0.23\u0026ndash;0.75) among unemployed women compared to employed women. Women who reported that distance to a health facility was not a big problem had a slightly higher frequency of tetanus injections (IRR\u0026thinsp;=\u0026thinsp;1.17; 95% CI: 1.02\u0026ndash;1.34). Similarly, those who were exposed to media had marginally higher uptake of tetanus injections (IRR\u0026thinsp;=\u0026thinsp;0.89; 95% CI: 0.78\u0026ndash;1.00). Antenatal care utilization was one of the strongest predictors; women who partially utilized ANC were 3.25 times (IRR\u0026thinsp;=\u0026thinsp;3.25; 95% CI: 2.82\u0026ndash;3.76) more likely, and those who adequately utilized ANC were 3.93 times (IRR\u0026thinsp;=\u0026thinsp;3.93; 95% CI: 3.32\u0026ndash;4.66) more likely to receive tetanus injections compared to those who did not utilize ANC at all. Lastly, women who gave birth in health institutions had a higher number of tetanus injections before birth (IRR\u0026thinsp;=\u0026thinsp;1.15; 95% CI: 1.00\u0026ndash;1.31) compared to those who delivered at home.\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\u003eMultivariate analysis on Zero Inflated Negative binomial fitted model for number of tetanus injections before birth and associated factors among pregnant women in Somaliland, SLHDS2020\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategorize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eZINB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP- value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI 95%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.077595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.8046417 1.443141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.055625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.7677881 1.451371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.079138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.7640372 1.524191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.141878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.7863617 1.658126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.062201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.6859778 1.644764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.075772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.6031659 1.918686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAwdal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarodijeh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.67677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.5474441 .8366472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSahil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.7128344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.5841645 .8698454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTogdheer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.8146551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.6778504 .9790699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.7097128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.5848672 .8612079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSanaag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.9949024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.8262838 1.197931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.112011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.990313 1.248664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMaternal educational status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.9481582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.8152577 1.102724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.041544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.7787875 1.392952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.009427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.6711849 1.518124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.634885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.358697 1.967216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.757817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.428757 2.162664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFourth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.320814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.078123 1.618135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHighest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.535132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25685 1.875029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMaternal employment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.4116004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.2268289 .746884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHusband employment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.082473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.9537884 1.228519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHusband's educational status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.098736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.9349306 1.291241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.9649039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.8588497 1.084054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedia exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.8861623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.7849613 1.000411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge of respondent at 1st birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.9843349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.8588045 1.128214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.8928846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.7181374 1.110154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.6785224 1.489722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eHealth care decision making\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespondent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHusband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.947987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.8176485 1.099102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespondent and Husband Jointly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.8656101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.7484405 1.001123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn Laws\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSomeone else\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.049522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.2511069 4.386564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.5529279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.0749544 4.078868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDistance from the health facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot a big problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBig problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.023021 1.344647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNumber of ANC visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enot utilized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epartially utilized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.251705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.815809 3.755079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eadequately utilized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.931047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.31883 4.656197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePlace of birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth institution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.146322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000178 1.31382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimipara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultipara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.8893924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.7330558 1.07907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egrand multipara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.9417395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.7308229 1.213527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the factors associated with the number of tetanus toxoid injections received by pregnant women in Somaliland using data from the 2020 Somaliland Demographic Health Survey. The findings revealed a significant association between TT immunization and several socio-demographic and health care access factors. Only 14.24% of the respondents received at least two doses, with 73.03% receiving none. The findings are lower compared to the studies conducted in Africa reported 62%(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) And Brazil reported 59.2%(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), and Turkey reported 27.8%(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The current finding also falls slightly below that of Sierra Leone, with coverage reported at 82.1%(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The disparity may be due to differences in healthcare infrastructure, maternal education levels, and antenatal care utilization. Moreover, Somaliland\u0026rsquo;s limited healthcare and financing accessibility challenges may further explain the low TT uptake.\u003c/p\u003e \u003cp\u003eOne of the strongest predictors of TT immunization was the region of residence, with women in some regions, such as Maroodijex, Togdheer, Saahil, and Sool, being significantly less likely to receive the TT injection compared to those from Awdal. These regional differences reflect variations in the availability and quality of maternal health services across Somaliland. A similar pattern was reported in a study from Nigeria and Ghana, where geographic inequalities were found to influence access to maternal immunization and Antenatal care.(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Women in remote areas often face difficulties such as a long distance to the health facilities and a lack of transportation, which limit their ability to receive essential services like TT immunization.\u003c/p\u003e \u003cp\u003eWomen from households in higher wealth quantiles were significantly more likely to receive TT immunization; for example, those in the middle wealth quantile had 1.76 times higher odds of obtaining the TT dose than women in the poorest category. These findings align with previous studies in Kenya(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), South Africa(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), Alexandra(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), and India(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), which indicate that economic status plays a crucial role in determining access to maternal health care services, including immunization. Financial constraints can hinder women\u0026rsquo;s ability to travel to health facilities or pay for services, thus reducing their likelihood of receiving recommended vaccines. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study also found that maternal employment status influences TT uptake. Unemployed women were 59% less likely to receive the TT immunization compared to those employed. Employment may empower women economically and socially, enabling them to make health-related decisions and access services independently. This is supported by research suggesting that working women are more likely to utilize maternal health services due to increased autonomy and financial resources.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough media exposure showed only borderline significance, it demonstrated a positive association with TT uptake (IRR\u0026thinsp;=\u0026thinsp;0.98, p\u0026thinsp;=\u0026thinsp;0.050). This suggests that exposure to health messages via radio, television, or print may influence maternal health behaviors by increasing awareness about the benefits and timing of TT immunization. A similar association was observed in studies from Ethiopia, which highlights the role of media in improving maternal knowledge and service utilization.(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)Women who weren\u0026rsquo;t exposed to the media may miss this opportunity.\u003c/p\u003e \u003cp\u003eAccess to health facilities was also a key determinant; women who reported that distance was not a major problem were more likely to receive the TT immunization (IRR\u0026thinsp;=\u0026thinsp;1.17, p\u0026thinsp;=\u0026thinsp;0.022). Long distances, poor roads, and a lack of transportation are known barriers in low-resource settings, especially in rural Somaliland, which shares similar challenges with regions in rural Ethiopia and northern Nigeria.(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Several barriers contribute to this issue; the expense and time involved in reaching far-off immunization centers are the primary factors. Their significant home responsibility, such as caring for children and managing the household, often keep them at home, making them reluctant to travel to distant clinics. Furthermore, the need for multiple vaccination visits, such as those for TT, can be exhausting for both women and their children if these centers are not conveniently located.(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the most striking findings was a strong association between antenatal care (ANC) attendance and TT uptake. Compared to those who did not attend ANC, women who partially utilized ANC were 3.25 times more likely to receive TT, while those who adequately utilized ANC were 3.93times more likely. This was consistent with studies reported in Kenya.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), Sierra Leon(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), Ghana(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), Mali(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), Malwa(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e), Pakistan(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), and Turkey(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). This confirms the critical role of ANC as an entry point for delivering maternal vaccines, as emphasized in global health recommendations. The low ANC utilization in this study (63.43% had no ANC visit) represents a significant missed opportunity for improving TT coverage and maternal health more broadly.\u003c/p\u003e \u003cp\u003eThe Place of delivery also had a modest but notable association with TT coverage. Women who gave birth in a health institution were 15% more likely to be immunized than those who delivered at home. This may be due to increased health education, follow-up visits, and postpartum counseling typically provided in a facility setting, as well as institutional promotion of immunization schedules.\u003c/p\u003e \u003cp\u003eThis study highlights that socioeconomic status, regional location, ANC utilization, maternal employment, and access to health information and services are critical determinants of TT immunization among pregnant women in Somaliland. The result emphasizes the need for multi-pronged interventions, including expanding ANC coverage to underserved regions, enhancing women\u0026rsquo;s economic empowerment through employment opportunities, strengthening community-based media campaigns to promote maternal immunization, and improving rural healthcare infrastructure to reduce physical barriers. These interventions should be integrated into broader maternal and child health programs to enhance TT coverage and contribute to the elimination of maternal and neonatal tetanus in Somaliland.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study concludes that tetanus toxoid immunization coverage among pregnant women in Somaliland is alarmingly low and far below the recommended global targets. The number of TT doses received is strongly influenced by both individual and structural factors. Women with resided in certain regions such as Marodijeh, Sahil, Togdheer, and Sool were significantly less likely to receive TT immunization. Indicating geographic disparities in the availability and access. Similarly, women from higher wealth quantiles were more likely to be immunized, suggesting that financial capacity enhances health care utilization. Employment status was also influential, with Employed women more likely to be vaccinated, possibly due to increased autonomy and resources. Media exposure had a borderline significant effect, indicating the potential of communication channels to promote maternal immunization. Physical access to health facilities and attendance of ANC visits were among the strongest predictors, with ANC providing an essential opportunity for administering the TT vaccine. Women who gave birth in a health institution also had a higher immunization rate, showing that facility-based care contributes positively to maternal health practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eEthical approval wasn't required for this study because I used publicly available, anonymized data from the Demographic and Health Survey (DHS). I obtained permission to download the DHS dataset from the Central Statistical Agency (CSA) through a request at \u0026nbsp;\u0026nbsp;https://microdata.nbs.gov.so/index.php/catalog/50\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe Somaliland Demographic and Health Survey (SLHDS) 2020 datasets used in this study are publicly accessible and can be obtained through an online request at https://microdata.nbs.gov.so/index.php/catalog/50, specifying the purpose of the research. The datasets analyzed during the current study are also available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis research received no external funding\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eH.J.Y. conducted data cleaning, analysis, and interpretation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eM.H.E. consulted on data analysis and publications.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA.A.F. prepared the background section.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA.A.H. wrote the discussion section.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA.A.O wrote the conclusion and discussion section\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude to the Somaliland Ministry of Health Development and the Somaliland Demographic and Health Survey (SLDHS) 2020 team for granting access to the dataset used in this study. We also acknowledge to colleagues and reviewers who provided valuable comments and constructive feedback that helped improve the quality of this manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOrganization WH. World health statistics 2024: monitoring health for the SDGs. sustainable development goals: World Health Organization; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheffield JS, Ramin SM. Tetanus in pregnancy. Am J Perinatol. 2004;21(04):173\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan R, Vandelaer J, Yakubu A, Raza AA, Zulu F. Maternal and neonatal tetanus elimination: from protecting women and newborns to protecting all. Int J women's health. 2015:171\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThwaites CL, Beeching NJ, Newton CR. 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TAF Prev Med Bull. 2009;8(6).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tetanus toxoid, immunization, Somaliland Demographic Health Survey, Zero-inflated negative binomial, Somaliland","lastPublishedDoi":"10.21203/rs.3.rs-8679360/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8679360/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTetanus Toxoid immunization is a critical public health intervention for reducing maternal and neonatal mortality. Despite global recommendations, Tetanus Toxoid coverage remains low in several low- and middle-income countries, including Somaliland.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis study aims to assess the prevalence and determinants of Tetanus Toxoid immunization among pregnant women in Somaliland using the 2020 Somaliland Demographic Health Survey.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional analytic study was conducted using the 2020 Somaliland Demographic Health Survey. Data from 2,584 women aged 15\u0026ndash;49 years. STATA 17 was used for analysis. Descriptive statistics were used to examine immunization coverage. The chi-square test was used to identify bivariate associations between TT uptake and explanatory variables. Given the nature of the count outcome and high proportion of zeros, a zero-inflated negative binomial regression model was applied. The result was interpreted using incidence rate ratios with 95% confidence intervals. The model with the lowest AIC and BIC values and the highest log likelihood was selected as the best fit.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOnly 14.24% of pregnant women received at least two Tetanus Toxoid doses, while 73.03% received none. ANC attendance (IRR\u0026thinsp;=\u0026thinsp;3.93; 95% CI: 3.32\u0026ndash;4.66), wealth index, maternal employment (IRR\u0026thinsp;=\u0026thinsp;0.41; 95% CI: 0.23\u0026ndash;0.75 for unemployed), institutional delivery, and distance to health facility (IRR\u0026thinsp;=\u0026thinsp;1.17; 95% CI: 1.02\u0026ndash;1.34) were significantly associated with Tetanus Toxoid uptake. Regional disparities and media exposure were also observed.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eTetanus Toxoid immunization among pregnant women in Somaliland is alarmingly low and shaped by both socio-demographic and reproductive health factors. Targeted interventions should focus on expanding ANC coverage, reducing geographic and economic barriers, empowering women, enhancing awareness through media, and improving physical access to healthcare facilities. These findings are aligned with SDG 3 in Somaliland.\u003c/p\u003e","manuscriptTitle":"Determinants of Tetanus Toxoid Immunization Among Pregnant Women in Somaliland: Evidence from the 2020 Nationwide Survey Using a Zero-Inflated Negative Binomial Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 00:53:53","doi":"10.21203/rs.3.rs-8679360/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"79edc2b0-4dd6-46f5-ae6f-4f876456c430","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-03T08:27:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 00:53:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8679360","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8679360","identity":"rs-8679360","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}