Effects of contraceptive use and antenatal care utilization on risks of pregnancy losses among reproductive women in Tanzania

Effects of contraceptive use and antenatal care utilization on risks of pregnancy losses among reproductive women in Tanzania | 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 Effects of contraceptive use and antenatal care utilization on risks of pregnancy losses among reproductive women in Tanzania Seth Kenedi Mbwambo, Jackson Josephat Kavishe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9396853/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 Pregnancy loss remains a critical public health challenge in Tanzania, where maternal healthcare access varies significantly across socio-economic groups. This study analyzes determinants of contraceptive use and antenatal care (ANC) utilization and their combined effects on pregnancy loss among women of reproductive age in Tanzania using Tanzania Demographic Health Survey 2022 data. Multinomial logit, double-hurdle model, and Zero-inflated Poisson captured multi-stage decisions and excess zeros. The empirical results reveal that wealth, partner’s education, media exposure, and health insurance influence contraceptive use and antenatal care uptake. Adequate ANC attendance and facility delivery reduce pregnancy loss. While the findings indicate that adequate ANC attendance and facility-based delivery significantly reduce the risk of pregnancy loss, the use of modern contraceptives was associated with a higher reported risk, suggesting potential selection bias or underlying issues related to the quality of contraceptive counseling and service timing. The findings underscore the urgency of prioritizing service quality alongside access. Integrated policy interventions should focus on enhancing maternal education and strengthening the continuum of care between family planning and maternal health services to reduce adverse pregnancy outcomes in Tanzania. Contraceptive Use Antenatal Care Utilization Pregnancy Loss Reproductive Women Tanzania 1. Introduction Pregnancy loss is one of the main maternal health challenges globally, affecting millions of women of reproductive age each year. Pregnancy loss refers to the spontaneous loss of an embryo or fetus before viability, including outcomes such as miscarriage, abortion, and stillbirth. Recent global estimates indicate that approximately 38.6 million pregnancy loss cases were recorded in 2021 (Xu et al., 2025 ), with about 23 million miscarriages and 1.8 million stillbirths occurring annually (UN-IGME, 2024). Despite sustained global efforts to improve maternal health under the Sustainable Development Goals (SDGs), pregnancy loss continues to pose significant health, social, and economic burdens worldwide, with approximately 2.6 million babies being stillborn out of 136 million births in the world (United Nations, 2023 ; Graham et al., 2016 ). Evidence shows that pregnancy loss is closely associated with inadequate access to reproductive healthcare services, particularly poor birth spacing and insufficient utilization of antenatal care (ANC) (Yehuala et al., 2025 ). This underscores the importance of integrated reproductive health interventions, including contraceptive use and adequate antenatal care, in reducing adverse pregnancy outcomes (Stuart et al., 2025 ). The burden of pregnancy loss is disproportionately high in low- and middle-income countries, particularly in Sub-Saharan Africa (SSA) which accounts for approximately 48% of global stillbirths, where access to reproductive health services remains limited. (UN-IGME, 2024; Goldenberg et al., 2018 ). In this region, low contraceptive use contributes to short birth intervals, which increase the risk of adverse pregnancy outcomes, including pregnancy loss (Belachew et al., 2023 ). Similarly, inadequate and inconsistent utilization of antenatal care services has been strongly linked to poor maternal and neonatal outcomes (Tolossa et al., 2024 ). Although antenatal care has the potential to promote improved reproductive health behaviors, including postpartum contraceptive uptake (McCarthy et al., 2024), many women in SSA continue to face barriers to accessing and fully utilizing these services. As a result, limited contraceptive use and suboptimal ANC utilization remain key contributors to pregnancy loss in the region. In Tanzania, notable progress has been made in expanding maternal and reproductive health services (Mpfubhusa et al., 2026 ; Prasad et al., 2021 ). Efforts such as promoting early initiation of antenatal care, the adoption of the eight-contact ANC model to improve the quality and frequency of care, and the decentralization of maternal health services to enhance access, particularly in rural areas, have contributed to increased ANC coverage (Kasagama et al., 2022 ; MoHCDGEC, 2018; MoHCDGEC, 2021). In addition, the integration of family planning services into broader healthcare programs has improved contraceptive uptake, with approximately 66% of women reported to use some form of contraception (UNFPA, 2018 ; TDHS-MS, 2022). Despite these achievements, significant challenges persist. The uptake of modern contraceptives remains uneven across regions (Kidayi et al., 2015 ), while disparities in the timing, frequency, and quality of antenatal care services continue to influence maternal health outcomes (Mahiti et al., 2025 ; Bintabara & Basinda, 2021 ). Socioeconomic and demographic inequalities further limit equitable access to reproductive health services in the country. Despite the growing body of literature on reproductive health, several critical gaps remain. Existing studies on antenatal care in Tanzania and Sub-Saharan Africa largely focus on the determinants of utilization while treating ANC as a single-stage decision, thereby overlooking the intensity and frequency of visits, which are essential for effective maternal care (Ntegwa, 2025 ; Eliufuo et al., 2024; Tibenderana et al., 2024 ). In addition, most studies examine contraceptive use and antenatal care separately, without considering their joint and complementary effects on pregnancy loss outcomes, limiting a comprehensive understanding of reproductive health dynamics (Mbona et al., 2025 ; Ntegwa & Pelizzo, 2025 ; Endawkie & Tsega, 2025 ). Furthermore, there exists a contextual gap, as many studies rely on cross-country or regional analyses in Sub-Saharan Africa, which may obscure country-specific differences in healthcare systems and socio-cultural factors relevant to Tanzania (Demissie et al., 2025 ; Aboagye et al., 2025 ). Finally, from a methodological perspective, much of the existing literature employs conventional approaches such as descriptive analysis and standard regression models, which do not adequately capture the complexity of healthcare decision-making processes, including multiple choice behavior, two-stage utilization decisions, and excess zero outcomes in pregnancy loss data (Tesema et al., 2021 ; Eliufuo et al., 2024; Ntegwa, 2025 ). These limitations highlight the need for a unified analytical framework supported by advanced econometric techniques. In light of these gaps, this study seeks to address the following research questions: What factors influence contraceptive use among women of reproductive age in Tanzania? What socioeconomic and demographic factors determine both the initiation and intensity of antenatal care utilization? And how do contraceptive use and antenatal care jointly affect the risk of pregnancy loss? By addressing these questions, this study contributes to the literature by integrating contraceptive use and antenatal care within a single analytical framework and applying advanced econometric models, including the Multinomial Logit Model, Double-Hurdle Model, and Zero-Inflated Poisson Model, to better capture the complexity of reproductive health behaviors and outcomes. The findings of this study are expected to provide important policy insights for improving maternal and reproductive health interventions in Tanzania. Specifically, the study informs the design of integrated healthcare strategies aims at enhancing equitable access to contraceptive services and improving the adequacy of antenatal care. Ultimately, this contributes to national and global efforts to reduce pregnancy loss and improve maternal and neonatal health outcomes. Furthermore, the study fills the methodological gaps that remain in studying reproductive health by employing double huddle, multinomial logit, and the zero-inflated poisson regression The remainder of this paper is organized as follows. Section 2 presents the theoretical foundations underpinning the study. Section 3 reviews the relevant empirical literature. Section 4 describes the data sources and outlines the research methodology. Section 5 presents the study’s empirical results, while Section 6 discusses the key findings in the context of existing literature. Section 7 summarizes the study and highlights its policy implications. Section 8 outlines the study’s limitations and suggests directions for future research. 2. Theoretical Foundation The Behavioral Model of Health Services Use, as revised by Andersen ( 1995 ), provides a sociological framework for analyzing contraceptive use and antenatal care (ANC) uptake. It posits that healthcare utilization is influenced by predisposing characteristics, enabling resources, and perceived need. In the Tanzanian context, realized access to services is shaped by maternal education, wealth quintiles, and the distance to health facilities, which act as structural barriers to service utilization (Gebrekidan et al., 2025 ; Mpfubhusa et al., 2026 ). Andersen’s model thus highlights the role of service entry barriers in shaping maternal health behaviors. Complementing this, Grossman’s ( 1972 ) Health Capital Model offers an economic perspective, framing health as a form of capital that individuals invest in to maintain or improve. Contraceptive use and intensive ANC visits, within this framework, are strategic investments that preserve health capital and prevent its rapid depreciation, which clinically manifests as pregnancy loss (Endawkie & Tsega, 2025 ). Grossman’s model emphasizes the economic costs of health production, including opportunity costs and travel time, which can reduce ANC uptake despite adequate health knowledge (Yehuala et al., 2025 ; Mpfubhusa et al., 2026 ). Integrating these perspectives allows for a comprehensive framework for understanding maternal health in Tanzania. Andersen’s model explains how social structures and personal characteristics influence service utilization, while Grossman’s model captures the economic rationale driving individuals to make health-preserving decisions. For instance, educated women may understand the benefits of ANC but still face high “shadow prices” for accessing care, affecting utilization and maternal outcomes (Tibenderana et al., 2024 ). 3. Empirical Literature Review 3.1 The Contraceptive use and risk of pregnancy loss Contraceptive use among women of reproductive age has been widely studied, but offers mixed results and contextual differences. Using Demographic and Health Survey (DHS) data from 23 Sub-Saharan African countries, Demissie et al. ( 2025 ) employed a multinomial multilevel model and found that both individual-level factors (such as education and age) and community-level characteristics significantly influence the use of short- and long-acting contraceptive methods. However, the use of pooled cross-country data limits the ability to capture country-specific dynamics, particularly in Tanzania, where regional disparities in healthcare access and cultural practices are pronounced. Similarly, Bolarinwa et al. ( 2021 ) applied spatial and multilevel analysis in Nigeria and revealed that both individual and community-level factors explain variations in contraceptive use. Nevertheless, the study does not explicitly model the relative probabilities of choosing among multiple contraceptive methods, thereby limiting the understanding of heterogeneous choice behavior. In Tanzania, Fente et al. ( 2025 ) examined contraceptive discontinuation using a multilevel binary logistic regression model and reported a discontinuation rate of 34%. While informative, the study focuses on discontinuation rather than the initial choice among alternative contraceptive methods, which is essential for understanding reproductive decision-making. Overall, the literature highlights the importance of socioeconomic and demographic factors in shaping contraceptive use but is limited by both contextual and methodological constraints. Many studies rely on cross-country analyses, while others apply binary models that fail to capture the multinomial nature of contraceptive choice behavior. These limitations justify the application of a Multinomial logit model to analyze the determinants of contraceptive use in Tanzania. 3.2 The antenatal care and risk of pregnancy loss Antenatal care (ANC) plays a critical role in improving maternal and neonatal health outcomes. Empirical evidence shows that ANC utilization is influenced by factors such as education, income, healthcare accessibility, and reproductive characteristics. Aboagye et al. ( 2025 ), using data from Sub-Saharan Africa, found that wealth and educational attainment are key determinants of disparities in ANC utilization. However, the cross-country nature of the study limits its applicability to country-specific contexts such as Tanzania. In Tanzania, Ntegwa ( 2025 ) employed a Poisson regression model and found that education, wealth status, urban residence, and media exposure significantly influence ANC utilization. Despite these insights, the study treats ANC utilization as a single-stage decision, failing to distinguish between the decision to initiate care and the intensity of subsequent visits. Similarly, Eliufuo et al. (2024) used multivariate logistic regression to analyze determinants of adequate ANC visits and found that access to information increases the likelihood of completing recommended visits, while higher parity and partner violence reduce completion rates. However, the study focuses only on completion and does not capture the two-stage decision-making process. Furthermore, Tibenderana et al. ( 2024 ) analyzed the adequacy of ANC components using a modified Poisson regression model and found that only 41% of women received adequate ANC services. While informative, the study treats ANC utilization as a single outcome, thereby overlooking the distinction between initiation and intensity of care. Taken together, these studies demonstrate that ANC utilization is influenced by multiple factors but are limited by their one-stage modeling approach, which does not adequately capture the sequential nature of healthcare utilization decisions. This provides strong justification for the use of a Double-Hurdle Model, which allows separate estimation of both the decision and intensity of ANC utilization. 3.3 The antenatal care, contraceptive use, and pregnancy outcomes A growing body of literature highlights the importance of reproductive health interventions in reducing adverse pregnancy outcomes, including pregnancy loss. Ahmed et al. ( 2026 ), using decomposition analysis, found that improvements in maternal healthcare services and increased contraceptive use contributed significantly to reductions in global maternal mortality. However, the study focuses on aggregate outcomes and does not explicitly examine pregnancy loss at the individual level, nor does it account for the count nature of such outcomes. At the country level, Ntegwa and Pelizzo ( 2025 ) used Propensity Score Matching to estimate the impact of antenatal care on adverse pregnancy outcomes in Tanzania. They found that adequate ANC reduces such outcomes by 5.6% to 9.3%. However, the study models pregnancy outcomes as a binary variable, thereby ignoring the frequency and repeated occurrence of pregnancy loss. Similarly, Mbona et al. ( 2025 ) found that contraceptive use reduces the likelihood of pregnancy termination, but the study focuses on termination rather than pregnancy loss, limiting its scope. At the regional level, Tesema et al. ( 2021 ) used a mixed-effects logistic regression model to estimate stillbirth prevalence in East Africa, while Endawkie and Tsega ( 2025 ) applied a negative binomial regression model to analyze pregnancy loss in Sub-Saharan Africa. Although these studies provide valuable insights, they do not account for the excess zero counts in pregnancy loss data and fail to jointly analyze the effects of contraceptive use and antenatal care utilization. Additionally, their regional focus limits their applicability to the Tanzanian context. Overall, the literature is constrained by conceptual, contextual, and methodological limitations, particularly the failure to model the joint effects of contraceptive use and antenatal care and the inability to address the statistical properties of pregnancy loss data. These limitations justify the use of a Zero-Inflated Poisson Model, which accounts for excess zeros and allows for a more robust analysis of the determinants of pregnancy loss. 4. Materials and Methods 4.1 Data The study utilizes secondary data from the National Bureau of Statistics (NBS) through the Tanzania Demographic and Health Survey (TDHS) 2022. The survey employed a stratified two-stage cluster sampling design to produce nationally representative data covering both Tanzania Mainland and Zanzibar, including urban and rural areas. A total of 40,394 samples, including the women of reproductive age (15–49 years), were included in the dataset. The study focused on key outcome variables such as contraceptive use, antenatal care utilization, and pregnancy outcomes, while also incorporating independent and control variables, including age, education, wealth index, household and family characteristics, and health insurance coverage. 4.2 Variables and measurement This study employs a range of socio-demographic, economic, behavioral, and reproductive health variables to examine the determinants of contraceptive use, antenatal care utilization, and pregnancy outcomes among women of reproductive age. The variables are measured using binary and categorical scales, consistent with the structure of the TDHS 2022 dataset. Binary variables are coded as 1 to indicate the presence of a characteristic and 0 otherwise, while categorical variables are grouped into meaningful classifications such as age groups, wealth quintiles, education levels, and fertility preferences, as indicated in Table 1 Table 1 The Description and Measurement of Variables Variables Description Measurement Sex The gender of the household head Binary (1 = male, 0 = female) Women’s Age The women in the reproductive age group Categorical (1 = 15–24, 25–34, 35–44, 44 and above) Radio listening Whether a respondent heard about the family loaning through the radio Categorical (1 = yes, 2 = no) Television watching Whether a respondent heard about family planning through television Binary (1 = yes, 0 = no) Social media use Whether a respondent heard about family palling through social networks (e.g., Instagram, Facebook, and Twitter) Binary (1 = yes, 0 = no) Marital status The status of whether the respondent is married or not married Binary (1 = married, 0 = never married) Residence The place where the respondent currently lives Binary (1 = urban, 0 = rural) Wife beating The beating of a wife is justified when she refuses to have sex with her husband. Binary (1 = yes, 0 = no) Access to health services Whether the women have accessed the health services for the past twelve months Binary (1 = yes, 0 = no) Decision maker Decision maker on the use of contraceptives Categorical (1 = respondents, 2 = partner/husband, 3 = the joint decision, 4 = someone else Chronic illness Whether a respondent has any chronic illness Binary (1 = yes, 0 = no) Family planning awareness Whether the respondent has been told about family planning when at the health facility Binary (1 = yes, 0 = no) Postpartum ovulation Can women get pregnant after birth and before the period Binary (1 = yes, 0 = no) Insurance coverage Whether the respondent is currently covered by any type of health insurance. Binary (1 = yes, 0 = no) Wealth index The wealth measure of a household’s economic status is based on the assets and living conditions Categorical (1 = poorest, 2 = poorer, 3 = middle, 4 = richer, 5 = richest Fertility preference A person’s or a couple’s desire to have children Categorical (1 = have another child, 2 = no more, 3 = infecund Partners/husband's education The highest level of education attained by a partner or a husband Categorical (1 = no education, 2 = primary education, 3 = secondary education, 4 = higher education) Husband's desire for children Indicate the husband’s preference regarding having additional children in the family. Binary (1 = yes, 0 = no) Waiting time The respondents waited at the health facility before getting the service Binary (1 = yes, 0 = no Distance to the facility Whether the distance to the health facility is a big problem or not Binary (1 = yes, 0 = no) Pregnancy history Indicate the woman’s record of all previous pregnancies and their outcomes. Categorical (1 = livebirth, 2 = stillbirth, 3 = miscarriage/abortion Antenatal care The number of antenatal care visits (at least 4 visits) during the pregnancy period Binary (1 = yes, 0 = no) Contraceptive use The current method of contraception that the respondent uses Categorical (1 = traditional, 2 = Modern, 3 = non-users) Place of delivery The place where the respondent was born Categorical (1 = at heath facility, 2 = at home, 3 = road,4 = others) Pressured to be pregnant Husband or family member pressured the respondent to become pregnant against their will Binary (1 = yes, 0 = no) Source: Compiled by authors 4.3 Model Specification The study employed three distinct models to examine the effects of contraceptive use, antenatal care (ANC), and the risk of pregnancy loss. First, a multinomial logit model was used to identify the factors influencing contraceptive use among women of reproductive age in Tanzania. Second, a double-hurdle model was applied to analyze the determinants and intensity of antenatal care utilization among pregnant women. Finally, a zero-inflated model was employed to assess the effects of ANC and contraceptive use on the risk of pregnancy loss. Based on these approaches, the following estimable equations were specified for each model The multinomial logit model is used to model categorical outcomes with more than two categories, e.g., type of contraceptive used (modern, traditional, none). The estimable equation is described as; $$\:P\left({Y}_{i}=j\right)=\frac{\text{exp}\left({X}_{i}{\beta\:}_{j}\right)}{1+\sum\:_{k=1}^{j-1}\text{e}\text{x}\text{p}({X}_{i}{\beta\:}_{k}\left)\right)},\:j=1,\:2,\:3\dots\:\dots\:.j-1$$ 1 Where Y i is the contraceptive use category for woman i , X i is the vector of independent variables (age, wealth, education, etc.), and β j is the vector of coefficients for category j 4.3.1 The double-huddle model The double-hurdle model analyzes decisions with two stages: the first hurdle examines whether an individual participates in an activity (e.g., attending antenatal care), while the second hurdle models the intensity of participation (e.g., number of visits) using a truncated regression. The truncation accounts for the fact that only participants have non-zero outcomes, ensuring unbiased estimation of the factors affecting intensity (Cragg, 1971 ). The double huddle model is defined as follows; Step 1: Participation decision (probit model) $$\:{D}_{i}={Z}_{i}\gamma\:+{\mu\:}_{i},\:\:{D}_{i}=\left\{\begin{array}{c}1\:if\:{D}_{i}>0\\\:0,\:otherwise\end{array}\right.$$ 2 Where, \(\:{D}_{i}=1\:\) If a woman uses ANC, \(\:{Z}_{i}\:\) is the vector of explanatory variables, \(\:\gamma\:\:\) is the vector of coefficients and \(\:{\mu\:}_{i}\) follows a normal distribution \(\:{\mu\:}_{i}\sim N(0,\:1)\) Step 2: (Intensity – Truncated regression) $$\:P\left({Y}_{i}=y\left|{D}_{i}\right.=1,\:{X}_{i}\right)=\frac{\frac{{\lambda\:}_{i}^{y}{e}^{-\lambda\:i}}{y!}}{1-{e}^{\lambda\:i}},\:y=1,\:2,\:3,\:4\dots\:\dots\:n$$ 3 Where \(\:\lambda\:i=\text{e}\text{x}\text{p}\left({X}_{i}\beta\:\right)\) , \(\:{X}_{i}\) is the expected number of ANC visits, the denominator adjusts for truncation at zero, y is the specific counts, and Y i is the number of antenatal care visits. 4.3.2 The zero-inflated Poisson (ZIP) model The Zero-Inflated Poisson (ZIP) model is used to analyze count data characterized by an excess number of zero observations. (Lambert, 1992 ). The model assumes that the observed zeros arise from two different processes: one group that will always have zero outcomes (structural zeros) and another group that may have positive counts or zeros generated from a standard Poisson process. (Greene, 2012 ). First process: for zero counts (logit model) $$\:Pr\left({Y}_{i}=0\left|{X}_{i}\right.\right)={\pi\:}_{i}+(1-{\pi\:}_{i}){e}^{-\lambda\:i}$$ 4 Where \(\:{\pi\:}_{i}=\frac{\text{exp}\left({W}_{i}\alpha\:\right)}{1+\text{exp}\left({W}_{i}\alpha\:\right)}\) Where \(\:{W}_{i}\:\) is the covariate in the logit model, \(\:\alpha\:\) is the coefficient in the logistic model Second process: for non-zero counts $$\:Pr\left({Y}_{i}=y\left|{X}_{i}\right.\right)=\left(1-{\pi\:}_{i}\right)\frac{{\lambda\:}_{i}^{y}{e}^{-\lambda\:i}}{y!},\:y=1,\:2\dots\:\dots\:.$$ 5 Where, \(\:\lambda\:i=\text{e}\text{x}\text{p}\left({X}_{i}\beta\:\right)\) , \(\:{Y}_{i}\:\) is the number of pregnancy losses \(\:{,\:\pi\:}_{i}\) is the probability of structural zeros, \(\:\lambda\:i\:\:is\:\) expected count if not a structural zero, \(\:{X}_{i}\) is the covariates, \(\:\beta\:\:\) factors associated with the number of losses among women at risk. 4.4 Study Analysis The study employed STATA 17 to analyze the dataset. Descriptive statistics were first used to examine trends and patterns in household and maternal characteristics before applying the comprehensive models, including the multinomial logistic, double-hurdle, and zero-inflated Poisson models. To ensure the reliability and validity of the estimates, each model was tested against its underlying assumptions and performance indicators, yielding robust, credible results that can inform policymakers and public health interventions. (Wooldridge, 2010 ). 5. Results 5.1 Household characteristics Table 2 presents the household and maternal characteristics of the study sample of 40,394 women of reproductive age in Tanzania. The majority of households were headed by males (74.51%), and most respondents resided in rural areas (72.32%). In terms of age, most women were between 35–44 years (40.1%), followed by 25–34 years (31.2%). Media exposure was moderate, with 46.58% of women reporting listening to the radio, 23.89% watching television, and 17.12% using social media. Most women were married (81.24%), and about 27.41% reported that wife-beating was justified if a wife refused sex. Access to health care was reported by 59.11% of women, while 73.02% reported no chronic illness. The contraceptive use decision was dominated by joint decisions (53.83%), with the respondent alone deciding 31.97% of cases, and the husband/partner in 13.30%. Family planning awareness was relatively low, with 44.71% reporting having been informed at a health facility. Postpartum ovulation knowledge was observed in 52.31% of women, while only 4.96% were covered by health insurance. Regarding economic status, the distribution across wealth quintiles was relatively balanced, with the middle category (22.27%) slightly higher. Fertility preference was almost evenly split, with 44.53% wanting another child, 42.00% not wanting more, and 13.47% undecided. Most partners had at least a primary education (42.58%), while 33.63% had no formal education. Table 2 Household Characteristics Variable Category Frequency Percent (%) Sex of household head Male 30,099 74.51 Female 10,295 25.49 Residence Urban 11,180 27.68 Rural 29,214 72.32 Women age 15–24 3645 1.32 25–34 12,622 7.70 35–44 16,225 14.16 Above 44 7,902 17.09 Listening to the radio Yes 18,815 46.58 No 21,579 53.42 Watching TV Yes 9,652 23.89 No 30,742 76.11 Use of social media Yes 6,916 17.12 No 33,478 82.88 Current marital status Never married 7,577 18.76 Married 32,817 81.24 Beating of the wife No 29,320 72.59 Yes 11,074 27.41 Access to health care Yes 23,878 59.11 No 16,516 40.89 Decision maker for contraception Respondent 10,493 31.97 Husband/partner 4,364 13.30 Joint decision 17,666 53.83 Someone else 294 0.89 Chronic illness No 28,253 73.02 Yes 12141 26.91 Family planning awareness Yes 10,676 44.71 No 13,202 55.29 Postpartum ovulation Yes 21,129 52.31 No 19,265 47.69 Covered by health insurance Yes 2,003 4.96 No 38,391 95.04 Wealth index Poorest 7,860 19.46 Poorer 8,079 20.00 Middle 8,994 22.27 Richer 8,075 19.99 Richest 7,386 18.28 Fertility preference Have another 17,987 44.53 Undecided 5,441 13.47 No more 16,966 42.00 Partner/Husband’s education No formal education 13,584 33.63 Primary education 17,200 42.58 Secondary education 7,307 18.09 Higher education 2,303 5.70 Husband’s desire for children Yes 10,524 26.76 No 20,713 73.24 Waiting time Yes 10,744 42.13 No 16,439 57.97 Distance to health facility Big problem 12,737 31.53 Not a big problem 27,657 68.47 Pregnancy history index Live birth 5,745 87.39 Still birth 790 12.02 Miscarriage/abortion 39 0.59 Contraceptive use Using a modern method 12,176 30.14 Using the traditional method 2,447 6.06 Non-user 25,771 56.98 Pressured to become pregnant No 31,475 95.91 Antenatal care Yes 38,342 94.94 No 2,052 5.06 Source: Authors computations Table 2 shows that 94.94% of women reported attending antenatal care, but 31.53% perceived distance to the health facility as a major barrier. Pregnancy history showed that 87.39% had experienced live births, 12.02% stillbirths, and only 0.59% miscarriages or abortions. In terms of contraceptive use, 30.14% were using modern methods, 6.06% traditional methods, and 56.98% were non-users. Notably, 95.91% of women reported not being pressured by family members or husbands to become pregnant. Overall, these findings provide a comprehensive profile of household, socio-economic, and reproductive characteristics relevant for analyzing maternal health behaviors in Tanzania. 5.2 Factors influencing contraceptive use among reproductive women in Tanzania Table 3 presents results from a multinomial logit model examining factors influencing contraceptive use among reproductive-age women in Tanzania, using non-contraceptive users as the reference category. The model was jointly significant, indicating a reasonably good fit. Women’s age significantly influenced contraceptive choice. Women aged 35–44 were 5.47 percentage points more likely to use modern methods compared to women above 44, while women aged 25–34 were less likely to use modern methods but slightly more likely to use traditional methods. Younger women (15–24) were more likely to use traditional contraceptives, suggesting that modern method uptake peaks in middle reproductive ages. Residence and household wealth also played significant roles. Urban women were slightly less likely to use modern methods but more likely to use traditional methods. Wealthier women, particularly those in the richer and middle categories, were more likely to use modern methods, indicating that economic status positively influences contraceptive uptake. Media exposure showed mixed effects: listening to the radio increased both modern and traditional method use, watching television slightly increased modern method use, while social media use was not statistically significant for modern methods. Table 3 The multinomial logit model for factors influencing contraceptive use among reproductive women in Tanzania Non-contraceptive user is the reference category Variable Modern Method (dy/dx) Traditional Method (dy/dx) Household head -0.0036*** (0.0003) -0.0000 (0.0002) Women’s age 15–24 0.0244 (0.0066) 0.0170*** (0.0036) 25–34 -0.0203*** (0.0042) 0.0086*** (0.0021) 35–44 0.0547*** (0.0103) 0.0096 (0.0062) Above 44 Ref. Ref. Listening to the radio 0.0164*** (0.0058) 0.0119*** (0.0028) Use of social media -0.0039 (0.0041) 0.0147 (0.0022) Watching TV 0.0010** (0.0046) -0.0077 (0.0023) Marital status -0.1392 (13.6801) -0.8711 (35.9910) Residence -0.0226*** (0.0078) 0.0116*** (0.0039) Wife beating 0.0039** (0.0030) 0.0063 (0.0013) Access to health services -0.0003 (0.0002) 0.0002 (0.0001) Decision maker on contraceptive use Respondent -0.1045*** (0.0086) -0.0026 (0.0048) Husband/ partner 0.0394*** (0.0063) 0.0373*** (0.0036) Joint decision of partners -0.1529*** (0.0172) -0.2000*** (0.0045) Other people Ref. Ref. Chronic illness -0.0273*** (0.0086) 0.0117** (0.0050) Family planning awareness 0.0347*** (0.0060) 0.0017 (0.0033) Postpartum ovulation -0.0087*** (0.0015) 0.0024*** (0.0007) Covered by health insurance 0.0042** (0.0135) 0.0124 (0.0061) Wealth index Poor 0.0251** (0.0098) 0.0033 (0.0050) Middle 0.0521*** (0.0098) 0.0161*** (0.0051) Richer 0.0610*** (0.0111) 0.0400*** (0.0058) Poorest Ref. Ref. Fertility preference Have another child 0.0105 (0.0189) 0.0314*** (0.0115) Undecided 0.1256** (0.0385) 0.3972 (0.7924) Infecund Ref. Ref. Partners/husbands’ education Primary education 0.3306*** (0.0961) 0.3095*** (0.0470) Secondary education 0.4760*** (0.1030) 0.0436 (0.0546) Higher education 0.2662** (0.1258) −0.1866** (0.0735) No education Ref. Ref. Number of obs = 23,878 Log likelihood = -19300.635 Pseudo R2 = 0.2533 LR chi2(50) = 2173.38 Prob > chi2 = 0.0000 *** =significance at 1%, ** = significance at 5%, * = significance at 10%, and robust standard errors in parentheses, Ref=reference/benchmark category, LR=likelihood ratio, the coefficient represents the marginal effects (dy/dx) Source: Authors computations Table 3 shows that Household decision-making dynamics strongly influenced contraceptive use. When the husband or partner decided on contraceptive use, women were more likely to use modern and traditional methods. In contrast, joint decision-making significantly reduced the probability of using modern and traditional methods relative to other decision-makers. Women who made decisions alone were less likely to use modern methods. Health and awareness factors were also significant. Family planning awareness increased the use, while women with chronic illnesses were less likely to use modern methods but more likely to use traditional methods. Understanding postpartum ovulation slightly reduced modern method use and increased traditional method use. Fertility preferences and partner education further shaped contraceptive behavior. Women desiring another child were more likely to use traditional methods, while those undecideds were more likely to use modern methods. Partner’s education had a strong effect: primary education increased both modern and traditional method use, secondary education increased modern method use, and higher education increased modern method use while reducing traditional method use. Health insurance slightly increased the modern method, but had no significant effect on traditional methods. 5.3 Determinants and intensity of antenatal care utilization among pregnant women Table 4 shows the Double Hurdle model estimates for the determinants of antenatal care (ANC) utilization (Hurdle 1) and the intensity of ANC visits (Hurdle 2) among women of reproductive age in Tanzania. The model was statistically significant, indicating that the included variables jointly influence both the decision to seek ANC and the number of visits attended. Partner or husband’s education significantly influenced ANC utilization: women whose partners had primary education were more likely to use ANC services, while secondary education slightly reduced the probability. In terms of intensity, women with partners having primary or secondary education attended fewer ANC visits, as indicated by negative coefficients in Hurdle 2. Table 4 Double huddle model for determinants and intensity of ANC utilization Huddle 1 (Determinants) Huddle 2 (Intensity of ANC use) Variable Coefficient Robust SE Coefficient Robust SE Partner/husband's education Primary 0.5623** 0.0643 -0.0650*** 0.0167 Secondary -0.1061** 0.0450 -0.0541*** 0.0157 Higher -0.0730 0.0681 -0.0121 0.0174 No education Ref. Ref. Ref. Ref. Husbands desire to have children 0.0632** 0.0310 0.00345 0.4097 Women’s age group 15–24 0.0066*** 0.0017 -0.0016*** 0.0003 25–34 0.3924** 0.0584 0.0492 0.0046 35–44 0.9273* 0.0754 0.0023 0.0086 Above 44 years Ref. Ref. Ref. Ref. Wealth Index Poorest 0.1116** 0.0468 0.0240*** 0.0087 Middle 0.2404*** 0.0497 0.0257*** 0.0088 Richer 0.3553*** 0.0622 0.0429*** 0.0109 Richest 0.3184 0.0799 0.1084 0.0144 Poorer Ref. Ref. Ref. Ref. Listening to the radio -0.1404*** 0.0287 0.0057 0.0063 Watching Television 0.0838*** 0.0215 0.0061 0.0039 Reading the magazine -0.0123 0.0266 0.0116** 0.0050 Marital status -0.0427 0.0807 -0.0172 0.0139 Residence 0.0282 0.0485 -0.0060 0.0084 Waiting time to get service -0.0241 0.0151 0.0045 0.0033 Pregnancy history 0.0010 0.0008 -0.0004*** 0.0001 Live birth 0.236* 0.007 0.4593 0.0052 Still birth 0.0034 0.6457 0.0378* 0.0069 Miscarriage/abortion Ref Ref. Ref. Ref. Distance to the health facility -0.2547*** 0.0383 0.0347*** 0.0061 Sex of household head -0.0190 0.0205 0.0106*** 0.0034 Marital status 0.0806 0.0535 -0.0062 0.0092 Covered by health insurance 0.0420** 0.0818 0.0171 0.0149 Number of obs = 40,394 Wald chi2(20) = 259.09 Log pseudolikelihood = -37584.559 Prob > chi2 = 0.0000 *** = significance at 1%, ** = significance at 5%, *= significance at 10%, and SE=standard errors, Ref=reference/benchmark category, ANC=antenatal care Source: Authors computations Table 4 further shows that wealth status and women’s age were also important determinants. Women in higher wealth categories, middle, richer, and richest, were more likely to use ANC, and the intensity of visits increased notably for the richer group. Younger women aged 15–24 had a small but significant increase in ANC utilization, while women aged 25–34 and 35–44 were more likely to use ANC services. Other factors influencing ANC utilization included media exposure, with television viewing increasing ANC uptake and radio listening reducing it. Pregnancy history, distance to the health facility, and health insurance also significantly affect ANC decisions and intensity of use. Notably, a greater distance to the facility reduced the likelihood of ANC utilization but increased the number of visits among those who sought care. 5.4 Effect of ANC and contraceptive use on risk of pregnancy losses The Zero-Inflated Poisson model examined the effect of antenatal care (ANC) and contraceptive use on the risk of pregnancy loss among women of reproductive age in Tanzania. The results in Table 5 show that attending ANC significantly reduces the risk of pregnancy loss, while a greater distance to a health facility increases the risk, highlighting the importance of access to maternal health services. Fertility preferences were strongly associated with pregnancy outcomes: women who desire more children had a higher risk of pregnancy loss, whereas women who were undecided about future fertility had a lower risk compared to infecund women. Partner education also influenced outcomes, with primary and secondary education reducing risk, while higher education was not significant. Place of delivery was critical: home delivery increased the risk, while health facility delivery substantially reduced it. Table 5 Zero-inflated poisson model for the effect of ANC and contraceptive use on risk of pregnancy loss Variable Coefficient Robust SE P-value Antenatal care -0.0712*** 0.0180 0.000 Distance to the health facility 0.0040*** 0.0009 0.000 Fertility preference More children 0.1426*** 0.0258 0.000 Undecided -0.2859*** 0.0472 0.000 Infecund Ref. Ref. Ref. Partners/husband's education Primary education -0.0703** 0.0287 0.014 Secondary education -0.2331** 0.0380 0.000 Higher education 0.0478 0.1295 0.712 No education Ref. Ref. Ref. Wife beating (refuses to have sex) 0.0161 0.0285 0.573 Place of delivery Home 0.3492** 0.0023 0.034 Health facility -0.7232*** 0.0056 0.000 Road 0.0023 0.0049 0.569 Others Ref. Ref. Ref. Marital status 0.0014** 0.0006 0.012 Pressured to be pregnant -0.0662** 0.0271 0.015 Women’s age Age 15–24 0.5143*** 0.0565 0.000 Age 25–34 0.1073** 0.0560 0.000 Age 35–44 -0.2930 0.0601 0.000 Above 44 years Ref. Ref. Ref. Sex of household head -0.0161 0.0300 0.591 Wealth index Poor 0.0732* 0.0411 0.074 Middle 0.3113*** 0.0402 0.000 Richer -0.4261** 0.0418 0.000 Poorest Ref. Ref. Ref. Chronic illness 0.0497* 0.0264 0.060 Contraceptive use Traditional -0.2618** 0.0467 0.000 Modern 0.2477** 0.0282 0.000 Non user Ref. Ref. Ref. Number of obs = 40,394 Log pseudolikelihood = -27087.15 Nonzero obs = 8,193 Zero obs = 32,201 Wald chi2(20) = 1043.17 Prob > chi2 = 0.0000 *** =significance at 1%, ** =significance at 5%, * =significance at 10%, Ref=reference /benchmark category, SE=standard errors, p-value=probability value ranging between 0 and 1 Source: Authors computations Table 5 further reveals that maternal age, socio-economic status, and contraceptive use further shaped pregnancy loss risk. Women aged 15–24 had the highest risk, with slight increases among women aged 25–34, while women aged 35–44 showed a lower risk. Wealth index showed a non-linear effect, with middle-income women at higher risk and richer women at lower risk. Chronic illness slightly increased risk, while using traditional contraceptives was protective. Other factors, such as marital status and being pressured to become pregnant, had smaller but significant effects. 6. Discussions The study provides important empirical insights into the determinants of contraceptive use, antenatal care (ANC) utilization, and their joint effects on pregnancy loss among women of reproductive age in Tanzania. The findings are broadly consistent with established theoretical frameworks, particularly Andersen’s Behavioral Model and the Health Capital Model (Andersen, 1995 ; Grossman, 1972 ), while also extending the empirical literature by uncovering context-specific and, in some cases, unexpected results. First, the multinomial logit results confirm that socioeconomic and demographic factors play a significant role in shaping contraceptive use. Consistent with prior studies (Demissie et al., 2025 ; Bolarinwa et al., 2021 ; Kidayi et al., 2015 ), wealth status and partner’s education were strong predictors of modern contraceptive uptake. These findings support the argument that higher socioeconomic status enhances both access to reproductive health services and the ability to afford modern contraceptive methods. Similarly, the positive effect of family planning awareness aligns with evidence from Mbona et al. ( 2025 ) and UNFPA ( 2018 ), which emphasize the importance of information dissemination in promoting contraceptive use. The influence of media exposure, particularly radio and television, further corroborates findings by Bolarinwa et al. ( 2021 ) and Demissie et al. ( 2025 ), who highlight the role of mass media in shaping reproductive health behaviors across Sub-Saharan Africa. However, some findings diverge from the existing literature. For instance, the negative association between urban residence and modern contraceptive use contradicts studies that typically report higher uptake in urban areas due to better access to healthcare services (Demissie et al., 2025 ; Aboagye et al., 2025 ). A plausible explanation is the existence of intra-urban inequalities, where women living in informal settlements may still face significant barriers to accessing quality reproductive health services (Bintabara & Basinda, 2021 ; Mahiti et al., 2025 ). In addition, the finding that joint decision-making reduces contraceptive use contrasts with studies suggesting that spousal communication enhances uptake (Bolarinwa et al., 2021 ; Demissie et al., 2025 ). This discrepancy may reflect underlying gender dynamics in Tanzania, where joint decision-making does not necessarily imply equal bargaining power, thereby limiting women’s autonomy in reproductive health decisions (Gebrekidan et al., 2025 ). Second, the double-hurdle model results provide strong evidence that ANC utilization is a two-stage process influenced by different sets of factors. In line with previous studies (Aboagye et al., 2025 ; Ntegwa, 2025 ; Eliufuo et al., 2024), wealth status, education, and access to health services significantly increase the likelihood of ANC utilization. These findings reinforce the view that financial and informational resources are critical determinants of healthcare access. The positive effect of health insurance coverage also supports findings from Gebrekidan et al. ( 2025 ) and Prasad et al. ( 2021 ), which highlight the role of financial protection and health system strengthening in improving maternal healthcare utilization in Tanzania. Nevertheless, the study reveals important nuances that extend the literature. For example, while a partner’s education increases the likelihood of initiating ANC, it reduces the intensity of visits. This finding contrasts with studies such as Eliufuo et al. (2024) and Tibenderana et al. ( 2024 ), which suggest that education improves the adequacy and completion of recommended ANC visits. One possible explanation is that more educated households may perceive fewer visits as adequate or face opportunity costs that limit repeated attendance, as suggested by the Health Capital Model (Grossman, 1972 ; Yehuala et al., 2025 ). Furthermore, the finding that distance to health facilities reduces ANC initiation but increases visit intensity among users is consistent with the notion of selective utilization, where only highly motivated women overcome access barriers and subsequently maximize service use (Tolossa et al., 2024 ; Tibenderana et al., 2024 ). Third, the Zero-Inflated Poisson results confirm that both ANC and contraceptive use significantly influence pregnancy loss, supporting the study’s main hypothesis. The negative association between ANC attendance and pregnancy loss is consistent with findings by Ntegwa and Pelizzo ( 2025 ), Tolossa et al. ( 2024 ), and Goldenberg et al. ( 2018 ), all of which demonstrate that adequate maternal healthcare reduces adverse pregnancy outcomes. Similarly, the strong protective effect of health facility delivery aligns with global evidence emphasizing the importance of skilled birth attendance in improving maternal and neonatal outcomes (Goldenberg et al., 2018 ; United Nations, 2023 ). However, the findings on contraceptive use present a mixed and somewhat unexpected pattern. While traditional contraceptive methods are associated with a reduced risk of pregnancy loss, modern methods are linked to an increased risk. This contradicts much of the existing literature, which generally finds modern contraception improves reproductive health outcomes (Ahmed et al., 2026 ; Mbona et al., 2025 ; Stuart et al., 2025 ). A possible explanation is selection bias, where women using modern contraceptives may have prior reproductive health complications, making them more susceptible to pregnancy loss (Endawkie & Tsega, 2025 ). Additionally, improper or inconsistent use of modern methods may reduce their effectiveness, highlighting gaps in counseling and service quality (McCarthy et al., 2024). This finding suggests that access alone is insufficient; the quality of reproductive health services is equally important. The study also finds that younger women (15–24 years) face a significantly higher risk of pregnancy loss, which is consistent with previous research (Yehuala et al., 2025 ; Endawkie & Tsega, 2025 ) linking younger maternal age to biological vulnerability and lower healthcare utilization. The non-linear relationship between wealth and pregnancy loss, where middle-income women face higher risks compared to richer women, further supports findings by Aboagye et al. ( 2025 ) and Bintabara and Basinda ( 2021 ) who found that persistent inequalities in maternal health outcomes. This suggests that improvements in income alone may not guarantee better health outcomes without corresponding improvements in healthcare access and quality. Overall, this study makes several important contributions to the literature. Unlike previous studies that examine contraceptive use and ANC separately (Ntegwa, 2025 ; Mbona et al., 2025 ; Eliufuo et al., 2024), this study integrates both factors within a single analytical framework, demonstrating their complementary effects on pregnancy outcomes. Methodologically, the application of multinomial logit, double-hurdle, and zero-inflated Poisson models addresses key limitations in prior research by capturing multi-stage decision-making processes and the excess zero nature of pregnancy loss data (Tesema et al., 2021 ; Endawkie & Tsega, 2025 ; Demissie et al., 2025 ). 7. Conclusion This study examined the determinants of contraceptive use and antenatal care (ANC) utilization, as well as their combined effects on pregnancy loss among women of reproductive age in Tanzania. The findings show that reproductive health behaviors are strongly influenced by socioeconomic and demographic factors, including wealth, education, media exposure, and health insurance. While these factors generally improve access to modern contraception and ANC services, some variations, such as lower contraceptive use in urban areas and differences in ANC visit intensity, highlight important contextual and behavioral dynamics. Importantly, the results confirm that adequate ANC utilization and health facility delivery significantly reduce the risk of pregnancy loss. However, the mixed effects observed for contraceptive methods suggest that improving access alone is not sufficient; the quality of services, proper usage, and informed decision-making are equally critical. From a policy perspective, several implications emerge. First, there is a need to strengthen family planning programs by enhancing education, awareness, and counseling to ensure the correct and consistent use of contraceptive methods. Second, improving both access to and the quality of ANC services is essential, with emphasis on encouraging women to complete the recommended number of visits. Third, addressing structural barriers such as distance to health facilities is crucial, particularly in rural and underserved areas, through improved infrastructure and community-based healthcare services. Fourth, promoting women’s autonomy and empowerment in reproductive health decision-making is vital for increasing the uptake of both contraceptive and maternal healthcare services. In conclusion, the study underscores the importance of integrated and context-specific reproductive health policies that simultaneously address contraceptive use, ANC utilization, and healthcare quality. Such a comprehensive approach is essential for reducing pregnancy loss and improving maternal health outcomes in Tanzania. 8. Limitation and Future Research Despite providing important insights, this study is subject to several limitations that should be acknowledged. First, the analysis is based on cross-sectional data from the TDHS 2022, which limits the ability to establish causal relationships between contraceptive use, ANC utilization, and pregnancy loss. The observed associations may therefore reflect correlations rather than definitive cause-and-effect linkages. Second, the study relies on self-reported data, which may be affected by recall bias and social desirability bias, particularly for sensitive issues such as contraceptive use and pregnancy outcomes. Third, some potentially important variables, such as quality of healthcare services, cultural beliefs, and provider-related factors, were not fully captured in the dataset, which may lead to omitted variable bias. Given these limitations, several avenues for future research are recommended. First, future studies should consider using longitudinal or panel data for better trends and patterns on causal relationships and track reproductive health behaviors over time. Second, incorporating qualitative or mixed-method approaches would provide deeper insights into the social, cultural, and behavioral factors influencing contraceptive use and ANC utilization. Third, further research is needed to explore the quality of reproductive health services, including counseling, availability of methods, and provider competence, for better understand their role in shaping outcomes. Declarations Funding: The authors received no financial support for the research, authorship, and/or publication of this article. Data availability statement The study used data from the Tanzania Demographic and Health Survey (TDHS) 2022 (Wave 5), obtained from the National Bureau of Statistics (NBS) microdata repository. Data codes are available from the corresponding author upon reasonable request. Authors contribution SKM conceptualized the study, developed the introduction, and conducted the empirical literature review. JJK was responsible for the methodology, data analysis, and discussion of the findings. SKM and JJK jointly wrote the conclusion and proofread the final manuscript. Disclosure statement : The authors report there are no competing interests to declare. References Aboagye, R. G., Osborne, A., Anyasodor, A. E., Yikindi, S. V., Adnani, Q. E. S., & Ahinkorah, B. O. (2025). Utilisation of eight or more antenatal care visits and its associated socio-economic-related inequalities in sub-Saharan Africa: A decomposition analysis. PLoS One , 20 (3), e0312412. https://doi.org/10.1371/journal.pone.0312412 Ahmed, S., Ali, M., Shah, I., & Tsui, A. O. (2026). Effect of maternity care improvement, fertility decline, and contraceptive use on global maternal mortality reduction between 2000 and 2023. The Lancet Global Health , 14 (1), e33–e48. https://doi.org/10.1016/S2214-109X(25)00409-7 Andersen, R. M. (1995). Revisiting the behavioral model and access to medical care: Does it matter? Journal of Health and Social Behavior , 36 (1), 1–10. https://doi.org/10.2307/2137284 Belachew, T. B., Asmamaw, D. B., & Negash, W. D. (2023). Short birth interval and its predictors among reproductive age women in high fertility countries in sub-Saharan Africa: a multilevel analysis of recent Demographic and Health Surveys. BMC Pregnancy and Childbirth , 23 (1), 81. https://doi.org/10.1186/s12884-023-05403-0 Bintabara, D., & Basinda, N. (2021). Twelve-year persistence of inequalities in antenatal care utilisation among women in Tanzania: a decomposition analysis of population-based cross-sectional surveys. British Medical Journal Open , 11 (4), e040450. https://doi.org/10.1136/bmjopen-2020-040450 Bolarinwa, O. A., Tessema, Z. T., Frimpong, J. B., Seidu, A. A., & Ahinkorah, B. O. (2021). Spatial distribution and factors associated with modern contraceptive use among women of reproductive age in Nigeria: A multilevel analysis. Plos one , 16 (12), e0258844. https://doi.org/10.1371/journal.pone.0258844 Cragg, J. G. (1971). Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica: journal of the Econometric Society , 829–844. https://doi.org/10.2307/1909582 Demissie, K. A., Jejaw, M., Teshale, G., Tiruneh, M. G., Tafere, T. Z., & Hagos, A. (2025). Multinomial multilevel analysis of factors affecting the use of modern contraceptives in sub-Saharan Africa: evidence from 2015 to 2023 Demographic Health Survey. Scientific Reports , 15 (1), 16751. https://doi.org/10.1038/s41598-025-01622-x Eliufoo, E., Majengo, V., Tian, Y., Bintabara, D., Moshi, F., & Li, Y. (2024). Determinants of adequate antenatal care visits among pregnant women in low-resource setting: evidence from Tanzania national survey. BMC Pregnancy and Childbirth , 24 (1), 790. https://doi.org/10.1186/s12884-024-06989-9 Endawkie, A., & Tsega, Y. (2025). Pregnancy loss and its predictors among ever-pregnant women in Sub-Saharan Africa: Multilevel mixed effect negative binomial regression. PLOS Global Public Health , 5 (4), e0004316. https://doi.org/10.1371/journal.pgph.0004316 Fente, B. M., Asnake, A. A., Negussie, M., Melaku Bezie, Y., Asmare, M. A., Asebe, Z., H. A., & Seifu, B. L. (2025). Prevalence and determinants of contraceptive discontinuation among reproductive age women: analysis of Tanzania demographic health survey. Frontiers in Global Women's Health , 6 , 1393020. https://doi.org/10.3389/fgwh.2025.1393020 Gebrekidan, A. Y., Woldegeorgis, B. Z., Kassie, G. A., Haile, K. E., Abrha, A. T., Asnake, A. A., & Asgedom, Y. S. (2025). Effective coverage of antenatal care and associated factors among pregnant women in Tanzania: a multilevel analysis. Front Glob Women’s Health . https://doi.org/10.3389/fgwh.2025.1477666 Goldenberg, R. L., McClure, E. M., & Saleem, S. (2018). Improving pregnancy outcomes in low- and middle-income countries. Reprod Health , 15 (Suppl 1), 88. https://doi.org/10.1186/s12978-018-0524-5 Graham, W., Woodd, S., Byass, P., Filippi, V., Gon, G., Virgo, S., & Singh, S. (2016). Diversity and divergence: the dynamic burden of poor maternal health. The Lancet , 388 (10056), 2164–2175. http://dx.doi.org/10.1016/S0140-6736(16)31533-1 Greene, W. H. (2012). Econometric analysis (7th ed.). Pearson. Grossman, M. (1972). On the concept of health capital and the demand for health. Journal of Political Economy , 80 (2), 223–255. https://doi.org/10.1086/259880 Kasagama, E., Todd, J., & Renju, J. (2022). Factors associated with changes in adequate antenatal care visits among pregnant women aged 15–49 years in Tanzania from 2004 to 2016. Bmc Pregnancy And Childbirth , 22 , 18. https://doi.org/10.1186/s12884-021-04350-y Kidayi, P. L., Msuya, S., Todd, J., Mtuya, C. C., Mtuy, T., & Mahande, M. J. (2015). Determinants of modern contraceptive use among women of reproductive age in Tanzania: Evidence from Tanzania Demographic and Health Survey data. Advances in Sexual Medicine , 5 , 43–52. http://dx.doi.org/10.4236/asm.2015.53006 Lambert, D. (1992). Zero-Inflated Poisson Regression, With an Application to Defects in Manufacturing. Technometrics , 34 (1), 1–14. https://doi.org/10.1080/00401706.1992.10485228 Mahiti, G. R., Chombo, S., & Luoga, P. (2025). Socio-demographic correlates of booking antenatal care in first trimester among pregnant women in Tanzania. Insights from Tanzania demographic health survey 2022. Frontiers in Reproductive Health , 7 , 1669621. https://doi.org/10.3389/frph.2025.1669621 Mbona, S. V., Chifurira, R., Ndlovu, B. D., & Ananth, A. (2025). Prevalence and determinants of pregnancy termination for childbearing women using the modified Poisson regression model: a cross-sectional study of the Tanzania Demographic and Health Survey (TDHS) 2022. Bmc Public Health , 25 (1), 56. https://doi.org/10.1186/s12889-024-21203-3 Ministry of Health [Tanzania Mainland], Ministry of Health [Zanzibar], National Bureau of Statistics, Office of the Chief Government Statistician [Zanzibar], & ICF (2022). Tanzania Demographic and Health Survey and Malaria Indicator Survey 2022. https://dhsprogram.com/pubs/pdf/FR382/FR382.pdf Mpfubhusa, F. L., Lihawa, R. M., & Mbwambo, S. K. (2026). Effect of digital health technologies on children's healthcare utilization: Evidence from vaccination uptake in Kigoma region, Tanzania. Health Education , 1–18. https://doi.org/10.1108/HE-09-2025-0164 Ntegwa, M. J. (2025). Determinants of quality antenatal care utilization in Tanzania: a generalized multilevel Poisson regression model approach. Discover Public Health , 22 (1), 734. https://doi.org/10.1186/s12982-025-01162-9 Ntegwa, M. J., & Pelizzo, R. (2025). Estimating the impact of prenatal health care services on adverse pregnancy outcomes in Tanzania: a propensity score matching approach. Frontiers in Global Women’s Health , 6 , 1589721. https://doi.org/10.3389/fgwh.2025.1589721 Ona, L., McCarthy, N., Fardousi, V., Tripathi, R., Stafford, K., Levin, F., Khan, M. Pepper, Oona, M. R., & Campbell (2024). Antenatal care interventions to increase contraceptive use following birth in low- and middle-income countries: Systematic review and narrative synthesis. Global Health: Science and Practice , 12 (5), e2400059. https://doi.org/10.9745/GHSP-D-24-00059 Prasad, N., Mwakatundu, N., Dominico, S., Masako, P., Mongo, W., Mwanshemele, Y., Maro, G., Subi, L., Chaote, P., Rusibamayila, N., Ruiz, A., Schmidt, K., Kasanga, M. G., Lobis, S., & Serbanescu, F. (2021). Improving maternal and reproductive health in Kigoma, Tanzania: A 13-year initiative. Global Health: Science and Practice , 9 (Suppl 1), S112–S127. https://doi.org/10.9745/GHSP-D-20-00494 Stuart, L., Desai, R., Hart, C., & Norris, S. A. (2025). Advocating for an integrated healthcare model for women of reproductive age in low- and middle-income countries. Critical Public Health , 35 (1). https://doi.org/10.1080/09581596.2025.2512842 Tesema, G. T., et al. (2021). Prevalence and associated factors of stillbirths in East Africa: A mixed-effects binary logistic regression analysis. BMC Pregnancy and Childbirth , 21 , 574. https://doi.org/10.1186/s12884-021-03883-6 Tibenderana, J. R., Kessy, S. A., Mlaponi, D. F., Mtenga, J. E., Gimonge, J., Mwaitete, N. L., & Moshi, F. V. (2024). The adequacy of ANC components received and associated factors among women of reproductive age in Tanzania. Plos one , 19 (11), e0301570. https://doi.org/10.1371/journal.pone.0301570 Tolossa, T., Gold, L., Lau, E. H., Dheresa, M., & Abimanyi-Ochom, J. (2024). Association between quality of antenatal care service utilisation and adverse birth outcomes among adolescent women in 22 Sub-Saharan African countries. A mixed-effects multilevel analysis. Sexual & Reproductive Healthcare , 42 , 101036. https://doi.org/10.1016/j.srhc.2024.101036 UNFPA (2018). Fact sheet: Family planning in Tanzania. United Nations Population Fund. Retrieved from https://tanzania.unfpa.org/sites/default/files/pub-pdf/familyplanning_A5_13nov_highres.pdf United Nations Inter-agency Group for Child Mortality Estimation (2024). Standing up for stillbirth: Current estimates and key interventions. United Nations Children’s Fund. https://childmortality.org/wp-content/uploads/2024/03/UN-IGME-2023-Stillbirth-Report.pdf United Nations (2023). The Sustainable Development Goals report 2023: Special edition. https://unstats.un.org/sdgs/report/2023/The-Sustainable-Development-Goals-Report-2023.pdf Wooldridge, J. M. (2010). Econometric analysis of cross-section and panel data (2nd ed.). MIT Press. Xu, S., Wang, J., & Ding, J. (2025). Global, regional, and national burden of pregnancy loss from 1990 to 2021: a systematic analysis of incidence and DALYs with projections to 2040. Journal of Gynecology Obstetrics and Human Reproduction , 103065. https://doi.org/10.1016/j.jogoh.2025.103065 Yehuala, T. Z., Mengesha, S. B., & Baykemagn, N. D. (2025). Predicting pregnancy loss and its determinants among reproductive-aged women using supervised machine learning algorithms in Sub-Saharan Africa. Front Glob Womens Health , 6 , 1456238. https://doi.org/10.3389/fgwh.2025.1456238 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9396853","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626563346,"identity":"80c7bd3c-84ef-402b-a878-74d8a9769526","order_by":0,"name":"Seth Kenedi Mbwambo","email":"","orcid":"","institution":"Moshi Co-operative University","correspondingAuthor":false,"prefix":"","firstName":"Seth","middleName":"Kenedi","lastName":"Mbwambo","suffix":""},{"id":626563347,"identity":"294b80b9-cbb8-4f27-9df5-677f98d5af75","order_by":1,"name":"Jackson Josephat Kavishe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYBACxgYGZhDNLAEkDgB5ciDegQekaDEGa0nAbxFYC4ME1IDEBhADnxbm9sOPDX9U3GGXbD9jePDnDpv0+WGHHwJtsZPTbcDhsJ4042SeM8+YpXlyDA7znknL3Xg7zQCoJdnY7AAuv+QwH2ZsO8wsx5CWAGLkbpydANJyIHEbLi39b5gP/gRp4X+WAGT8Tzecnf4Bv5YZOcwJvEAt0hLJBw7wth1IkJfOIWDLjGfGxjxnDjNLznh84DBvW7LhBumcggMJBrj9Ytif/FjyR8XhZInzic0ff7bZycvPTt/84UOFnRxOLQ0QOhkuYgBWaYBdOQjIQ2k7hEgDbtWjYBSMglEwMgEAg4Bm4DTqeRYAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Jackson","middleName":"Josephat","lastName":"Kavishe","suffix":""}],"badges":[],"createdAt":"2026-04-12 21:23:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9396853/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9396853/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108006745,"identity":"fef9198f-7e67-4e38-992f-9b7de809ffee","added_by":"auto","created_at":"2026-04-28 12:56:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":815313,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9396853/v1/f2b0a2a5-82fd-47fa-a409-2613cca8a7e4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of contraceptive use and antenatal care utilization on risks of pregnancy losses among reproductive women in Tanzania","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePregnancy loss is one of the main maternal health challenges globally, affecting millions of women of reproductive age each year. Pregnancy loss refers to the spontaneous loss of an embryo or fetus before viability, including outcomes such as miscarriage, abortion, and stillbirth. Recent global estimates indicate that approximately 38.6\u0026nbsp;million pregnancy loss cases were recorded in 2021 (Xu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), with about 23\u0026nbsp;million miscarriages and 1.8\u0026nbsp;million stillbirths occurring annually (UN-IGME, 2024). Despite sustained global efforts to improve maternal health under the Sustainable Development Goals (SDGs), pregnancy loss continues to pose significant health, social, and economic burdens worldwide, with approximately 2.6\u0026nbsp;million babies being stillborn out of 136\u0026nbsp;million births in the world (United Nations, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Graham et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Evidence shows that pregnancy loss is closely associated with inadequate access to reproductive healthcare services, particularly poor birth spacing and insufficient utilization of antenatal care (ANC) (Yehuala et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This underscores the importance of integrated reproductive health interventions, including contraceptive use and adequate antenatal care, in reducing adverse pregnancy outcomes (Stuart et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe burden of pregnancy loss is disproportionately high in low- and middle-income countries, particularly in Sub-Saharan Africa (SSA) which accounts for approximately 48% of global stillbirths, where access to reproductive health services remains limited. (UN-IGME, 2024; Goldenberg et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this region, low contraceptive use contributes to short birth intervals, which increase the risk of adverse pregnancy outcomes, including pregnancy loss (Belachew et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly, inadequate and inconsistent utilization of antenatal care services has been strongly linked to poor maternal and neonatal outcomes (Tolossa et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although antenatal care has the potential to promote improved reproductive health behaviors, including postpartum contraceptive uptake (McCarthy et al., 2024), many women in SSA continue to face barriers to accessing and fully utilizing these services. As a result, limited contraceptive use and suboptimal ANC utilization remain key contributors to pregnancy loss in the region.\u003c/p\u003e \u003cp\u003eIn Tanzania, notable progress has been made in expanding maternal and reproductive health services (Mpfubhusa et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Prasad et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Efforts such as promoting early initiation of antenatal care, the adoption of the eight-contact ANC model to improve the quality and frequency of care, and the decentralization of maternal health services to enhance access, particularly in rural areas, have contributed to increased ANC coverage (Kasagama et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; MoHCDGEC, 2018; MoHCDGEC, 2021). In addition, the integration of family planning services into broader healthcare programs has improved contraceptive uptake, with approximately 66% of women reported to use some form of contraception (UNFPA, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; TDHS-MS, 2022). Despite these achievements, significant challenges persist. The uptake of modern contraceptives remains uneven across regions (Kidayi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), while disparities in the timing, frequency, and quality of antenatal care services continue to influence maternal health outcomes (Mahiti et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Bintabara \u0026amp; Basinda, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Socioeconomic and demographic inequalities further limit equitable access to reproductive health services in the country.\u003c/p\u003e \u003cp\u003eDespite the growing body of literature on reproductive health, several critical gaps remain. Existing studies on antenatal care in Tanzania and Sub-Saharan Africa largely focus on the determinants of utilization while treating ANC as a single-stage decision, thereby overlooking the intensity and frequency of visits, which are essential for effective maternal care (Ntegwa, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Eliufuo et al., 2024; Tibenderana et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, most studies examine contraceptive use and antenatal care separately, without considering their joint and complementary effects on pregnancy loss outcomes, limiting a comprehensive understanding of reproductive health dynamics (Mbona et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ntegwa \u0026amp; Pelizzo, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Endawkie \u0026amp; Tsega, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, there exists a contextual gap, as many studies rely on cross-country or regional analyses in Sub-Saharan Africa, which may obscure country-specific differences in healthcare systems and socio-cultural factors relevant to Tanzania (Demissie et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Aboagye et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Finally, from a methodological perspective, much of the existing literature employs conventional approaches such as descriptive analysis and standard regression models, which do not adequately capture the complexity of healthcare decision-making processes, including multiple choice behavior, two-stage utilization decisions, and excess zero outcomes in pregnancy loss data (Tesema et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Eliufuo et al., 2024; Ntegwa, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These limitations highlight the need for a unified analytical framework supported by advanced econometric techniques.\u003c/p\u003e \u003cp\u003eIn light of these gaps, this study seeks to address the following research questions: What factors influence contraceptive use among women of reproductive age in Tanzania? What socioeconomic and demographic factors determine both the initiation and intensity of antenatal care utilization? And how do contraceptive use and antenatal care jointly affect the risk of pregnancy loss? By addressing these questions, this study contributes to the literature by integrating contraceptive use and antenatal care within a single analytical framework and applying advanced econometric models, including the Multinomial Logit Model, Double-Hurdle Model, and Zero-Inflated Poisson Model, to better capture the complexity of reproductive health behaviors and outcomes.\u003c/p\u003e \u003cp\u003eThe findings of this study are expected to provide important policy insights for improving maternal and reproductive health interventions in Tanzania. Specifically, the study informs the design of integrated healthcare strategies aims at enhancing equitable access to contraceptive services and improving the adequacy of antenatal care. Ultimately, this contributes to national and global efforts to reduce pregnancy loss and improve maternal and neonatal health outcomes. Furthermore, the study fills the methodological gaps that remain in studying reproductive health by employing double huddle, multinomial logit, and the zero-inflated poisson regression\u003c/p\u003e \u003cp\u003eThe remainder of this paper is organized as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the theoretical foundations underpinning the study. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reviews the relevant empirical literature. Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e4\u003c/span\u003e describes the data sources and outlines the research methodology. Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the study\u0026rsquo;s empirical results, while Section \u003cspan refid=\"Sec19\" class=\"InternalRef\"\u003e6\u003c/span\u003e discusses the key findings in the context of existing literature. Section \u003cspan refid=\"Sec20\" class=\"InternalRef\"\u003e7\u003c/span\u003e summarizes the study and highlights its policy implications. Section \u003cspan refid=\"Sec21\" class=\"InternalRef\"\u003e8\u003c/span\u003e outlines the study\u0026rsquo;s limitations and suggests directions for future research.\u003c/p\u003e"},{"header":"2. Theoretical Foundation","content":"\u003cp\u003eThe Behavioral Model of Health Services Use, as revised by Andersen (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), provides a sociological framework for analyzing contraceptive use and antenatal care (ANC) uptake. It posits that healthcare utilization is influenced by predisposing characteristics, enabling resources, and perceived need. In the Tanzanian context, realized access to services is shaped by maternal education, wealth quintiles, and the distance to health facilities, which act as structural barriers to service utilization (Gebrekidan et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mpfubhusa et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Andersen\u0026rsquo;s model thus highlights the role of service entry barriers in shaping maternal health behaviors.\u003c/p\u003e \u003cp\u003eComplementing this, Grossman\u0026rsquo;s (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1972\u003c/span\u003e) Health Capital Model offers an economic perspective, framing health as a form of capital that individuals invest in to maintain or improve. Contraceptive use and intensive ANC visits, within this framework, are strategic investments that preserve health capital and prevent its rapid depreciation, which clinically manifests as pregnancy loss (Endawkie \u0026amp; Tsega, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Grossman\u0026rsquo;s model emphasizes the economic costs of health production, including opportunity costs and travel time, which can reduce ANC uptake despite adequate health knowledge (Yehuala et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mpfubhusa et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIntegrating these perspectives allows for a comprehensive framework for understanding maternal health in Tanzania. Andersen\u0026rsquo;s model explains how social structures and personal characteristics influence service utilization, while Grossman\u0026rsquo;s model captures the economic rationale driving individuals to make health-preserving decisions. For instance, educated women may understand the benefits of ANC but still face high \u0026ldquo;shadow prices\u0026rdquo; for accessing care, affecting utilization and maternal outcomes (Tibenderana et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"3. Empirical Literature Review","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The Contraceptive use and risk of pregnancy loss\u003c/h2\u003e \u003cp\u003eContraceptive use among women of reproductive age has been widely studied, but offers mixed results and contextual differences. Using Demographic and Health Survey (DHS) data from 23 Sub-Saharan African countries, Demissie et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) employed a multinomial multilevel model and found that both individual-level factors (such as education and age) and community-level characteristics significantly influence the use of short- and long-acting contraceptive methods. However, the use of pooled cross-country data limits the ability to capture country-specific dynamics, particularly in Tanzania, where regional disparities in healthcare access and cultural practices are pronounced.\u003c/p\u003e \u003cp\u003eSimilarly, Bolarinwa et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) applied spatial and multilevel analysis in Nigeria and revealed that both individual and community-level factors explain variations in contraceptive use. Nevertheless, the study does not explicitly model the relative probabilities of choosing among multiple contraceptive methods, thereby limiting the understanding of heterogeneous choice behavior. In Tanzania, Fente et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) examined contraceptive discontinuation using a multilevel binary logistic regression model and reported a discontinuation rate of 34%. While informative, the study focuses on discontinuation rather than the initial choice among alternative contraceptive methods, which is essential for understanding reproductive decision-making.\u003c/p\u003e \u003cp\u003eOverall, the literature highlights the importance of socioeconomic and demographic factors in shaping contraceptive use but is limited by both contextual and methodological constraints. Many studies rely on cross-country analyses, while others apply binary models that fail to capture the multinomial nature of contraceptive choice behavior. These limitations justify the application of a Multinomial logit model to analyze the determinants of contraceptive use in Tanzania.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The antenatal care and risk of pregnancy loss\u003c/h2\u003e \u003cp\u003eAntenatal care (ANC) plays a critical role in improving maternal and neonatal health outcomes. Empirical evidence shows that ANC utilization is influenced by factors such as education, income, healthcare accessibility, and reproductive characteristics. Aboagye et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), using data from Sub-Saharan Africa, found that wealth and educational attainment are key determinants of disparities in ANC utilization. However, the cross-country nature of the study limits its applicability to country-specific contexts such as Tanzania.\u003c/p\u003e \u003cp\u003eIn Tanzania, Ntegwa (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) employed a Poisson regression model and found that education, wealth status, urban residence, and media exposure significantly influence ANC utilization. Despite these insights, the study treats ANC utilization as a single-stage decision, failing to distinguish between the decision to initiate care and the intensity of subsequent visits. Similarly, Eliufuo et al. (2024) used multivariate logistic regression to analyze determinants of adequate ANC visits and found that access to information increases the likelihood of completing recommended visits, while higher parity and partner violence reduce completion rates. However, the study focuses only on completion and does not capture the two-stage decision-making process.\u003c/p\u003e \u003cp\u003eFurthermore, Tibenderana et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) analyzed the adequacy of ANC components using a modified Poisson regression model and found that only 41% of women received adequate ANC services. While informative, the study treats ANC utilization as a single outcome, thereby overlooking the distinction between initiation and intensity of care.\u003c/p\u003e \u003cp\u003eTaken together, these studies demonstrate that ANC utilization is influenced by multiple factors but are limited by their one-stage modeling approach, which does not adequately capture the sequential nature of healthcare utilization decisions. This provides strong justification for the use of a Double-Hurdle Model, which allows separate estimation of both the decision and intensity of ANC utilization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The antenatal care, contraceptive use, and pregnancy outcomes\u003c/h2\u003e \u003cp\u003eA growing body of literature highlights the importance of reproductive health interventions in reducing adverse pregnancy outcomes, including pregnancy loss. Ahmed et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2026\u003c/span\u003e), using decomposition analysis, found that improvements in maternal healthcare services and increased contraceptive use contributed significantly to reductions in global maternal mortality. However, the study focuses on aggregate outcomes and does not explicitly examine pregnancy loss at the individual level, nor does it account for the count nature of such outcomes.\u003c/p\u003e \u003cp\u003eAt the country level, Ntegwa and Pelizzo (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) used Propensity Score Matching to estimate the impact of antenatal care on adverse pregnancy outcomes in Tanzania. They found that adequate ANC reduces such outcomes by 5.6% to 9.3%. However, the study models pregnancy outcomes as a binary variable, thereby ignoring the frequency and repeated occurrence of pregnancy loss. Similarly, Mbona et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found that contraceptive use reduces the likelihood of pregnancy termination, but the study focuses on termination rather than pregnancy loss, limiting its scope.\u003c/p\u003e \u003cp\u003eAt the regional level, Tesema et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) used a mixed-effects logistic regression model to estimate stillbirth prevalence in East Africa, while Endawkie and Tsega (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) applied a negative binomial regression model to analyze pregnancy loss in Sub-Saharan Africa. Although these studies provide valuable insights, they do not account for the excess zero counts in pregnancy loss data and fail to jointly analyze the effects of contraceptive use and antenatal care utilization. Additionally, their regional focus limits their applicability to the Tanzanian context.\u003c/p\u003e \u003cp\u003eOverall, the literature is constrained by conceptual, contextual, and methodological limitations, particularly the failure to model the joint effects of contraceptive use and antenatal care and the inability to address the statistical properties of pregnancy loss data. These limitations justify the use of a Zero-Inflated Poisson Model, which accounts for excess zeros and allows for a more robust analysis of the determinants of pregnancy loss.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Materials and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Data\u003c/h2\u003e \u003cp\u003eThe study utilizes secondary data from the National Bureau of Statistics (NBS) through the Tanzania Demographic and Health Survey (TDHS) 2022. The survey employed a stratified two-stage cluster sampling design to produce nationally representative data covering both Tanzania Mainland and Zanzibar, including urban and rural areas. A total of 40,394 samples, including the women of reproductive age (15\u0026ndash;49 years), were included in the dataset. The study focused on key outcome variables such as contraceptive use, antenatal care utilization, and pregnancy outcomes, while also incorporating independent and control variables, including age, education, wealth index, household and family characteristics, and health insurance coverage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Variables and measurement\u003c/h2\u003e \u003cp\u003eThis study employs a range of socio-demographic, economic, behavioral, and reproductive health variables to examine the determinants of contraceptive use, antenatal care utilization, and pregnancy outcomes among women of reproductive age. The variables are measured using binary and categorical scales, consistent with the structure of the TDHS 2022 dataset. Binary variables are coded as 1 to indicate the presence of a characteristic and 0 otherwise, while categorical variables are grouped into meaningful classifications such as age groups, wealth quintiles, education levels, and fertility preferences, as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eThe Description and Measurement of Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeasurement\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe gender of the household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;male, 0\u0026thinsp;=\u0026thinsp;female)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u0026rsquo;s Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe women in the reproductive age group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategorical (1\u0026thinsp;=\u0026thinsp;15\u0026ndash;24, 25\u0026ndash;34, 35\u0026ndash;44, 44 and above)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadio listening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether a respondent heard about the family loaning through the radio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategorical (1\u0026thinsp;=\u0026thinsp;yes, 2\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTelevision watching\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether a respondent heard about family planning through television\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial media use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether a respondent heard about family palling through social networks (e.g., Instagram, Facebook, and Twitter)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe status of whether the respondent is married or not married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;married, 0\u0026thinsp;=\u0026thinsp;never married)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe place where the respondent currently lives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;urban, 0\u0026thinsp;=\u0026thinsp;rural)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWife beating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe beating of a wife is justified when she refuses to have sex with her husband.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to health services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether the women have accessed the health services for the past twelve months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecision maker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecision maker on the use of contraceptives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategorical (1\u0026thinsp;=\u0026thinsp;respondents, 2\u0026thinsp;=\u0026thinsp;partner/husband, 3\u0026thinsp;=\u0026thinsp;the joint decision, 4\u0026thinsp;=\u0026thinsp;someone else\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether a respondent has any chronic illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily planning awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether the respondent has been told about family planning when at the health facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostpartum ovulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCan women get pregnant\u003c/p\u003e \u003cp\u003eafter birth and before the period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsurance coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether the respondent is \u003cb\u003ecurrently covered by any type of health insurance.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe wealth measure of a household\u0026rsquo;s \u003cb\u003eeconomic status\u003c/b\u003e is based on the assets and living conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategorical (1\u0026thinsp;=\u0026thinsp;poorest, 2\u0026thinsp;=\u0026thinsp;poorer, 3\u0026thinsp;=\u0026thinsp;middle, 4\u0026thinsp;=\u0026thinsp;richer, 5\u0026thinsp;=\u0026thinsp;richest\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFertility preference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA person\u0026rsquo;s or a couple\u0026rsquo;s \u003cb\u003edesire to have children\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategorical (1\u0026thinsp;=\u0026thinsp;have another child, 2\u0026thinsp;=\u0026thinsp;no more, 3\u0026thinsp;=\u0026thinsp;infecund\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartners/husband's education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe highest level of education attained by a partner or a husband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategorical (1\u0026thinsp;=\u0026thinsp;no education, 2\u0026thinsp;=\u0026thinsp;primary education, 3\u0026thinsp;=\u0026thinsp;secondary education, 4\u0026thinsp;=\u0026thinsp;higher education)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHusband's desire for children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicate the husband\u0026rsquo;s preference regarding having additional children in the family.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaiting time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe respondents waited at the health facility before getting the service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to the facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether the distance to the health facility is a big problem or not\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicate the woman\u0026rsquo;s record of all previous pregnancies and their outcomes.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategorical (1\u0026thinsp;=\u0026thinsp;livebirth, 2\u0026thinsp;=\u0026thinsp;stillbirth, 3\u0026thinsp;=\u0026thinsp;miscarriage/abortion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntenatal care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe number of antenatal care visits (at least 4 visits) during the pregnancy period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContraceptive use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe current method of contraception that the respondent uses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategorical (1\u0026thinsp;=\u0026thinsp;traditional, 2\u0026thinsp;=\u0026thinsp;Modern, 3\u0026thinsp;=\u0026thinsp;non-users)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe place where the respondent was born\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategorical (1\u0026thinsp;=\u0026thinsp;at heath facility, 2\u0026thinsp;=\u0026thinsp;at home, 3\u0026thinsp;=\u0026thinsp;road,4\u0026thinsp;=\u0026thinsp;others)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressured to be pregnant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHusband or family member pressured the respondent to become pregnant against their will\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinary (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eSource: Compiled by authors\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Model Specification\u003c/h2\u003e \u003cp\u003eThe study employed three distinct models to examine the effects of contraceptive use, antenatal care (ANC), and the risk of pregnancy loss. First, a multinomial logit model was used to identify the factors influencing contraceptive use among women of reproductive age in Tanzania. Second, a double-hurdle model was applied to analyze the determinants and intensity of antenatal care utilization among pregnant women. Finally, a zero-inflated model was employed to assess the effects of ANC and contraceptive use on the risk of pregnancy loss. Based on these approaches, the following estimable equations were specified for each model\u003c/p\u003e \u003cp\u003eThe multinomial logit model is used to model categorical outcomes with more than two categories, e.g., type of contraceptive used (modern, traditional, none). The estimable equation is described as;\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:P\\left({Y}_{i}=j\\right)=\\frac{\\text{exp}\\left({X}_{i}{\\beta\\:}_{j}\\right)}{1+\\sum\\:_{k=1}^{j-1}\\text{e}\\text{x}\\text{p}({X}_{i}{\\beta\\:}_{k}\\left)\\right)},\\:j=1,\\:2,\\:3\\dots\\:\\dots\\:.j-1$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere Y\u003csub\u003ei\u003c/sub\u003e is the contraceptive use category for woman \u003cem\u003ei\u003c/em\u003e, X\u003csub\u003ei\u003c/sub\u003e is the vector of independent variables (age, wealth, education, etc.), and β\u003csub\u003ej\u003c/sub\u003e is the vector of coefficients for category \u003cem\u003ej\u003c/em\u003e\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 The double-huddle model\u003c/h2\u003e \u003cp\u003eThe double-hurdle model analyzes decisions with two stages: the first hurdle examines whether an individual participates in an activity (e.g., attending antenatal care), while the second hurdle models the intensity of participation (e.g., number of visits) using a truncated regression. The truncation accounts for the fact that only participants have non-zero outcomes, ensuring unbiased estimation of the factors affecting intensity (Cragg, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). The double huddle model is defined as follows;\u003c/p\u003e \u003cp\u003eStep 1: Participation decision (probit model)\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{D}_{i}={Z}_{i}\\gamma\\:+{\\mu\\:}_{i},\\:\\:{D}_{i}=\\left\\{\\begin{array}{c}1\\:if\\:{D}_{i}\u0026gt;0\\\\\\:0,\\:otherwise\\end{array}\\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{D}_{i}=1\\:\\)\u003c/span\u003e\u003c/span\u003e If a woman uses ANC, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003eis the vector of explanatory variables, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\gamma\\:\\:\\)\u003c/span\u003e\u003c/span\u003eis the vector of coefficients and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e follows a normal distribution \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}\\sim N(0,\\:1)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eStep 2: (Intensity \u0026ndash; Truncated regression)\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:P\\left({Y}_{i}=y\\left|{D}_{i}\\right.=1,\\:{X}_{i}\\right)=\\frac{\\frac{{\\lambda\\:}_{i}^{y}{e}^{-\\lambda\\:i}}{y!}}{1-{e}^{\\lambda\\:i}},\\:y=1,\\:2,\\:3,\\:4\\dots\\:\\dots\\:n$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:i=\\text{e}\\text{x}\\text{p}\\left({X}_{i}\\beta\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the expected number of ANC visits, the denominator adjusts for truncation at zero, \u003cem\u003ey\u003c/em\u003e is the specific counts, and \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the number of antenatal care visits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 The zero-inflated Poisson (ZIP) model\u003c/h2\u003e \u003cp\u003eThe Zero-Inflated Poisson (ZIP) model is used to analyze count data characterized by an excess number of zero observations. (Lambert, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). The model assumes that the observed zeros arise from two different processes: one group that will always have zero outcomes (structural zeros) and another group that may have positive counts or zeros generated from a standard Poisson process. (Greene, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFirst process: for zero counts (logit model)\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:Pr\\left({Y}_{i}=0\\left|{X}_{i}\\right.\\right)={\\pi\\:}_{i}+(1-{\\pi\\:}_{i}){e}^{-\\lambda\\:i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\pi\\:}_{i}=\\frac{\\text{exp}\\left({W}_{i}\\alpha\\:\\right)}{1+\\text{exp}\\left({W}_{i}\\alpha\\:\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003eis the covariate in the logit model, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003e is the coefficient in the logistic model\u003c/p\u003e \u003cp\u003eSecond process: for non-zero counts\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:Pr\\left({Y}_{i}=y\\left|{X}_{i}\\right.\\right)=\\left(1-{\\pi\\:}_{i}\\right)\\frac{{\\lambda\\:}_{i}^{y}{e}^{-\\lambda\\:i}}{y!},\\:y=1,\\:2\\dots\\:\\dots\\:.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:i=\\text{e}\\text{x}\\text{p}\\left({X}_{i}\\beta\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003eis the number of pregnancy losses\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:\\pi\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the probability of structural zeros, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:i\\:\\:is\\:\\)\u003c/span\u003e\u003c/span\u003eexpected count if not a structural zero, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the covariates, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:\\)\u003c/span\u003e\u003c/span\u003e factors associated with the number of losses among women at risk.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Study Analysis\u003c/h2\u003e \u003cp\u003eThe study employed STATA 17 to analyze the dataset. Descriptive statistics were first used to examine trends and patterns in household and maternal characteristics before applying the comprehensive models, including the multinomial logistic, double-hurdle, and zero-inflated Poisson models. To ensure the reliability and validity of the estimates, each model was tested against its underlying assumptions and performance indicators, yielding robust, credible results that can inform policymakers and public health interventions. (Wooldridge, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Household characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the household and maternal characteristics of the study sample of 40,394 women of reproductive age in Tanzania. The majority of households were headed by males (74.51%), and most respondents resided in rural areas (72.32%). In terms of age, most women were between 35\u0026ndash;44 years (40.1%), followed by 25\u0026ndash;34 years (31.2%). Media exposure was moderate, with 46.58% of women reporting listening to the radio, 23.89% watching television, and 17.12% using social media. Most women were married (81.24%), and about 27.41% reported that wife-beating was justified if a wife refused sex. Access to health care was reported by 59.11% of women, while 73.02% reported no chronic illness.\u003c/p\u003e \u003cp\u003eThe contraceptive use decision was dominated by joint decisions (53.83%), with the respondent alone deciding 31.97% of cases, and the husband/partner in 13.30%. Family planning awareness was relatively low, with 44.71% reporting having been informed at a health facility. Postpartum ovulation knowledge was observed in 52.31% of women, while only 4.96% were covered by health insurance. Regarding economic status, the distribution across wealth quintiles was relatively balanced, with the middle category (22.27%) slightly higher. Fertility preference was almost evenly split, with 44.53% wanting another child, 42.00% not wanting more, and 13.47% undecided. Most partners had at least a primary education (42.58%), while 33.63% had no formal education.\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\u003eHousehold Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\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\u003ePercent (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\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\u003e30,099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11,180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29,214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove 44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eListening to the radio\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\u003e18,815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21,579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWatching TV\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\u003e9,652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30,742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of social media\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\u003e6,916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33,478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent marital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32,817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeating of the wife\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\u003e29,320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11,074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to health care\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\u003e23,878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16,516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecision maker for contraception\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\u003e10,493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHusband/partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJoint decision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17,666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSomeone else\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic illness\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\u003e28,253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily planning awareness\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\u003e10,676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostpartum ovulation\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\u003e21,129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19,265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovered by health insurance\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\u003e2,003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38,391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFertility preference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHave another\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17,987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUndecided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16,966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartner/Husband\u0026rsquo;s education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHusband\u0026rsquo;s desire for children\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\u003e10,524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20,713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaiting time\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\u003e10,744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16,439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to 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\u003e12,737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot a big problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27,657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy history index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLive birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStill birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiscarriage/abortion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContraceptive use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsing a modern method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsing the traditional method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-user\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25,771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressured to become pregnant\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\u003e31,475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntenatal care\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\u003e38,342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eSource: Authors computations\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that 94.94% of women reported attending antenatal care, but 31.53% perceived distance to the health facility as a major barrier. Pregnancy history showed that 87.39% had experienced live births, 12.02% stillbirths, and only 0.59% miscarriages or abortions. In terms of contraceptive use, 30.14% were using modern methods, 6.06% traditional methods, and 56.98% were non-users. Notably, 95.91% of women reported not being pressured by family members or husbands to become pregnant. Overall, these findings provide a comprehensive profile of household, socio-economic, and reproductive characteristics relevant for analyzing maternal health behaviors in Tanzania.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Factors influencing contraceptive use among reproductive women in Tanzania\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents results from a multinomial logit model examining factors influencing contraceptive use among reproductive-age women in Tanzania, using non-contraceptive users as the reference category. The model was jointly significant, indicating a reasonably good fit. Women\u0026rsquo;s age significantly influenced contraceptive choice. Women aged 35\u0026ndash;44 were 5.47 percentage points more likely to use modern methods compared to women above 44, while women aged 25\u0026ndash;34 were less likely to use modern methods but slightly more likely to use traditional methods. Younger women (15\u0026ndash;24) were more likely to use traditional contraceptives, suggesting that modern method uptake peaks in middle reproductive ages.\u003c/p\u003e \u003cp\u003eResidence and household wealth also played significant roles. Urban women were slightly less likely to use modern methods but more likely to use traditional methods. Wealthier women, particularly those in the richer and middle categories, were more likely to use modern methods, indicating that economic status positively influences contraceptive uptake. Media exposure showed mixed effects: listening to the radio increased both modern and traditional method use, watching television slightly increased modern method use, while social media use was not statistically significant for modern methods.\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\u003eThe multinomial logit model for factors influencing contraceptive use among reproductive women in Tanzania\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNon-contraceptive user is the reference category\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModern Method (dy/dx)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraditional Method (dy/dx)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0036*** (0.0003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0000 (0.0002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u0026rsquo;s age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0244 (0.0066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0170*** (0.0036)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0203*** (0.0042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0086*** (0.0021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0547*** (0.0103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0096 (0.0062)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove 44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eListening to the radio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0164*** (0.0058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0119*** (0.0028)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of social media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0039 (0.0041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0147 (0.0022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWatching TV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0010** (0.0046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0077 (0.0023)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1392 (13.6801)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.8711 (35.9910)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0226*** (0.0078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0116*** (0.0039)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWife beating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0039** (0.0030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0063 (0.0013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to health services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0003 (0.0002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0002 (0.0001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecision maker on contraceptive use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespondent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1045*** (0.0086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0026 (0.0048)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHusband/ partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0394*** (0.0063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0373*** (0.0036)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJoint decision of partners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1529*** (0.0172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2000*** (0.0045)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0273*** (0.0086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0117** (0.0050)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily planning awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0347*** (0.0060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0017 (0.0033)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostpartum ovulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0087*** (0.0015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0024*** (0.0007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovered by health insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0042** (0.0135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0124 (0.0061)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0251** (0.0098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0033 (0.0050)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0521*** (0.0098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0161*** (0.0051)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0610*** (0.0111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0400*** (0.0058)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFertility preference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave another child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0105 (0.0189)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0314*** (0.0115)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndecided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1256** (0.0385)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3972 (0.7924)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfecund\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartners/husbands\u0026rsquo; education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3306*** (0.0961)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3095*** (0.0470)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4760*** (0.1030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0436 (0.0546)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2662** (0.1258)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.1866** (0.0735)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNumber of obs\u0026thinsp;=\u0026thinsp;23,878 Log likelihood = -19300.635\u003c/p\u003e \u003cp\u003ePseudo R2 = 0.2533 LR chi2(50) = 2173.38\u003c/p\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2 = 0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003e*** =significance at 1%, ** = significance at 5%, * = significance at 10%, and robust standard errors in parentheses, Ref=reference/benchmark category, LR=likelihood ratio, the coefficient represents the marginal effects (dy/dx)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eSource: Authors computations\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that Household decision-making dynamics strongly influenced contraceptive use. When the husband or partner decided on contraceptive use, women were more likely to use modern and traditional methods. In contrast, joint decision-making significantly reduced the probability of using modern and traditional methods relative to other decision-makers. Women who made decisions alone were less likely to use modern methods.\u003c/p\u003e \u003cp\u003eHealth and awareness factors were also significant. Family planning awareness increased the use, while women with chronic illnesses were less likely to use modern methods but more likely to use traditional methods. Understanding postpartum ovulation slightly reduced modern method use and increased traditional method use.\u003c/p\u003e \u003cp\u003eFertility preferences and partner education further shaped contraceptive behavior. Women desiring another child were more likely to use traditional methods, while those undecideds were more likely to use modern methods. Partner\u0026rsquo;s education had a strong effect: primary education increased both modern and traditional method use, secondary education increased modern method use, and higher education increased modern method use while reducing traditional method use. Health insurance slightly increased the modern method, but had no significant effect on traditional methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Determinants and intensity of antenatal care utilization among pregnant women\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the Double Hurdle model estimates for the determinants of antenatal care (ANC) utilization (Hurdle 1) and the intensity of ANC visits (Hurdle 2) among women of reproductive age in Tanzania. The model was statistically significant, indicating that the included variables jointly influence both the decision to seek ANC and the number of visits attended. Partner or husband\u0026rsquo;s education significantly influenced ANC utilization: women whose partners had primary education were more likely to use ANC services, while secondary education slightly reduced the probability. In terms of intensity, women with partners having primary or secondary education attended fewer ANC visits, as indicated by negative coefficients in Hurdle 2.\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\u003eDouble huddle model for determinants and intensity of ANC utilization\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=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHuddle 1 (Determinants)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eHuddle 2 (Intensity of ANC use)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobust SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRobust SE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartner/husband's education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5623**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0650***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1061**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0541***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\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\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHusbands desire to have children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0632**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u0026rsquo;s age group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0066***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0016***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3924**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9273*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove 44 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1116**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0240***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2404***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0257***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3553***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0429***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eListening to the radio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1404***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWatching Television\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0838***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReading the magazine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0116**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaiting time to get service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0004***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLive birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.236*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStill birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0378*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiscarriage/abortion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to the health facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2547***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0347***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0106***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovered by health insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0420**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNumber of obs\u0026thinsp;=\u0026thinsp;40,394\u003c/p\u003e \u003cp\u003eWald chi2(20)\u0026thinsp;=\u0026thinsp;259.09\u003c/p\u003e \u003cp\u003eLog pseudolikelihood = -37584.559\u003c/p\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2 = 0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e*** = significance at 1%, ** = significance at 5%, *= significance at 10%, and SE=standard errors, Ref=reference/benchmark category, ANC=antenatal care\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eSource: Authors computations\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e further shows that wealth status and women\u0026rsquo;s age were also important determinants. Women in higher wealth categories, middle, richer, and richest, were more likely to use ANC, and the intensity of visits increased notably for the richer group. Younger women aged 15\u0026ndash;24 had a small but significant increase in ANC utilization, while women aged 25\u0026ndash;34 and 35\u0026ndash;44 were more likely to use ANC services. Other factors influencing ANC utilization included media exposure, with television viewing increasing ANC uptake and radio listening reducing it. Pregnancy history, distance to the health facility, and health insurance also significantly affect ANC decisions and intensity of use. Notably, a greater distance to the facility reduced the likelihood of ANC utilization but increased the number of visits among those who sought care.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Effect of ANC and contraceptive use on risk of pregnancy losses\u003c/h2\u003e \u003cp\u003eThe Zero-Inflated Poisson model examined the effect of antenatal care (ANC) and contraceptive use on the risk of pregnancy loss among women of reproductive age in Tanzania. The results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show that attending ANC significantly reduces the risk of pregnancy loss, while a greater distance to a health facility increases the risk, highlighting the importance of access to maternal health services. Fertility preferences were strongly associated with pregnancy outcomes: women who desire more children had a higher risk of pregnancy loss, whereas women who were undecided about future fertility had a lower risk compared to infecund women. Partner education also influenced outcomes, with primary and secondary education reducing risk, while higher education was not significant. Place of delivery was critical: home delivery increased the risk, while health facility delivery substantially reduced it.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eZero-inflated poisson model for the effect of ANC and contraceptive use on risk of pregnancy loss\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=\"left\" 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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobust SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntenatal care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0712***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to the health facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0040***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFertility preference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1426***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndecided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2859***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfecund\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartners/husband's education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0703**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2331**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\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\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWife beating (refuses to have sex)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3492**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.7232***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\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\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0014**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressured to be pregnant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0662**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u0026rsquo;s age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5143***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1073**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove 44 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0732*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3113***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.4261**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0497*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContraceptive use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraditional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2618**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2477**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon user\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNumber of obs\u0026thinsp;=\u0026thinsp;40,394 Log pseudolikelihood = -27087.15\u003c/p\u003e \u003cp\u003eNonzero obs = 8,193 Zero obs = 32,201\u003c/p\u003e \u003cp\u003eWald chi2(20)\u0026thinsp;=\u0026thinsp;1043.17 Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2 = 0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003e*** =significance at 1%, ** =significance at 5%, * =significance at 10%, Ref=reference /benchmark category, SE=standard errors, p-value=probability value ranging between 0 and 1\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eSource: Authors computations\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e further reveals that maternal age, socio-economic status, and contraceptive use further shaped pregnancy loss risk. Women aged 15\u0026ndash;24 had the highest risk, with slight increases among women aged 25\u0026ndash;34, while women aged 35\u0026ndash;44 showed a lower risk. Wealth index showed a non-linear effect, with middle-income women at higher risk and richer women at lower risk. Chronic illness slightly increased risk, while using traditional contraceptives was protective. Other factors, such as marital status and being pressured to become pregnant, had smaller but significant effects.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Discussions","content":"\u003cp\u003eThe study provides important empirical insights into the determinants of contraceptive use, antenatal care (ANC) utilization, and their joint effects on pregnancy loss among women of reproductive age in Tanzania. The findings are broadly consistent with established theoretical frameworks, particularly Andersen\u0026rsquo;s Behavioral Model and the Health Capital Model (Andersen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Grossman, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1972\u003c/span\u003e), while also extending the empirical literature by uncovering context-specific and, in some cases, unexpected results.\u003c/p\u003e \u003cp\u003eFirst, the multinomial logit results confirm that socioeconomic and demographic factors play a significant role in shaping contraceptive use. Consistent with prior studies (Demissie et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Bolarinwa et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kidayi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), wealth status and partner\u0026rsquo;s education were strong predictors of modern contraceptive uptake. These findings support the argument that higher socioeconomic status enhances both access to reproductive health services and the ability to afford modern contraceptive methods. Similarly, the positive effect of family planning awareness aligns with evidence from Mbona et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and UNFPA (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which emphasize the importance of information dissemination in promoting contraceptive use. The influence of media exposure, particularly radio and television, further corroborates findings by Bolarinwa et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Demissie et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who highlight the role of mass media in shaping reproductive health behaviors across Sub-Saharan Africa.\u003c/p\u003e \u003cp\u003eHowever, some findings diverge from the existing literature. For instance, the negative association between urban residence and modern contraceptive use contradicts studies that typically report higher uptake in urban areas due to better access to healthcare services (Demissie et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Aboagye et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A plausible explanation is the existence of intra-urban inequalities, where women living in informal settlements may still face significant barriers to accessing quality reproductive health services (Bintabara \u0026amp; Basinda, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mahiti et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In addition, the finding that joint decision-making reduces contraceptive use contrasts with studies suggesting that spousal communication enhances uptake (Bolarinwa et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Demissie et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This discrepancy may reflect underlying gender dynamics in Tanzania, where joint decision-making does not necessarily imply equal bargaining power, thereby limiting women\u0026rsquo;s autonomy in reproductive health decisions (Gebrekidan et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecond, the double-hurdle model results provide strong evidence that ANC utilization is a two-stage process influenced by different sets of factors. In line with previous studies (Aboagye et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ntegwa, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Eliufuo et al., 2024), wealth status, education, and access to health services significantly increase the likelihood of ANC utilization. These findings reinforce the view that financial and informational resources are critical determinants of healthcare access. The positive effect of health insurance coverage also supports findings from Gebrekidan et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Prasad et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which highlight the role of financial protection and health system strengthening in improving maternal healthcare utilization in Tanzania.\u003c/p\u003e \u003cp\u003eNevertheless, the study reveals important nuances that extend the literature. For example, while a partner\u0026rsquo;s education increases the likelihood of initiating ANC, it reduces the intensity of visits. This finding contrasts with studies such as Eliufuo et al. (2024) and Tibenderana et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which suggest that education improves the adequacy and completion of recommended ANC visits. One possible explanation is that more educated households may perceive fewer visits as adequate or face opportunity costs that limit repeated attendance, as suggested by the Health Capital Model (Grossman, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1972\u003c/span\u003e; Yehuala et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, the finding that distance to health facilities reduces ANC initiation but increases visit intensity among users is consistent with the notion of selective utilization, where only highly motivated women overcome access barriers and subsequently maximize service use (Tolossa et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tibenderana et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThird, the Zero-Inflated Poisson results confirm that both ANC and contraceptive use significantly influence pregnancy loss, supporting the study\u0026rsquo;s main hypothesis. The negative association between ANC attendance and pregnancy loss is consistent with findings by Ntegwa and Pelizzo (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), Tolossa et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and Goldenberg et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), all of which demonstrate that adequate maternal healthcare reduces adverse pregnancy outcomes. Similarly, the strong protective effect of health facility delivery aligns with global evidence emphasizing the importance of skilled birth attendance in improving maternal and neonatal outcomes (Goldenberg et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; United Nations, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the findings on contraceptive use present a mixed and somewhat unexpected pattern. While traditional contraceptive methods are associated with a reduced risk of pregnancy loss, modern methods are linked to an increased risk. This contradicts much of the existing literature, which generally finds modern contraception improves reproductive health outcomes (Ahmed et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Mbona et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Stuart et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A possible explanation is selection bias, where women using modern contraceptives may have prior reproductive health complications, making them more susceptible to pregnancy loss (Endawkie \u0026amp; Tsega, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, improper or inconsistent use of modern methods may reduce their effectiveness, highlighting gaps in counseling and service quality (McCarthy et al., 2024). This finding suggests that access alone is insufficient; the quality of reproductive health services is equally important.\u003c/p\u003e \u003cp\u003eThe study also finds that younger women (15\u0026ndash;24 years) face a significantly higher risk of pregnancy loss, which is consistent with previous research (Yehuala et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Endawkie \u0026amp; Tsega, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) linking younger maternal age to biological vulnerability and lower healthcare utilization. The non-linear relationship between wealth and pregnancy loss, where middle-income women face higher risks compared to richer women, further supports findings by Aboagye et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Bintabara and Basinda (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) who found that persistent inequalities in maternal health outcomes. This suggests that improvements in income alone may not guarantee better health outcomes without corresponding improvements in healthcare access and quality.\u003c/p\u003e \u003cp\u003eOverall, this study makes several important contributions to the literature. Unlike previous studies that examine contraceptive use and ANC separately (Ntegwa, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mbona et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Eliufuo et al., 2024), this study integrates both factors within a single analytical framework, demonstrating their complementary effects on pregnancy outcomes. Methodologically, the application of multinomial logit, double-hurdle, and zero-inflated Poisson models addresses key limitations in prior research by capturing multi-stage decision-making processes and the excess zero nature of pregnancy loss data (Tesema et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Endawkie \u0026amp; Tsega, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Demissie et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study examined the determinants of contraceptive use and antenatal care (ANC) utilization, as well as their combined effects on pregnancy loss among women of reproductive age in Tanzania. The findings show that reproductive health behaviors are strongly influenced by socioeconomic and demographic factors, including wealth, education, media exposure, and health insurance. While these factors generally improve access to modern contraception and ANC services, some variations, such as lower contraceptive use in urban areas and differences in ANC visit intensity, highlight important contextual and behavioral dynamics.\u003c/p\u003e \u003cp\u003eImportantly, the results confirm that adequate ANC utilization and health facility delivery significantly reduce the risk of pregnancy loss. However, the mixed effects observed for contraceptive methods suggest that improving access alone is not sufficient; the quality of services, proper usage, and informed decision-making are equally critical.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, several implications emerge. First, there is a need to strengthen family planning programs by enhancing education, awareness, and counseling to ensure the correct and consistent use of contraceptive methods. Second, improving both access to and the quality of ANC services is essential, with emphasis on encouraging women to complete the recommended number of visits. Third, addressing structural barriers such as distance to health facilities is crucial, particularly in rural and underserved areas, through improved infrastructure and community-based healthcare services. Fourth, promoting women\u0026rsquo;s autonomy and empowerment in reproductive health decision-making is vital for increasing the uptake of both contraceptive and maternal healthcare services.\u003c/p\u003e \u003cp\u003eIn conclusion, the study underscores the importance of integrated and context-specific reproductive health policies that simultaneously address contraceptive use, ANC utilization, and healthcare quality. Such a comprehensive approach is essential for reducing pregnancy loss and improving maternal health outcomes in Tanzania.\u003c/p\u003e"},{"header":"8. Limitation and Future Research","content":"\u003cp\u003eDespite providing important insights, this study is subject to several limitations that should be acknowledged. First, the analysis is based on cross-sectional data from the TDHS 2022, which limits the ability to establish causal relationships between contraceptive use, ANC utilization, and pregnancy loss. The observed associations may therefore reflect correlations rather than definitive cause-and-effect linkages. Second, the study relies on self-reported data, which may be affected by recall bias and social desirability bias, particularly for sensitive issues such as contraceptive use and pregnancy outcomes. Third, some potentially important variables, such as quality of healthcare services, cultural beliefs, and provider-related factors, were not fully captured in the dataset, which may lead to omitted variable bias.\u003c/p\u003e \u003cp\u003eGiven these limitations, several avenues for future research are recommended. First, future studies should consider using longitudinal or panel data for better trends and patterns on causal relationships and track reproductive health behaviors over time. Second, incorporating qualitative or mixed-method approaches would provide deeper insights into the social, cultural, and behavioral factors influencing contraceptive use and ANC utilization. Third, further research is needed to explore the quality of reproductive health services, including counseling, availability of methods, and provider competence, for better understand their role in shaping outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThe authors received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;\u003c/h2\u003e\n\u003ch3\u003eData availability statement\u003c/h3\u003e\n\u003cp\u003eThe study used data from the Tanzania Demographic and Health Survey (TDHS) 2022 (Wave 5), obtained from the National Bureau of Statistics (NBS) microdata repository. Data codes are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch3\u003eAuthors contribution\u003c/h3\u003e\n\u003cp\u003eSKM conceptualized the study, developed the introduction, and conducted the empirical literature review. JJK was responsible for the methodology, data analysis, and discussion of the findings. SKM and JJK jointly wrote the conclusion and proofread the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003estatement\u003c/strong\u003e: The authors report there are no competing interests to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAboagye, R. G., Osborne, A., Anyasodor, A. E., Yikindi, S. V., Adnani, Q. E. S., \u0026amp; Ahinkorah, B. O. (2025). Utilisation of eight or more antenatal care visits and its associated socio-economic-related inequalities in sub-Saharan Africa: A decomposition analysis. \u003cem\u003ePLoS One\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(3), e0312412. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0312412\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0312412\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed, S., Ali, M., Shah, I., \u0026amp; Tsui, A. O. (2026). Effect of maternity care improvement, fertility decline, and contraceptive use on global maternal mortality reduction between 2000 and 2023. \u003cem\u003eThe Lancet Global Health\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), e33\u0026ndash;e48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2214-109X(25)00409-7\u003c/span\u003e\u003cspan address=\"10.1016/S2214-109X(25)00409-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersen, R. M. (1995). Revisiting the behavioral model and access to medical care: Does it matter? \u003cem\u003eJournal of Health and Social Behavior\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(1), 1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2137284\u003c/span\u003e\u003cspan address=\"10.2307/2137284\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelachew, T. B., Asmamaw, D. B., \u0026amp; Negash, W. D. (2023). Short birth interval and its predictors among reproductive age women in high fertility countries in sub-Saharan Africa: a multilevel analysis of recent Demographic and Health Surveys. \u003cem\u003eBMC Pregnancy and Childbirth\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12884-023-05403-0\u003c/span\u003e\u003cspan address=\"10.1186/s12884-023-05403-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBintabara, D., \u0026amp; Basinda, N. (2021). Twelve-year persistence of inequalities in antenatal care utilisation among women in Tanzania: a decomposition analysis of population-based cross-sectional surveys. \u003cem\u003eBritish Medical Journal Open\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(4), e040450. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjopen-2020-040450\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2020-040450\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolarinwa, O. A., Tessema, Z. T., Frimpong, J. B., Seidu, A. A., \u0026amp; Ahinkorah, B. O. (2021). Spatial distribution and factors associated with modern contraceptive use among women of reproductive age in Nigeria: A multilevel analysis. \u003cem\u003ePlos one\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(12), e0258844. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0258844\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0258844\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCragg, J. G. (1971). Some statistical models for limited dependent variables with application to the demand for durable goods. \u003cem\u003eEconometrica: journal of the Econometric Society\u003c/em\u003e, 829\u0026ndash;844. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/1909582\u003c/span\u003e\u003cspan address=\"10.2307/1909582\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemissie, K. A., Jejaw, M., Teshale, G., Tiruneh, M. G., Tafere, T. Z., \u0026amp; Hagos, A. (2025). Multinomial multilevel analysis of factors affecting the use of modern contraceptives in sub-Saharan Africa: evidence from 2015 to 2023 Demographic Health Survey. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 16751. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-01622-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-01622-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEliufoo, E., Majengo, V., Tian, Y., Bintabara, D., Moshi, F., \u0026amp; Li, Y. (2024). Determinants of adequate antenatal care visits among pregnant women in low-resource setting: evidence from Tanzania national survey. \u003cem\u003eBMC Pregnancy and Childbirth\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(1), 790. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12884-024-06989-9\u003c/span\u003e\u003cspan address=\"10.1186/s12884-024-06989-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEndawkie, A., \u0026amp; Tsega, Y. (2025). Pregnancy loss and its predictors among ever-pregnant women in Sub-Saharan Africa: Multilevel mixed effect negative binomial regression. \u003cem\u003ePLOS Global Public Health\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(4), e0004316. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pgph.0004316\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgph.0004316\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFente, B. M., Asnake, A. A., Negussie, M., Melaku Bezie, Y., Asmare, M. A., Asebe, Z., H. A., \u0026amp; Seifu, B. L. (2025). Prevalence and determinants of contraceptive discontinuation among reproductive age women: analysis of Tanzania demographic health survey. \u003cem\u003eFrontiers in Global Women's Health\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, 1393020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgwh.2025.1393020\u003c/span\u003e\u003cspan address=\"10.3389/fgwh.2025.1393020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGebrekidan, A. Y., Woldegeorgis, B. Z., Kassie, G. A., Haile, K. E., Abrha, A. T., Asnake, A. A., \u0026amp; Asgedom, Y. S. (2025). Effective coverage of antenatal care and associated factors among pregnant women in Tanzania: a multilevel analysis. \u003cem\u003eFront Glob Women\u0026rsquo;s Health\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgwh.2025.1477666\u003c/span\u003e\u003cspan address=\"10.3389/fgwh.2025.1477666\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldenberg, R. L., McClure, E. M., \u0026amp; Saleem, S. (2018). Improving pregnancy outcomes in low- and middle-income countries. \u003cem\u003eReprod Health\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(Suppl 1), 88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12978-018-0524-5\u003c/span\u003e\u003cspan address=\"10.1186/s12978-018-0524-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraham, W., Woodd, S., Byass, P., Filippi, V., Gon, G., Virgo, S., \u0026amp; Singh, S. (2016). Diversity and divergence: the dynamic burden of poor maternal health. \u003cem\u003eThe Lancet\u003c/em\u003e, \u003cem\u003e388\u003c/em\u003e(10056), 2164\u0026ndash;2175. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/S0140-6736(16)31533-1\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(16)31533-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreene, W. H. (2012). Econometric analysis (7th ed.). Pearson.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrossman, M. (1972). On the concept of health capital and the demand for health. \u003cem\u003eJournal of Political Economy\u003c/em\u003e, \u003cem\u003e80\u003c/em\u003e(2), 223\u0026ndash;255. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1086/259880\u003c/span\u003e\u003cspan address=\"10.1086/259880\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKasagama, E., Todd, J., \u0026amp; Renju, J. (2022). Factors associated with changes in adequate antenatal care visits among pregnant women aged 15\u0026ndash;49 years in Tanzania from 2004 to 2016. \u003cem\u003eBmc Pregnancy And Childbirth\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e, 18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12884-021-04350-y\u003c/span\u003e\u003cspan address=\"10.1186/s12884-021-04350-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKidayi, P. L., Msuya, S., Todd, J., Mtuya, C. C., Mtuy, T., \u0026amp; Mahande, M. J. (2015). Determinants of modern contraceptive use among women of reproductive age in Tanzania: Evidence from Tanzania Demographic and Health Survey data. \u003cem\u003eAdvances in Sexual Medicine\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, 43\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.4236/asm.2015.53006\u003c/span\u003e\u003cspan address=\"10.4236/asm.2015.53006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLambert, D. (1992). Zero-Inflated Poisson Regression, With an Application to Defects in Manufacturing. \u003cem\u003eTechnometrics\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(1), 1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00401706.1992.10485228\u003c/span\u003e\u003cspan address=\"10.1080/00401706.1992.10485228\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahiti, G. R., Chombo, S., \u0026amp; Luoga, P. (2025). Socio-demographic correlates of booking antenatal care in first trimester among pregnant women in Tanzania. Insights from Tanzania demographic health survey 2022. \u003cem\u003eFrontiers in Reproductive Health\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 1669621. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/frph.2025.1669621\u003c/span\u003e\u003cspan address=\"10.3389/frph.2025.1669621\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMbona, S. V., Chifurira, R., Ndlovu, B. D., \u0026amp; Ananth, A. (2025). Prevalence and determinants of pregnancy termination for childbearing women using the modified Poisson regression model: a cross-sectional study of the Tanzania Demographic and Health Survey (TDHS) 2022. \u003cem\u003eBmc Public Health\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), 56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-024-21203-3\u003c/span\u003e\u003cspan address=\"10.1186/s12889-024-21203-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Health [Tanzania Mainland], Ministry of Health [Zanzibar], National Bureau of Statistics, Office of the Chief Government Statistician [Zanzibar], \u0026amp; ICF (2022). Tanzania Demographic and Health Survey and Malaria Indicator Survey 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/pubs/pdf/FR382/FR382.pdf\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/pubs/pdf/FR382/FR382.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMpfubhusa, F. L., Lihawa, R. M., \u0026amp; Mbwambo, S. K. (2026). Effect of digital health technologies on children's healthcare utilization: Evidence from vaccination uptake in Kigoma region, Tanzania. \u003cem\u003eHealth Education\u003c/em\u003e, 1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/HE-09-2025-0164\u003c/span\u003e\u003cspan address=\"10.1108/HE-09-2025-0164\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNtegwa, M. J. (2025). Determinants of quality antenatal care utilization in Tanzania: a generalized multilevel Poisson regression model approach. \u003cem\u003eDiscover Public Health\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 734. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12982-025-01162-9\u003c/span\u003e\u003cspan address=\"10.1186/s12982-025-01162-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNtegwa, M. J., \u0026amp; Pelizzo, R. (2025). Estimating the impact of prenatal health care services on adverse pregnancy outcomes in Tanzania: a propensity score matching approach. \u003cem\u003eFrontiers in Global Women\u0026rsquo;s Health\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, 1589721. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgwh.2025.1589721\u003c/span\u003e\u003cspan address=\"10.3389/fgwh.2025.1589721\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOna, L., McCarthy, N., Fardousi, V., Tripathi, R., Stafford, K., Levin, F., Khan, M. Pepper, Oona, M. R., \u0026amp; Campbell (2024). Antenatal care interventions to increase contraceptive use following birth in low- and middle-income countries: Systematic review and narrative synthesis. \u003cem\u003eGlobal Health: Science and Practice\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(5), e2400059. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.9745/GHSP-D-24-00059\u003c/span\u003e\u003cspan address=\"10.9745/GHSP-D-24-00059\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrasad, N., Mwakatundu, N., Dominico, S., Masako, P., Mongo, W., Mwanshemele, Y., Maro, G., Subi, L., Chaote, P., Rusibamayila, N., Ruiz, A., Schmidt, K., Kasanga, M. G., Lobis, S., \u0026amp; Serbanescu, F. (2021). Improving maternal and reproductive health in Kigoma, Tanzania: A 13-year initiative. \u003cem\u003eGlobal Health: Science and Practice\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(Suppl 1), S112\u0026ndash;S127. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.9745/GHSP-D-20-00494\u003c/span\u003e\u003cspan address=\"10.9745/GHSP-D-20-00494\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStuart, L., Desai, R., Hart, C., \u0026amp; Norris, S. A. (2025). Advocating for an integrated healthcare model for women of reproductive age in low- and middle-income countries. \u003cem\u003eCritical Public Health\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09581596.2025.2512842\u003c/span\u003e\u003cspan address=\"10.1080/09581596.2025.2512842\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTesema, G. T., et al. (2021). Prevalence and associated factors of stillbirths in East Africa: A mixed-effects binary logistic regression analysis. \u003cem\u003eBMC Pregnancy and Childbirth\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e, 574. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12884-021-03883-6\u003c/span\u003e\u003cspan address=\"10.1186/s12884-021-03883-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTibenderana, J. R., Kessy, S. A., Mlaponi, D. F., Mtenga, J. E., Gimonge, J., Mwaitete, N. L., \u0026amp; Moshi, F. V. (2024). The adequacy of ANC components received and associated factors among women of reproductive age in Tanzania. \u003cem\u003ePlos one\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(11), e0301570. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0301570\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0301570\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTolossa, T., Gold, L., Lau, E. H., Dheresa, M., \u0026amp; Abimanyi-Ochom, J. (2024). Association between quality of antenatal care service utilisation and adverse birth outcomes among adolescent women in 22 Sub-Saharan African countries. A mixed-effects multilevel analysis. \u003cem\u003eSexual \u0026amp; Reproductive Healthcare\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e, 101036. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.srhc.2024.101036\u003c/span\u003e\u003cspan address=\"10.1016/j.srhc.2024.101036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNFPA (2018). Fact sheet: Family planning in Tanzania. United Nations Population Fund. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tanzania.unfpa.org/sites/default/files/pub-pdf/familyplanning_A5_13nov_highres.pdf\u003c/span\u003e\u003cspan address=\"https://tanzania.unfpa.org/sites/default/files/pub-pdf/familyplanning_A5_13nov_highres.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations Inter-agency Group for Child Mortality Estimation (2024). Standing up for stillbirth: Current estimates and key interventions. United Nations Children\u0026rsquo;s Fund. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://childmortality.org/wp-content/uploads/2024/03/UN-IGME-2023-Stillbirth-Report.pdf\u003c/span\u003e\u003cspan address=\"https://childmortality.org/wp-content/uploads/2024/03/UN-IGME-2023-Stillbirth-Report.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations (2023). The Sustainable Development Goals report 2023: Special edition. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://unstats.un.org/sdgs/report/2023/The-Sustainable-Development-Goals-Report-2023.pdf\u003c/span\u003e\u003cspan address=\"https://unstats.un.org/sdgs/report/2023/The-Sustainable-Development-Goals-Report-2023.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWooldridge, J. M. (2010). \u003cem\u003eEconometric analysis of cross-section and panel data\u003c/em\u003e (2nd ed.). MIT Press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, S., Wang, J., \u0026amp; Ding, J. (2025). Global, regional, and national burden of pregnancy loss from 1990 to 2021: a systematic analysis of incidence and DALYs with projections to 2040. \u003cem\u003eJournal of Gynecology Obstetrics and Human Reproduction\u003c/em\u003e, 103065. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jogoh.2025.103065\u003c/span\u003e\u003cspan address=\"10.1016/j.jogoh.2025.103065\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYehuala, T. Z., Mengesha, S. B., \u0026amp; Baykemagn, N. D. (2025). Predicting pregnancy loss and its determinants among reproductive-aged women using supervised machine learning algorithms in Sub-Saharan Africa. \u003cem\u003eFront Glob Womens Health\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, 1456238. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgwh.2025.1456238\u003c/span\u003e\u003cspan address=\"10.3389/fgwh.2025.1456238\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":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":"Contraceptive Use, Antenatal Care Utilization, Pregnancy Loss, Reproductive Women, Tanzania","lastPublishedDoi":"10.21203/rs.3.rs-9396853/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9396853/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePregnancy loss remains a critical public health challenge in Tanzania, where maternal healthcare access varies significantly across socio-economic groups. This study analyzes determinants of contraceptive use and antenatal care (ANC) utilization and their combined effects on pregnancy loss among women of reproductive age in Tanzania using Tanzania Demographic Health Survey 2022 data. Multinomial logit, double-hurdle model, and Zero-inflated Poisson captured multi-stage decisions and excess zeros. The empirical results reveal that wealth, partner\u0026rsquo;s education, media exposure, and health insurance influence contraceptive use and antenatal care uptake. Adequate ANC attendance and facility delivery reduce pregnancy loss. While the findings indicate that adequate ANC attendance and facility-based delivery significantly reduce the risk of pregnancy loss, the use of modern contraceptives was associated with a higher reported risk, suggesting potential selection bias or underlying issues related to the quality of contraceptive counseling and service timing. The findings underscore the urgency of prioritizing service quality alongside access. Integrated policy interventions should focus on enhancing maternal education and strengthening the continuum of care between family planning and maternal health services to reduce adverse pregnancy outcomes in Tanzania.\u003c/p\u003e","manuscriptTitle":"Effects of contraceptive use and antenatal care utilization on risks of pregnancy losses among reproductive women in Tanzania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-27 14:14:01","doi":"10.21203/rs.3.rs-9396853/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":"c8523544-99d5-4018-b332-91e774d6700e","owner":[],"postedDate":"April 27th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-02T16:23:46+00:00","index":14,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T14:14:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-27 14:14:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9396853","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9396853","identity":"rs-9396853","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}