Social and Behavioural Factors Influencing Postnatal Care Service Utilisation Among Women in Tanzania from a Sociological Lens

Social and Behavioural Factors Influencing Postnatal Care Service Utilisation Among Women in Tanzania from a Sociological Lens | 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 Social and Behavioural Factors Influencing Postnatal Care Service Utilisation Among Women in Tanzania from a Sociological Lens Petro Tulla Ntemi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6907383/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Postnatal care (PNC) is a vital component of maternal and neonatal healthcare, significantly contributing to the reduction of preventable morbidity and mortality. Despite global and national policy efforts, the utilisation of PNC services remains suboptimal in Tanzania. This study investigates the socio-economic, cultural, and structural determinants associated with PNC service uptake among Tanzanian women, using Health Belief model as well as social action theory as theoretical framework. Methods A quantitative research design was employed, utilising secondary data from the 2015–2016 Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS). The sample comprised 6,994 women aged 15–49 who had given birth in the five years preceding the survey. Descriptive statistics, chi-square tests, and multivariate logistic regression analyses were conducted using STATA software, with statistical significance set at p < 0.05. Results The findings revealed that only 39.9% of women accessed PNC services within two days post-delivery. Significant associations were found between PNC utilisation and higher levels of education, wealth status, decision-making autonomy, exposure to maternal health information, and geographic accessibility (p < 0.05). Logistic regression analysis indicated that women with secondary or higher education and those from wealthier households were significantly more likely to utilise PNC services. Additionally, exposure to media and health decision-making autonomy were strong predictors of service uptake. Conversely, rural residence and poor accessibility to healthcare facilities were major barriers. Conclusions The study concludes that PNC service utilisation in Tanzania is hindered by intertwined socio-economic, cultural, and structural barriers. Interventions aimed at improving maternal health outcomes should focus on enhancing educational opportunities, strengthening healthcare infrastructure, expanding health insurance coverage, and promoting behavioural change through community engagement and media campaigns. These findings offer crucial insights for policymakers and healthcare stakeholders committed to improving maternal and child health in Tanzania. Postnatal care services utilisation women Figures Figure 1 Introduction Postnatal care (PNC) is a vital component of maternal and newborn healthcare, encompassing a range of medical, psychological, and social support services provided to women and their infants during the postpartum period (World Health Organization [WHO], 2013). These services include monitoring for complications, supporting breastfeeding practices, providing family planning counselling, and delivering essential neonatal care (Lawn et al., 2016 ). The World Health Organization (WHO) underscores the importance of PNC in reducing maternal and neonatal morbidity and mortality, particularly through the early identification and management of postpartum complications (WHO, 2018). As such, the WHO recommends a minimum of three postnatal check-ups within the first six weeks following childbirth, with the initial assessment occurring within 48 hours (WHO, 2016). Despite these global recommendations, the utilisation of PNC services remains considerably low in many low- and middle-income countries (LMICs), including Tanzania (Kruk et al., 2016 ). Early postnatal care plays a crucial role in preventing infections, detecting postpartum depression, and offering guidance on immunisation and nutrition for newborns (Say et al., 2014 ). However, socio-economic disparities, cultural practices, and systemic barriers continue to hinder access to and utilisation of these services across LMICs (Titaley et al., 2009 ). Evidence from Yaya et al. ( 2019 ) highlights that effective postnatal care is instrumental not only in improving immediate health outcomes but also in promoting long-term maternal and child well-being. These findings reinforce the urgency of improving accessibility, awareness, and quality of PNC services. In Tanzania, maternal mortality remains a significant public health concern, with postnatal complications contributing notably to these deaths (United Nations Population Fund [UNFPA], 2020). PNC is essential for managing conditions such as infections, postpartum haemorrhage, and neonatal health issues, thereby reducing the risk of fatal outcomes (Campbell & Graham, 2006 ). Nevertheless, multiple socio-economic, cultural, and health system challenges continue to limit the effective utilisation of PNC services across the country (Yaya et al., 2021 ). Worldwide, disparities in PNC coverage persist. While high-income countries report nearly universal utilisation of postnatal services, coverage in low-income regions, particularly sub-Saharan Africa, remains markedly low (Say et al., 2014 ). Factors such as maternal education, household income, and access to quality healthcare have been identified as key determinants of PNC uptake (Titaley et al., 2009 ). For instance, Koblinsky et al. ( 2016 ) reported that nearly 90% of women in high-income settings receive at least one postnatal visit, compared to less than 50% in sub-Saharan Africa. Similarly, research in Ethiopia by Mekonnen et al. ( 2019 ) found that women with higher levels of education and socio-economic status were significantly more likely to utilise PNC services. In Ghana, a study by Amoako et al. ( 2021 ) demonstrated that women with financial independence and decision-making autonomy were more inclined to access postnatal care. The utilisation of PNC services is influenced by a multitude of interrelated factors, which can be broadly categorised into socio-economic, cultural, and structural domains. Socio-economic determinants include maternal education, household income, employment status, and women’s decision-making power within households. Cultural beliefs and gender norms also play a significant role, particularly where traditional practices and community perceptions discourage the use of modern healthcare services. Furthermore, structural barriers such as the availability and quality of healthcare facilities, as well as geographic disparities, further impede access to postnatal care. Despite policy efforts and interventions, PNC service utilisation in Tanzania remains suboptimal. Many women do not receive timely postnatal check-ups, increasing the risk of maternal and neonatal complications. Socio-economic disparities, cultural beliefs, and healthcare infrastructure challenges contribute to low PNC uptake, particularly in rural areas. Understanding the factors influencing PNC utilisation is vital for developing effective interventions to improve maternal and newborn health outcomes. Limited research explores the combined influence of socio-economic, cultural, and structural factors on PNC service utilisation in Tanzania. This study aims to fill this gap. This study aims to quantitatively examine the influence of socio-economic, cultural, and structural factors on postnatal care (PNC) service utilisation among women in Tanzania, with a view to informing evidence-based policy interventions to improve service uptake. Despite policy efforts and interventions, PNC service utilisation in Tanzania remains suboptimal. Many women do not receive timely postnatal check-ups, increasing the risk of maternal and neonatal complications. Socio-economic disparities, cultural beliefs, and healthcare infrastructure challenges contribute to low PNC uptake, particularly in rural areas. Understanding the factors influencing PNC utilisation is vital for developing effective interventions to improve maternal and newborn health outcomes. Limited research explores the combined influence of socio-economic, cultural, and structural factors on PNC service utilisation in Tanzania. This study aims to fill this gap. This study aims to quantitatively examine the influence of socio-economic, cultural, and structural factors on postnatal care (PNC) service utilisation among women in Tanzania, with a view to informing evidence-based policy interventions to improve service uptake. The findings will inform policymakers, healthcare providers, and non-governmental organisations on strategies to improve access and uptake of PNC services, ultimately reducing maternal and neonatal mortality. Materials and methods Data source This is a quantitative study that utilised secondary data from the 2015–2016 Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS). The dataset comprised 6,924 women of reproductive age (15 to 49 years) who had given birth within the five years preceding the survey. The variables examined included socio-economic status, cultural beliefs, and access to healthcare facilities. Sample size and sampling technique The 2015–2016 Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS–MIS) employed a two-stage cluster sampling design to construct a nationally representative sample. In the first stage, 608 clusters, also referred to as primary sampling units (PSUs), were selected from a list of enumeration areas established by the 2012 Tanzania Population and Housing Census (National Bureau of Statistics [NBS] & ICF, 2016). In the second stage, a complete household listing was conducted within each selected cluster to create a sampling frame, from which 22 households per cluster were systematically chosen for participation. The survey targeted men and women aged 15 to 49 who were either usual residents of the selected households or present the night before the interview. For the present study, data were drawn from the women’s questionnaire, which focused on maternal and child health behaviours and outcomes. Of the 13,266 eligible women who had experienced at least one live birth in the five years preceding the survey (reflecting a 97% response rate), a sub-sample of 6,924 women who provided complete data on postnatal care (PNC) service utilisation was included in the analysis (NBS & ICF, 2016). Measurement of study variables Outcome variable The utilisation of postnatal care (PNC) services was the primary outcome variable, defined as a woman receiving a health check within two days after childbirth or following discharge from a health facility after her most recent delivery. This definition aligns with the guidelines of the World Health Organization (WHO) and the Tanzania Ministry of Health on postnatal care. The variable measured the percentage age distribution of women whose health was checked within 2 days after childbirth or being discharged from health facilities. This was coded as 0 = if was not checked and 1 = if was checked. The researcher included all women of reproductive age captured in the 2015–16 TDHS–MIS datasets. Independent variables The following were independent variables that were associated factors with PNC service utilisation. These factors include women’s education status, husband’s education status, media access, wealth quintile, place of residence, woman’s age, women’s employment status, husband’s employment status, health insurance, problems in accessing maternal health services, emotional autonomy, decision-making autonomy, payment for delivery, birth order, ANC visits, SBA, and zone of residence. This research is guided by the Health Belief Model (HBM) and Social Action Theory. The HBM provides a framework for understanding how individual perceptions of susceptibility, severity, benefits, and barriers influence health-related behaviours, including the decision to utilise PNC services. In contrast, Social Action Theory offers a broader perspective by examining how societal norms, cultural values, and collective behaviours shape maternal health practices. Together, these theoretical lenses offer valuable insights into the complex interplay of personal, cultural, and systemic factors that affect postnatal care service utilisation The researcher created a conceptual framework (Fig. 1 ) showing how Social Action Theory and Health Belief Model integrating to explain socio-economic, cultural, and structural factors influence postnatal care (PNC) service utilisation through behavioural and social theories at two levels: Structural and socio-cultural influences (SAT): Examines how gender roles, socio-economic status, maternal healthcare access, and cultural norms affect women’s healthcare choices. Individual perceptions and decision-making (HBM ) : Explains how perceived risks, benefits, barriers, and cues to action influence maternal healthcare decisions. Results Descriptive Results for PNC Service Utilisation The utilisation of PNC service as an outcome was based on univariate descriptive analysis, between the outcome variable and the independent variables show that only 2,750 (39.9%) women utilised PNC services, while 4,244 (60.1%) women did not. Bivariate Results of PNC Service Utilisation Table 1 presents the bivariate analysis of ANC visits among the respondent’s characteristics. The chi-squared analysis revealed a statistically significant relationship (p < 0.05) between the independent variables, as associated factors, and PNC service utilisation. These factors include women’s education status, husband’s education status, media access, wealth quintile, place of residence, woman’s age, women’s employment status, husband’s employment status, health insurance, problems in accessing maternal health services, emotional autonomy, decision-making autonomy, payment for delivery, birth order, ANC visits, SBA, and zone of residence. Table 1 Relationship between PNC service Utilisation and Women’s Characteristics Among Women Aged 15–49 Years. PNC service utilisation by women’s characteristics Characteristics Yes n (%) (N = 2,750) No n (%) (N = 6,044) p-value Women’s education status < 0.001 No education 358 (12.83) 1,010 (23.24) Primary 1,637 (63.63) 2,601 (65.37) Secondary and higher 755 (23.54) 633 (11.29) Husband's education status < 0.0001 No education 208 (8.89) 578 (16.08) Primary 1,381 (66.42) 2,298 (70.13) Secondary and higher 612 (24.7) 607 (13.79) Marital status 0.3701 Not married 1,053 (41.46) 1,702 (42.98) Married 1,697 58.54) 2,542 (57.02) Media Access < 0.001 No 316 (10.98) 949 (22.13) Yes 2,434 (89.02) 3,295 (77.81) Wealth quintile < 0.001 Poorest 364 (13.85) 1,072 (26.72) Poorer 433 (16.46) 917 (22.48) Middle 481 (16.74) 886 (20.35) Richer 715 (23.49) 828 (17.81) Richest 757 (29.46) 541 (12.64) Place of residence < 0.001 Rural 1,769 (58.39) 3,396 (77.39) Urban 981 (41.61) 848 (22.61) Woman's age 0.003 15–19 years 206 (8.07) 339 (8.86) 20–34 years 1,871 (69.47) 2,726 (64.12) 35–49 years 673 (22.46) 1,179 (22.47) Women's employment status < 0.001 Not employed 1576 (58.15) 1671 (39.02) Employed 1174 (41.82) 2573 (60.98) Husband's employment status < 0.001 Not employed 1722 (64.39) 2014 (48.49) Employed 1028 (35.61) 2230 (51.51) Health insurance < 0.001 No 2.,481 (89.71) 4,011 (94.09) Yes 269 (10.29) 233 (5.91) Problem in accessing maternal health services < 0.001 No 1,036 (36.29) 1,280 (28.69) Yes 1,714 (63.71) 2,964 (71.31) Household head sex 0.1127 Female 522 (20.09) 733 (17.95) Male 2,228 (79.91) 3,511 (82.05) Household head age 0.9066 15–24 years 128 (4.81 ) 197 (5.11 ) 25–60 years 2,309 (83.94) 3,555 (83.57) Above 60 years 313 (11.25 ) 492 (11.31 ) Emotional autonomy < 0.001 No 1,497 (57.25) 2,661 (63.9) Yes 1,253 (42.75) 1,583 (36.1) Decision making autonomy < 0.001 No 1,539 (66.72) 2,633 (75.44) Yes 671 (33.28) 860 (24.56) Paid for delivery < 0.0001 No 1,747 (65.1) 2,854 (70.57) Yes 1,003 (34.9) 1,390 (29.43) Birth order < 0.001 1–2 children 1,328 (51.39) 1,582 (38.74) 3–4 children 766 (27.97) 1,108 (26.48) 5 children or above 656 (20.64) 1,554 (34.81) ANC visits < 0.001 Less than 4 1151 (39.76) 2348 (55.11) At least 4 1588 (60.24) 1876 (44.89) Skilled birth attendant < 0.001 No 2124 (75.31) 3984 (93.32) Yes 626 (24.69) 260 (6.68) Zone of residence < 0.001 Western 195 (8.43) 423 (12.81) Northern 255 (11.34) 304 (8.9) Central 291 (12.05) 397 (10.67) Southern Highlands 346 (9.37) 215 (3.87) Southern 192 (6.81) 158 (3.56) Southwest highlands 208 (8.32) 570 (11.23) Lake 442 (17.64) 1,343 (35.46) Eastern 402 (23.25) 315 (11.31) Zanzibar 419 (2.74) 519 (2.18) Multivariable Overall Model of Factors Associated with PNC Service Utilisation Table 2 presents a multivariable logistic regression analysis was conducted to establish the association between the utilisation of postnatal care services (the outcome variable) and women’s demographic factors (independent variables) in health facilities across Tanzania. A total of seventeen independent variables were included in the final multivariable model, which were derived from the four interconnected blocks of the conceptual framework, as detailed in Chap. 6. The significance level was restricted to 5%. The results presented in Table 9.6 indicate that community context factors, such as place of residence, were significantly associated with PNC service utilisation. Women from urban areas were more likely to utilise PNC services compared to women from rural areas (reference category), with an adjusted odds ratio (aOR) of 1.50 (95% CI: 1.25–1.80, p = 0.000) after applying the 5% significance level. Furthermore, the zone of residence was significantly associated with the outcome variable. Women from the Southern Highlands were significantly more likely to utilise PNC services compared to those from the Western zone (reference category), with an aOR of 2.27 (95% CI: 1.78–2.90, p = 0.000). Additionally, women from the Southern zone had an aOR of 2.04 (95% CI: 1.53–2.71, p = 0.000), indicating that they were more likely to utilise PNC services. In contrast, women from the Lake zone were less likely to utilise PNC services, with an aOR of 0.53 (95% CI: 0.45–0.63, p = 0.000), after restricting the significance level to 5%. Regarding predisposing factors, maternal education was significantly associated with PNC service utilisation. Women with secondary or higher education were more likely to utilise PNC services compared to women with no education (reference category), with an aOR of 1.35 (95% CI: 1.10–1.68, p = 0.005) for each additional year of maternal education, after restricting the significance level to 5%. Additionally, employment status was significantly associated with the outcome variable. Employed women were less likely to utilise PNC services compared to unemployed women (reference category), with an aOR of 0.78 (95% CI: 0.66–0.93, p = 0.005). Furthermore, women with media access were more likely to utilise PNC services compared to those without media access (reference category), with an aOR of 1.54 (95% CI: 1.27–1.87, p = 0.000), after applying the 5% significance level. Enabling factors were also significantly associated with PNC service utilisation. Women with health insurance were more likely to utilise PNC services compared to those without health insurance (reference category), with an aOR of 1.47 (95% CI: 1.14–1.90, p = 0.003). Decision-making autonomy was also significantly associated with the outcome variable. Women with decision-making autonomy were more likely to utilise PNC services compared to women without decision-making autonomy (reference category), with an aOR of 1.32 (95% CI: 1.13–1.55, p = 0.001), after restricting the significance level to 5%. Additionally, the number of children a woman has was significantly associated with PNC service utilisation. Women with five or more children were less likely to utilise PNC services compared to those with 1–2 children (reference category), with an aOR of 0.74 (95% CI: 0.63–0.87, p = 0.000). The number of antenatal care visits was also significantly associated with the outcome variable. Women who had four or more ANC visits were more likely to utilise PNC services compared to those with fewer than four visits (reference category), with an aOR of 1.48 (95% CI: 1.29–1.70, p = 0.000). Finally, the presence of a skilled birth attendant was significantly associated with PNC service utilisation. Women assisted by skilled birth providers were more likely to utilise PNC services compared to women who were not assisted by a skilled provider (reference category), with an aOR of 3.22 (95% CI: 2.57–4.03, p = 0.000), after restricting the significance level to 5%, as summarised in Table below. Table 2 Multivariable logistic regression of independent variables associated with PNC service utilisation among women aged 15–49 years, after restricted to a 5% significance level. Variables Adjusted OR [aOR] (95% CI) P-value Community factors Place of residence Rural (reference) Urban 1.50(1.25, 1.80) 0.000 Zone of residence Western (reference category) Northern Central Southern Highlands 2.27(1.78,2.90) 0.000 Southern 2.04(1.53,2.71) 0.000 Southwest highlands .68(.51, .91) 0.009 Lake .53(.45,.63) 0.000 Eastern Zanzibar Predisposing factors Maternal education No education (reference category) Primary Secondary and higher 1.35(1.10,1.68) 0.005 Women employment status Not employed (reference category) Employed .78(.66,.93) 0.005 Household head age < 25 years (reference category) 25–60 years 60 and above years 1.38(1.09,1.76) 0.008 Media access No (reference category) Yes 1.54(1.27,1.87) 0.000 Enabling factors Health insurance No (reference category) Yes 1.47(1.14,1.90) 0.003 Need-based factors Decision-making autonomy No (reference category) Yes 1.32(1.13,1.55) 0.001 Birth order 1–2 (reference category) 3–4 Children 5 children or above .74(.63, .87) 0.000 Number of ANC Less than 4 (reference category) 4 or more 1.48(1.29,1.70) 0.000 Skilled birth provider No (reference category) Yes 3.22(2.57,4.03) 0.000 Model goodness of fit Number of Observations 5,658 Wald chi2 (35) 713.60 Prob > chi2 0.0000 Pseudo R2 0.1214 Hosmer-Lemeshow chi2 11.52 Log pseudolikelihood -3319.181 Discussion The findings indicate that the place of residence, particularly for women in urban areas, influenced the health belief theory constructs of perceived susceptibility, perceived self-efficacy, and cues to action regarding preparedness for PNC service utilisation. The study offers valuable insights into the factors associated with postnatal care service utilisation, specifically focusing on women’s socio-demographic characteristics two days after childbirth or after being discharged from health facilities in the context of Tanzania. The results reveal that socio-demographic differences in PNC service utilisation are closely linked to the constructs of the health belief model, particularly the perceived susceptibility to complications during childbirth, perceived self-efficacy to overcome barriers to PNC service utilisation, and cues to action related to childbirth preparedness. Women from both groups expressed similar views on the benefits of utilising PNC services, recognising the importance of maternal healthcare providers in addressing potential complications that may arise within two days after childbirth or following discharge. Manyeh et al. ( 2023 ) found that financial constraints and lack of awareness significantly hinder PNC service utilisation, as many women do not perceive postnatal visits as essential unless complications arise. Yaya et al. ( 2023 ) argue that increased community health interventions and media campaigns can significantly improve PNC service utilisation by altering perceived health risks and benefits. Nassoro et al. ( 2023 ) reported that rural women face more challenges accessing PNC services due to healthcare facility shortages, reinforcing the need for structural improvements. By applying the Health Belief Model, this study highlights that improving perceived benefits through targeted health education and financial incentives can enhance PNC service utilisation, particularly in underprivileged communities. Education played a critical role, as women with secondary or higher education were more likely to seek PNC services. Access to media and health insurance further facilitated utilisation by raising awareness and reducing financial barriers. Decision-making autonomy also emerged as a significant factor, empowering women to seek timely postnatal care. Geographical zones such as the Southern Highlands and urban areas showed higher PNC service utilisation. These findings align with the studies by Rani et al. ( 2008 ) and Sudhinaraset et al. (2016), which highlight the significance of education, autonomy, and socio-economic resources in the utilisation of maternal healthcare services. Seeking maternal healthcare during the postpartum period allows maternal healthcare service providers to identify complications related to childbirth and the postnatal phase. Evidence indicates that most childbirth-related complications, such as postpartum haemorrhage and various infections arise immediately after birth, posing a risk to the health and lives of both mothers and newborns. However, these complications can be prevented through the timely provision of postnatal care for both mothers and infants (Alemayeh et al., 2014). Furthermore, they identified key barriers to accessing these services, including costs, distance, transportation challenges, and instances of mistreatment by certain service providers. This study highlights the role of socio-demographic factors in shaping women’s perceptions and behaviours towards PNC service utilisation, underlining the importance of addressing these barriers to improve postnatal care access and outcomes. Women with secondary or higher education were more likely to use PNC services, with odds increasing by 1.35 compared to those with no education. This finding is consistent with previous research, such as that by Yadav ( 2015 ), which demonstrated that education increases women's knowledge and awareness of maternal health services, thereby enhancing their ability to make informed health decisions and influence household dynamics. Similarly, Bazant et al. ( 2009 ) found that educated women are more aware of the risks of maternal health complications and the importance of precautions. Educated women are also more likely to have the financial resources needed to access healthcare without relying on others (Chaka, 2022 ). Exavery et al. ( 2014 ) also found that Tanzanian women with primary and higher education had increased odds (by 17%) of utilising skilled birth attendants, while Hailu and Berhe ( 2014 ) found that educated women in Ethiopia were five times more likely to use skilled birth attendants. Women with access to media were 1.57 times more likely to utilise PNC services, a finding that aligns with studies by Iqbal et al. ( 2017 ) and Shibanuma et al. ( 2018 ), which indicated that exposure to mass media is positively associated with postpartum care. The study found that women exposed to media were more informed about the importance of maternal health care and the potential risks of not accessing care, as well as the benefits of maternal health policies (Fatema & Lariscy, 2020 ). This observation highlights the role of mass media in influencing PNC service utilisation. The study also found that women from poorer wealth quintiles were less likely to utilise PNC services, confirming findings from Iqbal et al. ( 2017 ), Wang and Hong (2015), Akinyemi et al. ( 2016 ), and Singh et al. (2015), which showed that women from wealthier households tend to utilise more postnatal services. Financial stability allows women to afford transport and medical costs associated with maternal healthcare service utilisation, a significant barrier for women from poorer backgrounds, especially those in rural areas (Zelalem Ayele et al., 2014 ). In contrast, women from rural areas faced additional challenges, including long distances to healthcare facilities, poor road infrastructure, and the high cost of transportation, which may deter them from accessing care (Ali et al., 2020 ). Women with health insurance were 1.47 times more likely to utilise PNC services, a finding consistent with Chaka ( 2022 ), which indicated that health insurance coverage is a key factor in increasing the utilisation of postpartum care. Moreover, women with decision-making autonomy were 1.32 times more likely to seek PNC services compared to those without such autonomy. This supports the notion that female autonomy, particularly in terms of financial independence and health-related decision-making, plays a critical role in utilising maternal healthcare services (Ghuman, Lee & Smith, 2006 ). The study found regional disparities in PNC service utilisation, with women from the Southern Highlands, Southern, and Eastern zones more likely to use PNC services compared to those in the Western zone. These findings are consistent with studies showing that women in more developed areas with better access to healthcare facilities are more likely to utilise postnatal care (Mumtaz, Bahk & Khang, 2019 ; Singh et al., 2012 ). Conversely, women in the Lake zone had decreased odds of using PNC services, likely due to factors such as limited infrastructure and healthcare access in rural areas (Iqbal et al., 2017 ). The results underscore the influence of geographic location on access to and utilisation of healthcare services. This study contributes valuable insights into the factors associated with PNC service utilisation in Tanzania. It highlights the critical role of maternal knowledge, socio-demographic factors, financial constraints, and regional disparities in shaping women's decisions regarding postnatal care. Addressing these factors, through both education and policy interventions, is crucial for improving maternal health outcomes in Tanzania and other similar settings. Conclusion The utilisation of postnatal care (PNC) services in Tanzania remains inadequate, largely due to persistent socio-economic disparities, entrenched cultural norms, and systemic weaknesses within the healthcare sector. These findings underscore the multifaceted nature of maternal healthcare access and highlight the importance of developing targeted interventions that respond to the unique challenges faced by different communities. Tackling these determinants and encouraging the optimal use of antenatal and postnatal services is vital for improving maternal and perinatal health outcomes. Achieving this requires coordinated efforts among healthcare providers, policymakers, and community-based organisations. Effectively addressing these barriers is essential to advancing the overall well-being of mothers and newborns. Declarations Ethics approval and consent to analyse This study was based on publicly available datasets derived from the 2015–16 Tanzania Demographic and Health Survey (TDHS), which are accessible online and have been anonymised to remove all personally identifiable information. Permission to utilise the DHS data was obtained by the researcher from the ICF Macro Institutional Review Board in Calverton, New York. Consent for publication: Not applicable Competing interests: The author declares no competing interests. 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Continuum of care in maternal, newborn and child health in Pakistan: Analysis of trends and determinants from 2006 to 2012. BMC Health Services Research , 17 (1), 189. https://doi.org/10.1186/s12913-017-2109-0 Koblinsky, M., Moyer, C. A., Calvert, C., Campbell, J., Campbell, O. M. R., Feigl, A. B., ... & Langer, A. (2016). Quality maternity care for every woman, everywhere: A call to action. The Lancet , 388 (10057), 2307–2320. https://doi.org/10.1016/S0140-6736(16)31333-2 Kruk, M. E., Kujawski, S., Moyer, C. A., Adanu, R. M., Afsana, K., Cohen, J., & Gage, A. D. (2016). Next generation maternal health: External shocks and health-system innovations. The Lancet , 388 (10057), 2296–2306. https://doi.org/10.1016/S0140-6736(16)31395-2 Lawn, J. E., Kinney, M., Belizan, J. M., Mason, E., McDougall, L., Larson, J., ... & Darmstadt, G. L. (2016). Accelerate progress—Countdown to 2015: Maternal, newborn and child survival. The Lancet , 387 (10032), 2121–2134. https://doi.org/10.1016/S0140-6736(15)00523-5 Manyeh, A. K., Nuertey, B. D., Agyemang, S. A., & Kukula, V. A. (2023). Barriers to postnatal care service utilization in sub-Saharan Africa: Evidence from Ghana. BMC Pregnancy and Childbirth , 23 , 120. https://doi.org/10.1186/s12884-023-05429-z Mekonnen, T., Dune, T., & Perz, J. (2019). Maternal health service utilization in Ethiopia: A systematic review and meta-analysis of determinants. BMC Pregnancy and Childbirth , 19 (1), 336. https://doi.org/10.1186/s12884-019-2470-4 Mumtaz, Z., Bahk, J., & Khang, Y. H. (2019). Inequality in the utilization of maternal healthcare services in Pakistan: Evidence from Pakistan Demographic and Health Surveys, 2006–2013. International Journal for Equity in Health , 18 (1), 118. https://doi.org/10.1186/s12939-019-1013-1 Nassoro, M. M., Selemani, M., & Mbarouk, A. G. (2023). Utilization of postnatal care services and associated factors among women in Zanzibar. Tanzania Journal of Health Research , 25 (1), 9–18. https://doi.org/10.4314/thrb.v25i1.3 National Bureau of Statistics (NBS) [Tanzania] & ICF. (2016). Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS) 2015–16 . NBS and ICF. Rani, M., Bonu, S., & Harvey, S. (2008). Differentials in the quality of antenatal care in India. International Journal for Quality in Health Care , 20 (1), 62–71. https://doi.org/10.1093/intqhc/mzm052 Say, L., Raine, R., & Pattinson, R. (2014). Postnatal care for mothers and newborns: Quality and coverage. The Lancet , 384 (9948), 1151–1161. https://doi.org/10.1016/S0140-6736(14)60496-7 Shibanuma, A., Yeji, F., Okawa, S., Mahama, E., Kikuchi, K., Enuameh, Y., ... & Jimba, M. (2018). The coverage of continuum of care in maternal, newborn and child health: A cross-sectional study of woman-child pairs in Ghana. BMJ Global Health , 3 (2), e000786. https://doi.org/10.1136/bmjgh-2018-000786 Singh, K., Story, W. T., & Moran, A. C. (2012). Assessing the influence of maternal and child health services on postnatal care in low-income countries. Journal of Health, Population and Nutrition , 30 (4), 378–392. Titaley, C. R., Dibley, M. J., & Roberts, C. L. (2009). Factors associated with underutilization of antenatal care services in Indonesia: Results of a 2002/2003 demographic and health survey. BMC Public Health , 9 (1), 243. https://doi.org/10.1186/1471-2458-9-243 United Nations Population Fund (UNFPA). (2020). Tanzania maternal health factsheet . https://tanzania.unfpa.org/en/publications/maternal-health-factsheet World Health Organization (WHO). (2013). Postnatal care for mothers and newborns: Highlights from the World Health Organization 2013 guidelines . https://www.who.int World Health Organization (WHO). (2016). Standards for improving quality of maternal and newborn care in health facilities . https://www.who.int World Health Organization (WHO). (2018). WHO recommendations on postnatal care of the mother and newborn . https://www.who.int Yadav, A. K. (2015). Impact of female education on maternal healthcare utilization in Nepal. International Journal of Population Research , 2015 , 1–8. https://doi.org/10.1155/2015/135763 Yaya, S., Bishwajit, G., Shah, V., & Gunawardena, N. (2019). Wealth, education and urban–rural inequality and maternal healthcare service usage in Malawi. BMJ Global Health , 4 (2), e000734. https://doi.org/10.1136/bmjgh-2018-000734 Yaya, S., Okonofua, F., Ntoimo, L., Uthman, O. A., & Bishwajit, G. (2021). Gender inequity as a barrier to women's access to skilled pregnancy care in rural Nigeria. Reproductive Health , 18 , 123. https://doi.org/10.1186/s12978-021-01193-4 Yaya, S., Oladimeji, O., & Ghose, B. (2023). Community-based strategies to improve postnatal care in sub-Saharan Africa: A review. Global Health Action , 16 (1), 2151283. https://doi.org/10.1080/16549716.2023.2151283 Zelalem Ayele, D., Belayihun, B., Teji, K., & Ayana, D. A. (2014). Factors affecting utilization of maternal health care services in Kombolcha District, Eastern Hararghe Zone, Oromia Regional State, Eastern Ethiopia. International Scholarly Research Notices , 2014 , 1–7. https://doi.org/10.1155/2014/917058. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6907383","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":493118379,"identity":"22385613-03c1-4dba-90f4-1c8f98d34dc2","order_by":0,"name":"Petro Tulla Ntemi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYLCCBAYGGT4IU0IORB54gE85G0hLAgMPkGZsAGoxBmtJIKSFAaGFIbEBai9OYC7ffHTDwx+HedjYzx5/8DHHIn1+2OGHQFvs5HQbsGuxbGNLu5GQANTCk5fYOHObRO7G22kGQC3JxmYHsGsxOMZjBtHCkGPYzAvSMjsBpOVA4jacWvi/QbTwvzFs/rtNIt1wdvoHAlp42CBaJIC2MG6TSJCXziFkSxrQYWnpQC1vDGf2bpMw3CCdU3AgwQCPXw4ffnbzh421HD9/jsGHn9vq5OVnp2/+8KHCTg6XFiyGgFUaEKscBOQbSFE9CkbBKBgFIwEAAFXNYRGPWWLMAAAAAElFTkSuQmCC","orcid":"","institution":"Institute of Social Work","correspondingAuthor":true,"prefix":"","firstName":"Petro","middleName":"Tulla","lastName":"Ntemi","suffix":""}],"badges":[],"createdAt":"2025-06-16 16:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6907383/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6907383/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88035023,"identity":"dc489084-d355-4e16-92b4-c0e1cffb9803","added_by":"auto","created_at":"2025-07-31 16:10:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104838,"visible":true,"origin":"","legend":"\u003cp\u003eA conceptual framework\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6907383/v1/5b1aae6456fd29d0c8b75c89.png"},{"id":92173900,"identity":"09fe47e6-957d-4dda-8d43-d5a6225e1b82","added_by":"auto","created_at":"2025-09-25 12:24:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":976467,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6907383/v1/d7176cec-832a-47d9-89ac-d8fe8f050e14.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Social and Behavioural Factors Influencing Postnatal Care Service Utilisation Among Women in Tanzania from a Sociological Lens","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePostnatal care (PNC) is a vital component of maternal and newborn healthcare, encompassing a range of medical, psychological, and social support services provided to women and their infants during the postpartum period (World Health Organization [WHO], 2013). These services include monitoring for complications, supporting breastfeeding practices, providing family planning counselling, and delivering essential neonatal care (Lawn et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The World Health Organization (WHO) underscores the importance of PNC in reducing maternal and neonatal morbidity and mortality, particularly through the early identification and management of postpartum complications (WHO, 2018). As such, the WHO recommends a minimum of three postnatal check-ups within the first six weeks following childbirth, with the initial assessment occurring within 48 hours (WHO, 2016).\u003c/p\u003e\u003cp\u003eDespite these global recommendations, the utilisation of PNC services remains considerably low in many low- and middle-income countries (LMICs), including Tanzania (Kruk et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Early postnatal care plays a crucial role in preventing infections, detecting postpartum depression, and offering guidance on immunisation and nutrition for newborns (Say et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, socio-economic disparities, cultural practices, and systemic barriers continue to hinder access to and utilisation of these services across LMICs (Titaley et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Evidence from Yaya et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) highlights that effective postnatal care is instrumental not only in improving immediate health outcomes but also in promoting long-term maternal and child well-being. These findings reinforce the urgency of improving accessibility, awareness, and quality of PNC services.\u003c/p\u003e\u003cp\u003eIn Tanzania, maternal mortality remains a significant public health concern, with postnatal complications contributing notably to these deaths (United Nations Population Fund [UNFPA], 2020). PNC is essential for managing conditions such as infections, postpartum haemorrhage, and neonatal health issues, thereby reducing the risk of fatal outcomes (Campbell \u0026amp; Graham, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Nevertheless, multiple socio-economic, cultural, and health system challenges continue to limit the effective utilisation of PNC services across the country (Yaya et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWorldwide, disparities in PNC coverage persist. While high-income countries report nearly universal utilisation of postnatal services, coverage in low-income regions, particularly sub-Saharan Africa, remains markedly low (Say et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Factors such as maternal education, household income, and access to quality healthcare have been identified as key determinants of PNC uptake (Titaley et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For instance, Koblinsky et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported that nearly 90% of women in high-income settings receive at least one postnatal visit, compared to less than 50% in sub-Saharan Africa. Similarly, research in Ethiopia by Mekonnen et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that women with higher levels of education and socio-economic status were significantly more likely to utilise PNC services. In Ghana, a study by Amoako et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrated that women with financial independence and decision-making autonomy were more inclined to access postnatal care.\u003c/p\u003e\u003cp\u003eThe utilisation of PNC services is influenced by a multitude of interrelated factors, which can be broadly categorised into socio-economic, cultural, and structural domains. Socio-economic determinants include maternal education, household income, employment status, and women\u0026rsquo;s decision-making power within households. Cultural beliefs and gender norms also play a significant role, particularly where traditional practices and community perceptions discourage the use of modern healthcare services. Furthermore, structural barriers such as the availability and quality of healthcare facilities, as well as geographic disparities, further impede access to postnatal care. Despite policy efforts and interventions, PNC service utilisation in Tanzania remains suboptimal. Many women do not receive timely postnatal check-ups, increasing the risk of maternal and neonatal complications. Socio-economic disparities, cultural beliefs, and healthcare infrastructure challenges contribute to low PNC uptake, particularly in rural areas. Understanding the factors influencing PNC utilisation is vital for developing effective interventions to improve maternal and newborn health outcomes. Limited research explores the combined influence of socio-economic, cultural, and structural factors on PNC service utilisation in Tanzania. This study aims to fill this gap. This study aims to quantitatively examine the influence of socio-economic, cultural, and structural factors on postnatal care (PNC) service utilisation among women in Tanzania, with a view to informing evidence-based policy interventions to improve service uptake. Despite policy efforts and interventions, PNC service utilisation in Tanzania remains suboptimal. Many women do not receive timely postnatal check-ups, increasing the risk of maternal and neonatal complications. Socio-economic disparities, cultural beliefs, and healthcare infrastructure challenges contribute to low PNC uptake, particularly in rural areas. Understanding the factors influencing PNC utilisation is vital for developing effective interventions to improve maternal and newborn health outcomes. Limited research explores the combined influence of socio-economic, cultural, and structural factors on PNC service utilisation in Tanzania. This study aims to fill this gap. This study aims to quantitatively examine the influence of socio-economic, cultural, and structural factors on postnatal care (PNC) service utilisation among women in Tanzania, with a view to informing evidence-based policy interventions to improve service uptake. The findings will inform policymakers, healthcare providers, and non-governmental organisations on strategies to improve access and uptake of PNC services, ultimately reducing maternal and neonatal mortality.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eData source\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis is a quantitative study that utilised secondary data from the 2015\u0026ndash;2016 Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS). The dataset comprised 6,924 women of reproductive age (15 to 49 years) who had given birth within the five years preceding the survey. The variables examined included socio-economic status, cultural beliefs, and access to healthcare facilities.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSample size and sampling technique\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 2015\u0026ndash;2016 Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS\u0026ndash;MIS) employed a two-stage cluster sampling design to construct a nationally representative sample. In the first stage, 608 clusters, also referred to as primary sampling units (PSUs), were selected from a list of enumeration areas established by the 2012 Tanzania Population and Housing Census (National Bureau of Statistics [NBS] \u0026amp; ICF, 2016). In the second stage, a complete household listing was conducted within each selected cluster to create a sampling frame, from which 22 households per cluster were systematically chosen for participation.\u003c/p\u003e\u003cp\u003eThe survey targeted men and women aged 15 to 49 who were either usual residents of the selected households or present the night before the interview. For the present study, data were drawn from the women\u0026rsquo;s questionnaire, which focused on maternal and child health behaviours and outcomes. Of the 13,266 eligible women who had experienced at least one live birth in the five years preceding the survey (reflecting a 97% response rate), a sub-sample of 6,924 women who provided complete data on postnatal care (PNC) service utilisation was included in the analysis (NBS \u0026amp; ICF, 2016).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasurement of study variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutcome variable\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe utilisation of postnatal care (PNC) services was the primary outcome variable, defined as a woman receiving a health check within two days after childbirth or following discharge from a health facility after her most recent delivery. This definition aligns with the guidelines of the World Health Organization (WHO) and the Tanzania Ministry of Health on postnatal care. The variable measured the percentage age distribution of women whose health was checked within 2 days after childbirth or being discharged from health facilities. This was coded as 0\u0026thinsp;=\u0026thinsp;if was not checked and 1\u0026thinsp;=\u0026thinsp;if was checked. The researcher included all women of reproductive age captured in the 2015\u0026ndash;16 TDHS\u0026ndash;MIS datasets.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIndependent variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe following were independent variables that were associated factors with PNC service utilisation. These factors include women\u0026rsquo;s education status, husband\u0026rsquo;s education status, media access, wealth quintile, place of residence, woman\u0026rsquo;s age, women\u0026rsquo;s employment status, husband\u0026rsquo;s employment status, health insurance, problems in accessing maternal health services, emotional autonomy, decision-making autonomy, payment for delivery, birth order, ANC visits, SBA, and zone of residence. This research is guided by the Health Belief Model (HBM) and Social Action Theory. The HBM provides a framework for understanding how individual perceptions of susceptibility, severity, benefits, and barriers influence health-related behaviours, including the decision to utilise PNC services. In contrast, Social Action Theory offers a broader perspective by examining how societal norms, cultural values, and collective behaviours shape maternal health practices. Together, these theoretical lenses offer valuable insights into the complex interplay of personal, cultural, and systemic factors that affect postnatal care service utilisation The researcher created a conceptual framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) showing how Social Action Theory and Health Belief Model integrating to explain socio-economic, cultural, and structural factors influence postnatal care (PNC) service utilisation through behavioural and social theories at two levels:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eStructural and socio-cultural influences (SAT): Examines how gender roles, socio-economic status, maternal healthcare access, and cultural norms affect women\u0026rsquo;s healthcare choices.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIndividual perceptions and decision-making (HBM\u003cb\u003e)\u003c/b\u003e: Explains how perceived risks, benefits, barriers, and cues to action influence maternal healthcare decisions.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eDescriptive Results for PNC Service Utilisation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe utilisation of PNC service as an outcome was based on univariate descriptive analysis, between the outcome variable and the independent variables show that only 2,750 (39.9%) women utilised PNC services, while 4,244 (60.1%) women did not.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBivariate Results of PNC Service Utilisation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the bivariate analysis of ANC visits among the respondent\u0026rsquo;s characteristics. The chi-squared analysis revealed a statistically significant relationship (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the independent variables, as associated factors, and PNC service utilisation. These factors include women\u0026rsquo;s education status, husband\u0026rsquo;s education status, media access, wealth quintile, place of residence, woman\u0026rsquo;s age, women\u0026rsquo;s employment status, husband\u0026rsquo;s employment status, health insurance, problems in accessing maternal health services, emotional autonomy, decision-making autonomy, payment for delivery, birth order, ANC visits, SBA, and zone of residence.\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\u003eRelationship between PNC service Utilisation and Women\u0026rsquo;s Characteristics Among Women Aged 15\u0026ndash;49 Years.\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003ePNC service utilisation by women\u0026rsquo;s characteristics\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes n (%)\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;2,750)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo n (%)\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;6,044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u0026rsquo;s education status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e358 (12.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,010 (23.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,637 (63.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,601 (65.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary and higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e755 (23.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e633 (11.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHusband's education status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e208 (8.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e578 (16.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,381 (66.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,298 (70.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary and higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e612 (24.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e607 (13.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3701\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,053 (41.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,702 (42.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,697 58.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,542 (57.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedia Access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e316 (10.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e949 (22.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,434 (89.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,295 (77.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWealth quintile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\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\u003e364 (13.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,072 (26.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e433 (16.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e917 (22.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e481 (16.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e886 (20.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e715 (23.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e828 (17.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRichest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e757 (29.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e541 (12.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlace of residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,769 (58.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,396 (77.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e981 (41.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e848 (22.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWoman'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\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;19 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e206 (8.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e339 (8.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;34 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,871 (69.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,726 (64.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;49 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e673 (22.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,179 (22.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen's employment status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot employed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1576 (58.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1671 (39.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1174 (41.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2573 (60.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHusband's employment status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot employed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1722 (64.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2014 (48.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1028 (35.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2230 (51.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.,481 (89.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,011 (94.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e269 (10.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e233 (5.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProblem in accessing maternal health services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,036 (36.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,280 (28.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,714 (63.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,964 (71.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold head sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1127\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e522 (20.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e733 (17.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,228 (79.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,511 (82.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold head 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\u003cp\u003e0.9066\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;24 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e128 (4.81 )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e197 (5.11 )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,309 (83.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,555 (83.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove 60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e313 (11.25 )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e492 (11.31 )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmotional autonomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,497 (57.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,661 (63.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,253 (42.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,583 (36.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDecision making autonomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,539 (66.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,633 (75.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e671 (33.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e860 (24.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaid for 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\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,747 (65.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,854 (70.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,003 (34.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,390 (29.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirth order\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;2 children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,328 (51.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,582 (38.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ndash;4 children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e766 (27.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,108 (26.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5 children or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e656 (20.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,554 (34.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANC visits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1151 (39.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2348 (55.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt least 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1588 (60.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1876 (44.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkilled birth attendant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2124 (75.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3984 (93.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e626 (24.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e260 (6.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZone of residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e195 (8.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e423 (12.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e255 (11.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e304 (8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e291 (12.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e397 (10.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern Highlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e346 (9.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e215 (3.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e192 (6.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e158 (3.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthwest highlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e208 (8.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e570 (11.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e442 (17.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,343 (35.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e402 (23.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e315 (11.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZanzibar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e419 (2.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e519 (2.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMultivariable Overall Model of Factors Associated with PNC Service Utilisation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents a multivariable logistic regression analysis was conducted to establish the association between the utilisation of postnatal care services (the outcome variable) and women\u0026rsquo;s demographic factors (independent variables) in health facilities across Tanzania. A total of seventeen independent variables were included in the final multivariable model, which were derived from the four interconnected blocks of the conceptual framework, as detailed in Chap.\u0026nbsp;6. The significance level was restricted to 5%. The results presented in Table\u0026nbsp;9.6 indicate that community context factors, such as place of residence, were significantly associated with PNC service utilisation. Women from urban areas were more likely to utilise PNC services compared to women from rural areas (reference category), with an adjusted odds ratio (aOR) of 1.50 (95% CI: 1.25\u0026ndash;1.80, p\u0026thinsp;=\u0026thinsp;0.000) after applying the 5% significance level. Furthermore, the zone of residence was significantly associated with the outcome variable. Women from the Southern Highlands were significantly more likely to utilise PNC services compared to those from the Western zone (reference category), with an aOR of 2.27 (95% CI: 1.78\u0026ndash;2.90, p\u0026thinsp;=\u0026thinsp;0.000). Additionally, women from the Southern zone had an aOR of 2.04 (95% CI: 1.53\u0026ndash;2.71, p\u0026thinsp;=\u0026thinsp;0.000), indicating that they were more likely to utilise PNC services. In contrast, women from the Lake zone were less likely to utilise PNC services, with an aOR of 0.53 (95% CI: 0.45\u0026ndash;0.63, p\u0026thinsp;=\u0026thinsp;0.000), after restricting the significance level to 5%.\u003c/p\u003e\u003cp\u003eRegarding predisposing factors, maternal education was significantly associated with PNC service utilisation. Women with secondary or higher education were more likely to utilise PNC services compared to women with no education (reference category), with an aOR of 1.35 (95% CI: 1.10\u0026ndash;1.68, p\u0026thinsp;=\u0026thinsp;0.005) for each additional year of maternal education, after restricting the significance level to 5%. Additionally, employment status was significantly associated with the outcome variable. Employed women were less likely to utilise PNC services compared to unemployed women (reference category), with an aOR of 0.78 (95% CI: 0.66\u0026ndash;0.93, p\u0026thinsp;=\u0026thinsp;0.005). Furthermore, women with media access were more likely to utilise PNC services compared to those without media access (reference category), with an aOR of 1.54 (95% CI: 1.27\u0026ndash;1.87, p\u0026thinsp;=\u0026thinsp;0.000), after applying the 5% significance level.\u003c/p\u003e\u003cp\u003eEnabling factors were also significantly associated with PNC service utilisation. Women with health insurance were more likely to utilise PNC services compared to those without health insurance (reference category), with an aOR of 1.47 (95% CI: 1.14\u0026ndash;1.90, p\u0026thinsp;=\u0026thinsp;0.003). Decision-making autonomy was also significantly associated with the outcome variable. Women with decision-making autonomy were more likely to utilise PNC services compared to women without decision-making autonomy (reference category), with an aOR of 1.32 (95% CI: 1.13\u0026ndash;1.55, p\u0026thinsp;=\u0026thinsp;0.001), after restricting the significance level to 5%. Additionally, the number of children a woman has was significantly associated with PNC service utilisation. Women with five or more children were less likely to utilise PNC services compared to those with 1\u0026ndash;2 children (reference category), with an aOR of 0.74 (95% CI: 0.63\u0026ndash;0.87, p\u0026thinsp;=\u0026thinsp;0.000). The number of antenatal care visits was also significantly associated with the outcome variable. Women who had four or more ANC visits were more likely to utilise PNC services compared to those with fewer than four visits (reference category), with an aOR of 1.48 (95% CI: 1.29\u0026ndash;1.70, p\u0026thinsp;=\u0026thinsp;0.000). Finally, the presence of a skilled birth attendant was significantly associated with PNC service utilisation. Women assisted by skilled birth providers were more likely to utilise PNC services compared to women who were not assisted by a skilled provider (reference category), with an aOR of 3.22 (95% CI: 2.57\u0026ndash;4.03, p\u0026thinsp;=\u0026thinsp;0.000), after restricting the significance level to 5%, as summarised in Table below.\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\u003eMultivariable logistic regression of independent variables associated with PNC service utilisation among women aged 15\u0026ndash;49 years, after restricted to a 5% significance level.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdjusted OR [aOR] (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\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\u003e\u003cem\u003eCommunity factors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlace of residence\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\u003eRural (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.50(1.25, 1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZone of residence\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\u003eWestern (reference category)\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\u003eNorthern\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\u003eCentral\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\u003eSouthern Highlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.27(1.78,2.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.04(1.53,2.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthwest highlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.68(.51, .91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.53(.45,.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern\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\u003eZanzibar\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\u003e\u003cem\u003ePredisposing factors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal 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\u003eNo education (reference category)\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\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\u003eSecondary and higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.35(1.10,1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen employment status\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\u003eNot employed (reference category)\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\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.78(.66,.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold head 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\u003e\u0026lt;\u0026thinsp;25 years (reference category)\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\u003e25\u0026ndash;60 years\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\u003e60 and above years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.38(1.09,1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedia access\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\u003eNo (reference category)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.54(1.27,1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEnabling factors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth insurance\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\u003eNo (reference category)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.47(1.14,1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNeed-based factors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDecision-making autonomy\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\u003eNo (reference category)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.32(1.13,1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirth order\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\u003e1\u0026ndash;2 (reference category)\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\u003e3\u0026ndash;4 Children\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\u003e5 children or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.74(.63, .87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of ANC\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\u003eLess than 4 (reference category)\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\u003e4 or more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.48(1.29,1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkilled birth provider\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\u003eNo (reference category)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.22(2.57,4.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel goodness of fit\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\u003eNumber of Observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,658\u003c/p\u003e\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\u003eWald chi2 (35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e713.60\u003c/p\u003e\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\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\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\u003ePseudo R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1214\u003c/p\u003e\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\u003eHosmer-Lemeshow chi2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.52\u003c/p\u003e\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\u003eLog pseudolikelihood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3319.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings indicate that the place of residence, particularly for women in urban areas, influenced the health belief theory constructs of perceived susceptibility, perceived self-efficacy, and cues to action regarding preparedness for PNC service utilisation. The study offers valuable insights into the factors associated with postnatal care service utilisation, specifically focusing on women\u0026rsquo;s socio-demographic characteristics two days after childbirth or after being discharged from health facilities in the context of Tanzania. The results reveal that socio-demographic differences in PNC service utilisation are closely linked to the constructs of the health belief model, particularly the perceived susceptibility to complications during childbirth, perceived self-efficacy to overcome barriers to PNC service utilisation, and cues to action related to childbirth preparedness. Women from both groups expressed similar views on the benefits of utilising PNC services, recognising the importance of maternal healthcare providers in addressing potential complications that may arise within two days after childbirth or following discharge. Manyeh et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that financial constraints and lack of awareness significantly hinder PNC service utilisation, as many women do not perceive postnatal visits as essential unless complications arise. Yaya et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) argue that increased community health interventions and media campaigns can significantly improve PNC service utilisation by altering perceived health risks and benefits. Nassoro et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported that rural women face more challenges accessing PNC services due to healthcare facility shortages, reinforcing the need for structural improvements. By applying the Health Belief Model, this study highlights that improving perceived benefits through targeted health education and financial incentives can enhance PNC service utilisation, particularly in underprivileged communities.\u003c/p\u003e\u003cp\u003eEducation played a critical role, as women with secondary or higher education were more likely to seek PNC services. Access to media and health insurance further facilitated utilisation by raising awareness and reducing financial barriers. Decision-making autonomy also emerged as a significant factor, empowering women to seek timely postnatal care. Geographical zones such as the Southern Highlands and urban areas showed higher PNC service utilisation. These findings align with the studies by Rani et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and Sudhinaraset et al. (2016), which highlight the significance of education, autonomy, and socio-economic resources in the utilisation of maternal healthcare services. Seeking maternal healthcare during the postpartum period allows maternal healthcare service providers to identify complications related to childbirth and the postnatal phase. Evidence indicates that most childbirth-related complications, such as postpartum haemorrhage and various infections arise immediately after birth, posing a risk to the health and lives of both mothers and newborns. However, these complications can be prevented through the timely provision of postnatal care for both mothers and infants (Alemayeh et al., 2014).\u003c/p\u003e\u003cp\u003eFurthermore, they identified key barriers to accessing these services, including costs, distance, transportation challenges, and instances of mistreatment by certain service providers. This study highlights the role of socio-demographic factors in shaping women\u0026rsquo;s perceptions and behaviours towards PNC service utilisation, underlining the importance of addressing these barriers to improve postnatal care access and outcomes.\u003c/p\u003e\u003cp\u003eWomen with secondary or higher education were more likely to use PNC services, with odds increasing by 1.35 compared to those with no education. This finding is consistent with previous research, such as that by Yadav (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which demonstrated that education increases women's knowledge and awareness of maternal health services, thereby enhancing their ability to make informed health decisions and influence household dynamics. Similarly, Bazant et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) found that educated women are more aware of the risks of maternal health complications and the importance of precautions. Educated women are also more likely to have the financial resources needed to access healthcare without relying on others (Chaka, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Exavery et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) also found that Tanzanian women with primary and higher education had increased odds (by 17%) of utilising skilled birth attendants, while Hailu and Berhe (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that educated women in Ethiopia were five times more likely to use skilled birth attendants.\u003c/p\u003e\u003cp\u003eWomen with access to media were 1.57 times more likely to utilise PNC services, a finding that aligns with studies by Iqbal et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Shibanuma et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which indicated that exposure to mass media is positively associated with postpartum care. The study found that women exposed to media were more informed about the importance of maternal health care and the potential risks of not accessing care, as well as the benefits of maternal health policies (Fatema \u0026amp; Lariscy, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This observation highlights the role of mass media in influencing PNC service utilisation. The study also found that women from poorer wealth quintiles were less likely to utilise PNC services, confirming findings from Iqbal et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Wang and Hong (2015), Akinyemi et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and Singh et al. (2015), which showed that women from wealthier households tend to utilise more postnatal services. Financial stability allows women to afford transport and medical costs associated with maternal healthcare service utilisation, a significant barrier for women from poorer backgrounds, especially those in rural areas (Zelalem Ayele et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In contrast, women from rural areas faced additional challenges, including long distances to healthcare facilities, poor road infrastructure, and the high cost of transportation, which may deter them from accessing care (Ali et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWomen with health insurance were 1.47 times more likely to utilise PNC services, a finding consistent with Chaka (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which indicated that health insurance coverage is a key factor in increasing the utilisation of postpartum care. Moreover, women with decision-making autonomy were 1.32 times more likely to seek PNC services compared to those without such autonomy. This supports the notion that female autonomy, particularly in terms of financial independence and health-related decision-making, plays a critical role in utilising maternal healthcare services (Ghuman, Lee \u0026amp; Smith, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The study found regional disparities in PNC service utilisation, with women from the Southern Highlands, Southern, and Eastern zones more likely to use PNC services compared to those in the Western zone. These findings are consistent with studies showing that women in more developed areas with better access to healthcare facilities are more likely to utilise postnatal care (Mumtaz, Bahk \u0026amp; Khang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Conversely, women in the Lake zone had decreased odds of using PNC services, likely due to factors such as limited infrastructure and healthcare access in rural areas (Iqbal et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The results underscore the influence of geographic location on access to and utilisation of healthcare services. This study contributes valuable insights into the factors associated with PNC service utilisation in Tanzania. It highlights the critical role of maternal knowledge, socio-demographic factors, financial constraints, and regional disparities in shaping women's decisions regarding postnatal care. Addressing these factors, through both education and policy interventions, is crucial for improving maternal health outcomes in Tanzania and other similar settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe utilisation of postnatal care (PNC) services in Tanzania remains inadequate, largely due to persistent socio-economic disparities, entrenched cultural norms, and systemic weaknesses within the healthcare sector. These findings underscore the multifaceted nature of maternal healthcare access and highlight the importance of developing targeted interventions that respond to the unique challenges faced by different communities. Tackling these determinants and encouraging the optimal use of antenatal and postnatal services is vital for improving maternal and perinatal health outcomes. Achieving this requires coordinated efforts among healthcare providers, policymakers, and community-based organisations. Effectively addressing these barriers is essential to advancing the overall well-being of mothers and newborns.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to analyse\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was based on publicly available datasets derived from the 2015–16 Tanzania Demographic and Health Survey (TDHS), which are accessible online and have been anonymised to remove all personally identifiable information. Permission to utilise the DHS data was obtained by the researcher from the ICF Macro Institutional Review Board in Calverton, New York.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ethere was no Funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe datasets generated during and/or analysed during the current study are available at https://dhsprogram.com/data/dataset_admin/index under the project titled Access to Health Services in Tanzania\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkinyemi, J. 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(2014). Factors affecting utilization of maternal health care services in Kombolcha District, Eastern Hararghe Zone, Oromia Regional State, Eastern Ethiopia. \u003cem\u003eInternational Scholarly Research Notices\u003c/em\u003e, \u003cem\u003e2014\u003c/em\u003e, 1\u0026ndash;7. https://doi.org/10.1155/2014/917058.\u003c/li\u003e\n\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":"Postnatal care, services utilisation, women","lastPublishedDoi":"10.21203/rs.3.rs-6907383/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6907383/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePostnatal care (PNC) is a vital component of maternal and neonatal healthcare, significantly contributing to the reduction of preventable morbidity and mortality. Despite global and national policy efforts, the utilisation of PNC services remains suboptimal in Tanzania. This study investigates the socio-economic, cultural, and structural determinants associated with PNC service uptake among Tanzanian women, using Health Belief model as well as social action theory as theoretical framework.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA quantitative research design was employed, utilising secondary data from the 2015–2016 Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS). The sample comprised 6,994 women aged 15–49 who had given birth in the five years preceding the survey. Descriptive statistics, chi-square tests, and multivariate logistic regression analyses were conducted using STATA software, with statistical significance set at p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings revealed that only 39.9% of women accessed PNC services within two days post-delivery. Significant associations were found between PNC utilisation and higher levels of education, wealth status, decision-making autonomy, exposure to maternal health information, and geographic accessibility (p \u0026lt; 0.05). Logistic regression analysis indicated that women with secondary or higher education and those from wealthier households were significantly more likely to utilise PNC services. Additionally, exposure to media and health decision-making autonomy were strong predictors of service uptake. Conversely, rural residence and poor accessibility to healthcare facilities were major barriers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study concludes that PNC service utilisation in Tanzania is hindered by intertwined socio-economic, cultural, and structural barriers. Interventions aimed at improving maternal health outcomes should focus on enhancing educational opportunities, strengthening healthcare infrastructure, expanding health insurance coverage, and promoting behavioural change through community engagement and media campaigns. These findings offer crucial insights for policymakers and healthcare stakeholders committed to improving maternal and child health in Tanzania.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Social and Behavioural Factors Influencing Postnatal Care Service Utilisation Among Women in Tanzania from a Sociological Lens","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-31 16:10:48","doi":"10.21203/rs.3.rs-6907383/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":"11f07b96-0cfd-4a76-b6e4-36da2c616d6e","owner":[],"postedDate":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-25T12:23:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-31 16:10:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6907383","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6907383","identity":"rs-6907383","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}