Summary measures of inequalities in facility delivery services utilization among reproductive-aged women: Evidence from the 2022 Tanzania Demographic and Health Survey

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Inequalities in the utilization of facility delivery services persist, potentially undermining progress toward reducing maternal mortality and morbidity. This study aims to assess the inequalities in facility delivery service utilization among reproductive-aged women in Tanzania, using the 2022 Tanzania Demographic and Health Survey (TDHS). Methods: This study employed an analytical cross-sectional design, utilizing nationally representative secondary data from the 2022 TDHS, and applied the World Health Organization (WHO) Health Equity Assessment Toolkit (HEAT) software. The study incorporated five variables as stratifiers for inequality; the WHO HEAT software utilized these stratifiers to evaluate disparities in several health and social indicators. Four measures were used to assess inequality, namely: Difference (D), Population Attributable Fraction (PAF), Population Attributable Risk (PAR), and Ratio (R). Result: The facility delivery coverage among women of reproductive age was 84.6% overall, with notable inequalities. Coverage was highest among the richest women (97%) and those with secondary or higher education (over 83%), while the poorest women and those with low education had substantially lower rates. Urban women had higher coverage (94%) compared to rural women (78%), and regional disparities ranged from 73% to 98%. Inequality analyses showed economic status as the strongest factor influencing facility delivery, with a 31.1 percentage point difference and a 1.5 times higher likelihood among the richest compared to the poorest. Education, residence, and region also contributed to disparities. Conclusion: This study presents several key summary measures of inequalities in facility delivery in Tanzania, primarily driven by economic status, educational attainment, residential location, and regional disparities. These disparities highlight the need for integrated strategies that aim to overcome financial, informational, and geographical barriers hindering equitable access to facility delivery services. birth inequalities summary measures facility delivery Figures Figure 1 Figure 2 Background Globally, maternal mortality and morbidity continue to be critical public health challenges, particularly in low- and middle-income countries (LMICs) such as Sub-Saharan Africa (SSA) [1]. Despite a notable 40% decline in the global maternal mortality ratio (MMR) from 328 to 197 deaths per 100,000 live births between 2000 and 2023 [2], progress remains insufficient to meet the Sustainable Development Goals (SDGs) target of reducing the global MMR to less than 70 per 100,000 live births by 2030 [3]. SSA bears the heaviest burden, accounting for approximately 70% of global maternal deaths, with a striking MMR of 545 deaths per 100,000 live births in 2020, far exceeding global averages [3,4]. Although some regions within the SSA have achieved substantial reductions in maternal mortality, the overall pace of progress has stagnated since 2015, underscoring the urgent need for intensified, targeted interventions [5]. Facility delivery, defined as childbirth occurring in health care institutions with skilled birth attendants, is widely recognized as a key intervention in reducing maternal mortality and morbidity [4]. Globally, coverage of facility deliveries has increased but remains uneven [6]. High-income countries report near-universal facility births, whereas several SSA countries lag, with facility delivery coverage rates as low as 24% in some settings [6,7]. The association between facility delivery and lower maternal mortality is well established in previous studies [4,7,8]. Facility births allow prompt management of obstetric complications such as hemorrhage, infection, and hypertensive disorders, which account for the majority of maternal deaths [3,4,8]. Therefore, improving access to and utilization of health facility delivery services is imperative for achieving SDG 3.1 on maternal mortality reduction [9]. Maternal mortality and morbidity remain significant concerns in Tanzania, though the country has made progress driven by national strategies and health system improvements [8]. The maternal mortality ratio in Tanzania was estimated at 556 deaths per 100,000 live births in 2016, with recent data indicating a decline to approximately 104 deaths per 100,000 live births by 2022, reflecting an 80% reduction over seven years attributable to enhanced political commitment and health interventions [10]. Strategies such as the expansion of Emergency Obstetric and Newborn Care (EmONC) facilities, increased skilled birth attendance, and improvements in referral networks have been pivotal [11]. Facility delivery coverage has increased substantially, with some regions reporting rates above 81.7% in 2020 [11,12]. However, national averages suggest ongoing gaps [11,12]. Several studies in Tanzania have examined the utilization of facility delivery, barriers to care, and the quality of care, highlighting the importance of both demand- and supply-side factors in influencing service uptake [13]. However, these studies demonstrate heterogeneity across regions and subpopulations, indicating persistent inequities in maternal health service access [8,13]. While the general status and determinants of facility delivery in Tanzania have been studied, limited research has focused explicitly on inequalities in facility delivery service use among reproductive-aged women [8,13–15]. Geographic disparities, socioeconomic status, education, and rural-urban divides are recognized as significant contributors globally and in SSA; however, a systematic understanding within the Tanzanian context remains sparse [13]. This gap challenges the design of equitable maternal health programs that meaningfully target underserved groups. Despite notable improvements in maternal health indicators and increased facility delivery coverage, inequalities in the utilization of facility delivery services persist in Tanzania, potentially undermining progress toward reducing maternal mortality and morbidity [8]. Understanding these inequalities is critical to tailoring interventions that enhance equitable access and improve maternal outcomes nationwide. This study aims to assess the extent and determinants of inequalities in facility-based delivery service utilization among reproductive-aged women in Tanzania, using the 2022 Tanzania Demographic and Health Survey (TDHS) for analysis. This study aims to utilize advanced methods by incorporating the World Health Organization (WHO) Health Equity Assessment Toolkit (HEAT) software to establish measures of inequality [16]. Addressing inequalities in facility delivery is essential to closing maternal health gaps and accelerating progress toward SDG targets. By identifying and quantifying disparities in service use, this research will provide evidence to inform policymakers, health planners, and stakeholders on targeted, context-specific strategies that promote universal and equitable access to facility-based childbirth services in Tanzania. Methods and materials Data source, setting, design, population, and sampling We used data from the recent TDHS, a population-based cross-sectional survey carried out approximately every five years, and collected information on various health indicators among men, women, children, and households [17]. Tanzania, located in East Africa, spans an area of 940,000 km 2 , including 60,000 km 2 of inland water. The 2022 national population census reports that the country had an estimated population of 61,741,120, with slightly more than half being women [18]. The country has nine geographical zones and thirty-two administrative regions, which the Ministry of Health uses as a framework for organizing data on both the mainland and Zanzibar. An analytical cross-sectional study was conducted utilizing the 2022 TDHS, which is based on nationally representative secondary data. The TDHS is funded by the U.S. Agency for International Development, with implementation overseen by the Ministry of Health in both the Mainland of Tanzania and Zanzibar, and in collaboration with multiple stakeholders. ICF International provided essential technical support for the survey implementation [18,19]. The detailed TDHS methodology is explained elsewhere [17]. In summary, the TDHS uses a two-stage, cluster sampling design to collect data on population demographics, reproductive health, and other self-reported social determinants of health. Specifically, 629 clusters were identified, from which 26 households were systematically selected within each cluster (primary sampling units), totaling 16,354 households in the survey. We used an individual file (IR), which contains information on women of reproductive age (15–49 years). Variable measurements Outcome variable The outcome variable for this study was facility delivery (coded 1 = Yes) if the mother gave birth at a health facility, (Coded as 0 = No) if the birth occurred elsewhere. Explanatory variables The study incorporated five variables as inequality stratifiers, which were identified from existing literature [20,21]. The WHO HEAT software included these stratifiers for evaluating disparities in several health and social indicators [16]. These stratifiers include age groups in women (15–24, 25–34 and 35–49), socioeconomic status (poorest (Quintile 1), poorer (Quintile 2), middle (Quintile 3), richer (Quintile 4) and richest (Quintile5)), educational levels (no or primary and secondary or higher), place of residence (rural and urban) and Tanzania’s geographical zones (southern highlands, southern, eastern, western, lake, Zanzibar, central, northern and south west highlands), birth order (1 and ≥ 2), marital status (married and unmarried), employment status (not working and working), distance to health facility (big problem and not a big problem), media exposure (yes and no) and ANC visits (no visit, < 4 and ≥ 4). The derivation, categorization, and recategorization of the variables based on the available data and literature [22–26]. Statistical analysis The WHO Health Equity Assessment Toolkit (HEAT) software version 3.1 [16] was used to assess facility delivery inequalities by age group, economic status, place of residence, education level, and Tanzanian zones. The inequality in facility delivery was analyzed through a 2-step stages. Firstly, the facility delivery among reproductive-aged women (15–49) years was disaggregated by the equity stratifiers. After that, four measures were used to evaluate inequality, namely: Difference (D), Population Attributable Fraction (PAF), Population Attributable Risk (PAR), and Ratio (R) as indicated by WHO [16]. D was calculated by subtracting the lowest proportion from the highest proportion of facility delivery between groups. To calculate PAR, let µ represent the population average of facility delivery, and Pref represent the proportion of facility delivery in the reference group of the variables used to assess inequalities. The PAR is then obtained by subtracting µ from Pref for the ordered variables (by age group, economic status, and education level). R was calculated by dividing the value of a stratifier in the most advantaged group by the value in the least advantaged group. The PAF was calculated by dividing PAR by the overall average µ, then multiplying by 100 (PAF = [PAR / µ] * 100). A zero (0) PAF or PAR shows no inequality, but a greater value indicates a proportional increase in inequality. The variation in facility delivery was investigated using the 95% uncertainty intervals (UIs), here referred to as CI. The lack of overlap in the CIs indicates that a statistically significant difference existed between the CIs, and vice versa. Results Percentage distribution of social-demographic characteristics of the respondents in TDHS 2022 Among women of reproductive age in Tanzania in 2022 (N = 4,969), the mean age was 28.45 years. The age group with the highest representation was 25–34 years at 42.14%. Most women resided in rural areas (69.95%). In terms of education, the most significant proportion had primary education (54.8%). Regarding wealth, 40.74% of women were classified as rich. For birth order, 50.37% of women had two or more children. The majority were married (57.19%) and working (63.86%). Most respondents reported that distance to a health facility was not a big problem (68.07%). In terms of media access, 50.09% reported no media exposure. For antenatal care, the highest proportion of women (69.49%) reported attending four or more ANC visits during their most recent pregnancy. See Table 1 for more percentage distribution. Table 1 Percentage distribution of social-demographic characteristics of the respondents in TDHS 2022 (N = 4,969) Variable Percentage n (%) Confidence Intervals Age categories 15–24 1820 (36.12) (34.25, 38.04) 25–34 2123 (42.14) (40.14, 44.16) 35–49 1095 (21.74) (20.29, 23.26) Mean (± SD) 28.45 (7.16) Residence Urban 1514 (30.05) (25.67, 34.83) Rural 3524 (69.95) (65.17, 74.33) Education No education 1013 (20.11) (17.9, 22.53) Primary 2761 (54.8) (52.77, 56.81) Secondary or higher 1264 (25.09) (22.99, 27.31) Wealth index Poor 2027 (40.22) (36.43, 44.14) Middle 958.9 (19.03) (17.12, 21.11) Rich 2053 (40.74) (36.48, 45.15) Birth order 1 2368 (49.63) (47.37, 51.89) ≥2 2403 (50.37) (48.11, 52.63) Marital status Unmarried 2157 (42.81) (40.86, 44.78) Married 2882 (57.19) (55.22, 59.14) Employment status Not working 1821 (36.14) (33.94, 38.41) Working 3217 (63.86) (61.59, 66.06) Distance to health care Big problem 1609 (31.93) (28.91, 35.12) Not a big problem 3430 (68.07) (64.88, 71.09) Media exposure No 2524 (50.09) (47.35, 52.82) Yes 2515 (49.91) (47.18, 52.65) ANC visits No ANC 452.3 (8.98) (8.01, 10.04) < 4 1085 (21.54) (19.74, 23.44) ≥ 4 3501 (69.49) (67.45, 71.45) Inequalities in facility delivery based on selected equity stratifiers in Tanzania Facility delivery coverage in Tanzania in 2022 varied significantly across socio-demographic and geographic subgroups (Fig. 1 ). Age-related differences were minimal, with women aged 15–24 and 25–34 years having slightly higher facility delivery rates (around 84%) compared to those aged 35–49 years (80%). Economic disparities were more pronounced. Women in the richest quintile had the highest facility delivery rate at nearly 97%, followed by those in the fourth (93%) and third (89%) quintiles. In contrast, women in the poorest quintile had substantially lower coverage at around 66%, indicating substantial wealth-related inequality. Educational attainment also showed a clear association with facility delivery. Women with secondary or higher education had a coverage rate of over 83%, compared to 68% among those with no or primary education. Marked differences were observed by place of residence. Urban women had the highest facility delivery coverage, at approximately 94%, while rural women lagged at about 78%, revealing a significant urban–rural gap. Regional disparities in facility delivery were also evident across Tanzania’s zones in 2022 (Fig. 2 ). The Southern Highlands and Southern zones had the highest facility delivery coverage, at 98% and 97% respectively, well above the national median of 84.6%. In contrast, the Northern and Western zones reported the lowest rates, at 73% and 80%, respectively. The Central, Lake, and Southwest Highlands zones had moderate coverage ranging between 78% and 85%. Meanwhile, the Eastern and Zanzibar zones reported relatively high facility delivery rates, at 91% and 87%, respectively. Inequality estimated indices of factors associated with Facility delivery among women of reproductive age The results indicate substantial economic, educational, residential, and regional inequalities in facility delivery among women of reproductive age in Tanzania in 2022. Economic-related inequality was the most pronounced, with a Difference (D) of 31.1 percentage points (95% CI: 31.0–31.1), suggesting that women in the richest group had markedly higher facility delivery rates compared to those in the poorest. The Population Attributable Fraction (PAF) was 16.6% (95% CI: 16.5–16.6), indicating that a significant proportion of facility deliveries could be achieved if economic disparities were eliminated. The Ratio was 1.5 (95% CI: 1.5–1.5), meaning that women in the richest group were 1.5 times more likely to deliver in health facilities than those in the poorest. Education-related disparities were also evident, with a Difference of 15.6 percentage points (95% CI: 15.5–15.6) and a PAF of 5.3% (95% CI: 5.3–5.3), highlighting that women with higher levels of education had higher facility delivery rates. The Ratio was 1.2 (95% CI: 1.2–1.2), pointing to modest relative differences across education levels. Place of residence presented a Difference of 16.4 percentage points (95% CI: 16.4–16.4), with urban women more likely to utilize facility delivery services than their rural counterparts. The PAF was 13.8% (95% CI: 13.8–13.8), and the Ratio was 1.2 (95% CI: 1.2–1.2), indicating a moderate disparity driven by geographic location. Regional (zone-level) inequalities were minimal in absolute terms, with a Difference of 5.3 percentage points (95% CI: 5.3–5.4) and a PAF of 5.6% (95% CI: 5.5–5.7) but showed a very high Ratio of 9.8 (95% CI: 9.8–9.8), suggesting stark relative differences in facility delivery coverage between the best and worst-performing zones. (Table 2 ) Table 2 Inequality estimated indices of factors associated with Facility delivery among women of reproductive age in Tanzania, 2022 Dimensions Estimates (95% CI) Economic Status Difference (D) 31.1 (31.0, 31.1) Population Attributable Fraction (PAF) 16.6 (16.5, 16.6) Population Attributable Risk (PAR) 13.8 (12.3, 15.2) Ratio (R) 1.5 (1.5, 1.5) Level of Education Difference (D) 15.6 (15.5, 15.6) Population Attributable Fraction (PAF) 5.3 (5.3, 5.3) Population Attributable Risk (PAR) 4.2 (3.3, 5.1) Ratio (R) 1.2 (1.2, 1.2) Place of residence Difference (D) 16.4 (16.4, 16.4) Population Attributable Fraction (PAF) 13.8 (13.8, 13.8) Population Attributable Risk (PAR) 11.5 (10.1, 12.8) Ratio (R) 1.2 (1.2, 1.2) Tanzania Zones Difference (D) 5.3 (5.3, 5.4) Population Attributable Fraction (PAF) 5.6 (5.5, 5.7) Population Attributable Risk (PAR) 4.6 (-0.5, 9.8) Ratio (R) 9.8 (9.8, 9.8) Discussion This study aimed to assess summary measures of inequalities in the use of facility delivery services among reproductive-aged women, using secondary analysis of the 2022 TDHS. The findings reveal stark economic disparities in facility delivery among the studied population. Findings reveal a substantial gap in access to facility delivery services between the richest and poorest groups whereby women in the wealthiest quintile are 1.5 times more likely to utilize facility delivery than those in the poorest quintile. This pronounced economic inequality highlights that despite universal maternal health policies, financial barriers remain a critical obstacle for poorer women, limiting their access to skilled birth attendance and safe delivery environments, which are essential for reducing maternal mortality [8]. Eliminating economic disparities could substantially increase facility deliveries, thereby potentially improving maternal and neonatal health outcomes. Educational disparities also play a significant role in the utilization of facility delivery services. Findings demonstrate that women with higher educational attainment are significantly more inclined to deliver in health facilities. Other studies have shown that education presumably enhances women’s awareness of the benefits of facility delivery, empowers them in health-related decision-making, and facilitates their navigation within the health system [27–29]. These educational inequalities suggest that improvements in female education and targeted health literacy interventions could reduce disparities, emphasizing the transformative impact of education on health behaviors and outcomes [8]. Geographical disparities further deepen facility delivery inequities, as evidenced by a difference between urban and rural populations, reflecting that urban women have moderately better access to facility delivery services compared to their rural counterparts. This may be attributed to better health infrastructure, transportation facilities, and availability of skilled healthcare personnel in urban areas [8,30,31]. Rural women often face considerable physical and socio-economic barriers, including limited healthcare accessibility and socio-cultural determinants that discourage facility delivery [32]. Consequently, spatial inequities necessitate policy and programmatic focus on improving rural health systems, transportation networks, and community outreach programs to enhance facility delivery rates in underserved areas [33]. Regional inequalities appear less pronounced, yet the exceptionally high Ratio of 9.8 indicates extreme relative disparities between zones. This implies that the proportional disparity in facility delivery across different zones is striking, highlighting zones with critically low coverage. This finding shows the importance of considering zone-specific factors, including service availability, quality of care, cultural norms, and local governance as seen by previous studies [15,33]. Addressing these regional disparities requires context-sensitive interventions that target the lowest-performing zones to elevate facility delivery coverage, ensuring equitable maternal health service distribution across the country. Strengths and limitations The strengths of this study lie primarily in its ability to provide a comprehensive and nuanced understanding of disparities across socioeconomic, educational, and geographic dimensions. Findings allow researchers and policymakers to quantify the magnitude and direction of inequalities, thereby identifying which population groups are most disadvantaged and require targeted interventions. Moreover, these measures capture variations within countries across different subpopulations, enabling tailored policy responses that address specific barriers to facility delivery, such as poverty, rural residence, and low education levels. The use of nationally representative, population-based data sets further strengthens the reliability and generalizability of the findings, as these data enable comparisons over time and between diverse contexts. However, limitations exist, the reliance on self-reported data, which can introduce recall bias and affect the accuracy of reported delivery locations. Cross-sectional study designs, common in these analyses, limit causal inferences, as they capture associations but not temporal sequences. Additionally, some important determinants, such as the quality of care or cultural factors influencing delivery choices, may not be fully captured or quantified by the indices, thus constraining the scope of inequality assessment. Therefore, while inequality indices are powerful for measuring disparities, they may sometimes overlook underlying structural and systemic factors, demanding complementary qualitative and mixed-methods research for a holistic understanding. The findings of this study can be interpreted cautiously with the above limitations. Future research should focus in minimizing the limitations above. Implications for practice and recommendation Educational campaigns should be designed to enhance awareness of the importance of facility deliveries, with a particular focus on rural and less-educated women. Enhancing community health worker programs may bridge gaps by providing localized support and facilitating access to maternal healthcare. Reform efforts must prioritize economic empowerment of poor women through subsidies, transport vouchers, or conditional cash transfers to mitigate financial constraints. Furthermore, investing in rural healthcare infrastructure and human resources is crucial to reducing urban-rural disparities. Regional development policies should incorporate maternal health indicators to allocate resources equitably and address disparities effectively. Conclusion This study presents several key summary measures of inequalities in facility delivery in Tanzania, primarily driven by economic status, educational attainment, residential location, and regional disparities. These disparities highlight the need for integrated strategies that aim to overcome financial, informational, and geographical barriers hindering equitable access to facility delivery services. There is a need for healthcare providers to recognize these disparities and adopt culturally sensitive, patient-centered approaches to encourage facility delivery, especially among disadvantaged groups. Ultimately, achieving equitable facility delivery in Tanzania requires a multisectoral approach that involves the health, education, transportation, and social protection sectors. Policymakers should support data-driven insights to design, implement, and monitor programs that prioritize the most marginalized populations, advancing Tanzania toward universal maternal health coverage and Sustainable Development Goal targets related to maternal and newborn health. Abbreviations ANC Antenatal Care CIs Confidence Intervals DHS Demographic and Health Survey EA Enumeration areas EmONC Emergency Obstetric and Newborn Care HEAT Health Equity Assessment LMICs Low- and middle-income MMR Maternal mortality ratio PAF Population Attributable Fraction PAR Population Attributable Risk SD Standard Deviation SDG Sustainable Development Goals SSA Sub-Saharan Africa TDHS Tanzania Demographic and Health Survey UIs Uncertainty Intervals WHO World Health Organization Declarations Acknowledgements We thank the DHS program for making the data available for this study and TILAM International for statistical consultation. Authors’ Contribution VGM, MJM, SAK, and EES conceptualized the idea and conducted formal analysis. MJM, EES, VGM, EDO, TPM, SAK, JRT, MHB, IPK, and AAN participated in the interpretation of the results, drafted the first draft of the manuscript, and reviewed all versions of the manuscript. All authors read and approved the final manuscript. Funding No funding received. Availability of data and materials DHS data are publicly available at https://dhsprogram.com. However, the dataset and STATA 18 “do-file” supporting the conclusion of this study are available and can be shared upon a reasonable request to the corresponding author. Ethics approval and consent to participate This study utilized secondary data from the 2022 TDHS, which was accessed through the DHS program website. The original study from the DHS program obtained ethical approval from the National Institute of Medical Research (NIMR) Ethics Committee in Tanzania and the ICF Macro Ethics Committee in Calverton, New York. The initial study adhered to the Declaration of Helsinki in this regard. Permission to download the data for this secondary analysis was granted by the DHS program upon acceptance of the proposed analysis plan under the designated account, with credentials available upon reasonable request via https://dhsprogram.com/data/dataset_admin/index.cfm. As this study involved secondary data analysis of publicly accessible datasets, no additional ethical approval was required. Informed consent was obtained from all participants during the initial survey, and all procedures adhered strictly to relevant guidelines and regulations. Further details regarding DHS data usage, methodology, and ethical standards can be found at http://goo.gl/ny8T6X. Consent for publication Not applicable. Competing interests Authors declared no competing interests. References Ekwuazi EK, Chigbu CO, Ngene NC. Reducing maternal mortality in low- and middle-income countries. Case Rep Womens Health. 2023;39:e00542. Lee S, Kim S, Lee H, Park J, Son Y, López Sánchez GF, et al. Global, Regional, and National Trends in Maternal Mortality Ratio Across 37 High Income Countries From 1990 to 2021, With Projections up to 2050: A Comprehensive Analysis From the WHO Mortality Database. J Korean Med Sci [Internet]. 2024 [cited 2025 May 14];40. Available from: https://doi.org/10.3346/jkms.2025.40.e85 UNICEF. Maternal mortality rates and statistics - UNICEF DATA [Internet]. 2023 [cited 2025 May 14]. Available from: https://data.unicef.org/topic/maternal-health/maternal-mortality/ Oyedele OK, Lawal TV. Global dominance of non-institutional delivery and the risky impact on maternal mortality spike in 25 Sub-Saharan African Countries. Glob Health Res Policy. 2025;10:10. Musarandega R, Nyakura M, Machekano R, Pattinson R, Munjanja SP. Causes of maternal mortality in Sub-Saharan Africa: A systematic review of studies published from 2015 to 2020. J Glob Health. 11:04048. Priebe J, Amuasi J, Dartanto T, Mombo-Ngoma G, Guigas M. Factors associated with skilled birth attendance in 37 low-income and middle-income countries: a secondary analysis of nationally representative, individual-level data. Lancet Glob Health. 2024;12:e1104–10. Straneo M, Hanson C, van den Akker T, Afolabi BB, Asefa A, Delamou A, et al. Inequalities in use of hospitals for childbirth among rural women in sub-Saharan Africa: a comparative analysis of 18 countries using Demographic and Health Survey data. BMJ Glob Health. 2024;9:e013029. Bintabara D. Addressing the huge poor–rich gap of inequalities in accessing safe childbirth care: A first step to achieving universal maternal health coverage in Tanzania. PLoS ONE. 2021;16:e0246995. Galgalo DA, Mokaya P, Chauhan S, Kiptulon EK, Wami GA, Várnagy Á, et al. Utilization of maternal health care services among pastoralist communities in Marsabit County, Kenya: a cross-sectional survey. Reprod Health. 2024;21:126. Ministry of Health (MoH) [Tanzania Mainland], Ministry of Health (MoH) [Zanzibar], National Bureau of Statistics (NBS), Office of the Chief Government Statistician (OCGS), and ICF. Tanzania Demographicand Health Survey and Malaria Indicator Survey 2022 Key Indicators Report. Dodoma, Rockville: MoH, NBS, OCGS, and ICF; 2023. CDC. Maternal and Reproductive Health in Tanzania Program [Internet]. Glob. Reprod. Health. 2024 [cited 2025 May 14]. Available from: https://www.cdc.gov/global-reproductive-health/php/maternal-reproductive-health-tanzania/index.html Shabani J, Masanja H, Kagoye S, Minja J, Bajaria S, Mlacha Y, et al. Quality of reporting and trends of emergency obstetric and neonatal care indicators: an analysis from Tanzania district health information system data between 2016 and 2020. BMC Pregnancy Childbirth. 2023;23:716. Binyaruka P, Foss A, Alibrahim A, Mziray N, Cassidy R, Borghi J. Supply-side factors influencing demand for facility-based delivery in Tanzania: a multilevel analysis. Health Econ Rev. 2023;13:52. Das G, Masoi TJ, Kibusi SM, Chaudhary A, Greenwald M, Goodman A. Patient and provider perspectives of disrespect and abuse during childbirth in Tanzania: a literature review. Open J Obstet Gynecol. 2021;11:1248–72. Bishanga DR, Drake M, Kim Y-M, Mwanamsangu AH, Makuwani AM, Zoungrana J, et al. Factors associated with institutional delivery: Findings from a cross-sectional study in Mara and Kagera regions in Tanzania. PLoS ONE. 2018;13:e0209672. WHO. Health Equity Assessment Toolkit: Technical Notes. 2021; Demographic and Health Surveys. Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS). 2022. NBS Tanzania. National Bureau of Statistics [Internet]. Manuf. Index. 2023 [cited 2023 Dec 16]. p. 4–7. Available from: https://www.nbs.go.tz/index.php/en/%0Ahttp://www.nigerianstat.gov.ng/ Ministry of Health. Tanzania Ministry of Health [Internet]. 2023 [cited 2023 Dec 16]. Available from: https://www.moh.go.tz/ Bhusal UP. Predictors of wealth-related inequality in institutional delivery: a decomposition analysis using Nepal multiple Indicator cluster survey (MICS) 2019. BMC Public Health. 2021;21:1–15. Randive B, San Sebastian M, De Costa A, Lindholm L. Inequalities in institutional delivery uptake and maternal mortality reduction in the context of cash incentive program, Janani Suraksha Yojana: Results from nine states in India. Soc Sci Med. 2014;123:1–6. Sadik W, Bayray A, Debie A, Gebremedhin T. Factors associated with institutional delivery practice among women in pastoral community of Dubti district, Afar region, Northeast Ethiopia: A community-based cross-sectional study. Reprod Health. 2019;16:1–8. Ketemaw A, Tareke M, Dellie E, Sitotaw G, Deressa Y, Tadesse G, et al. Factors associated with institutional delivery in Ethiopia: A cross sectional study. BMC Health Serv Res. 2020;20:1–6. Hassen SS, Jemal SS, Bambo M mesfin, Lelisho ME, Tareke SA, Merera AM, et al. Multilevel analysis of factors associated with utilization of institutional delivery in Ethiopia. Womens Health. 2022;18. Pathak P, Shrestha S, Devkota R, Thapa B. Factors Associated with the Utilization of Institutional Delivery Service among Mothers. J Nepal Health Res Counc. 2018;15:228–34. Tibenderana JR, Kessy SA, Mlaponi DF, Mtenga JE, Gimonge J, Mwaitete NL, et al. The Adequacy of ANC services received and associated factors among Women of Reproductive Age in Tanzania. PLoS ONE. 2024;1–15. Alam MB, Khanam SJ, Kabir MA, Chowdhury AR, Hassen TA, Das S, et al. Effects of Women’s Participation in Household Decision Making on Skilled Birth Attendants Supervised Delivery in Bangladesh. Health Serv Insights. 2025;18:11786329251316674. Doctor HV, Nkhana-Salimu S, Abdulsalam-Anibilowo M. Health facility delivery in sub-Saharan Africa: successes, challenges, and implications for the 2030 development agenda. BMC Public Health. 2018;18:1–12. Kifle MM, Kesete HF, Gaim HT, Angosom GS, Araya MB. Health facility or home delivery? Factors influencing the choice of delivery place among mothers living in rural communities of Eritrea. J Health Popul Nutr. 2018;37:1–15. Chen L, Chen T, Lan T, Chen C, Pan J. The contributions of population distribution, healthcare resourcing, and transportation infrastructure to spatial accessibility of health care. Inq J Health Care Organ Provis Financ. 2023;60:00469580221146041. Adhikari S, Lutz W, KC S. Rural/urban fertility differentials and the role of female education in declining birth rates: comparative analysis in Asia, Africa, and Latin America. Asian Popul Stud. 0:1–25. Ganle J. Addressing socio-cultural barriers to maternal healthcare in Ghana: perspectives of women and healthcare providers. J Womens Health Issues Care. 2014;6:2. Thasineku OC, Pandit S, Acharya D, Gurung YB. Associated factors for the utilization of institutional delivery services in Nepal: Findings from the Nepal Demographic Health Survey, 2022. PLOS One. 2025;20:e0322309. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6893727","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484723859,"identity":"90e6d89f-e46f-4c1c-bbc1-0876a8068c1c","order_by":0,"name":"Victoria Godfrey Majengo","email":"data:image/png;base64,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","orcid":"","institution":"Dodoma Regional Referral Hospital","correspondingAuthor":true,"prefix":"","firstName":"Victoria","middleName":"Godfrey","lastName":"Majengo","suffix":""},{"id":484723860,"identity":"05d4607a-39e9-48a3-9535-ef63fa0d4270","order_by":1,"name":"Sanun Ally Kessy","email":"","orcid":"","institution":"Benjamin Mkapa Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sanun","middleName":"Ally","lastName":"Kessy","suffix":""},{"id":484723861,"identity":"42965351-9968-4e69-86d8-416d09959283","order_by":2,"name":"Elihuruma Eliufoo Stephano","email":"","orcid":"","institution":"The University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Elihuruma","middleName":"Eliufoo","lastName":"Stephano","suffix":""},{"id":484723862,"identity":"e552e490-15db-4e2e-add7-a127659a3392","order_by":3,"name":"Jovin R. Tibenderana","email":"","orcid":"","institution":"St. Francis University","correspondingAuthor":false,"prefix":"","firstName":"Jovin","middleName":"R.","lastName":"Tibenderana","suffix":""},{"id":484723863,"identity":"14aad043-3757-4dcd-bec6-1e2231f95602","order_by":4,"name":"Erick Donard Oguma","email":"","orcid":"","institution":"The University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Erick","middleName":"Donard","lastName":"Oguma","suffix":""},{"id":484723864,"identity":"ccb12b95-7b19-419b-81cc-00d8f2ccfe10","order_by":5,"name":"Tegemea Patrick Mwalingo","email":"","orcid":"","institution":"The University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Tegemea","middleName":"Patrick","lastName":"Mwalingo","suffix":""},{"id":484723865,"identity":"f9fc190c-df5a-434e-a567-afd6b9ef7270","order_by":6,"name":"Mussa Hassan Bago","email":"","orcid":"","institution":"The University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Mussa","middleName":"Hassan","lastName":"Bago","suffix":""},{"id":484723866,"identity":"bba2c279-1397-4942-b84f-e34d6ac433ba","order_by":7,"name":"Immaculata P. Kessy","email":"","orcid":"","institution":"TILAM International","correspondingAuthor":false,"prefix":"","firstName":"Immaculata","middleName":"P.","lastName":"Kessy","suffix":""},{"id":484723867,"identity":"9d1d2722-9f4a-4500-922c-a93ab881348c","order_by":8,"name":"Azan Abubakar Nyundo","email":"","orcid":"","institution":"The University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Azan","middleName":"Abubakar","lastName":"Nyundo","suffix":""},{"id":484723868,"identity":"8f88fa39-e35c-459c-a3d6-f3efcb7034fc","order_by":9,"name":"Mtoro J. Mtoro","email":"","orcid":"","institution":"TILAM International","correspondingAuthor":false,"prefix":"","firstName":"Mtoro","middleName":"J.","lastName":"Mtoro","suffix":""}],"badges":[],"createdAt":"2025-06-14 11:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6893727/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6893727/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86709026,"identity":"049dcfa8-836d-4144-ade6-b97e0e63f49a","added_by":"auto","created_at":"2025-07-14 18:04:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40717,"visible":true,"origin":"","legend":"\u003cp\u003eInequalities in facility delivery based on selected equity stratifiers in Tanzania\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6893727/v1/7e8c756cd11db2778a4d5254.png"},{"id":86709028,"identity":"e18c0e8b-6a1d-4a0e-b21d-800ed4af74f6","added_by":"auto","created_at":"2025-07-14 18:04:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11627,"visible":true,"origin":"","legend":"\u003cp\u003eInequalities in facility delivery based on selected equity stratifiers in Tanzania\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6893727/v1/052c5439b3adb0f847038e5a.png"},{"id":90957759,"identity":"3a7e4a2f-4352-49c7-ba7c-93d7cdedea56","added_by":"auto","created_at":"2025-09-10 03:31:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1053082,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6893727/v1/8a11adf6-c389-4e45-ae57-625319ee1f5b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Summary measures of inequalities in facility delivery services utilization among reproductive-aged women: Evidence from the 2022 Tanzania Demographic and Health Survey","fulltext":[{"header":"Background","content":"\u003cp\u003eGlobally, maternal mortality and morbidity continue to be critical public health challenges, particularly in low- and middle-income countries (LMICs) such as Sub-Saharan Africa (SSA) [1]. Despite a notable 40% decline in the global maternal mortality ratio (MMR) from 328 to 197 deaths per 100,000 live births between 2000 and 2023 [2], progress remains insufficient to meet the Sustainable Development Goals (SDGs) target of reducing the global MMR to less than 70 per 100,000 live births by 2030 [3]. SSA bears the heaviest burden, accounting for approximately 70% of global maternal deaths, with a striking MMR of 545 deaths per 100,000 live births in 2020, far exceeding global averages [3,4]. Although some regions within the SSA have achieved substantial reductions in maternal mortality, the overall pace of progress has stagnated since 2015, underscoring the urgent need for intensified, targeted interventions [5].\u003c/p\u003e\u003cp\u003eFacility delivery, defined as childbirth occurring in health care institutions with skilled birth attendants, is widely recognized as a key intervention in reducing maternal mortality and morbidity [4]. Globally, coverage of facility deliveries has increased but remains uneven [6]. High-income countries report near-universal facility births, whereas several SSA countries lag, with facility delivery coverage rates as low as 24% in some settings [6,7]. The association between facility delivery and lower maternal mortality is well established in previous studies [4,7,8]. Facility births allow prompt management of obstetric complications such as hemorrhage, infection, and hypertensive disorders, which account for the majority of maternal deaths [3,4,8]. Therefore, improving access to and utilization of health facility delivery services is imperative for achieving SDG 3.1 on maternal mortality reduction [9].\u003c/p\u003e\u003cp\u003eMaternal mortality and morbidity remain significant concerns in Tanzania, though the country has made progress driven by national strategies and health system improvements [8]. The maternal mortality ratio in Tanzania was estimated at 556 deaths per 100,000 live births in 2016, with recent data indicating a decline to approximately 104 deaths per 100,000 live births by 2022, reflecting an 80% reduction over seven years attributable to enhanced political commitment and health interventions [10]. Strategies such as the expansion of Emergency Obstetric and Newborn Care (EmONC) facilities, increased skilled birth attendance, and improvements in referral networks have been pivotal [11]. Facility delivery coverage has increased substantially, with some regions reporting rates above 81.7% in 2020 [11,12]. However, national averages suggest ongoing gaps [11,12]. Several studies in Tanzania have examined the utilization of facility delivery, barriers to care, and the quality of care, highlighting the importance of both demand- and supply-side factors in influencing service uptake [13]. However, these studies demonstrate heterogeneity across regions and subpopulations, indicating persistent inequities in maternal health service access [8,13].\u003c/p\u003e\u003cp\u003eWhile the general status and determinants of facility delivery in Tanzania have been studied, limited research has focused explicitly on inequalities in facility delivery service use among reproductive-aged women [8,13\u0026ndash;15]. Geographic disparities, socioeconomic status, education, and rural-urban divides are recognized as significant contributors globally and in SSA; however, a systematic understanding within the Tanzanian context remains sparse [13]. This gap challenges the design of equitable maternal health programs that meaningfully target underserved groups.\u003c/p\u003e\u003cp\u003eDespite notable improvements in maternal health indicators and increased facility delivery coverage, inequalities in the utilization of facility delivery services persist in Tanzania, potentially undermining progress toward reducing maternal mortality and morbidity [8]. Understanding these inequalities is critical to tailoring interventions that enhance equitable access and improve maternal outcomes nationwide. This study aims to assess the extent and determinants of inequalities in facility-based delivery service utilization among reproductive-aged women in Tanzania, using the 2022 Tanzania Demographic and Health Survey (TDHS) for analysis. This study aims to utilize advanced methods by incorporating the World Health Organization (WHO) Health Equity Assessment Toolkit (HEAT) software to establish measures of inequality [16]. Addressing inequalities in facility delivery is essential to closing maternal health gaps and accelerating progress toward SDG targets. By identifying and quantifying disparities in service use, this research will provide evidence to inform policymakers, health planners, and stakeholders on targeted, context-specific strategies that promote universal and equitable access to facility-based childbirth services in Tanzania.\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData source, setting, design, population, and sampling\u003c/h2\u003e\u003cp\u003eWe used data from the recent TDHS, a population-based cross-sectional survey carried out approximately every five years, and collected information on various health indicators among men, women, children, and households [17]. Tanzania, located in East Africa, spans an area of 940,000 km\u003csup\u003e2\u003c/sup\u003e, including 60,000 km\u003csup\u003e2\u003c/sup\u003e of inland water. The 2022 national population census reports that the country had an estimated population of 61,741,120, with slightly more than half being women [18]. The country has nine geographical zones and thirty-two administrative regions, which the Ministry of Health uses as a framework for organizing data on both the mainland and Zanzibar. An analytical cross-sectional study was conducted utilizing the 2022 TDHS, which is based on nationally representative secondary data. The TDHS is funded by the U.S. Agency for International Development, with implementation overseen by the Ministry of Health in both the Mainland of Tanzania and Zanzibar, and in collaboration with multiple stakeholders. ICF International provided essential technical support for the survey implementation [18,19].\u003c/p\u003e\u003cp\u003eThe detailed TDHS methodology is explained elsewhere [17]. In summary, the TDHS uses a two-stage, cluster sampling design to collect data on population demographics, reproductive health, and other self-reported social determinants of health. Specifically, 629 clusters were identified, from which 26 households were systematically selected within each cluster (primary sampling units), totaling 16,354 households in the survey. We used an individual file (IR), which contains information on women of reproductive age (15\u0026ndash;49 years).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eVariable measurements\u003c/h3\u003e\n\u003cp\u003eOutcome variable\u003c/p\u003e\u003cp\u003eThe outcome variable for this study was facility delivery (coded 1\u0026thinsp;=\u0026thinsp;Yes) if the mother gave birth at a health facility, (Coded as 0\u0026thinsp;=\u0026thinsp;No) if the birth occurred elsewhere.\u003c/p\u003e\u003cp\u003eExplanatory variables\u003c/p\u003e\u003cp\u003eThe study incorporated five variables as inequality stratifiers, which were identified from existing literature [20,21]. The WHO HEAT software included these stratifiers for evaluating disparities in several health and social indicators [16]. These stratifiers include age groups in women (15\u0026ndash;24, 25\u0026ndash;34 and 35\u0026ndash;49), socioeconomic status (poorest (Quintile 1), poorer (Quintile 2), middle (Quintile 3), richer (Quintile 4) and richest (Quintile5)), educational levels (no or primary and secondary or higher), place of residence (rural and urban) and Tanzania\u0026rsquo;s geographical zones (southern highlands, southern, eastern, western, lake, Zanzibar, central, northern and south west highlands), birth order (1 and \u0026ge;\u0026thinsp;2), marital status (married and unmarried), employment status (not working and working), distance to health facility (big problem and not a big problem), media exposure (yes and no) and ANC visits (no visit, \u0026lt;\u0026thinsp;4 and \u0026ge;\u0026thinsp;4). The derivation, categorization, and recategorization of the variables based on the available data and literature [22\u0026ndash;26].\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe WHO Health Equity Assessment Toolkit (HEAT) software version 3.1 [16] was used to assess facility delivery inequalities by age group, economic status, place of residence, education level, and Tanzanian zones. The inequality in facility delivery was analyzed through a 2-step stages. Firstly, the facility delivery among reproductive-aged women (15\u0026ndash;49) years was disaggregated by the equity stratifiers. After that, four measures were used to evaluate inequality, namely: Difference (D), Population Attributable Fraction (PAF), Population Attributable Risk (PAR), and Ratio (R) as indicated by WHO [16]. D was calculated by subtracting the lowest proportion from the highest proportion of facility delivery between groups. To calculate PAR, let \u0026micro; represent the population average of facility delivery, and Pref represent the proportion of facility delivery in the reference group of the variables used to assess inequalities. The PAR is then obtained by subtracting \u0026micro; from Pref for the ordered variables (by age group, economic status, and education level). R was calculated by dividing the value of a stratifier in the most advantaged group by the value in the least advantaged group. The PAF was calculated by dividing PAR by the overall average \u0026micro;, then multiplying by 100 (PAF = [PAR / \u0026micro;] * 100). A zero (0) PAF or PAR shows no inequality, but a greater value indicates a proportional increase in inequality. The variation in facility delivery was investigated using the 95% uncertainty intervals (UIs), here referred to as CI. The lack of overlap in the CIs indicates that a statistically significant difference existed between the CIs, and vice versa.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003ePercentage distribution of social-demographic characteristics of the respondents in TDHS 2022\u003c/h2\u003e\u003cp\u003eAmong women of reproductive age in Tanzania in 2022 (N\u0026thinsp;=\u0026thinsp;4,969), the mean age was 28.45 years. The age group with the highest representation was 25\u0026ndash;34 years at 42.14%. Most women resided in rural areas (69.95%). In terms of education, the most significant proportion had primary education (54.8%).\u003c/p\u003e\u003cp\u003eRegarding wealth, 40.74% of women were classified as rich. For birth order, 50.37% of women had two or more children. The majority were married (57.19%) and working (63.86%). Most respondents reported that distance to a health facility was not a big problem (68.07%). In terms of media access, 50.09% reported no media exposure. For antenatal care, the highest proportion of women (69.49%) reported attending four or more ANC visits during their most recent pregnancy. See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for more percentage distribution.\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\u003ePercentage distribution of social-demographic characteristics of the respondents in TDHS 2022 (N\u0026thinsp;=\u0026thinsp;4,969)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercentage n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConfidence Intervals\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge categories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1820 (36.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(34.25, 38.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2123 (42.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(40.14, 44.16)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1095 (21.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(20.29, 23.26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28.45 (7.16)\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\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1514 (30.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(25.67, 34.83)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3524 (69.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(65.17, 74.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1013 (20.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(17.9, 22.53)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2761 (54.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(52.77, 56.81)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary or higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1264 (25.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(22.99, 27.31)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWealth index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2027 (40.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(36.43, 44.14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e958.9 (19.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(17.12, 21.11)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRich\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2053 (40.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(36.48, 45.15)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBirth order\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2368 (49.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(47.37, 51.89)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2403 (50.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(48.11, 52.63)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\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\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2157 (42.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(40.86, 44.78)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2882 (57.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(55.22, 59.14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEmployment status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot working\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1821 (36.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(33.94, 38.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3217 (63.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(61.59, 66.06)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistance to health care\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBig problem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1609 (31.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(28.91, 35.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot a big problem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3430 (68.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(64.88, 71.09)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedia exposure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2524 (50.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(47.35, 52.82)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2515 (49.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(47.18, 52.65)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eANC visits\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo ANC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e452.3 (8.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(8.01, 10.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1085 (21.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(19.74, 23.44)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3501 (69.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(67.45, 71.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eInequalities in facility delivery based on selected equity stratifiers in Tanzania\u003c/h2\u003e\u003cp\u003eFacility delivery coverage in Tanzania in 2022 varied significantly across socio-demographic and geographic subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Age-related differences were minimal, with women aged 15\u0026ndash;24 and 25\u0026ndash;34 years having slightly higher facility delivery rates (around 84%) compared to those aged 35\u0026ndash;49 years (80%).\u003c/p\u003e\u003cp\u003eEconomic disparities were more pronounced. Women in the richest quintile had the highest facility delivery rate at nearly 97%, followed by those in the fourth (93%) and third (89%) quintiles. In contrast, women in the poorest quintile had substantially lower coverage at around 66%, indicating substantial wealth-related inequality. Educational attainment also showed a clear association with facility delivery. Women with secondary or higher education had a coverage rate of over 83%, compared to 68% among those with no or primary education. Marked differences were observed by place of residence. Urban women had the highest facility delivery coverage, at approximately 94%, while rural women lagged at about 78%, revealing a significant urban\u0026ndash;rural gap.\u003c/p\u003e\u003cp\u003eRegional disparities in facility delivery were also evident across Tanzania\u0026rsquo;s zones in 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Southern Highlands and Southern zones had the highest facility delivery coverage, at 98% and 97% respectively, well above the national median of 84.6%. In contrast, the Northern and Western zones reported the lowest rates, at 73% and 80%, respectively. The Central, Lake, and Southwest Highlands zones had moderate coverage ranging between 78% and 85%. Meanwhile, the Eastern and Zanzibar zones reported relatively high facility delivery rates, at 91% and 87%, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInequality estimated indices of factors associated with Facility delivery among women of reproductive age\u003c/h3\u003e\n\u003cp\u003eThe results indicate substantial economic, educational, residential, and regional inequalities in facility delivery among women of reproductive age in Tanzania in 2022. Economic-related inequality was the most pronounced, with a Difference (D) of 31.1 percentage points (95% CI: 31.0\u0026ndash;31.1), suggesting that women in the richest group had markedly higher facility delivery rates compared to those in the poorest. The Population Attributable Fraction (PAF) was 16.6% (95% CI: 16.5\u0026ndash;16.6), indicating that a significant proportion of facility deliveries could be achieved if economic disparities were eliminated. The Ratio was 1.5 (95% CI: 1.5\u0026ndash;1.5), meaning that women in the richest group were 1.5 times more likely to deliver in health facilities than those in the poorest.\u003c/p\u003e\u003cp\u003eEducation-related disparities were also evident, with a Difference of 15.6 percentage points (95% CI: 15.5\u0026ndash;15.6) and a PAF of 5.3% (95% CI: 5.3\u0026ndash;5.3), highlighting that women with higher levels of education had higher facility delivery rates. The Ratio was 1.2 (95% CI: 1.2\u0026ndash;1.2), pointing to modest relative differences across education levels. Place of residence presented a Difference of 16.4 percentage points (95% CI: 16.4\u0026ndash;16.4), with urban women more likely to utilize facility delivery services than their rural counterparts. The PAF was 13.8% (95% CI: 13.8\u0026ndash;13.8), and the Ratio was 1.2 (95% CI: 1.2\u0026ndash;1.2), indicating a moderate disparity driven by geographic location.\u003c/p\u003e\u003cp\u003eRegional (zone-level) inequalities were minimal in absolute terms, with a Difference of 5.3 percentage points (95% CI: 5.3\u0026ndash;5.4) and a PAF of 5.6% (95% CI: 5.5\u0026ndash;5.7) but showed a very high Ratio of 9.8 (95% CI: 9.8\u0026ndash;9.8), suggesting stark relative differences in facility delivery coverage between the best and worst-performing zones. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\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\u003eInequality estimated indices of factors associated with Facility delivery among women of reproductive age in Tanzania, 2022\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDimensions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimates\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic Status\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifference (D)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(31.0, 31.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Attributable Fraction (PAF)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(16.5, 16.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Attributable Risk (PAR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(12.3, 15.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRatio (R)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.5, 1.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLevel of Education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifference (D)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(15.5, 15.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Attributable Fraction (PAF)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(5.3, 5.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Attributable Risk (PAR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(3.3, 5.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRatio (R)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.2, 1.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifference (D)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(16.4, 16.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Attributable Fraction (PAF)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(13.8, 13.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Attributable Risk (PAR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(10.1, 12.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRatio (R)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.2, 1.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTanzania Zones\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifference (D)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(5.3, 5.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Attributable Fraction (PAF)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(5.5, 5.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Attributable Risk (PAR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(-0.5, 9.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRatio (R)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(9.8, 9.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to assess summary measures of inequalities in the use of facility delivery services among reproductive-aged women, using secondary analysis of the 2022 TDHS. The findings reveal stark economic disparities in facility delivery among the studied population. Findings reveal a substantial gap in access to facility delivery services between the richest and poorest groups whereby women in the wealthiest quintile are 1.5 times more likely to utilize facility delivery than those in the poorest quintile. This pronounced economic inequality highlights that despite universal maternal health policies, financial barriers remain a critical obstacle for poorer women, limiting their access to skilled birth attendance and safe delivery environments, which are essential for reducing maternal mortality [8]. Eliminating economic disparities could substantially increase facility deliveries, thereby potentially improving maternal and neonatal health outcomes.\u003c/p\u003e\u003cp\u003eEducational disparities also play a significant role in the utilization of facility delivery services. Findings demonstrate that women with higher educational attainment are significantly more inclined to deliver in health facilities. Other studies have shown that education presumably enhances women\u0026rsquo;s awareness of the benefits of facility delivery, empowers them in health-related decision-making, and facilitates their navigation within the health system [27\u0026ndash;29]. These educational inequalities suggest that improvements in female education and targeted health literacy interventions could reduce disparities, emphasizing the transformative impact of education on health behaviors and outcomes [8].\u003c/p\u003e\u003cp\u003eGeographical disparities further deepen facility delivery inequities, as evidenced by a difference between urban and rural populations, reflecting that urban women have moderately better access to facility delivery services compared to their rural counterparts. This may be attributed to better health infrastructure, transportation facilities, and availability of skilled healthcare personnel in urban areas [8,30,31]. Rural women often face considerable physical and socio-economic barriers, including limited healthcare accessibility and socio-cultural determinants that discourage facility delivery [32]. Consequently, spatial inequities necessitate policy and programmatic focus on improving rural health systems, transportation networks, and community outreach programs to enhance facility delivery rates in underserved areas [33].\u003c/p\u003e\u003cp\u003eRegional inequalities appear less pronounced, yet the exceptionally high Ratio of 9.8 indicates extreme relative disparities between zones. This implies that the proportional disparity in facility delivery across different zones is striking, highlighting zones with critically low coverage. This finding shows the importance of considering zone-specific factors, including service availability, quality of care, cultural norms, and local governance as seen by previous studies [15,33]. Addressing these regional disparities requires context-sensitive interventions that target the lowest-performing zones to elevate facility delivery coverage, ensuring equitable maternal health service distribution across the country.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and limitations\u003c/h2\u003e\u003cp\u003eThe strengths of this study lie primarily in its ability to provide a comprehensive and nuanced understanding of disparities across socioeconomic, educational, and geographic dimensions. Findings allow researchers and policymakers to quantify the magnitude and direction of inequalities, thereby identifying which population groups are most disadvantaged and require targeted interventions. Moreover, these measures capture variations within countries across different subpopulations, enabling tailored policy responses that address specific barriers to facility delivery, such as poverty, rural residence, and low education levels. The use of nationally representative, population-based data sets further strengthens the reliability and generalizability of the findings, as these data enable comparisons over time and between diverse contexts. However, limitations exist, the reliance on self-reported data, which can introduce recall bias and affect the accuracy of reported delivery locations. Cross-sectional study designs, common in these analyses, limit causal inferences, as they capture associations but not temporal sequences.\u003c/p\u003e\u003cp\u003eAdditionally, some important determinants, such as the quality of care or cultural factors influencing delivery choices, may not be fully captured or quantified by the indices, thus constraining the scope of inequality assessment. Therefore, while inequality indices are powerful for measuring disparities, they may sometimes overlook underlying structural and systemic factors, demanding complementary qualitative and mixed-methods research for a holistic understanding. The findings of this study can be interpreted cautiously with the above limitations. Future research should focus in minimizing the limitations above.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eImplications for practice and recommendation\u003c/h2\u003e\u003cp\u003eEducational campaigns should be designed to enhance awareness of the importance of facility deliveries, with a particular focus on rural and less-educated women. Enhancing community health worker programs may bridge gaps by providing localized support and facilitating access to maternal healthcare. Reform efforts must prioritize economic empowerment of poor women through subsidies, transport vouchers, or conditional cash transfers to mitigate financial constraints. Furthermore, investing in rural healthcare infrastructure and human resources is crucial to reducing urban-rural disparities. Regional development policies should incorporate maternal health indicators to allocate resources equitably and address disparities effectively.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study presents several key summary measures of inequalities in facility delivery in Tanzania, primarily driven by economic status, educational attainment, residential location, and regional disparities. These disparities highlight the need for integrated strategies that aim to overcome financial, informational, and geographical barriers hindering equitable access to facility delivery services. There is a need for healthcare providers to recognize these disparities and adopt culturally sensitive, patient-centered approaches to encourage facility delivery, especially among disadvantaged groups.\u003c/p\u003e\u003cp\u003eUltimately, achieving equitable facility delivery in Tanzania requires a multisectoral approach that involves the health, education, transportation, and social protection sectors. Policymakers should support data-driven insights to design, implement, and monitor programs that prioritize the most marginalized populations, advancing Tanzania toward universal maternal health coverage and Sustainable Development Goal targets related to maternal and newborn health.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eANC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eAntenatal Care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eCIs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eConfidence Intervals\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eDHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eDemographic and Health Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eEnumeration areas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eEmONC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eEmergency Obstetric and Newborn Care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eHEAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eHealth Equity Assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eLMICs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eLow- and middle-income\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eMaternal mortality ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003ePopulation Attributable Fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003ePopulation Attributable Risk\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eSDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eSustainable Development Goals\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eSSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eSub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eTDHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eTanzania Demographic and Health Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eUIs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eUncertainty Intervals\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the DHS program for making the data available for this study and TILAM International for statistical consultation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVGM, MJM, SAK, and EES conceptualized the idea and conducted formal analysis. MJM, EES, VGM, EDO, TPM, SAK, JRT, MHB, IPK, and AAN participated in the interpretation of the results, drafted the first draft of the manuscript, and reviewed all versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDHS data are publicly available at https://dhsprogram.com. However, the dataset and STATA 18 \u0026ldquo;do-file\u0026rdquo; supporting the conclusion of this study are available and can be shared upon a reasonable request to the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized secondary data from the 2022 TDHS, which was accessed through the DHS program website. The original study from the DHS program obtained ethical approval from the National Institute of Medical Research (NIMR) Ethics Committee in Tanzania and the ICF Macro Ethics Committee in Calverton, New York. The initial study adhered to the Declaration of Helsinki in this regard. Permission to download the data for this secondary analysis was granted by the DHS program upon acceptance of the proposed analysis plan under the designated account, with credentials available upon reasonable request via https://dhsprogram.com/data/dataset_admin/index.cfm. As this study involved secondary data analysis of publicly accessible datasets, no additional ethical approval was required. Informed consent was obtained from all participants during the initial survey, and all procedures adhered strictly to relevant guidelines and regulations. Further details regarding DHS data usage, methodology, and ethical standards can be found at http://goo.gl/ny8T6X.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declared no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEkwuazi EK, Chigbu CO, Ngene NC. Reducing maternal mortality in low- and middle-income countries. Case Rep Womens Health. 2023;39:e00542. \u003c/li\u003e\n\u003cli\u003eLee S, Kim S, Lee H, Park J, Son Y, L\u0026oacute;pez S\u0026aacute;nchez GF, et al. Global, Regional, and National Trends in Maternal Mortality Ratio Across 37 High Income Countries From 1990 to 2021, With Projections up to 2050: A Comprehensive Analysis From the WHO Mortality Database. J Korean Med Sci [Internet]. 2024 [cited 2025 May 14];40. Available from: https://doi.org/10.3346/jkms.2025.40.e85\u003c/li\u003e\n\u003cli\u003eUNICEF. Maternal mortality rates and statistics - UNICEF DATA [Internet]. 2023 [cited 2025 May 14]. Available from: https://data.unicef.org/topic/maternal-health/maternal-mortality/\u003c/li\u003e\n\u003cli\u003eOyedele OK, Lawal TV. Global dominance of non-institutional delivery and the risky impact on maternal mortality spike in 25 Sub-Saharan African Countries. Glob Health Res Policy. 2025;10:10. \u003c/li\u003e\n\u003cli\u003eMusarandega R, Nyakura M, Machekano R, Pattinson R, Munjanja SP. Causes of maternal mortality in Sub-Saharan Africa: A systematic review of studies published from 2015 to 2020. J Glob Health. 11:04048. \u003c/li\u003e\n\u003cli\u003ePriebe J, Amuasi J, Dartanto T, Mombo-Ngoma G, Guigas M. Factors associated with skilled birth attendance in 37 low-income and middle-income countries: a secondary analysis of nationally representative, individual-level data. Lancet Glob Health. 2024;12:e1104\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eStraneo M, Hanson C, van den Akker T, Afolabi BB, Asefa A, Delamou A, et al. Inequalities in use of hospitals for childbirth among rural women in sub-Saharan Africa: a comparative analysis of 18 countries using Demographic and Health Survey data. BMJ Glob Health. 2024;9:e013029. \u003c/li\u003e\n\u003cli\u003eBintabara D. Addressing the huge poor\u0026ndash;rich gap of inequalities in accessing safe childbirth care: A first step to achieving universal maternal health coverage in Tanzania. PLoS ONE. 2021;16:e0246995. \u003c/li\u003e\n\u003cli\u003eGalgalo DA, Mokaya P, Chauhan S, Kiptulon EK, Wami GA, V\u0026aacute;rnagy \u0026Aacute;, et al. Utilization of maternal health care services among pastoralist communities in Marsabit County, Kenya: a cross-sectional survey. Reprod Health. 2024;21:126. \u003c/li\u003e\n\u003cli\u003eMinistry of Health (MoH) [Tanzania Mainland], Ministry of Health (MoH) [Zanzibar], National Bureau of Statistics (NBS), Office of the Chief Government Statistician (OCGS), and ICF. Tanzania Demographicand Health Survey and Malaria Indicator Survey 2022 Key Indicators Report. Dodoma, Rockville: MoH, NBS, OCGS, and ICF; 2023. \u003c/li\u003e\n\u003cli\u003eCDC. Maternal and Reproductive Health in Tanzania Program [Internet]. Glob. Reprod. Health. 2024 [cited 2025 May 14]. Available from: https://www.cdc.gov/global-reproductive-health/php/maternal-reproductive-health-tanzania/index.html\u003c/li\u003e\n\u003cli\u003eShabani J, Masanja H, Kagoye S, Minja J, Bajaria S, Mlacha Y, et al. Quality of reporting and trends of emergency obstetric and neonatal care indicators: an analysis from Tanzania district health information system data between 2016 and 2020. BMC Pregnancy Childbirth. 2023;23:716. \u003c/li\u003e\n\u003cli\u003eBinyaruka P, Foss A, Alibrahim A, Mziray N, Cassidy R, Borghi J. Supply-side factors influencing demand for facility-based delivery in Tanzania: a multilevel analysis. Health Econ Rev. 2023;13:52. \u003c/li\u003e\n\u003cli\u003eDas G, Masoi TJ, Kibusi SM, Chaudhary A, Greenwald M, Goodman A. Patient and provider perspectives of disrespect and abuse during childbirth in Tanzania: a literature review. Open J Obstet Gynecol. 2021;11:1248\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eBishanga DR, Drake M, Kim Y-M, Mwanamsangu AH, Makuwani AM, Zoungrana J, et al. Factors associated with institutional delivery: Findings from a cross-sectional study in Mara and Kagera regions in Tanzania. PLoS ONE. 2018;13:e0209672. \u003c/li\u003e\n\u003cli\u003eWHO. Health Equity Assessment Toolkit: Technical Notes. 2021; \u003c/li\u003e\n\u003cli\u003eDemographic and Health Surveys. Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS). 2022. \u003c/li\u003e\n\u003cli\u003eNBS Tanzania. National Bureau of Statistics [Internet]. Manuf. Index. 2023 [cited 2023 Dec 16]. p. 4\u0026ndash;7. Available from: https://www.nbs.go.tz/index.php/en/%0Ahttp://www.nigerianstat.gov.ng/\u003c/li\u003e\n\u003cli\u003eMinistry of Health. Tanzania Ministry of Health [Internet]. 2023 [cited 2023 Dec 16]. Available from: https://www.moh.go.tz/\u003c/li\u003e\n\u003cli\u003eBhusal UP. Predictors of wealth-related inequality in institutional delivery: a decomposition analysis using Nepal multiple Indicator cluster survey (MICS) 2019. BMC Public Health. 2021;21:1\u0026ndash;15. \u003c/li\u003e\n\u003cli\u003eRandive B, San Sebastian M, De Costa A, Lindholm L. Inequalities in institutional delivery uptake and maternal mortality reduction in the context of cash incentive program, Janani Suraksha Yojana: Results from nine states in India. Soc Sci Med. 2014;123:1\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eSadik W, Bayray A, Debie A, Gebremedhin T. Factors associated with institutional delivery practice among women in pastoral community of Dubti district, Afar region, Northeast Ethiopia: A community-based cross-sectional study. Reprod Health. 2019;16:1\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eKetemaw A, Tareke M, Dellie E, Sitotaw G, Deressa Y, Tadesse G, et al. Factors associated with institutional delivery in Ethiopia: A cross sectional study. BMC Health Serv Res. 2020;20:1\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eHassen SS, Jemal SS, Bambo M mesfin, Lelisho ME, Tareke SA, Merera AM, et al. Multilevel analysis of factors associated with utilization of institutional delivery in Ethiopia. Womens Health. 2022;18. \u003c/li\u003e\n\u003cli\u003ePathak P, Shrestha S, Devkota R, Thapa B. Factors Associated with the Utilization of Institutional Delivery Service among Mothers. J Nepal Health Res Counc. 2018;15:228\u0026ndash;34. \u003c/li\u003e\n\u003cli\u003eTibenderana JR, Kessy SA, Mlaponi DF, Mtenga JE, Gimonge J, Mwaitete NL, et al. The Adequacy of ANC services received and associated factors among Women of Reproductive Age in Tanzania. PLoS ONE. 2024;1\u0026ndash;15. \u003c/li\u003e\n\u003cli\u003eAlam MB, Khanam SJ, Kabir MA, Chowdhury AR, Hassen TA, Das S, et al. Effects of Women\u0026rsquo;s Participation in Household Decision Making on Skilled Birth Attendants Supervised Delivery in Bangladesh. Health Serv Insights. 2025;18:11786329251316674. \u003c/li\u003e\n\u003cli\u003eDoctor HV, Nkhana-Salimu S, Abdulsalam-Anibilowo M. Health facility delivery in sub-Saharan Africa: successes, challenges, and implications for the 2030 development agenda. BMC Public Health. 2018;18:1\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eKifle MM, Kesete HF, Gaim HT, Angosom GS, Araya MB. Health facility or home delivery? Factors influencing the choice of delivery place among mothers living in rural communities of Eritrea. J Health Popul Nutr. 2018;37:1\u0026ndash;15. \u003c/li\u003e\n\u003cli\u003eChen L, Chen T, Lan T, Chen C, Pan J. The contributions of population distribution, healthcare resourcing, and transportation infrastructure to spatial accessibility of health care. Inq J Health Care Organ Provis Financ. 2023;60:00469580221146041. \u003c/li\u003e\n\u003cli\u003eAdhikari S, Lutz W, KC S. Rural/urban fertility differentials and the role of female education in declining birth rates: comparative analysis in Asia, Africa, and Latin America. Asian Popul Stud. 0:1\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eGanle J. Addressing socio-cultural barriers to maternal healthcare in Ghana: perspectives of women and healthcare providers. J Womens Health Issues Care. 2014;6:2. \u003c/li\u003e\n\u003cli\u003eThasineku OC, Pandit S, Acharya D, Gurung YB. Associated factors for the utilization of institutional delivery services in Nepal: Findings from the Nepal Demographic Health Survey, 2022. PLOS One. 2025;20:e0322309.\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":"birth, inequalities, summary measures, facility, delivery","lastPublishedDoi":"10.21203/rs.3.rs-6893727/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6893727/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eGlobally, coverage of facility deliveries has increased but remains uneven despite the association between facility delivery and lower maternal mortality being well established in previous studies. Inequalities in the utilization of facility delivery services persist, potentially undermining progress toward reducing maternal mortality and morbidity. This study aims to assess the inequalities in facility delivery service utilization among reproductive-aged women in Tanzania, using the 2022 Tanzania Demographic and Health Survey (TDHS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis study employed an analytical cross-sectional design, utilizing nationally representative secondary data from the 2022 TDHS, and applied the World Health Organization (WHO) Health Equity Assessment Toolkit (HEAT) software. The study incorporated five variables as stratifiers for inequality; the WHO HEAT software utilized these stratifiers to evaluate disparities in several health and social indicators. Four measures were used to assess inequality, namely: Difference (D), Population Attributable Fraction (PAF), Population Attributable Risk (PAR), and Ratio (R).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult:\u003c/strong\u003e The facility delivery coverage among women of reproductive age was 84.6% overall, with notable inequalities. Coverage was highest among the richest women (97%) and those with secondary or higher education (over 83%), while the poorest women and those with low education had substantially lower rates. Urban women had higher coverage (94%) compared to rural women (78%), and regional disparities ranged from 73% to 98%. Inequality analyses showed economic status as the strongest factor influencing facility delivery, with a 31.1 percentage point difference and a 1.5 times higher likelihood among the richest compared to the poorest. Education, residence, and region also contributed to disparities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study presents several key summary measures of inequalities in facility delivery in Tanzania, primarily driven by economic status, educational attainment, residential location, and regional disparities. These disparities highlight the need for integrated strategies that aim to overcome financial, informational, and geographical barriers hindering equitable access to facility delivery services.\u003c/p\u003e","manuscriptTitle":"Summary measures of inequalities in facility delivery services utilization among reproductive-aged women: Evidence from the 2022 Tanzania Demographic and Health Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 18:04:00","doi":"10.21203/rs.3.rs-6893727/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":"590e62ed-dd38-4571-876e-afc0aabc8853","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-19T13:23:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 18:04:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6893727","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6893727","identity":"rs-6893727","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}