Women’s desire to limit childbearing and associated factors in Tanzania: Evidence from the 2022 Demographic and Health Survey

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Mtoro, Elihuruma Eliufoo Stephano, Mariam Mkoma, Joanes Faustine Mboineki This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6951161/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Reproductive Health → Version 1 posted 9 You are reading this latest preprint version Abstract Background High fertility rates in Tanzania significantly contribute to rapid population growth, posing challenges to the country’s socioeconomic development and straining the already burdened health system. Women’s desire to control the number of children they have influences population growth and is critical for improving maternal and child health outcomes. This study aimed to determine the prevalence and associated factors of women’s desire to limit childbearing in Tanzania. Methods An Analytical cross-sectional study using secondary data from the 2022 Tanzania Demographic and Health Surveys was conducted. The study included 2,851 participants selected through a two-stage sampling method. A generalized Poisson regression model was used to determine factors associated with the desire to limit childbearing. Adjusted prevalence ratios (APRs) with 95% confidence intervals (CIs) were reported, and statistical significance was set at p < 0.05. Results The prevalence of desire to limit childbearing was 20.2% (95% CI: 18.4–22.2). Women aged 25–34 years (APR = 2.90, 95%CI: 1.34–6.28) and those aged 35–49 years (APR = 7.43, 95%CI: 3.42–16.11) were more likely to have a desire to limit childbearing compared to those aged 15–24 years. Women in primary education (APR = 1.35, 95%CI: 1.08–1.69), women in rich quintile (APR = 1.26, 95%CI: 1.03–1.54), working women (APR = 1.60, 95%CI: 1.31–1.95), increase in number of children (APR = 1.26, 95%CI: 1.21–1.30) were associated with higher likelihood of desire to limit children. Conclusion Nearly one in five women in Tanzania showed a desire to limit childbearing. A multifaceted approach, including comprehensive family planning awareness and improved access to sexual and reproductive health that addresses sociodemographic and geographical disparities, would be essential. Desire to limit Childbearing Reproductive Health Fertility Tanzania Background Globally, there has been a worldwide decline in fertility over the past half-century. However, sub-Saharan Africa (SSA) stands out with persistently high fertility rates [ 1 ]. The United Nations estimated the total fertility rate (TFR) in SSA at 4.7 births per woman between 2015 and 2020, which is more than twice the level observed in other regions [ 1 , 2 ]. This late and slow fertility transition in SSA is caused by a lack of demand for limiting family size, religious attitudes toward fertility, and low contraceptive use [ 1 , 3 ]. SSA's population is expected to almost double to over 2 billion by mid-century, growing three times faster than the global average and potentially becoming the most populous region globally by 2070 [ 4 ]. Tanzania's population has increased significantly, with a 37% rise over the past decade to nearly 63 million people [ 5 ]. Projections suggest a 2–3% annual growth rate until 2050, driven by a high fertility rate [ 6 ]. Tanzania is projected to be one of eight countries responsible for over half of the global population increase in the next three decades, with five of these countries located in Africa [ 7 ]. The high population growth in Tanzania puts significant pressure on essential services like education, healthcare, and food supply [ 8 ]. The desire to limit childbearing is a critical aspect of reproductive health, influencing population dynamics and individual well-being [ 1 ]. This has been cited in Sustainable Development Goals (SGD) Target 3.7, which states, " By 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmes" [ 9 ]. Recent studies highlight a significant desire among women in SSA to limit childbearing, with a sizable number, approximately 8 million, expressing a demand for limiting future births [ 10 ]. However, the uptake of female permanent contraception among women with a demand for family planning to limit childbearing remains low in many SSA countries, with a pooled percentage of 4.13% between 2010 and 2018 [ 11 ]. This percentage varied significantly across countries, ranging from 0.26% in Angola to 26.85% in Malawi [ 11 ]. A study involving 232,784 childbearing women across 32 Sub-Saharan African countries found that 64.95% desired more children, with rates ranging from 34.9% in South Africa to 89.43% in Niger [ 12 ]. Factors consistently associated with a lower desire for more children include older age, higher educational attainment of both women and their partners, higher parity, current contraceptive use, and exposure to media [ 13 ]. Conversely, women in rural areas and those with lower socioeconomic status are more likely to desire more children [ 14 ]. Additionally, women who lack sole decision-making capacity regarding their lives tend to have a higher likelihood of desiring more children [ 1 , 13 ]. In Tanzania, women of reproductive age often have an average of four or five children, compared to the global average of two [ 15 ]. Cultural values often prioritize larger families, and for poorer families, children can provide security in old age, given the limited social protections available [ 15 , 16 ]. While Tanzania offers various contraceptive options and has made efforts to reduce barriers, only 34% of married women use any form of contraception [ 17 ]. Male dominance in family planning decision-making is a recognized barrier to contraceptive use in Tanzania [ 18 ]. Studies indicate that when disagreements arise between spouses regarding fertility preferences, men tend to desire more children than their wives [ 19 ]. Despite the growing desire among women in SSA to limit childbearing and the recognized importance of family planning programs [ 1 ], high fertility rates persist, notably in Tanzania [ 20 ]. The influence of male dominance, cultural norms, and socioeconomic disparities continues to impede women's ability to achieve their desired family size, contributing to a high unmet need for contraception [ 17 , 18 ]. While previous research has identified various determinants of fertility desire in SSA [ 13 , 14 , 21 , 22 ], there is a continuous need for up-to-date, country-specific evidence that incorporates recent demographic and health survey data to inform targeted interventions. This study aims to utilize evidence from the 2022 Tanzania Demographic and Health Survey (TDHS) to provide current data on the status of women's desire to limit childbearing and its associated factors in a country facing considerable population growth [ 6 ] and healthcare strain [ 8 ]. This research can inform and strengthen existing fertility control programs and policies in Tanzania and aid policymakers and public health practitioners in designing interventions that are sensitive to the cultural, geographical, and socioeconomic factors. The findings of this study will ensure that national fertility estimations and expectations are met and hence improve women's reproductive health outcomes and contribute to sustainable development in Tanzania and similar low-income settings. Materials and Methods Data source, design, setting, population, and sampling An analytical cross-sectional study was conducted using secondary data from the 2022 TDHS, which was conducted from 24 February to 21 July 2022 across all regions in Tanzania. Tanzania is in East Africa, spanning a total area of 945,087 km 2 . According to the 2022 National Population census, the country’s population is approximately 62 million, with slightly over half being women [ 6 ]. TDHS is a population-based cross-sectional survey that captures nationally representative data across demographics, reproductive health, and self-reported social determinants of health indices. The target population for the TDHS includes women of reproductive age (15–49 years), men, children, and households across the 32 administrative regions in Tanzania. This study specifically focuses on women of reproductive age in sexual unions. The TDHS methodology is explained elsewhere in detail [ 5 ]. Briefly, TDHS uses a two-stage sampling strategy. The country is divided into urban and rural areas within each region, which are found in eight geographical zones. Firstly, Primary sampling Units (PSUs), which are census enumeration areas (EAs), are selected, followed by households. From each selected PSU, a complete household listing is performed. This is then followed by a selection of a specific number of households using equal probability sampling. This study utilized individual file records with 15,254 women of reproductive age. For this analysis, our final sample was 2,851 (weighted) after excluding women who were sterilized, declared infecund, and unmarried women. This exclusion ensured our analysis focused on the desire to limit childbearing among women actively in a sexual union, providing a more relevant and comprehensives understanding for this specific population. Variable measurements Outcome variable In the 2022 TDHS, eligible female respondents were asked about their desire for more children. The responses were (i) Wants within 2 years, (ii) wants after 2 + years, (iii) wants, unsure about timing, (iv) undecided, and (v) wants no more. A binary variable was created based on these responses, coded as ‘1’ for ‘YES’ if a woman wants no more children and coded as ‘0’ for ‘NO’ if otherwise [ 14 ]. Explanatory variables The variables selected for this study are based on the available data from the 2022 TDHS and previous literature. We include age category in years (15–24, 25–34 or 35–49), education level (no formal education, primary education or secondary/higher), wealth index (poor, middle or rich), working status (working or not working), parity (none, 1–2 or 3+), media exposure (yes or no by aggerating listening to radio, reading newspaper or watching television), exposure to family planning messages (yes or no), family planning knowledge (yes or no), family planning methods use (yes or no), distance to the health facility (big problem or not a big problem), visited health facility in the past 12 months (yes or no), age at first marriage in years (< 15, 15–19 or ≥ 20), place of residence (rural or urban) and geographical zones (Western, Northern, Central, Southern, Southwest Highlands, Lake, Eastern, and Zanzibar). Statistical analysis To address the complex survey design of the TDHS, we applied individual sampling weights (v005/1,000,000), accounted for PSU (v021), and stratified the data (v023) to ensure representative estimates and control for sampling biases. Data management and analysis were performed using STATA 18 (STATA Corp, College Station, TX). We used descriptive statistics and bivariate analysis to estimate the association between the desire to limit childbearing and sociodemographic characteristics. A generalized Poisson regression model was used to determine factors associated with women’s desire to limit childbearing. We used this approach as Odds Ratio may overestimate the association for non-rare outcomes (> 10%), and this decision was supported by the lowest Akaike Information Criterion, suggesting a better fit. Univariate analyses were performed by fitting each explanatory variable against the response variable to estimate the Crude Prevalence Ratio (CPR). Variables with p < 0.05 were considered for multivariable analysis. Thereafter, multivariable analysis adjusted for potential confounders, including women’s age, was fitted to estimate Adjusted Prevalence Ratio (APR) with corresponding 95% Confidence Intervals (CI). A variance inflation factor was used to assess for multicollinearity between independent variables before fitting a multivariable regression model. Statistical significance was set at p < 0.05. Results Sociodemographic characteristics A total of 2,851 women were included in this study. Nearly four in ten (39.9%) were aged 25–34 years, and the overall mean age was 31.5 (standard deviation = 8.4). More than half (58.5%) had completed primary education. In terms of socioeconomic status, 43.1% were in a rich quintile and 66.5% were working. The majority (97.7%) had family planning knowledge, 79.3% had exposure to family planning messages, and just 37.6% were currently using contraceptives. Over half (55.1%) had more than two children. More than 62.0% visited health facilities in the past 12 months, and just 3.1% saw community healthcare workers during the same period. (Table 1 ). Prevalence of desire to limit childbearing The prevalence of the desire to limit childbearing was 20.2% (95% CI: 18.4–22.2). The prevalence of the desire to limit childbearing was significantly higher among women aged 35–49 years compared to women aged 15–24 years (44.8% versus 2.3%) (p < 0.001). Women with primary education had a higher prevalence of desire to limit childbearing (24.1%) compared to those with no formal education (19.4%). Working women had a significantly higher prevalence of desire to limit childbearing compared to non-working women (24.7% versus 11.3%) (p < 0.001). Higher parity women (3+) had a higher proportion of desire to limit childbearing compared to nulliparous women (33.7% versus 0.8%) (p < 0.001). (Table 1 ). Table 1 Sociodemographic characteristics and distribution of desire to limit children among women in sexual unions in Tanzania (N = 2,851) Variables N (%) Desire to limit childbearing, n(%) p-value No Yes Age in years < 0.001 15–24 735 (25.8) 718 (97.7) 17 (2.3) 25–34 1137 (39.9) 1016 (89.4) 121 (10.6) 35–39 980 (34.4) 541 (55.2) 439 (44.8) Mean (± SD) 31.5 (8.4) Education Level < 0.001 No formal education 558 (19.6) 450 (80.6) 108 (19.4) Primary 1669 (58.5) 1267 (75.9) 401 (24.1) Secondary/higher 624 (21.9) 558 (89.4) 66 (10.6) Wealth Index 0.626 Poor 1069 (37.5) 854 (79.8) 216 (20.2) Middle 554 (19.4) 432 (78.0) 121 (22.0) Rich 1228 (43.1) 989 (80.6) 239 (19.4) Working status < 0.001 Not working 954 (33.5) 846 (88.7) 108 (11.3) Working 1897 (66.5) 1430 (75.3) 467 (24.7) Media exposure 0.990 No 966 (33.9) 771 (79.8) 195 (20.2) Yes 1885 (66.1) 1504 (79.8) 381 (20.2) Family planning knowledge 0.219 No 65 (2.3) 58 (88.4) 7 (11.6) Yes 2785 (97.7) 2217 (79.6) 568 (20.4) Exposure to family planning messages 0.812 No 591 (20.7) 469 (79.4) 122 (20.6) Yes 2260 (79.3) 1806 (79.9) 454 (20.1) Currently using family planning method 0.091 No 1779 (62.4) 1440 (80.9) 339 (19.1) Yes 1072 (37.6) 835 (77.9) 237 (22.1) Parity < 0.001 None 209 (7.3) 207 (99.2) 2 (0.8) 1–2 1073 (37.6) 1027 (95.7) 46 (4.3) 3+ 1569 (55.1) 1040 (66.3) 529 (33.7) Age at first marriage 0.849 <15 212 (7.4) 165 (77.9) 47 (22.1) 15–19 1511 (53.0) 1209 (80.0) 302 (20.0) ≥20 1128 (39.6) 901 (79.8) 227 (20.2) Visited health facility in the past 12 months 0.717 No 1083 (38.0) 861 (80.2) 214 (19.7) Yes 1768 (62.0) 1406 (79.5) 362 (20.5) Distance to the health facility 0.423 Big problem 870 (30.5) 684 (78.6) 186 (21.4) Not a big problem 1981 (69.5) 1590 (80.3) 390 (19.7) Residence 0.688 Urban 895 (31.4) 720 (80.5) 175 (19.5) Rural 1956 (68.6) 1555 (79.5) 401 (20.5) Geographical zones 0.024 Western 241 (8.5) 199 (82.7) 42 (17.3) Northern 307 (10.8) 220 (71.5) 87 (28.5) Central 294 (10.3) 221 (75.1) 73 (24.9) Southern 612 (21.5) 501 (81.8) 111 (18.2) Lake 828 (29.0) 668 (80.6) 160 (19.4) Eastern 482 (16.9) 393 (81.6) 89 (18.4) Zanzibar 86 (3.0) 73 (84.8) 13 (15.2) Factors associated with the desire to limit childbearing In the adjusted generalized regression model, women aged (APR = 2.90, 95%CI: 1.34–6.28) and those aged (APR = 7.43, 95%CI: 3.42–16.12) were more likely to have a desire to limit childbearing compared to women aged 15–24 years. Women in the rich quintile were 26% more likely to have a desire to limit childbearing compared to those in the poor quintile (APR = 1.26, 95%CI:1.03–1.54). Additionally, women who were working (APR = 1.60, 95%CI: 1.31–1.95) were 60% more likely to have a desire to limit childbearing than their counterparts. Increase in the number of children associated with a higher likelihood of willingness to limit childbearing (APR = 1.26, 95%CI: 1.21–1.30). (Table 2 ). Table 2 Generalized Poisson Regression model for factors associated with the desire to limit childbearing among women in sexual unions in Tanzania Variables Crude p-value Adjusted p-value PR (95%CI) PR (95%CI) Age in years 15–24 Ref Ref 25–34 4.65 (2.14–10.12) < 0.001 2.90 (1.34–6.28) 0.007 35–39 19.62 (9.20–41.80) < 0.001 7.43 (3.42–16.11) < 0.001 Education Level No formal education Ref Ref Primary 1.24 (0.98–1.75) 0.059 1.35 (1.08–1.69) 0.008 Secondary/higher 0.54 (0.38–0.78) 0.001 1.19 (0.80–1.76) 0.400 Wealth Index Poor Ref Ref Middle 1.12 (0.84–1.49) 0.451 0.97 (0.80–1.18) 0.747 Rich 0.95 (0.74–1.23) 0.716 1.26 (1.03–1.54) 0.025 Working status Not working Ref Ref Working 2.19 (1.75–2.75) < 0.001 1.60 (1.31–1.95) < 0.001 Media exposure No Ref - Yes 1.01 (0.80–1.26) 0.990 Family planning knowledge No Ref - Yes 1.75 (0.76–4.03) 0.184 Currently using family planning method No Ref - Yes 1.16 (0.97–1.38) 0.109 Parity 1.38 (1.35–1.42) < 0.001 1.26 (1.21–1.30) < 0.001 Residence Urban Ref - Rural 1.06 (0.86–1.29) 0.634 Geographical zones Western Ref Ref Northern 1.64 (1.16–2.42) 0.011 1.80 (1.30–2.50) < 0.001 Central 1.44 (0.97–2.16) 0.081 1.33 (0.94–1.87) 0.103 Southern 1.05 (0.73–1.51) 0.805 1.11 (0.81–1.52) 0.504 Lake 1.12 (0.77–1.61) 0.559 1.20 (0.87–1.64) 0.265 Eastern 1.06 (0.70–1.60) 0.771 1.15 (0.80–1.65) 0.459 Zanzibar 0.87 (0.56–1.37) 0.560 0.69 (0.46–1.05) 0.087 PR; Prevalence Ratio, CI ; Confidence Intervals Discussion This study aimed to assess the women’s desire to limit childbearing and associated factors in Tanzania by analyzing the 2022 TDHS. Our study found that one in five women (20.2%; 95% CI: 18.4–22.2) had the desire to limit childbearing. This prevalence is lower than those reported in previous studies in Ethiopia, ranging from 33–37.7% [ 14 , 21 ], Kenya (33%) [ 23 ], Mozambique (28%) [ 24 ], and significantly lower than in Nepal (80%) [ 25 ]. These variations might be attributed to differences in study design, context, and sample characteristics. The Ethiopian studies were conducted in rural settings, while the Kenyan study was longitudinal among postpartum women. Despite a lower national prevalence, our finding underscores a critical gap in fulfilling women’s fertility desires, highlighting an urgent need for sexual and reproductive health stakeholders to develop evidence-based interventions to address this unmet need. In multivariable analyses, women’s age, education, rich quintile, working status, parity, and geographical zones were associated with the desire to limit childbearing. Women aged 25–34 and 35–49 years were more likely to have a desire to limit childbearing, aligning with previous studies in SSA [ 22 , 26 ]. Older women may have more children and prefer to limit the number of pregnancies than their younger counterparts with no children [ 14 ]. Regarding the number of children, we found that the increase in the number of children was associated with the desire to limit childbearing [ 21 , 27 ]. This suggests that as women have more children, their preference for smaller sizes intensifies, likely driven by the increased socioeconomic demands associated with a larger household [ 10 ]. Women with primary education were more likely to limit childbearing than those with no formal education. Educated women may have greater awareness of family planning methods, enhancing autonomy in reproductive decision-making and reducing high-risk fertility behaviors [ 28 ]. Our study further revealed that employed women were more likely to demonstrate a desire to limit childbearing. Employment often offers competing demands on women’s time and resources, such as balancing career aspirations with family chores. Additionally, economic empowerment gained through work could enhance women’s autonomy regarding their reproductive health [ 29 ]. Contrarily, a study in SSA found that Zambian working women were less likely to desire to limit childbearing [ 10 ]. This could be attributed to a smaller sample size and a difference in the timing of the DHS survey. Our study revealed that women in the rich quintile were more likely to express a desire to limit childbearing. This association is often observed globally, women with higher socioeconomic status have greater access to information, education, and family planning services, which empower them to make informed decisions about their fertility [ 30 , 31 ]. Additionally, wealthier households normally prioritize investments in the education and well-being of fewer children rather than having a large family. Lastly, women in the northern zone showed a significantly higher desire to limit childbearing than those in the western zone. The north zone is often characterized by urbanization, access to education, and health infrastructure. These factors may contribute to improved awareness in family planning and a stronger inclination towards smaller family sizes [ 32 ]. Comparative evidence supports these findings, underscoring an urgent need for integrated and focused health programs Targeted interventions focused are essential to meet Tanzania’s reproductive health goals and promote inclusive, and sustainable development. Strengths and limitations This study's strengths include the use of nationally representative 2022 TDHS data and a large sample, enhancing the generalizability of our findings to Tanzanian women of reproductive age. Moreover, a generalised Poisson regression model with a robust variance estimator effectively accounts for survey design complexities and strengthens the validity of our findings. However, the cross-sectional nature of the data prevents establishing causality between the examined factors and the desire to limit childbearing. Since most of the variables were self-reported, there is a possibility of recall and social desirability biases. Implications for practice and policy recommendations The overall prevalence of women’s desire to limit childbearing underscores a critical yet underexplored opportunity for advancing sexual and reproductive health rights. Policy makers and reproductive health stakeholders should. Re-evaluate and strengthen comprehensive family planning programs. This necessitates a client-oriented and multifaceted approach to emphasize counselling and consider women’s unique sociodemographics, whilst also strategically linking family planning initiatives with broader programs for girls ' education and women’s empowerment, ultimately enhancing access and awareness to all women of reproductive age in Tanzania as stipulated in the SDG 3.7. Additionally, empowering women to make informed decisions about their fertility desires is a fundamental human right that should remain central to family planning programs. Conclusion Nearly one in five women in Tanzania showed a desire to limit childbearing. The desire to limit childbearing was associated with sociodemographic and regional characteristics. A multifaceted approach, including comprehensive family planning awareness and improved access to sexual and reproductive health that addresses sociodemographic and geographical disparities, would be essential. These findings provide valuable insight to guide national strategies aiming at improving sexual and reproductive health rights, promoting equitable access to reproductive services among women of reproductive age in Tanzania. Abbreviations APR Adjusted prevalence rate CI Confidence interval CPR Crude prevalence rate DHS Demographic and Health Survey EA Enumeration area PR Prevalence rate SSA Sub-Saharan Africa SDG Sustainable Development Goals TDHS Tanzania Demographic and Health Survey TFR Total fertility rate Declarations Acknowledgements We are grateful to the DHS Program for providing the data for this study and to TILAM International for their methodological and statistical consultation. Authors’ Contribution MJM and EES conceptualized the idea and conducted formal analysis. MJM, EES, MM and JFN interpreted the results, drafted the manuscript, and reviewed all versions of the manuscript. All authors read and approved the final manuscript. Funding None Availability of data and materials The raw data supporting the conclusions of this article will be made available by the authors without undue reservation. The complete dataset is available at https://dhsprogram.com. Ethics approval and consent to participate This study utilized publicly available, de-identified data from the 2022 TDHS, accessible online through the DHS program. The original survey received ethical approval from both the National Institute of Medical Research Ethics Committee in Tanzania and the ICF Macro Ethics Committee in Calverton, New York. Permission to use 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 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 and ethical standards can be found at http://goo.gl/ny8T6X. Consent for publication Not applicable. Competing interests The authors have no conflicts of interest to declare. References Bongaarts J. Trends in fertility and fertility preferences in sub-Saharan Africa: the roles of education and family planning programs. Genus. 2020;76:32. Tesfa D, Tiruneh SA, Gebremariam AD, Azanaw MM, Engidaw MT, Kefale B et al. The pooled estimate of the total fertility rate in sub-Saharan Africa using recent (2010–2018) Demographic and Health Survey data. Front Public Health [Internet]. 2023 [cited 2025 Jun 21];10. Available from: https://www.frontiersin.org/journals/public-health/articles/ 10.3389/fpubh.2022.1053302/full World Bank Open Data. 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Available from: https://link.springer.com/article/ 10.1186/s12884-019-2245-3 Determined to stop?. Longitudinal analysis of the desire to have no more children in rural Mozambique: Population Studies: Vol 71, No 3 [Internet]. [cited 2025 Jun 19]. Available from: https://www.tandfonline.com/doi/abs/ 10.1080/00324728.2017.1334957 Fertility Limiting Intention and Contraceptive Use among Currently Married Men in Nepal. Evidence from Nepal Demographic and Health Survey 2016 - Paudel – 2018 - BioMed Research International - Wiley Online Library [Internet]. [cited 2025 Jun 19]. Available from: https://onlinelibrary.wiley.com/doi/full/ 10.1155/2018/5970705 Araban M, Karimy M, Armoon B, Zamani-Alavijeh F. Factors related to childbearing intentions among women: a cross-sectional study in health centers, Saveh, Iran. J Egypt Public Health Assoc. 2020;95:6. Adebowale AS, Palamuleni ME. Family planning needs to limit childbearing are unmet, yet our parity is high: characterizing and unveiling the predictive factors. BMC Womens Health. 2023;23:492. Woldeamanuel BT, Gessese GT, Demie TG, Handebo S, Biratu TD. Women’s education, contraception use, and high-risk fertility behavior: A cross-sectional analysis of the demographic and health survey in Ethiopia. Front Glob Womens Health [Internet]. 2023 [cited 2025 Jun 20];4. Available from: https://www.frontiersin.org/journals/global-womens-health/articles/ 10.3389/fgwh.2023.1071461/full Chowdhury S, Rahman MM, Haque MA. Role of women’s empowerment in determining fertility and reproductive health in Bangladesh: a systematic literature review. AJOG Glob Rep. 2023;3:100239. Asebe HA. Factors influencing high-risk fertility practices among women of childbearing age in Tanzania: using DHS 2022. BMC Public Health. 2025;25:1–8. Negash WD, Eshetu HB, Asmamaw DB. Intention to use contraceptives and its correlates among reproductive age women in selected high fertility sub-saharan Africa countries: a multilevel mixed effects analysis. BMC Public Health. 2023;23:257. Larsen U, Hollos M. Women’s empowerment and fertility decline among the Pare of Kilimanjaro region, Northern Tanzania. Soc Sci Med. 2003;57:1099–115. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Reproductive Health → Version 1 posted Editorial decision: Revision requested 03 Aug, 2025 Reviews received at journal 03 Aug, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviews received at journal 14 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers invited by journal 01 Jul, 2025 Editor assigned by journal 24 Jun, 2025 Submission checks completed at journal 24 Jun, 2025 First submitted to journal 22 Jun, 2025 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-6951161","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":479212651,"identity":"92cb460e-9f35-496f-ae01-02046231fa4a","order_by":0,"name":"Mtoro J. Mtoro","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYNACHoYEBgbmA0CWhAxh1WxwLWwJIC08RGphAGnhMYDoJgR05zcf+/BBxi6Pf/aZz69u1FjwMLAfProBnxazY2zJM2fwJBdLnMvdZp1zDOgwnrS0G/i18Bgz8/AwJzac4d1mnMMG1CLBY0aMlvrE+Wd4nhnn/CNey+HEDWd4mB/nthGlJS2ZcQbP8WLDM2xmzLl9EjxsBP1y+PBhho891XlyZ5gff875VifHz374GF4tYMDYA6bYJMAkQeVg8ANMMn8gTvUoGAWjYBSMNAAA+BlCoA3AKzYAAAAASUVORK5CYII=","orcid":"","institution":"TILAM International","correspondingAuthor":true,"prefix":"","firstName":"Mtoro","middleName":"J.","lastName":"Mtoro","suffix":""},{"id":479212653,"identity":"fde5d5fe-c504-48a7-a0a6-65a78220c355","order_by":1,"name":"Elihuruma Eliufoo Stephano","email":"","orcid":"","institution":"The University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Elihuruma","middleName":"Eliufoo","lastName":"Stephano","suffix":""},{"id":479212655,"identity":"bf73f158-e5cb-43e4-9308-c5be117d6b76","order_by":2,"name":"Mariam Mkoma","email":"","orcid":"","institution":"Mwananyamala Regional Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mariam","middleName":"","lastName":"Mkoma","suffix":""},{"id":479212657,"identity":"3c12faa1-fc6c-4ad7-b8fd-328e0f72d79b","order_by":3,"name":"Joanes Faustine Mboineki","email":"","orcid":"","institution":"The University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Joanes","middleName":"Faustine","lastName":"Mboineki","suffix":""}],"badges":[],"createdAt":"2025-06-22 19:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6951161/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6951161/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12978-025-02260-2","type":"published","date":"2026-01-07T15:57:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100069108,"identity":"244d4911-994e-41d4-b451-a33547989d61","added_by":"auto","created_at":"2026-01-12 16:09:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1221452,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6951161/v1/4755cc31-321c-4df4-90b4-302cb2749853.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Women’s desire to limit childbearing and associated factors in Tanzania: Evidence from the 2022 Demographic and Health Survey","fulltext":[{"header":"Background","content":"\u003cp\u003eGlobally, there has been a worldwide decline in fertility over the past half-century. However, sub-Saharan Africa (SSA) stands out with persistently high fertility rates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The United Nations estimated the total fertility rate (TFR) in SSA at 4.7 births per woman between 2015 and 2020, which is more than twice the level observed in other regions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This late and slow fertility transition in SSA is caused by a lack of demand for limiting family size, religious attitudes toward fertility, and low contraceptive use [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. SSA's population is expected to almost double to over 2\u0026nbsp;billion by mid-century, growing three times faster than the global average and potentially becoming the most populous region globally by 2070 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Tanzania's population has increased significantly, with a 37% rise over the past decade to nearly 63\u0026nbsp;million people [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Projections suggest a 2\u0026ndash;3% annual growth rate until 2050, driven by a high fertility rate [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Tanzania is projected to be one of eight countries responsible for over half of the global population increase in the next three decades, with five of these countries located in Africa [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The high population growth in Tanzania puts significant pressure on essential services like education, healthcare, and food supply [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe desire to limit childbearing is a critical aspect of reproductive health, influencing population dynamics and individual well-being [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This has been cited in Sustainable Development Goals (SGD) Target 3.7, which states, \" By 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmes\" [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Recent studies highlight a significant desire among women in SSA to limit childbearing, with a sizable number, approximately 8\u0026nbsp;million, expressing a demand for limiting future births [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the uptake of female permanent contraception among women with a demand for family planning to limit childbearing remains low in many SSA countries, with a pooled percentage of 4.13% between 2010 and 2018 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This percentage varied significantly across countries, ranging from 0.26% in Angola to 26.85% in Malawi [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A study involving 232,784 childbearing women across 32 Sub-Saharan African countries found that 64.95% desired more children, with rates ranging from 34.9% in South Africa to 89.43% in Niger [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Factors consistently associated with a lower desire for more children include older age, higher educational attainment of both women and their partners, higher parity, current contraceptive use, and exposure to media [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Conversely, women in rural areas and those with lower socioeconomic status are more likely to desire more children [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Additionally, women who lack sole decision-making capacity regarding their lives tend to have a higher likelihood of desiring more children [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Tanzania, women of reproductive age often have an average of four or five children, compared to the global average of two [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Cultural values often prioritize larger families, and for poorer families, children can provide security in old age, given the limited social protections available [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. While Tanzania offers various contraceptive options and has made efforts to reduce barriers, only 34% of married women use any form of contraception [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Male dominance in family planning decision-making is a recognized barrier to contraceptive use in Tanzania [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Studies indicate that when disagreements arise between spouses regarding fertility preferences, men tend to desire more children than their wives [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the growing desire among women in SSA to limit childbearing and the recognized importance of family planning programs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], high fertility rates persist, notably in Tanzania [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The influence of male dominance, cultural norms, and socioeconomic disparities continues to impede women's ability to achieve their desired family size, contributing to a high unmet need for contraception [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. While previous research has identified various determinants of fertility desire in SSA [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], there is a continuous need for up-to-date, country-specific evidence that incorporates recent demographic and health survey data to inform targeted interventions. This study aims to utilize evidence from the 2022 Tanzania Demographic and Health Survey (TDHS) to provide current data on the status of women's desire to limit childbearing and its associated factors in a country facing considerable population growth [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and healthcare strain [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This research can inform and strengthen existing fertility control programs and policies in Tanzania and aid policymakers and public health practitioners in designing interventions that are sensitive to the cultural, geographical, and socioeconomic factors. The findings of this study will ensure that national fertility estimations and expectations are met and hence improve women's reproductive health outcomes and contribute to sustainable development in Tanzania and similar low-income settings.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source, design, setting, population, and sampling\u003c/h2\u003e \u003cp\u003eAn analytical cross-sectional study was conducted using secondary data from the 2022 TDHS, which was conducted from 24 February to 21 July 2022 across all regions in Tanzania. Tanzania is in East Africa, spanning a total area of 945,087 km\u003csup\u003e2\u003c/sup\u003e. According to the 2022 National Population census, the country\u0026rsquo;s population is approximately 62\u0026nbsp;million, with slightly over half being women [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. TDHS is a population-based cross-sectional survey that captures nationally representative data across demographics, reproductive health, and self-reported social determinants of health indices. The target population for the TDHS includes women of reproductive age (15\u0026ndash;49 years), men, children, and households across the 32 administrative regions in Tanzania. This study specifically focuses on women of reproductive age in sexual unions.\u003c/p\u003e \u003cp\u003eThe TDHS methodology is explained elsewhere in detail [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Briefly, TDHS uses a two-stage sampling strategy. The country is divided into urban and rural areas within each region, which are found in eight geographical zones. Firstly, Primary sampling Units (PSUs), which are census enumeration areas (EAs), are selected, followed by households. From each selected PSU, a complete household listing is performed. This is then followed by a selection of a specific number of households using equal probability sampling. This study utilized individual file records with 15,254 women of reproductive age. For this analysis, our final sample was 2,851 (weighted) after excluding women who were sterilized, declared infecund, and unmarried women. This exclusion ensured our analysis focused on the desire to limit childbearing among women actively in a sexual union, providing a more relevant and comprehensives understanding for this specific population.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariable measurements\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcome variable\u003c/h2\u003e \u003cp\u003eIn the 2022 TDHS, eligible female respondents were asked about their desire for more children. The responses were (i) Wants within 2 years, (ii) wants after 2\u0026thinsp;+\u0026thinsp;years, (iii) wants, unsure about timing, (iv) undecided, and (v) wants no more. A binary variable was created based on these responses, coded as \u0026lsquo;1\u0026rsquo; for \u0026lsquo;YES\u0026rsquo; if a woman wants no more children and coded as \u0026lsquo;0\u0026rsquo; for \u0026lsquo;NO\u0026rsquo; if otherwise [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExplanatory variables\u003c/h3\u003e\n\u003cp\u003eThe variables selected for this study are based on the available data from the 2022 TDHS and previous literature. We include age category in years (15\u0026ndash;24, 25\u0026ndash;34 or 35\u0026ndash;49), education level (no formal education, primary education or secondary/higher), wealth index (poor, middle or rich), working status (working or not working), parity (none, 1\u0026ndash;2 or 3+), media exposure (yes or no by aggerating listening to radio, reading newspaper or watching television), exposure to family planning messages (yes or no), family planning knowledge (yes or no), family planning methods use (yes or no), distance to the health facility (big problem or not a big problem), visited health facility in the past 12 months (yes or no), age at first marriage in years (\u0026lt;\u0026thinsp;15, 15\u0026ndash;19 or \u0026ge;\u0026thinsp;20), place of residence (rural or urban) and geographical zones (Western, Northern, Central, Southern, Southwest Highlands, Lake, Eastern, and Zanzibar).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo address the complex survey design of the TDHS, we applied individual sampling weights (v005/1,000,000), accounted for PSU (v021), and stratified the data (v023) to ensure representative estimates and control for sampling biases. Data management and analysis were performed using STATA 18 (STATA Corp, College Station, TX).\u003c/p\u003e \u003cp\u003eWe used descriptive statistics and bivariate analysis to estimate the association between the desire to limit childbearing and sociodemographic characteristics. A generalized Poisson regression model was used to determine factors associated with women\u0026rsquo;s desire to limit childbearing. We used this approach as Odds Ratio may overestimate the association for non-rare outcomes (\u0026gt;\u0026thinsp;10%), and this decision was supported by the lowest Akaike Information Criterion, suggesting a better fit. Univariate analyses were performed by fitting each explanatory variable against the response variable to estimate the Crude Prevalence Ratio (CPR). Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered for multivariable analysis. Thereafter, multivariable analysis adjusted for potential confounders, including women\u0026rsquo;s age, was fitted to estimate Adjusted Prevalence Ratio (APR) with corresponding 95% Confidence Intervals (CI). A variance inflation factor was used to assess for multicollinearity between independent variables before fitting a multivariable regression model. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic characteristics\u003c/h2\u003e \u003cp\u003eA total of 2,851 women were included in this study. Nearly four in ten (39.9%) were aged 25\u0026ndash;34 years, and the overall mean age was 31.5 (standard deviation\u0026thinsp;=\u0026thinsp;8.4). More than half (58.5%) had completed primary education. In terms of socioeconomic status, 43.1% were in a rich quintile and 66.5% were working. The majority (97.7%) had family planning knowledge, 79.3% had exposure to family planning messages, and just 37.6% were currently using contraceptives. Over half (55.1%) had more than two children. More than 62.0% visited health facilities in the past 12 months, and just 3.1% saw community healthcare workers during the same period. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrevalence of desire to limit childbearing\u003c/h3\u003e\n\u003cp\u003eThe prevalence of the desire to limit childbearing was 20.2% (95% CI: 18.4\u0026ndash;22.2). The prevalence of the desire to limit childbearing was significantly higher among women aged 35\u0026ndash;49 years compared to women aged 15\u0026ndash;24 years (44.8% versus 2.3%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Women with primary education had a higher prevalence of desire to limit childbearing (24.1%) compared to those with no formal education (19.4%). Working women had a significantly higher prevalence of desire to limit childbearing compared to non-working women (24.7% versus 11.3%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher parity women (3+) had a higher proportion of desire to limit childbearing compared to nulliparous women (33.7% versus 0.8%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics and distribution of desire to limit children among women in sexual unions in Tanzania (N\u0026thinsp;=\u0026thinsp;2,851)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eDesire to limit childbearing, n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge in years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e735 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e718 (97.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1137 (39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1016 (89.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e121 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e980 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e541 (55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e439 (44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.5 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e558 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e450 (80.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1669 (58.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1267 (75.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e401 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary/higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e624 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e558 (89.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.626\u003c/p\u003e \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\u003e1069 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e854 (79.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e216 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e554 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e432 (78.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e121 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1228 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e989 (80.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e239 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorking 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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot working\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e954 (33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e846 (88.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1897 (66.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1430 (75.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e467 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedia exposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e966 (33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e771 (79.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1885 (66.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1504 (79.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e381 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily planning knowledge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58 (88.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2785 (97.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2217 (79.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e568 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExposure to family planning messages\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e591 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e469 (79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2260 (79.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1806 (79.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e454 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrently using family planning method\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1779 (62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1440 (80.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e339 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1072 (37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e835 (77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e237 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e209 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e207 (99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1073 (37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1027 (95.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1569 (55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1040 (66.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e529 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at first marriage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e212 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165 (77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1511 (53.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1209 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e302 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1128 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e901 (79.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e227 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVisited health facility in the past 12 months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1083 (38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e861 (80.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e214 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1768 (62.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1406 (79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e362 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistance to the health facility\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.423\u003c/p\u003e \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\u003e870 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e684 (78.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot a big problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1981 (69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1590 (80.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e390 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.688\u003c/p\u003e \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\u003e895 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e720 (80.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e175 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1956 (68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1555 (79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e401 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeographical 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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e241 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e199 (82.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e307 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e220 (71.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87 (28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e294 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e221 (75.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73 (24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e612 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e501 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e828 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e668 (80.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e482 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e393 (81.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZanzibar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73 (84.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with the desire to limit childbearing\u003c/h2\u003e \u003cp\u003eIn the adjusted generalized regression model, women aged (APR\u0026thinsp;=\u0026thinsp;2.90, 95%CI: 1.34\u0026ndash;6.28) and those aged (APR\u0026thinsp;=\u0026thinsp;7.43, 95%CI: 3.42\u0026ndash;16.12) were more likely to have a desire to limit childbearing compared to women aged 15\u0026ndash;24 years. Women in the rich quintile were 26% more likely to have a desire to limit childbearing compared to those in the poor quintile (APR\u0026thinsp;=\u0026thinsp;1.26, 95%CI:1.03\u0026ndash;1.54). Additionally, women who were working (APR\u0026thinsp;=\u0026thinsp;1.60, 95%CI: 1.31\u0026ndash;1.95) were 60% more likely to have a desire to limit childbearing than their counterparts. Increase in the number of children associated with a higher likelihood of willingness to limit childbearing (APR\u0026thinsp;=\u0026thinsp;1.26, 95%CI: 1.21\u0026ndash;1.30). (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\u003eGeneralized Poisson Regression model for factors associated with the desire to limit childbearing among women in sexual unions in Tanzania\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge in years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.65 (2.14\u0026ndash;10.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.90 (1.34\u0026ndash;6.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.62 (9.20\u0026ndash;41.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.43 (3.42\u0026ndash;16.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24 (0.98\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35 (1.08\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary/higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.38\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19 (0.80\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.400\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12 (0.84\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.80\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.747\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.74\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26 (1.03\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorking 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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot working\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.19 (1.75\u0026ndash;2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.60 (1.31\u0026ndash;1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedia exposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.80\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily planning knowledge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.75 (0.76\u0026ndash;4.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrently using family planning method\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16 (0.97\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38 (1.35\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26 (1.21\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (0.86\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeographical 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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64 (1.16\u0026ndash;2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80 (1.30\u0026ndash;2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44 (0.97\u0026ndash;2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33 (0.94\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (0.73\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 (0.81\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12 (0.77\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20 (0.87\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (0.70\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15 (0.80\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZanzibar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.56\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69 (0.46\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ePR; Prevalence Ratio, CI ; Confidence Intervals\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to assess the women\u0026rsquo;s desire to limit childbearing and associated factors in Tanzania by analyzing the 2022 TDHS. Our study found that one in five women (20.2%; 95% CI: 18.4\u0026ndash;22.2) had the desire to limit childbearing. This prevalence is lower than those reported in previous studies in Ethiopia, ranging from 33\u0026ndash;37.7% [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], Kenya (33%) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], Mozambique (28%) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and significantly lower than in Nepal (80%) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These variations might be attributed to differences in study design, context, and sample characteristics. The Ethiopian studies were conducted in rural settings, while the Kenyan study was longitudinal among postpartum women. Despite a lower national prevalence, our finding underscores a critical gap in fulfilling women\u0026rsquo;s fertility desires, highlighting an urgent need for sexual and reproductive health stakeholders to develop evidence-based interventions to address this unmet need.\u003c/p\u003e \u003cp\u003eIn multivariable analyses, women\u0026rsquo;s age, education, rich quintile, working status, parity, and geographical zones were associated with the desire to limit childbearing. Women aged 25\u0026ndash;34 and 35\u0026ndash;49 years were more likely to have a desire to limit childbearing, aligning with previous studies in SSA [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Older women may have more children and prefer to limit the number of pregnancies than their younger counterparts with no children [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Regarding the number of children, we found that the increase in the number of children was associated with the desire to limit childbearing [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This suggests that as women have more children, their preference for smaller sizes intensifies, likely driven by the increased socioeconomic demands associated with a larger household [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWomen with primary education were more likely to limit childbearing than those with no formal education. Educated women may have greater awareness of family planning methods, enhancing autonomy in reproductive decision-making and reducing high-risk fertility behaviors [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our study further revealed that employed women were more likely to demonstrate a desire to limit childbearing. Employment often offers competing demands on women\u0026rsquo;s time and resources, such as balancing career aspirations with family chores. Additionally, economic empowerment gained through work could enhance women\u0026rsquo;s autonomy regarding their reproductive health [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Contrarily, a study in SSA found that Zambian working women were less likely to desire to limit childbearing [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This could be attributed to a smaller sample size and a difference in the timing of the DHS survey.\u003c/p\u003e \u003cp\u003eOur study revealed that women in the rich quintile were more likely to express a desire to limit childbearing. This association is often observed globally, women with higher socioeconomic status have greater access to information, education, and family planning services, which empower them to make informed decisions about their fertility [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, wealthier households normally prioritize investments in the education and well-being of fewer children rather than having a large family. Lastly, women in the northern zone showed a significantly higher desire to limit childbearing than those in the western zone. The north zone is often characterized by urbanization, access to education, and health infrastructure. These factors may contribute to improved awareness in family planning and a stronger inclination towards smaller family sizes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Comparative evidence supports these findings, underscoring an urgent need for integrated and focused health programs Targeted interventions focused are essential to meet Tanzania\u0026rsquo;s reproductive health goals and promote inclusive, and sustainable development.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study's strengths include the use of nationally representative 2022 TDHS data and a large sample, enhancing the generalizability of our findings to Tanzanian women of reproductive age. Moreover, a generalised Poisson regression model with a robust variance estimator effectively accounts for survey design complexities and strengthens the validity of our findings. However, the cross-sectional nature of the data prevents establishing causality between the examined factors and the desire to limit childbearing. Since most of the variables were self-reported, there is a possibility of recall and social desirability biases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImplications for practice and policy recommendations\u003c/h2\u003e \u003cp\u003eThe overall prevalence of women\u0026rsquo;s desire to limit childbearing underscores a critical yet underexplored opportunity for advancing sexual and reproductive health rights. Policy makers and reproductive health stakeholders should. Re-evaluate and strengthen comprehensive family planning programs. This necessitates a client-oriented and multifaceted approach to emphasize counselling and consider women\u0026rsquo;s unique sociodemographics, whilst also strategically linking family planning initiatives with broader programs for girls ' education and women\u0026rsquo;s empowerment, ultimately enhancing access and awareness to all women of reproductive age in Tanzania as stipulated in the SDG 3.7. Additionally, empowering women to make informed decisions about their fertility desires is a fundamental human right that should remain central to family planning programs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eNearly one in five women in Tanzania showed a desire to limit childbearing. The desire to limit childbearing was associated with sociodemographic and regional characteristics. A multifaceted approach, including comprehensive family planning awareness and improved access to sexual and reproductive health that addresses sociodemographic and geographical disparities, would be essential. These findings provide valuable insight to guide national strategies aiming at improving sexual and reproductive health rights, promoting equitable access to reproductive services among women of reproductive age in Tanzania.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAPR\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAdjusted prevalence rate\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eConfidence interval\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCPR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eCrude prevalence rate\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDHS\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eDemographic and Health Survey\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eEA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eEnumeration area\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePrevalence rate\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSSA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSub-Saharan Africa\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSDG\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSustainable Development Goals\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTDHS\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTanzania Demographic and Health Survey\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTFR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal fertility rate\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the DHS Program for providing the data for this study and to TILAM International for their methodological and statistical consultation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMJM and EES conceptualized the idea and conducted formal analysis. MJM, EES, MM and JFN interpreted the results, drafted 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\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors without undue reservation. The complete dataset is available at https://dhsprogram.com.\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 publicly available, de-identified data from the 2022 TDHS, accessible online through the DHS program. The original survey received ethical approval from both the National Institute of Medical Research Ethics Committee in Tanzania and the ICF Macro Ethics Committee in Calverton, New York. Permission to use 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 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 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\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBongaarts J. Trends in fertility and fertility preferences in sub-Saharan Africa: the roles of education and family planning programs. Genus. 2020;76:32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTesfa D, Tiruneh SA, Gebremariam AD, Azanaw MM, Engidaw MT, Kefale B et al. The pooled estimate of the total fertility rate in sub-Saharan Africa using recent (2010\u0026ndash;2018) Demographic and Health Survey data. Front Public Health [Internet]. 2023 [cited 2025 Jun 21];10. 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Family planning needs to limit childbearing are unmet, yet our parity is high: characterizing and unveiling the predictive factors. BMC Womens Health. 2023;23:492.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoldeamanuel BT, Gessese GT, Demie TG, Handebo S, Biratu TD. Women\u0026rsquo;s education, contraception use, and high-risk fertility behavior: A cross-sectional analysis of the demographic and health survey in Ethiopia. Front Glob Womens Health [Internet]. 2023 [cited 2025 Jun 20];4. 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Factors influencing high-risk fertility practices among women of childbearing age in Tanzania: using DHS 2022. BMC Public Health. 2025;25:1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNegash WD, Eshetu HB, Asmamaw DB. Intention to use contraceptives and its correlates among reproductive age women in selected high fertility sub-saharan Africa countries: a multilevel mixed effects analysis. BMC Public Health. 2023;23:257.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarsen U, Hollos M. Women\u0026rsquo;s empowerment and fertility decline among the Pare of Kilimanjaro region, Northern Tanzania. Soc Sci Med. 2003;57:1099\u0026ndash;115.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"reproductive-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"reph","sideBox":"Learn more about [Reproductive Health](http://reproductive-health-journal.biomedcentral.com)","snPcode":"12978","submissionUrl":"https://submission.nature.com/new-submission/12978/3","title":"Reproductive Health","twitterHandle":"@Reprod_Health","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Desire to limit, Childbearing, Reproductive Health, Fertility, Tanzania","lastPublishedDoi":"10.21203/rs.3.rs-6951161/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6951161/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHigh fertility rates in Tanzania significantly contribute to rapid population growth, posing challenges to the country\u0026rsquo;s socioeconomic development and straining the already burdened health system. Women\u0026rsquo;s desire to control the number of children they have influences population growth and is critical for improving maternal and child health outcomes. This study aimed to determine the prevalence and associated factors of women\u0026rsquo;s desire to limit childbearing in Tanzania.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAn Analytical cross-sectional study using secondary data from the 2022 Tanzania Demographic and Health Surveys was conducted. The study included 2,851 participants selected through a two-stage sampling method. A generalized Poisson regression model was used to determine factors associated with the desire to limit childbearing. Adjusted prevalence ratios (APRs) with 95% confidence intervals (CIs) were reported, and statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of desire to limit childbearing was 20.2% (95% CI: 18.4\u0026ndash;22.2). Women aged 25\u0026ndash;34 years (APR\u0026thinsp;=\u0026thinsp;2.90, 95%CI: 1.34\u0026ndash;6.28) and those aged 35\u0026ndash;49 years (APR\u0026thinsp;=\u0026thinsp;7.43, 95%CI: 3.42\u0026ndash;16.11) were more likely to have a desire to limit childbearing compared to those aged 15\u0026ndash;24 years. Women in primary education (APR\u0026thinsp;=\u0026thinsp;1.35, 95%CI: 1.08\u0026ndash;1.69), women in rich quintile (APR\u0026thinsp;=\u0026thinsp;1.26, 95%CI: 1.03\u0026ndash;1.54), working women (APR\u0026thinsp;=\u0026thinsp;1.60, 95%CI: 1.31\u0026ndash;1.95), increase in number of children (APR\u0026thinsp;=\u0026thinsp;1.26, 95%CI: 1.21\u0026ndash;1.30) were associated with higher likelihood of desire to limit children.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eNearly one in five women in Tanzania showed a desire to limit childbearing. A multifaceted approach, including comprehensive family planning awareness and improved access to sexual and reproductive health that addresses sociodemographic and geographical disparities, would be essential.\u003c/p\u003e","manuscriptTitle":"Women’s desire to limit childbearing and associated factors in Tanzania: Evidence from the 2022 Demographic and Health Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 06:52:54","doi":"10.21203/rs.3.rs-6951161/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-03T14:27:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-03T10:05:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261499725762179261220999281055719047184","date":"2025-07-14T23:21:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-14T11:14:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213883371206139999974700521867918602064","date":"2025-07-14T09:20:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-01T17:58:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-24T08:36:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-24T08:33:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Reproductive Health","date":"2025-06-22T19:36:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"reproductive-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"reph","sideBox":"Learn more about [Reproductive Health](http://reproductive-health-journal.biomedcentral.com)","snPcode":"12978","submissionUrl":"https://submission.nature.com/new-submission/12978/3","title":"Reproductive Health","twitterHandle":"@Reprod_Health","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"540f6708-609f-42b3-a047-2e26a5a15190","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:01:21+00:00","versionOfRecord":{"articleIdentity":"rs-6951161","link":"https://doi.org/10.1186/s12978-025-02260-2","journal":{"identity":"reproductive-health","isVorOnly":false,"title":"Reproductive Health"},"publishedOn":"2026-01-07 15:57:28","publishedOnDateReadable":"January 7th, 2026"},"versionCreatedAt":"2025-07-02 06:52:54","video":"","vorDoi":"10.1186/s12978-025-02260-2","vorDoiUrl":"https://doi.org/10.1186/s12978-025-02260-2","workflowStages":[]},"version":"v1","identity":"rs-6951161","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6951161","identity":"rs-6951161","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}