Unmet need for family planning among married women in Tanzania: Insights from the 2022 Demographic and Health Survey

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Understanding factors associated with unmet need for FP is critical for effective policy formulation and improving maternal and child health outcomes. Therefore, this study aimed to determine the prevalence and associated factors of unmet need for FP among women in sexual unions in Tanzania. Methods An analytical cross-sectional study was conducted using secondary data from the 2022 TDHS. A generalized logistic regression model was used to determine factors associated with unmet need for FP. Odds ratio and 95% confidence Intervals (CI) were computed to estimate the strength and magnitude of association. Statistical significance was set at p < 0.05. Results The prevalence of unmet need for family planning was 20.7% (95%CI: 18.9–22.6). For the factors, women aged 25–34 years (AOR = 0.47, 95%CI: 0.35–0.63) and those aged 35–49 years (AOR = 0.37, 95%CI: 0.25–0.54) had lower odds of unmet need for FP than their counterparts. Women who were working had lower odds of unmet need for FP compared to their counterparts (AOR = 0.71, 95%CI: 0.56–0.87). The increase in the number of children was associated with a higher likelihood of unmet need for FP (AOR = 1.30, 95%CI: 1.22–1.38). Women with a history of pregnancy termination were 26% less likely to have unmet need for FP than their counterparts (AOR = 0.74, 95%CI: 0.53–0.97). Conclusion The established findings of this study indicate that factors such as age, parity, and employment status significantly influence the likelihood of unmet need, with younger and non-working women often facing greater challenges. Addressing this public health challenge necessitates a concerted and targeted approach, focusing on enhancing the accessibility and quality of family planning services, particularly for rural and younger populations. Family planning contraception unmet married women Tanzania Background Family planning stands as a cornerstone of global public health, widely recognized for its profound benefits in improving maternal and newborn health, alongside fostering broader socioeconomic well-being [ 1 ]. Historically, contraception, once a novelty in the 1960s with less than 10% usage in developing regions, has evolved into a global norm, signifying a major public health achievement of the 20th century [ 2 ]. In 2023, the global contraceptive prevalence of any method was estimated at 65%, with modern methods accounting for 59% [ 3 ]. This remarkable adoption, with an estimated 748 million women globally utilizing a modern method, underscores its widespread acceptance and impact [ 4 ]. Family planning not only prevents unintended pregnancies and reduces unsafe abortions and maternal deaths but also contributes to healthier children, enhanced women's agency, and increased labor force participation [ 5 ]. Despite these significant advancements and the acknowledged cost-effectiveness, a persistent challenge remains: the unmet need for family planning [ 6 ]. Globally, the unmet need for family planning continues to be a critical public health concern, with an estimated 160 million women and adolescents experiencing this in 2019–2021 [ 7 ]. This issue is particularly pronounced in Sub-Saharan Africa (SSA) and South Asia, where over half of these women reside [ 4 ]. While the percentage of married fecund women with unmet need globally decreased substantially from 22% in 1970 to 12% in 2010, the SSA region continues to struggle with a high unmet need of 25% [ 8 ]. This high unmet need in SSA is attributed to factors such as insufficient knowledge of methods, societal opposition, and fear of health side effects [ 9 ]. Studies reveal a low prevalence of modern contraceptive use across SSA, with rates varying significantly by country, ranging from 4% in Chad to 60.5% in Namibia, and an overall prevalence of 25.4% [ 10 ]. The overall prevalence of unmet need for family planning among married women in the sub-region was recorded at 22.9% for the period under study [ 10 ], with South Sudan (35%), the Central African Republic (29%), and Vanuatu (28%) having the highest unmet need in 2019 [ 10 – 13 ]. These figures underscore the considerable disparities and challenges that persist despite global progress, necessitating focused interventions to address the diverse barriers to contraceptive uptake [ 11 ]. Despite the substantial body of research on family planning [ 8 , 10 , 11 ], a notable gap exists in understanding the precise dynamics and determinants of unmet need at a country-specific level, especially within the context of recent demographic and health survey data. While previous studies have highlighted the national unmet need for contraceptives in Tanzania, such as the 2015–2016 Tanzania Demographic and Health Survey (TDHS) report indicating 22% [ 14 , 15 ], there remains a need for a more current and comprehensive analysis. The literature generally acknowledges that factors like age, education, number of living children, partner discussion, and concerns about side effects influence unmet need [ 16 ]. However, there is a recognized demand for additional research to better understand how factors such as consistency and duration of contraceptive use influence method effectiveness, and to identify the most effective platforms for sexual and reproductive health interventions, particularly for vulnerable groups [ 17 ]. This study aims to bridge these identified gaps by utilizing the insights from the 2022 TDHS, which provides up-to-date and comprehensive data on awareness and use of family planning methods, fertility preferences, and related health issues. This study will provide current and reliable estimates of unmet need among married women in Tanzania, allowing for a deeper exploration of contributing factors and regional variations. The findings will be crucial for policymakers and program managers in evaluating and improving existing family planning programs, ensuring that interventions are evidence-based and effectively tailored to meet the needs and preferences of women in Tanzania. 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 between 24 February and 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 approximately population is approximately 62 million, with slightly over half being women [ 18 ]. 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. However, his study specifically focuses on women of reproductive age in sexual unions. The TDHS methodology is explained elsewhere in detail [ 19 ]. In summary, the TDHS employs a two-stage sampling design. The country is first stratified by urban and rural areas within each region, which are grouped into eight distinct geographical zones. In the first stage, Primary Sampling Units (PSUs) which correspond to census enumeration areas are selected. This is followed by the second stage, where a household listing is conducted within each selected PSU, from which a fixed number of households are chosen using equal probability sampling. This study utilized individual file records with 15,254 women of reproductive age. For this analysis, our final sample was 3,033 (weighted) after excluding unmarried women. Dependent variable The outcome variable for this study was unmet need for FP. This was defined according to the standard DHS criteria. Unmet need for FP refers to women of reproductive age who are not using any contraception methods despite expressing a desire to delay or limit future births. Women were considered to have a need for spacing if they were fecund and wished to postpone their next birth by at least two years, were uncertain about whether they wanted another child, were currently pregnant with a pregnancy reported as mistimed or were postpartum amenorrheic and reported that their last birth was mistimed. Additionally, women were classified as having unmet need for limiting childbearing if they were fecund and did not want any more childbearing, were currently pregnant with a pregnancy reported as unwanted or were postpartum amenorrheic and reported that their last birth was unwanted. Pregnant and postpartum amenorrheic women were included based on their retrospective fertility preferences. Only those not currently using any contraceptive method and meeting these criteria were classified as having an unmet need for FP coded as ‘1’ and ‘0’ if otherwise. Independent variables The selection of variables for this study is based on the available data from the 2022 TDHS and previous literature. We included 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), 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 (< 15, 15–19 or ≥ 20), sex of household head (male or female), ever terminated a pregnancy (yes or no), covered by medical insurance (yes or no), place of residence (rural or urban) and geographical zones (Western, Northern, Central, Southern, Southwest Highlands, Lake, Eastern, and Zanzibar). Data management and analysis To address the complex survey design of the TDHS, we applied individual sampling weights (v005/1,000,000), accounted for primary sampling units (v021), and stratified the data (v023) to ensure representative estimates and control for sampling biases. All analysis were performed using STATA 18 (STATA Corp, College Station, TX). We used descriptive statistics for frequency distribution, whereas mean and standard deviation were used for continuous variable and frequency with percentages for categorical variables. Bivariate analysis using Pearson chi-squared test was used to determine the association between unmet need for FP and sociodemographic characteristics. A generalized logistic regression model with robust variance estimator was used to identify factors associated with unmet need for FP. Univariate analyses were performed by fitting each explanatory variable against response variable to estimate Crude Odds Ratio (COR). Variables with p < 0.05 were selected for multivariable regression. Thereafter, a multivariable regression model adjusted for potential confounders, including women’s age, was fitted to estimate Adjusted Odds Ratio (AOR) with corresponding 95% Confidence Intervals (CI). Parity was fitted as continuous variable to maintain the full variability in the data and minimize the potential residual confounding introduced by arbitrary categorization [ 20 ]. We compared logistic and Modified Poisson regression models using Akaike Information Criterion (AIC) as odds ratio may overestimate association for non-rare outcome (> 10%). However, logistic model was chosen as it has lowest AIC suggesting a better model fit. 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 Table 1 presents sociodemographic characteristics of our study participants. A total of 3,033 married women where analyzed, with the mean age of 32.2 years (standard deviation = 8.7). Over one third, 37.7% were aged 25–34 years and 38.0% were aged 35–49 years. Nearly six in ten (58.9) women had attained primary education and 67.6% were working. Regarding socioeconomic status, 37.3% were from poor households and 42.8% from rich households. The vast majority (97.6%) were knowledgeable about FP and 78.9% were exposed to FP messages. Prevalence of unmet need for family planning The overall prevalence of unmet need for FP was 20.7% (95%CI: 18.9–22.6) among married women in Tanzania. The prevalence of unmet need for FP was higher (24.5% [95%CI:20.9–28.6]) among women aged 15–24 years. Women in poorest quintile, (24.7% [95%CI: 21.7–28.0]) had significantly higher proportion of unmet need for FP than their counterparts. Regarding working status, women who were not working had higher proportion of unmet need for FP than their counterparts (25.0% [95%CI:21.8–28.6]). Media access also played a significant role as women with media exposure had lower proportion of unmet need for FP than their counterparts (18,9% [95%CI:16.9–21.1]). Regarding idea number for children, women with no zero desire for children had significantly higher proportion of unmet need for FP (50.1% [39.8–60.4]). (Table 1 ). Table 1 Sociodemographic characteristics and distribution of unmet need for FP among women in sexual unions (N = 3,033) Characteristics n (%) Unmet need (n) Prevalence of unmet need for FP, %(95%CI) p-value Age in years 0.017 15–24 737 (24.3) 181 24.5 (20.9–28.6) 25–34 1,144 (37.7) 208 18.2 (15.8–20.9) 35–39 1,153 (38.0) 238 20.7 (18.0-23.6) Mean (± SD) 32.2 (8.7) Education Level 0.081 No formal education 597 (19.7) 137 23.1 (19.1–27.5) Primary 1,786 (58.9) 379 21.2 (18.9–23.6) Secondary/higher 650 (21.4) 111 17.1 (13.7–21.0) Wealth Index 0.003 Poor 1,133 (37.3) 280 24.7 (21.7–28.0) Middle 603 (19.9) 114 19.0 (15.6–23.0) Rich 1,298 (42.8) 233 17.9 (15.3–20.9) Working status 0.001 Not working 984 (32.4) 246 25.0 (21.8–28.6) Working 2,050 (67.6) 381 18.6 (16.7–20.7) Media exposure 0.004 No 1,025 (33.8) 247 24.1 (21.1–27.3) Yes 2,008 (66.2) 380 18.9 (16.9–21.1) FP knowledge 0.053 No 72 (2.4) 21 29.6 (20.4–40.8) Yes 2,961 (97.6) 606 20.5 (18.7–22.4) Exposure to FP messages 0.336 No 641 (21.1) 142 22.2 (18.9–25.9) Yes 2,392 (78.9) 485 20.3 (18.3–22.4) Parity < 0.001 None 218 (7.2) 9 3.9 (2.1–7.3) 1–2 1,088 (35.9) 217 20.0 (17.3–22.9) ≥ 3 1,727 (56.9) 401 23.2 (20.9–25.8) Visited health facility in the past 12 months 0.475 No 1,183 (39.0) 255 21.6 (18.6–24.9) Yes 1,851 (61.0) 372 20.1 (17.9–22.5) Distance to the health facility 0.467 Big problem 925 (30.5) 200 21.6 (18.6–25.1) Not a big problem 2,109 (69.5) 427 20.3 (18.2–22.5) Covered by medical insurance 0.012 no 2.862 (94.4) 605 21.1 (19.2–23.1) yes 171 (5.6) 22 12.8 (8.3–19.3) Age at first marriage (years) 0.234 < 15 224 (7.4) 43 19.4 (13.8–26.5) 15–19 1,609 (53) 356 22.1 (19.7–24.7) ≥ 20 1,200 (39.6) 228 19.0 (16.4–21.9) Ideal number for children < 0.001 None 89 (2.9) 45 50.1 (39.8–60.4) 1–3 280 (9.2) 39 14.0 (9.2–20.6) ≥ 4 2,579 (85) 519 20.1 (18.3–22.0) Non-numeric response 84 (2.8) 24 28.7 (19.2–40.4) Sex of Household Head 0.474 Male 2,546 (83.9) 519 20.4 (18.5–22.4) Female 488 (16.1) 108 22.1 (18.0-26.9) Ever terminated a pregnancy 0.029 No 2,492 (82.1) 537 21.6 (19.6–23.6) Yes 541 (17.9) 90 16.6 (13.1–20.8) Residence 0.012 Urban 949 (31.3) 160 16.8 (13.7–20.6) Rural 2,084 (68.7) 467 22.4 (20.4–24.6) Geographical zones < 0.001 Western 258 (8.5) 57 22.4 (16.4–29.8) Northern 330 (10.9) 66 20.0 (15.0-25.9) Central 327 (10.8) 55 17.0 (12.2–23.1) Southern 639 (21.1) 107 16.7 (13.9–19.9) Lake 888 (29.3) 240 27.1 (23.3–31.2) Eastern 501 (16.5) 78 15.6 (11.7–20.5) Zanzibar 90 (3.0) 30 25.4 (21.4–29.9) FP; Family Planning SD; Confidence Interval Factors associated with the unmet need for family planning From the adjusted model of our regression analysis, women aged 25–34 years (AOR = 0.47, 95%CI: 0.35–0.63) and those aged 35–49 years (AOR = 0.37, 95%CI: 0.25–0.54) had lower odds of unmet need for FP than their counterparts. Women who were working had lower odds of unmet need for FP compared to their counterparts (AOR = 0.71, 95%CI: 0.56–0.87). The increase in the number of children was associated with a higher likelihood of unmet need for FP (AOR = 1.30, 95%CI: 1.22–1.38). Women with history of pregnancy termination were 26% less likely to have unmet need for FP than their counterparts (AOR = 0.74, 95%CI: 0.53–0.97). (Table 2 ) Table 2: Weighted logistic regression model for factors associated with unmet need for FP among married women in Tanzania (N=3,033) Characteristics Crude OR (95%CI) p-value Adjusted OR (95%CI) p-value Age in years 15-24 Ref Ref 25-34 0.69 (0.52-0.90) 0.006 0.47 (0.35-0.63) <0.001 35-39 0.80 (0.61-1.05) 0.112 0.37 (0.25-0.54) <0.001 Education Level No formal education Ref Ref Primary 0.90 (0.69-1.16) 0.416 1.14 (0.85-1.53) 0.392 Secondary/higher 0.69 (0.49-0.96) 0.030 1.19 (0.78-1.81) 0.433 Wealth Index Poor Ref Ref Middle 0.72 (0.54-0.95) 0.019 0.81 (0.60-1.10) 0.185 Rich 0.67 (0.52-0.84) 0.001 0.99 (0.69-1.45) 0.988 Working status Not working Ref Ref Working 0.69 (0.55-0.85) 0.001 0.71 (0.56-0.87) 0.004 Media exposure No Ref Yes 0.74 (0.60-0.91) 0.004 0.87 (0.68-1.12) 0.285 FP knowledge No Ref - Yes 0.61 (0.36-1.06) 0.078 Exposure to FP messages No Ref - Yes 0.89 (0.70-1.14) 0.359 Parity 1.16 (1.11-1.22) <0.001 1.30 (1.22-1.38) <0.001 Visited health facility in the past 12 months No Ref - Yes 0.92 (0.4-1.13) 0.421 Distance to the health facility Big problem 1.09 (0.87-1.36) 0.450 Not a big problem Ref - Covered by medical insurance no Ref Ref yes 0.55 (0.31-0.97) 0.038 0.70 (0.39-1.28) 0.252 Ideal number for children 1.01 (0.99-1.04) 0.079 - Ever terminated a pregnancy No Ref Ref Yes 0.72 (0.54-0.96) 0.027 0.74 (0.53-0.97) 0.043 Residence Urban Ref Ref Rural 1.43 (1.11-1.83) 0.005 1.06 (0.75-1.48) 0.747 Geographical zones Western 0.85 (0.55-1.31) 0.452 0.70 (0.44-1.14) 0.152 Northern 0.73 (0.47-1.13) 0.161 0.78 (0.47-1.30) 0.344 Central 0.60 (0.39-0.92) 0.021 0.57 (0.35-0.95) 0.031 Southern 0.59 (0.42-0.83) 0.002 0.64 (0.43-0.95) 0.028 Lake 1.09 (0.78-1.52) 0.616 0.98 (0.66-1.45) 0.908 Eastern 0.54 (0.36-0.82) 0.004 0.68 (0.43-1.08) 0.103 Zanzibar Ref Ref Ref; Reference category, OR; Odds Ratio, CI: Confidence Intervals Discussion This study aimed to assess unmet need for family planning among married women in Tanzania by examining the 2022 TDHS. The overall prevalence of unmet need for FP among married women in Tanzania was 20.7%. This figure aligns with other assessments for Tanzania, such as the 2012–2017 report, which indicated a national unmet need of 22% for contraceptives among married women [ 21 , 22 ]. Another study found that 20% of married women in Tanzania had an unmet need for contraception, while for adolescents, the rates were 30% in 2004/05, 25.3% in 2010, and 26.5% in 2015/16 [ 15 , 23 ]. Specifically, women aged 15–24 years show a prevalence of 24.5% (95% CI: 20.9–28.6), which is consistent with previous research highlighting higher unmet needs among younger adolescents [ 24 ]. In comparison to global and regional figures, the level of unmet need for modern contraception in Tanzania is higher than the World Health Organization (WHO) cut-off point of 25% [ 21 ], indicating a significant public health concern requiring targeted interventions. While the global percentage of married fecund women with unmet need decreased, SSA still faces a high unmet need [ 10 ]. In the Magu Health and Demographic Surveillance System site, the unmet need for modern contraception among women aged 15–49 years was 30.9% in 2012 and 31.6% in 2017 [ 21 ]. These variations across studies may be attributed to differences in the social context of the communities studied, data collection methods, and the calculation of fertility desire, as well as the characteristics and sizes of the sampled populations. This study assessed several factors that are associated with unmet needs for FP. The current finding that women aged 25–34 and 35–49 had lower odds of unmet need for FP than their counterparts is consistent with other studies [ 25 , 26 ]. For instance, a study in the Democratic Republic of Congo (DRC) also found that young women and middle-aged women were less likely to have unmet needs for family planning compared to adolescents [ 25 ]. Similarly, in Nepal, lower odds of total unmet need for FP were observed in women aged 20–34 years and 35–49 years compared to those under 20 years [ 27 ]. These consistent findings suggest that as women age, they may achieve their ideal family size, resulting in a decreased likelihood of unmet needs. However, some studies indicate that women between 15 and 24 years old were more likely to have unmet needs for family planning compared to those above 35 years [ 28 ]. In Guinea, adolescents aged 15–19 years had a higher likelihood of unmet need for contraception than young women aged 20–24 years [ 29 ]. This study found that working women had lower odds of unmet need for FP compared to their counterparts, which is supported by several studies [ 26 , 28 ]. Research indicates that employed women are less likely to have unmet needs than those who are not working [ 26 ]. This aligns with findings from other studies, which suggest that employment, along with education and wealth, contributes to women's empowerment, which is positively associated with contraceptive use [ 23 , 30 ]. Moreover, when the spouse is employed, the probability of unmet need significantly decreases. However, a study in rural Uganda found that while off-farm wage-employed women were more likely to use traditional contraception, employment was not associated with greater use of modern contraceptives [ 30 ]. An increase in the number of children was associated with a higher likelihood of unmet need for FP contrasts with some evidence from Turkey, where a lower likelihood of unmet need was observed among women with at least one child compared to women without children [ 26 ]. This disparity is due to women with more children in Turkey tending to use FP services more as their desire to have further children decreases [ 26 ]. However, other low- and middle-income countries have shown that strong traditions and the importance of having a male child can lead women to have more children, contributing to unmet need for FP [ 26 , 29 ]. In Guinea, women with one or two children, or more than two children were more likely to have an unmet need for contraception [ 29 ]. Regarding pregnancy termination, the current study's finding that women with a history of pregnancy termination were 26% less likely to have unmet need for FP suggests that such experiences might lead to increased awareness or motivation for contraception [ 31 ]. This is supported by research indicating that women with a history of pregnancy termination showed higher odds of knowing about modern methods of FP [ 31 ]. However, some studies highlight that terminations of pregnancies indicate a high unmet need for contraception and unintended pregnancies as an underlying factor [ 32 ]. Strengths and limitations of the study This study leverages nationally representative data, a significant strength that allows for broad generalizability of its findings across Tanzania. The use of such a comprehensive dataset provides robust statistical power, offering valuable knowledge into a critical public health issue. However, the study is subject to inherent limitations associated with secondary data analysis. These limitations typically include the cross-sectional nature of the data, which restricts the ability to infer causality between identified factors and unmet need. Furthermore, reliance on self-reported data may introduce recall bias or social desirability bias, potentially affecting the accuracy of responses regarding sensitive topics like family planning and pregnancy history. Implications for practice and policy recommendations The findings of this study carry significant implications for both public health practice and policy in Tanzania, particularly in reducing the unmet needs for family planning among married women. Given the disproportionately high unmet need among adolescent girls and younger women (15–24 years) and those residing in rural areas, interventions must be specifically tailored to these demographic groups. Policy initiatives should prioritize enhancing accessibility to comprehensive family planning services in rural settings, potentially through expanding mobile outreach services or strengthening community health worker programs that can deliver information and methods directly to underserved populations. Furthermore, the positive association between women's employment and lower unmet need suggests that policies promoting women's economic empowerment, alongside educational attainment, could serve as effective indirect strategies for improving contraceptive uptake. Lastly, integrating family planning services within broader healthcare systems, such as HIV care and treatment clinics as seen in the previous study [ 21 ], could help reduce unmet needs, though careful evaluation of such integrated models is necessary to ensure effectiveness. Conclusion The persistent high prevalence of unmet need for family planning among married women in Tanzania remains a substantial public health concern, echoing global challenges in reproductive health and hindering progress towards Sustainable Development Goals. The established findings of this study, utilizing the 2022 TDHS, indicate that factors such as age, parity, and employment status significantly influence the likelihood of unmet need, with younger and non-working women often facing greater challenges. Addressing this public health challenge necessitates a concerted and targeted approach, focusing on enhancing the accessibility and quality of family planning services, particularly for rural and younger populations. Ultimately, a holistic strategy that combines the best programmatic interventions, supportive policy frameworks, and investments in women's empowerment will be crucial in closing the gap of unmet need and improving sexual and reproductive health outcomes across Tanzania. Abbreviations AIC Akaike Information Criterion AOR Adjusted odds ratio CI Confidence Intervals COR Crude Odds Ratio DHS Demographic and Health Survey DRC Democratic Republic of Congo FP Family Planning PSUs Primary Sampling Units SD Standard Deviation SSA Sub-Saharan Africa TDHS Tanzania and Demographic and Health Survey WHO World Health Organization Declarations Acknowledgements We thank the DHS program for making the data available for this study and TILAM International for methodological and statistical consultation. Authors’ Contribution MJM and EES conceptualized the idea and conducted formal analysis. MJM, VGM, AAJ and EES interpreted the results, drafted the manuscript, and reviewed all versions of the manuscript. All authors read and approved the final manuscript. Funding There was no funding for this study. 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. 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Kagoye SA, Jahanpour O, Obure J, Mahande MJ, Renju J. Trends and determinants of unmet need for modern contraception among adolescent girls and young women in Tanzania, 2004-2016 [Internet]. medRxiv; 2022 [cited 2025 Feb 11]. p. 2022.06.07.22276109. Available from: https://www.medrxiv.org/content/10.1101/2022.06.07.22276109v1 Nkenguye W, Ismail H, Urassa EP, Yongolo NM, Kagoye S, Msuya SE. Factors Associated with Modern Contraceptive Use Among Out of School Adolescent Girls in Majengo and Njoro Wards of Moshi Municipality, Tanzania. East Afr Health Res J. 2023;7:32–9. Ngole BE, Joho AA. Factors Influencing Modern Family Planning Utilization and Barriers in Women of Reproductive Age in the Iringa Region, Tanzania: A Mixed-Methods Study. SAGE Open Nurs. 2025;11:23779608251313897. Alhassan RHAH, Haggerty CL, Fapohunda A, Affan NJ, Anto-Ocrah M. Exploring the Use of Digital Educational Tools for Sexual and Reproductive Health in Sub-Saharan Africa: Systematic Review. JMIR Public Health Surveill. 2025;11:e63309. The United Republic of Tanzania (URT), Ministry of Finance and Planning, Tanzania, National Bureau of Statistics and President’s Office - Finance and Planning, Office of the, Chief Government Statistician, Zanzibar, Chief Government Statistician, Zanzibar. The 2022 Population and Housing Census: Administrative Units Population Distribution Report; Tanzania Zanzibar, [Internet]. The United Republic of Tanzania (URT); 2022. Available from: https://sensa.nbs.go.tz/publication/volume1c.pdf Ministry of Health (MoH) [Tanzania Mainland], Ministry of Health (MoH) [Zanzibar], National Bureau of Statistics (NBS), Office of the Chief Government Statistician (OCGS), and ICF. Tanzania Demographicand Health Survey and Malaria Indicator Survey 2022 Key Indicators Report. Dodoma, Rockville: MoH, NBS, OCGS, and ICF; 2023. Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med. 2006;25:127–41. Mkwashapi D, Renju J, Mahande M, Wringe A, Changalucha J, Urassa M, et al. Unmet need for modern contraception by HIV status: findings from community—based studies implemented before and after earlier ART initiation program in rural Tanzania. Reprod Health. 2023;20:153. Rwabilimbo AG, Ahmed KY, Mshokela JB, Arora A, Ogbo FA, Collaboration (GloMACH) on behalf of the GM and CHR. Trends and Drivers of Unmet Need for Family Planning in Currently Married Tanzanian Women between 1999 and 2016. Int J Environ Res Public Health. 2023;20:2262. Kabagenyi A, Wasswa R, Kayemba V. Multilevel mixed effects analysis of individual and community factors associated with unmet need for contraception among married women in four East African countries. SSM - Popul Health. 2024;25:101602. Mkande AS, Mosha IH. A Qualitative Exploration of Perceptions and Experiences of Adolescent Girls and Young Women on Modern Contraceptive Methods Use in Malinyi District, Morogoro, Tanzania. East Afr Health Res J. 2025;8:363. Mosuse MA, Gadeyne S. Prevalence and factors associated with unmet need for family planning among women of reproductive age (15–49) in the Democratic Republic of Congo: A multilevel mixed-effects analysis. PLOS ONE. 2022;17:e0275869. Ökem ZG, Pekkurnaz D. Determinants of unmet need for family planning: Evidence from the 2018 Turkey Demographic and Health Survey. J Biosoc Sci. 2024;56:90–103. C SPK, Adhikari B, Pandey AR, Pandey M, Kakchapati S, Giri S, et al. Unmet need for family planning and associated factors among currently married women in Nepal: A further analysis of Nepal Demographic and Health Survey—2022. PLOS ONE. 2024;19:e0303634. Workie DL, Zike DT, Fenta HM, Mekonnen MA. A binary logistic regression model with complex sampling design of unmet need for family planning among all women aged (15-49) in Ethiopia. Afr Health Sci. 2017;17:637. Sidibé S, Grovogui FM, Kourouma K, Kolié D, Camara BS, Delamou A, et al. Unmet need for contraception and its associated factors among adolescent and young women in Guinea: A multilevel analysis of the 2018 Demographic and Health Surveys. Front Glob Womens Health [Internet]. 2022 [cited 2025 Jul 1];3. Available from: https://www.frontiersin.org/journals/global-womens-health/articles/10.3389/fgwh.2022.932997/full Van Den Broeck G. Women’s employment and family planning in rural Uganda. Women Health. 2020;60:517–33. Woytowich DJ, Xie B. Effects of HIV status and history of pregnancy termination on trends of family planning knowledge among women in nine fragile and conflict-affected countries. 2021 [cited 2025 Jul 1]. Available from: https://www.researchsquare.com/article/rs-814775/v1 Coulson J, Sharma V, Wen H. Understanding the global dynamics of continuing unmet need for family planning and unintended pregnancy. China Popul Dev Stud. 2023;7:1–14. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7023104","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":479904040,"identity":"0bf22037-78c7-41c8-9f69-81f2b7792f5e","order_by":0,"name":"Mtoro Jabar 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":"Jabar","lastName":"Mtoro","suffix":""},{"id":479904041,"identity":"8ce7be10-f885-4242-b3c3-6abfda7a394e","order_by":1,"name":"Victoria Godfrey Majengo","email":"","orcid":"","institution":"Dodoma Regional Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"Victoria","middleName":"Godfrey","lastName":"Majengo","suffix":""},{"id":479904042,"identity":"426039d5-14ef-4d19-b136-4e057d9e06d7","order_by":2,"name":"Angelina Alphonse Joho","email":"","orcid":"","institution":"The University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Angelina","middleName":"Alphonse","lastName":"Joho","suffix":""},{"id":479904043,"identity":"532dfeff-e824-4595-8a47-b3c86ffc2149","order_by":3,"name":"Elihuruma Eliufoo Stephano","email":"","orcid":"","institution":"The University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Elihuruma","middleName":"Eliufoo","lastName":"Stephano","suffix":""}],"badges":[],"createdAt":"2025-07-01 19:23:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7023104/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7023104/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86855715,"identity":"b6f78b5d-7162-475d-950e-137b388d1961","added_by":"auto","created_at":"2025-07-16 11:01:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1376295,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7023104/v1/0cbad664-dce3-4f40-8b6b-8c57f9272e4f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unmet need for family planning among married women in Tanzania: Insights from the 2022 Demographic and Health Survey","fulltext":[{"header":"Background","content":"\u003cp\u003eFamily planning stands as a cornerstone of global public health, widely recognized for its profound benefits in improving maternal and newborn health, alongside fostering broader socioeconomic well-being [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Historically, contraception, once a novelty in the 1960s with less than 10% usage in developing regions, has evolved into a global norm, signifying a major public health achievement of the 20th century [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In 2023, the global contraceptive prevalence of any method was estimated at 65%, with modern methods accounting for 59% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This remarkable adoption, with an estimated 748\u0026nbsp;million women globally utilizing a modern method, underscores its widespread acceptance and impact [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Family planning not only prevents unintended pregnancies and reduces unsafe abortions and maternal deaths but also contributes to healthier children, enhanced women's agency, and increased labor force participation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite these significant advancements and the acknowledged cost-effectiveness, a persistent challenge remains: the unmet need for family planning [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGlobally, the unmet need for family planning continues to be a critical public health concern, with an estimated 160\u0026nbsp;million women and adolescents experiencing this in 2019\u0026ndash;2021 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This issue is particularly pronounced in Sub-Saharan Africa (SSA) and South Asia, where over half of these women reside [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While the percentage of married fecund women with unmet need globally decreased substantially from 22% in 1970 to 12% in 2010, the SSA region continues to struggle with a high unmet need of 25% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This high unmet need in SSA is attributed to factors such as insufficient knowledge of methods, societal opposition, and fear of health side effects [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Studies reveal a low prevalence of modern contraceptive use across SSA, with rates varying significantly by country, ranging from 4% in Chad to 60.5% in Namibia, and an overall prevalence of 25.4% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The overall prevalence of unmet need for family planning among married women in the sub-region was recorded at 22.9% for the period under study [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], with South Sudan (35%), the Central African Republic (29%), and Vanuatu (28%) having the highest unmet need in 2019 [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These figures underscore the considerable disparities and challenges that persist despite global progress, necessitating focused interventions to address the diverse barriers to contraceptive uptake [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the substantial body of research on family planning [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], a notable gap exists in understanding the precise dynamics and determinants of unmet need at a country-specific level, especially within the context of recent demographic and health survey data. While previous studies have highlighted the national unmet need for contraceptives in Tanzania, such as the 2015\u0026ndash;2016 Tanzania Demographic and Health Survey (TDHS) report indicating 22% [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], there remains a need for a more current and comprehensive analysis. The literature generally acknowledges that factors like age, education, number of living children, partner discussion, and concerns about side effects influence unmet need [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, there is a recognized demand for additional research to better understand how factors such as consistency and duration of contraceptive use influence method effectiveness, and to identify the most effective platforms for sexual and reproductive health interventions, particularly for vulnerable groups [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study aims to bridge these identified gaps by utilizing the insights from the 2022 TDHS, which provides up-to-date and comprehensive data on awareness and use of family planning methods, fertility preferences, and related health issues. This study will provide current and reliable estimates of unmet need among married women in Tanzania, allowing for a deeper exploration of contributing factors and regional variations. The findings will be crucial for policymakers and program managers in evaluating and improving existing family planning programs, ensuring that interventions are evidence-based and effectively tailored to meet the needs and preferences of women in Tanzania.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eData source, design, setting, population and sampling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAn analytical cross-sectional study was conducted using secondary data from the 2022 TDHS, which was conducted between 24 February and 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 approximately population is approximately 62\u0026nbsp;million, with slightly over half being women [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. 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. However, his 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=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In summary, the TDHS employs a two-stage sampling design. The country is first stratified by urban and rural areas within each region, which are grouped into eight distinct geographical zones. In the first stage, Primary Sampling Units (PSUs) which correspond to census enumeration areas are selected. This is followed by the second stage, where a household listing is conducted within each selected PSU, from which a fixed number of households are chosen using equal probability sampling. This study utilized individual file records with 15,254 women of reproductive age. For this analysis, our final sample was 3,033 (weighted) after excluding unmarried women.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDependent variable\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe outcome variable for this study was unmet need for FP. This was defined according to the standard DHS criteria. Unmet need for FP refers to women of reproductive age who are not using any contraception methods despite expressing a desire to delay or limit future births. Women were considered to have a need for spacing if they were fecund and wished to postpone their next birth by at least two years, were uncertain about whether they wanted another child, were currently pregnant with a pregnancy reported as mistimed or were postpartum amenorrheic and reported that their last birth was mistimed. Additionally, women were classified as having unmet need for limiting childbearing if they were fecund and did not want any more childbearing, were currently pregnant with a pregnancy reported as unwanted or were postpartum amenorrheic and reported that their last birth was unwanted. Pregnant and postpartum amenorrheic women were included based on their retrospective fertility preferences. Only those not currently using any contraceptive method and meeting these criteria were classified as having an unmet need for FP coded as \u0026lsquo;1\u0026rsquo; and \u0026lsquo;0\u0026rsquo; if otherwise.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIndependent variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe selection of variables for this study is based on the available data from the 2022 TDHS and previous literature. We included 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 \u0026ge;\u0026thinsp;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), 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 (\u0026lt;\u0026thinsp;15, 15\u0026ndash;19 or \u0026ge;\u0026thinsp;20), sex of household head (male or female), ever terminated a pregnancy (yes or no), covered by medical insurance (yes or no), place of residence (rural or urban) and geographical zones (Western, Northern, Central, Southern, Southwest Highlands, Lake, Eastern, and Zanzibar).\u003c/p\u003e\u003cp\u003e\u003cb\u003eData management and analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo address the complex survey design of the TDHS, we applied individual sampling weights (v005/1,000,000), accounted for primary sampling units (v021), and stratified the data (v023) to ensure representative estimates and control for sampling biases. All analysis were performed using STATA 18 (STATA Corp, College Station, TX).\u003c/p\u003e\u003cp\u003eWe used descriptive statistics for frequency distribution, whereas mean and standard deviation were used for continuous variable and frequency with percentages for categorical variables. Bivariate analysis using Pearson chi-squared test was used to determine the association between unmet need for FP and sociodemographic characteristics. A generalized logistic regression model with robust variance estimator was used to identify factors associated with unmet need for FP. Univariate analyses were performed by fitting each explanatory variable against response variable to estimate Crude Odds Ratio (COR). Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected for multivariable regression. Thereafter, a multivariable regression model adjusted for potential confounders, including women\u0026rsquo;s age, was fitted to estimate Adjusted Odds Ratio (AOR) with corresponding 95% Confidence Intervals (CI). Parity was fitted as continuous variable to maintain the full variability in the data and minimize the potential residual confounding introduced by arbitrary categorization [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. We compared logistic and Modified Poisson regression models using Akaike Information Criterion (AIC) as odds ratio may overestimate association for non-rare outcome (\u0026gt;\u0026thinsp;10%). However, logistic model was chosen as it has lowest AIC suggesting a better model fit. 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"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eSociodemographic characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents sociodemographic characteristics of our study participants. A total of 3,033 married women where analyzed, with the mean age of 32.2 years (standard deviation\u0026thinsp;=\u0026thinsp;8.7). Over one third, 37.7% were aged 25\u0026ndash;34 years and 38.0% were aged 35\u0026ndash;49 years. Nearly six in ten (58.9) women had attained primary education and 67.6% were working. Regarding socioeconomic status, 37.3% were from poor households and 42.8% from rich households. The vast majority (97.6%) were knowledgeable about FP and 78.9% were exposed to FP messages.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrevalence of unmet need for family planning\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe overall prevalence of unmet need for FP was 20.7% (95%CI: 18.9\u0026ndash;22.6) among married women in Tanzania. The prevalence of unmet need for FP was higher (24.5% [95%CI:20.9\u0026ndash;28.6]) among women aged 15\u0026ndash;24 years. Women in poorest quintile, (24.7% [95%CI: 21.7\u0026ndash;28.0]) had significantly higher proportion of unmet need for FP than their counterparts. Regarding working status, women who were not working had higher proportion of unmet need for FP than their counterparts (25.0% [95%CI:21.8\u0026ndash;28.6]). Media access also played a significant role as women with media exposure had lower proportion of unmet need for FP than their counterparts (18,9% [95%CI:16.9\u0026ndash;21.1]). Regarding idea number for children, women with no zero desire for children had significantly higher proportion of unmet need for FP (50.1% [39.8\u0026ndash;60.4]). (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 unmet need for FP among women in sexual unions (N\u0026thinsp;=\u0026thinsp;3,033)\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=\"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\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnmet need (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePrevalence of unmet need for FP, %(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\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\u003e0.017\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e737 (24.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.5 (20.9\u0026ndash;28.6)\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\u003e25\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,144 (37.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.2 (15.8\u0026ndash;20.9)\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\u003e35\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,153 (38.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.7 (18.0-23.6)\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\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.2 (8.7)\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\u003e0.081\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e597 (19.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.1 (19.1\u0026ndash;27.5)\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\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,786 (58.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.2 (18.9\u0026ndash;23.6)\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\u003eSecondary/higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e650 (21.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.1 (13.7\u0026ndash;21.0)\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\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\u003e0.003\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,133 (37.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.7 (21.7\u0026ndash;28.0)\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\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e603 (19.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.0 (15.6\u0026ndash;23.0)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,298 (42.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.9 (15.3\u0026ndash;20.9)\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\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\u003e0.001\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e984 (32.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.0 (21.8\u0026ndash;28.6)\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\u003eWorking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,050 (67.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.6 (16.7\u0026ndash;20.7)\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\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,025 (33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.1 (21.1\u0026ndash;27.3)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,008 (66.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.9 (16.9\u0026ndash;21.1)\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\u003eFP 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\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.6 (20.4\u0026ndash;40.8)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,961 (97.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.5 (18.7\u0026ndash;22.4)\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 FP 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\u003e0.336\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e641 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.2 (18.9\u0026ndash;25.9)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,392 (78.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.3 (18.3\u0026ndash;22.4)\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\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e218 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.9 (2.1\u0026ndash;7.3)\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\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,088 (35.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.0 (17.3\u0026ndash;22.9)\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\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,727 (56.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.2 (20.9\u0026ndash;25.8)\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\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\u003e0.475\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,183 (39.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.6 (18.6\u0026ndash;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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,851 (61.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.1 (17.9\u0026ndash;22.5)\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\u003e0.467\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e925 (30.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.6 (18.6\u0026ndash;25.1)\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\u003eNot a big problem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,109 (69.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.3 (18.2\u0026ndash;22.5)\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\u003eCovered by medical insurance\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\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.862 (94.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.1 (19.2\u0026ndash;23.1)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e171 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.8 (8.3\u0026ndash;19.3)\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\u003e\u003cb\u003eAge at first marriage (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\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e224 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.4 (13.8\u0026ndash;26.5)\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\u003e15\u0026ndash;19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,609 (53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.1 (19.7\u0026ndash;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\u0026ge;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,200 (39.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.0 (16.4\u0026ndash;21.9)\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\u003e\u003cb\u003eIdeal number for children\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e89 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.1 (39.8\u0026ndash;60.4)\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\u003e1\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e280 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.0 (9.2\u0026ndash;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\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,579 (85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.1 (18.3\u0026ndash;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\u003eNon-numeric response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.7 (19.2\u0026ndash;40.4)\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\u003eSex of Household Head\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\u003e0.474\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,546 (83.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.4 (18.5\u0026ndash;22.4)\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\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e488 (16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.1 (18.0-26.9)\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\u003e\u003cb\u003eEver terminated a pregnancy\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\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,492 (82.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.6 (19.6\u0026ndash;23.6)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e541 (17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.6 (13.1\u0026ndash;20.8)\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\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\u003e0.012\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e949 (31.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.8 (13.7\u0026ndash;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\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,084 (68.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.4 (20.4\u0026ndash;24.6)\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\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\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e258 (8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.4 (16.4\u0026ndash;29.8)\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\u003eNorthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e330 (10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.0 (15.0-25.9)\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\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e327 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.0 (12.2\u0026ndash;23.1)\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\u003eSouthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e639 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.7 (13.9\u0026ndash;19.9)\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\u003eLake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e888 (29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.1 (23.3\u0026ndash;31.2)\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\u003eEastern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e501 (16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.6 (11.7\u0026ndash;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\u003eZanzibar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.4 (21.4\u0026ndash;29.9)\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\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eFP; Family Planning SD; Confidence Interval\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFactors associated with the unmet need for family planning\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFrom the adjusted model of our regression analysis, women aged 25\u0026ndash;34 years (AOR\u0026thinsp;=\u0026thinsp;0.47, 95%CI: 0.35\u0026ndash;0.63) and those aged 35\u0026ndash;49 years (AOR\u0026thinsp;=\u0026thinsp;0.37, 95%CI: 0.25\u0026ndash;0.54) had lower odds of unmet need for FP than their counterparts. Women who were working had lower odds of unmet need for FP compared to their counterparts (AOR\u0026thinsp;=\u0026thinsp;0.71, 95%CI: 0.56\u0026ndash;0.87). The increase in the number of children was associated with a higher likelihood of unmet need for FP (AOR\u0026thinsp;=\u0026thinsp;1.30, 95%CI: 1.22\u0026ndash;1.38). Women with history of pregnancy termination were 26% less likely to have unmet need for FP than their counterparts (AOR\u0026thinsp;=\u0026thinsp;0.74, 95%CI: 0.53\u0026ndash;0.97). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eTable 2: Weighted logistic regression model for factors associated with unmet need for FP among married women in Tanzania (N=3,033)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude \u003cstrong\u003eOR (95%CI)\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted \u003cstrong\u003eOR (95%CI)\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge in years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e15-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e25-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.69 (0.52-0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e0.47 (0.35-0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e35-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.80 (0.61-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e0.37 (0.25-0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.90 (0.69-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e1.14 (0.85-1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eSecondary/higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.69 (0.49-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e1.19 (0.78-1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.72 (0.54-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e0.81 (0.60-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eRich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.67 (0.52-0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e0.99 (0.69-1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eNot working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eWorking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.69 (0.55-0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e0.71 (0.56-0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedia exposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.74 (0.60-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e0.87 (0.68-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFP knowledge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.61 (0.36-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure to FP messages\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.89 (0.70-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e1.16 (1.11-1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e1.30 (1.22-1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVisited health facility in the past 12 months\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.92 (0.4-1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistance to the health facility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eBig problem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e1.09 (0.87-1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eNot a big problem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCovered by medical insurance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.55 (0.31-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e0.70 (0.39-1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIdeal number for children\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e1.01 (0.99-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEver terminated a pregnancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.72 (0.54-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e0.74 (0.53-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e1.43 (1.11-1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e1.06 (0.75-1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeographical zones\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.85 (0.55-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e0.70 (0.44-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eNorthern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.73 (0.47-1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e0.78 (0.47-1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.60 (0.39-0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e0.57 (0.35-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eSouthern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.59 (0.42-0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e0.64 (0.43-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eLake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e1.09 (0.78-1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e0.98 (0.66-1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eEastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003e0.54 (0.36-0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003e0.68 (0.43-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1667%;\"\u003e\n \u003cp\u003eZanzibar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6667%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eRef; Reference category, OR; Odds Ratio, CI: Confidence Intervals\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to assess unmet need for family planning among married women in Tanzania by examining the 2022 TDHS. The overall prevalence of unmet need for FP among married women in Tanzania was 20.7%. This figure aligns with other assessments for Tanzania, such as the 2012\u0026ndash;2017 report, which indicated a national unmet need of 22% for contraceptives among married women [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Another study found that 20% of married women in Tanzania had an unmet need for contraception, while for adolescents, the rates were 30% in 2004/05, 25.3% in 2010, and 26.5% in 2015/16 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Specifically, women aged 15\u0026ndash;24 years show a prevalence of 24.5% (95% CI: 20.9\u0026ndash;28.6), which is consistent with previous research highlighting higher unmet needs among younger adolescents [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In comparison to global and regional figures, the level of unmet need for modern contraception in Tanzania is higher than the World Health Organization (WHO) cut-off point of 25% [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], indicating a significant public health concern requiring targeted interventions. While the global percentage of married fecund women with unmet need decreased, SSA still faces a high unmet need [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In the Magu Health and Demographic Surveillance System site, the unmet need for modern contraception among women aged 15\u0026ndash;49 years was 30.9% in 2012 and 31.6% in 2017 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These variations across studies may be attributed to differences in the social context of the communities studied, data collection methods, and the calculation of fertility desire, as well as the characteristics and sizes of the sampled populations.\u003c/p\u003e\u003cp\u003eThis study assessed several factors that are associated with unmet needs for FP. The current finding that women aged 25\u0026ndash;34 and 35\u0026ndash;49 had lower odds of unmet need for FP than their counterparts is consistent with other studies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. For instance, a study in the Democratic Republic of Congo (DRC) also found that young women and middle-aged women were less likely to have unmet needs for family planning compared to adolescents [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Similarly, in Nepal, lower odds of total unmet need for FP were observed in women aged 20\u0026ndash;34 years and 35\u0026ndash;49 years compared to those under 20 years [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These consistent findings suggest that as women age, they may achieve their ideal family size, resulting in a decreased likelihood of unmet needs. However, some studies indicate that women between 15 and 24 years old were more likely to have unmet needs for family planning compared to those above 35 years [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In Guinea, adolescents aged 15\u0026ndash;19 years had a higher likelihood of unmet need for contraception than young women aged 20\u0026ndash;24 years [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study found that working women had lower odds of unmet need for FP compared to their counterparts, which is supported by several studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Research indicates that employed women are less likely to have unmet needs than those who are not working [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This aligns with findings from other studies, which suggest that employment, along with education and wealth, contributes to women's empowerment, which is positively associated with contraceptive use [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Moreover, when the spouse is employed, the probability of unmet need significantly decreases. However, a study in rural Uganda found that while off-farm wage-employed women were more likely to use traditional contraception, employment was not associated with greater use of modern contraceptives [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAn increase in the number of children was associated with a higher likelihood of unmet need for FP contrasts with some evidence from Turkey, where a lower likelihood of unmet need was observed among women with at least one child compared to women without children [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This disparity is due to women with more children in Turkey tending to use FP services more as their desire to have further children decreases [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, other low- and middle-income countries have shown that strong traditions and the importance of having a male child can lead women to have more children, contributing to unmet need for FP [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In Guinea, women with one or two children, or more than two children were more likely to have an unmet need for contraception [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Regarding pregnancy termination, the current study's finding that women with a history of pregnancy termination were 26% less likely to have unmet need for FP suggests that such experiences might lead to increased awareness or motivation for contraception [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This is supported by research indicating that women with a history of pregnancy termination showed higher odds of knowing about modern methods of FP [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, some studies highlight that terminations of pregnancies indicate a high unmet need for contraception and unintended pregnancies as an underlying factor [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths and limitations of the study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study leverages nationally representative data, a significant strength that allows for broad generalizability of its findings across Tanzania. The use of such a comprehensive dataset provides robust statistical power, offering valuable knowledge into a critical public health issue. However, the study is subject to inherent limitations associated with secondary data analysis. These limitations typically include the cross-sectional nature of the data, which restricts the ability to infer causality between identified factors and unmet need. Furthermore, reliance on self-reported data may introduce recall bias or social desirability bias, potentially affecting the accuracy of responses regarding sensitive topics like family planning and pregnancy history.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImplications for practice and policy recommendations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe findings of this study carry significant implications for both public health practice and policy in Tanzania, particularly in reducing the unmet needs for family planning among married women. Given the disproportionately high unmet need among adolescent girls and younger women (15\u0026ndash;24 years) and those residing in rural areas, interventions must be specifically tailored to these demographic groups. Policy initiatives should prioritize enhancing accessibility to comprehensive family planning services in rural settings, potentially through expanding mobile outreach services or strengthening community health worker programs that can deliver information and methods directly to underserved populations. Furthermore, the positive association between women's employment and lower unmet need suggests that policies promoting women's economic empowerment, alongside educational attainment, could serve as effective indirect strategies for improving contraceptive uptake. Lastly, integrating family planning services within broader healthcare systems, such as HIV care and treatment clinics as seen in the previous study [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], could help reduce unmet needs, though careful evaluation of such integrated models is necessary to ensure effectiveness.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe persistent high prevalence of unmet need for family planning among married women in Tanzania remains a substantial public health concern, echoing global challenges in reproductive health and hindering progress towards Sustainable Development Goals. The established findings of this study, utilizing the 2022 TDHS, indicate that factors such as age, parity, and employment status significantly influence the likelihood of unmet need, with younger and non-working women often facing greater challenges. Addressing this public health challenge necessitates a concerted and targeted approach, focusing on enhancing the accessibility and quality of family planning services, particularly for rural and younger populations. Ultimately, a holistic strategy that combines the best programmatic interventions, supportive policy frameworks, and investments in women's empowerment will be crucial in closing the gap of unmet need and improving sexual and reproductive health outcomes across Tanzania.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.8752%;\"\u003e\n \u003cp\u003eAkaike Information Criterion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003eAOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.8752%;\"\u003e\n \u003cp\u003eAdjusted odds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.8752%;\"\u003e\n \u003cp\u003eConfidence Intervals\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003eCOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.8752%;\"\u003e\n \u003cp\u003eCrude Odds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003eDHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.8752%;\"\u003e\n \u003cp\u003eDemographic and Health Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003eDRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.8752%;\"\u003e\n \u003cp\u003eDemocratic Republic of Congo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003eFP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.8752%;\"\u003e\n \u003cp\u003eFamily Planning\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003ePSUs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.8752%;\"\u003e\n \u003cp\u003ePrimary Sampling Units\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.8752%;\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003eSSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.8752%;\"\u003e\n \u003cp\u003eSub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003eTDHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.8752%;\"\u003e\n \u003cp\u003eTanzania and Demographic and Health Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.8752%;\"\u003e\n \u003cp\u003eWorld Health Organization\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the DHS program for making the data available for this study and TILAM International for methodological and statistical consultation. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMJM and EES conceptualized the idea and conducted formal analysis. MJM, VGM, AAJ and EES 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\u003eThere was no funding for this study. \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. \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\u003eNone declared. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBowring AL, Schwartz S, Lyons C, Rao A, Olawore O, Njindam IM, et al. Unmet Need for Family Planning and Experience of Unintended Pregnancy Among Female Sex Workers in Urban Cameroon: Results From a National Cross-Sectional Study. Glob Health Sci Pract. 2020;8:82\u0026ndash;99. \u003c/li\u003e\n\u003cli\u003eChandra-Mouli V, Akwara E. Improving access to and use of contraception by adolescents: What progress has been made, what lessons have been learnt, and what are the implications for action? 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Unmet need for family planning and associated factors among currently married women in Nepal: A further analysis of Nepal Demographic and Health Survey\u0026mdash;2022. PLOS ONE. 2024;19:e0303634. \u003c/li\u003e\n\u003cli\u003eWorkie DL, Zike DT, Fenta HM, Mekonnen MA. A binary logistic regression model with complex sampling design of unmet need for family planning among all women aged (15-49) in Ethiopia. Afr Health Sci. 2017;17:637. \u003c/li\u003e\n\u003cli\u003eSidib\u0026eacute; S, Grovogui FM, Kourouma K, Koli\u0026eacute; D, Camara BS, Delamou A, et al. Unmet need for contraception and its associated factors among adolescent and young women in Guinea: A multilevel analysis of the 2018 Demographic and Health Surveys. Front Glob Womens Health [Internet]. 2022 [cited 2025 Jul 1];3. Available from: https://www.frontiersin.org/journals/global-womens-health/articles/10.3389/fgwh.2022.932997/full\u003c/li\u003e\n\u003cli\u003eVan Den Broeck G. Women\u0026rsquo;s employment and family planning in rural Uganda. Women Health. 2020;60:517\u0026ndash;33. \u003c/li\u003e\n\u003cli\u003eWoytowich DJ, Xie B. Effects of HIV status and history of pregnancy termination on trends of family planning knowledge among women in nine fragile and conflict-affected countries. 2021 [cited 2025 Jul 1]. Available from: https://www.researchsquare.com/article/rs-814775/v1\u003c/li\u003e\n\u003cli\u003eCoulson J, Sharma V, Wen H. Understanding the global dynamics of continuing unmet need for family planning and unintended pregnancy. China Popul Dev Stud. 2023;7:1\u0026ndash;14. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Family planning, contraception, unmet, married women, Tanzania","lastPublishedDoi":"10.21203/rs.3.rs-7023104/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7023104/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIn Tanzania, unmet need for family planning (FP) presents a major challenge for preventing unintended pregnancies and their associated maternal and child health morbidities. Understanding factors associated with unmet need for FP is critical for effective policy formulation and improving maternal and child health outcomes. Therefore, this study aimed to determine the prevalence and associated factors of unmet need for FP among women in sexual unions in Tanzania.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eAn analytical cross-sectional study was conducted using secondary data from the 2022 TDHS. A generalized logistic regression model was used to determine factors associated with unmet need for FP. Odds ratio and 95% confidence Intervals (CI) were computed to estimate the strength and magnitude of association. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe prevalence of unmet need for family planning was 20.7% (95%CI: 18.9\u0026ndash;22.6). For the factors, women aged 25\u0026ndash;34 years (AOR\u0026thinsp;=\u0026thinsp;0.47, 95%CI: 0.35\u0026ndash;0.63) and those aged 35\u0026ndash;49 years (AOR\u0026thinsp;=\u0026thinsp;0.37, 95%CI: 0.25\u0026ndash;0.54) had lower odds of unmet need for FP than their counterparts. Women who were working had lower odds of unmet need for FP compared to their counterparts (AOR\u0026thinsp;=\u0026thinsp;0.71, 95%CI: 0.56\u0026ndash;0.87). The increase in the number of children was associated with a higher likelihood of unmet need for FP (AOR\u0026thinsp;=\u0026thinsp;1.30, 95%CI: 1.22\u0026ndash;1.38). Women with a history of pregnancy termination were 26% less likely to have unmet need for FP than their counterparts (AOR\u0026thinsp;=\u0026thinsp;0.74, 95%CI: 0.53\u0026ndash;0.97).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe established findings of this study indicate that factors such as age, parity, and employment status significantly influence the likelihood of unmet need, with younger and non-working women often facing greater challenges. Addressing this public health challenge necessitates a concerted and targeted approach, focusing on enhancing the accessibility and quality of family planning services, particularly for rural and younger populations.\u003c/p\u003e","manuscriptTitle":"Unmet need for family planning among married women in Tanzania: Insights from the 2022 Demographic and Health Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-08 14:54:52","doi":"10.21203/rs.3.rs-7023104/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"540f6708-609f-42b3-a047-2e26a5a15190","owner":[],"postedDate":"July 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-16T10:53:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-08 14:54:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7023104","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7023104","identity":"rs-7023104","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}