Individual and community-level determinants of knowledge about fertile periods among women of childbearing age in Tanzania: A multilevel mixed effect analysis of a recent demographic and health survey

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Mtoro, Elihuruma Eliufoo Stephano This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7354220/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Knowledge of fertile periods among women of childbearing age remains crucial for reproductive health and family planning globally. Despite its importance, awareness is generally low which contributes to high rates of unintended pregnancies. This study aims to assess the individual and community-level correlates of knowledge about fertile periods among women of childbearing age in Tanzania. Methods An analytical cross-sectional study was conducted using secondary data from the 2022 Tanzania Demographic and Health Survey. Given the survey complex design, a multilevel mixed-effect logistic regression was used to identify determinants of knowledge about fertility periods among women of childbearing age. Adjusted odds ratio (AOR) with corresponding confidence intervals (CI) were computed to estimated strength and magnitude of association. Results Of all women of childbearing age, 22.6% (95%CI: 21.4–23.8), had correct knowledge about fertility period. At the individual level; older age, primary education or higher, living in rich household, parity, media exposure, mobile phone ownership, internet use was significantly associated with fertility knowledge. At the community level women in rural areas and geographical zones associated with fertility knowledge. Conclusion One in five women had correct knowledge about the fertility period. This finding underscores the need for multifaceted approach and context-specific intervention to address individual and community level disparities, aimed at improving maternal health outcomes and fertility goals in Tanzania. Fertility period Knowledge Childbearing Women Tanzania Background Globally, fertility rates have generally been declining over recent decades, with the average number of children per woman being around 2.3 in 2023 [ 1 ]. Global fertility patterns reveal significant regional disparities, with Sub-Saharan Africa (SSA) standing out as the world's only region maintaining fertility rates substantially above replacement level [ 2 , 3 ]. In 2022, the fertility rate in Sub-Saharan Africa amounted to 4.53 children per woman, while the overall African fertility rate for 2024 was 4.1 children per woman [ 4 , 5 ]. SSA is the world's only region with an above-replacement total fertility rate (TFR), currently estimated at 4.3 to 4.6 [ 6 ]. Despite recent demographic transitions observed globally, the median pace of fertility decline in sub-Saharan Africa (0.03 per year) is less than one-third the pace in other regions (0.12 and 0.13, respectively) [ 7 ]. This slower demographic transition has profound implications for population dynamics, with projections indicating that sub-Saharan Africa will account for one in every two children born on the planet by 2100 [ 8 ]. Tanzania mirrors this regional trend, maintaining a high fertility rate of about five children per woman, which remains well above the global average [ 9 ]. Knowledge of fertile periods among women of childbearing age remains crucial for reproductive health and family planning globally [ 10 ]. Research on women’s understanding of fertile periods reveals significant gaps worldwide, especially in SSA [ 11 , 12 ]. Despite its importance, awareness is generally low, with only about 25% of women worldwide and similarly low levels observed in SSA correctly identifying their fertile window [ 10 ]. Studies show that only 20% of women aged 10 to 24 have accurate knowledge about the fertile window, underscoring a broad lack of understanding of this vital aspect of reproductive health [ 11 ]. While knowing the fertile period is an efficient family planning method, many women lack correct information, leading to unintended pregnancies [ 13 ]. Recent multilevel analyses across various East African countries have consistently identified key factors influencing this knowledge, including age, education level, awareness of family planning, and proximity to health facilities [ 13 , 14 ]. Understanding the fertility window helps women choose when to engage or abstain from sex to either prevent or achieve pregnancy, making it a crucial part of reproductive autonomy [ 13 ]. Nevertheless, the impact of community-level factors on this knowledge remains unexplored, mainly in many African contexts, highlighting the need for comprehensive studies that examine both individual and environmental influences to guide targeted reproductive health interventions and policies. This limited knowledge contributes to high rates of unintended pregnancies, which pose significant health, social, and economic challenges, especially in low- and middle-income countries [ 15 ]. The persistently high fertility rates in SSA present significant challenges to achieving the Sustainable Development Goals (SDGs), particularly SDG 3.7, which aims to "ensure universal access to sexual and reproductive health-care services, including for family planning, information & education, & the integration of reproductive health into national strategies" [ 16 ]. SDG 3.7 supports universal access to sexual and reproductive health care services, including family planning, while SDG 5.6 supports universal access to sexual and reproductive rights [ 17 ]. However, in SSA, high fertility rates, averaging around 4.8 children per woman in countries like Tanzania, combined with low fertility awareness, exacerbate the challenges in achieving these goals. The relationship between fertility and sustainable development is multifaceted, affecting not only individual health outcomes but also broader socioeconomic development, resource allocation, and environmental sustainability across the region [ 18 ]. Enhancing fertility knowledge can empower women to make informed reproductive choices, potentially contributing to fertility decline and better maternal and child health outcomes [ 19 ]. Tanzania exhibits a high fertility status, with a notable prevalence of high-risk fertility behaviors, such as early motherhood, short birth intervals, and high parity among women of childbearing age [ 20 ]. These sustained high fertility levels in Tanzania contribute to rapid population growth and place considerable pressure on the country's health, education, and social services [ 21 ]. Research shows that knowledge of fertile periods remains limited among Tanzanian women, impacting their ability to practice effective family planning [ 22 ]. While some studies have measured reproductive knowledge in segments of the population [ 22 – 25 ], a comprehensive analysis focused on fertile period awareness among women of childbearing age using a national survey is scarce. The persistence of high fertility rates in Tanzania [ 9 ], despite ongoing family planning initiatives, suggests significant gaps in reproductive health knowledge and service utilization among women of childbearing age [ 20 ]. While previous studies have examined contraceptive use and fertility preferences [ 6 , 20 ], there remains insufficient understanding of women's knowledge about fertile periods and the individual and community-level factors that influence this knowledge. This knowledge gap is particularly concerning given that understanding fertile periods is fundamental to effective family planning, whether for natural family planning methods or informed contraceptive use [ 15 ]. The lack of comprehensive, multilevel analysis examining both individual characteristics and community contexts that influence fertile period knowledge represents a critical research gap. This study addresses this gap by conducting a multilevel mixed-effect analysis using recent Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS) data to identify the individual and community-level correlates of knowledge about fertile periods among women of childbearing age in Tanzania. This study aims to provide evidence-based insights to inform targeted interventions and policy decisions aimed at improving reproductive health literacy and supporting Tanzania's progress toward achieving the SDGs related to reproductive health and family planning by employing advanced statistical methods that account for the hierarchical nature of the data. Materials and methods Data source, design, setting, population and sampling Using the secondary data from the 2022 TDHS-MIS, analytical cross-sectional study was conducted. The TDHS-MIS is a nationally representative survey that was conducted between 24 February and 21 July 2022 across all regions of Tanzania. The target population for the TDHS-MIS includes women of childbearing age (15–49 years), men, children, and households across the 32 administrative regions in Tanzania. However, his study specifically focuses on women of childbearing age. The TDHS-MIS uses a two-stage sampling approach. The country is first stratified by urban and rural areas within each region across the eight distinct geographical zones. Primary Sampling Units (PSUs) which correspond to census enumeration areas are then 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 childbearing age. Study variables Dependent variable: The outcome variable in this study was women’s accurate knowledge of the fertility period. In the TDHS-MIS, women of childbearing age were asked, “When is the ovulation time?” The response options included: “during her period,” “after the period ended,” “middle of the cycle,” “before the period begins,” “at any time,” “other”, and “don’t know.” For this analysis, women who responded “middle of the cycle” were classified as having correct knowledge (coded as “1”), while all other responses were considered incorrect (coded as “0”) [26,27]. Independent variables: The selection of variables for this study is based on the available data from the 2022 TDHS-MIS and literature. Individual-level variables We included woman’s age category in years (15-24, 25-34 or 35-49), education level (no formal education, primary education or secondary/higher), marital status (never married or ever married), 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 response), parity (none, 1-2 or 3+), current contraceptive use (yes or no), sex of household head (male or female), exposure to family planning messages (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), owns a mobile phone (yes or no), internet use (yes or no) and menstruated in the last six weeks (yes or no). Community-level variables Place of residence (rural or urban) and geographical zones (Western, Northern, Central, Southern, Southwest Highlands, Lake, Eastern, and Zanzibar). Community media exposure was computed by aggregating proportion of women with media exposure, “High” if proportion was greater or equal to 50% and “Low” if less or equal to “50%”. 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 (clusters), and stratified the data to ensure representative estimates and control for sampling biases. Data cleaning, coding, and analysis were performed using STATA 18 (STATA Corp, College Station, TX). The analyses began with a descriptive analysis and a Pearson chi-square test of independence (χ 2 ) to determine the association between knowledge of fertility period and the explanatory variables. Random effects and model fitness Considering the hierarchical structure of the data (women within households within clusters), a multilevel logistic regression model was used to examine the association between the individual and community-levels factors with fertility knowledge in Tanzania using the 2022 DHS-MIS dataset. The Stata command “melogit” was used in fitting these models. Therefore, a weighted mixed–effect logistic regression model was employed to determine the determinants of fertility knowledge. This model was selected as it accounts for hierarchical nature of the data and the complex survey design [28]. At the first level, women were modelled from households (Individual level) and at the second level, households were modelled from PSUs (community levels). Four models were constructed in this study. The first model was the empty model/null model (Model 0), which is the model that shows the variance in the outcome variable (fertility knowledge), attributed to the clustering of PSUs, however, this model has no explanatory variable included. The second model contained only the individual-level factors (Model I), while the third model contained the community-level factors (Model II). The final model was the complete model (Model III) that simultaneously included the individual and community level factors. The mixed-effects regression model consisted of fixed and random effects. The fixed effects (measures of association) showed results of the association between the selected explanatory variables and the outcome variable (fertility knowledge) and were reported as adjusted odds ratios (AOR) with their 95% confidence intervals (CI). Random effects (measures of variation) such as Intra-class Correlation Coefficient (ICC), Median Odds Ratio (MOR), and Proportion change in variance (PCV) were computed to measure the variation of fertility knowledge across clusters [29,30]. Both Akaike’s Information Criterion (AIC) and Bayesian Information Criteria (BIC) were used to measure how well the different models fitted the data. Deviance = −2 (log likelihood ratio) was used to compare the models due to the nested nature of the model; the model with the lowest deviance was selected as the best-fit model. A variance inflation factor (VIF) was used to assess for multicollinearity between independent variables before fitting a multivariable regression model. All analyses were two tailed and statistically significant was set at p<0.05. Results Sociodemographic characteristics Among all women of childbearing age analyzed, 38.1% were aged 15-24 years. More than half (53.3%) had attained primary education. Over two third (73.5%) were ever married and 71.6% were headed by male. Nearly half (48.0%) were from rich household and only 30.9% were current contraceptive users. In terms of communication channels, 59.4% owns a mobile phone and 14.3% were internet uses. Most of women (70.0%) and 65.7% were menstruated in the last weeks. Regarding location, 64.3% were from rural setting and 29.2% were the residents of lake zone. (Table 1). Prevalence of knowledge about fertility periods The overall prevalence of knowledge about fertility periods was 22.6% (95%CI: 21.4-23.8). Women aged 25-34 had significantly higher proportion of knowledge about fertility periods compared to women aged 15-24 years (25.9% versus 19.4%). Women with secondary/higher education (30.3%) had higher proportion of knowledge about fertility periods than those with no formal education (14.7%). Similarly, women in rich quintile (26.8%) shower a higher proportion of knowledge about fertility periods than those in poor quintile (17.9%). (Table 1). Table 1: Sociodemographic characteristics and distribution of fertility knowledge among women of childbearing age in Tanzania, 2022 Demographic and Health Survey (N=15,254) Characteristics Total N (%) Knowledgeable about fertility period, n (%) p-value No Yes Woman’s age (years) <0.001 15-24 5810 (38.1) 4683 (80.6) 1127 (19.4) 25-34 4609 (30.2) 3414 (74.1) 1195 (25.9) 35-49 4835 (31.7) 3718 (76.9) 1117 (23.1) Mean (±SD) Education level <0.001 No formal education 2450 (16.1) 2090 (85.3) 360 (14.7) Primary 8123 (53.3) 6462 (79.6) 1661 (20.4) Secondary/Higher 4681 (30.7) 3263 (69.7) 1418 (30.3) Marital status 0.021 Never Married 4047 (26.5) 3198 (79.0) 849 (21.0) Ever Married 11207 (73.5) 8617 (76.9) 2590 (23.1) Wealth index <0.001 Poor 5044 (33.1) 4143 (82.1) 901 (17.9) Middle 2880 (18.9) 2305 (80.0) 575 (20.0) Rich 7330 (48.0) 5367 (73.2) 1963 (26.8) Working 0.383 Not working 5452 (35.7) 4255 (78.0) 1197 (22.0) Working 9802 (64.3) 7560 (77.1) 2242 (22.9) Media exposure <0.001 No 4690 (30.7) 3897 (82.1) 793 (16.9) Yes 10564 (69.3) 7918 (74.9) 2646 (25.1) Parity <0.001 None 3874 (25.4) 3127 (80.7) 746 (19.3) 1-2 4579 (30) 3378 (73.8) 1201 (26.2) 3+ 6802 (44.6) 5310 (78.1) 1492 (21.9) Current contraceptive use <0.001 No 10536 (69.1) 8342 (79.2) 2194 (20.8) Yes 4718 (30.9) 3473 (73.6) 1245 (26.4) Sex of household head 0.402 Male 10918 (71.6) 8483 (77.7) 2435 (22.3) Female 4336 (28.4) 3333 (76.9) 1004 (23.1) Visited health facility last 12 months <0.001 No 7167 (47) 5757 (80.3) 1410 (19.7) Yes 8087 (53) 6058 (74.9) 2029 (25.1) Owns a mobile phone <0.001 No 6193 (40.6) 5219 (84.3) 975 (15.7) Yes 9061 (59.4) 6597 (72.8) 2464 (27.2) Internet use <0.001 No 13072 (85.7) 10422 (79.7) 2650 (20.3) Yes 2182 (14.3) 1393 (63.8) 789 (36.2) Distance to the health facility <0.001 Big problem 4393 (28.8) 3536 (80.5) 857 (19.5) Not a big problem 10861 (71.2) 8279 (76.2) 2582 (23.8) Exposure to family planning messages <0.001 No 3354 (30.0) 2917 (87.0) 437 (13.0) Yes 11900 (70.0) 8898 (74.8) 3002 (25.2) Menstruated in last six weeks 0.088 No 5229 (34.3) 4107 (78.5) 1122 (21.5) Yes 10025 (65.7) 7708 (76.9) 2317 (23.1) Community media exposure <0.001 Low 3165 (20.8) 2624 (82.9) 541 (17.1) High 12089 (79.2) 9191 (76.0) 2898 (24.0) Place of residence 0.011 Urban 5446 (35.7) 4084 (75.0) 1362 (25.0) Rural 9808 (64.3) 7731 (78.8) 2077 (21.2) Geographical zone <0.001 Western 1268 (8.3) 1054 (83.1) 214 (16.9) Northern 1733 (11.4) 1359 (78.4) 374 (21.6) Central 1573 (10.3) 1096 (69.7) 477 (30.3) Southern 3051 (20.0) 2183 (71.6) 868 (28.4) Lake 4454 (29.2) 3714 (83.4) 740 (16.6) Eastern 2657 (17.4) 1988 (74.8) 669 (25.2) Zanzibar 517 (3.4) 421 (81.4) 96 (18.6) Determinants of knowledge on fertility periods A mixed-effects regression model revealed a significant association between several demographic characteristics with knowledge about fertility periods. Compared to women aged 25-34 (AOR=1.23, 95%CI: 1.04-1.44) and aged 35-49 (AOR=1.21, 95%CI: 1.01-1.45) had higher odds of being knowledgeable about fertility periods than their younger counterparts. Women in secondary education (AOR=1.92, 95%CI: 1.92-2.81) and in primary education (AOR=1.30, (95%CI: 1.11-1.52%) had higher odds of being knowledgeable about fertility periods than those with no formal education. Women from rich households had higher odds of being knowledgeable about fertility periods compared to those in poor households (AOR=1.20, (95%CI: 1.01-1.41). Women with media exposure (AOR=1.17, 95%CI: 1.02-1.34) had higher odds of being knowledgeable about fertility period compared to their counterparts. Measures of variation and model fitness A null model was employed to assess the significance of community-level variation knowledge about fertility periods. This model demonstrated significant variation across localities (variance =0.68, p< 0.001), with 17.0% of the total variation attributed to between-cluster differences and 83.0% to within-cluster differences. The MOR in the null model was 1.14, indicating substantial variation of community-level effects. Model I, which included individual-level variables, showed that 16.3% of the variation was attributable to individual differences, with an MOR of 1.14. Model II, incorporating community-level variables, resulted in an MOR of 1.12 and an ICC of 13.6%, indicating cluster-level variation. The final model (Model III), including individual and community-level variables, exhibited the lowest deviance and highest likelihood ratio (LLR), suggesting the best model fit. In Model III, 13.4% of the variation was attributed to individual and community-level factors, with an MOR of 1.11. (Table 2). Table 2: Multilevel Mixed-effect regression for determinants of fertility periods among women of childbearing age in Tanzania, 2022 Demographic and Health Survey (15,254) Characteristics Model 0 Model I Model II Model III AOR (95%CI) AOR (95%CI) AOR (95%CI) Age group 15-24 Ref Ref 25-34 1.23 (1.04-1.45)* 1.23 (1.04-1.44)* 35-49 1.21 (1.01-1.45)* 1.21 (1.01-1.45)* Education No formal education Ref Ref Primary 1.32 (1.13-1.54)* 1.30 (1.11-1.52)* Secondary/Higher 2.31 (1.90-2.79)* 2.32 (1.92-2.81)* Wealth index Poor Ref Ref Middle 0.93 (0.79-1.08) 0.94 (0.81-1.10) Rich 1.10 (0.94-1.29) 1.20 (1.01-1.41)* Parity None Ref Ref 1-2 1.31 (1.11-1.54)* 1.29 (1.09-1.52)* 3+ 1.32 (1.07-1.64)* 1.33 (1.07-1.64)* Media exposure No Ref Ref Yes 1.18 (1.03-1.36)* 1.17 (1.02-1.34)* Visited health facility in the past 12 months No Ref Ref Yes 1.12 (0.99-1.27) 1.12 (0.99-1.27) Owns mobile phone No Ref Ref Yes 1.53 (1.34-1.75)* 1.54 (1.35-1.76)* Internet use No Yes 1.36 (1.09-1.68)* 1.38 (1.11-1.72)* Community media exposure Low Ref Ref High 1.56 (1.25-1.95)* 1.16 (0.92-1.46) Residence Urban Ref Ref Rural 0.85 (0.72-0.99)* 1.27 (1.06-1.53)* Geographical zones Western Ref Ref Northern 1.28 (0.89-1.84) 0.97 (0.68-1.39) Central 1.98 (1.41-2.77)* 1.82 (1.31-2.53)* Southern 1.81 (1.30-2.52)* 1.68 (1.21-2.35)* Lake 0.78 (0.55-1.10) 0.74 (0.52-1.05) Eastern 1.34 (0.95-1.89) 1.14 (0.82-1.60) Zanzibar 0.91 (0.65-1.27) 0.56 (0.40-0.78)* Random effects Variance 0.68 0.64 0.52 0.51 PCV (%) Ref 5.9% 23.5% 25.0% ICC (%) 17.0% 16.3% 13.6% 13.4% MOR 1.14 1.14 1.12 1.11 Model fitness AIC 15507.02 15018.19 15410.72 14934.52 BIC 15522.29 15125.04 15487.04 15102.44 Deviance 15503.02 14990.19 15390.72 14890.52 *p < 0.05, ICC; Intra-class Correlation Coefficient, PCV; Proportional Change in Variance, MOR; Median Odds Ratio, AIC; Akaike Information Criterion, BIC; Bayesian Information Criterion, Ref; Reference category. Discussion The aim of this study was to determine the knowledge of fertility periods and its individual and community level determinants among women of childbearing age in Tanzania, using the 2022 TDHS-MIS data. In the multilevel logistic regression model; women aged ≥ 25 years, primary education or higher, living in rich household, parity, media exposure, mobile phone ownership, internet use, rural area and geographical zones were found to be significantly associated with the knowledge about fertility period. This study found that only 22.6% (95%CI: 21.4–23.8) of women correctly knew about fertility period. This finding lower than those reported in Kenya (38.1%), Sierra Leone (39.8%), Ethiopia (23.6%) and Ghana (42.3%) [ 26 , 31 – 33 ]. This difference could be attributed to cultural context, education exposure, reproductive health information, as well as timing of DHS. Nonetheless, our finding was higher than those in India (21.2%) and a multicounty study in SSA (15.5%) [ 27 , 34 ], suggesting that while awareness remains generally low across many LMICs, contextual factors such as health education programs, access to information, and social norms may influence knowledge about fertility periods. The current study revealed having correct knowledge about fertility period was higher among women aged ≥ 25 years. This finding corroborates with previous studies conducted in SSA [ 27 , 31 , 35 ], which consistently report a positive association between age and fertility knowledge. As women grow older, they are more likely to have multiple opportunities to learn about ovulation through personal experience during pregnancy or engagement with health services which collectively enhance their understating of fertility. Educated women had higher odds of being knowledgeable about fertility period compared to their counterparts, which is consistent studies in LMICs [ 33 , 35 ]. Education my enhance comprehension of biological concepts, increase ability to process and retain health information. Additionally, health education on family planning and fertility awareness has shown to improved women’s reproductive health awareness [ 36 ]. Media serves as cornerstone in disseminating wide range of health-related information. In the context of reproductive health, access to media platform such as radio, television and newspaper, and digital channels plays a pivotal role in increasing awareness and dispel misconception. In this study, women with media exposure, owned a mobile phone and used the internet had significantly higher odds of possessing correct knowledge about fertility period. Our finding aligns with previous studies in the other parts of the world [ 37 ]. This finding entails that media can be used to leverage reproductive health information and improve fertility awareness among women of childbearing age. Women in rich households had significantly higher odds of being knowledge about fertility periods. Women in wealthy household may have access to better education, health service and reliable information regarding reproductive health [ 31 ]. This places women in rich household at a distinct advantage in acquiring and retaining knowledge about fertility period. This study revealed that women with higher parity had higher odds of possessing correct knowledge about fertility periods than nulliparous women, consistently with previous studies [ 27 , 38 ]. Women with childbearing experience are equipped with repeated interactions with health services that encompasses antenatal care, delivery and postnatal care and family planning programs, all of which provides depth of information regarding reproductive health. Additionally, multiparous women may have sought fertility-related information to better plan and space their pregnancies, thereby enhancing fertility period literacy [ 39 ]. Interestingly, women residing in rural areas had higher odds of correct knowledge about fertility periods compared to their urban counterparts. This is in contrast to previous studies in SSA [ 32 , 35 ]. A possible explanation for this could that women in rural areas rely information from older women and community healthcare workers. Additionally, rural health programs aimed at reducing high risk fertility behaviors may have integrated this awareness in households and in facility context for better maternal and child health outcomes [ 40 ]. Traditions and norms in rural areas may alco encourage open conversion about menstruation, thereby reinforcing knowledge retention over time. Strength and limitations This study benefited from several strengths; first, it was conducted using large data from large national survey with adequate sample size. Secondly, the use of multilevel logistic regression accounted for hierarchical nature of the TDHS-MIS enhance validity of our findings. However, this study has some limitation. The cross-sectional design limit causal relationship and the reliance of self-reported may have introduced recall or socio desirability biases. Conclusion This study reveals a significant gap in fertility knowledge among women of childbearing age in Tanzania, with only 22.6% exhibited correct knowledge. In the multilevel regression analysis; age, education, wealth status, media exposure, mobile phone ownership and internet uses was associated with fertility knowledge at the individual level. At the community level, residence and geographic location showed significant association regarding knowledge about fertility period. These findings highlight a critical need for multifaceted and context-specific interventions to improve fertility knowledge. Concerted effort should focus on leveraging digital platforms to reach more women, establishing comprehensive educational programs specifically for younger women, those with lower education level and those in disadvantageous communities. In light of addressing these individual and community level-disparities, public health interventions may effectively empower women to make informed reproductive choices, ultimately contributing to maternal health outcomes and family planning goals in Tanzania. Abbreviations AIC Akaike Information Criterion AOR Adjusted odds ratio CI Confidence Intervals DHS Demographic and Health Survey ICC Intra-class Correlation Coefficient MOR Median Odds Ratio PCV Proportion change in variance PSUs Primary Sampling Units SD Standard Deviation SDG Sustainable Development Goal SSA Sub-Saharan Africa TDHS-MIS Tanzania and Demographic and Health Survey and Malaria Indicator Survey TFR Total fertility rate VIF Variance inflation factor 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 conceptualized the idea and conducted formal analysis. MJM 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. Further details regarding DHS data usage and ethical standards can be found at http://goo.gl/ny8T6X. Consent for publication Not applicable. Competing interests None declared. References Dattani S, Rodés-Guirao L, Roser M. Fertility Rate. Our World Data [Internet]. 2025 [cited 2025 July 24]; Available from: https://ourworldindata.org/fertility-rate Aboagye RG, Donkoh IE, Okyere J, Seidu A-A, Ahinkorah BO, Yaya S. Association between sexual violence and multiple high-risk fertility behaviours among women of reproductive age in sub-Saharan Africa. BMC Public Health. 2024;24:432. Ahinkorah BO, Seidu A-A, Armah-Ansah EK, Budu E, Ameyaw EK, Agbaglo E, et al. Drivers of desire for more children among childbearing women in sub-Saharan Africa: implications for fertility control. BMC Pregnancy Childbirth. 2020;20:778. Statista. 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Why sub-Saharan Africa might exceed its projected population size by 2100. The Lancet. 2020;396:1131–3. Tanzania - fertility rate 2012-2022 [Internet]. Statista. [cited 2024 Dec 17]. Available from: https://www.statista.com/statistics/447693/fertility-rate-in-tanzania/ Wolde M, Kassie A, Shitu K, Azene ZN. Knowledge of Fertile Period and Its Determinants Among Women of Childbearing age in Ethiopia: A Multilevel Analysis Based on 2016 Ethiopian Demographic and Health Survey. Front Public Health. 2022;10:828967. Zegeye AF, Tamir TT, Mekonen EG, Workneh BS, Negash WD, Mekonnen CK. Individual and community-level determinants of knowledge about fertile periods among adolescent girls and young women (10–24 years) in Sub-Saharan Africa: A multilevel mixed effect analysis of a recent demographic and health survey. Heliyon [Internet]. 2024 [cited 2025 Feb 17];10. Available from: https://www.cell.com/heliyon/abstract/S2405-8440(24)02787-7 Zegeye B, Idriss-Wheeler D, Oladimeji O, Yaya S. Exploring health insurance and knowledge of the ovulatory cycle: evidence from Demographic and Health Surveys of 29 countries in Sub-Saharan Africa. Reprod Health. 2023;20:129. Iyanda AE, Dinkins BJ, Osayomi T, Adeusi TJ, Lu Y, Oppong JR. Fertility knowledge, contraceptive use and unintentional pregnancy in 29 African countries: a cross-sectional study. Int J Public Health. 2020;65:445–55. 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. Sung S, Mikes BA, Abramovitz A. Natural Family Planning. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 [cited 2025 July 24]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK546661/ United Nations (UN). THE 17 GOALS | Sustainable Development [Internet]. [cited 2025 Apr 19]. Available from: https://sdgs.un.org/goals Wallengren E, Guthold R, Newby H, Moller A-B, Marsh AD, Fagan L, et al. Relevance of the Sustainable Development Goals (SDGs) to Adolescent Health Measurement: A Systematic Mapping of the SDG Framework and Global Adolescent Health Indicators. J Adolesc Health. 2024;74:S47–55. Haq SMA, Chowdhury MAF, Ahmed KJ, Chowdhury MTA. Environmental quality and its impact on total fertility rate: an econometric analysis from a new perspective. BMC Public Health. 2023;23:2397. Chowdhury S, Rahman MM, Haque MdA. Role of women’s empowerment in determining fertility and reproductive health in Bangladesh: a systematic literature review. AJOG Glob Rep. 2023;3:100239. Asebe HA. Factors influencing high-risk fertility practices among women of childbearing age in Tanzania: using DHS 2022. BMC Public Health. 2025;25:1391. Dept IMFA. Fostering Human Capital in Tanzania’s Rapidly Growing Population. IMF Staff Ctry Rep [Internet]. 2025 [cited 2025 July 24];2025. Available from: https://www.elibrary.imf.org/view/journals/002/2025/164/article-A002-en.xml Kassim M, Ndumbaro F. Factors affecting family planning literacy among women of childbearing age in the rural Lake zone, Tanzania. BMC Public Health. 2022;22:646. 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. 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. Massenga J, Noronha R, Awadhi B, Bishanga DR, Safari O, Njonge L, et al. Family Planning Uptake in Kagera and Mara Regions in Tanzania: A Cross-Sectional Community Survey. Int J Environ Res Public Health. 2021;18:1651. Wolde M, Kassie A, Shitu K, Azene ZN. Knowledge of Fertile Period and Its Determinants Among Women of Childbearing age in Ethiopia: A Multilevel Analysis Based on 2016 Ethiopian Demographic and Health Survey. Front Public Health [Internet]. 2022 [cited 2025 Aug 9];10. Available from: https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.828967/full Individual and community-level determinants of knowledge of ovulatory cycle among women of reproductive age in 29 African countries: a multilevel analysis | BMC Women’s Health [Internet]. [cited 2025 Aug 9]. Available from: https://link.springer.com/article/10.1186/s12905-022-01984-8 Rabe-Hesketh S, Skrondal A. Multilevel Modelling of Complex Survey Data. J R Stat Soc Ser A Stat Soc. 2006;169:805–27. Dong N, Reinke WM, Herman KC, Bradshaw CP, Murray DW. Meaningful Effect Sizes, Intraclass Correlations, and Proportions of Variance Explained by Covariates for Planning Two- and Three-Level Cluster Randomized Trials of Social and Behavioral Outcomes. Eval Rev. 2016;40:334–77. Merlo J, Wagner P, Ghith N, Leckie G. An original stepwise multilevel logistic regression analysis of discriminatory accuracy: The case of neighbourhoods and health. PLoS ONE. 2016;11:1–31. Individual and community-level factors associated with ovulatory cycle knowledge among women in Ghana: a multilevel analysis of recent demographic and health survey data | Contraception and Reproductive Medicine [Internet]. [cited 2025 Aug 9]. Available from: https://link.springer.com/article/10.1186/s40834-025-00343-w Knowledge of fertility period among reproductive age women in Kenya: a multilevel analysis based on 2022 Kenyan demographic and health survey | Contraception and Reproductive Medicine [Internet]. [cited 2025 Aug 9]. Available from: https://link.springer.com/article/10.1186/s40834-024-00287-7 Ameyaw EK, Woytowich D, Gbagbo FY, Amoah PA. Assessing geographical variation in ovulatory cycle knowledge among women of reproductive age in Sierra Leone: Analysis of the 2019 Demographic and Health Survey. PLOS ONE. 2024;19:e0300239. Demographic and socio-economic correlates of knowledge of the ovulatory cycle among tribal women in India: Evidence from the nationally representative survey (NFHS-5) | BMC Public Health [Internet]. [cited 2025 Aug 9]. Available from: https://link.springer.com/article/10.1186/s12889-024-18296-1 Full article: Knowledge of the Ovulatory Period and Associated Factors Among Reproductive Women in Ethiopia: A Population-Based Study Using the 2016 Ethiopian Demographic Health Survey [Internet]. [cited 2025 Aug 9]. Available from: https://www.tandfonline.com/doi/full/10.2147/IJWH.S267675 García D, Vassena R, Prat A, Vernaeve V. Increasing fertility knowledge and awareness by tailored education: a randomized controlled trial. Reprod Biomed Online. 2016;32:113–20. Knowledge of the ovulatory cycle and its determinants among women of reproductive age in Papua New Guinea: Insights from a population-based study | PLOS One [Internet]. [cited 2025 Aug 9]. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0324255 Individual and community-level determinants of knowledge of ovulatory cycle among women of childbearing age in Ethiopia: A multilevel analysis based on 2016 Ethiopian Demographic and Health Survey | PLOS One [Internet]. [cited 2025 Aug 9]. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0254094 Spatial distribution and factors associated with unmet need for contraception among women in Ghana | Reproductive Health [Internet]. [cited 2025 Aug 9]. Available from: https://link.springer.com/article/10.1186/s12978-024-01935-6 Asebe HA. Factors influencing high-risk fertility practices among women of childbearing age in Tanzania: using DHS 2022. BMC Public Health. 2025;25:1391. 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Mtoro","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYNACHoYEBgbmA0CWhAxh1WxwLWwJIC08RGphAGnhMYDoJgR05zcf+/BBxi6Pf/aZz69u1FjwMLAfProBnxazY2zJM2fwJBdLnMvdZp1zDOgwnrS0G/i18Bgz8/AwJzac4d1mnMMG1CLBY0aMlvrE+Wd4nhnn/CNey+HEDWd4mB/nthGlJS2ZcQbP8WLDM2xmzLl9EjxsBP1y+PBhho891XlyZ5gff875VifHz374GF4tYMDYA6bYJMAkQeVg8ANMMn8gTvUoGAWjYBSMNAAA+BlCoA3AKzYAAAAASUVORK5CYII=","orcid":"","institution":"TILAM International","correspondingAuthor":true,"prefix":"","firstName":"Mtoro","middleName":"J.","lastName":"Mtoro","suffix":""},{"id":510357309,"identity":"a3f5b068-2fa8-4bde-8048-05ddd1d393b5","order_by":1,"name":"Elihuruma Eliufoo Stephano","email":"","orcid":"","institution":"University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Elihuruma","middleName":"Eliufoo","lastName":"Stephano","suffix":""}],"badges":[],"createdAt":"2025-08-12 09:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7354220/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7354220/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92958091,"identity":"b42c8169-c66d-447b-b9d9-ace3fa74a706","added_by":"auto","created_at":"2025-10-07 14:24:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1315617,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7354220/v1/5a2f4781-7fcc-4222-801e-f32cc62537f9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Individual and community-level determinants of knowledge about fertile periods among women of childbearing age in Tanzania: A multilevel mixed effect analysis of a recent demographic and health survey","fulltext":[{"header":"Background","content":"\u003cp\u003eGlobally, fertility rates have generally been declining over recent decades, with the average number of children per woman being around 2.3 in 2023 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Global fertility patterns reveal significant regional disparities, with Sub-Saharan Africa (SSA) standing out as the world's only region maintaining fertility rates substantially above replacement level [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In 2022, the fertility rate in Sub-Saharan Africa amounted to 4.53 children per woman, while the overall African fertility rate for 2024 was 4.1 children per woman [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. SSA is the world's only region with an above-replacement total fertility rate (TFR), currently estimated at 4.3 to 4.6 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite recent demographic transitions observed globally, the median pace of fertility decline in sub-Saharan Africa (0.03 per year) is less than one-third the pace in other regions (0.12 and 0.13, respectively) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This slower demographic transition has profound implications for population dynamics, with projections indicating that sub-Saharan Africa will account for one in every two children born on the planet by 2100 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Tanzania mirrors this regional trend, maintaining a high fertility rate of about five children per woman, which remains well above the global average [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eKnowledge of fertile periods among women of childbearing age remains crucial for reproductive health and family planning globally [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Research on women\u0026rsquo;s understanding of fertile periods reveals significant gaps worldwide, especially in SSA [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Despite its importance, awareness is generally low, with only about 25% of women worldwide and similarly low levels observed in SSA correctly identifying their fertile window [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Studies show that only 20% of women aged 10 to 24 have accurate knowledge about the fertile window, underscoring a broad lack of understanding of this vital aspect of reproductive health [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While knowing the fertile period is an efficient family planning method, many women lack correct information, leading to unintended pregnancies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Recent multilevel analyses across various East African countries have consistently identified key factors influencing this knowledge, including age, education level, awareness of family planning, and proximity to health facilities [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Understanding the fertility window helps women choose when to engage or abstain from sex to either prevent or achieve pregnancy, making it a crucial part of reproductive autonomy [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Nevertheless, the impact of community-level factors on this knowledge remains unexplored, mainly in many African contexts, highlighting the need for comprehensive studies that examine both individual and environmental influences to guide targeted reproductive health interventions and policies. This limited knowledge contributes to high rates of unintended pregnancies, which pose significant health, social, and economic challenges, especially in low- and middle-income countries [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe persistently high fertility rates in SSA present significant challenges to achieving the Sustainable Development Goals (SDGs), particularly SDG 3.7, which aims to \"ensure universal access to sexual and reproductive health-care services, including for family planning, information \u0026amp; education, \u0026amp; the integration of reproductive health into national strategies\" [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. SDG 3.7 supports universal access to sexual and reproductive health care services, including family planning, while SDG 5.6 supports universal access to sexual and reproductive rights [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, in SSA, high fertility rates, averaging around 4.8 children per woman in countries like Tanzania, combined with low fertility awareness, exacerbate the challenges in achieving these goals. The relationship between fertility and sustainable development is multifaceted, affecting not only individual health outcomes but also broader socioeconomic development, resource allocation, and environmental sustainability across the region [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Enhancing fertility knowledge can empower women to make informed reproductive choices, potentially contributing to fertility decline and better maternal and child health outcomes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTanzania exhibits a high fertility status, with a notable prevalence of high-risk fertility behaviors, such as early motherhood, short birth intervals, and high parity among women of childbearing age [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These sustained high fertility levels in Tanzania contribute to rapid population growth and place considerable pressure on the country's health, education, and social services [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Research shows that knowledge of fertile periods remains limited among Tanzanian women, impacting their ability to practice effective family planning [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. While some studies have measured reproductive knowledge in segments of the population [\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], a comprehensive analysis focused on fertile period awareness among women of childbearing age using a national survey is scarce.\u003c/p\u003e\u003cp\u003eThe persistence of high fertility rates in Tanzania [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], despite ongoing family planning initiatives, suggests significant gaps in reproductive health knowledge and service utilization among women of childbearing age [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. While previous studies have examined contraceptive use and fertility preferences [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], there remains insufficient understanding of women's knowledge about fertile periods and the individual and community-level factors that influence this knowledge. This knowledge gap is particularly concerning given that understanding fertile periods is fundamental to effective family planning, whether for natural family planning methods or informed contraceptive use [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The lack of comprehensive, multilevel analysis examining both individual characteristics and community contexts that influence fertile period knowledge represents a critical research gap. This study addresses this gap by conducting a multilevel mixed-effect analysis using recent Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS) data to identify the individual and community-level correlates of knowledge about fertile periods among women of childbearing age in Tanzania. This study aims to provide evidence-based insights to inform targeted interventions and policy decisions aimed at improving reproductive health literacy and supporting Tanzania's progress toward achieving the SDGs related to reproductive health and family planning by employing advanced statistical methods that account for the hierarchical nature of the data.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eData source, design, setting, population and sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the secondary data from the 2022 TDHS-MIS, analytical cross-sectional study was conducted. The TDHS-MIS is a nationally representative survey that was conducted between 24 February and 21 July 2022 across all regions of Tanzania. \u0026nbsp;The target population for the TDHS-MIS includes women of childbearing age (15\u0026ndash;49 years), men, children, and households across the 32 administrative regions in Tanzania. However, his study specifically focuses on women of childbearing age.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe TDHS-MIS uses a two-stage sampling approach. The country is first stratified by urban and rural areas within each region across the eight distinct geographical zones. Primary Sampling Units (PSUs) which correspond to census enumeration areas are then 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 childbearing age.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDependent variable:\u0026nbsp;\u003c/strong\u003eThe outcome variable in this study was women\u0026rsquo;s accurate knowledge of the fertility period. In the TDHS-MIS, women of childbearing age were asked, \u0026ldquo;When is the ovulation time?\u0026rdquo; The response options included: \u0026ldquo;during her period,\u0026rdquo; \u0026ldquo;after the period ended,\u0026rdquo; \u0026ldquo;middle of the cycle,\u0026rdquo; \u0026ldquo;before the period begins,\u0026rdquo; \u0026ldquo;at any time,\u0026rdquo; \u0026ldquo;other\u0026rdquo;, and \u0026ldquo;don\u0026rsquo;t know.\u0026rdquo; For this analysis, women who responded \u0026ldquo;middle of the cycle\u0026rdquo; were classified as having correct knowledge (coded as \u0026ldquo;1\u0026rdquo;), while all other responses were considered incorrect (coded as \u0026ldquo;0\u0026rdquo;) [26,27].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent variables:\u0026nbsp;\u003c/strong\u003eThe selection of variables for this study is based on the available data from the 2022 TDHS-MIS and literature.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndividual-level variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe included woman\u0026rsquo;s age category in years (15-24, 25-34 or 35-49), education level (no formal education, primary education or secondary/higher), marital status (never married or ever married), wealth index (poor, middle or rich), working status (working or not working), parity (none, 1-2 or \u0026ge;3), media exposure (yes or no by aggerating listening to radio, reading newspaper or watching television response), parity (none, 1-2 or 3+), current contraceptive use (yes or no), sex of household head (male or female), exposure to family planning messages (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;15, 15-19 or \u0026ge;20), owns a mobile phone (yes or no), internet use (yes or no) and menstruated in the last six weeks (yes or no).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCommunity-level variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlace of residence (rural or urban) and geographical zones (Western, Northern, Central, Southern, Southwest Highlands, Lake, Eastern, and Zanzibar). Community media exposure was computed by aggregating proportion of women with media exposure, \u0026ldquo;High\u0026rdquo; if proportion was greater or equal to 50% and \u0026ldquo;Low\u0026rdquo; if less or equal to \u0026ldquo;50%\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData management and analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo address the complex survey design of the TDHS, we applied individual sampling weights (v005/1,000,000), accounted for primary sampling units (clusters), and stratified the data to ensure representative estimates and control for sampling biases. Data cleaning, coding, and analysis were performed using STATA 18 (STATA Corp, College Station, TX). The analyses began with a descriptive analysis and a Pearson chi-square test of independence (\u0026chi;\u003csup\u003e2\u003c/sup\u003e) to determine the association between knowledge of fertility period and the explanatory variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRandom effects and model fitness\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering the hierarchical structure of the data (women within households within clusters), a multilevel logistic regression model was used to examine the association between the individual and community-levels factors with fertility knowledge in Tanzania using the 2022 DHS-MIS dataset. The Stata command \u0026ldquo;melogit\u0026rdquo; was used in fitting these models. \u0026nbsp;Therefore, a weighted mixed\u0026ndash;effect logistic regression model was employed to determine the determinants of fertility knowledge. This model was selected as it accounts for hierarchical nature of the data and the complex survey design [28]. At the first level, women were modelled from households (Individual level) and at the second level, households were modelled from PSUs (community levels). Four models were constructed in this study. The first model was the empty model/null model (Model 0), which is the model that shows the variance in the outcome variable (fertility knowledge), attributed to the clustering of PSUs, however, this model has no explanatory variable included. The second model contained only the individual-level factors (Model I), while the third model contained the community-level factors (Model II). The final model was the complete model (Model III) that simultaneously included the individual and community level factors.\u003c/p\u003e\n\u003cp\u003eThe mixed-effects regression model consisted of fixed and random effects. The fixed effects (measures of association) showed results of the association between the selected explanatory variables and the outcome variable (fertility knowledge) and were reported as adjusted odds ratios (AOR) with their 95% confidence intervals (CI). Random effects (measures of variation) such as Intra-class Correlation Coefficient (ICC), Median Odds Ratio (MOR), and Proportion change in variance (PCV) were computed to measure the variation of fertility knowledge across clusters [29,30]. Both Akaike\u0026rsquo;s Information Criterion (AIC) and Bayesian Information Criteria (BIC) were used to measure how well the different models fitted the data. Deviance\u0026thinsp;=\u0026thinsp;\u0026minus;2 (log likelihood ratio) was used to compare the models due to the nested nature of the model; the model with the lowest deviance was selected as the best-fit model. A variance inflation factor (VIF) was used to assess for multicollinearity between independent variables before fitting a multivariable regression model. All analyses were two tailed and statistically significant was set at p\u0026lt;0.05.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSociodemographic characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong all women of childbearing age analyzed, 38.1% were aged 15-24 years. More than half (53.3%) had attained primary education. Over two third (73.5%) were ever married and 71.6% were headed by male. Nearly half (48.0%) were from rich household and only 30.9% were current contraceptive users. In terms of communication channels, 59.4% owns a mobile phone and 14.3% were internet uses. Most of women (70.0%) and 65.7% were menstruated in the last weeks. Regarding location, 64.3% were from rural setting and 29.2% were the residents of lake zone. (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence of knowledge about fertility periods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall prevalence of knowledge about fertility periods was 22.6% (95%CI: 21.4-23.8). Women aged 25-34 had significantly higher proportion of knowledge about fertility periods compared to women aged 15-24 years (25.9% versus 19.4%). Women with secondary/higher education (30.3%) had higher proportion of knowledge about fertility periods than those with no formal education (14.7%). Similarly, women in rich quintile (26.8%) shower a higher proportion of knowledge about fertility periods than those in poor quintile (17.9%). \u0026nbsp;(Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1: Sociodemographic characteristics and distribution of fertility knowledge among women of childbearing age in Tanzania, 2022 Demographic and Health Survey (N=15,254)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKnowledgeable about fertility period, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWoman\u0026rsquo;s age (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e15-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e5810 (38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e4683 (80.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1127 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e25-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4609 (30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3414 (74.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1195 (25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e35-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4835 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3718 (76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1117 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eMean (\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2450 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2090 (85.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e360 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e8123 (53.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e6462 (79.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1661 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eSecondary/Higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4681 (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3263 (69.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1418 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNever Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4047 (26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3198 (79.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e849 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eEver Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e11207 (73.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e8617 (76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2590 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth index\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e5044 (33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e4143 (82.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e901 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2880 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2305 (80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e575 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eRich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e7330 (48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e5367 (73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1963 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNot working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e5452 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e4255 (78.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1197 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eWorking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e9802 (64.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e7560 (77.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2242 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedia exposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4690 (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3897 (82.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e793 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e10564 (69.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e7918 (74.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2646 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e3874 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3127 (80.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e746 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4579 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3378 (73.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1201 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e3+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e6802 (44.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e5310 (78.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1492 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent contraceptive use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e10536 (69.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e8342 (79.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2194 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4718 (30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3473 (73.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1245 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex of household head\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e10918 (71.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e8483 (77.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2435 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4336 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3333 (76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1004 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVisited health facility last 12 months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e7167 (47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e5757 (80.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1410 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e8087 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e6058 (74.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2029 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOwns a mobile phone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e6193 (40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e5219 (84.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e975 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e9061 (59.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e6597 (72.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2464 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternet use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e13072 (85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e10422 (79.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2650 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2182 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1393 (63.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e789 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\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: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eBig problem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4393 (28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3536 (80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e857 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eNot a big problem\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e10861 (71.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e8279 (76.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2582 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure to family planning messages\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e3354 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2917 (87.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e437 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e11900 (70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e8898 (74.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e3002 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenstruated in last six weeks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e5229 (34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e4107 (78.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1122 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e10025 (65.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e7708 (76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2317 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity media exposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e3165 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2624 (82.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e541 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e12089 (79.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e9191 (76.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2898 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e5446 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e4084 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1362 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e9808 (64.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e7731 (78.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2077 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeographical zone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1268 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1054 (83.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e214 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eNorthern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1733 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1359 (78.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e374 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1573 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1096 (69.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e477 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eSouthern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e3051 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2183 (71.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e868 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eLake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4454 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3714 (83.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e740 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eEastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2657 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1988 (74.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e669 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 198px;\"\u003e\n \u003cp\u003eZanzibar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e517 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e421 (81.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e96 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeterminants of knowledge on fertility periods\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA mixed-effects regression model revealed a significant association between several demographic characteristics with knowledge about fertility periods. Compared to women aged 25-34 (AOR=1.23, 95%CI: 1.04-1.44) and aged 35-49 (AOR=1.21, 95%CI: 1.01-1.45) had higher odds of being knowledgeable about fertility periods than their younger counterparts. Women in secondary education (AOR=1.92, 95%CI: 1.92-2.81) and in primary education (AOR=1.30, (95%CI: 1.11-1.52%) had higher odds of being knowledgeable about fertility periods than those with no formal education. Women from rich households had higher odds of being knowledgeable about fertility periods compared to those in poor households (AOR=1.20, (95%CI: 1.01-1.41). Women with media exposure (AOR=1.17, 95%CI: 1.02-1.34) had higher odds of being knowledgeable about fertility period compared to their counterparts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures of variation and model fitness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA null model was employed to assess the significance of community-level variation knowledge about fertility periods. This model demonstrated significant variation across localities (variance =0.68, p\u0026lt; 0.001), with 17.0% of the total variation attributed to between-cluster differences and 83.0% to within-cluster differences. The MOR in the null model was 1.14, indicating substantial variation of community-level effects. Model I, which included individual-level variables, showed that 16.3% of the variation was attributable to individual differences, with an MOR of 1.14. Model II, incorporating community-level variables, resulted in an MOR of 1.12 and an ICC of 13.6%, indicating cluster-level variation. The final model (Model III), including individual and community-level variables, exhibited the lowest deviance and highest likelihood ratio (LLR), suggesting the best model fit. In Model III, 13.4% of the variation was attributed to individual and community-level factors, with an MOR of 1.11. (Table 2). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: Multilevel Mixed-effect regression for determinants of fertility periods among women of childbearing age in Tanzania, 2022 Demographic and Health Survey (15,254)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"654\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel III\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\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: 149px;\"\u003e\n \u003cp\u003e15-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e25-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.23 (1.04-1.45)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.23 (1.04-1.44)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e35-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.21 (1.01-1.45)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.21 (1.01-1.45)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\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: 149px;\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.32 (1.13-1.54)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.30 (1.11-1.52)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eSecondary/Higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e2.31 (1.90-2.79)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e2.32 (1.92-2.81)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\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: 149px;\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e0.93 (0.79-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.94 (0.81-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eRich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.10 (0.94-1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.20 (1.01-1.41)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\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: 149px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.31 (1.11-1.54)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.29 (1.09-1.52)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e3+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.32 (1.07-1.64)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.33 (1.07-1.64)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedia exposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\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: 149px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.18 (1.03-1.36)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.17 (1.02-1.34)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVisited health facility in the past 12 months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\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: 149px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.12 (0.99-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.12 (0.99-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOwns mobile phone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\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: 149px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.53 (1.34-1.75)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.54 (1.35-1.76)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternet use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\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: 149px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\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: 149px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.36 (1.09-1.68)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.38 (1.11-1.72)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity media exposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\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: 149px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e1.56 (1.25-1.95)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.16 (0.92-1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\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: 149px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.85 (0.72-0.99)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.27 (1.06-1.53)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeographical zones\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\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: 149px;\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eNorthern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e1.28 (0.89-1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.97 (0.68-1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e1.98 (1.41-2.77)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.82 (1.31-2.53)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eSouthern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e1.81 (1.30-2.52)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.68 (1.21-2.35)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eLake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.78 (0.55-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.74 (0.52-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eEastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e1.34 (0.95-1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.14 (0.82-1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eZanzibar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.91 (0.65-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.56 (0.40-0.78)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRandom effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\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: 149px;\"\u003e\n \u003cp\u003eVariance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003ePCV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e5.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e23.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e25.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eICC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e17.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e16.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e13.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e13.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eMOR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel fitness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\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: 149px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e15507.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e15018.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e15410.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e14934.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e15522.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e15125.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e15487.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e15102.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eDeviance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e15503.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e14990.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e15390.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e14890.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ICC; Intra-class Correlation Coefficient, PCV; Proportional Change in Variance, MOR; Median Odds Ratio, AIC; Akaike Information Criterion, BIC; Bayesian Information Criterion, Ref; Reference category.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of this study was to determine the knowledge of fertility periods and its individual and community level determinants among women of childbearing age in Tanzania, using the 2022 TDHS-MIS data. In the multilevel logistic regression model; women aged\u0026thinsp;\u0026ge;\u0026thinsp;25 years, primary education or higher, living in rich household, parity, media exposure, mobile phone ownership, internet use, rural area and geographical zones were found to be significantly associated with the knowledge about fertility period.\u003c/p\u003e\u003cp\u003eThis study found that only 22.6% (95%CI: 21.4\u0026ndash;23.8) of women correctly knew about fertility period. This finding lower than those reported in Kenya (38.1%), Sierra Leone (39.8%), Ethiopia (23.6%) and Ghana (42.3%) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This difference could be attributed to cultural context, education exposure, reproductive health information, as well as timing of DHS. Nonetheless, our finding was higher than those in India (21.2%) and a multicounty study in SSA (15.5%) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], suggesting that while awareness remains generally low across many LMICs, contextual factors such as health education programs, access to information, and social norms may influence knowledge about fertility periods.\u003c/p\u003e\u003cp\u003eThe current study revealed having correct knowledge about fertility period was higher among women aged\u0026thinsp;\u0026ge;\u0026thinsp;25 years. This finding corroborates with previous studies conducted in SSA [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], which consistently report a positive association between age and fertility knowledge. As women grow older, they are more likely to have multiple opportunities to learn about ovulation through personal experience during pregnancy or engagement with health services which collectively enhance their understating of fertility. Educated women had higher odds of being knowledgeable about fertility period compared to their counterparts, which is consistent studies in LMICs [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Education my enhance comprehension of biological concepts, increase ability to process and retain health information. Additionally, health education on family planning and fertility awareness has shown to improved women\u0026rsquo;s reproductive health awareness [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMedia serves as cornerstone in disseminating wide range of health-related information. In the context of reproductive health, access to media platform such as radio, television and newspaper, and digital channels plays a pivotal role in increasing awareness and dispel misconception.\u003c/p\u003e\u003cp\u003eIn this study, women with media exposure, owned a mobile phone and used the internet had significantly higher odds of possessing correct knowledge about fertility period. Our finding aligns with previous studies in the other parts of the world [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This finding entails that media can be used to leverage reproductive health information and improve fertility awareness among women of childbearing age.\u003c/p\u003e\u003cp\u003eWomen in rich households had significantly higher odds of being knowledge about fertility periods. Women in wealthy household may have access to better education, health service and reliable information regarding reproductive health [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This places women in rich household at a distinct advantage in acquiring and retaining knowledge about fertility period. This study revealed that women with higher parity had higher odds of possessing correct knowledge about fertility periods than nulliparous women, consistently with previous studies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Women with childbearing experience are equipped with repeated interactions with health services that encompasses antenatal care, delivery and postnatal care and family planning programs, all of which provides depth of information regarding reproductive health. Additionally, multiparous women may have sought fertility-related information to better plan and space their pregnancies, thereby enhancing fertility period literacy [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInterestingly, women residing in rural areas had higher odds of correct knowledge about fertility periods compared to their urban counterparts. This is in contrast to previous studies in SSA [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. A possible explanation for this could that women in rural areas rely information from older women and community healthcare workers. Additionally, rural health programs aimed at reducing high risk fertility behaviors may have integrated this awareness in households and in facility context for better maternal and child health outcomes [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Traditions and norms in rural areas may alco encourage open conversion about menstruation, thereby reinforcing knowledge retention over time.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eStrength and limitations\u003c/h2\u003e\u003cp\u003eThis study benefited from several strengths; first, it was conducted using large data from large national survey with adequate sample size. Secondly, the use of multilevel logistic regression accounted for hierarchical nature of the TDHS-MIS enhance validity of our findings. However, this study has some limitation. The cross-sectional design limit causal relationship and the reliance of self-reported may have introduced recall or socio desirability biases.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reveals a significant gap in fertility knowledge among women of childbearing age in Tanzania, with only 22.6% exhibited correct knowledge. In the multilevel regression analysis; age, education, wealth status, media exposure, mobile phone ownership and internet uses was associated with fertility knowledge at the individual level. At the community level, residence and geographic location showed significant association regarding knowledge about fertility period. These findings highlight a critical need for multifaceted and context-specific interventions to improve fertility knowledge. Concerted effort should focus on leveraging digital platforms to reach more women, establishing comprehensive educational programs specifically for younger women, those with lower education level and those in disadvantageous communities. In light of addressing these individual and community level-disparities, public health interventions may effectively empower women to make informed reproductive choices, ultimately contributing to maternal health outcomes and family planning goals in 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: 151px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\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: 151px;\"\u003e\n \u003cp\u003eAOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\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: 151px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\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: 151px;\"\u003e\n \u003cp\u003eDHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\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: 151px;\"\u003e\n \u003cp\u003eICC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eIntra-class Correlation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eMedian Odds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eProportion change in variance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePSUs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\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: 151px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\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: 151px;\"\u003e\n \u003cp\u003eSDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eSustainable Development Goal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eSSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\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: 151px;\"\u003e\n \u003cp\u003eTDHS-MIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eTanzania and Demographic and Health Survey and Malaria Indicator Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eTotal fertility rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eVariance inflation factor\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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMJM conceptualized the idea and conducted formal analysis. MJM 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors without undue reservation. The complete dataset is available at https://dhsprogram.com.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized publicly available, de-identified data from the 2022 TDHS, accessible online through the DHS program. The original survey received ethical approval from both the National Institute of Medical Research Ethics Committee in Tanzania and the ICF Macro Ethics Committee in Calverton, New York. Permission to use the data for this secondary analysis was granted by the DHS program upon acceptance of the proposed analysis plan under the designated account, with credentials available upon request via https://dhsprogram.com/data/dataset_admin/index.cfm. As this study involved secondary data analysis of publicly accessible datasets, no additional ethical approval was required. Informed consent was obtained from all participants during the initial survey, and all procedures adhered strictly to relevant guidelines and regulations. Further details regarding DHS data usage and ethical standards can be found at http://goo.gl/ny8T6X.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDattani S, Rod\u0026eacute;s-Guirao L, Roser M. Fertility Rate. Our World Data [Internet]. 2025 [cited 2025 July 24]; Available from: https://ourworldindata.org/fertility-rate\u003c/li\u003e\n\u003cli\u003eAboagye RG, Donkoh IE, Okyere J, Seidu A-A, Ahinkorah BO, Yaya S. Association between sexual violence and multiple high-risk fertility behaviours among women of reproductive age in sub-Saharan Africa. BMC Public Health. 2024;24:432. \u003c/li\u003e\n\u003cli\u003eAhinkorah BO, Seidu A-A, Armah-Ansah EK, Budu E, Ameyaw EK, Agbaglo E, et al. Drivers of desire for more children among childbearing women in sub-Saharan Africa: implications for fertility control. BMC Pregnancy Childbirth. 2020;20:778. \u003c/li\u003e\n\u003cli\u003eStatista. Sub-Saharan Africa - fertility rate 2013-2023 [Internet]. Statista. [cited 2025 July 24]. Available from: https://www.statista.com/statistics/805638/fertility-rate-in-sub-saharan-africa/\u003c/li\u003e\n\u003cli\u003eWoldeamanuel BT, Gessese GT, Demie TG, Handebo S, Biratu TD. Women\u0026rsquo;s education, contraception use, and high-risk fertility behavior: A cross-sectional analysis of the demographic and health survey in Ethiopia. Front Glob Womens Health. 2023;4:1071461. \u003c/li\u003e\n\u003cli\u003eTesfa D, Tiruneh SA, Gebremariam AD, Azanaw MM, Engidaw MT, Kefale B, et al. 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Available from: https://sdgs.un.org/goals\u003c/li\u003e\n\u003cli\u003eWallengren E, Guthold R, Newby H, Moller A-B, Marsh AD, Fagan L, et al. Relevance of the Sustainable Development Goals (SDGs) to Adolescent Health Measurement: A Systematic Mapping of the SDG Framework and Global Adolescent Health Indicators. J Adolesc Health. 2024;74:S47\u0026ndash;55. \u003c/li\u003e\n\u003cli\u003eHaq SMA, Chowdhury MAF, Ahmed KJ, Chowdhury MTA. Environmental quality and its impact on total fertility rate: an econometric analysis from a new perspective. BMC Public Health. 2023;23:2397. \u003c/li\u003e\n\u003cli\u003eChowdhury S, Rahman MM, Haque MdA. Role of women\u0026rsquo;s empowerment in determining fertility and reproductive health in Bangladesh: a systematic literature review. AJOG Glob Rep. 2023;3:100239. \u003c/li\u003e\n\u003cli\u003eAsebe HA. Factors influencing high-risk fertility practices among women of childbearing age in Tanzania: using DHS 2022. BMC Public Health. 2025;25:1391. \u003c/li\u003e\n\u003cli\u003eDept IMFA. Fostering Human Capital in Tanzania\u0026rsquo;s Rapidly Growing Population. IMF Staff Ctry Rep [Internet]. 2025 [cited 2025 July 24];2025. Available from: https://www.elibrary.imf.org/view/journals/002/2025/164/article-A002-en.xml\u003c/li\u003e\n\u003cli\u003eKassim M, Ndumbaro F. Factors affecting family planning literacy among women of childbearing age in the rural Lake zone, Tanzania. BMC Public Health. 2022;22:646. \u003c/li\u003e\n\u003cli\u003eMkande 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. \u003c/li\u003e\n\u003cli\u003eNgole 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. \u003c/li\u003e\n\u003cli\u003eMassenga J, Noronha R, Awadhi B, Bishanga DR, Safari O, Njonge L, et al. Family Planning Uptake in Kagera and Mara Regions in Tanzania: A Cross-Sectional Community Survey. Int J Environ Res Public Health. 2021;18:1651. \u003c/li\u003e\n\u003cli\u003eWolde M, Kassie A, Shitu K, Azene ZN. Knowledge of Fertile Period and Its Determinants Among Women of Childbearing age in Ethiopia: A Multilevel Analysis Based on 2016 Ethiopian Demographic and Health Survey. Front Public Health [Internet]. 2022 [cited 2025 Aug 9];10. Available from: https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.828967/full\u003c/li\u003e\n\u003cli\u003eIndividual and community-level determinants of knowledge of ovulatory cycle among women of reproductive age in 29 African countries: a multilevel analysis | BMC Women\u0026rsquo;s Health [Internet]. [cited 2025 Aug 9]. Available from: https://link.springer.com/article/10.1186/s12905-022-01984-8\u003c/li\u003e\n\u003cli\u003eRabe-Hesketh S, Skrondal A. Multilevel Modelling of Complex Survey Data. J R Stat Soc Ser A Stat Soc. 2006;169:805\u0026ndash;27. \u003c/li\u003e\n\u003cli\u003eDong N, Reinke WM, Herman KC, Bradshaw CP, Murray DW. Meaningful Effect Sizes, Intraclass Correlations, and Proportions of Variance Explained by Covariates for Planning Two- and Three-Level Cluster Randomized Trials of Social and Behavioral Outcomes. Eval Rev. 2016;40:334\u0026ndash;77. \u003c/li\u003e\n\u003cli\u003eMerlo J, Wagner P, Ghith N, Leckie G. An original stepwise multilevel logistic regression analysis of discriminatory accuracy: The case of neighbourhoods and health. PLoS ONE. 2016;11:1\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eIndividual and community-level factors associated with ovulatory cycle knowledge among women in Ghana: a multilevel analysis of recent demographic and health survey data | Contraception and Reproductive Medicine [Internet]. [cited 2025 Aug 9]. Available from: https://link.springer.com/article/10.1186/s40834-025-00343-w\u003c/li\u003e\n\u003cli\u003eKnowledge of fertility period among reproductive age women in Kenya: a multilevel analysis based on 2022 Kenyan demographic and health survey | Contraception and Reproductive Medicine [Internet]. [cited 2025 Aug 9]. Available from: https://link.springer.com/article/10.1186/s40834-024-00287-7\u003c/li\u003e\n\u003cli\u003eAmeyaw EK, Woytowich D, Gbagbo FY, Amoah PA. Assessing geographical variation in ovulatory cycle knowledge among women of reproductive age in Sierra Leone: Analysis of the 2019 Demographic and Health Survey. PLOS ONE. 2024;19:e0300239. \u003c/li\u003e\n\u003cli\u003eDemographic and socio-economic correlates of knowledge of the ovulatory cycle among tribal women in India: Evidence from the nationally representative survey (NFHS-5) | BMC Public Health [Internet]. [cited 2025 Aug 9]. Available from: https://link.springer.com/article/10.1186/s12889-024-18296-1\u003c/li\u003e\n\u003cli\u003eFull article: Knowledge of the Ovulatory Period and Associated Factors Among Reproductive Women in Ethiopia: A Population-Based Study Using the 2016 Ethiopian Demographic Health Survey [Internet]. [cited 2025 Aug 9]. Available from: https://www.tandfonline.com/doi/full/10.2147/IJWH.S267675\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a D, Vassena R, Prat A, Vernaeve V. Increasing fertility knowledge and awareness by tailored education: a randomized controlled trial. Reprod Biomed Online. 2016;32:113\u0026ndash;20. \u003c/li\u003e\n\u003cli\u003eKnowledge of the ovulatory cycle and its determinants among women of reproductive age in Papua New Guinea: Insights from a population-based study | PLOS One [Internet]. [cited 2025 Aug 9]. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0324255\u003c/li\u003e\n\u003cli\u003eIndividual and community-level determinants of knowledge of ovulatory cycle among women of childbearing age in Ethiopia: A multilevel analysis based on 2016 Ethiopian Demographic and Health Survey | PLOS One [Internet]. [cited 2025 Aug 9]. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0254094\u003c/li\u003e\n\u003cli\u003eSpatial distribution and factors associated with unmet need for contraception among women in Ghana | Reproductive Health [Internet]. [cited 2025 Aug 9]. Available from: https://link.springer.com/article/10.1186/s12978-024-01935-6\u003c/li\u003e\n\u003cli\u003eAsebe HA. Factors influencing high-risk fertility practices among women of childbearing age in Tanzania: using DHS 2022. BMC Public Health. 2025;25:1391. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fertility period, Knowledge, Childbearing Women, Tanzania","lastPublishedDoi":"10.21203/rs.3.rs-7354220/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7354220/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eKnowledge of fertile periods among women of childbearing age remains crucial for reproductive health and family planning globally. Despite its importance, awareness is generally low which contributes to high rates of unintended pregnancies. This study aims to assess the individual and community-level correlates of knowledge about fertile periods among women of childbearing age in Tanzania.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eAn analytical cross-sectional study was conducted using secondary data from the 2022 Tanzania Demographic and Health Survey. Given the survey complex design, a multilevel mixed-effect logistic regression was used to identify determinants of knowledge about fertility periods among women of childbearing age. Adjusted odds ratio (AOR) with corresponding confidence intervals (CI) were computed to estimated strength and magnitude of association.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOf all women of childbearing age, 22.6% (95%CI: 21.4\u0026ndash;23.8), had correct knowledge about fertility period. At the individual level; older age, primary education or higher, living in rich household, parity, media exposure, mobile phone ownership, internet use was significantly associated with fertility knowledge. At the community level women in rural areas and geographical zones associated with fertility knowledge.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOne in five women had correct knowledge about the fertility period. This finding underscores the need for multifaceted approach and context-specific intervention to address individual and community level disparities, aimed at improving maternal health outcomes and fertility goals in Tanzania.\u003c/p\u003e","manuscriptTitle":"Individual and community-level determinants of knowledge about fertile periods among women of childbearing age in Tanzania: A multilevel mixed effect analysis of a recent demographic and health survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 15:12:50","doi":"10.21203/rs.3.rs-7354220/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":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-07T14:23:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 15:12:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7354220","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7354220","identity":"rs-7354220","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}