The prevalence and correlates of early marriages for women aged 20-29 in Zimbabwe: An analysis of the 2019 Multiple Indicator Cluster Survey (MICS)

The prevalence and correlates of early marriages for women aged 20-29 in Zimbabwe: An analysis of the 2019 Multiple Indicator Cluster Survey (MICS) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The prevalence and correlates of early marriages for women aged 20-29 in Zimbabwe: An analysis of the 2019 Multiple Indicator Cluster Survey (MICS) E Bvurume, K Mangombe, K Mhlanga, C Lwanga This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5262707/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 Early marriage remains a significant public health issue, particularly in sub-Saharan Africa. It has the potential to deprive adolescent girls of their sexual and reproductive rights and expose them to the risks associated with early and high-risk fertility behavior. This study analyzes the prevalence and factors associated with early marriages in Zimbabwe. Methods The analysis utilized data from the 2019 Zimbabwe Multiple Indicator Cluster Survey. A pooled weighted sub-sample of 2219 women aged 20–29 was used for the study. Statistical analysis was carried out using the R-Studio software package, version 3, considering complex survey design analysis. Logistic regression models were used to examine the correlations between individual-level factors and early marital behavior. Results According to the study, 40.9% of women were married before reaching adulthood. The research found a strong correlation between early sexual initiation as strongly related to early marriage (OR = 7.311, p < 0.005), living in the Mashonaland provinces, particularly Mashonaland East, and an increased likelihood of child marriage (OR = 4.1169, p < 0.005). Furthermore, religious affiliation and level of education were also identified as independent factors that elevate the risk of child marriage. Conclusion The research highlighted the necessity of implementing coordinated strategies across different policy and community levels to empower girls, provide education, and ensure protection. These strategies are crucial for addressing the structural, sociocultural, and individual barriers effectively. The study underscored the importance of the evidence presented, which can guide policy-making and the development of targeted interventions to combat child marriage in Zimbabwe. Furthermore, it recommended that future qualitative research should delve deeper into community and intergenerational factors and utilize mixed methods to explore the issues associated with high early marriage rates in the Mashonaland provinces. Correlates Prevalence Early Marriage Logistic Regression Zimbabwe 1 Background The issue of child marriage, defined as marriage before reaching the age of 18, remains a significant global concern with severe implications for human rights and the well-being of young girls 1 . Shockingly, the number of girls worldwide who have been married before the age of 18 exceeds 650 million, and projections indicate that this number could soar to 700 million by 2030 if decisive action is not taken 2 , 3 . Of particular concern is the region of sub-Saharan Africa, which exhibits some of the highest rates of child marriage globally, with over 50 million girls being married during childhood 4 . Regional trends suggest that Eastern and Southern Africa account for over 50 million child brides, with Ethiopia reporting the highest absolute numbers 4 . Previous research has shown that child marriages are associated with adverse lifelong outcomes for young women, including increased exposure to intimate partner violence 5 , implications for maternal and child health, and the loss of opportunities due to lack of education 1 . In Zimbabwe, data from the latest Multiple Indicator Cluster Survey (MICS) conducted in 2019 revealed that one-third of women are married by the age of 18, with significantly higher rates reported in rural areas as compared to urban areas 6 . Factors such as wealth status have consistently emerged as key determinants associated with child marriage, with poverty driving normalization and providing financial incentives for early unions 7 – 9 . Furthermore, higher levels of education have been linked to delayed marriage by providing opportunities and empowerment 10 , 11 . Access to mass media has the potential to shift social norms, favouring education over domestic roles 12 , 13 . Additionally, certain ethnic and religious practices have traditionally promoted early unions 14 , 15 , while larger families face greater financial strain, leading to pressure for early marriage 16 , 17 . Moreover, rural residents can isolate girls from information and opportunities 6 , 18 , and early sexual initiation is correlated with an increased risk of marriage due to associated stigma, dropout rates, and pregnancy 19 . Finally, housing conditions also impact perceived risks and parental concerns over privacy 20 , 21 . 2 Methods 2.1 Study design This study was a quantitative correlational design among women aged 20–29 years in Zimbabwe. 2.2 Sample size, sampling procedure, and selection criteria This research utilized data from the 2019 Multiple Indicator Cluster Survey (MICS) to examine the factors related to early marriages in Zimbabwe. The 2019 MICS is part of UNICEF's series of nationally representative cross-sectional household survey programs, aimed at filling socio-demographic data gaps for monitoring the well-being of children, women, and men. Conducted periodically by the Zimbabwe National Statistical Agency, the survey covered 10703 households across 462 clusters in all 10 provinces of Zimbabwe, using a stratified two-stage cluster sampling design 6 . Clusters were selected using probability proportional to size, with 26 households selected per cluster using systematic sampling. All women aged 15–49 in the selected households were eligible for interview; for this study, a subsample of 2169 women aged 20–29 was used. The age range was chosen based on a previous study conducted in Zambia 22 . 2.3 Survey instruments The dependent variable in this study was derived from the general inquiry on age at first marriage: "When did you get married for the first time?" A binary variable was constructed based on this question to indicate whether the female respondent got married before the age of 18. In this analysis, individuals who married before age 18 were coded as 1 (Yes) and those who married at 18 or older were coded as 0 (No). The independent variables included wealth, education, urban or rural residence, family size, early sexual debut, religion, ethnicity, and media exposure. These variables were sourced from previous research on child marriage 10 , 16 , 23 , 24 . The respondent's age at the time of the survey was treated as a continuous variable and later categorized into 20–24 and 25–29 age groups. Place of residence was categorized as urban or rural, and provinces were identified as Bulawayo, Manicaland, Mashonaland Central, Mashonaland East, Mashonaland West, Matabeleland North, Matabeleland South, Midlands, Masvingo, and Harare. Marital status was grouped as married or single, while family size was categorized as small or large. Ethnicity was classified as Shona, Ndebele, and other languages, and education level as primary, secondary, and tertiary. The wealth index was divided into quintiles, denoted as low, medium, and high. The number of bedrooms was coded as one, two, or three and over, and exposure to mass media was categorized as low or high. Religious affiliation was classified as Mainline Churches, Pentecostal, Apostolic, Traditional, and other. 2.4 Data analysis The data were weighted to address sampling imbalances, complex survey design, and nonresponse. Frequency distributions were utilized to provide a description and summary of the characteristics of the respondents in the sample. Subsequently, a comparison was made between the characteristics of women who experienced early marriage and those who did not. The relationship between the dependent variable, which measured whether or not a woman married before the age of 18, and each independent variable was established at the bivariate level and was tested using a chi-squared test, set at p < 0.05. According to the data and as observed in Table 1 , approximately 40.9% of the women reported having married before the age of 18. Independent factors included in the final model were selected based on their statistical significance (p < 0.05) at the bivariate level. The results of the logistic regression model were presented in the form of adjusted odds ratios (AORs) with their corresponding 95% confidence intervals in Table 2 . Before fitting the model, all independent variables were tested for multicollinearity using the variance inflation factor (VIF) (results not presented here). 2.5 Ethical consideration An analysis was conducted on a population-based dataset that is publicly available on the UNICEF MICS website. The researcher obtained official permission to access the dataset from data archives officials. Informed consent and voluntary participation were obtained from all participants before any data was collected. To ensure privacy, the final data was separated from unique identifiers such as names and locations, guaranteeing confidentiality and anonymity to all participants. 3 Results 3.1 Distribution of the study population by socio-demographic characteristics A subsample of 2219 women was included in the analysis drawn from the 2019, Multiple Indicator Cluster Survey. Table 1 shows the distribution of women included in the analysis by background characteristics and it also displays results at the Bivariate-level, cross tabulations with a chi-square test that was used to analyze the association between early marriage and selected predictor variables. The majority of female respondents were from rural areas, making up 58.4% of the sample. 85.2% of the respondents reported being married or in a union, and 63% of them came from small families with fewer than 6 members. A significant proportion, around 86.1%, identified as Shona and were aged 25 or over, making up 52.6% of the sample. 75.2% of the women had completed secondary education. In terms of wealth distribution, 33.6% were in the lower category, 45.4% in the medium, and 21% in the high category. When it comes to the number of bedrooms, 36.2% reported having 2 bedrooms, while 40.9% had one bedroom and 22.9% had three or more bedrooms. Regarding religious affiliation, 26.2% were affiliated with mainline churches, 18.2% with the Pentecostal church, close to 30.8% with the apostolic faith, and only 3.6% with the traditional church. The study also found that early sexual debut was strongly associated with child marriage, with 11.1% of women who had their sexual debut before the age of 15 years getting married before 18. The study also revealed that background factors such as place of residence, religion, ethnicity, mass media exposure, and wealth quintile were significantly associated with early marriages. However, the number of bedrooms, marital status, and family size showed no strong correlations with early marriage as indicated by p-values greater than 0.05 Table 1 Percentage distribution of selected characteristics of respondents (n = 2219) Variables Frequency Percent Χ 2 -test (p-values) Early marriage (Outcome variable) Early marriage 908 40.9 Late marriage 1311 59.1 Age 0.015 20–24 1051 47.4 25–29 1168 52.6 Place of residence 0.000 Urban 923 41.6 Rural 1297 58.4 Province 0.000 Bulawayo 108 4.9 Manicaland 317 14.3 Mashonaland Central 191 5.4 Mashonaland East 211 9.5 Mashonaland West 284 12.8 Matabeleland North 105 4.7 Matabeleland South 89 4.0 Midlands 226 10.2 Masvingo 245 11.0 Harare 442 19.9 Marital status 0.287 union 1904 85.8 Not union 316 14.2 Family size 0.691 Small 1398 63.0 Big 822 37.0 Ethnicity 0.008 Shona 1910 86.1 Other languages 309 13.9 Education 0.000 Primary 423 19.1 Secondary 1668 75.2 Tertiary 128 5.7 Wealth Quintile 0.000 Low 747 33.6 Medium 1007 45.4 High 466 21.0 Number of bedrooms 0.923 1 bed 909 40.9 2 beds 804 36.2 3 + beds 507 22.9 Mass Media Exposure 0.001 Low 1368 61.6 High 852 38.4 Religion 0.000 Mainline churches 581 26.2 Pentecostal 403 18.2 Apostolic 683 30.8 Traditional 79 3.6 Other religion 473 21.2 Sexual Debut 0.000 Early 246 11.1 Late 1973 88.9 3.2 Analysis of factors associated with early marriage behaviour The results from the Logistic Regression Model, as shown in Table 2 , elucidate the various factors associated with early marriage among women in Zimbabwe. Predictor variables that demonstrated statistical significance in the bivariate analysis were included in the logistic regression model. The analysis revealed that individuals falling within the age group of 25–29 years exhibited a diminished impact on increasing the likelihood of early marriage (AOR = 0.682; 95% CI = 0.569–0.818). Furthermore, women residing in rural areas displayed a higher prevalence of child marriage compared to their urban counterparts, with living in rural areas significantly increasing the odds of early marriage (AOR = 1.089; 95% CI = 0.799–1.485). The study also identified specific provinces, such as Manicaland, Mashonaland Central, Mashonaland East, Mashonaland West, Masvingo, and Harare, as having higher odds of women getting married before the age of 18, in contrast to Bulawayo province. Notably, Mashonaland East exhibited the most prominent effect in increasing the odds of early marriage. Furthermore, women affiliated with the Apostolic Sect demonstrated a higher likelihood of getting married before the age of 18 (AOR = 1.439; 95% CI = 1.107–1.870) compared to those affiliated with the Mainline religion. Similarly, individuals belonging to other religions presented 1.55 times higher odds of early marriage (AOR = 1.551; 95% CI = 1.137–2.116) compared to Mainline denominations. Educational attainment was found to reduce the probability of marrying before the age of 18, with individuals holding secondary and tertiary education exhibiting lower odds compared to those with primary education (AOR = 0.425, 95% CI = 0.319–0.566 and AOR = 0.155, 95% CI = 0.076–0.314, respectively). High media exposure was also associated with reduced chances of marrying before 18 compared to low media exposure (AOR = 0.838; 95% CI = 0.652–1.075). Additionally, belonging to the medium and high wealth quintile was linked to a lower likelihood of marrying before 18. Similar trends were observed among those belonging to other language categories as their ethnic group. Table 2 Logistic Regression Parameter Estimates and Adjusted Odds Ratio of Early marriage Prevalence Variables P-values AOR (95% CI) Age 20–24 1 1 25–29 0.000 *** 0.682 (0.569;0.818) Place of residence Urban 1 1 Rural 0.6555 1.089 (0.799;1.485) Province Bulawayo 1 1 Manicaland 0.0145*** 2.824 (1.265;6.304) Mashonaland Central 0.0120*** 2.870 (1.294;6.363) Mashonaland East 0.0006*** 4.125 (1.880;9.049) Mashonaland West 0.0083*** 2.915 (1.356;6.268) Matabeleland North 0.0560 2.292 (0.999;5.261) Matabeleland South 0.3056 1.470 (0.664;3.256) Midlands 0.0890 2.074 (0.950;4.528) Masvingo 0.0179 2.734 (1.258;5.938) Harare 0.0087*** 2.857 (1.344;6.073) Ethnicity Shona 1 1 Other languages 0.5043 0.868 (0.549;1.374) Education Primary 1 1 Secondary 0.0000*** 0.425 (0.319;0.566) Tertiary 0.0000*** 0.155 (0.076;0.314) Wealth Quintile Low 1 1 Medium 0.3012 0.856 (0.642; 1.140) High 0.1433 0.678 (0.424;1.086) Mass Media Exposure Low 1 1 High 0.1575 0.838 (0.652;1.075) Religion Mainline churches 1 1 Pentecostal 0.7851 0.956 (0.699;1.322) Apostolic 0.0098*** 1.439 (1.107;1.870) Traditional 0.3432 1.340 (0.750;2.396) Other religion 0.0077*** 1.551 (1.137;2.116) Sexual Debut Late 1 1 Early 0.0000*** 7.311 (4.796; 11.145) 4 Discussions The study aimed to examine early marriage prevalence among women in Zimbabwe. 39.4% of women were married before 18, indicating a persistent challenge of child marriage. This finding aligns with previous regional prevalence in Western and Central Africa 25 . Early sexual debut is identified as the most influential demographic predictor, significantly increasing the likelihood of child marriage. Specifically, early sexual debut before the age of 15 raises the odds of child marriage by 7 times. Similar findings were reported in Nigeria 26 and in a study examining 37 sub-Saharan African countries 5 . Research in sub-Saharan Africa has revealed that the impact of early sexual debut is heightened in economically disadvantaged households that lack sufficient pregnancy management resources 27 . The differences in provinces align with existing research on the social and economic factors influencing early marriage in Zimbabwe. According to Chigwada et al. 19 , poverty and limited access to education and employment opportunities in provinces like Mashonaland East and Manicaland may lead to greater pressure for early marriage. Additionally, urban areas like Harare may experience higher rates due to migration, social pressures, and limited access to support services for vulnerable girls 28 . Research by Chipfakacha 29 emphasizes the role of cultural norms and traditions in perpetuating early marriage in rural Zimbabwe. The Apostolic Sect strictly adheres to traditional values and interpretations of scripture, placing a strong emphasis on marriage for women over education or career aspirations 29 . This can lead to early marriage being encouraged or even mandated in some Apostolic communities as a way to maintain social order and uphold traditional values 30 . These communities often have stricter social norms and limited access to education and economic opportunities, creating a more vulnerable environment for girls and increasing the likelihood of early marriage as a perceived solution to economic hardship or social pressure. A study has revealed a significant association between religious affiliation and the likelihood of early marriage in Zimbabwe, with the influence of religious institutions and practices being highlighted 30 . Specifically, the Apostolic Sect's emphasis on traditional values and interpretations of scripture creates a context where early marriage is encouraged, and this aligns with the influence of the microsystem on individual behavior. Family and religious community interactions play a crucial role in shaping attitudes and norms around early marriage, with pressure from family members and religious leaders contributing to the higher likelihood of early marriage. Having a higher level of education decreases the likelihood of girls getting married before the age of 18 to 31 . Girls with tertiary education had significantly lower odds (0.155) of marrying early. This is because education provides girls with more opportunities and empowerment 31 . Educated girls have greater ambitions beyond marriage and motherhood, which reduces parental pressure to marry them off early for economic or cultural 11 . Finally, media exposure played a significant role in reducing early marriages as shown by results in a decline in odds due to higher media exposure. Numerous studies have shown that girls who are exposed to more media tend to wait longer to get married. In rural Bangladesh, researchers Amin et al. 12 examined data from over 1000 households with access to television. 5 Conclusions This study identifies early sexual debut, specific provinces, Age, religiosity, and education level as key predictors of child marriage in Zimbabwe. A comprehensive approach is needed to address the complex factors driving this practice, including accessible educational and economic programs for girls, community outreach efforts, and strengthening legal protections. Protecting girls' rights requires coordinated strategies across different levels, focusing on policy reform, community mobilization, and gender equality initiatives. Declarations Acknowledgments The authors would like to thank the participants of this research for their candid insights. Author contributions. The authors made significant contributions to the manuscript as follows: EM, KM 1 , and KM 2 were involved in the conceptualization, design, analysis, and interpretation of the study, as well as in drafting the manuscript. CL, EM, and KM 2 contributed to the acquisition and analysis of data. KM 1 CL were involved in the drafting and critical revision of the manuscript. All authors approved the manuscript for submission. Funding The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Data availability Data are available from the authors upon reasonable request. Emmanuel Bvurume (Email: [email protected] ) Code availability Not applicable Ethics approval and consent to participate All participants were taken through a written informed consent process before participation. The researcher obtained official permission to access the dataset from data archives officials. Competing interest, the authors declare no competing interests. References UNICEF, End Child, Marriage. Empower women | UNICEF Zimbabwe [Internet]. 2023 [cited 2024 Feb 27]. https://www.unicef.org/zimbabwe/end-child-marriage-empower-women Kurebwa DKJ. Early marriage in Zimbabwe: A male perspective. [Internet]. SAGE Open, 8(4), 2158244018802389; 2018. https://doi.org/10.1177/2158244018802389 UNICEF. Child marriage. [Internet]. 2018. https://www.unicef.org/protection/child-marriage UNICEF. 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[Internet]. Economic Development and Cultural Change, 65(1), 95–122.; 2017. https://doi.org/10.1086/689543 Chipfakacha V. Factors influencing early marriage among rural Zimbabwean adolescent girls: A qualitative study. [Internet]. International Journal of Africa Nursing Sciences, 12, 100199.; 2020. https://doi.org/10.1016/j.ijans.2020.100199 Makururu S. Religious Affiliation -Child Marriages Nexus in Zimbabwe: A Case of Marange. 2019. Ehsan H, Ghafoori̇ N, Akrami̇ SO. The Impact of Poverty and Education on Female Child Marriage in Afghanistan Evidence from 2015 Afghanistan Demographic and Health Survey. 19 Mayıs Sosyal Bilimler Dergisi [Internet]. 2021 Jun 30 [cited 2024 Feb 1];2(2):418–31. http://dergipark.org.tr/en/doi/ 10.52835/19maysbd.897102 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5262707","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375430357,"identity":"9bc4afc6-fad9-4366-8bee-2382ce9d2bae","order_by":0,"name":"E Bvurume","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYBACAxiDH0QkFJCiRbIBpMUAt0pMLQYHULh4gLlEjtln3hwbOePzi59ueGDAIM8vdgC/FssZOcazebelGZvdeGZ2A+gww5mzEwg47MwZY2bebYcTt904ANaSYHCbOC3/6zfPOP6NSC3He0BaDiQY8PcQaYtle1sx49xtyYYzbvCUAbVIEPaLOTPzZoa32+zk+fuPb7v5o8JGnl+agBYEkACrlCBWOQjwHyBF9SgYBaNgFIwkAAA5EULSEW2R1wAAAABJRU5ErkJggg==","orcid":"","institution":"University of Zimbabwe","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"E","middleName":"","lastName":"Bvurume","suffix":""},{"id":375430358,"identity":"edfc2907-0340-4119-b45b-036e8854e565","order_by":1,"name":"K Mangombe","email":"","orcid":"","institution":"University of Zimbabwe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"K","middleName":"","lastName":"Mangombe","suffix":""},{"id":375430359,"identity":"062e036d-ed30-4ae6-bc43-afa39e64bc78","order_by":2,"name":"K Mhlanga","email":"","orcid":"","institution":"University of Zimbabwe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"K","middleName":"","lastName":"Mhlanga","suffix":""},{"id":375430360,"identity":"4f9197fa-2e3b-4275-86b2-055d461b7fb4","order_by":3,"name":"C Lwanga","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"C","middleName":"","lastName":"Lwanga","suffix":""}],"badges":[],"createdAt":"2024-10-14 16:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5262707/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5262707/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71657693,"identity":"1ff382a6-af63-436f-aa39-89586d76b34d","added_by":"auto","created_at":"2024-12-17 13:10:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":687236,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5262707/v1/e28f412b-5bb1-4775-a89d-bcf7bc829bef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The prevalence and correlates of early marriages for women aged 20-29 in Zimbabwe: An analysis of the 2019 Multiple Indicator Cluster Survey (MICS)","fulltext":[{"header":"1 Background","content":"\u003cp\u003eThe issue of child marriage, defined as marriage before reaching the age of 18, remains a significant global concern with severe implications for human rights and the well-being of young girls \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Shockingly, the number of girls worldwide who have been married before the age of 18 exceeds 650\u0026nbsp;million, and projections indicate that this number could soar to 700\u0026nbsp;million by 2030 if decisive action is not taken \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Of particular concern is the region of sub-Saharan Africa, which exhibits some of the highest rates of child marriage globally, with over 50\u0026nbsp;million girls being married during childhood \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Regional trends suggest that Eastern and Southern Africa account for over 50\u0026nbsp;million child brides, with Ethiopia reporting the highest absolute numbers \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Previous research has shown that child marriages are associated with adverse lifelong outcomes for young women, including increased exposure to intimate partner violence \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, implications for maternal and child health, and the loss of opportunities due to lack of education\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn Zimbabwe, data from the latest Multiple Indicator Cluster Survey (MICS) conducted in 2019 revealed that one-third of women are married by the age of 18, with significantly higher rates reported in rural areas as compared to urban areas \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Factors such as wealth status have consistently emerged as key determinants associated with child marriage, with poverty driving normalization and providing financial incentives for early unions \u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Furthermore, higher levels of education have been linked to delayed marriage by providing opportunities and empowerment \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Access to mass media has the potential to shift social norms, favouring education over domestic roles \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Additionally, certain ethnic and religious practices have traditionally promoted early unions \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, while larger families face greater financial strain, leading to pressure for early marriage \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Moreover, rural residents can isolate girls from information and opportunities \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and early sexual initiation is correlated with an increased risk of marriage due to associated stigma, dropout rates, and pregnancy \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Finally, housing conditions also impact perceived risks and parental concerns over privacy \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eThis study was a quantitative correlational design among women aged 20\u0026ndash;29 years in Zimbabwe.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample size, sampling procedure, and selection criteria\u003c/h2\u003e \u003cp\u003eThis research utilized data from the 2019 Multiple Indicator Cluster Survey (MICS) to examine the factors related to early marriages in Zimbabwe. The 2019 MICS is part of UNICEF's series of nationally representative cross-sectional household survey programs, aimed at filling socio-demographic data gaps for monitoring the well-being of children, women, and men. Conducted periodically by the Zimbabwe National Statistical Agency, the survey covered 10703 households across 462 clusters in all 10 provinces of Zimbabwe, using a stratified two-stage cluster sampling design \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Clusters were selected using probability proportional to size, with 26 households selected per cluster using systematic sampling. All women aged 15\u0026ndash;49 in the selected households were eligible for interview; for this study, a subsample of 2169 women aged 20\u0026ndash;29 was used. The age range was chosen based on a previous study conducted in Zambia \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Survey instruments\u003c/h2\u003e \u003cp\u003eThe dependent variable in this study was derived from the general inquiry on age at first marriage: \"When did you get married for the first time?\" A binary variable was constructed based on this question to indicate whether the female respondent got married before the age of 18. In this analysis, individuals who married before age 18 were coded as 1 (Yes) and those who married at 18 or older were coded as 0 (No). The independent variables included wealth, education, urban or rural residence, family size, early sexual debut, religion, ethnicity, and media exposure. These variables were sourced from previous research on child marriage \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The respondent's age at the time of the survey was treated as a continuous variable and later categorized into 20\u0026ndash;24 and 25\u0026ndash;29 age groups. Place of residence was categorized as urban or rural, and provinces were identified as Bulawayo, Manicaland, Mashonaland Central, Mashonaland East, Mashonaland West, Matabeleland North, Matabeleland South, Midlands, Masvingo, and Harare. Marital status was grouped as married or single, while family size was categorized as small or large. Ethnicity was classified as Shona, Ndebele, and other languages, and education level as primary, secondary, and tertiary. The wealth index was divided into quintiles, denoted as low, medium, and high. The number of bedrooms was coded as one, two, or three and over, and exposure to mass media was categorized as low or high. Religious affiliation was classified as Mainline Churches, Pentecostal, Apostolic, Traditional, and other.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis\u003c/h2\u003e \u003cp\u003eThe data were weighted to address sampling imbalances, complex survey design, and nonresponse. Frequency distributions were utilized to provide a description and summary of the characteristics of the respondents in the sample. Subsequently, a comparison was made between the characteristics of women who experienced early marriage and those who did not. The relationship between the dependent variable, which measured whether or not a woman married before the age of 18, and each independent variable was established at the bivariate level and was tested using a chi-squared test, set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. According to the data and as observed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, approximately 40.9% of the women reported having married before the age of 18. Independent factors included in the final model were selected based on their statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) at the bivariate level. The results of the logistic regression model were presented in the form of adjusted odds ratios (AORs) with their corresponding 95% confidence intervals in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Before fitting the model, all independent variables were tested for multicollinearity using the variance inflation factor (VIF) (results not presented here).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Ethical consideration\u003c/h2\u003e \u003cp\u003eAn analysis was conducted on a population-based dataset that is publicly available on the UNICEF MICS website. The researcher obtained official permission to access the dataset from data archives officials. Informed consent and voluntary participation were obtained from all participants before any data was collected. To ensure privacy, the final data was separated from unique identifiers such as names and locations, guaranteeing confidentiality and anonymity to all participants.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Distribution of the study population by socio-demographic characteristics\u003c/h2\u003e \u003cp\u003eA subsample of 2219 women was included in the analysis drawn from the 2019, Multiple Indicator Cluster Survey. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the distribution of women included in the analysis by background characteristics and it also displays results at the Bivariate-level, cross tabulations with a chi-square test that was used to analyze the association between early marriage and selected predictor variables.\u003c/p\u003e \u003cp\u003eThe majority of female respondents were from rural areas, making up 58.4% of the sample. 85.2% of the respondents reported being married or in a union, and 63% of them came from small families with fewer than 6 members. A significant proportion, around 86.1%, identified as Shona and were aged 25 or over, making up 52.6% of the sample. 75.2% of the women had completed secondary education. In terms of wealth distribution, 33.6% were in the lower category, 45.4% in the medium, and 21% in the high category. When it comes to the number of bedrooms, 36.2% reported having 2 bedrooms, while 40.9% had one bedroom and 22.9% had three or more bedrooms. Regarding religious affiliation, 26.2% were affiliated with mainline churches, 18.2% with the Pentecostal church, close to 30.8% with the apostolic faith, and only 3.6% with the traditional church. The study also found that early sexual debut was strongly associated with child marriage, with 11.1% of women who had their sexual debut before the age of 15 years getting married before 18. The study also revealed that background factors such as place of residence, religion, ethnicity, mass media exposure, and wealth quintile were significantly associated with early marriages. However, the number of bedrooms, marital status, and family size showed no strong correlations with early marriage as indicated by p-values greater than 0.05\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentage distribution of selected characteristics of respondents (n\u0026thinsp;=\u0026thinsp;2219)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eΧ\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e-test (p-values)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEarly marriage\u003c/b\u003e \u003cem\u003e(Outcome variable)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly marriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate marriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003e0.015\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003e0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProvince\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003e\u003cem\u003e0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulawayo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManicaland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMashonaland Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMashonaland East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMashonaland West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatabeleland North\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatabeleland South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMidlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasvingo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003e0.287\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003e0.691\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBig\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003e0.008\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShona\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther languages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003e0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth Quintile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003e0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of bedrooms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003e0.923\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 bed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 beds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003csup\u003e+\u003c/sup\u003e beds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMass Media Exposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cem\u003e0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMainline churches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePentecostal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApostolic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraditional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther religion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSexual Debut\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003e0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Analysis of factors associated with early marriage behaviour\u003c/h2\u003e \u003cp\u003eThe results from the Logistic Regression Model, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, elucidate the various factors associated with early marriage among women in Zimbabwe. Predictor variables that demonstrated statistical significance in the bivariate analysis were included in the logistic regression model. The analysis revealed that individuals falling within the age group of 25\u0026ndash;29 years exhibited a diminished impact on increasing the likelihood of early marriage (AOR\u0026thinsp;=\u0026thinsp;0.682; 95% CI\u0026thinsp;=\u0026thinsp;0.569\u0026ndash;0.818). Furthermore, women residing in rural areas displayed a higher prevalence of child marriage compared to their urban counterparts, with living in rural areas significantly increasing the odds of early marriage (AOR\u0026thinsp;=\u0026thinsp;1.089; 95% CI\u0026thinsp;=\u0026thinsp;0.799\u0026ndash;1.485).\u003c/p\u003e \u003cp\u003eThe study also identified specific provinces, such as Manicaland, Mashonaland Central, Mashonaland East, Mashonaland West, Masvingo, and Harare, as having higher odds of women getting married before the age of 18, in contrast to Bulawayo province. Notably, Mashonaland East exhibited the most prominent effect in increasing the odds of early marriage. Furthermore, women affiliated with the Apostolic Sect demonstrated a higher likelihood of getting married before the age of 18 (AOR\u0026thinsp;=\u0026thinsp;1.439; 95% CI\u0026thinsp;=\u0026thinsp;1.107\u0026ndash;1.870) compared to those affiliated with the Mainline religion. Similarly, individuals belonging to other religions presented 1.55 times higher odds of early marriage (AOR\u0026thinsp;=\u0026thinsp;1.551; 95% CI\u0026thinsp;=\u0026thinsp;1.137\u0026ndash;2.116) compared to Mainline denominations. Educational attainment was found to reduce the probability of marrying before the age of 18, with individuals holding secondary and tertiary education exhibiting lower odds compared to those with primary education (AOR\u0026thinsp;=\u0026thinsp;0.425, 95% CI\u0026thinsp;=\u0026thinsp;0.319\u0026ndash;0.566 and AOR\u0026thinsp;=\u0026thinsp;0.155, 95% CI\u0026thinsp;=\u0026thinsp;0.076\u0026ndash;0.314, respectively). High media exposure was also associated with reduced chances of marrying before 18 compared to low media exposure (AOR\u0026thinsp;=\u0026thinsp;0.838; 95% CI\u0026thinsp;=\u0026thinsp;0.652\u0026ndash;1.075). Additionally, belonging to the medium and high wealth quintile was linked to a lower likelihood of marrying before 18. Similar trends were observed among those belonging to other language categories as their ethnic group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic Regression Parameter Estimates and Adjusted Odds Ratio of Early marriage Prevalence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.682 (0.569;0.818)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.089 (0.799;1.485)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProvince\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulawayo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManicaland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0145***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.824 (1.265;6.304)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMashonaland Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0120***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.870 (1.294;6.363)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMashonaland East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0006***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.125 (1.880;9.049)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMashonaland West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0083***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.915 (1.356;6.268)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatabeleland North\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.292 (0.999;5.261)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatabeleland South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.470 (0.664;3.256)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMidlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.074 (0.950;4.528)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasvingo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.734 (1.258;5.938)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0087***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.857 (1.344;6.073)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShona\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther languages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.868 (0.549;1.374)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.425 (0.319;0.566)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.155 (0.076;0.314)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth Quintile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.856 (0.642; 1.140)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.678 (0.424;1.086)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMass Media Exposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.838 (0.652;1.075)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMainline churches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePentecostal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.956 (0.699;1.322)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApostolic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0098***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.439 (1.107;1.870)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraditional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.340 (0.750;2.396)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther religion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0077***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.551 (1.137;2.116)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSexual Debut\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.311 (4.796; 11.145)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussions","content":"\u003cp\u003eThe study aimed to examine early marriage prevalence among women in Zimbabwe. 39.4% of women were married before 18, indicating a persistent challenge of child marriage. This finding aligns with previous regional prevalence in Western and Central Africa \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Early sexual debut is identified as the most influential demographic predictor, significantly increasing the likelihood of child marriage. Specifically, early sexual debut before the age of 15 raises the odds of child marriage by 7 times. Similar findings were reported in Nigeria \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and in a study examining 37 sub-Saharan African countries \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Research in sub-Saharan Africa has revealed that the impact of early sexual debut is heightened in economically disadvantaged households that lack sufficient pregnancy management resources \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe differences in provinces align with existing research on the social and economic factors influencing early marriage in Zimbabwe. According to Chigwada et al. \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, poverty and limited access to education and employment opportunities in provinces like Mashonaland East and Manicaland may lead to greater pressure for early marriage. Additionally, urban areas like Harare may experience higher rates due to migration, social pressures, and limited access to support services for vulnerable girls \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Research by Chipfakacha \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e emphasizes the role of cultural norms and traditions in perpetuating early marriage in rural Zimbabwe. The Apostolic Sect strictly adheres to traditional values and interpretations of scripture, placing a strong emphasis on marriage for women over education or career aspirations \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. This can lead to early marriage being encouraged or even mandated in some Apostolic communities as a way to maintain social order and uphold traditional values \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. These communities often have stricter social norms and limited access to education and economic opportunities, creating a more vulnerable environment for girls and increasing the likelihood of early marriage as a perceived solution to economic hardship or social pressure. A study has revealed a significant association between religious affiliation and the likelihood of early marriage in Zimbabwe, with the influence of religious institutions and practices being highlighted \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Specifically, the Apostolic Sect's emphasis on traditional values and interpretations of scripture creates a context where early marriage is encouraged, and this aligns with the influence of the microsystem on individual behavior. Family and religious community interactions play a crucial role in shaping attitudes and norms around early marriage, with pressure from family members and religious leaders contributing to the higher likelihood of early marriage.\u003c/p\u003e \u003cp\u003eHaving a higher level of education decreases the likelihood of girls getting married before the age of 18 to \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Girls with tertiary education had significantly lower odds (0.155) of marrying early. This is because education provides girls with more opportunities and empowerment \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Educated girls have greater ambitions beyond marriage and motherhood, which reduces parental pressure to marry them off early for economic or cultural \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Finally, media exposure played a significant role in reducing early marriages as shown by results in a decline in odds due to higher media exposure. Numerous studies have shown that girls who are exposed to more media tend to wait longer to get married. In rural Bangladesh, researchers Amin et al. \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e examined data from over 1000 households with access to television.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThis study identifies early sexual debut, specific provinces, Age, religiosity, and education level as key predictors of child marriage in Zimbabwe. A comprehensive approach is needed to address the complex factors driving this practice, including accessible educational and economic programs for girls, community outreach efforts, and strengthening legal protections. Protecting girls' rights requires coordinated strategies across different levels, focusing on policy reform, community mobilization, and gender equality initiatives.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch4\u003eAcknowledgments\u0026nbsp;The authors would like to thank the participants of this research for their candid insights.\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions.\u0026nbsp;\u003c/strong\u003eThe authors made significant contributions to the manuscript as follows: \u0026nbsp;EM, KM\u003csup\u003e1\u003c/sup\u003e, and KM\u003csup\u003e2\u003c/sup\u003e\u0026nbsp; \u0026nbsp;were involved in the conceptualization, design, analysis, and interpretation of the study, as well as in drafting the manuscript. \u0026nbsp;CL, EM, and KM\u003csup\u003e2\u003c/sup\u003e contributed to the acquisition and analysis of data. KM\u003csup\u003e1\u003c/sup\u003e CL were involved in the drafting and critical revision of the manuscript. All authors approved the manuscript for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eData are available from the authors upon reasonable request. Emmanuel Bvurume (Email: [email protected])\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003eAll participants were taken through a written informed consent process before participation. The researcher obtained official permission to access the dataset from data archives officials.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest,\u0026nbsp;\u003c/strong\u003ethe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUNICEF, End Child, Marriage. Empower women | UNICEF Zimbabwe [Internet]. 2023 [cited 2024 Feb 27]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unicef.org/zimbabwe/end-child-marriage-empower-women\u003c/span\u003e\u003cspan address=\"https://www.unicef.org/zimbabwe/end-child-marriage-empower-women\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurebwa DKJ. Early marriage in Zimbabwe: A male perspective. [Internet]. 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The Impact of Poverty and Education on Female Child Marriage in Afghanistan Evidence from 2015 Afghanistan Demographic and Health Survey. 19 Mayıs Sosyal Bilimler Dergisi [Internet]. 2021 Jun 30 [cited 2024 Feb 1];2(2):418\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dergipark.org.tr/en/doi/\u003c/span\u003e\u003cspan address=\"http://dergipark.org.tr/en/doi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.52835/19maysbd.897102\u003c/span\u003e\u003cspan address=\"10.52835/19maysbd.897102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"Correlates, Prevalence, Early Marriage, Logistic Regression, Zimbabwe","lastPublishedDoi":"10.21203/rs.3.rs-5262707/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5262707/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEarly marriage remains a significant public health issue, particularly in sub-Saharan Africa. It has the potential to deprive adolescent girls of their sexual and reproductive rights and expose them to the risks associated with early and high-risk fertility behavior. This study analyzes the prevalence and factors associated with early marriages in Zimbabwe.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe analysis utilized data from the 2019 Zimbabwe Multiple Indicator Cluster Survey. A pooled weighted sub-sample of 2219 women aged 20\u0026ndash;29 was used for the study. Statistical analysis was carried out using the R-Studio software package, version 3, considering complex survey design analysis. Logistic regression models were used to examine the correlations between individual-level factors and early marital behavior.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAccording to the study, 40.9% of women were married before reaching adulthood. The research found a strong correlation between early sexual initiation as strongly related to early marriage (OR\u0026thinsp;=\u0026thinsp;7.311, p\u0026thinsp;\u0026lt;\u0026thinsp;0.005), living in the Mashonaland provinces, particularly Mashonaland East, and an increased likelihood of child marriage (OR\u0026thinsp;=\u0026thinsp;4.1169, p\u0026thinsp;\u0026lt;\u0026thinsp;0.005). Furthermore, religious affiliation and level of education were also identified as independent factors that elevate the risk of child marriage.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe research highlighted the necessity of implementing coordinated strategies across different policy and community levels to empower girls, provide education, and ensure protection. These strategies are crucial for addressing the structural, sociocultural, and individual barriers effectively. The study underscored the importance of the evidence presented, which can guide policy-making and the development of targeted interventions to combat child marriage in Zimbabwe. Furthermore, it recommended that future qualitative research should delve deeper into community and intergenerational factors and utilize mixed methods to explore the issues associated with high early marriage rates in the Mashonaland provinces.\u003c/p\u003e","manuscriptTitle":"The prevalence and correlates of early marriages for women aged 20-29 in Zimbabwe: An analysis of the 2019 Multiple Indicator Cluster Survey (MICS)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-08 11:44:05","doi":"10.21203/rs.3.rs-5262707/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":"29526c35-0b20-446a-b85f-128720981b80","owner":[],"postedDate":"November 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-17T13:09:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-08 11:44:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5262707","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5262707","identity":"rs-5262707","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}