Factors Influencing the Uptake of Modern Contraceptives among Women in Tanzania: A survey of Public Health Facilities in Dodoma City Council

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Thirty- eight per cent of reproductive-aged women currently use modern contraceptive methods, and 22% have an unmet need for family planning. Objectives: To determine the factors influencing the uptake of modern contraceptives among women attending public health facilities in Dodoma City Council. Methods An analytical cross-sectional study using a quantitative approach was conducted among 362 women of reproductive age who attended public health facilities in Dodoma City and 32 healthcare workers. The participants were selected using simple random sampling. Data was collected from 6 September to 15 October 2021. A structured questionnaire was used. Data was analyzed using descriptive and inferential statistics at the 0.05 level of significance. The strength of association was determined using a logistic regression model. Results Of the 362 participants, 46.6% were between 24 and 34 years old. Most (60.2%) were married, and 48.9% had completed secondary education. About a third (32.6%) had a monthly income of 300,000 to 399,999 TZS. A statistically significant association at 0.05 was observed between uptake of contraceptives and education (p = 0.043), marital status (p = 0.024), partner’s support (p = 0.001), awareness, preference, and satisfaction (p = 0.000). Conclusion The study revealed that education, marital status, awareness, preference, and satisfaction of the respondents have a positive significant effect on the uptake of modern contraceptives. Uptake of contraceptives Women of reproductive age Family planning Dodoma Background Contraception is described as pregnancy prevention by inhibiting the normal process of ovulation, fertilization and implantation. The use of modern contraceptive methods allows couples and individuals to attain their desired number of children [ 1 ]. To attain target 3.7 of Sustainable Development Goal (SDG) 3, which underscores that by 2030, the world should ensure universal access to sexual and reproductive health services, the use of modern contraceptive methods is crucial [ 2 ]. It was well reported that the reductions in unintended pregnancies and maternal and neonatal mortality can lead to the achievement of SDG 3 targets 3.1 and 3.2, which aim to reduce the global maternal mortality ratio to less than 70 per 100,000 live births and to end all preventable deaths of children under five by 2030 [ 2 ]. In 2019, there were approximately 1.9 billion women of reproductive age. Among these women, 1.1 billion needed family planning services but only 842 million used modern contraception [ 3 ]. According to the World Fertility and Family Planning Report of 2020 [ 4 ], the use of contraceptives among women of reproductive age in 2019 was above 55% in 37 countries and below 20% in 23 countries. Several factors, such as cultural and religious myths and misconceptions, undermine modern contraception [ 5 ]. In addition, partner-related factors and socio-demographic characteristics (age, education level, and religion) are associated with modern contraceptive use [ 6 ]. In low and middle-income countries, 214 million women who wanted to avoid pregnancy in 2019 were not using any method of contraception [ 7 ], and most of them were from Sub-Saharan African countries [ 7 ]. The reasons for this might be inadequate knowledge, poor accessibility to modern contraceptive methods, preference for having many children, among others [ 8 ] Moreover, United Nations Department of Economic and Social Affairs and Population Division data show that more than 20% of the unmet need for family planning was in 15 countries from Sub-Saharan Africa [ 9 ]. In Tanzania, 38% of reproductive-aged women currently use modern contraceptive methods, and 22% have an unmet need for family planning [ 10 ] .Teenage pregnancies among 15-19-year-olds range from 23–27% and are higher among rural than urban women [ 10 ]. The total fertility ratio (TFR) was 5.4 children per woman of childbearing age in 2010 and 5.2 in 2015 [ 10 ]. Modern contraceptive methods currently available in the public sector supply chain in Tanzania include Oral contraceptive pills, injectable contraceptives (DMPA), condoms, intrauterine devices (IUCD), and contraceptive implants [ 11 ]. The Tanzanian Ministry of Health has implemented a series of national strategies and policies to facilitate the use of contraceptive services [ 12 ]. The main objective of these guidelines is to increase the contraceptive prevalence rate (CPR) and reduce the unmet need for family planning. The National target was to attain a national modern contraceptive prevalence rate (CPR) of 45% by 2020 and reduce the current unmet need for family planning (FP) to 10% by 2020[ 12 ]. Nevertheless, this country’s target was not achieved [ 13 ]. In the Dodoma region, 14.7% of family planning needs are unmet [ 10 ]. Despite existing data on the unmet need for family planning and the low contraceptive prevalence rate in Tanzania, no studies have been conducted on the factors influencing contraceptive use among women of reproductive age. Thus, this study aims to determine these factors to help policymakers and planners identify areas on which their programs and policies should focus shortly to improve the uptake of modern contraceptives. Methods Study setting The study was conducted at the public health facilities of the Dodoma City Council. Due to its convenience, Dodoma City Council was chosen as the area of study. It is a rapidly urbanizing city in Tanzania following the reallocation of government offices to Dodoma from Dar es Salaam in 2017, and the need for family planning services is high. The City Council has 35 public health facilities that provide family planning services, out of which two are hospitals, one is a health centre, and thirty-two are dispensaries. Study design and population An analytical cross-sectional design was used to investigate the factors that influence modern contraceptive use among women in public health facilities in Dodoma City. The study populations were health facility workers in reproductive and child health clinics that offer family planning and women of reproductive age who visited these clinics. Sampling Two samples were used according to the study populations. One comprised health facility workers, and the other was for women seeking family planning services. The city has 35 health facilities offering family planning services. The representative sample size in this population was calculated using Yamane’s formula to obtain 32 health facilities. These facilities were selected using simple random sampling aided by computer-generated numbers. For the case of clients attending RCH clinics, Fischer’s formula was used to determine the sample size based on the contraceptive prevalence rate from a previous study, which was 38%. Therefore, the determined sample size was 362 women of reproductive age. Since there are 32 health facilities involved, 12 women were selected using simple random sampling by employing the lottery method from each clinic. Data collection tools Data were captured through two structured questionnaires. These were for women seeking family planning services and health care workers offering reproductive health services. Theses tools contained open and closed questions on various aspects of socio-demographic factors that are associated with their clients’ contraceptive use( age, marital status, educational level, income, awareness support from the partner, cultural and religion beliefs), the supply chain factors( forecasting, stock out, order fill rate, lead time and inventory management), and the health care system & policy factors (includes staff training, counselling skills, availability of guideline and standard, location and distance of the health facility, financing, satisfaction ) affecting uptake of contraceptives. Data collection procedures Contact was made with the City council to inform the selected facilities about the study and to arrange visiting dates. Two to three health facilities were visited per day. The questionnaires were pre-tested in two facilities in Dodoma City before data collection. The problems encountered, time spent, and other suggestions for improvement were considered and used to make final adjustments to the questionnaire. Those facilities used for pre-testing were not included in the data collection process. The questionnaires had sections that addressed all the specific objectives, which were socio-demographic, supply chain, and health system and policy factors that impact reproductive health services use. To ensure internal validity, the researcher and supervisor reviewed the overall content of the questionnaire for readability, clarity, and completeness regarding the questions to be included in the study. The questionnaires were structured so that some questions were designed to ask similar information in different ways to ensure consistency. Furthermore, the questionnaires were translated into Swahili to avoid language barriers. External validity was achieved by using simple random sampling to ensure that the data was collected according to the determined sample sizes. To ensure reliability, the responses from the pretest were compared and observed to concur. Ethical approval was obtained before data collection. The participants willingly consented to participate in the study. The confidentiality of the data was maintained by coding the questionnaires and keeping them in a secure place only accessible to the principal investigator. Data analysis The collected data were analyzed using SPSS version 23 to obtain descriptive statistics, such as percentages and frequencies, on the selected public health facilities. Furthermore, the inferential analysis of data was carried out whereby; the bivariate and multivariate logistic regression models were applied to examine the association between the uptake of modern contraceptives and study variables at a 5% statistical level of significance. Fischer's exact test and Pearson's Chi-square tests were used to ascertain the correlation between the study variables and the uptake of modern contraceptives. The odds ratio (OR) estimated the extent of the association at 95% confidence interval (CI). Results Socio-demographic characteristics According to Table 1 , a relative majority (46.7%) of the respondents were 24–34 years of age (60.2%) were married, and 48.9% had attained a secondary level of education. A comparatively higher proportion (32.6%) had a monthly income of 300,000– 399,999 Tanzanian Shillings (TZS). Table 1 Socio-demographic characteristics of the respondents (n = 362) Socio-demographic Characteristic Description Observations Percentage Age in years 15–24 125 34.53 25–34 169 46.69 35–44 57 15.75 45–54 11 3.04 Marital Status Single 108 29.83 Married 218 60.22 Divorced 7 1.93 Widow 3 0.83 Separated 26 7.18 Level of education No education 4 1.1 Primary 61 16.85 Secondary 177 48.9 Certificate 60 16.57 Diploma 34 9.39 Bachelor Degree 24 6.63 Monthly income 10,000–99,999 35 9.67 100,000–199,999 57 15.75 200,000–299,999 61 16.85 300,000–399,999 118 32.6 400,000–499,999 66 18.23 500,000 and above 25 6.91 Uptake of modern contraceptive The results showed that the majority of women (74.6%) use modern contraceptives (Table 2). The types of modern contraceptives preferred were injectable methods (39.7%), followed by pills (23.6%), implants (22.5%), and condoms (7.49%). The main reasons for preferred contraceptives were minimal side effects (50.19%) and convenience of use (47.9%). In addition, 23.4% of participants experienced side effects. The most prevalent side effects were nausea and vomiting (41.2%), heavy bleeding (33.8%), and lower abdominal pains (11.8%). Table 2: Frequency of modern contraceptive use (n = 267) Variable Description Frequency Percent Type of contraceptives preferred Pills 63 23.6 Injectable 106 39.7 Condoms 20 7.49 Implants 60 22.47 Copper T 18 6.74 Reason for the choice of contraceptives Advised by the health provider 4 1.54 Few side effects 134 50.19 Only available choice 1 0.37 Convenience 128 47.94 Occurrence of side effects Experienced side effects 70 26.32 Did not experience side effects 196 73.68 Type of side effects Nausea and vomiting 28 41.18 Heavy bleeding 23 33.82 Headache 6 8.82 Lower abdominal pain 8 11.76 Delayed period 3 4.41 Association between Uptake of Modern Contraceptives and study participants characteristics The associations between the socio-demographic characteristics and uptake of contraceptives were determined using chi-square at a 0.05 level of significance, and the results are presented in Table 3 . The characteristics that had a statistically significant association with the uptake of contraceptives were marital status (p = 0.024), level of education (p = 0.043), level of satisfaction (p = 0.001), and availability of support (p = 0.001), and status of awareness (p = 0.000). Therefore, this suggests that these variables influence the uptake of modern contraceptives in the community. Table 3 Association between the Uptake of Modern Contraceptives and socio-demographic characteristics (n = 362) Variable Uptake Of Contraceptives Person chi-square P-value Yes No Marital status Married 106 148 5.0818 0.024* Not Married 59 49 Education status Educated 128 169 4.1095 0.043* Not Educated 37 28 Preference status Prefer 51 138 55.14 0.000* Not Preferred 114 59 Status of satisfaction Satisfied 110 49 63.68 0.000* Not Satisfied 55 148 Support status Supported 136 131 11.7674 0.001* Not Supported 29 66 Awareness status Uptake Of Contraceptives Fisher’s exact P-value Yes No Aware 95 0 0.000 0.000 Not Aware 70 197 *- statistically significant relationship Factors Influencing the Uptake of Modern Contraceptives To understand factors that influence the uptake of modern contraceptives, multivariate logistic regression models were employed (Table 4 ). The only factors significantly associated with the uptake of modern contraceptives were awareness status (AOR = 1.0679, P = 0.000), preference status (AOR = 1.0117, P = 0.010), and satisfaction status (AOR = 1.0416, P = 0.036). Table 4 Predictors of Uptake of modern contraceptives Variable Description Bivariate Analysis Multivariate Analysis Crude Odds Ratio (95% CI) P-Value Adjusted Odds Ratio (95% CI) P-Value Education status dummy 1 = educated 0 = not educated 1.0275 (0.79–1.32) 0.034* 1.0047 (0.09–1.21) 0.106 Marital status dummy 1 = married 0 = otherwise 0.9948 (0.76–1.31) 0.665 0.9985 (0.08–0.16) 0.848 Awareness status dummy 1 = yes 0 = no 1.4590 (0.69–3.05) 0.000* 1.0679 (0.46–17.30) 0.000* Preference status dummy 1 = preferred 0 = not preferred 1.0657 (0.84–1.34) 0.001* 1.0117 (0.52–1.26) 0.010* Support status dummy 1 = supported 0 = not supported 1.3958 (0.78–2.48) 0.021* 1.0667 (0.54–1.42) 0.262 Satisfaction status dummy 1 = satisfied 0 = not satisfied 1.2626 (0.62–2.55) 0.009* 1.0416 (0.53–0.94) 0.036* *- statistically significant association, CI-confidence interval Quality of service and other factors that affect the uptake of contraceptives The findings revealed that 77.4% of respondents were counselled on how to use contraceptives by health providers. One hundred and fifty-six (58.9%) received advice from health providers on dealing with the side effects. In addition, only 11.7% of the respondents were forced to take a specific method of modern contraceptives. Most (76.6%) of the respondents were satisfied with the service quality. The reasons for dissatisfaction for the remaining respondents were long waiting time (45.2%), lack of a contraceptive method of choice (41.9%), and lack of Privacy/confidentiality (12.9%) when delivering services. The majority (56.3%) of the facilities did not use guidelines. There was a statistically significant association between the uptake of modern contraceptives and service satisfaction level (p = 0.001). In examining the provision of contraceptive management, the majority (71.9%) of the staff had been trained on family planning services and 78.1% on the Logistic Management Information System (LMIS). The latter covered four aspects, namely: assessment of stock status, knowledge of minimum and maximum stock level, reporting and requesting health commodities through the the-LMIS system, and ensuring adequate physical storage of contraceptives. The most frequent interval of supportive supervision visits during the 6 months before the study was once a month (68.8%) and twice in six months (28.1%). Regarding the costs incurred by the respondents on the uptake of contraceptives, 73.2% did not incur any costs. For the distance from the respondents’ residence to the health facilities, most (75.5%) respondents resided between 1 to 5 kilometres, while 23.4% lived between 6 to 10 kilometres. The modes of reaching the health facilities were mainly walking (57.6%) and public service vehicles (33.9%). Supply chain factors that affect the uptake of contraceptives The nurses (62.5%) were primarily responsible for ordering contraceptives, followed by pharmacists (28.1%). The considerations in forecasting the number of contraceptives in health facilities were mainly based on consumption data (71.9%) and service utilization data (18.75%). Concerning the lead time, 78.1% of the health facilities received orders within three months. The majority (53.1%) of the health facilities received the full quantity of all the contraceptives ordered. Regarding stock levels, 68.8% of the health facilities had minimum and maximum stock levels of 3 and 6 months, respectively—moreover, 71.9% of health facilities received resupply once every three months. Regarding the availability of modern contraceptives in health facilities, only 34.38% of health facilities had all the available types. The reported contraceptives that were often not available included implants (38.1%), oral contraceptive pills (28.8%), Copper T (23.8%), and injectables (9.5%). Most health facilities (65.6%) used the Information Communication Technologies (ICT) System for reporting and ordering contraceptives. The reported ICT tools used were computers (76.19%) and mobile phones (23.81%). Moreover, 65.6% of the health facilities lacked an internet connection for recording health information, diagnosis, and service delivery. Discussion This study aimed to assess the factors affecting contraceptive uptake among women attending public health facilities in Dodoma City Council. The study reveals that age was a significant factor affecting FP uptake, whereby the majority of the respondents associated with the uptake of modern contraceptives were in the aged between 25–34 years. Similar results have been obtained in other studies in Ethiopia, where the results show that contraceptive use is highest among 24-35-year-olds [ 14 ]. The reason could be that this age category is usually married and, therefore, likely to get pregnant easily. The use of DMPA injections was comparatively high at 39.7%, similar to the findings from the Tanzania Demographic Health Survey, which showed that women prefer Depo-Provera to any other modern family plan [ 10 ]. This drug is convenient and reliable since it is administered quarterly. Noncompliance is, therefore, likely to be minimal [ 7 ]. However, this result is contrary to that in Ghana, where condoms are the most widely used, probably due to the role condoms play in both the prevention of sexually transmitted infections and the control of unintended pregnancies [ 15 ]. The main determinants of contraceptive use were level of education, access to health facilities, the influence of social media, and neighbourhood. In this case, education was found to be the main source of information influencing awareness of modern contraceptive use. This is probably because the importance of family planning is relayed in schools, and women can make informed choices. However, this differs from other studies in Ghana, where television was identified as the main source of information influencing awareness on the uptake of modern contraceptives [ 16 ]. Reliable income had a positive correlation with the uptake of modern contraceptives. Individuals with higher monthly incomes are more likely to be aware of the need for child spacing so that they can have adequate time to work and, therefore, tend to use family planning more frequently. This finding is consistent with Edo state, Nigeria [ 17 ]. Residing close to health facilities was found to increase the use of modern contraceptive methods. The time and costs incurred to reach the facilities were minimal; this finding is consistent with that in Senegal, where people travelling long distances to reach health facilities are less likely to use contraceptives [ 18 ]. Most of the respondents were counselled on how to use contraceptives by healthcare providers. This service was found to be significantly associated with the increase in uptake of contraceptives. Counselling enhances interaction with clients and informs them on a broad range of contraceptive method choices. Health care providers trained in Family Planning counselling were found to be more useful to their clients’ satisfaction with using Family Planning methods. Pre-counselling improves the contraceptive uptake rate significantly [ 19 ]. The utilization of contraceptives is enhanced when couples are in agreement and support one another. In Ethiopia, women who discuss the issue of modern contraceptives with their husbands and are supported are more likely to use modern contraceptive methods than women who aren’t supported[ 20 ]. However, in this study, most respondents did not get support from their partners. Several studies are underscoring the challenge of culture to the uptake of family planning services [ 21 , 22 ]. The majority of the respondents' traditional beliefs did not support the use of modern contraceptives, believing that they cause harm and the perception of having many kids as a sign of wealth. That explains why most studies point to the desire to have more children as a barrier to family planning. Moreover, in Africa, traditionally, women are considered responsible for increasing family size, and failure to do so attracts negative judgment from society [ 23 ]. The reported contraceptives that were often not available included implants, oral contraceptive pills, Copper T, and injectables. Comparatively, in some other sub-Saharan African countries, the proportion of health facilities that report experiencing contraceptive stock-outs in the past six months is estimated to be 50% or more [ 24 ]. The main reasons for stock-outs could be improper planning, forecasting, and quantification of contraceptives by less-skilled health workers and non-pharmaceutical personnel. Most providers reported that their health facilities order contraceptive replenishment from the Medical Stores Department through the electronic logistics management information system (e-LMIS). The level of technology use in the health facilities was low; in most facilities, there was no internet system. This finding is consistent with that of Kenya, where most respondents reported that the level of technology affects the efficiency of medicines availability in their health facility [ 25 ]. Furthermore, there were significant health system and policy factors constraints. Health system and policy factors considered were the lack of privacy and confidentiality, limited information they provided to clients at a health facility, the lack of qualified health staff incentives, the lack of family planning standards & guidelines, and the financial implications of a full range of services. This finding is consistent with that of Ethiopia, which shows that the inadequate availability of key resources, such as trained staff, information, education, communication materials and other family planning guidelines and standards in clinics, is a major constraint to the performance of quality family planning services [ 26 ]. Strengths, limitations, and future research Strengths The study will help policymakers and planners to identify areas on which their programs and policies should focus shortly to improve the uptake of modern contraceptives. For donors and implementing partners, the study will enable them to know where they can support the service providers. The study findings will show the reasons behind the choice and uptake of contraceptives. This may form the basis for coming up with customized mitigation strategies to enhance the uptake Limitations There might have been biases of information from the participants; secondly, The study did not focus on private facilities offering modern contraceptive methods, and The study excluded male and female sterilization and the lactation amenorrhea method (LAM) as the modern methods of contraception. Future Research This survey was conducted in Dodoma City Council and might not represent the whole country. Therefore, it is recommended that the study be extended to other parts of the country in the future. Conclusion There are many health benefits of contraceptive use, including the prevention of unwanted childbearing, improved birth spacing, reduction of maternal and infant mortality, and improvements in the lives of women and children in general. The study revealed that socio-demographic factors have a positive significant effect on the uptake of modern contraceptives. In addition, supply chain factors significantly contribute to the uptake of modern contraceptives. Furthermore, the healthcare system and policy factors, namely, the quality of services delivered and training staff to provide modern contraceptive services by health facilities, also significantly contribute to the uptake of modern contraceptives. Declarations Author Contribution V.K provided inputs in different sections of the manuscripts such as spelling errors and formatP.K The main Supervisor in my thesis provides technical support throughout the processO.S Co-supervisor in my thesis provides technical support throughout the processN.J Support formatting of the document and other technical inputs in different sections References Lasong J, Zhang Y, Gebremedhin SA, Opoku S, Abaidoo CS, Mkandawire T, et al. Determinants of modern contraceptive use among married women of reproductive age: A cross-sectional study in rural Zambia. BMJ Open. 2020;10:1–10. Sakarya THE, Of J. 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Res. 2015. pp. 245–72. http://gssrr.org/index.php?journal=JournalOfBasicAndApplied . Tafese F, Woldie M, Megerssa B. Quality of family planning services in primary health centres of Jimma Zone, Southwest Ethiopia. Ethiop J Health Sci. 2013;23:245–54. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Submission checks completed at journal 28 Mar, 2024 Editor assigned by journal 28 Mar, 2024 First submitted to journal 28 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4181194","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":284917054,"identity":"9eb05dda-508e-4544-a29a-bf9ec444c4d4","order_by":0,"name":"Machumu Stephen Miyeye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACZhhDAkRUMDAYkKjlDDFa4ACkhbGNCC267TyGj3kqtsnzz+4x3cw777C8OXvzAYYfFdtwajE7zGNszHPmtuGMO2fMbvNuO2y4s+dYAmPPmdv4tJhJ87bdZtwgkQPWwrjhRo4BM2MbXi3mv3n/3baHaJlz2J4YLWbMvA23EyFaGg4nEqGFrVhyzrHbyTNupJXdnHMsPXnDmWMJB/H65fzhjR/e1Ny27Z+RvO3Gmxpr2w3Hmw8++FGBWwsDA4cBEw+C1wwmD+BRDwTsDxh/IHh1+BWPglEwCkbBiAQAeBpeVK1I36sAAAAASUVORK5CYII=","orcid":"","institution":"Ministry of Health, Community Development, Gender, Elderly and Children","correspondingAuthor":true,"prefix":"","firstName":"Machumu","middleName":"Stephen","lastName":"Miyeye","suffix":""},{"id":284917055,"identity":"e345baf2-805c-4bac-8c89-c99261d8c6b2","order_by":1,"name":"Vedaste KAGISHA","email":"","orcid":"","institution":"University of Rwanda","correspondingAuthor":false,"prefix":"","firstName":"Vedaste","middleName":"","lastName":"KAGISHA","suffix":""},{"id":284917057,"identity":"2978e311-a4fe-4194-8c40-b47ca0e0088e","order_by":2,"name":"Peter Karimi","email":"","orcid":"","institution":"University of Rwanda","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Karimi","suffix":""},{"id":284917059,"identity":"84b389d7-67ae-4324-8cac-1aa7313d2d65","order_by":3,"name":"Omary Swalehe","email":"","orcid":"","institution":"University of Rwanda","correspondingAuthor":false,"prefix":"","firstName":"Omary","middleName":"","lastName":"Swalehe","suffix":""},{"id":284917064,"identity":"bd4606f1-90b7-4e59-b19e-a74c645e094a","order_by":4,"name":"Ngenzi Joseph Lune","email":"","orcid":"","institution":"University of Rwanda","correspondingAuthor":false,"prefix":"","firstName":"Ngenzi","middleName":"Joseph","lastName":"Lune","suffix":""}],"badges":[],"createdAt":"2024-03-28 09:39:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4181194/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4181194/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54040090,"identity":"185f7020-9fb6-4719-84af-c3b1704aa32e","added_by":"auto","created_at":"2024-04-03 17:29:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":506477,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4181194/v1/6fcd07c4-faaf-481c-81b3-b7edf515ead3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors Influencing the Uptake of Modern Contraceptives among Women in Tanzania: A survey of Public Health Facilities in Dodoma City Council","fulltext":[{"header":"Background","content":"\u003cp\u003eContraception is described as pregnancy prevention by inhibiting the normal process of ovulation, fertilization and implantation. The use of modern contraceptive methods allows couples and individuals to attain their desired number of children [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. To attain target 3.7 of Sustainable Development Goal (SDG) 3, which underscores that by 2030, the world should ensure universal access to sexual and reproductive health services, the use of modern contraceptive methods is crucial [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It was well reported that the reductions in unintended pregnancies and maternal and neonatal mortality can lead to the achievement of SDG 3 targets 3.1 and 3.2, which aim to reduce the global maternal mortality ratio to less than 70 per 100,000 live births and to end all preventable deaths of children under five by 2030 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn 2019, there were approximately 1.9\u0026nbsp;billion women of reproductive age. Among these women, 1.1\u0026nbsp;billion needed family planning services but only 842\u0026nbsp;million used modern contraception [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to the World Fertility and Family Planning Report of 2020 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], the use of contraceptives among women of reproductive age in 2019 was above 55% in 37 countries and below 20% in 23 countries.\u003c/p\u003e \u003cp\u003eSeveral factors, such as cultural and religious myths and misconceptions, undermine modern contraception [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition, partner-related factors and socio-demographic characteristics (age, education level, and religion) are associated with modern contraceptive use [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn low and middle-income countries, 214\u0026nbsp;million women who wanted to avoid pregnancy in 2019 were not using any method of contraception [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and most of them were from Sub-Saharan African countries [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The reasons for this might be inadequate knowledge, poor accessibility to modern contraceptive methods, preference for having many children, among others [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eMoreover, United Nations Department of Economic and Social Affairs and Population Division data show that more than 20% of the unmet need for family planning was in 15 countries from Sub-Saharan Africa [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Tanzania, 38% of reproductive-aged women currently use modern contraceptive methods, and 22% have an unmet need for family planning [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] .Teenage pregnancies among 15-19-year-olds range from 23\u0026ndash;27% and are higher among rural than urban women [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The total fertility ratio (TFR) was 5.4 children per woman of childbearing age in 2010 and 5.2 in 2015 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Modern contraceptive methods currently available in the public sector supply chain in Tanzania include Oral contraceptive pills, injectable contraceptives (DMPA), condoms, intrauterine devices (IUCD), and contraceptive implants [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Tanzanian Ministry of Health has implemented a series of national strategies and policies to facilitate the use of contraceptive services [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The main objective of these guidelines is to increase the contraceptive prevalence rate (CPR) and reduce the unmet need for family planning. The National target was to attain a national modern contraceptive prevalence rate (CPR) of 45% by 2020 and reduce the current unmet need for family planning (FP) to 10% by 2020[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Nevertheless, this country\u0026rsquo;s target was not achieved [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In the Dodoma region, 14.7% of family planning needs are unmet [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Despite existing data on the unmet need for family planning and the low contraceptive prevalence rate in Tanzania, no studies have been conducted on the factors influencing contraceptive use among women of reproductive age. Thus, this study aims to determine these factors to help policymakers and planners identify areas on which their programs and policies should focus shortly to improve the uptake of modern contraceptives.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting\u003c/h2\u003e \u003cp\u003eThe study was conducted at the public health facilities of the Dodoma City Council. Due to its convenience, Dodoma City Council was chosen as the area of study. It is a rapidly urbanizing city in Tanzania following the reallocation of government offices to Dodoma from Dar es Salaam in 2017, and the need for family planning services is high. The City Council has 35 public health facilities that provide family planning services, out of which two are hospitals, one is a health centre, and thirty-two are dispensaries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eAn analytical cross-sectional design was used to investigate the factors that influence modern contraceptive use among women in public health facilities in Dodoma City. The study populations were health facility workers in reproductive and child health clinics that offer family planning and women of reproductive age who visited these clinics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSampling\u003c/h2\u003e \u003cp\u003eTwo samples were used according to the study populations. One comprised health facility workers, and the other was for women seeking family planning services. The city has 35 health facilities offering family planning services. The representative sample size in this population was calculated using Yamane\u0026rsquo;s formula to obtain 32 health facilities. These facilities were selected using simple random sampling aided by computer-generated numbers. For the case of clients attending RCH clinics, Fischer\u0026rsquo;s formula was used to determine the sample size based on the contraceptive prevalence rate from a previous study, which was 38%. Therefore, the determined sample size was 362 women of reproductive age. Since there are 32 health facilities involved, 12 women were selected using simple random sampling by employing the lottery method from each clinic.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData collection tools\u003c/h2\u003e \u003cp\u003eData were captured through two structured questionnaires. These were for women seeking family planning services and health care workers offering reproductive health services. Theses tools contained open and closed questions on various aspects of socio-demographic factors that are associated with their clients\u0026rsquo; contraceptive use( age, marital status, educational level, income, awareness support from the partner, cultural and religion beliefs), the supply chain factors( forecasting, stock out, order fill rate, lead time and inventory management), and the health care system \u0026amp; policy factors (includes staff training, counselling skills, availability of guideline and standard, location and distance of the health facility, financing, satisfaction ) affecting uptake of contraceptives.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData collection procedures\u003c/h2\u003e \u003cp\u003eContact was made with the City council to inform the selected facilities about the study and to arrange visiting dates. Two to three health facilities were visited per day. The questionnaires were pre-tested in two facilities in Dodoma City before data collection. The problems encountered, time spent, and other suggestions for improvement were considered and used to make final adjustments to the questionnaire. Those facilities used for pre-testing were not included in the data collection process. The questionnaires had sections that addressed all the specific objectives, which were socio-demographic, supply chain, and health system and policy factors that impact reproductive health services use. To ensure internal validity, the researcher and supervisor reviewed the overall content of the questionnaire for readability, clarity, and completeness regarding the questions to be included in the study. The questionnaires were structured so that some questions were designed to ask similar information in different ways to ensure consistency.\u003c/p\u003e \u003cp\u003eFurthermore, the questionnaires were translated into Swahili to avoid language barriers. External validity was achieved by using simple random sampling to ensure that the data was collected according to the determined sample sizes. To ensure reliability, the responses from the pretest were compared and observed to concur. Ethical approval was obtained before data collection. The participants willingly consented to participate in the study. The confidentiality of the data was maintained by coding the questionnaires and keeping them in a secure place only accessible to the principal investigator.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe collected data were analyzed using SPSS version 23 to obtain descriptive statistics, such as percentages and frequencies, on the selected public health facilities.\u003c/p\u003e \u003cp\u003eFurthermore, the inferential analysis of data was carried out whereby; the bivariate and multivariate logistic regression models were applied to examine the association between the uptake of modern contraceptives and study variables at a 5% statistical level of significance. Fischer's exact test and Pearson's Chi-square tests were used to ascertain the correlation between the study variables and the uptake of modern contraceptives. The odds ratio (OR) estimated the extent of the association at 95% confidence interval (CI).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSocio-demographic characteristics\u003c/h2\u003e \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a relative majority (46.7%) of the respondents were 24\u0026ndash;34 years of age (60.2%) were married, and 48.9% had attained a secondary level of education. A comparatively higher proportion (32.6%) had a monthly income of 300,000\u0026ndash; 399,999 Tanzanian Shillings (TZS).\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\u003eSocio-demographic characteristics of the respondents (n\u0026thinsp;=\u0026thinsp;362)\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-demographic\u003c/p\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eAge in years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eLevel of education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCertificate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eMonthly income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,000\u0026ndash;99,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100,000\u0026ndash;199,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200,000\u0026ndash;299,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300,000\u0026ndash;399,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400,000\u0026ndash;499,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500,000 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.91\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=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eUptake of modern contraceptive\u003c/h2\u003e \u003cp\u003eThe results showed that the majority of women (74.6%) use modern contraceptives (Table\u0026nbsp;2). The types of modern contraceptives preferred were injectable methods (39.7%), followed by pills (23.6%), implants (22.5%), and condoms (7.49%). The main reasons for preferred contraceptives were minimal side effects (50.19%) and convenience of use (47.9%). In addition, 23.4% of participants experienced side effects. The most prevalent side effects were nausea and vomiting (41.2%), heavy bleeding (33.8%), and lower abdominal pains (11.8%). \u003c/p\u003e\u003cp\u003e\u003cb\u003eTable\u0026nbsp;2: Frequency of modern contraceptive use\u003c/b\u003e \u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003e(n\u0026thinsp;=\u0026thinsp;267)\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eType of contraceptives preferred\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInjectable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCondoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImplants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCopper T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eReason for the choice of contraceptives\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvised by the health provider\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFew side effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnly available choice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConvenience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eOccurrence of side effects\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperienced side effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDid not experience side effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eType of side effects\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNausea and vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeavy bleeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeadache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower abdominal pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.41\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between Uptake of Modern Contraceptives and study participants characteristics\u003c/h2\u003e \u003cp\u003eThe associations between the socio-demographic characteristics and uptake of contraceptives were determined using chi-square at a 0.05 level of significance, and the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The characteristics that had a statistically significant association with the uptake of contraceptives were marital status (p\u0026thinsp;=\u0026thinsp;0.024), level of education (p\u0026thinsp;=\u0026thinsp;0.043), level of satisfaction (p\u0026thinsp;=\u0026thinsp;0.001), and availability of support (p\u0026thinsp;=\u0026thinsp;0.001), and status of awareness (p\u0026thinsp;=\u0026thinsp;0.000). Therefore, this suggests that these variables influence the uptake of modern contraceptives in the community.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between the Uptake of Modern Contraceptives and socio-demographic characteristics (n\u0026thinsp;=\u0026thinsp;362)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUptake Of Contraceptives\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePerson chi-square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.024*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eEducation status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.043*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Educated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePreference status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrefer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Preferred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eStatus of satisfaction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eSupport status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.7674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAwareness status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUptake Of Contraceptives\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eFisher\u0026rsquo;s exact\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAware\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Aware\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e*- statistically significant relationship\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eFactors Influencing the Uptake of Modern Contraceptives\u003c/h2\u003e \u003cp\u003eTo understand factors that influence the uptake of modern contraceptives, multivariate logistic regression models were employed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The only factors significantly associated with the uptake of modern contraceptives were awareness status (AOR\u0026thinsp;=\u0026thinsp;1.0679, P\u0026thinsp;=\u0026thinsp;0.000), preference status (AOR\u0026thinsp;=\u0026thinsp;1.0117, P\u0026thinsp;=\u0026thinsp;0.010), and satisfaction status (AOR\u0026thinsp;=\u0026thinsp;1.0416, P\u0026thinsp;=\u0026thinsp;0.036).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictors of Uptake of modern contraceptives\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eBivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrude Odds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted Odds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation status dummy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;educated\u003c/p\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;not educated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0275\u003c/p\u003e \u003cp\u003e(0.79\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0047\u003c/p\u003e \u003cp\u003e(0.09\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status dummy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;married\u003c/p\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9948\u003c/p\u003e \u003cp\u003e(0.76\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9985\u003c/p\u003e \u003cp\u003e(0.08\u0026ndash;0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAwareness status dummy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;yes\u003c/p\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4590\u003c/p\u003e \u003cp\u003e(0.69\u0026ndash;3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0679\u003c/p\u003e \u003cp\u003e(0.46\u0026ndash;17.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePreference status dummy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;preferred\u003c/p\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;not preferred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0657\u003c/p\u003e \u003cp\u003e(0.84\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0117\u003c/p\u003e \u003cp\u003e(0.52\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSupport status dummy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;supported\u003c/p\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;not supported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3958\u003c/p\u003e \u003cp\u003e(0.78\u0026ndash;2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0667\u003c/p\u003e \u003cp\u003e(0.54\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSatisfaction status dummy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;satisfied\u003c/p\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;not satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2626\u003c/p\u003e \u003cp\u003e(0.62\u0026ndash;2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0416\u003c/p\u003e \u003cp\u003e(0.53\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.036*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*-\u003cem\u003estatistically significant association, CI-confidence interval\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eQuality of service and other factors that affect the uptake of contraceptives\u003c/h2\u003e \u003cp\u003eThe findings revealed that 77.4% of respondents were counselled on how to use contraceptives by health providers. One hundred and fifty-six (58.9%) received advice from health providers on dealing with the side effects. In addition, only 11.7% of the respondents were forced to take a specific method of modern contraceptives. Most (76.6%) of the respondents were satisfied with the service quality. The reasons for dissatisfaction for the remaining respondents were long waiting time (45.2%), lack of a contraceptive method of choice (41.9%), and lack of Privacy/confidentiality (12.9%) when delivering services. The majority (56.3%) of the facilities did not use guidelines. There was a statistically significant association between the uptake of modern contraceptives and service satisfaction level (p\u0026thinsp;=\u0026thinsp;0.001). In examining the provision of contraceptive management, the majority (71.9%) of the staff had been trained on family planning services and 78.1% on the Logistic Management Information System (LMIS). The latter covered four aspects, namely: assessment of stock status, knowledge of minimum and maximum stock level, reporting and requesting health commodities through the the-LMIS system, and ensuring adequate physical storage of contraceptives.\u003c/p\u003e \u003cp\u003eThe most frequent interval of supportive supervision visits during the 6 months before the study was once a month (68.8%) and twice in six months (28.1%).\u003c/p\u003e \u003cp\u003eRegarding the costs incurred by the respondents on the uptake of contraceptives, 73.2% did not incur any costs. For the distance from the respondents\u0026rsquo; residence to the health facilities, most (75.5%) respondents resided between 1 to 5 kilometres, while 23.4% lived between 6 to 10 kilometres. The modes of reaching the health facilities were mainly walking (57.6%) and public service vehicles (33.9%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSupply chain factors that affect the uptake of contraceptives\u003c/h2\u003e \u003cp\u003eThe nurses (62.5%) were primarily responsible for ordering contraceptives, followed by pharmacists (28.1%). The considerations in forecasting the number of contraceptives in health facilities were mainly based on consumption data (71.9%) and service utilization data (18.75%). Concerning the lead time, 78.1% of the health facilities received orders within three months. The majority (53.1%) of the health facilities received the full quantity of all the contraceptives ordered. Regarding stock levels, 68.8% of the health facilities had minimum and maximum stock levels of 3 and 6 months, respectively\u0026mdash;moreover, 71.9% of health facilities received resupply once every three months.\u003c/p\u003e \u003cp\u003eRegarding the availability of modern contraceptives in health facilities, only 34.38% of health facilities had all the available types. The reported contraceptives that were often not available included implants (38.1%), oral contraceptive pills (28.8%), Copper T (23.8%), and injectables (9.5%). Most health facilities (65.6%) used the Information Communication Technologies (ICT) System for reporting and ordering contraceptives. The reported ICT tools used were computers (76.19%) and mobile phones (23.81%). Moreover, 65.6% of the health facilities lacked an internet connection for recording health information, diagnosis, and service delivery.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to assess the factors affecting contraceptive uptake among women attending public health facilities in Dodoma City Council. The study reveals that age was a significant factor affecting FP uptake, whereby the majority of the respondents associated with the uptake of modern contraceptives were in the aged between 25\u0026ndash;34 years.\u003c/p\u003e \u003cp\u003eSimilar results have been obtained in other studies in Ethiopia, where the results show that contraceptive use is highest among 24-35-year-olds [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The reason could be that this age category is usually married and, therefore, likely to get pregnant easily.\u003c/p\u003e \u003cp\u003eThe use of DMPA injections was comparatively high at 39.7%, similar to the findings from the Tanzania Demographic Health Survey, which showed that women prefer Depo-Provera to any other modern family plan [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This drug is convenient and reliable since it is administered quarterly. Noncompliance is, therefore, likely to be minimal [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, this result is contrary to that in Ghana, where condoms are the most widely used, probably due to the role condoms play in both the prevention of sexually transmitted infections and the control of unintended pregnancies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe main determinants of contraceptive use were level of education, access to health facilities, the influence of social media, and neighbourhood. In this case, education was found to be the main source of information influencing awareness of modern contraceptive use. This is probably because the importance of family planning is relayed in schools, and women can make informed choices. However, this differs from other studies in Ghana, where television was identified as the main source of information influencing awareness on the uptake of modern contraceptives [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eReliable income had a positive correlation with the uptake of modern contraceptives. Individuals with higher monthly incomes are more likely to be aware of the need for child spacing so that they can have adequate time to work and, therefore, tend to use family planning more frequently. This finding is consistent with Edo state, Nigeria [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Residing close to health facilities was found to increase the use of modern contraceptive methods. The time and costs incurred to reach the facilities were minimal; this finding is consistent with that in Senegal, where people travelling long distances to reach health facilities are less likely to use contraceptives [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost of the respondents were counselled on how to use contraceptives by healthcare providers. This service was found to be significantly associated with the increase in uptake of contraceptives. Counselling enhances interaction with clients and informs them on a broad range of contraceptive method choices. Health care providers trained in Family Planning counselling were found to be more useful to their clients\u0026rsquo; satisfaction with using Family Planning methods. Pre-counselling improves the contraceptive uptake rate significantly [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe utilization of contraceptives is enhanced when couples are in agreement and support one another. In Ethiopia, women who discuss the issue of modern contraceptives with their husbands and are supported are more likely to use modern contraceptive methods than women who aren\u0026rsquo;t supported[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, in this study, most respondents did not get support from their partners. Several studies are underscoring the challenge of culture to the uptake of family planning services [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe majority of the respondents' traditional beliefs did not support the use of modern contraceptives, believing that they cause harm and the perception of having many kids as a sign of wealth. That explains why most studies point to the desire to have more children as a barrier to family planning. Moreover, in Africa, traditionally, women are considered responsible for increasing family size, and failure to do so attracts negative judgment from society [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe reported contraceptives that were often not available included implants, oral contraceptive pills, Copper T, and injectables. Comparatively, in some other sub-Saharan African countries, the proportion of health facilities that report experiencing contraceptive stock-outs in the past six months is estimated to be 50% or more [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The main reasons for stock-outs could be improper planning, forecasting, and quantification of contraceptives by less-skilled health workers and non-pharmaceutical personnel. Most providers reported that their health facilities order contraceptive replenishment from the Medical Stores Department through the electronic logistics management information system (e-LMIS).\u003c/p\u003e \u003cp\u003eThe level of technology use in the health facilities was low; in most facilities, there was no internet system. This finding is consistent with that of Kenya, where most respondents reported that the level of technology affects the efficiency of medicines availability in their health facility [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Furthermore, there were significant health system and policy factors constraints. Health system and policy factors considered were the lack of privacy and confidentiality, limited information they provided to clients at a health facility, the lack of qualified health staff incentives, the lack of family planning standards \u0026amp; guidelines, and the financial implications of a full range of services. This finding is consistent with that of Ethiopia, which shows that the inadequate availability of key resources, such as trained staff, information, education, communication materials and other family planning guidelines and standards in clinics, is a major constraint to the performance of quality family planning services [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths, limitations, and future research\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eStrengths\u003c/h2\u003e \u003cp\u003eThe study will help policymakers and planners to identify areas on which their programs and policies should focus shortly to improve the uptake of modern contraceptives. For donors and implementing partners, the study will enable them to know where they can support the service providers. The study findings will show the reasons behind the choice and uptake of contraceptives. This may form the basis for coming up with customized mitigation strategies to enhance the uptake\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThere might have been biases of information from the participants; secondly, The study did not focus on private facilities offering modern contraceptive methods, and The study excluded male and female sterilization and the lactation amenorrhea method (LAM) as the modern methods of contraception.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eFuture Research\u003c/h2\u003e \u003cp\u003eThis survey was conducted in Dodoma City Council and might not represent the whole country. Therefore, it is recommended that the study be extended to other parts of the country in the future.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThere are many health benefits of contraceptive use, including the prevention of unwanted childbearing, improved birth spacing, reduction of maternal and infant mortality, and improvements in the lives of women and children in general. The study revealed that socio-demographic factors have a positive significant effect on the uptake of modern contraceptives. In addition, supply chain factors significantly contribute to the uptake of modern contraceptives. Furthermore, the healthcare system and policy factors, namely, the quality of services delivered and training staff to provide modern contraceptive services by health facilities, also significantly contribute to the uptake of modern contraceptives.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eV.K provided inputs in different sections of the manuscripts such as spelling errors and formatP.K The main Supervisor in my thesis provides technical support throughout the processO.S Co-supervisor in my thesis provides technical support throughout the processN.J Support formatting of the document and other technical inputs in different sections\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLasong J, Zhang Y, Gebremedhin SA, Opoku S, Abaidoo CS, Mkandawire T, et al. 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BMC Public Health. 2015;15:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDurowade KA, Omokanye LO, Elegbede OE, Adetokunbo S, Olomofe CO, Ajiboye AD, et al. Barriers to Contraceptive Uptake among Women of Reproductive Age in a Semi-Urban Community of Ekiti State, Southwest Nigeria. Ethiop J Health Sci. 2017;27:121\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOoms GI, Kibira D, Reed T, Van Den Ham HA, Mantel-Teeuwisse AK, Buckland-Merrett G. Access to sexual and reproductive health commodities in East and Southern Africa: A cross-country comparison of availability, affordability and stock-outs in Kenya, Tanzania, Uganda and Zambia. BMC Public Health BMC Public Health. 2020;20:1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWanjala Wasike D, Mugambi F. Effects of Pharmaceutical Procurement Processes on Performance of Public Health Facilities in Mombasa County, Kenya [Internet]. Int. J. Sci. Basic Appl. Res. Int. J. Sci. Basic Appl. Res. 2015. pp. 245\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gssrr.org/index.php?journal=JournalOfBasicAndApplied\u003c/span\u003e\u003cspan address=\"http://gssrr.org/index.php?journal=JournalOfBasicAndApplied\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTafese F, Woldie M, Megerssa B. Quality of family planning services in primary health centres of Jimma Zone, Southwest Ethiopia. Ethiop J Health Sci. 2013;23:245\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Uptake of contraceptives, Women of reproductive age, Family planning, Dodoma","lastPublishedDoi":"10.21203/rs.3.rs-4181194/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4181194/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eTanzania has a relatively high unmet need and low use of family planning methods. Thirty- eight per cent of reproductive-aged women currently use modern contraceptive methods, and 22% have an unmet need for family planning.\u003c/p\u003e\u003ch2\u003eObjectives:\u003c/h2\u003e \u003cp\u003eTo determine the factors influencing the uptake of modern contraceptives among women attending public health facilities in Dodoma City Council.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAn analytical cross-sectional study using a quantitative approach was conducted among 362 women of reproductive age who attended public health facilities in Dodoma City and 32 healthcare workers. The participants were selected using simple random sampling. Data was collected from 6 September to 15 October 2021. A structured questionnaire was used. Data was analyzed using descriptive and inferential statistics at the 0.05 level of significance. The strength of association was determined using a logistic regression model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the 362 participants, 46.6% were between 24 and 34 years old. Most (60.2%) were married, and 48.9% had completed secondary education. About a third (32.6%) had a monthly income of 300,000 to 399,999 TZS. A statistically significant association at 0.05 was observed between uptake of contraceptives and education (p\u0026thinsp;=\u0026thinsp;0.043), marital status (p\u0026thinsp;=\u0026thinsp;0.024), partner\u0026rsquo;s support (p\u0026thinsp;=\u0026thinsp;0.001), awareness, preference, and satisfaction (p\u0026thinsp;=\u0026thinsp;0.000).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study revealed that education, marital status, awareness, preference, and satisfaction of the respondents have a positive significant effect on the uptake of modern contraceptives.\u003c/p\u003e","manuscriptTitle":"Factors Influencing the Uptake of Modern Contraceptives among Women in Tanzania: A survey of Public Health Facilities in Dodoma City Council","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-03 17:21:35","doi":"10.21203/rs.3.rs-4181194/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"checksComplete","content":"","date":"2024-03-28T10:04:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-28T10:04:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2024-03-28T09:37:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4a2b75ec-f9a0-45c2-bebe-70dea362dddc","owner":[],"postedDate":"April 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-04-10T20:29:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-03 17:21:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4181194","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4181194","identity":"rs-4181194","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}