Knowledge of and preference for abortion legality in Kenya: A National Cross- Sectional Survey

Knowledge of and preference for abortion legality in Kenya: A National Cross- Sectional Survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Knowledge of and preference for abortion legality in Kenya: A National Cross- Sectional Survey Kenneth Juma, MUTUKU Esther, ATHERO Sherine, AKUKU Isaiah, Boniface Ayanbekongshie USHIE, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4761401/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Inadequate knowledge of abortion laws implies those seeking care cannot know their legal entitlements, service providers cannot practice with legal protection, and governments can escape legal responsibility for the adverse effects of their laws. There is limited understanding of the public’s views of abortion in Kenya. This study assessed the knowledge and preference for abortion legality in Kenya. Methods We conducted a nationwide cross-sectional survey using mobile telephones to collect data from adults 18 or older. The sample was drawn from a database owned by a mobile-survey provider with over 12 million telephone numbers across Kenya. We used a random digit dialing approach to select and recruit participants. Trained research assistants administered telephonic interviews using a structured questionnaires that had questions assessing knowledge of conditions for legally sanctioned abortion and their preferences for conditions the law should permit. We summarized continuous variables into means with standard deviations while categorical variables into proportions. Multivariate analyses were performed to assess associations between knowledge of abortion laws and independent variables. Results Of the 8942 respondents, 76%, 74%, and 33% correctly knew that the Kenyan law allows abortion when a woman's life and health are at risk and when the pregnancy results from rape, respectively. Being female (AOR = 1.22 [95% CI: 1.09–1.38]; p < 0.001), age group 25–34 (1.21 [1.03–1.43]; 0.024) and having university education (2.57; [1.79–3.70]; <0.001) were associated with higher knowledge of abortion laws. Majority of respondents preferred that the law allow abortion when a woman’s life (72%) or health (65%) or mental health (43%) is at risk. One-quarter wanted abortions allowed for rape (29%) and incest (28%) while only 15% approved abortion if the pregnant was unwanted. The latent class analysis characterized that most respondents were conditional supporters of abortion legalization (43%), whereas individuals opposed to abortion legalization in all/most circumstances represented 29% of respondents. Conclusion Information on abortion legality preferences offers insights into public acceptability and opportunities for abortion legal and policy reforms. There is need for comprehensive dissemination of the Kenya abortion law, focusing specifically on groups and communities that typically struggle to access information and services. Abortion Public opinions Abortion law legal preferences sub-Saharan Africa Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Access to safe abortion and post-abortion care depends on several factors, including the legal and political context of abortion, access to information, stigma, and the readiness of health facilities to provide high-quality abortion-related services ( 1 ). Critically, the use of safe abortion services varies by women's awareness and knowledge of abortion law and availability of services ( 2 ). Achieving universal access to sexual and reproductive health and rights requires unrestricted access to accurate information that facilitates women’s autonomy and informed decisions regarding their reproductive health ( 3 ). Nevertheless, research shows that women, healthcare providers, and even policymakers worldwide have limited or inaccurate knowledge of abortion laws and policies in their countries ( 1 ). A recent systematic review in Africa showed low levels of women's general knowledge of abortion laws in their countries and critical differentials in knowledge based on geography, wealth, and education ( 4 ). Despite restrictive abortion laws across many African countries, abortion remains commonplace ( 5 , 6 ). For instance, in Kenya, abortion is only permitted if, in the opinion of a trained health professional, there is a need for emergency treatment; or the life or health of the mother is in danger; or if permitted by any other written law ( 7 ). Additionally, abortion is permitted on grounds of rape and defilement based on a Kenyan High Court ruling of 2019 ( 8 ). Paradoxically, the 1964 Kenyan Penal Code (Sections 158–160) criminalizes abortion and imposes harsh penalties on those who contravene the law. Despite these legal restrictions of abortion, in 2012, an estimated 464,000 induced abortions occurred in Kenya, with a majority of them unsafe, resulting in moderate to severe complications, some of which needed intensive care and attendance by highly-skilled health providers ( 9 – 11 ). Knowledge of abortion law greatly influences access and utilization of safe abortion and post-abortion care services ( 12 ). Differences in knowledge drive inequalities, especially for marginalized women who may have greater demand for abortion services, but would be disproportionately affected and experience arbitrary denials of services or poor quality of care, as reported in Mexico ( 3 ). A study by Mekuria et al, among young females undergraduates in Ethiopia, suggested that the main impediment to utilizing safe abortion services was inadequate knowledge of the abortion law ( 2 , 13 ). Notably, in a context where communities do not know the law regulating access to abortions, those seeking care cannot know their legal entitlements, service providers cannot practice with legal protection, and governments can escape legal responsibility for the adverse effects of their laws, and also fail to disseminate laws appropriately ( 4 ). As such, assessing the awareness and knowledge of abortion laws in a country is critical, especially in Kenya where abortion is legally allowed for multiple indications ( 7 ). Neglecting knowledge of abortion law can translate to high social and economic costs and inhibit the efforts to expand access to safe abortion care. Further, while common, the inconsistencies in the national laws and policy around abortion (especially the constitution vs the penal code), often impedes safe and evidence-based practice around abortion ( 14 , 15 ). Further, understanding knowledge of abortion laws can be critical in influencing community perceptions of abortion, which tend to be largely negative and cultivates stigma, discrimination and ostracization of women known to have terminated their pregnancies, their providers, friends and family ( 16 ). In 2022, a study reported that healthcare providers conveyed concerns pertaining to the delivery of abortion services, citing fear, intimidation, harassment, and prosecution under restrictive laws around safe abortion access and lack of clear safe abortion care policies and guidelines within the Ministry of Health ( 17 ). It is helpful to understand the public’s knowledge and preferences for abortion laws to inform an understanding of the barriers to women’s access to safe and legal abortion in Kenya. Few studies have previously touched on knowledge and preferences towards abortion laws in Kenya, and there remains a gap in understanding community members' viewpoints on this subject. These knowledge gaps sometimes stem from the vague and broad terms of the law, which breed uncertainty and even conflict when unaccompanied by accessible regulation or guidelines. Inadequate community level knowledge impedes the populace’s ability to hold their leaders and decision-makers accountable to meeting their aspirations. The aim of this study was to examine the public’s knowledge of the current abortion law in Kenya. Moreover, we explored their preferences for grounds under which they would want abortion allowed. Material and Methods Study design and population The study was a cross-sectional survey with a nationally representative sample of adults across the 47 counties in Kenya. Respondents were individuals 18 years or older, and of diverse sociodemographic characteristics, including sex, age groups, religion, education, marital status, employment, and residential areas. The choice of adults (18 years or older) eased the process of acquiring informed consents for participants and avoided the need to seek parental assent before engaging those below age 18, especially as these were phone-based interviews. As such, those eligible for the survey, were individuals 18 years or older, who had a working phone number and had been living in Kenya for the past six months. Estimating the Sample size Based on a 2020 survey, 15% of respondents in Kenya indicated support for abortion (which represented some 6% who said “yes” to abortion” and 9% who supported abortion conditionally, i.e., “dependent” on the situation of the woman or pregnancy) ( 18 ). We based our estimation on this 15% prevalence to determine a robust sample capable of producing stable estimates of the proportion of people who expressed positive opinions toward abortion with sufficient power (0.8) and a 95% confidence interval. Using the formula for calculating proportions from a single population; n = Zα/22 *p*(1-p) / MOE 2 , Where we assumed that the known p = 0.15 (from the 2020 study), and a margin of error (MOE) of 1% (0.01). Thus, solving for n in the above equation gave the sample size of 8487. Additionally, we inflated the sample by 5% to account for non-response and incomplete interviews, which brought the final sample size to 8912. Sampling procedure The research team worked collaboratively with an owner of the mobile telephone database in Kenya. Geopoll owns and maintains a telephone database and operates mobile and online data collection services across several countries in Africa. The GeoPoll User Database has detailed demographic and location information of all individuals in the database. The study respondents were selected using a two-stage stratified random sampling and a random digit dialing approach, from an existing Geopoll database of over 12 million telephone numbers from all 47 counties of Kenya. Stage one involved selecting primary sampling units—counties—using the probability proportional to size method. The second stage involved randomly selecting GeoPoll-indexed mobile phone users from a representative sample. To achieve randomness from the robust database, we randomly extracted a pool/frame of 50,000 mobile numbers, representative by age, sex, and location. From this pool of numbers, we randomly dialed users until we achieved the desired quotas. This process of extracting a frame of numbers and dialing from it was repeated until the targeted quotas were met. In cases of a non-response, such numbers were redialed four hours later to ensure all potential participants had equal chances of selection. If the selected respondent was unreachable or refused to participate, they were replaced by a randomly generated phone number of a participant from a pool or pre-sampled phone numbers. Respondents’ phone numbers were randomly selected for each targeted county and run through a software system that eliminated duplicate phone numbers. As such, only unique phone numbers were retained in the finalized database of phone numbers to be targeted. The selection included diverse categories of respondents without a preponderance towards any subgroup. Data collection and data collection tools Trained research assistants conducted the data collection between September and November 2022 with 8942 interviews completed. Research assistants were trained in research ethics, informed consent processes, phone etiquette, interviewing techniques and the study questionnaire. We used a structured questionnaire previously used in other countries to test people's knowledge of abortion and abortion laws. The questionnaire had five main segments including a) demographic characteristics, b) the mosaic of opinions on induced abortion, c) the Stigmatizing Attitudes, Beliefs, and Actions Scale (SABAS), d) the abortion listing experiment, and e) knowledge of abortion law section. For this paper, we only focus on the data collected using the abortion legal knowledge segment of the questionnaire. The knowledge segment presented a list of 14 potential conditions under which the law allows abortion. The interviewer asked the respondents, "To the best of your knowledge, under which conditions is abortion legal in Kenya?" The grounds for abortion were not read to the respondents – the interviewer only recorded their unprompted responses. However, we probed them by asking, "Is there any other ground" until they did not have any other grounds to mention. For the preferred grounds for abortion, we asked respondents whether they would want abortion to be legal under each ground, one by one. The interviewers administered the study questionnaire through telephone interviews, which took an average of 25 minutes to complete. To interview 8942 respondents, 126,482 phone numbers were dialed. Of this number, 59821 were on automatic answering machines, 2321 required callbacks, 28484 calls were either declined, disconnected or were refusals, and 26914 did not respond/no answer. Study variables and measurements The main independent variables included socio-demographic characteristics, such as age, residence, marital status, religion and educational levels. The assessment of knowledge regarding abortion law involved the utilization of three variables with 'yes' or 'no' responses. Likewise, the evaluation of respondent preferred abortion law employed the same approach but with 14 binary items. Data analysis procedure We summarized and presented demographic characteristics as frequencies and compared the knowledge categories between them using Pearson's Chi-squared test. For the knowledge questions, we coded the correct responses as ‘1’ and incorrect/do not know as ‘0’. The knowledge scores were generated by summing the correct responses across three knowledge indicator variables. The scores were categorized into "high knowledge," "average knowledge," and "poor knowledge." Items were deemed to be of “poor knowledge” if the composite sum of correct responses was either zero or one, “average knowledge” if the composite sum score was two, and "high knowledge" if all three composite sum of correct responses was three for those who answered (yes). With similar dummy coding as before, we also created another continuous knowledge score with all the 14 items – including the reasons abortions are not allowed in Kenyan laws. We used the knowledge score in multivariable-adjusted analysis of participants’ preference conditions they would support. We graphed the proportions of preferences chosen by respondents and subsequently stratified them by gender. We also conducted a multivariable multinomial logistic regression to identify predictors of high knowledge of abortion laws. Similarly, we fitted a multivariable linear regression model to identify correlates of endorsing/preferred conditions under which abortion could be allowed. We employed latent class analysis (LCA) to fully characterize preferences of the Kenyan population about abortion laws. For categorical data, LCA is a factor analogue that, according to McCutcheon ( 19 ), discovers an unobserved collection of latent classes that "explains" the correlations between a set of observed categorical variables/indicators. Put differently, LCA offers a method for uncovering a small number of underlying subgroups with various characteristics. In this study, we asked the participants 14 questions (items) about abortion situations they would support. The questions examined the willingness of the study participants to oppose or approve abortion legalization for various conditions. With the 14 binary items, we first fitted a sequence of base models with one to ten classes using poLCA function ( 20 ) in R 4.3.1 (R Core Team, 2023) and temporarily retained a four class-model, which had the lowest Bayesian information criterion (BIC). We then evaluated models with two to five classes using glca ( 21 ) functions in R and extracted a variety of fit indices (Table S2). Finally, we retained a four-class model that had the lowest BIC, better entropy (0.82) that shows good quality of classification and was theoretically sound. We handled missing data by list-wise deletion. Data analysis was done in R (version 4.3.1) and Stata (version 17). All statistical tests were 2-tailed, and a 5% significance threshold maintained. RESULTS Sociodemographic characteristics of participants The mean age of respondents was 33 years (SD, 10), with the highest proportion above 35 years (46%). A quarter of the respondents were between ages 18 to 24 years while one-third were 25 to 34 years (Table 1 ). Of the 8,942 individuals interviewed, there was an even split between men -- 4,464 50%) and women -- 4,478 (50%). More respondents had tertiary level education (post-secondary) (43%) with very few not having any formal education (3%). A slight majority of respondents were married (54%), and most identified as Christians (93%). Table 1 Sociodemographic characteristics of study participants Characteristic Overall, N = 8,942 Age Mean (SD; Range) 33 years (SD,10; 18–85)* Age groups 18–24 2,219 (24.8%) 25–34 2,640 (29.5%) 35+ 4,083 (45.7%) Sex Male 4,464 (49.9%) Female 4,478 (50.1%) Highest Level of Education None** 228 (2.6%) Primary 1,481 (16.6%) Secondary 3,348 (37.5%) University/College/Technical school 3882 (43.4%) Marital status Married/Partnered 4,790 (53.6%) Single/divorced/widowed 4,149 (46.4%) Religion Christian 8,303 (92.9%) Islam 526 (5.9%) Other (Agnostic, Atheist, Spiritual, Hindu) 110 (1.2%) **SD = standard deviation; None- no formal education Knowledge of Kenyan abortion laws Figure 1 presents the responses of participants to the question on “under which conditions does the existing Kenyan law allow abortion?”. Based on the three conditions under which the Kenyan laws allow for safe legal abortion, those reporting correctly were − 76% for when a woman's life is at risk; 74% for physical health at risk; and only 33% knew that abortion is allowed when the pregnancy is because of rape (Fig. 1 ). At the same time, a considerable majority knew that abortion is not allowed because a pregnant girl or woman is unmarried (89%), woman is HIV positive (88%), woman is unable to care for child (87%), or that the girl is still in school (86%), or girl is 16 years or younger (83%) (Fig. 1 ). County level variations in knowledge of abortion law are presented in Fig. 2 . More information on the levels of knowledge is in Table S1 . Knowledge of abortion law by sociodemographic characteristics Table 2 presents the knowledge levels by sociodemographic characteristics. The proportion of males and females across the knowledge scores were about the same (p = 0.47). Similarly, there was no significant difference in the levels of knowledge by religious identity. However, we found statistically significant differences in knowledge level by other sociodemographic characteristics. Among those with average to high knowledge, respondents aged 25–34 were the majority compared to the other two age groups. Those with university level education were more in the average and higher knowledge categories compared to all the other education categories (including those with no education, primary and secondary level education). Most of the partnered participants had average knowledge (poor, 31.5% vs. average, 43.0% vs. high, 25.5%). The unpartnered participants had a close but significantly different distribution by knowledge scores (poor, 29.1% vs. average, 41.5% vs. high, 29.4%). Generally, across the levels of knowledge, we found that clustering was around average knowledge (Table 2 ). Table 2 Knowledge score across sociodemographic characteristics Characteristic Overall Knowledge score N = 8,942 1 Poor knowledge , N = 2,711 1 Average knowledge , N = 3,774 1 High knowledge , N = 2,435 1 p -value 2 Sex 0.47 Male 4,464 (49.9%) 1,365 (30.6%) 1,899 (42.6%) 1,190 (26.7%) Female 4,478 (50.1%) 1,346 (30.1%) 1,875 (42.0%) 1,245 (27.9%) Age group < 0.001 18–24 2,219 (24.8%) 716 (32.3%) 893 (40.3%) 607 (27.4%) 25–34 2,640 (29.5%) 683 (26.0%) 1,184 (45.0%) 762 (29.0%) 35+ 4,083 (45.7%) 1,312 (32.2%) 1,697 (41.6%) 1,066 (26.2%) Education level < 0.001 None/Don't know 228 (2.6%) 90 (39.5%) 89 (39.0%) 49 (21.5%) Primary 1,481 (16.6%) 632 (42.8%) 552 (37.4%) 293 (19.8%) Secondary 3,348 (37.5%) 1,080 (32.3%) 1,433 (42.9%) 827 (24.8%) University 3,882 (43.4%) 908 (23.5%) 1,700 (43.9%) 1,264 (32.6%) Marital status < 0.001 Partnered 4,790 (53.6%) 1,506 (31.5%) 2,055 (43.0%) 1,218 (25.5%) Unpartnered 4,149 (46.4%) 1,205 (29.1%) 1,718 (41.5%) 1,215 (29.4%) Religion 0.080 Christian 8,303 (92.9%) 2,507 (30.3%) 3,531 (42.6%) 2,244 (27.1%) Others 636 (7.1%) 204 (32.1%) 242 (38.1%) 189 (29.8%) 1 Frequency (%) 2 Pearson’s Chi-squared test Note: The knowledge score categories were obtained by cutting the composite sum of scores into three groups: “Poor knowledge” if in the composite score was 0 or 1, “Average knowledge” if a composite score of 2 and "High knowledge" if composite score is 3. Determinants of knowledge of abortion laws Table 3 shows the determinants of high of knowledge of abortion laws in Kenya. Our analysis shows that sex, age group, education and marital status were predictors of levels of knowledge of abortion laws. Specifically, females were associated with a 22% increase in odds of knowledge of abortion laws compared to males (AOR = 1.22 [95% CI: 1.09–1.38]; p < 0.001). The odds of high knowledge for the age group 25–34 increased by 21% in comparison to ages 18–24 (1.21 [1.03–1.43]; 0.024). Respondents with university education were more knowledgeable than those with no education (2.57; [1.79–3.70]; <0.001). Similarly, unpartnered had respondents had higher knowledge than the partnered/married (1.26; [1.11–1.43]; <0.001). Table 3 The determinants of knowledge of legal indications of abortion in Kenya Variable Poor vs. Average Poor vs. High AOR [95% CI] 1 p -value AOR [95% CI] 1 p -value Sex Male Ref. Ref. Female 1.12 [1.01–1.25] 0.029 1.22 [1.09–1.38] < 0.001 Age group (years) 18–24 Ref. Ref. 25–34 1.29 [1.11–1.50] < 0.001 1.21 [1.03–1.43] 0.024 35+ 1.11 [0.96–1.29] 0.2 1.11 [0.95–1.31] 0.2 Religion Christian Ref. Ref. Others 0.91 [0.75–1.11] 0.4 1.16 [0.94–1.43] 0.2 Highest education level None/don't know Ref. Ref. Primary 0.89 [0.65–1.21] 0.4 0.86 [0.59–1.25] 0.4 Secondary 1.37 [1.01–1.87] 0.044 1.42 [0.98–2.04] 0.061 University 1.85 [1.36–2.52] < 0.001 2.57 [1.79–3.70] < 0.001 Marital status Partnered Ref. Ref. Unpartnered 1.05 [0.93–1.18] 0.4 1.26 [1.11–1.43] < 0.001 1 AOR = Adjusted Odds Ratio; Bold p -value = Significant estimate at 0.05 level; Ref. = Referent category; estimates are from multinomial logistic regression model. Abortion preference: Conditions survey participants preferred abortion be allowed Figure 3 presents situations respondents preferred abortion to be allowed. None of the options received absolute approval by all respondents (100%). About 72% and 65% wanted abortions allowed when the life or physical health of the woman are at risk, respectively. Slightly less than half of respondents (43%) would approve of abortion if the mental health of the women were at risk. Surprisingly, only about one-quarter would want abortions allowed in cases of rape (29%) and incest (28%). The least acceptable conditions for abortion according to this survey were, if the pregnant girl or woman was unmarried (7%), on economic grounds (11%), the woman is HIV positive (11%) and if the girl is still in school (12%). Figure 4 shows the breakdown by sex of situations respondents preferred abortion should be allowed. In general, there was no major difference across men and women regarding situations in which respondents felt they would want to see abortion. Latent Class Analysis (LCA): Exploring the preferences towards Kenyan abortion law The LCA explored preferences towards various potential legal provisions for abortion, and categorized the respondents into four classes. Class 1- represents participants in favor of abortion in all 14 conditions (characterized by very high probabilities - mostly above 90%), we labelled this group as legalization proponents , and it represented 7% of the sample. Class 2 represented respondents who tended to favor abortion when there was risk to women’s physical health (84%), risk to women’s life (93%), and mental health risk (77%). These respondents in Class 2 also exhibited moderate levels of support for abortion when a pregnancy results from rape (66%), mental incapacity (61%), pregnancy from incest (59%), and fetal anomaly (53%). We labeled this class as moderate supporters of abortion legalization , and comprised 21% of the sample. Class 3 represented individuals supportive of abortion for health reasons: that is, when physical health is at risk (84 %), and when life is at risk ( 93%), but were moderate on mental health risk (43%) and opposed the rest of the conditions. We labelled this group as conditional supporters , and it represented the highest proportion of the sample (43%). Class 4 represented respondents with very low probabilities of support for abortion in any circumstance (29%), since they consistently opposed all scenarios and were on the conservative side of each opinion statement, we labelled this group as legalization opponents . Figure 5 presents profile plot of the classes with the item-response probabilities for the listed conditions under which abortion could be allowed. Table 4 summarizes the correlates of the total number of conditions for abortion that participants preferred or would support. Our results show that female participants (-0.42 [95% CI: -0.55 to -0.28; p < 0.001]), unpartnered participants (-0.52 [-0.67 to -0.38]; p < 0.001), those with university education (-0.75 [-1.2 to -0.32]; p < 0.001) and those of non-Christian religious affiliations (-0.52 [-0.77 to -0.27]; p < 0.001) endorsed fewer items compared to the corresponding referent category. However, increasing knowledge of abortion laws (0.67 [0.64 to 0.70]; p < 0.001) correlated with endorsing more of the abortion conditions. Table 4 Correlates of the number of conditions for abortion that participants endorsed, presented as multivariable-adjusted mean differences Characteristic Coef. [95% CI] p -value (Intercept) 3.8 [3.3 to 4.4] < 0.001 Sex Male Ref. Female -0.42 [-0.55 to -0.28] < 0.001 Age (years) 18–24 Ref. 25–34 -0.03 [-0.22 to 0.16] 0.8 35+ 0.12 [-0.07 to 0.31] 0.2 Marital status Partnered Ref. Unpartnered -0.52 [-0.67 to -0.38] < 0.001 Highest education level None/Don't know Ref. Primary 0.31 [-0.12 to 0.74] 0.2 Secondary -0.09 [-0.51 to 0.34] 0.7 University -0.75 [-1.2 to -0.32] < 0.001 Religious affiliation Christian Ref. Others -0.52 [-0.77 to -0.27] < 0.001 Knowledge score 0.67 [0.64 to 0.70] < 0.001 1 Coef. = Coefficient; Bold p -value = Significant estimate at 0.05 level; Ref. = Referent category; estimates are from multivariable-linear regression model. Dominant survey language included in the adjustment of model estimates Discussion This nationwide survey aimed to describe knowledge of abortion laws in Kenya and the situations people would prefer abortions allowed. In general, the study findings revealed that respondents had fair knowledge of two conditions under which abortion is legal in Kenya—threat to a pregnant woman’s life and health. However, only 33% knew that a woman could have access to legal abortion if her pregnancy emanated from rape. There were no significant differences in levels of knowledge between males and females. These findings are not so different from other studies globally that suggest that women, health providers, and even decision makers worldwide sometimes have limited or inaccurate knowledge of the abortion laws and policies in their country ( 2 , 4 , 22 , 23 ). A systematic review assessing women’s awareness and knowledge of abortion laws found that, of the 16 studies reviewed, women’s awareness and correct knowledge of the legal status was less than 50% in nine studies, and in six studies, knowledge of legalization/liberalization ranged between 32.3% − 68.2% ( 4 ). The variations in knowledge of abortion laws across countries could be linked to several factors, including poor information dissemination of the laws ( 24 ). Nonetheless, inadequacy in knowledge of abortion laws has a direct impact on how women enter the health system and whether they can successfully navigate its bureaucratic measures, including in countries with some permissive laws. In Kenya, where abortion is not permitted except under limited conditions, knowledge of abortion laws is very important. Knowledge of abortion laws is a key determinant of the utilization of safe abortion services ( 2 ). In situations where knowledge levels are low, women rely on the knowledge and judgement of health providers as to the law and what services they can legally offer. In an Ethiopian study, a major reason cited by young females for not utilizing the safe abortion services was inadequate knowledge on the abortion law ( 13 ). Information vacuums on abortion laws lead to inconsistent, arbitrary, and even discriminatory practice, including harassments by the police and denial of services by medical and state authorities often in bad faith. At times, contradictory laws cause confusion, such as where the constitution provides for abortion albeit under limited conditions, but the penal code criminalizes the same practice. Findings showed that most people correctly identified conditions abortions are disallowed in Kenya, while at the same time, fewer people correctly pointed to conditions under which abortions are allowed in Kenya. This finding reflects the chilling effect of criminal laws around abortion and the pattern of stretching abortion laws to prohibit situations that are actually allowed by law. More individuals with post-secondary education had higher levels of knowledge on the Kenyan abortion law, as was those above 35 years. Other studies in the region have reported similar findings where higher levels of education dovetailed greater percentages of correct general awareness and knowledge, even though this pattern was highly variable between studies. In Ghana, for instance, women with formal education had nearly 85% greater awareness compared to women without any formal education (92% vs. 8%) ( 22 ). In Ethiopia, higher awareness and knowledge was seen among women living in predominantly urban areas and with better access to comprehensive abortion care. These regions in Ethiopia had higher abortion rates than most other parts of the country ( 3 ). Further, an assessment in Nepal also reported that variations in proportion of women with correct knowledge existed across socioeconomic strata, and that women in urban areas and with higher education had higher levels of knowledge compared to those living in rural areas or with lower levels of education ( 24 ). About 72% and 65% wanted abortions allowed when the life or physical health of the woman are at risk respectively. None of the options scored a 100% approval, and the two reasons more preferred are the same grounds under which the current law already allows for abortion. Interestingly, almost half of respondents (43%) indicated they would approve of abortion if the mental health of the woman were at risk. Surprisingly, only about one-quarter would want abortions allowed in cases of rape (29%) and incest (28%). No previous studies in Kenya or elsewhere in the region have collected this data point, but it remains valuable since community-level perceptions on situations under which abortions should be allowable offer possible avenues for improving abortion access and programming ( 25 ). Knowledge of abortion legality is critical to protect and expand access to safe abortion care services, especially in contexts like Kenya where abortion is available for multiple indications ( 26 ). It is also pertinent to note the 15% of respondents who wanted abortion allowed if the pregnant woman does not want the pregnancy. A recent PEW poll reported 11% of respondents in Kenya supported legalization of abortion ( 27 ). This 15% reported in our study is significant for several reasons; including 1) it is not conditioned on any exceptions – it is based on the right of the woman to decide whether they want to continue a pregnancy or not. 2) It is higher than the 9% reported in the Ipsos’s 2020 survey, which increased to 15% “depending on the conditions” ( 18 ). For our study, if we average the conditions under which respondents would want to see abortion become allowable (legal), we found a higher percentage at 27%. Conclusion The study findings show that the knowledge of the existing abortion law is sub-optimal. Sex, age, and educational levels were associated with various levels of knowledge of abortion laws. Most people in Kenya preferred abortion to be allowable in conditions where the health or life of the women is at risk and when the mental health of the woman is at stake. There is need to comprehensively disseminate information on the abortion law, and where women can get safe and legal abortion to avert the challenges of unsafe abortion in Kenya. These efforts should pay attention to groups and communities that typically struggle to access information and safe abortion care services. Study Limitations Our study is the first large-scale, nationally representative survey on knowledge and preference of abortion laws in Kenya. Nonetheless, the study had some limitations that are critically discussed in this section. First, being a phone-based survey, only respondents with active phone numbers and who could be reached at the time of the survey were interviewed. As such, there is a possibility of systematic exclusion of people without active phone numbers and those who could not be reached at the time of survey. However, we made several re-dial attempts for individuals who could not be reached, and only dropped reaching out to them after the several attempts failed. Even among telephone owners, the focused on the 12 million phone numbers in GeoPoll’s database, implying that the other millions of people in Kenya with phone numbers did not have the same chances of participation in the study because GeoPoll’s database did not include their mobile numbers. Finally, due to the sensitive nature of abortion, social desirability bias and recall bias might have affected the responses of the respondents. We however assured all respondents of the confidentiality and privacy of their answers in the survey. Notwithstanding these challenges, this study presents strong and reliable findings on the knowledge of, and preferences towards abortion laws in Kenya. Declarations Ethics approval and consent to participate Scientific and ethical approval to conduct this study was obtained from the AMREF ethics and scientific research committee (P1240/2022). The National Commission for Science, Technology and Innovation, granted a research permit (NACOST-NACOSTI/P/22/19653). All participants provided informed verbal consent before participating in the study. Consent for publication: Not applicable Availability of Data and Materials All data and materials are available on request from the corresponding author. Also, according to the APHRC policies (the organization hosting the datasets), all deidentified datasets will be publicly available on the APHRC microdata portal after 3 years (https://aphrc.org/microdata-portal/). Competing interests The authors declare no competing interests. Funding The research was supported by a grant from the African Regional Office of the Swedish International Development Cooperation Agency, Sida (Contribution No. 12103), for APHRC’s Challenging the Politics of Social Exclusion project. Authors’ contributions KJ and BU conceptualized the original study, and KJ, EM, BU, SA, were primarily involved in the data collection. KJ, EM, AA, and IA were responsible for data cleaning and analysis, and all authors reviewed, edited, and approved the final manuscript. Acknowledgement We are indebted to the Geopoll team for their role in setting up the data collection systems, supporting the training of field teams, and collecting data. We appreciate the hard work and diligence of the field teams for collecting the study data. We also thank all the respondents who gave their time to participating in this study. References Erdman JN, Johnson BR. Access to knowledge and the Global Abortion Policies Database. Int J Gynecol Obstet [Internet]. 2018 Jul 1 [cited 2023 Jun 5];142(1):120–4. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/ijgo.12509 Mekonnen BD, Wubneh CA. Knowledge, Attitude, and Associated Factors towards Safe Abortion among Private College Female Students in Gondar City, Northwest Ethiopia: A Cross-Sectional Study. Adv Prev Med [Internet]. 2020 Nov 4 [cited 2023 Jun 5];2020:1–8. Available from: /pmc/articles/PMC7657704/ Sheehy G, Dozier JL, Mickler AK, Yihdego M, Karp C, Zimmerman LA. Regional and residential disparities in knowledge of abortion legality and availability of facility-based abortion services in Ethiopia. Contracept X. 2021 Jan 1;3:100066. Assifi AR, Berger B, Tunçalp Ö, Khosla R, Ganatra B. Women’s Awareness and Knowledge of Abortion Laws: A Systematic Review. PLoS One [Internet]. 2016 Mar 1 [cited 2023 Jun 5];11(3). Available from: /pmc/articles/PMC4807003/ Atuhaire S. Abortion among adolescents in Africa: A review of practices, consequences, and control strategies. Int J Health Plann Manage [Internet]. 2019 Oct 1 [cited 2023 Mar 30];34(4):e1378–86. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/hpm.2842 Singh S, Remez L, Sedgh G, Kwok L, Tsuyoshi O. Abortion Worldwide 2017: Uneven Progress and Unequal Access, New York: Guttmacher Institute, 2018 [Internet]. New York; 2018. Available from: https://www.guttmacher.org/sites/default/files/report_pdf/abortion-worldwide-2017.pdf National Council for Law Reporting. Laws of Kenya. The Constitution of Kenya 2010. Kenya; 2010. Ipas. Kenya restores standards and guidelines for comprehensive reproductive health, including abortion. Ipas Newsletter [Internet]. 2019 Jun 14; Available from: https://www.ipas.org/news/kenya-restores-standards-and-guidelines-for-comprehensive-reproductive-health-including-abortion/ Mutua MM, Achia TNO, Maina BW, Izugbara CO. A cross-sectional analysis of Kenyan postabortion care services using a nationally representative sample. Int J Gynecol Obstet [Internet]. 2017 Sep 1;138(3):276–82. Available from: https://doi.org/10.1002/ijgo.12239 Ministry of Health Kenya, African Population and Health Research Center (APHRC), Guttmacher Institute. Incidence and Complications of Unsafe Abortion in Kenya: Key findings of a national study. Nairobi; 2013. Mohamed SF, Izugbara C, Moore AM, Mutua M, Kimani-Murage EW, Ziraba AK, et al. The estimated incidence of induced abortion in Kenya: a cross-sectional study. BMC Pregnancy Childbirth [Internet]. 2015;15(1):185. Available from: https://doi.org/10.1186/s12884-015-0621-1 Klu D, Yeboah I, Kayi EA, Okyere J, Essiaw MN. Utilization of abortion services from an unsafe provider and associated factors among women with history of induced abortion in Ghana. BMC Pregnancy Childbirth [Internet]. 2022 Dec 1 [cited 2023 Jun 5];22(1):1–8. Available from: https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-022-05034-x Mekuria M, Daba D, Girma T, Birhanu A. Assessment of knowledge on abortion law and factors affecting it among regular undergraduate female students of Ambo University, Oromia Region, Ethiopia, 2018: a cross sectional study. Contracept Reprod Med 2020 51 [Internet]. 2020 Dec 1 [cited 2023 Jun 6];5(1):1–8. Available from: https://contraceptionmedicine.biomedcentral.com/articles/10.1186/s40834-020-00136-3 Scheinerman N, Callahan KP. Legal Discrepancies and Expectations of Women: Abortion, Fetal Therapy, and NICU Care. Hastings Cent Rep [Internet]. 2023 Mar 1 [cited 2023 Jun 6];53(2):36–43. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/hast.1472 de Londras F, Cleeve A, Rodriguez MI, Farrell A, Furgalska M, Lavelanet AF. The impact of provider restrictions on abortion-related outcomes: a synthesis of legal and health evidence. Reprod Health [Internet]. 2022 Dec 1 [cited 2023 Jun 6];19(1):95. Available from: /pmc/articles/PMC9014563/ Ushie BA, Juma K, Kimemia G, Ouedraogo R, Bangha M, Mutua M. Community perception of abortion, women who abort and abortifacients in Kisumu and Nairobi counties, Kenya. PLoS One. 2019;14(12). Kimemia GW, Kabiru CW, Ushie BA. Ethical concerns facing abortion researchers in restrictive settings: the need for guidelines. Sex Reprod Heal Matters [Internet]. 2023 Dec 1 [cited 2023 Jun 6];31(1):2193315. Available from: /pmc/articles/PMC10134914/ Kenya Christian Professional Forum. IPSOS: KCPF Perceptions Study on Abortion, Homosexuality and 2010 Constitution [Internet]. Nairobi; 2020. Available from: https://www.theelephant.info/documents/ipsos-kcpf-perceptions-study-on-abortion-homosexuality-and-2010-constitution/ Mccutcheon AL. Sexual Morality, Pro-Life Values, and Attitudes toward Abortion: A Simultaneous Latent Structure Analysis for 1978-1983. Sociol Methods Res. 1987;16(2):256–75. Linzer DA, Lewis JB. poLCA: An R package for polytomous variable latent class analysis. J Stat Softw. 2011 Jun;42(10):1–29. Kim Y, Jeon S, Chang C, Chung H. glca: An R Package for Multiple-Group Latent Class Analysis. Appl Psychol Meas. 2022 Jul;46(5):439–41. Id SA, Uwumboriyhie V, Id G, Id EO. Knowledge and attitude towards Ghana’s abortion law: A cross-sectional study among female undergraduate students. Chikhungu LC, editor. PLOS Glob Public Heal [Internet]. 2023 Apr 21 [cited 2023 Jun 6];3(4):e0001719. Available from: https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0001719 Becker D, Garcia SG, Larsen U. Knowledge and opinions about abortion law among Mexican youth. Int Fam Plan Perspect. 2002;28(4):205–13. Adhikari R. Knowledge on legislation of abortion and experience of abortion among female youth in Nepal: A cross sectional study. Reprod Health [Internet]. 2016 Apr 27 [cited 2023 Jun 6];13(1):1–9. Available from: https://reproductive-health-journal.biomedcentral.com/articles/10.1186/s12978-016-0166-4 Hessini L, Brookman-Amissah E, Crane BB. Global policy change and women’s access to safe abortion: the impact of the World Health Organization’s guidance in Africa. Afr J Reprod Health. 2006;10(3):14–27. Mutua MM, Manderson L, Musenge E, Achia TNO. Policy, law and post-abortion care services in Kenya. PLoS One [Internet]. 2018 Sep 21;13(9):e0204240–e0204240. Available from: https://pubmed.ncbi.nlm.nih.gov/30240408 Fetterolf J, Clancy L. Support for legal abortion is widespread in many countries, especially in Europe. Pew Research Center. 2023. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Jul, 2024 Editor assigned by journal 23 Jul, 2024 Submission checks completed at journal 22 Jul, 2024 First submitted to journal 18 Jul, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4761401","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330655275,"identity":"f77562f5-97db-4d77-a76c-53fb7c38585a","order_by":0,"name":"Kenneth 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Center","correspondingAuthor":false,"prefix":"","firstName":"Anthony","middleName":"","lastName":"Ajayi","suffix":""}],"badges":[],"createdAt":"2024-07-18 09:24:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4761401/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4761401/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62801153,"identity":"51064125-9fc8-489e-9df4-298050ae2b8c","added_by":"auto","created_at":"2024-08-19 16:03:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":995199,"visible":true,"origin":"","legend":"\u003cp\u003eRespondent's knowledge of current abortion laws in Kenya\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4761401/v1/8485edf4053363bcad1bf094.png"},{"id":62801152,"identity":"1417beca-442e-41dc-8a14-ad0e59f42ffc","added_by":"auto","created_at":"2024-08-19 16:03:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":473092,"visible":true,"origin":"","legend":"\u003cp\u003eProportion with high knowledge of abortion laws by counties\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4761401/v1/3787f571b804a358fad4defc.png"},{"id":62800865,"identity":"434204fc-e374-44b6-9a72-4b073dcc14d7","added_by":"auto","created_at":"2024-08-19 15:55:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31830,"visible":true,"origin":"","legend":"\u003cp\u003eResponders’ preferred conditions for allowing abortion\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4761401/v1/5f229f7d42bb4f05ed1b7f03.png"},{"id":62799977,"identity":"dd09b9c1-e80f-4fbc-a2b4-f165378d09a7","added_by":"auto","created_at":"2024-08-19 15:47:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":36343,"visible":true,"origin":"","legend":"\u003cp\u003eSituations study participants would want to see abortion allowed by sex\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4761401/v1/dbe6b19cbfe914165b9598e8.png"},{"id":62799982,"identity":"06b69ac2-54ce-40ba-af40-a5835801b1f7","added_by":"auto","created_at":"2024-08-19 15:47:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":650073,"visible":true,"origin":"","legend":"\u003cp\u003eProfiles of participants’ regarding preference toward abortion\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4761401/v1/f930f3fae1ac26da00586b15.png"},{"id":62801329,"identity":"7204cfee-bd64-4a6f-a104-4663e84cc625","added_by":"auto","created_at":"2024-08-19 16:16:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2832593,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4761401/v1/c0e1a34c-7e2a-4508-ae94-e787eb4701dc.pdf"},{"id":62799979,"identity":"bda3fcda-09c6-40a4-9856-8c7cbedaad26","added_by":"auto","created_at":"2024-08-19 15:47:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23400,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4761401/v1/412f5902b4cd371d02eb8c57.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Knowledge of and preference for abortion legality in Kenya: A National Cross- Sectional Survey","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccess to safe abortion and post-abortion care depends on several factors, including the legal and political context of abortion, access to information, stigma, and the readiness of health facilities to provide high-quality abortion-related services (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Critically, the use of safe abortion services varies by women's awareness and knowledge of abortion law and availability of services (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Achieving universal access to sexual and reproductive health and rights requires unrestricted access to accurate information that facilitates women\u0026rsquo;s autonomy and informed decisions regarding their reproductive health (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Nevertheless, research shows that women, healthcare providers, and even policymakers worldwide have limited or inaccurate knowledge of abortion laws and policies in their countries (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). A recent systematic review in Africa showed low levels of women's general knowledge of abortion laws in their countries and critical differentials in knowledge based on geography, wealth, and education (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite restrictive abortion laws across many African countries, abortion remains commonplace (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). For instance, in Kenya, abortion is only permitted if, in the opinion of a trained health professional, there is a need for emergency treatment; or the life or health of the mother is in danger; or if permitted by any other written law (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Additionally, abortion is permitted on grounds of rape and defilement based on a Kenyan High Court ruling of 2019 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Paradoxically, the 1964 Kenyan Penal Code (Sections 158\u0026ndash;160) criminalizes abortion and imposes harsh penalties on those who contravene the law. Despite these legal restrictions of abortion, in 2012, an estimated 464,000 induced abortions occurred in Kenya, with a majority of them unsafe, resulting in moderate to severe complications, some of which needed intensive care and attendance by highly-skilled health providers (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKnowledge of abortion law greatly influences access and utilization of safe abortion and post-abortion care services (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Differences in knowledge drive inequalities, especially for marginalized women who may have greater demand for abortion services, but would be disproportionately affected and experience arbitrary denials of services or poor quality of care, as reported in Mexico (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). A study by Mekuria et al, among young females undergraduates in Ethiopia, suggested that the main impediment to utilizing safe abortion services was inadequate knowledge of the abortion law (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Notably, in a context where communities do not know the law regulating access to abortions, those seeking care cannot know their legal entitlements, service providers cannot practice with legal protection, and governments can escape legal responsibility for the adverse effects of their laws, and also fail to disseminate laws appropriately (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs such, assessing the awareness and knowledge of abortion laws in a country is critical, especially in Kenya where abortion is legally allowed for multiple indications (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Neglecting knowledge of abortion law can translate to high social and economic costs and inhibit the efforts to expand access to safe abortion care. Further, while common, the inconsistencies in the national laws and policy around abortion (especially the constitution vs the penal code), often impedes safe and evidence-based practice around abortion (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther, understanding knowledge of abortion laws can be critical in influencing community perceptions of abortion, which tend to be largely negative and cultivates stigma, discrimination and ostracization of women known to have terminated their pregnancies, their providers, friends and family (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In 2022, a study reported that healthcare providers conveyed concerns pertaining to the delivery of abortion services, citing fear, intimidation, harassment, and prosecution under restrictive laws around safe abortion access and lack of clear safe abortion care policies and guidelines within the Ministry of Health (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). It is helpful to understand the public\u0026rsquo;s knowledge and preferences for abortion laws to inform an understanding of the barriers to women\u0026rsquo;s access to safe and legal abortion in Kenya.\u003c/p\u003e \u003cp\u003eFew studies have previously touched on knowledge and preferences towards abortion laws in Kenya, and there remains a gap in understanding community members' viewpoints on this subject. These knowledge gaps sometimes stem from the vague and broad terms of the law, which breed uncertainty and even conflict when unaccompanied by accessible regulation or guidelines. Inadequate community level knowledge impedes the populace\u0026rsquo;s ability to hold their leaders and decision-makers accountable to meeting their aspirations.\u003c/p\u003e \u003cp\u003eThe aim of this study was to examine the public\u0026rsquo;s knowledge of the current abortion law in Kenya. Moreover, we explored their preferences for grounds under which they would want abortion allowed.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThe study was a cross-sectional survey with a nationally representative sample of adults across the 47 counties in Kenya. Respondents were individuals 18 years or older, and of diverse sociodemographic characteristics, including sex, age groups, religion, education, marital status, employment, and residential areas. The choice of adults (18 years or older) eased the process of acquiring informed consents for participants and avoided the need to seek parental assent before engaging those below age 18, especially as these were phone-based interviews. As such, those eligible for the survey, were individuals 18 years or older, who had a working phone number and had been living in Kenya for the past six months.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEstimating the Sample size\u003c/h2\u003e \u003cp\u003eBased on a 2020 survey, 15% of respondents in Kenya indicated support for abortion (which represented some 6% who said \u0026ldquo;yes\u0026rdquo; to abortion\u0026rdquo; and 9% who supported abortion conditionally, i.e., \u0026ldquo;dependent\u0026rdquo; on the situation of the woman or pregnancy) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). We based our estimation on this 15% prevalence to determine a robust sample capable of producing stable estimates of the proportion of people who expressed positive opinions toward abortion with sufficient power (0.8) and a 95% confidence interval. Using the formula for calculating proportions from a single population;\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;Zα/22 *p*(1-p) / MOE\u003csup\u003e2\u003c/sup\u003e,\u003c/p\u003e \u003cp\u003eWhere we assumed that the known \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15 (from the 2020 study), and a margin of error (MOE) of 1% (0.01). Thus, solving for \u003cem\u003en\u003c/em\u003e in the above equation gave the sample size of 8487. Additionally, we inflated the sample by 5% to account for non-response and incomplete interviews, which brought the final sample size to 8912.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling procedure\u003c/h3\u003e\n\u003cp\u003eThe research team worked collaboratively with an owner of the mobile telephone database in Kenya. Geopoll owns and maintains a telephone database and operates mobile and online data collection services across several countries in Africa. The GeoPoll User Database has detailed demographic and location information of all individuals in the database. The study respondents were selected using a two-stage stratified random sampling and a random digit dialing approach, from an existing Geopoll database of over 12\u0026nbsp;million telephone numbers from all 47 counties of Kenya. Stage one involved selecting primary sampling units\u0026mdash;counties\u0026mdash;using the probability proportional to size method. The second stage involved randomly selecting GeoPoll-indexed mobile phone users from a representative sample. To achieve randomness from the robust database, we randomly extracted a pool/frame of 50,000 mobile numbers, representative by age, sex, and location. From this pool of numbers, we randomly dialed users until we achieved the desired quotas. This process of extracting a frame of numbers and dialing from it was repeated until the targeted quotas were met. In cases of a non-response, such numbers were redialed four hours later to ensure all potential participants had equal chances of selection. If the selected respondent was unreachable or refused to participate, they were replaced by a randomly generated phone number of a participant from a pool or pre-sampled phone numbers. Respondents\u0026rsquo; phone numbers were randomly selected for each targeted county and run through a software system that eliminated duplicate phone numbers. As such, only unique phone numbers were retained in the finalized database of phone numbers to be targeted. The selection included diverse categories of respondents without a preponderance towards any subgroup.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData collection and data collection tools\u003c/h2\u003e \u003cp\u003eTrained research assistants conducted the data collection between September and November 2022 with 8942 interviews completed. Research assistants were trained in research ethics, informed consent processes, phone etiquette, interviewing techniques and the study questionnaire. We used a structured questionnaire previously used in other countries to test people's knowledge of abortion and abortion laws. The questionnaire had five main segments including a) demographic characteristics, b) the mosaic of opinions on induced abortion, c) the Stigmatizing Attitudes, Beliefs, and Actions Scale (SABAS), d) the abortion listing experiment, and e) knowledge of abortion law section. For this paper, we only focus on the data collected using the abortion legal knowledge segment of the questionnaire. The knowledge segment presented a list of 14 potential conditions under which the law allows abortion. The interviewer asked the respondents, \"To the best of your knowledge, under which conditions is abortion legal in Kenya?\" The grounds for abortion were not read to the respondents \u0026ndash; the interviewer only recorded their unprompted responses. However, we probed them by asking, \"Is there any other ground\" until they did not have any other grounds to mention. For the preferred grounds for abortion, we asked respondents whether they would want abortion to be legal under each ground, one by one. The interviewers administered the study questionnaire through telephone interviews, which took an average of 25 minutes to complete. To interview 8942 respondents, 126,482 phone numbers were dialed. Of this number, 59821 were on automatic answering machines, 2321 required callbacks, 28484 calls were either declined, disconnected or were refusals, and 26914 did not respond/no answer.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy variables and measurements\u003c/h3\u003e\n\u003cp\u003eThe main independent variables included socio-demographic characteristics, such as age, residence, marital status, religion and educational levels. The assessment of knowledge regarding abortion law involved the utilization of three variables with 'yes' or 'no' responses. Likewise, the evaluation of respondent preferred abortion law employed the same approach but with 14 binary items.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analysis procedure\u003c/h2\u003e \u003cp\u003eWe summarized and presented demographic characteristics as frequencies and compared the knowledge categories between them using Pearson's Chi-squared test. For the knowledge questions, we coded the correct responses as \u0026lsquo;1\u0026rsquo; and incorrect/do not know as \u0026lsquo;0\u0026rsquo;. The knowledge scores were generated by summing the correct responses across three knowledge indicator variables. The scores were categorized into \"high knowledge,\" \"average knowledge,\" and \"poor knowledge.\" Items were deemed to be of \u0026ldquo;poor knowledge\u0026rdquo; if the composite sum of correct responses was either zero or one, \u0026ldquo;average knowledge\u0026rdquo; if the composite sum score was two, and \"high knowledge\" if all three composite sum of correct responses was three for those who answered (yes). With similar dummy coding as before, we also created another continuous knowledge score with all the 14 items \u0026ndash; including the reasons abortions are not allowed in Kenyan laws. We used the knowledge score in multivariable-adjusted analysis of participants\u0026rsquo; preference conditions they would support. We graphed the proportions of preferences chosen by respondents and subsequently stratified them by gender.\u003c/p\u003e \u003cp\u003e We also conducted a multivariable multinomial logistic regression to identify predictors of high knowledge of abortion laws. Similarly, we fitted a multivariable linear regression model to identify correlates of endorsing/preferred conditions under which abortion could be allowed. We employed latent class analysis (LCA) to fully characterize preferences of the Kenyan population about abortion laws. For categorical data, LCA is a factor analogue that, according to McCutcheon (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), discovers an unobserved collection of latent classes that \"explains\" the correlations between a set of observed categorical variables/indicators. Put differently, LCA offers a method for uncovering a small number of underlying subgroups with various characteristics. In this study, we asked the participants 14 questions (items) about abortion situations they would support. The questions examined the willingness of the study participants to oppose or approve abortion legalization for various conditions. With the 14 binary items, we first fitted a sequence of base models with one to ten classes using poLCA function (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) in R 4.3.1 (R Core Team, 2023) and temporarily retained a four class-model, which had the lowest Bayesian information criterion (BIC). We then evaluated models with two to five classes using glca (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) functions in R and extracted a variety of fit indices (Table S2). Finally, we retained a four-class model that had the lowest BIC, better entropy (0.82) that shows good quality of classification and was theoretically sound. We handled missing data by list-wise deletion. Data analysis was done in R (version 4.3.1) and Stata (version 17). All statistical tests were 2-tailed, and a 5% significance threshold maintained.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic characteristics of participants\u003c/h2\u003e \u003cp\u003eThe mean age of respondents was 33 years (SD, 10), with the highest proportion above 35 years (46%). A quarter of the respondents were between ages 18 to 24 years while one-third were 25 to 34 years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of the 8,942 individuals interviewed, there was an even split between men -- 4,464 50%) and women -- 4,478 (50%). More respondents had tertiary level education (post-secondary) (43%) with very few not having any formal education (3%). A slight majority of respondents were married (54%), and most identified as Christians (93%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics of study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall, N\u0026thinsp;=\u0026thinsp;8,942\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD; Range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 years (SD,10; 18\u0026ndash;85)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,219 (24.8%)\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=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,640 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,083 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,464 (49.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,478 (50.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHighest Level of Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e228 (2.6%)\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=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,481 (16.6%)\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=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,348 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity/College/Technical school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3882 (43.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried/Partnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,790 (53.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle/divorced/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,149 (46.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,303 (92.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIslam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e526 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther (Agnostic, Atheist, Spiritual, Hindu)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e**SD\u0026thinsp;=\u0026thinsp;standard deviation; None- no formal education\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\u003eKnowledge of Kenyan abortion laws\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the responses of participants to the question on \u0026ldquo;under which conditions does the existing Kenyan law allow abortion?\u0026rdquo;. Based on the three conditions under which the Kenyan laws allow for safe legal abortion, those reporting correctly were \u0026minus;\u0026thinsp;76% for when a woman's life is at risk; 74% for physical health at risk; and only 33% knew that abortion is allowed when the pregnancy is because of rape (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At the same time, a considerable majority knew that abortion is not allowed because a pregnant girl or woman is unmarried (89%), woman is HIV positive (88%), woman is unable to care for child (87%), or that the girl is still in school (86%), or girl is 16 years or younger (83%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). County level variations in knowledge of abortion law are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. More information on the levels of knowledge is in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eKnowledge of abortion law by sociodemographic characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the knowledge levels by sociodemographic characteristics. The proportion of males and females across the knowledge scores were about the same (p\u0026thinsp;=\u0026thinsp;0.47). Similarly, there was no significant difference in the levels of knowledge by religious identity. However, we found statistically significant differences in knowledge level by other sociodemographic characteristics. Among those with average to high knowledge, respondents aged 25\u0026ndash;34 were the majority compared to the other two age groups. Those with university level education were more in the average and higher knowledge categories compared to all the other education categories (including those with no education, primary and secondary level education). Most of the partnered participants had average knowledge (poor, 31.5% vs. average, 43.0% vs. high, 25.5%). The unpartnered participants had a close but significantly different distribution by knowledge scores (poor, 29.1% vs. average, 41.5% vs. high, 29.4%). Generally, across the levels of knowledge, we found that clustering was around average knowledge (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKnowledge score across sociodemographic characteristics\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=\"left\" 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=\"left\" 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\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eKnowledge score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;8,942\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePoor knowledge\u003c/b\u003e,\u003c/p\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;2,711\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eAverage knowledge\u003c/b\u003e,\u003c/p\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;3,774\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHigh knowledge\u003c/b\u003e,\u003c/p\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;2,435\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,464 (49.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,365 (30.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,899 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,190 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,478 (50.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,346 (30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,875 (42.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,245 (27.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,219 (24.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e716 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e893 (40.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e607 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,640 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e683 (26.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,184 (45.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e762 (29.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,083 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,312 (32.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,697 (41.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,066 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone/Don't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (39.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49 (21.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,481 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e632 (42.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e552 (37.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e293 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,348 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,080 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,433 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e827 (24.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,882 (43.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e908 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,700 (43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,264 (32.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,790 (53.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,506 (31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,055 (43.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,218 (25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnpartnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,149 (46.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,205 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,718 (41.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,215 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,303 (92.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,507 (30.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,531 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,244 (27.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e636 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204 (32.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e242 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e189 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e Frequency (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e Pearson\u0026rsquo;s Chi-squared test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNote: The knowledge score categories were obtained by cutting the composite sum of scores into three groups: \u0026ldquo;Poor knowledge\u0026rdquo; if in the composite score was 0 or 1, \u0026ldquo;Average knowledge\u0026rdquo; if a composite score of 2 and \"High knowledge\" if composite score is 3.\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDeterminants of knowledge of abortion laws\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the determinants of high of knowledge of abortion laws in Kenya. Our analysis shows that sex, age group, education and marital status were predictors of levels of knowledge of abortion laws. Specifically, females were associated with a 22% increase in odds of knowledge of abortion laws compared to males (AOR\u0026thinsp;=\u0026thinsp;1.22 [95% CI: 1.09\u0026ndash;1.38]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The odds of high knowledge for the age group 25\u0026ndash;34 increased by 21% in comparison to ages 18\u0026ndash;24 (1.21 [1.03\u0026ndash;1.43]; 0.024). Respondents with university education were more knowledgeable than those with no education (2.57; [1.79\u0026ndash;3.70]; \u0026lt;0.001). Similarly, unpartnered had respondents had higher knowledge than the partnered/married (1.26; [1.11\u0026ndash;1.43]; \u0026lt;0.001).\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe determinants of knowledge of legal indications of abortion in Kenya\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\u003ePoor vs. Average\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePoor vs. High\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAOR [95% CI]\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eAOR [95% CI]\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12 [1.01\u0026ndash;1.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22 [1.09\u0026ndash;1.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29 [1.11\u0026ndash;1.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21 [1.03\u0026ndash;1.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11 [0.96\u0026ndash;1.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 [0.95\u0026ndash;1.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 [0.75\u0026ndash;1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16 [0.94\u0026ndash;1.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHighest education level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone/don't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89 [0.65\u0026ndash;1.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86 [0.59\u0026ndash;1.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37 [1.01\u0026ndash;1.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42 [0.98\u0026ndash;2.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85 [1.36\u0026ndash;2.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.57 [1.79\u0026ndash;3.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnpartnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 [0.93\u0026ndash;1.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26 [1.11\u0026ndash;1.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e AOR\u0026thinsp;=\u0026thinsp;Adjusted Odds Ratio; Bold \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;Significant estimate at 0.05 level; Ref. = Referent category; estimates are from multinomial logistic regression model.\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=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAbortion preference: Conditions survey participants preferred abortion be allowed\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents situations respondents preferred abortion to be allowed. None of the options received absolute approval by all respondents (100%). About 72% and 65% wanted abortions allowed when the life or physical health of the woman are at risk, respectively. Slightly less than half of respondents (43%) would approve of abortion if the mental health of the women were at risk. Surprisingly, only about one-quarter would want abortions allowed in cases of rape (29%) and incest (28%). The least acceptable conditions for abortion according to this survey were, if the pregnant girl or woman was unmarried (7%), on economic grounds (11%), the woman is HIV positive (11%) and if the girl is still in school (12%). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the breakdown by sex of situations respondents preferred abortion should be allowed. In general, there was no major difference across men and women regarding situations in which respondents felt they would want to see abortion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLatent Class Analysis (LCA): Exploring the preferences towards Kenyan abortion law\u003c/h2\u003e \u003cp\u003eThe LCA explored preferences towards various potential legal provisions for abortion, and categorized the respondents into four classes. Class 1- represents participants in favor of abortion in all 14 conditions (characterized by very high probabilities - mostly above 90%), we labelled this group as \u003cem\u003elegalization proponents\u003c/em\u003e, and it represented 7% of the sample.\u003c/p\u003e \u003cp\u003eClass 2 represented respondents who tended to favor abortion when there was risk to women\u0026rsquo;s physical health (84%), risk to women\u0026rsquo;s life (93%), and mental health risk (77%). These respondents in Class 2 also exhibited moderate levels of support for abortion when a pregnancy results from rape (66%), mental incapacity (61%), pregnancy from incest (59%), and fetal anomaly (53%). We labeled this class as \u003cem\u003emoderate supporters of abortion legalization\u003c/em\u003e, and comprised 21% of the sample.\u003c/p\u003e \u003cp\u003eClass 3 represented individuals supportive of abortion for health reasons: that is, when physical health is at risk (84\u003cem\u003e%), and when life is at risk (\u003c/em\u003e93%), but were moderate on mental health risk (43%) and opposed the rest of the conditions. We labelled this group as \u003cem\u003econditional supporters\u003c/em\u003e, and it represented the highest proportion of the sample (43%).\u003c/p\u003e \u003cp\u003eClass 4 represented respondents with very low probabilities of support for abortion in any circumstance (29%), since they consistently opposed all scenarios and were on the conservative side of each opinion statement, we labelled this group as \u003cem\u003elegalization opponents\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents profile plot of the classes with the item-response probabilities for the listed conditions under which abortion could be allowed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the correlates of the total number of conditions for abortion that participants preferred or would support. Our results show that female participants (-0.42 [95% CI: -0.55 to -0.28; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]), unpartnered participants (-0.52 [-0.67 to -0.38]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), those with university education (-0.75 [-1.2 to -0.32]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and those of non-Christian religious affiliations (-0.52 [-0.77 to -0.27]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) endorsed fewer items compared to the corresponding referent category. However, increasing knowledge of abortion laws (0.67 [0.64 to 0.70]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) correlated with endorsing more of the abortion conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelates of the number of conditions for abortion that participants endorsed, presented as multivariable-adjusted mean differences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef. [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-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\u003e(Intercept)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8 [3.3 to 4.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.42 [-0.55 to -0.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.03 [-0.22 to 0.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12 [-0.07 to 0.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnpartnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.52 [-0.67 to -0.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHighest education level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone/Don't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.31 [-0.12 to 0.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.09 [-0.51 to 0.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.75 [-1.2 to -0.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligious affiliation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.52 [-0.77 to -0.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKnowledge score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67 [0.64 to 0.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e Coef. = Coefficient; Bold \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;Significant estimate at 0.05 level; Ref. = Referent category; estimates are from multivariable-linear regression model. Dominant survey language included in the adjustment of model estimates\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis nationwide survey aimed to describe knowledge of abortion laws in Kenya and the situations people would prefer abortions allowed. In general, the study findings revealed that respondents had fair knowledge of two conditions under which abortion is legal in Kenya\u0026mdash;threat to a pregnant woman\u0026rsquo;s life and health. However, only 33% knew that a woman could have access to legal abortion if her pregnancy emanated from rape. There were no significant differences in levels of knowledge between males and females. These findings are not so different from other studies globally that suggest that women, health providers, and even decision makers worldwide sometimes have limited or inaccurate knowledge of the abortion laws and policies in their country (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). A systematic review assessing women\u0026rsquo;s awareness and knowledge of abortion laws found that, of the 16 studies reviewed, women\u0026rsquo;s awareness and correct knowledge of the legal status was less than 50% in nine studies, and in six studies, knowledge of legalization/liberalization ranged between 32.3% \u0026minus;\u0026thinsp;68.2% (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The variations in knowledge of abortion laws across countries could be linked to several factors, including poor information dissemination of the laws (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Nonetheless, inadequacy in knowledge of abortion laws has a direct impact on how women enter the health system and whether they can successfully navigate its bureaucratic measures, including in countries with some permissive laws.\u003c/p\u003e \u003cp\u003eIn Kenya, where abortion is not permitted except under limited conditions, knowledge of abortion laws is very important. Knowledge of abortion laws is a key determinant of the utilization of safe abortion services (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In situations where knowledge levels are low, women rely on the knowledge and judgement of health providers as to the law and what services they can legally offer. In an Ethiopian study, a major reason cited by young females for not utilizing the safe abortion services was inadequate knowledge on the abortion law (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Information vacuums on abortion laws lead to inconsistent, arbitrary, and even discriminatory practice, including harassments by the police and denial of services by medical and state authorities often in bad faith. At times, contradictory laws cause confusion, such as where the constitution provides for abortion albeit under limited conditions, but the penal code criminalizes the same practice.\u003c/p\u003e \u003cp\u003eFindings showed that most people correctly identified conditions abortions are disallowed in Kenya, while at the same time, fewer people correctly pointed to conditions under which abortions are allowed in Kenya. This finding reflects the chilling effect of criminal laws around abortion and the pattern of stretching abortion laws to prohibit situations that are actually allowed by law.\u003c/p\u003e \u003cp\u003eMore individuals with post-secondary education had higher levels of knowledge on the Kenyan abortion law, as was those above 35 years. Other studies in the region have reported similar findings where higher levels of education dovetailed greater percentages of correct general awareness and knowledge, even though this pattern was highly variable between studies. In Ghana, for instance, women with formal education had nearly 85% greater awareness compared to women without any formal education (92% vs. 8%) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In Ethiopia, higher awareness and knowledge was seen among women living in predominantly urban areas and with better access to comprehensive abortion care. These regions in Ethiopia had higher abortion rates than most other parts of the country (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Further, an assessment in Nepal also reported that variations in proportion of women with correct knowledge existed across socioeconomic strata, and that women in urban areas and with higher education had higher levels of knowledge compared to those living in rural areas or with lower levels of education (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAbout 72% and 65% wanted abortions allowed when the life or physical health of the woman are at risk respectively. None of the options scored a 100% approval, and the two reasons more preferred are the same grounds under which the current law already allows for abortion. Interestingly, almost half of respondents (43%) indicated they would approve of abortion if the mental health of the woman were at risk. Surprisingly, only about one-quarter would want abortions allowed in cases of rape (29%) and incest (28%). No previous studies in Kenya or elsewhere in the region have collected this data point, but it remains valuable since community-level perceptions on situations under which abortions should be allowable offer possible avenues for improving abortion access and programming (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Knowledge of abortion legality is critical to protect and expand access to safe abortion care services, especially in contexts like Kenya where abortion is available for multiple indications (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). It is also pertinent to note the 15% of respondents who wanted abortion allowed if the pregnant woman does not want the pregnancy. A recent PEW poll reported 11% of respondents in Kenya supported legalization of abortion (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This 15% reported in our study is significant for several reasons; including 1) it is not conditioned on any exceptions \u0026ndash; it is based on the right of the woman to decide whether they want to continue a pregnancy or not. 2) It is higher than the 9% reported in the Ipsos\u0026rsquo;s 2020 survey, which increased to 15% \u0026ldquo;depending on the conditions\u0026rdquo; (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). For our study, if we average the conditions under which respondents would want to see abortion become allowable (legal), we found a higher percentage at 27%.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study findings show that the knowledge of the existing abortion law is sub-optimal. Sex, age, and educational levels were associated with various levels of knowledge of abortion laws. Most people in Kenya preferred abortion to be allowable in conditions where the health or life of the women is at risk and when the mental health of the woman is at stake. There is need to comprehensively disseminate information on the abortion law, and where women can get safe and legal abortion to avert the challenges of unsafe abortion in Kenya. These efforts should pay attention to groups and communities that typically struggle to access information and safe abortion care services.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStudy Limitations\u003c/h2\u003e \u003cp\u003eOur study is the first large-scale, nationally representative survey on knowledge and preference of abortion laws in Kenya. Nonetheless, the study had some limitations that are critically discussed in this section. First, being a phone-based survey, only respondents with active phone numbers and who could be reached at the time of the survey were interviewed. As such, there is a possibility of systematic exclusion of people without active phone numbers and those who could not be reached at the time of survey. However, we made several re-dial attempts for individuals who could not be reached, and only dropped reaching out to them after the several attempts failed. Even among telephone owners, the focused on the 12\u0026nbsp;million phone numbers in GeoPoll\u0026rsquo;s database, implying that the other millions of people in Kenya with phone numbers did not have the same chances of participation in the study because GeoPoll\u0026rsquo;s database did not include their mobile numbers. Finally, due to the sensitive nature of abortion, social desirability bias and recall bias might have affected the responses of the respondents. We however assured all respondents of the confidentiality and privacy of their answers in the survey. Notwithstanding these challenges, this study presents strong and reliable findings on the knowledge of, and preferences towards abortion laws in Kenya.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScientific and ethical approval to conduct this study was obtained from the AMREF ethics and scientific research committee (P1240/2022). The National Commission for Science, Technology and Innovation, granted a research permit (NACOST-NACOSTI/P/22/19653). All participants provided informed verbal consent before participating in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data and materials are available on request from the corresponding author. Also, according to the APHRC policies (the organization hosting the datasets), all deidentified datasets will be publicly available on the APHRC microdata portal after 3 years (https://aphrc.org/microdata-portal/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was supported by a grant from the African Regional Office of the Swedish International Development Cooperation Agency, Sida (Contribution No. 12103), for APHRC\u0026rsquo;s Challenging the Politics of Social Exclusion project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKJ and BU conceptualized the original study, and KJ, EM, BU, SA, were primarily involved in the data collection. KJ, EM, AA, and IA were responsible for data cleaning and analysis, and all authors reviewed, edited, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are indebted to the Geopoll team for their role in setting up the data collection systems, supporting the training of field teams, and collecting data. We appreciate the hard work and diligence of the field teams for collecting the study data. We also thank all the respondents who gave their time to participating in this study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eErdman JN, Johnson BR. Access to knowledge and the Global Abortion Policies Database. Int J Gynecol Obstet [Internet]. 2018 Jul 1 [cited 2023 Jun 5];142(1):120\u0026ndash;4. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/ijgo.12509\u003c/li\u003e\n\u003cli\u003eMekonnen BD, Wubneh CA. Knowledge, Attitude, and Associated Factors towards Safe Abortion among Private College Female Students in Gondar City, Northwest Ethiopia: A Cross-Sectional Study. Adv Prev Med [Internet]. 2020 Nov 4 [cited 2023 Jun 5];2020:1\u0026ndash;8. Available from: /pmc/articles/PMC7657704/\u003c/li\u003e\n\u003cli\u003eSheehy G, Dozier JL, Mickler AK, Yihdego M, Karp C, Zimmerman LA. Regional and residential disparities in knowledge of abortion legality and availability of facility-based abortion services in Ethiopia. Contracept X. 2021 Jan 1;3:100066. \u003c/li\u003e\n\u003cli\u003eAssifi AR, Berger B, Tun\u0026ccedil;alp \u0026Ouml;, Khosla R, Ganatra B. Women\u0026rsquo;s Awareness and Knowledge of Abortion Laws: A Systematic Review. PLoS One [Internet]. 2016 Mar 1 [cited 2023 Jun 5];11(3). Available from: /pmc/articles/PMC4807003/\u003c/li\u003e\n\u003cli\u003eAtuhaire S. Abortion among adolescents in Africa: A review of practices, consequences, and control strategies. Int J Health Plann Manage [Internet]. 2019 Oct 1 [cited 2023 Mar 30];34(4):e1378\u0026ndash;86. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/hpm.2842\u003c/li\u003e\n\u003cli\u003eSingh S, Remez L, Sedgh G, Kwok L, Tsuyoshi O. Abortion Worldwide 2017: Uneven Progress and Unequal Access, New York: Guttmacher Institute, 2018 [Internet]. New York; 2018. Available from: https://www.guttmacher.org/sites/default/files/report_pdf/abortion-worldwide-2017.pdf\u003c/li\u003e\n\u003cli\u003eNational Council for Law Reporting. Laws of Kenya. The Constitution of Kenya 2010. Kenya; 2010. \u003c/li\u003e\n\u003cli\u003eIpas. Kenya restores standards and guidelines for comprehensive reproductive health, including abortion. Ipas Newsletter [Internet]. 2019 Jun 14; Available from: https://www.ipas.org/news/kenya-restores-standards-and-guidelines-for-comprehensive-reproductive-health-including-abortion/\u003c/li\u003e\n\u003cli\u003eMutua MM, Achia TNO, Maina BW, Izugbara CO. A cross-sectional analysis of Kenyan postabortion care services using a nationally representative sample. Int J Gynecol Obstet [Internet]. 2017 Sep 1;138(3):276\u0026ndash;82. Available from: https://doi.org/10.1002/ijgo.12239\u003c/li\u003e\n\u003cli\u003eMinistry of Health Kenya, African Population and Health Research Center (APHRC), Guttmacher Institute. Incidence and Complications of Unsafe Abortion in Kenya: Key findings of a national study. Nairobi; 2013. \u003c/li\u003e\n\u003cli\u003eMohamed SF, Izugbara C, Moore AM, Mutua M, Kimani-Murage EW, Ziraba AK, et al. The estimated incidence of induced abortion in Kenya: a cross-sectional study. BMC Pregnancy Childbirth [Internet]. 2015;15(1):185. Available from: https://doi.org/10.1186/s12884-015-0621-1\u003c/li\u003e\n\u003cli\u003eKlu D, Yeboah I, Kayi EA, Okyere J, Essiaw MN. Utilization of abortion services from an unsafe provider and associated factors among women with history of induced abortion in Ghana. BMC Pregnancy Childbirth [Internet]. 2022 Dec 1 [cited 2023 Jun 5];22(1):1\u0026ndash;8. Available from: https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-022-05034-x\u003c/li\u003e\n\u003cli\u003eMekuria M, Daba D, Girma T, Birhanu A. Assessment of knowledge on abortion law and factors affecting it among regular undergraduate female students of Ambo University, Oromia Region, Ethiopia, 2018: a cross sectional study. Contracept Reprod Med 2020 51 [Internet]. 2020 Dec 1 [cited 2023 Jun 6];5(1):1\u0026ndash;8. Available from: https://contraceptionmedicine.biomedcentral.com/articles/10.1186/s40834-020-00136-3\u003c/li\u003e\n\u003cli\u003eScheinerman N, Callahan KP. Legal Discrepancies and Expectations of Women: Abortion, Fetal Therapy, and NICU Care. Hastings Cent Rep [Internet]. 2023 Mar 1 [cited 2023 Jun 6];53(2):36\u0026ndash;43. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/hast.1472\u003c/li\u003e\n\u003cli\u003ede Londras F, Cleeve A, Rodriguez MI, Farrell A, Furgalska M, Lavelanet AF. The impact of provider restrictions on abortion-related outcomes: a synthesis of legal and health evidence. Reprod Health [Internet]. 2022 Dec 1 [cited 2023 Jun 6];19(1):95. Available from: /pmc/articles/PMC9014563/\u003c/li\u003e\n\u003cli\u003eUshie BA, Juma K, Kimemia G, Ouedraogo R, Bangha M, Mutua M. Community perception of abortion, women who abort and abortifacients in Kisumu and Nairobi counties, Kenya. PLoS One. 2019;14(12). \u003c/li\u003e\n\u003cli\u003eKimemia GW, Kabiru CW, Ushie BA. Ethical concerns facing abortion researchers in restrictive settings: the need for guidelines. Sex Reprod Heal Matters [Internet]. 2023 Dec 1 [cited 2023 Jun 6];31(1):2193315. Available from: /pmc/articles/PMC10134914/\u003c/li\u003e\n\u003cli\u003eKenya Christian Professional Forum. 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Chikhungu LC, editor. PLOS Glob Public Heal [Internet]. 2023 Apr 21 [cited 2023 Jun 6];3(4):e0001719. Available from: https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0001719\u003c/li\u003e\n\u003cli\u003eBecker D, Garcia SG, Larsen U. Knowledge and opinions about abortion law among Mexican youth. Int Fam Plan Perspect. 2002;28(4):205\u0026ndash;13. \u003c/li\u003e\n\u003cli\u003eAdhikari R. Knowledge on legislation of abortion and experience of abortion among female youth in Nepal: A cross sectional study. Reprod Health [Internet]. 2016 Apr 27 [cited 2023 Jun 6];13(1):1\u0026ndash;9. Available from: https://reproductive-health-journal.biomedcentral.com/articles/10.1186/s12978-016-0166-4\u003c/li\u003e\n\u003cli\u003eHessini L, Brookman-Amissah E, Crane BB. Global policy change and women\u0026rsquo;s access to safe abortion: the impact of the World Health Organization\u0026rsquo;s guidance in Africa. Afr J Reprod Health. 2006;10(3):14\u0026ndash;27. \u003c/li\u003e\n\u003cli\u003eMutua MM, Manderson L, Musenge E, Achia TNO. Policy, law and post-abortion care services in Kenya. PLoS One [Internet]. 2018 Sep 21;13(9):e0204240\u0026ndash;e0204240. Available from: https://pubmed.ncbi.nlm.nih.gov/30240408\u003c/li\u003e\n\u003cli\u003eFetterolf J, Clancy L. Support for legal abortion is widespread in many countries, especially in Europe. Pew Research Center. 2023. \u003c/li\u003e\n\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-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Abortion, Public opinions, Abortion law, legal preferences, sub-Saharan Africa","lastPublishedDoi":"10.21203/rs.3.rs-4761401/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4761401/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInadequate knowledge of abortion laws implies those seeking care cannot know their legal entitlements, service providers cannot practice with legal protection, and governments can escape legal responsibility for the adverse effects of their laws. There is limited understanding of the public\u0026rsquo;s views of abortion in Kenya. This study assessed the knowledge and preference for abortion legality in Kenya.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a nationwide cross-sectional survey using mobile telephones to collect data from adults 18 or older. The sample was drawn from a database owned by a mobile-survey provider with over 12\u0026nbsp;million telephone numbers across Kenya. We used a random digit dialing approach to select and recruit participants. Trained research assistants administered telephonic interviews using a structured questionnaires that had questions assessing knowledge of conditions for legally sanctioned abortion and their preferences for conditions the law should permit. We summarized continuous variables into means with standard deviations while categorical variables into proportions. Multivariate analyses were performed to assess associations between knowledge of abortion laws and independent variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the 8942 respondents, 76%, 74%, and 33% correctly knew that the Kenyan law allows abortion when a woman's life and health are at risk and when the pregnancy results from rape, respectively. Being female (AOR\u0026thinsp;=\u0026thinsp;1.22 [95% CI: 1.09\u0026ndash;1.38]; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), age group 25\u0026ndash;34 (1.21 [1.03\u0026ndash;1.43]; 0.024) and having university education (2.57; [1.79\u0026ndash;3.70]; \u0026lt;0.001) were associated with higher knowledge of abortion laws. Majority of respondents preferred that the law allow abortion when a woman\u0026rsquo;s life (72%) or health (65%) or mental health (43%) is at risk. One-quarter wanted abortions allowed for rape (29%) and incest (28%) while only 15% approved abortion if the pregnant was unwanted. The latent class analysis characterized that most respondents were conditional supporters of abortion legalization (43%), whereas individuals opposed to abortion legalization in all/most circumstances represented 29% of respondents.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eInformation on abortion legality preferences offers insights into public acceptability and opportunities for abortion legal and policy reforms. There is need for comprehensive dissemination of the Kenya abortion law, focusing specifically on groups and communities that typically struggle to access information and services.\u003c/p\u003e","manuscriptTitle":"Knowledge of and preference for abortion legality in Kenya: A National Cross- Sectional Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-19 15:47:09","doi":"10.21203/rs.3.rs-4761401/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-23T10:27:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-23T09:25:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-22T12:02:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-07-18T09:23:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"006ba593-c405-4754-b1d4-e0717f2c5415","owner":[],"postedDate":"August 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T07:25:06+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-19 15:47:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4761401","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4761401","identity":"rs-4761401","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}