Factors Associated with Risky Sexual Behaviour among Women Informal Cross-border Traders in Lusaka, Zambia

Factors Associated with Risky Sexual Behaviour among Women Informal Cross-border Traders in Lusaka, Zambia | 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 Factors Associated with Risky Sexual Behaviour among Women Informal Cross-border Traders in Lusaka, Zambia Simson Mwale, Musonda Lemba, Million Phiri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8258951/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background Women informal cross-border traders in Lusaka, Zambia, operate in highly mobile and economically vulnerable contexts that may heighten their exposure to risky sexual behaviour and HIV. This study examined the socio-demographic, migratory, and behavioural factors associated with risky sexual behaviour among women informal cross-border traders, focusing on identifying key predictors using a binary logistic regression approach. Methods A cross-sectional survey was conducted in 2019 among 499 women engaged in informal cross-border trade at the Common Market for Eastern and Southern Africa (COMESA) Market in Lusaka. Data were collected via a structured questionnaire capturing socio-demographic characteristics, sexual behaviour, mobility patterns, and social lifestyle factors. Binary logistic regression was used to identify determinants of risky sexual behaviour, with odds ratios (ORs) and 95% confidence intervals (CIs) reported. Results The majority of respondents (67.7%, n = 338) were classified as high-risk and 32.3% (n = 161) as low-risk. Behavioural factors were the strongest predictors of risky sexual behaviour. Women with two or more sexual partners were 14 times more likely to engage in risky sexual behaviour (OR = 14.57, 95% CI: 3.06–69.34, p < 0.01). Inconsistent condom use also significantly increased risk: “sometimes” use (OR = 21.47, 95% CI: 2.68-171.74, p < 0.01) and “never” use (OR = 47.82, 95% CI: 5.65-405.03, p < 0.01) compared to consistent use. Educational level and literacy were protective; women with secondary (OR = 0.21, 95% CI: 0.02–2.73) and tertiary education (OR = 0.20, 95% CI: 0.01–4.49) had lower odds of risky sexual behaviour. Women residing in densely populated settlements such as Kanyama, Mandevu, and Matero demonstrated higher sexual risk. Age, marital status, and religious affiliation showed trends but were not statistically significant in the adjusted model. Conclusion Risky sexual behaviour among women informal cross-border traders in Lusaka was largely driven by behavioural factors, particularly multiple sexual partnerships and inconsistent condom use, while education and literacy conferred protective effects. Interventions should prioritise sexual health education, condom negotiation skills, literacy programmes, and support for women in high-density settlements to reduce HIV vulnerability. Women cross-border traders risky sexual behaviour HIV vulnerability Zambia binary logistic regression Introduction Women’s participation in informal cross-border trade has become an increasingly important economic activity across sub-Saharan Africa, including Zambia. This form of trade provides vital livelihood opportunities, particularly in contexts where formal employment is scarce and poverty rates are high [ 1 , 2 ]. Women who engage in this trade, commonly referred to as “Women Informal Cross-Border Traders”, play a critical role in local and regional economies by facilitating the movement of goods and services across borders. However, despite its economic significance, women informal cross-border traders face numerous socioeconomic challenges, including vulnerabilities related to sexual health [ 3 , 4 ]. One of the most pressing concerns for this population is the increased likelihood of engaging in risky sexual behaviours, which heightens their susceptibility to human immunodeficiency virus (HIV) and other sexually transmitted infections (STIs). Evidence indicates that women engaged in cross-border trade may engage in high-risk sexual behaviours such as multiple sexual partnerships, transactional sex, and inconsistent condom use [ 5 , 6 ]. These behaviours are often shaped by structural and contextual factors, including gendered power dynamics within trading environments, economic pressures, limited access to healthcare, and inadequate sexual health education [ 7 , 8 ]. While the economic contributions of women informal cross-border traders are well documented, research examining how socio-demographic characteristics influence their sexual risk-taking remains limited. In Zambia, where HIV prevalence remains high among women, understanding the demographic determinants of risky sexual behaviour is critical for developing targeted interventions. Factors such as age, marital status, education, literacy, religious affiliation, and place of residence have been shown in other contexts to influence sexual behaviours and HIV risk [ 9 , 10 , 11 ]. For instance, women with lower education or literacy levels are more likely to engage in risky sexual practices, while residence in densely populated or economically disadvantaged areas may exacerbate vulnerability to transactional sex and coercion. Despite recognition of the public health implications of risky sexual behaviour among women informal cross-border traders, there is a paucity of empirical evidence specifically examining the influence of demographic factors on sexual risk in this population. Most studies have focused on broader urban populations or migrant workers, leaving women in informal cross-border trade, particularly in Zambian border towns and urban markets, understudied [ 12 , 13 ]. Therefore, this study aimed to investigate the demographic factors associated with risky sexual behaviour among women informal cross-border traders in Lusaka, Zambia, using a binary logistic regression approach to identify key predictors of HIV vulnerability. The findings are intended to inform targeted behavioural and structural interventions to reduce risky sexual behaviour and improve sexual health outcomes in this high-risk population. Methods and Data Data Source Data for this study were collected in 2019 from 499 women engaged in informal cross-border trade at the Common Market for Eastern and Southern Africa (COMESA) Market in Lusaka, Zambia. Structured questionnaires were used to collect information on socio-demographic characteristics, including marital status, age, education level, literacy, religious affiliation, and place of residence, as well as self-reported sexual behaviours. Respondents were selected using a probability sampling technique, with eligibility criteria requiring that participants be at least 18 years old, members of the Cross-Border Traders Association (CBTA), actively engaged in cross-border trading, and owning or renting a shop or container at the COMESA Market. This approach was adopted to focus on women whose demographic characteristics could influence higher levels of risky sexual behaviour. Study Context The study was conducted at the COMESA Market, a major centre for informal cross-border trade in Lusaka. Established in 1998 by informal traders who founded the CBTA, the market had a membership of approximately 42,350 cross-border traders by 2015, of which 70% were women [ 14 , 15 ]. At the time of the survey in January 2019, the market had 1,299 shops and containers distributed across 14 sections and zones, of which 49% (632) were owned and rented by women. The study achieved a response rate of 99.8%, with 499 women participating. Study Design and Sample Size A three-stage multi-stage sampling technique was employed. In the first stage, women informal cross-border traders were identified based on the specific business sections within the market. In the second stage, stratified sampling was used to select representative trading units from each section and zone. In the third stage, simple random sampling was applied to select individual respondents from the sampled trading units. This process ensured that the 499 participants were representative of the population of women traders operating at the COMESA Market. Study Measurements Outcome Variable The dependent variable for this study was risky sexual behaviour, measured through a composite variable, “RISK_LEVEL,” derived from 12 survey items assessing sexual risk practices. These items included having more than one sexual partner, voluntary sexual activity, multiple partners in the past 12 months, transactional sex, unprotected sex, inconsistent condom use, and knowledge of STIs. Each item was coded 1 if it indicated high-risk behaviour and 0 otherwise. A total score for each respondent was calculated by summing all items, with scores ranging from 0 (risk-free) to 11 (very high-risk). The total scores were further categorised into a new variable, RISK_STATUS, with scores 0–5 classified as low-risk and 6–11 as high-risk. These scores reflected the extent to which women informal cross-border traders engaged in behaviours that exposed them to heightened HIV vulnerability, such as inconsistent condom use, multiple sexual partnerships, and transactional sex. As shown in Table 1 , respondents were distributed across the full scale of the index; however, the majority fell within the mid-to-high range of risk. Only a small proportion of women scored very low, such as those with scores of 0 (2%) or 1 (1.2%), indicating minimal involvement in sexual risk-taking. In contrast, a large segment of the participants had scores between 6 and 8, with a peak at score 7, which accounted for 28.3% of the sample. This distribution suggested that high levels of engagement in risky sexual practices were common among the women surveyed, reinforcing the need to examine the demographic and socioeconomic factors shaping their vulnerability. Table 1 Recoding of the dependent variable Variable measurement Survey Results, 2019 (N = 499) Number Percent RISK_LEVEL 0 10 2 1 6 1.2 2 17 3.4 3 29 5.8 4 42 8.4 5 57 11.4 6 86 17.2 7 141 28.3 8 54 10.8 9 33 6.6 10 21 4.2 11 3 0.6 RISK_STATUS Low risk 161 32.3 High risk 338 67.7 For analytic purposes, the risk index was recoded into a binary outcome variable to distinguish between women at low risk and those at high risk of engaging in sexual behaviours associated with increased HIV exposure. Following recoding, 161 respondents (32.3%) were classified as low risk, while 338 (67.7%) were categorised as high risk. This distribution indicated that nearly two-thirds of the women were at elevated risk. The categorisation was crucial for the binary logistic regression, as it enabled the model to estimate the likelihood of being in the high-risk group based on demographic predictors such as age, education level, marital status, settlement of residence, and religious affiliation. The high prevalence of risky sexual behaviour observed in this distribution was consistent with the broader study findings, which showed that factors such as lower educational attainment, residence in densely populated settlements, and limited literacy significantly increased the odds of risky sexual behaviour. The dependent variable, therefore, not only captured the behavioural vulnerabilities present among these traders but also provided a strong foundation for identifying the demographic characteristics that had the greatest influence on sexual risk-taking. Overall, the distribution supported the conclusion that women engaged in informal cross-border trade experienced substantial and multifaceted sexual health risks shaped by social, economic, and cultural determinants. Independent Variables The independent variables in this study were selected based on their established relevance to HIV risk and risky sexual behaviour, as reported in previous studies [ 2 , 16 , 17 ]. These variables were classified as individual-level demographic factors and included marital status, age, education, literacy, religious affiliation, and place of residence. Marital status was categorised as married, unmarried (never married), widowed, divorced, or separated. Age was recorded in single years and grouped into five categories: below 25 years, 25–34 years, 35–44 years, 45–54 years, and above 55 years. Education was measured as none (no formal education), primary, secondary, or tertiary. Literacy was assessed through the ability to read and write, coded as yes/no. Religious affiliation was categorised as Catholic, Muslim, Jehovah’s Witness, Masonic, Pentecostal, Protestant, and Seventh Day Adventist (SDA). Place of residence was classified according to the socioeconomic characteristics of seven locations: Chawama (high density, low income), Kabwata (medium density, middle income), Kanyama (high density, low income), Lusaka Central (low density, high income), Mandevu (high density, middle income), Matero (high density, low to middle income), Munali (low density, middle to high income), and beyond Lusaka (low density, low to middle income). Statistical Analysis Data analysis was conducted at three levels: univariate, bivariate, and multivariate. Univariate analysis employed descriptive statistics to summarise respondents’ demographic characteristics and their risk levels. Bivariate analysis was performed using chi-square tests to assess the association between each independent variable and risky sexual behaviour. Multivariate analysis utilised a binary logistic regression model to identify significant predictors of risky sexual behaviour while adjusting for potential confounders. All statistical analyses were conducted using Stata version 13, with significance set at p < 0.05. Odds ratios (ORs) with 95% confidence intervals (CIs) were reported to indicate the strength and direction of associations. Results Table 2 Background characteristics of respondents Background characteristics Survey Results, 2019 (N = 499) Number Percent Marital status Married 196 39.36 Unmarried 135 27.11 Widowed 81 16.27 Divorced 58 11.65 Separated 28 5.62 Age (years) Below 25 25 5.02 25–34 168 33.73 35–44 175 35.14 45–54 90 18.07 Above 55 40 6.03 Educational level None 31 6.33 Primary (Grade 1–7) 139 28.37 Secondary (Grade 8–12) 280 57.14 Tertiary (college, university) 40 8.16 Ability to write Yes 406 82.52 No 86 17.48 Ability to read Yes 386 78.46 No 102 21.54 Religious affiliation Catholic 172 34.47 Muslim 26 5.21 Jehovah Witness 37 7.41 Masonic 20 4.01 Pentecostal 97 19.44 Protestant 111 22.24 SDA 36 7.21 Residence Chawama 59 11.90 Kabwata 66 13.31 Kanyama 135 27.22 Lusaka Central 9 1.81 Mandevu 99 19.96 Matero 69 13.91 Munali 47 9.48 Outside Lusaka 12 2.42 Table 2 presented the background characteristics of the 499 women informal cross-border traders who participated in the 2019 survey. The marital status distribution showed that the largest proportion of respondents were married (39.36%), followed by unmarried women (27.11%). Widowed women accounted for 16.27% of the sample, while 11.65% were divorced and 5.62% were separated. This distribution suggested that a substantial proportion of participants were either single, widowed, or had previously been married, which may have influenced their economic independence, mobility, and exposure to risky situations related to cross-border trading activities. The age distribution indicated that the majority of respondents were in the economically active age groups. Women aged 35–44 constituted the largest group (35.14%), closely followed by those aged 25–34 (33.73%). Women aged 45–54 represented 18.07% of the sample, while those younger than 25 years accounted for only 5.02%. Participants above 55 years comprised 6.03%. This pattern demonstrated that informal cross-border trading attracted mostly women in their prime working years, who may have faced unique pressures related to supporting households and sustaining livelihoods, factors potentially linked to heightened sexual risk-taking, as noted in the study findings. Educational attainment varied considerably across respondents. More than half of the women (57.14%) had attained secondary-level education, while 28.37% had only primary schooling. A smaller proportion (8.16%) had tertiary education, and 6.33% reported having no formal education. Literacy indicators further revealed that the majority of respondents were able to write (82.52%) and read (78.46%), although a notable minority lacked these skills. The presence of a sizeable group with limited education and literacy aligned with the regression findings, which showed that lower levels of education were associated with increased likelihood of engaging in risky sexual behaviour. Religious affiliation was diverse within the sample. Catholics constituted the largest religious group (34.47%), followed by Protestants (22.24%) and Pentecostals (19.44%). Smaller proportions identified as Jehovah’s Witness (7.41%), Seventh-Day Adventist (7.21%), Masonic (4.01%), and Muslim (5.21%). These religious differences were relevant to the study because some faith communities emphasised behavioural norms and restrictions that might influence sexual decision-making. For example, the regression results suggested that Muslim women were less likely to engage in risky sexual practices compared to other groups. The distribution of respondents by place of residence showed that a large proportion lived in high-density, low-income settlements. Kanyama (27.22%), Mandevu (19.96%), and Matero (13.91%) together accounted for over half of the respondents. Other settlements such as Chawama (11.90%), Kabwata (13.31%), and Munali (9.48%) represented smaller shares, while only 1.81% lived in Lusaka Central and 2.42% resided outside the city. The concentration of traders in densely populated townships corresponded with the study’s findings, which indicated that women living in these areas, especially Kanyama, Mandevu, and Matero, had higher odds of engaging in risky sexual behaviour due to socioeconomic hardships, overcrowding, and limited access to social support systems. Overall, the background characteristics suggested that the women engaged in informal cross-border trade were predominantly in their prime working ages, often from lower-income residential areas, and with varying levels of education and literacy. These demographic patterns provided important context for understanding the socioeconomic pressures and vulnerabilities that shaped their engagement in risky sexual behaviour. Table 3 Percent distribution of risky sexual behaviour by background characteristics of respondents Background characteristics Survey Results 2019 (N = 499) Low risk High risk N (%) N (%) p-value Marital status 0.244 Married 57(29.08) 139(70.92) Unmarried 46(34.07) 89(65.93) Widowed 32(39.51) 49(60.93) Divorced 14(24.14) 44(75.86) Separated 11(39.29) 17(60.71) Age (years) 0.129 Below 25 14(56.00) 11(44.00) 25–34 53(31.55) 115(68.45) 35–44 54(30.86) 121(69.14) 45–54 28(31.11) 62(68.89) Above 55 11(27.50) 29(72.50) Educational level 0.000** None 5(16.13) 26(83.87) Primary 26(18.71) 113(81.29) Secondary 102(36.43) 178(63.57) Tertiary 25(62.50) 15(37.50) Ability to write 0.005** Yes 136(35.23) 250(64.77) No 22(20.75) 84(79.25) Ability to read 0.014** Yes 140(34.48) 266(65.52) No 18(20.93) 68(79.07) Religious affiliation 0.019* Catholic 48(27.91) 124(72.09) Muslim 14(53.85) 12(46.15) Jehovah Witness 8(21.62) 29(78.38) Masonic 7(35.00) 13(65.00) Pentecostal 41(42.27) 56(57.73) Protestant 30(27.03) 81(72.97) SDA 13(36.11) 23(63.89) Residence 0.002** Chawama 27(45.76) 32(54.24) Kabwata 33(50.00) 33(50.00) Kanyama 36(26.67) 99(73.33) Lusaka Central 2(22.22) 7(77.78) Mandevu 28(28.28) 71(71.72) Matero 19(27.54) 50(72.46) Munali 12(25.53) 35(74.47) Outside Lusaka 1(8.33) 11(91.67) *p < 0.05, ** p < 0.01 Table 3 presented the distribution of risky sexual behaviour across various background characteristics of the respondents and showed how each characteristic was associated with either low-risk or high-risk sexual behaviour. Although differences were observed across marital status groups, the association was not statistically significant (p = 0.244). Married women demonstrated a high-risk prevalence of 70.92%, while divorced women exhibited the highest proportion of high-risk behaviour at 75.86%. Widowed (60.93%) and separated (60.71%) women showed moderately high-risk levels, whereas unmarried women had a slightly lower prevalence (65.93%). These findings indicated that marital status alone did not significantly influence risk levels, although divorced and married women tended to be more likely to engage in high-risk behaviours. Age differences in sexual risk-taking were also not statistically significant (p = 0.129). However, the pattern showed that women aged 25–34 and 35–44, who formed the majority of the sample, had similar high-risk proportions of 68.45% and 69.14%, respectively. Women above 55 years exhibited the highest proportion of high-risk behaviour at 72.50%, whereas the youngest group, below 25 years, recorded the lowest high-risk prevalence at 44%. Despite the lack of statistical significance, these results suggested that younger women were less likely to engage in risky sexual behaviour, while older women, particularly those above 35 years, tended to report more high-risk practices. Educational level showed a highly significant association with risky sexual behaviour (p = 0.000). Women with no formal education had the highest proportion of high-risk behaviour (83.87%), followed closely by those with only primary schooling (81.29%). In contrast, women with secondary education reported a much lower high-risk prevalence (63.57%), while those with tertiary education showed the lowest at 37.50%. This strong gradient indicated that education had a protective effect, with higher educational attainment associated with lower levels of sexual risk-taking. These results aligned with the logistic regression findings, which also demonstrated that higher education significantly reduced the odds of engaging in risky sexual behaviour. Literacy variables further supported this pattern. Both the ability to write (p = 0.005) and the ability to read (p = 0.014) were statistically significant. Women who were unable to write reported a high-risk prevalence of 79.25%, compared to 64.77% among those who could write. Similarly, women who could not read had a higher high-risk prevalence (79.07%) compared to those who were literate (65.52%). These results suggested that literacy skills played an important role in shaping sexual decision-making, likely due to increased access to information, health messages, and safer sex knowledge. Religious affiliation was also significantly associated with risky sexual behaviour (p = 0.019). Muslims showed the lowest high-risk prevalence (46.15%), suggesting a relatively protective influence of religious norms or practices. In contrast, Jehovah’s Witnesses (78.38%), Protestants (72.97%), and Catholics (72.09%) reported much higher proportions of high-risk behaviour. Pentecostals (57.73%) and Seventh-Day Adventists (63.89%) had moderately high levels. These variations indicated that religious norms and community expectations may have shaped behavioural patterns differently across denominations. Residence demonstrated a strong significant association with risky sexual behaviour (p = 0.002). Women residing in high-density areas such as Kanyama (73.33%), Mandevu (71.72%), Matero (72.46%), and Munali (74.47%) exhibited substantially higher proportions of high-risk behaviour. Conversely, women from Kabwata (50.00%) and Chawama (54.24%) reported lower risk levels. The highest proportion of high-risk behaviour was found among women living outside Lusaka (91.67%), though this category had a small sample size. These findings reinforced the conclusion that socioeconomic conditions associated with densely populated settlements, such as poverty, overcrowding, and limited access to services, were important drivers of risky sexual behaviour among informal cross-border traders. Overall, the bivariate results indicated that education, literacy, religion, and residence were significantly associated with risky sexual behaviour, while marital status and age did not show statistically significant associations. These relationships provided a foundation for the multivariate analysis, which further explored the independent effects of these demographic predictors. Table 4 Percent distribution of risky sexual behaviour by migratory, sexual and social lifestyle characteristics of respondents Migratory, sexual and social lifestyle characteristics Survey Results 2019 (N = 499) Low risk High risk N (%) N (%) p-value Average time out 0.000** Not gone out 25(54.35) 21(45.65) 1–5 times 85(25.60) 247(74.40) 6 + time 33(33.00) 67(67.00) Economic status 0.989 Good 61(29.76) 144(70.24) Bad 79(29.70) 187(70.30) Sexual partners (current) 0.000** 1 partner 122(34.56) 231(65.44) 2 + partners 15(12.40) 106(87.60) Paid sex 0.039* Yes 16(20.51) 62(79.49) No 131(32.27) 337(69.73) Frequency of condom use 0.000** Every time 6(54.55) 5(45.45) Almost every time 32(76.19) 10(23.81) Sometimes 23(13.86) 143(86.14) Never 12(7.14) 156(92.86) Heard about STIs 0.023* Yes 134(28.88) 330(71.12) No 8(57.14) 6(42.86) Alcohol consumption (ever taken) 0.076 Yes 46(26.59) 127(73.41) No 110(34.38) 210(65.63) Drug consumption (ever taken) 0.910 Yes 3(30.00) 7(70.00) No 153(31.68) 337(68.36) **p < 0.05, *** p < 0.01 Table 4 presented the distribution of risky sexual behaviour according to migratory, sexual, and social lifestyle characteristics of the respondents. The amount of time women spent away from home due to cross-border trading showed a statistically significant association with sexual risk-taking (p = 0.000). Women who did not travel reported the lowest proportion of high-risk behaviour (45.65%), while those who travelled frequently, particularly between one and five times, exhibited a much higher prevalence of high-risk behaviour (74.40%). Even women who travelled six or more times reported high-risk levels of 67%. These results suggested that increased mobility exposed traders to conditions that heightened sexual vulnerability, likely due to time spent away from partners, limited supervision, and exposure to unfamiliar environments. Economic status did not show a significant association with risky sexual behaviour (p = 0.989). Both women who reported having a “good” economic status (70.24%) and those with a “bad” economic status (70.30%) had nearly identical proportions of high-risk behaviour. This finding indicated that income levels alone did not explain variations in sexual risk-taking within this population. The number of current sexual partners had a highly significant relationship with risky sexual behaviour (p = 0.000). Women with only one partner had a high-risk prevalence of 65.44%, whereas those with two or more partners recorded a substantially higher prevalence of 87.60%. This pattern clearly demonstrated that having multiple concurrent partners significantly increased the likelihood of engaging in high-risk sexual behaviour. Paid sex also showed a statistically significant association (p = 0.039). Women who reported engaging in paid sex had a high-risk prevalence of 79.49%, compared to 69.73% among those who did not. This implied that transactional sex activities heightened vulnerability to engaging in risky sexual practices, possibly due to reduced bargaining power or inconsistent condom use. Condom use frequency was strongly and significantly associated with risky sexual behaviour (p = 0.000). Women who reported using condoms “every time” had the lowest prevalence of high-risk behaviour (45.45%). However, those who used condoms only “sometimes” (86.14%) or “never” (92.86%) had extremely high levels of sexual risk. Interestingly, women reporting “almost every time” had an unusually low high-risk prevalence (23.81%), indicating a strong protective effect when condom use was near consistent. Overall, inconsistent or non-use of condoms was strongly associated with elevated sexual risk. Knowledge about STIs also exhibited a significant association with risky sexual behaviour (p = 0.023). Women who had heard of STIs showed a high-risk prevalence of 71.12%. Surprisingly, those who had not heard of STIs displayed a lower high-risk proportion of 42.86%, although the sample size for this group was very small. The finding suggested that lack of STI knowledge did not necessarily correspond directly with lower risk, possibly due to limited awareness of what constitutes risky behaviour. Alcohol consumption did not show a statistically significant effect on risky sexual behaviour (p = 0.076), although a trend was observed. Women who had ever consumed alcohol had a higher prevalence of high-risk behaviour (73.41%) than those who had not (65.63%). While not statistically significant, this pattern pointed to the potential influence of alcohol on sexual decision-making. Similarly, drug consumption did not show a significant association (p = 0.910). Both women who had ever used drugs (70.00%) and those who had not (68.36%) showed comparable levels of high-risk behaviour, suggesting that drug use was not a major determinant of sexual risk in this sample. Overall, the findings demonstrated that mobility, multiple sexual partnerships, engagement in paid sex, condom use habits, and STI knowledge were significant predictors of risky sexual behaviour at the bivariate level. Conversely, economic status, alcohol consumption, and drug use did not show significant associations. These results highlighted the behavioural and mobility-related factors that most strongly shaped sexual risk among informal cross-border traders, providing a foundation for the subsequent multivariate analysis. Table 5 Determinants of risky sexual behaviour among women cross-border traders Background Characteristics (N = 499) Odds Rat. (95% Conf. Interval) Marital status Married Ref. Unmarried 1.18 (0.46–3.04) Widowed 2.21 (0.50–9.74) Divorced 0.83 (0.22–3.18) Separated 1.51 (0.21–10.69) Age (years) Below 25 Ref. 25–34 1.00 (0.12–8.34) 35–44 2.18 (0.25–19.36) 45–54 2.42 (0.22–26.90) Above 55 6.14 (0.43– 88.53) Educational level None Ref. Primary (Grade 1–7) 0.52 (0.06–4.66) Secondary (Grade 8–12) 0.21 (0.02–2.73) Tertiary (college, university) 0.20 (0.01–4.49) Ability to write Yes Ref. No 2.27 (0.06–82.99) Ability to read Yes Ref. No 0.17 (0.00–7.63) Religious affiliation Catholic Ref. Muslim 1.35 (0.18–10.37) Jehovah Witness 1.77 (0.39–8.02) Masonic 2.53 (0.31–20.78) Pentecostal 0.94 (0.31–2.83) Protestant 2.01 (0.66–6.15) SDA 0.69 (0.16–3.05) Residence Chawama Ref. Kabwata 0.47 (0.09–2.37) Kanyama 2.31 (0.50–10.71) Lusaka Central 6.29 (0.07–575.02) Mandevu 0.34 (0.08–1.41) Matero 0.76 (0.16–3.58) Munali 1.23 (0.22–6.91) Outside Lusaka 5.84 (0.22–153.93) *p < 0.05, ** p < 0.01; Ref: Reference Table 5 presented the results of the binary logistic regression analysis that examined the determinants of risky sexual behaviour among women informal cross-border traders. Overall, the findings indicated that none of the background characteristics included in the model reached statistical significance at either the 5% or 1% levels. This meant that although some variables showed notable trends in terms of odds ratios, these effects were not strong enough to conclude statistically meaningful associations after controlling for other factors. Regarding marital status, unmarried women had 1.18 times higher odds of engaging in risky sexual behaviour compared to married women, while widowed women had more than twice the odds (OR = 2.21). Separated women also showed elevated odds (OR = 1.51). Conversely, divorced women had slightly lower odds (OR = 0.83). However, all confidence intervals were wide and crossed 1.0, indicating that marital status did not significantly predict risky sexual behaviour in the adjusted model. Age patterns suggested that the likelihood of risky sexual behaviour increased with age, although the results were not statistically significant. Compared to women below 25 years, those aged 35–44 had more than twice the odds (OR = 2.18), and those aged 45–54 had similar elevated odds (OR = 2.42). Women above 55 years showed the highest odds of risky behaviour (OR = 6.14), although the confidence interval was extremely wide (0.43–88.53), suggesting substantial uncertainty and lack of statistical significance. These results implied a general trend of increased risk with age, but the model did not confirm age as a significant determinant. Educational level showed consistent protective effects, as women with primary, secondary, and tertiary education all exhibited lower odds of engaging in risky sexual behaviour compared to women with no formal education. For instance, secondary education was associated with a substantial reduction in odds (OR = 0.21), and tertiary education showed an even stronger protective trend (OR = 0.20). However, none of the confidence intervals excluded 1.0, indicating that education did not have a statistically significant effect in the adjusted model despite the clear directional trend. This finding contrasted with the bivariate results, where education emerged as a strong significant predictor. For literacy variables, women who were unable to write had higher odds of risky sexual behaviour (OR = 2.27), while those who could not read had considerably lower odds (OR = 0.17). Both relationships, however, were statistically insignificant, and the very wide confidence intervals suggested instability in the model estimates for these categories. Religion also did not demonstrate significant associations. Compared with Catholic women, the odds were higher among Jehovah’s Witnesses (OR = 1.77), Protestants (OR = 2.01), Masonic respondents (OR = 2.53), and Muslims (OR = 1.35). Meanwhile, Pentecostal women (OR = 0.94) and Seventh-Day Adventists (OR = 0.69) had slightly lower odds. None of these effects reached statistical significance, indicating that religious affiliation did not independently determine risky sexual behaviour when adjusting for other factors. Residence-based differences also lacked statistical significance, although notable trends emerged. Women living in Kanyama had more than twice the odds of risky sexual behaviour (OR = 2.31) compared to those in Chawama, while residents of Lusaka Central had very high odds (OR = 6.29) but also extremely wide confidence intervals, reflecting small sample sizes. Women residing in Mandevu (OR = 0.34) and Kabwata (OR = 0.47) had lower odds, suggesting potentially protective contextual factors in these areas. However, the lack of significance meant these patterns could not be considered reliable predictors. Overall, the logistic regression results suggested that although several demographic variables showed directional effects consistent with theoretical expectations and bivariate findings, none emerged as statistically significant determinants of risky sexual behaviour after adjusting for confounders. This may have been influenced by sample size limitations within certain categories, wide confidence intervals, and clustering effects within the population. The findings highlighted the complexity of predicting risky sexual behaviour and indicated that unmeasured behavioural, structural, or contextual factors may play a more substantial role than background characteristics alone. Table 6 Determinants of risky sexual behaviour among women cross-border traders (continues) Background Characteristics (N = 499) Odds Rat. (95% Conf. Interval) Average time out Not gone out Ref. 1–5 times 1.59 (0.29–8.56) 6 + time 1.25 (0.20–7.64) Economic status Good Ref. Bad 1.51 (0.67–3.42) Sexual partners (current) 1 partner Ref. 2 + partners 14.57** (3.06–69.34) Paid sex Yes Ref. No 1.26 (0.36– 4.42) Frequency of condom use Every time Ref. Almost every time 0.24 (0.02–2.36) Sometimes 21.47** (2.68–171.74) Never 47.82** (5.65–405.03) Heard about STIs Yes Ref. No 0.24 (0.01–4.00) Alcohol consumption (ever taken) Yes Ref. No 2.08 (0.92–4.72) Drug consumption (ever taken) Yes Ref. No 2.71 (0.19–38.27) *p < 0.05, ** p < 0.01; Ref: Reference Table 6 presented the continuation of the logistic regression results, focusing on migratory, sexual, and lifestyle characteristics as determinants of risky sexual behaviour among women informal cross-border traders. The results showed that frequency of travel, economic status, STI knowledge, alcohol use, and drug use were not statistically significant predictors of sexual risk in the adjusted model. However, the number of sexual partners and frequency of condom use emerged as strong and highly significant determinants of risky sexual behaviour. With respect to mobility, women who travelled between one and five times had higher odds of engaging in risky sexual behaviour (OR = 1.59) compared to those who had not travelled, while those who travelled six or more times had slightly elevated odds (OR = 1.25). However, both associations were statistically insignificant, suggesting that mobility alone did not independently predict risky sexual behaviour once other factors were controlled for. Economic status also appeared unrelated to risk behaviour; women who perceived their economic situation as “bad” had only slightly higher odds (OR = 1.51) than those who reported a “good” status, and the confidence interval included 1.0, indicating no significant effect. Sexual partnership patterns emerged as one of the strongest predictors. Women who reported having two or more sexual partners had substantially higher odds of engaging in risky sexual behaviour (OR = 14.57, 95% CI: 3.06–69.34), and this association was highly significant at the 1% level. This finding confirmed that multiple concurrent partnerships were a major driver of sexual risk among the traders. Paid sex did not significantly predict risky sexual behaviour in the adjusted model. Although women who did not engage in paid sex had slightly higher odds (OR = 1.26) relative to those who did, the effect was statistically insignificant, indicating that transactional sex was not an independent determinant once other variables were included. Frequency of condom use was the most powerful determinant in the model. Compared to women who used condoms every time, those who used them only sometimes had dramatically higher odds of risky behaviour (OR = 21.47, 95% CI: 2.68–171.74), and those who never used condoms had even higher odds (OR = 47.82, 95% CI: 5.65–405.03). Both effects were highly significant at the 1% level. Even women who used condoms almost every time had lower odds (OR = 0.24), although this association was not statistically significant. These results clearly demonstrated that condom inconsistency was a critical predictor of risky sexual behaviour and underscored the protective role of consistent condom use. Knowledge about STIs, alcohol consumption, and drug use showed no statistically significant effects. Women who had not heard about STIs had lower odds (OR = 0.24), but the wide confidence interval indicated uncertainty in the estimate. Women who abstained from alcohol had higher odds of risky behaviour (OR = 2.08), though this effect was not significant. Drug use also failed to show a significant relationship, with women who had never used drugs reporting higher odds (OR = 2.71), but again with extremely wide confidence intervals. Overall, the findings from Table 6 indicated that multiple sexual partnerships and inconsistent or non-use of condoms were the most significant determinants of risky sexual behaviour among women informal cross-border traders. These behavioural factors far outweighed demographic and socioeconomic predictors in the adjusted model, highlighting the central role of sexual practices rather than structural characteristics in shaping HIV vulnerability within this population. Discussion The current study identified several key behavioural determinants of risky sexual behaviour among women informal cross-border traders in Lusaka, with multiple sexual partners and inconsistent condom use emerging as the strongest predictors. Women who reported having two or more sexual partners had substantially elevated odds of engaging in risky sexual behaviour (OR ≈ 14.6). This finding is consistent with broader epidemiological evidence that multiple and concurrent partnerships are major drivers of HIV risk [ 18 ]. In the Zambian context, multiple sexual partnerships have been identified as a key social driver of HIV infection, particularly among economically vulnerable women [ 19 ]. The high prevalence of multiple partners among traders may reflect transactional dynamics, mobility-related opportunities, and economic necessity, which align with patterns documented in sub-Saharan African populations [ 5 ]. Frequency of condom use was another critical determinant. Compared to women who used condoms at every sexual encounter, those who reported “sometimes” or “never” using condoms had dramatically higher odds of risky sexual behaviour (ORs ≈ 21.5 and ≈ 47.8, respectively). These results corroborate previous findings in Zambia, where inconsistent condom use among women, particularly in transactional or mobile contexts, has been shown to increase vulnerability to HIV and STIs [ 20 ]. Socio-cultural barriers, including trust, relationship dynamics, and gender power imbalances, may limit condom negotiation, especially in marital or transactional partnerships [ 5 ]. These factors highlight the need for tailored interventions that promote consistent condom use and empower women to negotiate safer sex, even within economically or socially dependent relationships. Although mobility was initially associated with higher risky sexual behaviour in bivariate analyses, it was not a statistically significant predictor in the adjusted model. This suggests that mobility alone may not independently increase sexual risk once behavioural factors such as number of partners and condom use are considered. Previous studies on migration and HIV risk have similarly indicated that the relationship between mobility and sexual risk is often mediated by behavioural choices rather than mobility itself [ 21 ]. Therefore, interventions targeting mobile populations, such as cross-border traders, should address behavioural drivers alongside structural vulnerabilities. Contrary to expectations, self-perceived economic status did not significantly predict risky sexual behaviour in this study. This may reflect a relative homogeneity in economic conditions among cross-border traders, whereby differences in “good” versus “bad” status were insufficient to influence sexual risk once behaviour was accounted for. Additionally, transactional sexual practices among women in trading contexts may be influenced by broader structural and relational factors rather than immediate economic standing [ 18 ]. Interestingly, women who reported not having heard of STIs exhibited lower (though non-significant) odds of risky sexual behaviour. This counterintuitive finding may be due to selection or reporting bias; women who are more sexually active may have greater exposure to sexual health information, creating an association between STI knowledge and higher observed risk. Similar observations have been reported in Zambia, where knowledge alone did not always translate into safer sexual practices due to socio-cultural and structural barriers [ 21 ]. Substance use, including alcohol and drug consumption, did not emerge as significant predictors of risky sexual behaviour after adjusting for other variables. Although substance use is often linked to risky sexual practices, in this population, sexual behaviour particularly number of partners and condom use appeared to be more proximal determinants of risk. This aligns with studies in Zambia showing that in certain high-mobility populations, sexual behaviour may outweigh substance use in predicting HIV and STI vulnerability [ 5 ]. Demographic characteristics such as education, age, marital status, religion, and residence were not statistically significant in the multivariate model, although they showed important trends. Higher education was associated with lower odds of risky sexual behaviour, suggesting that education may exert a protective effect indirectly through improved sexual health knowledge and decision-making [ 20 ]. Older women showed higher odds of risk, which may reflect cumulative exposure or different types of partnerships, but these associations were not statistically significant. Marital status, religion, and residence demonstrated variation in risk but lost significance when behavioural variables were included, indicating that sexual practices may be more critical than demographic characteristics in determining risk. The dominance of behavioural factors in predicting risky sexual behaviour has important implications for intervention. Efforts to reduce HIV and STI vulnerability among women informal cross-border traders should prioritise condom promotion and negotiation skills, behavioural change programmes targeting sexual practices, and sexual health education tailored to mobile women, potentially delivered in markets or cross-border points. Although economic status was not a significant predictor, addressing structural vulnerabilities, such as limited access to credit or social safety nets, may further support risk reduction strategies [ 18 ]. Some limitations of the study should be noted. Wide confidence intervals for several variables suggest limited statistical power, particularly for smaller subgroups. Self-reported sexual behaviours may be influenced by social desirability bias. Additionally, unmeasured factors, such as partner power dynamics, gender-based violence, or access to health services, could influence sexual risk. The cross-sectional design limits causal inference, and future research should consider longitudinal or mixed-methods approaches to better understand the mechanisms linking mobility, economic vulnerability, and sexual behaviour. In sum, the study found that multiple sexual partnerships and inconsistent condom use were the strongest predictors of risky sexual behaviour among women informal cross-border traders in Lusaka. Demographic and structural factors appeared less influential once behavioural variables were accounted for. These findings highlight the need for interventions that focus on behavioural risk reduction, empower women to negotiate condom use, and address structural vulnerabilities, thereby reducing HIV and STI exposure among this high-risk population. Conclusion This study investigated the determinants of risky sexual behaviour among women informal cross-border traders in Lusaka, Zambia, using a binary logistic regression approach. The findings demonstrated that behavioural factors, particularly having multiple sexual partners and inconsistent condom use, were the strongest predictors of high-risk sexual behaviour. Women who reported two or more sexual partners were substantially more likely to engage in risky sexual practices, while inconsistent or non-use of condoms dramatically increased the likelihood of risk. These results align with previous research highlighting that behavioural practices, rather than demographic or structural factors alone, play a pivotal role in HIV and STI vulnerability [ 5 , 20 ]. Although demographic variables such as age, education, marital status, religion, and residence showed some trends, they did not remain statistically significant in the adjusted models. This suggests that while socio-demographic characteristics may shape women’s sexual behaviours indirectly, proximal behavioural choices are the most critical determinants of risk. Mobility, economic status, alcohol and drug consumption, and STI knowledge were also not significant predictors in the multivariate analysis, indicating that interventions targeting sexual behaviour itself may yield the most immediate impact. The study underscores the urgent need for targeted interventions for women informal cross-border traders, including programmes that promote consistent condom use, sexual health education, negotiation skills, and economic empowerment strategies. Addressing these behavioural determinants can significantly reduce HIV and STI risk among this vulnerable population. Moreover, structural interventions that consider mobility, occupational hazards, and gender-based vulnerabilities remain important to complement behavioural strategies [ 3 , 19 ]. In conclusion, women informal cross-border traders in Lusaka face high levels of risky sexual behaviour, primarily driven by multiple sexual partnerships and inconsistent condom use. Tailored interventions addressing these behaviours, supported by broader structural and policy measures, are essential to safeguard the sexual and reproductive health of this high-risk population and to contribute to Zambia’s broader HIV prevention goals. Study Limitations The study had several limitations. Its cross-sectional design prevented establishing causal relationships between demographic factors and risky sexual behaviour, providing only associations. Data were self-reported, making them susceptible to recall and social desirability biases, which may have led to underreporting of risky behaviours such as multiple sexual partners or inconsistent condom use. The study focused solely on women traders at the COMESA Market in Lusaka, limiting the generalisability of findings to other markets, border towns, or rural areas. Additionally, while key demographic variables were examined, other factors such as partner characteristics, negotiation power, psychosocial influences, and access to sexual health services were not included, leaving potential residual confounding. Despite these constraints, the study provides important evidence on demographic determinants of risky sexual behaviour among women informal cross-border traders, offering a foundation for targeted HIV prevention interventions. Abbreviations CBTA : Cross-Border Traders Association CI : Confidence Interval COMESA : Common Market for Eastern and Southern Africa HIV : Human Immunodeficiency Virus OR : Odds Ratio SDA : Seventh Day Adventist STIs : Sexually Transmitted Infections Declarations Ethical approval and consent to participate Ethical approval for this study was obtained from the University of Zambia’s School of Humanities and Social Sciences Research Ethics Committee (HSSREC: 2018-June-005, No. 153760602331). All respondents received an information sheet explaining the purpose, procedures, and voluntary nature of the study. Written informed consent was obtained from each participant prior to data collection. Questionnaires were administered in a private and secure location to ensure confidentiality. Participation was restricted to women aged 18 years and above who were members of the CBTA and who owned or rented a shop or container at the COMESA Market. Consent for publication Not applicable Availability of data and materials The data are available upon request from the authors. Competing interests The authors declare that they have no competing interests. Funding No funding was received. Author contributions The conceptualisation and data curation for the project were carried out by Simson Mwale, Musonda Lemba, and Million Phiri, who also contributed to the formal analysis. The methodology was developed by Simson Mwale and Musonda Lemba, while the original draft was written by Simson Mwale. Finally, all authors reviewed and edited the manuscript. Acknowledgements The authors extend their gratitude to the managers of COMESA market and CBTA members who agreed to participate in this study. References United Nations Development Programme Zambia. UNDP Zambia 2023 Annual Report. UNDP Zambia; 2023. Salia JG, et al. High Mobility and STIs/HIV among Women Informal Cross Border Traders in Southern Mozambique: Exploring Knowledge, Risk Perception, and Sexual Behaviours. Int J Environ Res Public Health. 2020;17:4724. Amnesty International. Women traders at risk: Gender-based vulnerabilities in Southern Africa; 2024. World Bank & World Trade Organization. Report on Trade and Gender: Informal Cross-Border Trade in Africa. World Bank / WTO; 2020. Malama K, Phiri T, Chola M. Sexual behaviour and HIV risk among female cross-border traders in Zambia. BMC Public Health. 2021;21(1):1004. https://doi.org/10.1186/s12889-021-11004-7 . Mushota C, et al. HIV risks among women traders in Zambia’s cross-border regions. Zambia J HIV/AIDS. 2020;24(3):120–30. Bwalya J, et al. Social isolation and HIV vulnerability among informal cross-border traders in Zambia. Afr J AIDS Res. 2018;17(2):113–21. Lungu E, Kasonde M. Gender-based violence and HIV among Zambian informal cross-border traders. Zambian J Public Health. 2019;20(3):155–63. Musheke M, Mulenga M, Kabemba D. The role of education in shaping sexual behaviors and HIV risk in Zambia. Zambian J Soc Sci. 2017;18(3):34–42. Mwaba P. Informal cross-border trade as a livelihood strategy for women in Southern Africa. Dev Stud Q. 2021;37(4):72–89. Mwanza M, et al. The intersection of age, gender, and HIV vulnerability in Zambia. Zambian J Public Health. 2020;25(2):22–8. United Nations Conference on Trade and Development. Women in Informal Cross Border Trade in Zambia: A Small-Scale Trader’s Guide to Trade Rules and Procedures. UNCTAD; 2023. Buvé A, Bishikwabo-Nsarhaza K, Mutangadura G. The spread and effect of HIV-1 infection in sub-Saharan Africa. Lancet. 2014;359:2011–7. Kakoma. Interview with the then President of CBTA, Interviewed by Simson Mwale, 6 July 2015, at 11:00, Lusaka. Lungu J. June. Interview with the then Secretary General of Cross-Border Traders Association of Zambia (CBTA), Interviewed by Simson Mwale, 26 2010, at 10:30, Lusaka. Jawando JO, Adeyemi EO. Sexual Exchange and Cross-Border Trade: Implications for HIV/AIDS in Nigeria. SAGE Open. 2020;1–12. Phiri M, Lemba M, Chomba C, Kanyamuna V. Examining differentials in HIV transmission risk behaviour and its associated factors among men in Southern African countries. Humanit Soc Sci Commun. 2022;9(1):295. United Nations. Global HIV/AIDS trends and drivers: 2022 report. New York, NY: United Nations; 2022. UN Zambia. Common country analysis: HIV and gender dynamics. Lusaka: United Nations Zambia; 2022. Guttmacher Institute. Sexual and reproductive health in Zambia: Current trends and challenges. 2025. Simbeye L, Chirwa T, Mwale M. Knowledge and behaviour regarding sexually transmitted infections among women in Lusaka, Zambia. J Health Popul Nutr. 2024;43(1):12. https://doi.org/10.1186/s41043-024-00212-5 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8258951","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":559759286,"identity":"a6c49b80-7272-4369-98d8-979c74989ee8","order_by":0,"name":"Simson 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17:19:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1599637,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8258951/v1/1ca69039-f8a1-475e-923c-9a9a3090a500.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors Associated with Risky Sexual Behaviour among Women Informal Cross-border Traders in Lusaka, Zambia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWomen\u0026rsquo;s participation in informal cross-border trade has become an increasingly important economic activity across sub-Saharan Africa, including Zambia. This form of trade provides vital livelihood opportunities, particularly in contexts where formal employment is scarce and poverty rates are high [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Women who engage in this trade, commonly referred to as \u0026ldquo;Women Informal Cross-Border Traders\u0026rdquo;, play a critical role in local and regional economies by facilitating the movement of goods and services across borders. However, despite its economic significance, women informal cross-border traders face numerous socioeconomic challenges, including vulnerabilities related to sexual health [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. One of the most pressing concerns for this population is the increased likelihood of engaging in risky sexual behaviours, which heightens their susceptibility to human immunodeficiency virus (HIV) and other sexually transmitted infections (STIs).\u003c/p\u003e \u003cp\u003eEvidence indicates that women engaged in cross-border trade may engage in high-risk sexual behaviours such as multiple sexual partnerships, transactional sex, and inconsistent condom use [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These behaviours are often shaped by structural and contextual factors, including gendered power dynamics within trading environments, economic pressures, limited access to healthcare, and inadequate sexual health education [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. While the economic contributions of women informal cross-border traders are well documented, research examining how socio-demographic characteristics influence their sexual risk-taking remains limited.\u003c/p\u003e \u003cp\u003eIn Zambia, where HIV prevalence remains high among women, understanding the demographic determinants of risky sexual behaviour is critical for developing targeted interventions. Factors such as age, marital status, education, literacy, religious affiliation, and place of residence have been shown in other contexts to influence sexual behaviours and HIV risk [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For instance, women with lower education or literacy levels are more likely to engage in risky sexual practices, while residence in densely populated or economically disadvantaged areas may exacerbate vulnerability to transactional sex and coercion.\u003c/p\u003e \u003cp\u003eDespite recognition of the public health implications of risky sexual behaviour among women informal cross-border traders, there is a paucity of empirical evidence specifically examining the influence of demographic factors on sexual risk in this population. Most studies have focused on broader urban populations or migrant workers, leaving women in informal cross-border trade, particularly in Zambian border towns and urban markets, understudied [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, this study aimed to investigate the demographic factors associated with risky sexual behaviour among women informal cross-border traders in Lusaka, Zambia, using a binary logistic regression approach to identify key predictors of HIV vulnerability. The findings are intended to inform targeted behavioural and structural interventions to reduce risky sexual behaviour and improve sexual health outcomes in this high-risk population.\u003c/p\u003e"},{"header":"Methods and Data","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source\u003c/h2\u003e \u003cp\u003eData for this study were collected in 2019 from 499 women engaged in informal cross-border trade at the Common Market for Eastern and Southern Africa (COMESA) Market in Lusaka, Zambia. Structured questionnaires were used to collect information on socio-demographic characteristics, including marital status, age, education level, literacy, religious affiliation, and place of residence, as well as self-reported sexual behaviours. Respondents were selected using a probability sampling technique, with eligibility criteria requiring that participants be at least 18 years old, members of the Cross-Border Traders Association (CBTA), actively engaged in cross-border trading, and owning or renting a shop or container at the COMESA Market. This approach was adopted to focus on women whose demographic characteristics could influence higher levels of risky sexual behaviour.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Context\u003c/h3\u003e\n\u003cp\u003eThe study was conducted at the COMESA Market, a major centre for informal cross-border trade in Lusaka. Established in 1998 by informal traders who founded the CBTA, the market had a membership of approximately 42,350 cross-border traders by 2015, of which 70% were women [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. At the time of the survey in January 2019, the market had 1,299 shops and containers distributed across 14 sections and zones, of which 49% (632) were owned and rented by women. The study achieved a response rate of 99.8%, with 499 women participating.\u003c/p\u003e\n\u003ch3\u003eStudy Design and Sample Size\u003c/h3\u003e\n\u003cp\u003eA three-stage multi-stage sampling technique was employed. In the first stage, women informal cross-border traders were identified based on the specific business sections within the market. In the second stage, stratified sampling was used to select representative trading units from each section and zone. In the third stage, simple random sampling was applied to select individual respondents from the sampled trading units. This process ensured that the 499 participants were representative of the population of women traders operating at the COMESA Market.\u003c/p\u003e\n\u003ch3\u003eStudy Measurements\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eOutcome Variable\u003c/h2\u003e \u003cp\u003eThe dependent variable for this study was risky sexual behaviour, measured through a composite variable, \u0026ldquo;RISK_LEVEL,\u0026rdquo; derived from 12 survey items assessing sexual risk practices. These items included having more than one sexual partner, voluntary sexual activity, multiple partners in the past 12 months, transactional sex, unprotected sex, inconsistent condom use, and knowledge of STIs. Each item was coded 1 if it indicated high-risk behaviour and 0 otherwise. A total score for each respondent was calculated by summing all items, with scores ranging from 0 (risk-free) to 11 (very high-risk). The total scores were further categorised into a new variable, RISK_STATUS, with scores 0\u0026ndash;5 classified as low-risk and 6\u0026ndash;11 as high-risk.\u003c/p\u003e \u003cp\u003eThese scores reflected the extent to which women informal cross-border traders engaged in behaviours that exposed them to heightened HIV vulnerability, such as inconsistent condom use, multiple sexual partnerships, and transactional sex. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, respondents were distributed across the full scale of the index; however, the majority fell within the mid-to-high range of risk. Only a small proportion of women scored very low, such as those with scores of 0 (2%) or 1 (1.2%), indicating minimal involvement in sexual risk-taking. In contrast, a large segment of the participants had scores between 6 and 8, with a peak at score 7, which accounted for 28.3% of the sample. This distribution suggested that high levels of engagement in risky sexual practices were common among the women surveyed, reinforcing the need to examine the demographic and socioeconomic factors shaping their vulnerability.\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\u003eRecoding of the dependent variable\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=\"char\" char=\".\" 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\u003eVariable measurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSurvey Results, 2019 (N\u0026thinsp;=\u0026thinsp;499)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRISK_LEVEL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRISK_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\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor analytic purposes, the risk index was recoded into a binary outcome variable to distinguish between women at low risk and those at high risk of engaging in sexual behaviours associated with increased HIV exposure. Following recoding, 161 respondents (32.3%) were classified as low risk, while 338 (67.7%) were categorised as high risk. This distribution indicated that nearly two-thirds of the women were at elevated risk. The categorisation was crucial for the binary logistic regression, as it enabled the model to estimate the likelihood of being in the high-risk group based on demographic predictors such as age, education level, marital status, settlement of residence, and religious affiliation.\u003c/p\u003e \u003cp\u003eThe high prevalence of risky sexual behaviour observed in this distribution was consistent with the broader study findings, which showed that factors such as lower educational attainment, residence in densely populated settlements, and limited literacy significantly increased the odds of risky sexual behaviour. The dependent variable, therefore, not only captured the behavioural vulnerabilities present among these traders but also provided a strong foundation for identifying the demographic characteristics that had the greatest influence on sexual risk-taking. Overall, the distribution supported the conclusion that women engaged in informal cross-border trade experienced substantial and multifaceted sexual health risks shaped by social, economic, and cultural determinants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIndependent Variables\u003c/h2\u003e \u003cp\u003eThe independent variables in this study were selected based on their established relevance to HIV risk and risky sexual behaviour, as reported in previous studies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These variables were classified as individual-level demographic factors and included marital status, age, education, literacy, religious affiliation, and place of residence. Marital status was categorised as married, unmarried (never married), widowed, divorced, or separated. Age was recorded in single years and grouped into five categories: below 25 years, 25\u0026ndash;34 years, 35\u0026ndash;44 years, 45\u0026ndash;54 years, and above 55 years. Education was measured as none (no formal education), primary, secondary, or tertiary. Literacy was assessed through the ability to read and write, coded as yes/no. Religious affiliation was categorised as Catholic, Muslim, Jehovah\u0026rsquo;s Witness, Masonic, Pentecostal, Protestant, and Seventh Day Adventist (SDA). Place of residence was classified according to the socioeconomic characteristics of seven locations: Chawama (high density, low income), Kabwata (medium density, middle income), Kanyama (high density, low income), Lusaka Central (low density, high income), Mandevu (high density, middle income), Matero (high density, low to middle income), Munali (low density, middle to high income), and beyond Lusaka (low density, low to middle income).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData analysis was conducted at three levels: univariate, bivariate, and multivariate. Univariate analysis employed descriptive statistics to summarise respondents\u0026rsquo; demographic characteristics and their risk levels. Bivariate analysis was performed using chi-square tests to assess the association between each independent variable and risky sexual behaviour. Multivariate analysis utilised a binary logistic regression model to identify significant predictors of risky sexual behaviour while adjusting for potential confounders. All statistical analyses were conducted using Stata version 13, with significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Odds ratios (ORs) with 95% confidence intervals (CIs) were reported to indicate the strength and direction of associations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\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\u003eBackground characteristics of respondents\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBackground characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSurvey Results, 2019 (N\u0026thinsp;=\u0026thinsp;499)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.62\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\u003eBelow 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.02\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove 55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational 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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary (Grade 1\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary (Grade 8\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary (college, university)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbility to write\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbility to read\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.54\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\u003eCatholic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJehovah Witness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasonic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePentecostal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtestant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChawama\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKabwata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKanyama\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLusaka Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMandevu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatero\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMunali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutside Lusaka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presented the background characteristics of the 499 women informal cross-border traders who participated in the 2019 survey. The marital status distribution showed that the largest proportion of respondents were married (39.36%), followed by unmarried women (27.11%). Widowed women accounted for 16.27% of the sample, while 11.65% were divorced and 5.62% were separated. This distribution suggested that a substantial proportion of participants were either single, widowed, or had previously been married, which may have influenced their economic independence, mobility, and exposure to risky situations related to cross-border trading activities. The age distribution indicated that the majority of respondents were in the economically active age groups. Women aged 35\u0026ndash;44 constituted the largest group (35.14%), closely followed by those aged 25\u0026ndash;34 (33.73%). Women aged 45\u0026ndash;54 represented 18.07% of the sample, while those younger than 25 years accounted for only 5.02%. Participants above 55 years comprised 6.03%. This pattern demonstrated that informal cross-border trading attracted mostly women in their prime working years, who may have faced unique pressures related to supporting households and sustaining livelihoods, factors potentially linked to heightened sexual risk-taking, as noted in the study findings.\u003c/p\u003e \u003cp\u003eEducational attainment varied considerably across respondents. More than half of the women (57.14%) had attained secondary-level education, while 28.37% had only primary schooling. A smaller proportion (8.16%) had tertiary education, and 6.33% reported having no formal education. Literacy indicators further revealed that the majority of respondents were able to write (82.52%) and read (78.46%), although a notable minority lacked these skills. The presence of a sizeable group with limited education and literacy aligned with the regression findings, which showed that lower levels of education were associated with increased likelihood of engaging in risky sexual behaviour. Religious affiliation was diverse within the sample. Catholics constituted the largest religious group (34.47%), followed by Protestants (22.24%) and Pentecostals (19.44%). Smaller proportions identified as Jehovah\u0026rsquo;s Witness (7.41%), Seventh-Day Adventist (7.21%), Masonic (4.01%), and Muslim (5.21%). These religious differences were relevant to the study because some faith communities emphasised behavioural norms and restrictions that might influence sexual decision-making. For example, the regression results suggested that Muslim women were less likely to engage in risky sexual practices compared to other groups.\u003c/p\u003e \u003cp\u003eThe distribution of respondents by place of residence showed that a large proportion lived in high-density, low-income settlements. Kanyama (27.22%), Mandevu (19.96%), and Matero (13.91%) together accounted for over half of the respondents. Other settlements such as Chawama (11.90%), Kabwata (13.31%), and Munali (9.48%) represented smaller shares, while only 1.81% lived in Lusaka Central and 2.42% resided outside the city. The concentration of traders in densely populated townships corresponded with the study\u0026rsquo;s findings, which indicated that women living in these areas, especially Kanyama, Mandevu, and Matero, had higher odds of engaging in risky sexual behaviour due to socioeconomic hardships, overcrowding, and limited access to social support systems. Overall, the background characteristics suggested that the women engaged in informal cross-border trade were predominantly in their prime working ages, often from lower-income residential areas, and with varying levels of education and literacy. These demographic patterns provided important context for understanding the socioeconomic pressures and vulnerabilities that shaped their engagement in risky sexual behaviour.\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\u003ePercent distribution of risky sexual behaviour by background characteristics of respondents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBackground\u003c/p\u003e \u003cp\u003echaracteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSurvey Results 2019 (N\u0026thinsp;=\u0026thinsp;499)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.244\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57(29.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139(70.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46(34.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89(65.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32(39.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49(60.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14(24.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44(75.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11(39.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17(60.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\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 \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.129\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14(56.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11(44.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53(31.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115(68.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54(30.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121(69.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28(31.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62(68.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove 55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11(27.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29(72.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational 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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.000**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5(16.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26(83.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26(18.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113(81.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102(36.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178(63.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25(62.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15(37.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbility to write\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136(35.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e250(64.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22(20.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84(79.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbility to read\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.014**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140(34.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e266(65.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18(20.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68(79.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\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 \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.019*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatholic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48(27.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124(72.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14(53.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12(46.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJehovah Witness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8(21.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29(78.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasonic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7(35.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13(65.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePentecostal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41(42.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56(57.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtestant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30(27.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81(72.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13(36.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23(63.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChawama\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27(45.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32(54.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKabwata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33(50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33(50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKanyama\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36(26.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99(73.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLusaka Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2(22.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(77.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMandevu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28(28.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71(71.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatero\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19(27.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50(72.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMunali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12(25.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35(74.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutside Lusaka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1(8.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11(91.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presented the distribution of risky sexual behaviour across various background characteristics of the respondents and showed how each characteristic was associated with either low-risk or high-risk sexual behaviour. Although differences were observed across marital status groups, the association was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.244). Married women demonstrated a high-risk prevalence of 70.92%, while divorced women exhibited the highest proportion of high-risk behaviour at 75.86%. Widowed (60.93%) and separated (60.71%) women showed moderately high-risk levels, whereas unmarried women had a slightly lower prevalence (65.93%). These findings indicated that marital status alone did not significantly influence risk levels, although divorced and married women tended to be more likely to engage in high-risk behaviours. Age differences in sexual risk-taking were also not statistically significant (p\u0026thinsp;=\u0026thinsp;0.129). However, the pattern showed that women aged 25\u0026ndash;34 and 35\u0026ndash;44, who formed the majority of the sample, had similar high-risk proportions of 68.45% and 69.14%, respectively. Women above 55 years exhibited the highest proportion of high-risk behaviour at 72.50%, whereas the youngest group, below 25 years, recorded the lowest high-risk prevalence at 44%. Despite the lack of statistical significance, these results suggested that younger women were less likely to engage in risky sexual behaviour, while older women, particularly those above 35 years, tended to report more high-risk practices.\u003c/p\u003e \u003cp\u003eEducational level showed a highly significant association with risky sexual behaviour (p\u0026thinsp;=\u0026thinsp;0.000). Women with no formal education had the highest proportion of high-risk behaviour (83.87%), followed closely by those with only primary schooling (81.29%). In contrast, women with secondary education reported a much lower high-risk prevalence (63.57%), while those with tertiary education showed the lowest at 37.50%. This strong gradient indicated that education had a protective effect, with higher educational attainment associated with lower levels of sexual risk-taking. These results aligned with the logistic regression findings, which also demonstrated that higher education significantly reduced the odds of engaging in risky sexual behaviour. Literacy variables further supported this pattern. Both the ability to write (p\u0026thinsp;=\u0026thinsp;0.005) and the ability to read (p\u0026thinsp;=\u0026thinsp;0.014) were statistically significant. Women who were unable to write reported a high-risk prevalence of 79.25%, compared to 64.77% among those who could write. Similarly, women who could not read had a higher high-risk prevalence (79.07%) compared to those who were literate (65.52%). These results suggested that literacy skills played an important role in shaping sexual decision-making, likely due to increased access to information, health messages, and safer sex knowledge.\u003c/p\u003e \u003cp\u003eReligious affiliation was also significantly associated with risky sexual behaviour (p\u0026thinsp;=\u0026thinsp;0.019). Muslims showed the lowest high-risk prevalence (46.15%), suggesting a relatively protective influence of religious norms or practices. In contrast, Jehovah\u0026rsquo;s Witnesses (78.38%), Protestants (72.97%), and Catholics (72.09%) reported much higher proportions of high-risk behaviour. Pentecostals (57.73%) and Seventh-Day Adventists (63.89%) had moderately high levels. These variations indicated that religious norms and community expectations may have shaped behavioural patterns differently across denominations.\u003c/p\u003e \u003cp\u003eResidence demonstrated a strong significant association with risky sexual behaviour (p\u0026thinsp;=\u0026thinsp;0.002). Women residing in high-density areas such as Kanyama (73.33%), Mandevu (71.72%), Matero (72.46%), and Munali (74.47%) exhibited substantially higher proportions of high-risk behaviour. Conversely, women from Kabwata (50.00%) and Chawama (54.24%) reported lower risk levels. The highest proportion of high-risk behaviour was found among women living outside Lusaka (91.67%), though this category had a small sample size. These findings reinforced the conclusion that socioeconomic conditions associated with densely populated settlements, such as poverty, overcrowding, and limited access to services, were important drivers of risky sexual behaviour among informal cross-border traders. Overall, the bivariate results indicated that education, literacy, religion, and residence were significantly associated with risky sexual behaviour, while marital status and age did not show statistically significant associations. These relationships provided a foundation for the multivariate analysis, which further explored the independent effects of these demographic predictors.\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\u003ePercent distribution of risky sexual behaviour by migratory, sexual and social lifestyle characteristics of respondents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMigratory, sexual and social lifestyle characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSurvey Results 2019 (N\u0026thinsp;=\u0026thinsp;499)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage time out\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.000**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot gone out\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25(54.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21(45.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;5 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85(25.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e247(74.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026thinsp;+\u0026thinsp;time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33(33.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67(67.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEconomic 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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.989\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61(29.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144(70.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79(29.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e187(70.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSexual partners (current)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.000**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122(34.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e231(65.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026thinsp;+\u0026thinsp;partners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15(12.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106(87.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePaid sex\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.039*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16(20.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62(79.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131(32.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e337(69.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrequency of condom use\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.000**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvery time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6(54.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5(45.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlmost every time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32(76.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10(23.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23(13.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143(86.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12(7.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e156(92.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeard about STIs\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.023*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e134(28.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e330(71.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8(57.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(42.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption (ever taken)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.076\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46(26.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127(73.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110(34.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e210(65.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrug consumption (ever taken)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.910\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3(30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(70.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e153(31.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e337(68.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e**p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presented the distribution of risky sexual behaviour according to migratory, sexual, and social lifestyle characteristics of the respondents. The amount of time women spent away from home due to cross-border trading showed a statistically significant association with sexual risk-taking (p\u0026thinsp;=\u0026thinsp;0.000). Women who did not travel reported the lowest proportion of high-risk behaviour (45.65%), while those who travelled frequently, particularly between one and five times, exhibited a much higher prevalence of high-risk behaviour (74.40%). Even women who travelled six or more times reported high-risk levels of 67%. These results suggested that increased mobility exposed traders to conditions that heightened sexual vulnerability, likely due to time spent away from partners, limited supervision, and exposure to unfamiliar environments. Economic status did not show a significant association with risky sexual behaviour (p\u0026thinsp;=\u0026thinsp;0.989). Both women who reported having a \u0026ldquo;good\u0026rdquo; economic status (70.24%) and those with a \u0026ldquo;bad\u0026rdquo; economic status (70.30%) had nearly identical proportions of high-risk behaviour. This finding indicated that income levels alone did not explain variations in sexual risk-taking within this population.\u003c/p\u003e \u003cp\u003eThe number of current sexual partners had a highly significant relationship with risky sexual behaviour (p\u0026thinsp;=\u0026thinsp;0.000). Women with only one partner had a high-risk prevalence of 65.44%, whereas those with two or more partners recorded a substantially higher prevalence of 87.60%. This pattern clearly demonstrated that having multiple concurrent partners significantly increased the likelihood of engaging in high-risk sexual behaviour. Paid sex also showed a statistically significant association (p\u0026thinsp;=\u0026thinsp;0.039). Women who reported engaging in paid sex had a high-risk prevalence of 79.49%, compared to 69.73% among those who did not. This implied that transactional sex activities heightened vulnerability to engaging in risky sexual practices, possibly due to reduced bargaining power or inconsistent condom use.\u003c/p\u003e \u003cp\u003eCondom use frequency was strongly and significantly associated with risky sexual behaviour (p\u0026thinsp;=\u0026thinsp;0.000). Women who reported using condoms \u0026ldquo;every time\u0026rdquo; had the lowest prevalence of high-risk behaviour (45.45%). However, those who used condoms only \u0026ldquo;sometimes\u0026rdquo; (86.14%) or \u0026ldquo;never\u0026rdquo; (92.86%) had extremely high levels of sexual risk. Interestingly, women reporting \u0026ldquo;almost every time\u0026rdquo; had an unusually low high-risk prevalence (23.81%), indicating a strong protective effect when condom use was near consistent. Overall, inconsistent or non-use of condoms was strongly associated with elevated sexual risk.\u003c/p\u003e \u003cp\u003eKnowledge about STIs also exhibited a significant association with risky sexual behaviour (p\u0026thinsp;=\u0026thinsp;0.023). Women who had heard of STIs showed a high-risk prevalence of 71.12%. Surprisingly, those who had not heard of STIs displayed a lower high-risk proportion of 42.86%, although the sample size for this group was very small. The finding suggested that lack of STI knowledge did not necessarily correspond directly with lower risk, possibly due to limited awareness of what constitutes risky behaviour. Alcohol consumption did not show a statistically significant effect on risky sexual behaviour (p\u0026thinsp;=\u0026thinsp;0.076), although a trend was observed. Women who had ever consumed alcohol had a higher prevalence of high-risk behaviour (73.41%) than those who had not (65.63%). While not statistically significant, this pattern pointed to the potential influence of alcohol on sexual decision-making.\u003c/p\u003e \u003cp\u003eSimilarly, drug consumption did not show a significant association (p\u0026thinsp;=\u0026thinsp;0.910). Both women who had ever used drugs (70.00%) and those who had not (68.36%) showed comparable levels of high-risk behaviour, suggesting that drug use was not a major determinant of sexual risk in this sample. Overall, the findings demonstrated that mobility, multiple sexual partnerships, engagement in paid sex, condom use habits, and STI knowledge were significant predictors of risky sexual behaviour at the bivariate level. Conversely, economic status, alcohol consumption, and drug use did not show significant associations. These results highlighted the behavioural and mobility-related factors that most strongly shaped sexual risk among informal cross-border traders, providing a foundation for the subsequent multivariate analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDeterminants of risky sexual behaviour among women cross-border traders\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBackground Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;499)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Rat.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(95% Conf. Interval)\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\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\u003eMarried\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\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.46\u0026ndash;3.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.50\u0026ndash;9.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.22\u0026ndash;3.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.21\u0026ndash;10.69)\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\u003eBelow 25\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\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.12\u0026ndash;8.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.25\u0026ndash;19.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.22\u0026ndash;26.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove 55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.43\u0026ndash; 88.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational 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\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 (Grade 1\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.06\u0026ndash;4.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary (Grade 8\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.02\u0026ndash;2.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary (college, university)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.01\u0026ndash;4.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbility to write\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\u003eYes\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.06\u0026ndash;82.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbility to read\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\u003eYes\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.00\u0026ndash;7.63)\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\u003eCatholic\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\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.18\u0026ndash;10.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJehovah Witness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.39\u0026ndash;8.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasonic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.31\u0026ndash;20.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePentecostal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.31\u0026ndash;2.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtestant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.66\u0026ndash;6.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.16\u0026ndash;3.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChawama\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\u003eKabwata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.09\u0026ndash;2.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKanyama\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.50\u0026ndash;10.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLusaka Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.07\u0026ndash;575.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMandevu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.08\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatero\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.16\u0026ndash;3.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMunali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.22\u0026ndash;6.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutside Lusaka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.22\u0026ndash;153.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Ref: Reference\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presented the results of the binary logistic regression analysis that examined the determinants of risky sexual behaviour among women informal cross-border traders. Overall, the findings indicated that none of the background characteristics included in the model reached statistical significance at either the 5% or 1% levels. This meant that although some variables showed notable trends in terms of odds ratios, these effects were not strong enough to conclude statistically meaningful associations after controlling for other factors. Regarding marital status, unmarried women had 1.18 times higher odds of engaging in risky sexual behaviour compared to married women, while widowed women had more than twice the odds (OR\u0026thinsp;=\u0026thinsp;2.21). Separated women also showed elevated odds (OR\u0026thinsp;=\u0026thinsp;1.51). Conversely, divorced women had slightly lower odds (OR\u0026thinsp;=\u0026thinsp;0.83). However, all confidence intervals were wide and crossed 1.0, indicating that marital status did not significantly predict risky sexual behaviour in the adjusted model.\u003c/p\u003e \u003cp\u003eAge patterns suggested that the likelihood of risky sexual behaviour increased with age, although the results were not statistically significant. Compared to women below 25 years, those aged 35\u0026ndash;44 had more than twice the odds (OR\u0026thinsp;=\u0026thinsp;2.18), and those aged 45\u0026ndash;54 had similar elevated odds (OR\u0026thinsp;=\u0026thinsp;2.42). Women above 55 years showed the highest odds of risky behaviour (OR\u0026thinsp;=\u0026thinsp;6.14), although the confidence interval was extremely wide (0.43\u0026ndash;88.53), suggesting substantial uncertainty and lack of statistical significance. These results implied a general trend of increased risk with age, but the model did not confirm age as a significant determinant. Educational level showed consistent protective effects, as women with primary, secondary, and tertiary education all exhibited lower odds of engaging in risky sexual behaviour compared to women with no formal education. For instance, secondary education was associated with a substantial reduction in odds (OR\u0026thinsp;=\u0026thinsp;0.21), and tertiary education showed an even stronger protective trend (OR\u0026thinsp;=\u0026thinsp;0.20). However, none of the confidence intervals excluded 1.0, indicating that education did not have a statistically significant effect in the adjusted model despite the clear directional trend. This finding contrasted with the bivariate results, where education emerged as a strong significant predictor.\u003c/p\u003e \u003cp\u003eFor literacy variables, women who were unable to write had higher odds of risky sexual behaviour (OR\u0026thinsp;=\u0026thinsp;2.27), while those who could not read had considerably lower odds (OR\u0026thinsp;=\u0026thinsp;0.17). Both relationships, however, were statistically insignificant, and the very wide confidence intervals suggested instability in the model estimates for these categories. Religion also did not demonstrate significant associations. Compared with Catholic women, the odds were higher among Jehovah\u0026rsquo;s Witnesses (OR\u0026thinsp;=\u0026thinsp;1.77), Protestants (OR\u0026thinsp;=\u0026thinsp;2.01), Masonic respondents (OR\u0026thinsp;=\u0026thinsp;2.53), and Muslims (OR\u0026thinsp;=\u0026thinsp;1.35). Meanwhile, Pentecostal women (OR\u0026thinsp;=\u0026thinsp;0.94) and Seventh-Day Adventists (OR\u0026thinsp;=\u0026thinsp;0.69) had slightly lower odds. None of these effects reached statistical significance, indicating that religious affiliation did not independently determine risky sexual behaviour when adjusting for other factors.\u003c/p\u003e \u003cp\u003eResidence-based differences also lacked statistical significance, although notable trends emerged. Women living in Kanyama had more than twice the odds of risky sexual behaviour (OR\u0026thinsp;=\u0026thinsp;2.31) compared to those in Chawama, while residents of Lusaka Central had very high odds (OR\u0026thinsp;=\u0026thinsp;6.29) but also extremely wide confidence intervals, reflecting small sample sizes. Women residing in Mandevu (OR\u0026thinsp;=\u0026thinsp;0.34) and Kabwata (OR\u0026thinsp;=\u0026thinsp;0.47) had lower odds, suggesting potentially protective contextual factors in these areas. However, the lack of significance meant these patterns could not be considered reliable predictors. Overall, the logistic regression results suggested that although several demographic variables showed directional effects consistent with theoretical expectations and bivariate findings, none emerged as statistically significant determinants of risky sexual behaviour after adjusting for confounders. This may have been influenced by sample size limitations within certain categories, wide confidence intervals, and clustering effects within the population. The findings highlighted the complexity of predicting risky sexual behaviour and indicated that unmeasured behavioural, structural, or contextual factors may play a more substantial role than background characteristics alone.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDeterminants of risky sexual behaviour among women cross-border traders (continues)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBackground Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;499)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Rat.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(95% Conf. Interval)\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\u003eAverage time out\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\u003eNot gone out\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\u003e1\u0026ndash;5 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.29\u0026ndash;8.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026thinsp;+\u0026thinsp;time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.20\u0026ndash;7.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEconomic 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\u003eGood\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\u003eBad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.67\u0026ndash;3.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSexual partners (current)\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\u003e1 partner\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\u003e2\u0026thinsp;+\u0026thinsp;partners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.57**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(3.06\u0026ndash;69.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePaid sex\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\u003eYes\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.36\u0026ndash; 4.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrequency of condom use\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\u003eEvery time\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\u003eAlmost every time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.02\u0026ndash;2.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.47**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(2.68\u0026ndash;171.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.82**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(5.65\u0026ndash;405.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeard about STIs\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\u003eYes\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.01\u0026ndash;4.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption (ever taken)\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\u003eYes\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.92\u0026ndash;4.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrug consumption (ever taken)\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\u003eYes\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.19\u0026ndash;38.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Ref: Reference\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presented the continuation of the logistic regression results, focusing on migratory, sexual, and lifestyle characteristics as determinants of risky sexual behaviour among women informal cross-border traders. The results showed that frequency of travel, economic status, STI knowledge, alcohol use, and drug use were not statistically significant predictors of sexual risk in the adjusted model. However, the number of sexual partners and frequency of condom use emerged as strong and highly significant determinants of risky sexual behaviour. With respect to mobility, women who travelled between one and five times had higher odds of engaging in risky sexual behaviour (OR\u0026thinsp;=\u0026thinsp;1.59) compared to those who had not travelled, while those who travelled six or more times had slightly elevated odds (OR\u0026thinsp;=\u0026thinsp;1.25). However, both associations were statistically insignificant, suggesting that mobility alone did not independently predict risky sexual behaviour once other factors were controlled for. Economic status also appeared unrelated to risk behaviour; women who perceived their economic situation as \u0026ldquo;bad\u0026rdquo; had only slightly higher odds (OR\u0026thinsp;=\u0026thinsp;1.51) than those who reported a \u0026ldquo;good\u0026rdquo; status, and the confidence interval included 1.0, indicating no significant effect.\u003c/p\u003e \u003cp\u003eSexual partnership patterns emerged as one of the strongest predictors. Women who reported having two or more sexual partners had substantially higher odds of engaging in risky sexual behaviour (OR\u0026thinsp;=\u0026thinsp;14.57, 95% CI: 3.06\u0026ndash;69.34), and this association was highly significant at the 1% level. This finding confirmed that multiple concurrent partnerships were a major driver of sexual risk among the traders. Paid sex did not significantly predict risky sexual behaviour in the adjusted model. Although women who did not engage in paid sex had slightly higher odds (OR\u0026thinsp;=\u0026thinsp;1.26) relative to those who did, the effect was statistically insignificant, indicating that transactional sex was not an independent determinant once other variables were included.\u003c/p\u003e \u003cp\u003eFrequency of condom use was the most powerful determinant in the model. Compared to women who used condoms every time, those who used them only sometimes had dramatically higher odds of risky behaviour (OR\u0026thinsp;=\u0026thinsp;21.47, 95% CI: 2.68\u0026ndash;171.74), and those who never used condoms had even higher odds (OR\u0026thinsp;=\u0026thinsp;47.82, 95% CI: 5.65\u0026ndash;405.03). Both effects were highly significant at the 1% level. Even women who used condoms almost every time had lower odds (OR\u0026thinsp;=\u0026thinsp;0.24), although this association was not statistically significant. These results clearly demonstrated that condom inconsistency was a critical predictor of risky sexual behaviour and underscored the protective role of consistent condom use.\u003c/p\u003e \u003cp\u003eKnowledge about STIs, alcohol consumption, and drug use showed no statistically significant effects. Women who had not heard about STIs had lower odds (OR\u0026thinsp;=\u0026thinsp;0.24), but the wide confidence interval indicated uncertainty in the estimate. Women who abstained from alcohol had higher odds of risky behaviour (OR\u0026thinsp;=\u0026thinsp;2.08), though this effect was not significant. Drug use also failed to show a significant relationship, with women who had never used drugs reporting higher odds (OR\u0026thinsp;=\u0026thinsp;2.71), but again with extremely wide confidence intervals. Overall, the findings from Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e indicated that multiple sexual partnerships and inconsistent or non-use of condoms were the most significant determinants of risky sexual behaviour among women informal cross-border traders. These behavioural factors far outweighed demographic and socioeconomic predictors in the adjusted model, highlighting the central role of sexual practices rather than structural characteristics in shaping HIV vulnerability within this population.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study identified several key behavioural determinants of risky sexual behaviour among women informal cross-border traders in Lusaka, with multiple sexual partners and inconsistent condom use emerging as the strongest predictors. Women who reported having two or more sexual partners had substantially elevated odds of engaging in risky sexual behaviour (OR\u0026thinsp;\u0026asymp;\u0026thinsp;14.6). This finding is consistent with broader epidemiological evidence that multiple and concurrent partnerships are major drivers of HIV risk [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In the Zambian context, multiple sexual partnerships have been identified as a key social driver of HIV infection, particularly among economically vulnerable women [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The high prevalence of multiple partners among traders may reflect transactional dynamics, mobility-related opportunities, and economic necessity, which align with patterns documented in sub-Saharan African populations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrequency of condom use was another critical determinant. Compared to women who used condoms at every sexual encounter, those who reported \u0026ldquo;sometimes\u0026rdquo; or \u0026ldquo;never\u0026rdquo; using condoms had dramatically higher odds of risky sexual behaviour (ORs\u0026thinsp;\u0026asymp;\u0026thinsp;21.5 and \u0026asymp;\u0026thinsp;47.8, respectively). These results corroborate previous findings in Zambia, where inconsistent condom use among women, particularly in transactional or mobile contexts, has been shown to increase vulnerability to HIV and STIs [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Socio-cultural barriers, including trust, relationship dynamics, and gender power imbalances, may limit condom negotiation, especially in marital or transactional partnerships [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These factors highlight the need for tailored interventions that promote consistent condom use and empower women to negotiate safer sex, even within economically or socially dependent relationships.\u003c/p\u003e \u003cp\u003eAlthough mobility was initially associated with higher risky sexual behaviour in bivariate analyses, it was not a statistically significant predictor in the adjusted model. This suggests that mobility alone may not independently increase sexual risk once behavioural factors such as number of partners and condom use are considered. Previous studies on migration and HIV risk have similarly indicated that the relationship between mobility and sexual risk is often mediated by behavioural choices rather than mobility itself [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, interventions targeting mobile populations, such as cross-border traders, should address behavioural drivers alongside structural vulnerabilities. Contrary to expectations, self-perceived economic status did not significantly predict risky sexual behaviour in this study. This may reflect a relative homogeneity in economic conditions among cross-border traders, whereby differences in \u0026ldquo;good\u0026rdquo; versus \u0026ldquo;bad\u0026rdquo; status were insufficient to influence sexual risk once behaviour was accounted for. Additionally, transactional sexual practices among women in trading contexts may be influenced by broader structural and relational factors rather than immediate economic standing [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, women who reported not having heard of STIs exhibited lower (though non-significant) odds of risky sexual behaviour. This counterintuitive finding may be due to selection or reporting bias; women who are more sexually active may have greater exposure to sexual health information, creating an association between STI knowledge and higher observed risk. Similar observations have been reported in Zambia, where knowledge alone did not always translate into safer sexual practices due to socio-cultural and structural barriers [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Substance use, including alcohol and drug consumption, did not emerge as significant predictors of risky sexual behaviour after adjusting for other variables. Although substance use is often linked to risky sexual practices, in this population, sexual behaviour particularly number of partners and condom use appeared to be more proximal determinants of risk. This aligns with studies in Zambia showing that in certain high-mobility populations, sexual behaviour may outweigh substance use in predicting HIV and STI vulnerability [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDemographic characteristics such as education, age, marital status, religion, and residence were not statistically significant in the multivariate model, although they showed important trends. Higher education was associated with lower odds of risky sexual behaviour, suggesting that education may exert a protective effect indirectly through improved sexual health knowledge and decision-making [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Older women showed higher odds of risk, which may reflect cumulative exposure or different types of partnerships, but these associations were not statistically significant. Marital status, religion, and residence demonstrated variation in risk but lost significance when behavioural variables were included, indicating that sexual practices may be more critical than demographic characteristics in determining risk. The dominance of behavioural factors in predicting risky sexual behaviour has important implications for intervention. Efforts to reduce HIV and STI vulnerability among women informal cross-border traders should prioritise condom promotion and negotiation skills, behavioural change programmes targeting sexual practices, and sexual health education tailored to mobile women, potentially delivered in markets or cross-border points. Although economic status was not a significant predictor, addressing structural vulnerabilities, such as limited access to credit or social safety nets, may further support risk reduction strategies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSome limitations of the study should be noted. Wide confidence intervals for several variables suggest limited statistical power, particularly for smaller subgroups. Self-reported sexual behaviours may be influenced by social desirability bias. Additionally, unmeasured factors, such as partner power dynamics, gender-based violence, or access to health services, could influence sexual risk. The cross-sectional design limits causal inference, and future research should consider longitudinal or mixed-methods approaches to better understand the mechanisms linking mobility, economic vulnerability, and sexual behaviour. In sum, the study found that multiple sexual partnerships and inconsistent condom use were the strongest predictors of risky sexual behaviour among women informal cross-border traders in Lusaka. Demographic and structural factors appeared less influential once behavioural variables were accounted for. These findings highlight the need for interventions that focus on behavioural risk reduction, empower women to negotiate condom use, and address structural vulnerabilities, thereby reducing HIV and STI exposure among this high-risk population.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study investigated the determinants of risky sexual behaviour among women informal cross-border traders in Lusaka, Zambia, using a binary logistic regression approach. The findings demonstrated that behavioural factors, particularly having multiple sexual partners and inconsistent condom use, were the strongest predictors of high-risk sexual behaviour. Women who reported two or more sexual partners were substantially more likely to engage in risky sexual practices, while inconsistent or non-use of condoms dramatically increased the likelihood of risk. These results align with previous research highlighting that behavioural practices, rather than demographic or structural factors alone, play a pivotal role in HIV and STI vulnerability [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although demographic variables such as age, education, marital status, religion, and residence showed some trends, they did not remain statistically significant in the adjusted models. This suggests that while socio-demographic characteristics may shape women\u0026rsquo;s sexual behaviours indirectly, proximal behavioural choices are the most critical determinants of risk. Mobility, economic status, alcohol and drug consumption, and STI knowledge were also not significant predictors in the multivariate analysis, indicating that interventions targeting sexual behaviour itself may yield the most immediate impact.\u003c/p\u003e \u003cp\u003eThe study underscores the urgent need for targeted interventions for women informal cross-border traders, including programmes that promote consistent condom use, sexual health education, negotiation skills, and economic empowerment strategies. Addressing these behavioural determinants can significantly reduce HIV and STI risk among this vulnerable population. Moreover, structural interventions that consider mobility, occupational hazards, and gender-based vulnerabilities remain important to complement behavioural strategies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In conclusion, women informal cross-border traders in Lusaka face high levels of risky sexual behaviour, primarily driven by multiple sexual partnerships and inconsistent condom use. Tailored interventions addressing these behaviours, supported by broader structural and policy measures, are essential to safeguard the sexual and reproductive health of this high-risk population and to contribute to Zambia\u0026rsquo;s broader HIV prevention goals.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStudy Limitations\u003c/h2\u003e \u003cp\u003eThe study had several limitations. Its cross-sectional design prevented establishing causal relationships between demographic factors and risky sexual behaviour, providing only associations. Data were self-reported, making them susceptible to recall and social desirability biases, which may have led to underreporting of risky behaviours such as multiple sexual partners or inconsistent condom use. The study focused solely on women traders at the COMESA Market in Lusaka, limiting the generalisability of findings to other markets, border towns, or rural areas. Additionally, while key demographic variables were examined, other factors such as partner characteristics, negotiation power, psychosocial influences, and access to sexual health services were not included, leaving potential residual confounding. Despite these constraints, the study provides important evidence on demographic determinants of risky sexual behaviour among women informal cross-border traders, offering a foundation for targeted HIV prevention interventions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCBTA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;: Cross-Border Traders Association\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;: Confidence Interval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCOMESA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;: Common Market for Eastern and Southern Africa\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHIV\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;: \u0026nbsp;Human Immunodeficiency Virus\u003c/p\u003e\n\u003cp\u003eOR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;: Odds Ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSDA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;: Seventh Day Adventist\u003c/p\u003e\n\u003cp\u003eSTIs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; : \u0026nbsp;Sexually Transmitted Infections\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the University of Zambia\u0026rsquo;s School of Humanities and Social Sciences Research Ethics Committee (HSSREC: 2018-June-005, No. 153760602331). All respondents received an information sheet explaining the purpose, procedures, and voluntary nature of the study. Written informed consent was obtained from each participant prior to data collection. Questionnaires were administered in a private and secure location to ensure confidentiality. Participation was restricted to women aged 18 years and above who were members of the CBTA and who owned or rented a shop or container at the COMESA Market.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are available upon request from the authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe conceptualisation and data curation for the project were carried out by Simson Mwale, Musonda Lemba, and Million Phiri, who also contributed to the formal analysis. The methodology was developed by Simson Mwale and Musonda Lemba, while the original draft was written by Simson Mwale. Finally, all authors reviewed and edited the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their gratitude to the managers of COMESA market and CBTA members who agreed to participate in this study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUnited Nations Development Programme Zambia. UNDP Zambia 2023 Annual Report. UNDP Zambia; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalia JG, et al. High Mobility and STIs/HIV among Women Informal Cross Border Traders in Southern Mozambique: Exploring Knowledge, Risk Perception, and Sexual Behaviours. Int J Environ Res Public Health. 2020;17:4724.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmnesty International. Women traders at risk: Gender-based vulnerabilities in Southern Africa; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Bank \u0026amp; World Trade Organization. Report on Trade and Gender: Informal Cross-Border Trade in Africa. World Bank / WTO; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalama K, Phiri T, Chola M. Sexual behaviour and HIV risk among female cross-border traders in Zambia. BMC Public Health. 2021;21(1):1004. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-021-11004-7\u003c/span\u003e\u003cspan address=\"10.1186/s12889-021-11004-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMushota C, et al. HIV risks among women traders in Zambia\u0026rsquo;s cross-border regions. Zambia J HIV/AIDS. 2020;24(3):120\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBwalya J, et al. Social isolation and HIV vulnerability among informal cross-border traders in Zambia. Afr J AIDS Res. 2018;17(2):113\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLungu E, Kasonde M. Gender-based violence and HIV among Zambian informal cross-border traders. Zambian J Public Health. 2019;20(3):155\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMusheke M, Mulenga M, Kabemba D. The role of education in shaping sexual behaviors and HIV risk in Zambia. Zambian J Soc Sci. 2017;18(3):34\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMwaba P. Informal cross-border trade as a livelihood strategy for women in Southern Africa. Dev Stud Q. 2021;37(4):72\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMwanza M, et al. The intersection of age, gender, and HIV vulnerability in Zambia. Zambian J Public Health. 2020;25(2):22\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations Conference on Trade and Development. Women in Informal Cross Border Trade in Zambia: A Small-Scale Trader\u0026rsquo;s Guide to Trade Rules and Procedures. UNCTAD; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuv\u0026eacute; A, Bishikwabo-Nsarhaza K, Mutangadura G. The spread and effect of HIV-1 infection in sub-Saharan Africa. Lancet. 2014;359:2011\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKakoma. Interview with the then President of CBTA, Interviewed by Simson Mwale, 6 July 2015, at 11:00, Lusaka.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLungu J. June. Interview with the then Secretary General of Cross-Border Traders Association of Zambia (CBTA), Interviewed by Simson Mwale, 26 2010, at 10:30, Lusaka.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJawando JO, Adeyemi EO. Sexual Exchange and Cross-Border Trade: Implications for HIV/AIDS in Nigeria. SAGE Open. 2020;1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhiri M, Lemba M, Chomba C, Kanyamuna V. Examining differentials in HIV transmission risk behaviour and its associated factors among men in Southern African countries. Humanit Soc Sci Commun. 2022;9(1):295.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations. Global HIV/AIDS trends and drivers: 2022 report. New York, NY: United Nations; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUN Zambia. Common country analysis: HIV and gender dynamics. Lusaka: United Nations Zambia; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuttmacher Institute. Sexual and reproductive health in Zambia: Current trends and challenges. 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimbeye L, Chirwa T, Mwale M. Knowledge and behaviour regarding sexually transmitted infections among women in Lusaka, Zambia. J Health Popul Nutr. 2024;43(1):12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s41043-024-00212-5\u003c/span\u003e\u003cspan address=\"10.1186/s41043-024-00212-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":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":"Women cross-border traders, risky sexual behaviour, HIV vulnerability, Zambia, binary logistic regression","lastPublishedDoi":"10.21203/rs.3.rs-8258951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8258951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWomen informal cross-border traders in Lusaka, Zambia, operate in highly mobile and economically vulnerable contexts that may heighten their exposure to risky sexual behaviour and HIV. This study examined the socio-demographic, migratory, and behavioural factors associated with risky sexual behaviour among women informal cross-border traders, focusing on identifying key predictors using a binary logistic regression approach.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional survey was conducted in 2019 among 499 women engaged in informal cross-border trade at the Common Market for Eastern and Southern Africa (COMESA) Market in Lusaka. Data were collected via a structured questionnaire capturing socio-demographic characteristics, sexual behaviour, mobility patterns, and social lifestyle factors. Binary logistic regression was used to identify determinants of risky sexual behaviour, with odds ratios (ORs) and 95% confidence intervals (CIs) reported.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe majority of respondents (67.7%, n\u0026thinsp;=\u0026thinsp;338) were classified as high-risk and 32.3% (n\u0026thinsp;=\u0026thinsp;161) as low-risk. Behavioural factors were the strongest predictors of risky sexual behaviour. Women with two or more sexual partners were 14 times more likely to engage in risky sexual behaviour (OR\u0026thinsp;=\u0026thinsp;14.57, 95% CI: 3.06\u0026ndash;69.34, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Inconsistent condom use also significantly increased risk: \u0026ldquo;sometimes\u0026rdquo; use (OR\u0026thinsp;=\u0026thinsp;21.47, 95% CI: 2.68-171.74, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and \u0026ldquo;never\u0026rdquo; use (OR\u0026thinsp;=\u0026thinsp;47.82, 95% CI: 5.65-405.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) compared to consistent use. Educational level and literacy were protective; women with secondary (OR\u0026thinsp;=\u0026thinsp;0.21, 95% CI: 0.02\u0026ndash;2.73) and tertiary education (OR\u0026thinsp;=\u0026thinsp;0.20, 95% CI: 0.01\u0026ndash;4.49) had lower odds of risky sexual behaviour. Women residing in densely populated settlements such as Kanyama, Mandevu, and Matero demonstrated higher sexual risk. Age, marital status, and religious affiliation showed trends but were not statistically significant in the adjusted model.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eRisky sexual behaviour among women informal cross-border traders in Lusaka was largely driven by behavioural factors, particularly multiple sexual partnerships and inconsistent condom use, while education and literacy conferred protective effects. Interventions should prioritise sexual health education, condom negotiation skills, literacy programmes, and support for women in high-density settlements to reduce HIV vulnerability.\u003c/p\u003e","manuscriptTitle":"Factors Associated with Risky Sexual Behaviour among Women Informal Cross-border Traders in Lusaka, Zambia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 11:40:17","doi":"10.21203/rs.3.rs-8258951/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-13T11:57:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-12T10:14:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42391532772665845808634997231901791213","date":"2026-01-14T12:10:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-19T12:11:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81215451941639481375270717877445480820","date":"2025-12-12T11:24:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-11T09:46:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-03T05:34:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-03T00:52:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-03T00:51:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-12-02T09:43:52+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":"5b43ad65-e279-48d9-ada4-53298fa9ae43","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T06:54:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-16 11:40:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8258951","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8258951","identity":"rs-8258951","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}