Economic Consequences of HIV Status and Viral Load Level Among Households: Evidence from Twelve Sub-Saharan African Countries

Economic Consequences of HIV Status and Viral Load Level Among Households: Evidence from Twelve Sub-Saharan African Countries | 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 Economic Consequences of HIV Status and Viral Load Level Among Households: Evidence from Twelve Sub-Saharan African Countries Boniface Simpoli Yohana, John Massito, Arbogust Moshi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7283006/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: HIV/AIDS is the leading cause of death in sub-Saharan Africa. Families facing numerous challenges, often exacerbated by the pandemic, frequently turn to their communities and external economic support for assistance. Methods: The PHIA project data (2015-2022) from 12 sub-Saharan countries focused on both adolescents and adults aged > 15 years and above. This study examined income after work, with HIV status, viral load, and sociodemographic factors as predictors. Descriptive statistics and econometric modelling were used to analyze the determinants of working and receiving income. Results: Our analysis revealed a strong connection between health status and labor force engagement among individuals in Sub-Saharan Africa. Suppressed viral load in HIV-positive individuals significantly increased the odds of working and earning income by 2.1 times, underscoring the economic value of ART adherence. Behavioral factors such as alcohol use also played a role: those consuming alcohol frequently had 1.27 times higher odds of economic participation. Conversely, poor health outcomes and larger household sizes diminished employment prospects. Age, gender, and marital status influenced economic engagement, with older adults, females, and married individuals showing reduced odds. Higher education and wealth status substantially boosted labor participation. Rural residents were more economically active than urban dwellers. These findings emphasize the critical intersection between health and socioeconomic resilience Conclusions: Our study demonstrates that health status, particularly viral suppression among HIV-positive individuals, plays a pivotal role in enhancing income generation. Behavioral and demographic factors also shaped employment outcomes, with younger, educated, wealthier individuals showing higher odds. Poor health, female gender, older age, and urban residence reduced income opportunities. These findings highlight the vital link between clinical well-being and socioeconomic empowerment. Working and receiving income Viral Load Suppression Socioeconomic Determinants and sub-Saharan Africa Multiple logistic analysis Figures Figure 1 1.0 Introduction A healthy population is one of the fundamental factors for the economic growth and development of any nation(1,2). Healthy individuals are an important form of human capital that can enhance workers’ productivity by raising physical capacities, such as strength and endurance, as well as mental capacities, such as cognitive functioning and reasoning ability(3,4). In addition, global health is considered a fundamental human right and a worldwide social goal; a physically and mentally healthier society is more energetic and stronger, which creates activity in production activities (5). According to the theory of production, output depends on capital, land, and labour; however, labour productivity depends on labour skills and health(6). Several studies that have attempted to clarify cross differences in household welfare and productivity rate typically conclude that healthy household size and composition, education status with good health, type of economic activities, traditional and cultural practices, access to credit, and technological utilization are among the key explanatory variables (7,8). Healthy workers lose less time from work due to illness and are more productive(9–11). (12)The study on human capital, described health as a capital-productive asset and an economic growth and development engine.(12). Fifty percent of the economic growth and development differentials between developed and developing nations are attributable to ill health and low life expectancy (5). In general, poor health status affects household social and economic development in several ways(13). First, it increases treatment and other costs due to regular checkups and treatment costs (14). Second, the impact on food consumption in poorly healthy households due to treatment costs had sold assets and switched from more expensive to cheaper non-nutrient commodities(15,16). Finally, poor health reduces individual time, energy, and work motivation, thereby reducing productivity (17,18). The HIV/AIDS epidemic is a serious problem among low-income communities in most developing countries, including India(19). This is evidenced by the fact that, in 2020, 36 million adults over the age of 15 were living with HIV (20). In Sub-Saharan African countries (SSA), with an estimated 28.5 million HIV + people, Kenya has become the nation with the second-largest number of people living with HIV/AIDS, approximately 1.5 million, after South Africa, approximately 5.0 million (21,22). Estimates reveal that roughly one out of ten HIV-positive persons in the world are from Sub-Saharan Africa, and the first HIV + case was reported in Kinshasa in the 1970s (23). However, the impact of these diseases varies from one place to another and is mostly classified politically, socially, and economically(24). In terms of socio-economic impact, these include the loss of jobs, income, and death of family members, such as parents, reduced education and knowledge of the new generation, reduced interest in being well educated, and less investment in health (25,26). From a national perspective, the pandemic has significant implications for government spending (27). The health and social care services domains may be severely affected by the inadequate use of the health workforce in HIV-related problems (15). Within these 40 years of disease experience, there have been several research on the impact of Acquired Immune Deficiency Syndrome (AIDS) and Human Immunodeficiency Virus (HIV) on household means of livelihood(28–31). Despite the findings of these studies, HIV/AIDS has continued to negatively impact household welfare, with tragic consequences for the income level and living standards of affected households (32–34). 1.1 Conceptual Framework of Economic Consequences of HIV Status and Viral Load The conceptual framework of the economic consequences of HIV status and viral load explores the bidirectional relationship between individual health outcomes and socioeconomic conditions. HIV-positive individuals, particularly those with unsuppressed viral loads, often face heightened barriers to employment, reduced productivity, and increased healthcare expenditures, which collectively exacerbate financial vulnerability. Conversely, economic instability such as poverty, limited access to healthcare, or inadequate social protection can hinder treatment adherence and viral suppression, perpetuating poor health outcomes. Conversely, economic instability, such as poverty, limited access to healthcare, or inadequate social protection can hinder treatment adherence and viral suppression, perpetuating poor health outcomes. Diagram 1 illustrates how health-related factors, human behavior, non-monetary elements, and socio-demographic characteristics influence an individual's likelihood of participating in the labor force. 2.0 Materials and Methods 2.1 Study Area The study area is 12 Sub-Saharan African countries where the Population HIV Impact Assessment Survey (PHIA) was conducted. These 12 countries are heterogeneous states with multi-ethnic groups. 2.2 Data Types This study employs quantitative cross-sectional data from the Population HIV Impact Assessment Survey (PHIA) for twelve (12) available sub-Saharan African countries. The PHIA are standardized national household survey conducted in low- and middle-income countries that ask questions about demographic and socioeconomic behavior and outcomes. 2.3 Study population and sample A total of twelve countries were included in the analysis, covering a range of years, geographic sizes, and population demographics. Table 1 presents the study population characteristics and sample sizes across the selected countries. The number of HIV-positive individuals, average household size, and total household count vary considerably, reflecting contextual heterogeneity across the regions. Table 1 The Study Population and Sample Size Country Year Area Size Total Population HIV-Positive Number Households Household Size Cameroon 2017/2018 475,650 25,076,747 490.00 13,500 3.0 Cote d’ivoire 2017/2018 322,462 25,493,988 442.00 11,654 2.1 Eswatini 2016/2017 17,363 1,093,238 287.00 5,740 26.8 Ethiopia 2017/2018 1,112,000 111,129,438 395.00 11,810 1.1 Lesotho 2016/2017 30,355 2,170,617 418.00 10,450 25.6 Malawi 2015/2016 118,480 17,405,624 500.00 13,234 10.6 Namibia 2017 824,292 2,364,534 465.00 11,625 8.3 Rwanda 2018/2019 26,338 12,835,028 375.00 11,250 2.95 Tanzania 2022/2023 945,087 63,876,544 526.00 15,780 4.6 Uganda 2016/2017 241,038 40,127,085 523.00 13,075 5.8 Zambia 2016 752,614 16,767,761 515.00 12,352 11.0 Zimbabwe 2015/2016 399,757 14,452,704 500.00 14,085 12.9 2.4 Method of Data Analysis The Binary Logistic Regression Technique (BLRT) was employed to predict the odds/livelihoods of working and receiving income among both affected and unaffected households in SSA. The BLRT is a type of regression that is applied when the predicted variable is a dichotomy, while the predictor variables are of any form(35). The dependent variable usually takes the value of one (1) with a probability of success ψ or otherwise zero (0) with a probability of failure of 1 – ψ. 2.5 Diagnostic Test The diagnostic statistical test assesses the study variables before analysis, ensuring model accuracy and reliable findings for decision-making. The answer depends on the data type. For cross-sectional binary variables in logistic regression, the assumptions include binary dependent variables, independent observations, minimal multicollinearity, linearity with log odds, large sample size, and heteroscedasticity tests. Table 2 shows the binary, multicollinearity, linearity, sample size, and heteroscedasticity tests. Table 2 Diagnostic statistical test Variable Types Binary Test Multicollinearity Test Linearity Test Sample Size Test Heteroskedasticity Test HIV status B N/A N/A 100,593 Viral load level B N/A N/A 100,593 Age group C 0.79163 0.997 23,687 chi2(1) = 102.4 Prob > chi2 = 0.00 Gender B 0.73977 N/A 85,502 Marital Status B 0.68707 N/A 83,001 Education Level C 0.77757 0.122 19,478 Individual Wealth C 0.64677 0.487 29,691 Number of Partners C 0.45179 N/A 10,160 Condom Use B 0.43960 N/A 100,731 Payment for Sex B 0.9703 N/A 83,001 Alcohol Status C 0.86479 0.066 17,640 Household Size C 0.95922 N/A 11,459 Residential B 0.78657 N/A 100,731 Food shortage B 0.83259 N/A 100,731 NB: B-binary variable, C-categorical variable, N/A if the test does not qualify for variable type, statistical significance defined as p-value < 0.05. The results in Table 2 above show that the viral load level variable is binary with two responses (1- suppressed, 0-not suppressed), which satisfies the assumption of a binary dependent variable. The independent test results showed that each originated from a unique respondent without any repetition. For the Multicollinearity test, a 1/VIF value for all variables is greater than the 0.25 cut-off point value, implying that there is no relation among the independent variables. The linearity test using the Box-Tidwell test results shows that all four ordinary categorical independent variables have linear relationships with the odds of the dependent variables as the p-value is greater than 5% (0.05). The linearity test using the Box-Tidwell test results shows that all four ordinary categorical independent variables have linear relationships with the odds of the dependent variables, as the p-value is greater than 5% (0.05). For the sample size, all variables were found to have a large sample size, with a minimum observed number of partners of approximately 10,160. Finally, the heteroscedasticity test result of Prob > chi2 = 0.000 is less than 0.05, we reject the null hypothesis of heteroscedasticity. Based on these diagnostic test results (presence of non-constant variance), the use of vce(robust) in the logistic model makes the odds result more precise and reliable. 3.0 Results 3.1 Descriptive Findings Table 3 The Working Status by Country for all Individuals and HIV Positive (N = 347,611) Country Not Working Working and Receiving Income Total All Individual HIV Positive All Individual HIV Positive All Individual HIV Positive Cameroon 3,317 120 10,671 474 13,988 594 (23.71%) (20.2%) (76.29%) (79.8%) (100%) (100%) Cote d’ivoire 10,326 240 8,601 203 18,927 443 (54.56%) (54.18%) (45.44%) (45.82%) (100%) (100%) Eswatini 7,318 1,657 4,568 1,409 11,886 3,066 (61.57%) (54.04%) (38.43%) (45.96%) (100%) (100%) Ethiopia 11,492 316 8,678 298 20,170 614 (56.98%) (51.47%) (43.02%) (48.53%) (100%) (100%) Lesotho 8,304 1,913 4,791 1,347 13,095 3,260 (63.4%) (58.68%) (36.59%) (41.32%) (100%) (100%) Malawi 13,655 1,443 6,212 834 19,867 2,277 (68.73%) (63.37%) (31.27%) (36.63%) (100%) (100%) Namibia 10,961 1,564 7,834 882 18,795 2,446 (58.32%) (63.94%) (41.68%) (36.06%) (100%) (100%) Rwanda 18,362 523 12,353 411 30,715 934 (59.78%) (56%) (40.22%) (44%) (100%) (100%) Tanzania 19,954 1,035 14,153 906 34,107 1,941 (58.5%) (53.32%) (41.5%) (46.68%) (100%) (100%) Uganda 14,591 741 15,374 1,069 29,965 1,810 (48.69%) (40.94%) (51.31%) (59.06%) (100) (100%) Zambia 14,697 1,561 7,107 967 21,804 2,528 (67.41%) (61.75%) (32.59%) (38.25%) (100) (100%) Zimbabwe 16,415 2,277 8,651 1,313 25,066 3,590 (65.49%) (63.43%) (34.5%) (36.57%) (100%) (100%) Total 149,392 13,390 108,993 10,113 258,385 23,503 (57.82%) (56.97%) (42.18%) (43.03%) (100%) (100%) Table 3 shows the working status by country, all individuals, and HIV-positive individuals. Cameroon had a high percentage of approximately 76.29% and 79.8% of all individuals and HIV-positive individuals, respectively, who received income after working. This indicates that HIV treatment is high in Cameroon, making labour productivity unaffected. Uganda is the second country, and more than half of its population is working frequently (approximately 51.31%), and 59.06% of all adult individuals and HIV-positive individuals, respectively, receive income after working. This also indicates that HIV cases do not affect labour productivity because of viral load suppression due to antiretroviral therapy (ART) treatment. Malawi is the country found to have a very low percentage of about 31.27% and 36.63% of all adult individuals and HIV-positive individuals, respectively, who receive income after working. However, Namibia found a lower percentage of HIV-positive adults who receive income after working (approximately 36.06%) compared to the adult population (approximately 41.68%) who receive income after working. This indicates that HIV affects labour productivity in Namibia, as only a few HIV-positive individuals were connected to antiretroviral therapy (ART). 3.2 Multiple Regression Analysis Table 4 Multiple Regression Analysis of Factors for Working and Receiving Income after Working (N = 347,611) Working Individual Income OR Robust Std. Err. z P > z [95% CI] Socio-Demographic Factors Age group 15–25 Ref 26–35 1.17 0.0639829 2.95 0.003 (1.06–1.31) 36–45 1.14 0.0658971 2.19 0.029 (1.01–1.27) 46–55 0.90 0.0610543 -1.53 0.126 (0.79–1.03) 56–65 0.31 0.0228344 -15.91 0.000 (0.27–0.36) Gender Male Ref Female 0.28 0.0126065 -28.38 0.000 (0.26–0.31) Marital Status Not Married Ref Married 0.64 0.0349526 -8.12 0.000 (0.58–0.72) Education Level Primary level Ref Secondary level 1.03 0.0423328 0.74 0.458 (0.95–1.12) Tertiary level 0.87 0.1640756 -0.74 0.462 (0.60–1.26) University level 2.82 0.2487955 11.72 0.000 (2.37–3.35) Individual Wealth Lower Ref Second 1.04 0.0582079 0.77 0.440 (0.94–1.16) Middle 1.37 0.0759247 5.64 0.000 (1.23–1.53) Fourth 2.66 0.1893272 13.79 0.000 (2.32–3.06) Highest 2.99 0.2495208 13.15 0.000 (2.54–3.52) Health Related Factors HIV status Negative (-) Ref Positive (+) 0.96 0.045285 -0.94 0.349 (0.87–1.05) HIV viral load unsuppressed Ref suppressed 2.14 0.0658971 2.19 0.029 (1.01–1.27) Human Behavior Factors Number_Partner Abstain Ref One partner 0.96 0.0927089 -0.43 0.664 (0.79–1.16) Multpartners 1.31 0.1086903 3.31 0.001 (1.12–1.54) Payment Yes Ref No 1.34 0.1153621 3.38 0.001 (1.13–1.58) Alcohol Status Never Ref Monthly or less 1.13 0.0757076 1.89 0.059 (1.00-1.29) 2-4Times A Month 1.15 0.1007497 1.59 0.112 (0.97–1.36) 2–3 Times A Week 1.27 0.1157675 2.65 0.008 (1.06–1.52) 4 or More Times a Week 1.04 0.1052178 0.41 0.687 (0.85–1.27) Non-Monetary Poverty Factors Household Size Below National Average Ref Within National Average 0.87 0.0626243 -1.96 0.049 (0.75-1.00) Above National Average 0.83 0.0628672 -2.52 0.012 (0.71–0.96) Residential Urban Ref Rural 1.30 0.0838255 4.01 0.000 (1.14–1.47) _cons 0.89 0.1565927 -0.65 0.516 (0.63–1.26) Note: Statistical significance was defined as p < 0.05. Table 4 and Fig. 1 present the results of the econometric models. The multiple logit model shows that most explanatory variables were significantly associated with the possibility of working. Our findings indicated that the odds of working were independently associated with age, sex, education level, individual wealth, Viral load level for positive individuals, number of partners, payment for sex, alcohol intake, household size, and residential location. Our findings indicated that the odds of working and receiving income were independently associated with age, sex, education level, individual wealth, Viral load level for positive individuals, number of partners, payment for sex, alcohol intake, household size, and residential location. For instance, older age reduced the odds ratio (0.3 less times less compared to young age) of working and receiving income (p < 0.05). Being female is also 0.3 times the odds of working and receiving income compared to being male. Married individuals had 0.6 odds of less time working and receiving income compared to unmarried individuals. Higher education was found to increase the odds of working and receiving income 2.8 times compared with primary education. Similarly, individuals with high wealth status were found to have 2.9 times higher odds of working and receiving income compared to those in the lower wealth quintile. On health-related factors, the individual with suppressed viral load was found to have 2.1 times higher odds of working and receiving income compared to those who were unsuppressed. Additionally, those with multiple partners were more likely to receive income after working than those who abstained (1.3 OR). Individuals who do not pay for sex increase the probability of working and receiving income (1.3 OR) compared with those who pay (OR = 1.3, CI: 1.12–1.54). Individuals who use alcohol four or more times a week have 1.27 times more likelihood of working and receiving income compared to patients who never use alcohol (OR = 1.27, CI: 1.06–1.52). Individuals from household sizes above the national average have 0.8 times less likelihood of working and receiving income compared to individuals from household sizes below the national average (OR = 0.8, CI: 0.71–0.96). Lastly, individuals from rural areas have 1.27 times more likelihood of working and receiving income compared to individuals from urban areas (OR = 1.27, CI: 1.15–1.47). Additionally, those with multiple partners were more likely to receive income after working than those who abstained (1.3 OR). Individuals who do not pay for sex increase the probability of working and receiving income (1.3 OR) compared with those who pay (OR = 1.3, CI: 1.12–1.54). Individuals who use alcohol four or more times a week have 1.27 times more likelihood of working and receiving income compared to patients who never use alcohol (OR = 1.27, CI: 1.06–1.52). Individuals from household size above the national average have 0.8 times less likelihood of working and receiving income compared to individuals from household size below the national average (OR = 0.8, CI: 0.71–0.96). Lastly, individuals from rural areas have 1.27 times more likelihood of working and receiving income compared to individuals from urban areas (OR = 1.27, CI: 1.15–1.4 4.0 Discussion Working and receiving income were associated with individual age, such that the young adult age population aged (26–35) years was found to be 1.17 times more likely to work and receive income compared to the rest of the population. This result has several implications for the study. First, based on most of the SSA population census, more than 65% of its people are young adults under 35 years of age; hence, they are more likely to dominate our category. Second, technological issues, nowadays due to the high level of technological innovation (computerization), make it easier for the young adult generation to adapt compared to the adult population, and make it easier for them to secure more jobs than their counterparts(6,10). In this study, gender was found to be significantly related to working and receiving income, consistent with the existing literature. The results show that the female population has 0.2 times the likelihood of working and receiving income as males. This result is consistent with most previous studies, which found that the household income of males can be more seriously affected by HIV/AIDS than the household income of females because a large part of the household income is generated by males(36,37). This is mainly channelled in labour division and culture issues among SSA countries, as many women play an important role in housework, such as raising kids and cooking for their husbands, which prohibits them from paid jobs(38,39). Also, the facts are that regardless of their big in number in terms of population, their percentage of working is very small, and(13,40) in their study provide an argument that when both female and male households work and if male household heads are affected by HIV/AIDS or any disability that prevents him no longer work, generally their female counterparts often take over the role of household financial supporter. In addition, a study conducted in China, specifically in the province of Yunnan, found that local customs and culture dictate that women provide financial support, while men are less likely to work outside the home(41). Regarding marital status, the results are controversial, as many married people (approximately 81%) work and receive income compared to unmarried individuals. However, married individuals were found to be statistically significant, with 0.6 times less likelihood of working and receiving income compared to unmarried individuals. A study conducted by (42) provides a supportive argument that married individuals may have additional responsibilities within the household, such as caregiving, child-rearing, or managing household affairs, which can limit their availability for full-time employment or career advancement. In HIV/AIDS couple families, the other spouse may take on caregiving responsibilities, which can impact their ability to maintain full-time employment or pursue career opportunities that require significant time commitments. Hence, living alone, being separated, or being divorced may significantly increase the probability of working and receiving income. Regarding education level, most of the working and receiving population (approximately 38.5%) is at the secondary level. However, those with a university-level education are 2.8 times more likely to be working and receiving income than those with primary education. The results are similar to those of several studies(43,44) which found that the higher the education, the higher the chance of being employed, and this is mainly due to the high working skills of university-level individuals. In addition, the education level may define the level of exposure of an individual, which is critical when it comes to assessing the output per worker. We are told that the trade-off between efficiency and justice no longer holds in the global knowledge-driven economy. However, other studies on business turnover argue that employees with high education levels, who are young and inexperienced, tend to have a low level of satisfaction with jobs and careers and have a lower commitment to the organization; these negative attitudes are associated with turnover intention(45,46). In terms of individual wealth, most of the working and receiving population (approximately 24.5%) belongs to the higher wealth quintile. Individuals belonging to the higher wealth quintile are 2.9 times more likely to work and receive income than those belonging to the lower wealth quintile. This result is channelled as individuals from higher wealth quintiles can easily access education and skills, job opportunities, asset ownership and investment, health and well-being, social capital and networks, risk aversion, and policy and economic environment(47,48). Hence, understanding these factors highlights the complex interplay between wealth, socioeconomic opportunities, and income generation. This result is consistent with findings from a previous by (49), who found that individual wealth heterogeneity matters for aggregate fluctuations in employment, output, and wages based on the overall employment shape. The employment rate across the wealth quintile is nearly flat, especially among households, except for the first wealth quintile. In terms of viral load level, finding that individuals with suppressed viral load have 2.1 times higher odds of working and receiving income aligns with broader evidence linking viral suppression to improved socioeconomic outcomes. A study in Zambia found that longer ART duration and multi-month drug dispensation significantly increased viral suppression, which in turn correlated with better employment stability(50). Similarly, research in Chile has shown that unemployment is associated with higher odds of having a detectable viral load, reinforcing the bidirectional relationship between health and economic productivity(51). Moreover, a meta-analysis revealed that monetary incentives improved viral suppression and retention in care, indirectly enhancing economic participation(52). These findings suggest that viral suppression not only improves clinical outcomes but also facilitates labor market engagement, especially in resource-limited settings. In terms of several partners, the majority of working and receiving incomes, about 59.8% belong to one partner. However, multipartners were found to be 1.3 times more likely to work and receive income compared to an individual with no partner. These results are channelled in several ways, firstly, having multiple partners could increase financial responsibilities and obligations, motivating individuals to seek employment or work actively to meet these obligations(53). Secondly, a study conducted by (54)found that individuals with multiple partners may exhibit characteristics associated with risk-taking behavior, which can extend to their economic activities. This behavior may lead them to pursue more entrepreneurial opportunities or take on higher-risk but potentially higher-reward occupations. Lastly, a study conducted by (55)provides an argument that having multiple partners can expand an individual's social network and connections, which may provide access to employment opportunities, referrals, or business partnerships. The study found that 62.5% of individuals who never consume alcohol are employed and receive income. However, individuals who drink alcohol two to three times a week were found to be 1.27 times more likely to work and earn income compared to abstainers (56). Several arguments may explain this association. Firstly, in some cultural contexts, alcohol consumption is closely intertwined with social activities, networking, and business engagements. Supporting this, research by (57) suggests that certain occupations or industries may exhibit higher levels of alcohol use among workers. For example, in sectors such as hospitality, entertainment, or various skilled trades, socializing and networking over drinks may be common practice, potentially influencing employment patterns. Moreover, a study conducted by (58) notes that, in some cases, individuals with alcohol use disorders may engage in informal or irregular employment to sustain their drinking habits. While these forms of work may lack stability, they still contribute to income generation. Nonetheless, it is crucial to recognize that higher alcohol consumption is associated with negative health and behavioral outcomes. Alcohol intoxication can lead to behavioral problems and mental changes, including inappropriate behavior, mood instability, poor judgment, slurred speech, memory issues, attention deficits, and impaired coordination. In terms of household size, most individuals who are employed and earn income, approximately 73.4% belong to households with a size below the national average. Conversely, individuals from households above the national average in size were found to be 0.82 times less likely to work and receive income compared to those from smaller households. This pattern may be influenced by several factors. First, larger households often face challenges in accessing education and skill development opportunities, which are essential for improving employability and income-generation prospects(59). Second, they typically experience greater economic pressures due to increased consumption needs and expenses. These financial constraints may limit opportunities for household members, including adults, to engage in paid employment(60). Lastly, in terms of residence location, most individuals who are employed and receive income, approximately 58.0% reside in rural areas. Furthermore, individuals from rural areas were found to be 1.2 times more likely to work and earn income compared to those from urban areas. This finding is largely attributed to the agricultural sector, which employs over 60% of the population in Sub-Saharan African countries and is predominantly concentrated in rural areas. Consequently, rural residents may have greater employment opportunities compared to their urban counterparts. 5.0 Conclusions This study highlights the complex factors influencing income generation among individuals living with HIV in Sub-Saharan Africa. Socio-demographic and health-related variables such as age, gender, education, wealth, marital status, alcohol use, household size, partner count, and residence significantly affect labor force participation. Notably, viral load suppression is positively linked to economic engagement, emphasizing the dual benefit of consistent ART use. The findings advocate for policies that combine viral monitoring with economic empowerment, particularly for vulnerable groups. Women, those who are married, and individuals from larger households face unique constraints. Addressing these disparities calls for integrated interventions. HIV programs should embed livelihood strategies to support inclusive socioeconomic recovery. 6.0 Study Limitations These findings should be interpreted with caution due to certain study limitations. Our recruitment strategy was restricted to 12 out of 54 Sub-Saharan African countries, primarily because the HIV Impact Assessment Survey (PHIA) was not conducted across the entire region. This constraint reflects the availability of data rather than a deliberate exclusion. Abbreviations ART: Antiretroviral therapy PHIA: Population HIV Impact Assessment AIDS: Acquired Immunodeficiency Syndrome AOR: Adjusted Odds Ratio SSA: Sub-Saharan African Countries HIV: Human Immunodeficiency Virus Declarations Acknowledgements- The author gratefully acknowledges the Centers for Disease Control and Prevention (CDC) for their valuable support in providing access to datasets from twelve countries upon request through their website. Authors' contributions: - Conceptualization, B.Y. and J.M.; methodology, B.Y; software, B.Y; validation, B.Y., J.M. and A.M.; formal analysis, A.M.; investigation, B.Y.; resources, B.Y.; data curation, B.Y.; writing—original draft preparation, B.Y.; writing—review and editing J.M. and A.M.; All authors have read and agreed to the published version of the manuscript. Funding: No funding Availability of data and material: The study datasets and materials for all twelve sub-Saharan African countries are available on the website of the Centre for Disease Control (CDC). https://phia-data.icap.columbia.edu/datasets Ethics approval and consent to participate The study protocol underwent a thorough review process, ensuring ethical clearance was obtained from the respective Ministries of Health in each country. Additionally, informed consent was secured from all participants before to their interviews. Consent for publication: Not applicable Clinical Trial This study did not involve a clinical trial and, therefore, was not registered in any clinical trial registry. This study did not involve a clinical trial and, therefore, was not registered in any clinical trial registry. Competing interests: All authors declare no competing interests References Bloom D, Canning D, Kotschy R, Prettner K, Schünemann J. Health and Economic Growth: Reconciling the Micro and Macro Evidence. Cambridge, MA; 2019 Jun. Ogundari K, Awokuse T. Human capital contribution to economic growth in Sub-Saharan Africa: Does health status matter more than education? Econ Anal Policy. 2018 Jun;58:131–40. Puiu IA, Bîlbîie A. Measuring productivity in the healthcare sector: a bibliometric and content analysis. Health Econ Rev. 2025 Mar 18;15(1):24. Puiu IA, Bîlbîie A. Measuring productivity in the healthcare sector: a bibliometric and content analysis. Health Econ Rev. 2025 Mar 18;15(1):24. World Health Organization. Global action plan on physical activity 2018-2030: more active people for a healthier world. World Health Organization. 2019. Roziana Baharin, Rizqon Halal Syah Aji, Ishak Yussof, Nasir Mohd Saukani. Impact of Human Resource Investment on Labor Productivity in Indonesia. Iranian Journal of Management Studies (IJMS). 2019; Kokabisaghi F. Assessment of the Effects of Economic Sanctions on Iranians’ Right to Health by Using Human Rights Impact Assessment Tool: A Systematic Review. Int J Health Policy Manag. 2018 Jan 20;7(5):374–93. Libois F, Somville V. Fertility, household size and poverty in Nepal. World Dev. 2018 Mar;103:311–22. Mora Z, Suharyanto A, Yahya M. Effect of Work Safety and Work Healthy Towards Employee’s Productivity in PT. Sisirau Aceh Tamiang. Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences. 2020 May 8;3(2):753–60. Brunner B, Igic I, Keller AC, Wieser S. Who gains the most from improving working conditions? Health-related absenteeism and presenteeism due to stress at work. The European Journal of Health Economics. 2019 Nov 15;20(8):1165–80. Tzenios N. The Impact of Health Literacy on Employee Productivity: An Empirical Investigation. 2019. AWOGBEMI TO. HUMAN CAPITAL DEVELOPMENT AND NIGERIA’S ECONOMIC GROWTH. Journal of Public Administration, Finance and Law. 2023;(27):67–76. Achdou Y, Han J, Lasry JM, Lions PL, Moll B. Income and Wealth Distribution in Macroeconomics: A Continuous-Time Approach. Rev Econ Stud. 2022 Jan 10;89(1):45–86. Asare Vitenu-Sackey P, Barfi R. The Impact of Covid-19 Pandemic on the Global Economy: Emphasis on Poverty Alleviation and Economic Growth. The Economics and Finance Letters. 2021;8(1):32–43. Sriram S, Khan MM. Effect of health insurance program for the poor on out-of-pocket inpatient care cost in India: evidence from a nationally representative cross-sectional survey. BMC Health Serv Res. 2020 Dec 7;20(1):839. Dean LT, Moss SL, Ransome Y, Frasso-Jaramillo L, Zhang Y, Visvanathan K, et al. “It still affects our economic situation”: long-term economic burden of breast cancer and lymphedema. Supportive Care in Cancer. 2019 May 18;27(5):1697–708. Karanika-Murray M, Biron C. The health-performance framework of presenteeism: Towards understanding an adaptive behaviour. Human Relations. 2020 Feb 18;73(2):242–61. Wolkoff P, Azuma K, Carrer P. Health, work performance, and risk of infection in office-like environments: The role of indoor temperature, air humidity, and ventilation. Int J Hyg Environ Health. 2021 Apr;233:113709. Mominur Rahman Md, Islam F, Saidur Rahaman Md, Sultana NA, Fahim NF, Ahmed M. Studies on the prevalence of HIV/AIDS in Bangladesh including other developing countries. Advances in Traditional Medicine. 2023 Sep 20;23(3):647–58. Govender RD, Hashim MJ, Khan MA, Mustafa H, Khan G. Global Epidemiology of HIV/AIDS: A Resurgence in North America and Europe. J Epidemiol Glob Health. 2021;11(3):296. Mukiri E. HIV Oral Self-testing (HiVST) Strategy for Increased HIV Testing Among Female Sex Workers (Fsw) in Nairobi County, Kenya. 2023. USMAN SUNUSI USMAN, ALIYU MUHAMMAD MAIGORO, GANA MUHAMMAD LAWAN, JIBRIN ADAMU DAMAZAI, ABUBAKAR MUHAMMAD KURFI, IBRAHIM ADAM ABDULLAHI, et al. Comparative study on sero-prevalence and risk factors of HIV/AIDs between intra-city and long-distance commercial drivers in Kano State. International Journal of Science and Research Archive. 2023 Jul 30;9(2):077–89. Faria NR, Vidal N, Lourenco J, Raghwani J, Sigaloff KCE, Tatem AJ, et al. Distinct rates and patterns of spread of the major HIV-1 subtypes in Central and East Africa. PLoS Pathog. 2019 Dec 6;15(12):e1007976. Collier DA, Monit C, Gupta RK. The Impact of HIV-1 Drug Escape on the Global Treatment Landscape. Cell Host Microbe. 2019 Jul;26(1):48–60. Chilunda V, Calderon TM, Martinez-Aguado P, Berman JW. The impact of substance abuse on HIV-mediated neuropathogenesis in the current ART era. Brain Res. 2019 Dec;1724:146426. Portilla-Tamarit J, Reus S, Portilla I, Fuster Ruiz-de-Apodaca MJ, Portilla J. Impact of Advanced HIV Disease on Quality of Life and Mortality in the Era of Combined Antiretroviral Treatment. J Clin Med. 2021 Feb 11;10(4):716. Sapiano TN. Fiscal Decentralization and the HIV Treatment Cascade in Low-and Middle-Income Countries: Identifying Opportunities to Sustain Progress Made Under the President’s Emergency Plan for Aids Relief . 2023. Zhang Q, Yang H, Fan J, Duan L, Chen D, Feng X, et al. Older people living with human immunodeficiency virus/acquired immune deficiency syndrome in Chinese rural areas: perceived stigma and associated factors. Trans R Soc Trop Med Hyg. 2019 Aug 1;113(8):477–82. Liu XJ, McGoogan JM, Wu ZY. Human immunodeficiency virus/acquired immunodeficiency syndrome prevalence, incidence, and mortality in China, 1990 to 2017: a secondary analysis of the Global Burden of Disease Study 2017 data. Chin Med J (Engl). 2021 May 20;134(10):1175–80. Davy-Mendez T, Napravnik S, Wohl DA, Durr AL, Zakharova O, Farel CE, et al. Hospitalization Rates and Outcomes Among Persons Living With Human Immunodeficiency Virus in the Southeastern United States, 1996–2016. Clinical Infectious Diseases. 2020 Oct 23;71(7):1616–23. Kim JM, Kim NJ, Choi JY, Chin BS. History of Acquired Immune Deficiency Syndrome in Korea. Infect Chemother. 2020;52(2):234. Mcinziba A. Exploring how the management of household incomes impact on antiretroviral therapy adherence behaviour of people living with HIV in the Western Cape, South Africa. 2021. Fauk NK, Ward PR, Hawke K, Mohamadi L, Mwanri L. A narrative systematic review of quantitative and qualitative evidence on the impacts of HIV on women living with HIV and their families in developing countries. 2019. Osobase AO. Factors Influencing Food Expenditure Patterns Among HIV/AIDS Affected Households in Lagos State of Nigeria. Türk Akademik Sosyal Bilimler Araştırma Dergisi. 2019; Boateng EY, Abaye DA. A Review of the Logistic Regression Model with Emphasis on Medical Research. Journal of Data Analysis and Information Processing. 2019;07(04):190–207. Osobase AO, Dauda RO, Nwakeze NM. The Impact of HIV/AIDS on Household Income: A case study of Lagos State, Nigeria. 2018. Raghupathi V, Raghupathi W. The influence of education on health: an empirical assessment of OECD countries for the period 1995–2015. Archives of Public Health. 2020 Dec 6;78(1):20. Musyafaah NL, Novitasari Y, Rahmawati TL. Division the Husband and Wife Roles to Live a Domestic Life During the Pandemic Covid 19 in the Mubadala Perspective. Ulul Albab: Jurnal Studi dan Penelitian Hukum Islam. 2022 Jan 8;5(1):19. Sancak. Factors that Influence Employment of Women in the City of Van. In Women’s Economic Empowerment in Turkey (pp. 17-27). Routledge. Routledge. 2019; Aburto JM, Villavicencio F, Basellini U, Kjærgaard S, Vaupel JW. Dynamics of life expectancy and life span equality. Proceedings of the National Academy of Sciences. 2020 Mar 10;117(10):5250–9. Wang X, Cheng Z. Cross-Sectional Studies. Chest. 2020 Jul;158(1):S65–71. Ugargol AP, Bailey A. Family caregiving for older adults: gendered roles and caregiver burden in emigrant households of Kerala, India. Asian Popul Stud. 2018 May 4;14(2):194–210. Szromek AR, Wolniak R. Job Satisfaction and Problems among Academic Staff in Higher Education. Sustainability. 2020 Jun 15;12(12):4865. Guerrero M, Urbano D, Cunningham JA, Gajón E. Determinants of Graduates’ Start-Ups Creation across a Multi-Campus Entrepreneurial University: The Case of Monterrey Institute of Technology and Higher Education. Journal of Small Business Management. 2018 Jan;56(1):150–78. Abate J, Schaefer T, Pavone T. UNDERSTANDING GENERATIONAL IDENTITY, JOB BURNOUT, JOB SATISFACTION, JOB TENURE AND TURNOVER INTENTION. Journal of Organizational Culture, Communications and Conflict. 2018; Bhagwandeen TP. Relationship between intrinsic job satisfaction, extrinsic job satisfaction, and employee turnover intentions. 2021. Pietrolucci A, Albertini M. Not all wealth is the same: types and levels of wealth and children’s university enrolment. Eur Sociol Rev. 2023 Oct 20;39(5):789–803. Prieto J. New approaches to measuring economic and social well-being in Chile (Doctoral dissertation, London School of Economics and Political Science). 2020; Pietrolucci A, Albertini M. Not all wealth is the same: types and levels of wealth and children’s university enrolment. Eur Sociol Rev. 2023 Oct 20;39(5):789–803. Lamba L, Kazonga E, Nyirenda C, Chilyabanyama R. Viral Load Suppression Among Adults with HIV on Antiretroviral Therapy: Outcomes from a Lusaka District Hospital, Zambia. International Journal of Translational Medical Research and Public Health. 2025 Feb 14;9:e004. Leiva-Escobar I, Cortes CP, Lamadrid A. Employment Status and HIV Viral Load in Chilean Adult Population: A Propensity Score Analysis. AIDS Behav. 2025 Apr 9;29(4):1256–65. Zhu Z, Guo L, Yang M, Cheng J. The effectiveness of monetary incentives in improving viral suppression, treatment adherence, and retention in care among the general population of people living with HIV: a systematic review and meta-analysis. AIDS Res Ther. 2025 Jun 2;22(1):57. Balven R, Fenters V, Siegel DS, Waldman D. Academic Entrepreneurship: The Roles of Identity, Motivation, Championing, Education, Work-Life Balance, and Organizational Justice. Academy of Management Perspectives. 2018 Feb;32(1):21–42. Czerwonka M. Cultural, cognitive and personality traits in risk-taking behaviour: evidence from Poland and the United States of America. Economic Research-Ekonomska Istraživanja. 2019 Jan 1;32(1):894–908. Neumeyer X, Santos SC. Sustainable business models, venture typologies, and entrepreneurial ecosystems: A social network perspective. J Clean Prod. 2018 Jan;172:4565–79. Geusens F, Bigman-Galimore CA, Beullens K. A cross-cultural comparison of the processes underlying the associations between sharing of and exposure to alcohol references and drinking intentions. New Media Soc. 2020 Jan 3;22(1):49–69. Pachito D V., Pega F, Bakusic J, Boonen E, Clays E, Descatha A, et al. The effect of exposure to long working hours on alcohol consumption, risky drinking and alcohol use disorder: A systematic review and meta-analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury. Environ Int. 2021 Jan;146:106205. Dordoye EK, Afun LA, Alalbila TM. Perception and Attitude of Employers towards Employees with AUD in an Emerging Economy: A Qualitative Enquiry. Open J Psychiatr. 2021;11(02):107–23. Ugargol AP, Bailey A. Family caregiving for older adults: gendered roles and caregiver burden in emigrant households of Kerala, India. Asian Popul Stud. 2018 May 4;14(2):194–210. Trlifajová L, Hurrle J. Work must pay: Does it? Precarious employment and employment motivation for low-income households. J Eur Soc Policy. 2019 Jul 25;29(3):376–95. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7283006","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":528410704,"identity":"234cbe40-f827-41b3-9c19-35279b8ed794","order_by":0,"name":"Boniface Simpoli Yohana","email":"data:image/png;base64,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","orcid":"","institution":"National Bureau of Statistics","correspondingAuthor":true,"prefix":"","firstName":"Boniface","middleName":"Simpoli","lastName":"Yohana","suffix":""},{"id":528410705,"identity":"82e29098-e518-469a-8d28-56c8de5637fb","order_by":1,"name":"John Massito","email":"","orcid":"","institution":"University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Massito","suffix":""},{"id":528410706,"identity":"304300e0-0a6f-4b1b-b680-3bb00139dcb9","order_by":2,"name":"Arbogust Moshi","email":"","orcid":"","institution":"University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Arbogust","middleName":"","lastName":"Moshi","suffix":""}],"badges":[],"createdAt":"2025-08-03 11:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7283006/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7283006/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93515928,"identity":"1b52074e-3cab-4d89-9b3b-1d7a22069786","added_by":"auto","created_at":"2025-10-14 16:32:28","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":867424,"visible":true,"origin":"","legend":"","description":"","filename":"EconomicConsequencesofHIVStatusandViralLoadLevelAmongHouseholdsEvidencefromTwelveSubSaharanAfricanCountries.docx","url":"https://assets-eu.researchsquare.com/files/rs-7283006/v1/a8c36ec74938bb25a138ec96.docx"},{"id":93515925,"identity":"b680aadd-9abe-4099-9d83-1f5fdc5b7772","added_by":"auto","created_at":"2025-10-14 16:32:28","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5879,"visible":true,"origin":"","legend":"","description":"","filename":"5e2fa847114a4a22964af71c4dce09b2.json","url":"https://assets-eu.researchsquare.com/files/rs-7283006/v1/c8ec43ee9806f576e22c97e5.json"},{"id":93515933,"identity":"686a9218-defd-4ffd-aae2-466bc15f4532","added_by":"auto","created_at":"2025-10-14 16:32:28","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":123367,"visible":true,"origin":"","legend":"","description":"","filename":"5e2fa847114a4a22964af71c4dce09b21enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7283006/v1/14a8632d906874580c2bbc27.xml"},{"id":93516564,"identity":"28421125-de2f-492b-ac5d-54da6f3b72f9","added_by":"auto","created_at":"2025-10-14 16:40:28","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55770,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7283006/v1/81849436d5d473dc1f5bfacb.png"},{"id":93515926,"identity":"03b9298a-2f33-465a-acc1-2778747810c4","added_by":"auto","created_at":"2025-10-14 16:32:28","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":558466,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7283006/v1/e255a0ce05ade00e4506e53a.png"},{"id":93516563,"identity":"533c7f3b-35d9-4bd6-b2c0-050a0917a304","added_by":"auto","created_at":"2025-10-14 16:40:28","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12158,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7283006/v1/9fecf75f9b3ed9eb3cd2a63b.png"},{"id":93515931,"identity":"4ada389e-7c48-4c07-b729-828eaf1d82c5","added_by":"auto","created_at":"2025-10-14 16:32:28","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":43512,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7283006/v1/6b67019c227540b770133920.png"},{"id":93516566,"identity":"003acbb4-57a6-4fc6-9ecf-3ff6b5953254","added_by":"auto","created_at":"2025-10-14 16:40:28","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122480,"visible":true,"origin":"","legend":"","description":"","filename":"5e2fa847114a4a22964af71c4dce09b21structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7283006/v1/e665bd1a247f696586ae5089.xml"},{"id":93515934,"identity":"d9a7320f-e86a-4f5e-9b51-c90af2921b04","added_by":"auto","created_at":"2025-10-14 16:32:29","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126040,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7283006/v1/b2d339b3c3f858fb26b6b2cc.html"},{"id":93516731,"identity":"cde2ee4f-86a2-40ae-9f02-89e218a3ede6","added_by":"auto","created_at":"2025-10-14 16:48:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":116474,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7283006/v1/3a9efc55b2acc3f6308f24a7.jpg"},{"id":93823112,"identity":"19ad3c04-f3c6-4a62-b4c1-8f9f5630deb9","added_by":"auto","created_at":"2025-10-18 05:16:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1434165,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7283006/v1/f451f064-4d8f-4863-ba65-91b3bcdfb73f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Economic Consequences of HIV Status and Viral Load Level Among Households: Evidence from Twelve Sub-Saharan African Countries","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eA healthy population is one of the fundamental factors for the economic growth and development of any nation(1,2). Healthy individuals are an important form of human capital that can enhance workers\u0026rsquo; productivity by raising physical capacities, such as strength and endurance, as well as mental capacities, such as cognitive functioning and reasoning ability(3,4). In addition, global health is considered a fundamental human right and a worldwide social goal; a physically and mentally healthier society is more energetic and stronger, which creates activity in production activities (5). According to the theory of production, output depends on capital, land, and labour; however, labour productivity depends on labour skills and health(6). Several studies that have attempted to clarify cross differences in household welfare and productivity rate typically conclude that healthy household size and composition, education status with good health, type of economic activities, traditional and cultural practices, access to credit, and technological utilization are among the key explanatory variables (7,8).\u003c/p\u003e\u003cp\u003eHealthy workers lose less time from work due to illness and are more productive(9\u0026ndash;11). (12)The study on human capital, described health as a capital-productive asset and an economic growth and development engine.(12). Fifty percent of the economic growth and development differentials between developed and developing nations are attributable to ill health and low life expectancy (5). In general, poor health status affects household social and economic development in several ways(13). First, it increases treatment and other costs due to regular checkups and treatment costs (14). Second, the impact on food consumption in poorly healthy households due to treatment costs had sold assets and switched from more expensive to cheaper non-nutrient commodities(15,16). Finally, poor health reduces individual time, energy, and work motivation, thereby reducing productivity (17,18). The HIV/AIDS epidemic is a serious problem among low-income communities in most developing countries, including India(19). This is evidenced by the fact that, in 2020, 36\u0026nbsp;million adults over the age of 15 were living with HIV (20). In Sub-Saharan African countries (SSA), with an estimated 28.5\u0026nbsp;million HIV\u0026thinsp;+\u0026thinsp;people, Kenya has become the nation with the second-largest number of people living with HIV/AIDS, approximately 1.5\u0026nbsp;million, after South Africa, approximately 5.0\u0026nbsp;million (21,22). Estimates reveal that roughly one out of ten HIV-positive persons in the world are from Sub-Saharan Africa, and the first HIV\u0026thinsp;+\u0026thinsp;case was reported in Kinshasa in the 1970s (23). However, the impact of these diseases varies from one place to another and is mostly classified politically, socially, and economically(24). In terms of socio-economic impact, these include the loss of jobs, income, and death of family members, such as parents, reduced education and knowledge of the new generation, reduced interest in being well educated, and less investment in health (25,26). From a national perspective, the pandemic has significant implications for government spending (27). The health and social care services domains may be severely affected by the inadequate use of the health workforce in HIV-related problems (15). Within these 40 years of disease experience, there have been several research on the impact of Acquired Immune Deficiency Syndrome (AIDS) and Human Immunodeficiency Virus (HIV) on household means of livelihood(28\u0026ndash;31). Despite the findings of these studies, HIV/AIDS has continued to negatively impact household welfare, with tragic consequences for the income level and living standards of affected households (32\u0026ndash;34).\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Conceptual Framework of Economic Consequences of HIV Status and Viral Load\u003c/h2\u003e\u003cp\u003eThe conceptual framework of the economic consequences of HIV status and viral load explores the bidirectional relationship between individual health outcomes and socioeconomic conditions. HIV-positive individuals, particularly those with unsuppressed viral loads, often face heightened barriers to employment, reduced productivity, and increased healthcare expenditures, which collectively exacerbate financial vulnerability. Conversely, economic instability such as poverty, limited access to healthcare, or inadequate social protection can hinder treatment adherence and viral suppression, perpetuating poor health outcomes. Conversely, economic instability, such as poverty, limited access to healthcare, or inadequate social protection can hinder treatment adherence and viral suppression, perpetuating poor health outcomes. Diagram 1 illustrates how health-related factors, human behavior, non-monetary elements, and socio-demographic characteristics influence an individual's likelihood of participating in the labor force.\u003c/p\u003e\u003c/div\u003e"},{"header":"2.0 Materials and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Area\u003c/h2\u003e\u003cp\u003eThe study area is 12 Sub-Saharan African countries where the Population HIV Impact Assessment Survey (PHIA) was conducted. These 12 countries are heterogeneous states with multi-ethnic groups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Types\u003c/h2\u003e\u003cp\u003eThis study employs quantitative cross-sectional data from the Population HIV Impact Assessment Survey (PHIA) for twelve (12) available sub-Saharan African countries. The PHIA are standardized national household survey conducted in low- and middle-income countries that ask questions about demographic and socioeconomic behavior and outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Study population and sample\u003c/h2\u003e\u003cp\u003eA total of twelve countries were included in the analysis, covering a range of years, geographic sizes, and population demographics. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the study population characteristics and sample sizes across the selected countries. The number of HIV-positive individuals, average household size, and total household count vary considerably, reflecting contextual heterogeneity across the regions.\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\u003eThe Study Population and Sample Size\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eArea Size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHIV-Positive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNumber Households\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHousehold Size\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCameroon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017/2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e475,650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25,076,747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e490.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13,500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCote d\u0026rsquo;ivoire\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017/2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e322,462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25,493,988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e442.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11,654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEswatini\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2016/2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17,363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,093,238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e287.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5,740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e26.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthiopia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017/2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,112,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e111,129,438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e395.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11,810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLesotho\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2016/2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30,355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,170,617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e418.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10,450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e25.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalawi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015/2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e118,480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17,405,624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e500.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13,234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNamibia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e824,292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,364,534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e465.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11,625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRwanda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2018/2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26,338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12,835,028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e375.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11,250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTanzania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2022/2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e945,087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63,876,544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e526.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15,780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUganda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2016/2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e241,038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40,127,085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e523.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13,075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZambia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e752,614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16,767,761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e515.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12,352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZimbabwe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015/2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e399,757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14,452,704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e500.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14,085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e12.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Method of Data Analysis\u003c/h2\u003e\u003cp\u003eThe Binary Logistic Regression Technique (BLRT) was employed to predict the odds/livelihoods of working and receiving income among both affected and unaffected households in SSA. The BLRT is a type of regression that is applied when the predicted variable is a dichotomy, while the predictor variables are of any form(35). The dependent variable usually takes the value of one (1) with a probability of success ψ or otherwise zero (0) with a probability of failure of 1 \u0026ndash; ψ.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Diagnostic Test\u003c/h2\u003e\u003cp\u003eThe diagnostic statistical test assesses the study variables before analysis, ensuring model accuracy and reliable findings for decision-making. The answer depends on the data type. For cross-sectional binary variables in logistic regression, the assumptions include binary dependent variables, independent observations, minimal multicollinearity, linearity with log odds, large sample size, and heteroscedasticity tests. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the binary, multicollinearity, linearity, sample size, and heteroscedasticity tests.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiagnostic statistical test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Types\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinary Test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMulticollinearity Test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLinearity Test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSample Size Test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHeteroskedasticity Test\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIV status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e100,593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViral load level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e100,593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.79163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23,687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"11\" rowspan=\"12\"\u003e\u003cp\u003echi2(1)\u0026thinsp;=\u0026thinsp;102.4\u003c/p\u003e\u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u0026thinsp;=\u0026thinsp;0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.73977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e85,502\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.68707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e83,001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.77757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19,478\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual Wealth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.64677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29,691\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Partners\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.45179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10,160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCondom Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.43960\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e100,731\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePayment for Sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e83,001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.86479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17,640\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.95922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11,459\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidential\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.78657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e100,731\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFood shortage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.83259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e100,731\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\u003eNB: B-binary variable, C-categorical variable, N/A if the test does not qualify for variable type, statistical significance defined as p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e above show that the viral load level variable is binary with two responses (1- suppressed, 0-not suppressed), which satisfies the assumption of a binary dependent variable. The independent test results showed that each originated from a unique respondent without any repetition. For the Multicollinearity test, a 1/VIF value for all variables is greater than the 0.25 cut-off point value, implying that there is no relation among the independent variables. The linearity test using the Box-Tidwell test results shows that all four ordinary categorical independent variables have linear relationships with the odds of the dependent variables as the p-value is greater than 5% (0.05). The linearity test using the Box-Tidwell test results shows that all four ordinary categorical independent variables have linear relationships with the odds of the dependent variables, as the p-value is greater than 5% (0.05). For the sample size, all variables were found to have a large sample size, with a minimum observed number of partners of approximately 10,160. Finally, the heteroscedasticity test result of Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u0026thinsp;=\u0026thinsp;0.000 is less than 0.05, we reject the null hypothesis of heteroscedasticity. Based on these diagnostic test results (presence of non-constant variance), the use of vce(robust) in the logistic model makes the odds result more precise and reliable.\u003c/p\u003e\u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Descriptive Findings\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Working Status by Country for all Individuals and HIV Positive (N\u0026thinsp;=\u0026thinsp;347,611)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eNot Working\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eWorking and Receiving Income\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eTotal\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\u003eAll Individual\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHIV Positive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAll Individual\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHIV Positive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAll Individual\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHIV Positive\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCameroon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10,671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13,988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e594\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(23.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(20.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(76.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(79.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCote d\u0026rsquo;ivoire\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18,927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e443\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(54.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(54.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(45.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(45.82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEswatini\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4,568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11,886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3,066\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(61.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(54.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(38.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(45.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthiopia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11,492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20,170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e614\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(56.98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(51.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(43.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(48.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLesotho\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,913\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4,791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13,095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3,260\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(63.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(58.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(36.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(41.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalawi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13,655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19,867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2,277\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(68.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(63.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(31.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(36.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNamibia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7,834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18,795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2,446\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(58.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(63.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(41.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(36.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRwanda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18,362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12,353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30,715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e934\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(59.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(40.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTanzania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19,954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14,153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34,107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1,941\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(58.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(53.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(41.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(46.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUganda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14,591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15,374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29,965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1,810\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(48.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(40.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(51.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(59.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZambia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14,697\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7,107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21,804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2,528\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(67.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(61.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(32.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(38.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZimbabwe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16,415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25,066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3,590\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(65.49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(63.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(34.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(36.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e149,392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13,390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e108,993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10,113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e258,385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23,503\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(57.82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(56.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(42.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(43.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(100%)\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=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the working status by country, all individuals, and HIV-positive individuals. Cameroon had a high percentage of approximately 76.29% and 79.8% of all individuals and HIV-positive individuals, respectively, who received income after working. This indicates that HIV treatment is high in Cameroon, making labour productivity unaffected. Uganda is the second country, and more than half of its population is working frequently (approximately 51.31%), and 59.06% of all adult individuals and HIV-positive individuals, respectively, receive income after working. This also indicates that HIV cases do not affect labour productivity because of viral load suppression due to antiretroviral therapy (ART) treatment. Malawi is the country found to have a very low percentage of about 31.27% and 36.63% of all adult individuals and HIV-positive individuals, respectively, who receive income after working. However, Namibia found a lower percentage of HIV-positive adults who receive income after working (approximately 36.06%) compared to the adult population (approximately 41.68%) who receive income after working. This indicates that HIV affects labour productivity in Namibia, as only a few HIV-positive individuals were connected to antiretroviral therapy (ART).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Multiple Regression Analysis\u003c/h2\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\u003eMultiple Regression Analysis of Factors for Working and Receiving Income after Working (N\u0026thinsp;=\u0026thinsp;347,611)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorking Individual Income\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRobust Std. Err.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;z\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[95% CI]\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\u003eSocio-Demographic Factors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26\u0026ndash;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0639829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.06\u0026ndash;1.31)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e36\u0026ndash;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0658971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.01\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e46\u0026ndash;55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0610543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.79\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e56\u0026ndash;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0228344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-15.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.27\u0026ndash;0.36)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0126065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-28.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.26\u0026ndash;0.31)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot Married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\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\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0349526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-8.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.58\u0026ndash;0.72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0423328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.95\u0026ndash;1.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1640756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.60\u0026ndash;1.26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2487955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(2.37\u0026ndash;3.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIndividual Wealth\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0582079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.94\u0026ndash;1.16)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0759247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.23\u0026ndash;1.53)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFourth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1893272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(2.32\u0026ndash;3.06)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHighest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2495208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(2.54\u0026ndash;3.52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth Related Factors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHIV status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative (-)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive (+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.045285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.87\u0026ndash;1.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHIV viral load\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eunsuppressed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esuppressed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0658971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.01\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHuman Behavior Factors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber_Partner\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbstain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOne partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0927089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.79\u0026ndash;1.16)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultpartners\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1086903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.12\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePayment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1153621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.13\u0026ndash;1.58)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlcohol Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonthly or less\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0757076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.00-1.29)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2-4Times A Month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1007497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.97\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ndash;3 Times A Week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1157675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.06\u0026ndash;1.52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4 or More Times a Week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1052178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.85\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNon-Monetary Poverty Factors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold Size\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow National Average\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWithin National Average\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0626243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.75-1.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove National Average\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0628672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.71\u0026ndash;0.96)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidential\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0838255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.14\u0026ndash;1.47)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1565927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.63\u0026ndash;1.26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\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 and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e present the results of the econometric models. The multiple logit model shows that most explanatory variables were significantly associated with the possibility of working.\u003c/p\u003e\u003cp\u003eOur findings indicated that the odds of working were independently associated with age, sex, education level, individual wealth, Viral load level for positive individuals, number of partners, payment for sex, alcohol intake, household size, and residential location. Our findings indicated that the odds of working and receiving income were independently associated with age, sex, education level, individual wealth, Viral load level for positive individuals, number of partners, payment for sex, alcohol intake, household size, and residential location.\u003c/p\u003e\u003cp\u003eFor instance, older age reduced the odds ratio (0.3 less times less compared to young age) of working and receiving income (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Being female is also 0.3 times the odds of working and receiving income compared to being male. Married individuals had 0.6 odds of less time working and receiving income compared to unmarried individuals. Higher education was found to increase the odds of working and receiving income 2.8 times compared with primary education. Similarly, individuals with high wealth status were found to have 2.9 times higher odds of working and receiving income compared to those in the lower wealth quintile. On health-related factors, the individual with suppressed viral load was found to have 2.1 times higher odds of working and receiving income compared to those who were unsuppressed.\u003c/p\u003e\u003cp\u003eAdditionally, those with multiple partners were more likely to receive income after working than those who abstained (1.3 OR). Individuals who do not pay for sex increase the probability of working and receiving income (1.3 OR) compared with those who pay (OR\u0026thinsp;=\u0026thinsp;1.3, CI: 1.12\u0026ndash;1.54). Individuals who use alcohol four or more times a week have 1.27 times more likelihood of working and receiving income compared to patients who never use alcohol (OR\u0026thinsp;=\u0026thinsp;1.27, CI: 1.06\u0026ndash;1.52). Individuals from household sizes above the national average have 0.8 times less likelihood of working and receiving income compared to individuals from household sizes below the national average (OR\u0026thinsp;=\u0026thinsp;0.8, CI: 0.71\u0026ndash;0.96). Lastly, individuals from rural areas have 1.27 times more likelihood of working and receiving income compared to individuals from urban areas (OR\u0026thinsp;=\u0026thinsp;1.27, CI: 1.15\u0026ndash;1.47).\u003c/p\u003e\u003cp\u003eAdditionally, those with multiple partners were more likely to receive income after working than those who abstained (1.3 OR). Individuals who do not pay for sex increase the probability of working and receiving income (1.3 OR) compared with those who pay (OR\u0026thinsp;=\u0026thinsp;1.3, CI: 1.12\u0026ndash;1.54). Individuals who use alcohol four or more times a week have 1.27 times more likelihood of working and receiving income compared to patients who never use alcohol (OR\u0026thinsp;=\u0026thinsp;1.27, CI: 1.06\u0026ndash;1.52). Individuals from household size above the national average have 0.8 times less likelihood of working and receiving income compared to individuals from household size below the national average (OR\u0026thinsp;=\u0026thinsp;0.8, CI: 0.71\u0026ndash;0.96). Lastly, individuals from rural areas have 1.27 times more likelihood of working and receiving income compared to individuals from urban areas (OR\u0026thinsp;=\u0026thinsp;1.27, CI: 1.15\u0026ndash;1.4\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eWorking and receiving income were associated with individual age, such that the young adult age population aged (26\u0026ndash;35) years was found to be 1.17 times more likely to work and receive income compared to the rest of the population. This result has several implications for the study. First, based on most of the SSA population census, more than 65% of its people are young adults under 35 years of age; hence, they are more likely to dominate our category. Second, technological issues, nowadays due to the high level of technological innovation (computerization), make it easier for the young adult generation to adapt compared to the adult population, and make it easier for them to secure more jobs than their counterparts(6,10).\u003c/p\u003e\u003cp\u003eIn this study, gender was found to be significantly related to working and receiving income, consistent with the existing literature. The results show that the female population has 0.2 times the likelihood of working and receiving income as males. This result is consistent with most previous studies, which found that the household income of males can be more seriously affected by HIV/AIDS than the household income of females because a large part of the household income is generated by males(36,37). This is mainly channelled in labour division and culture issues among SSA countries, as many women play an important role in housework, such as raising kids and cooking for their husbands, which prohibits them from paid jobs(38,39). Also, the facts are that regardless of their big in number in terms of population, their percentage of working is very small, and(13,40) in their study provide an argument that when both female and male households work and if male household heads are affected by HIV/AIDS or any disability that prevents him no longer work, generally their female counterparts often take over the role of household financial supporter. In addition, a study conducted in China, specifically in the province of Yunnan, found that local customs and culture dictate that women provide financial support, while men are less likely to work outside the home(41).\u003c/p\u003e\u003cp\u003eRegarding marital status, the results are controversial, as many married people (approximately 81%) work and receive income compared to unmarried individuals. However, married individuals were found to be statistically significant, with 0.6 times less likelihood of working and receiving income compared to unmarried individuals. A study conducted by (42) provides a supportive argument that married individuals may have additional responsibilities within the household, such as caregiving, child-rearing, or managing household affairs, which can limit their availability for full-time employment or career advancement. In HIV/AIDS couple families, the other spouse may take on caregiving responsibilities, which can impact their ability to maintain full-time employment or pursue career opportunities that require significant time commitments. Hence, living alone, being separated, or being divorced may significantly increase the probability of working and receiving income.\u003c/p\u003e\u003cp\u003eRegarding education level, most of the working and receiving population (approximately 38.5%) is at the secondary level. However, those with a university-level education are 2.8 times more likely to be working and receiving income than those with primary education. The results are similar to those of several studies(43,44) which found that the higher the education, the higher the chance of being employed, and this is mainly due to the high working skills of university-level individuals. In addition, the education level may define the level of exposure of an individual, which is critical when it comes to assessing the output per worker. We are told that the trade-off between efficiency and justice no longer holds in the global knowledge-driven economy. However, other studies on business turnover argue that employees with high education levels, who are young and inexperienced, tend to have a low level of satisfaction with jobs and careers and have a lower commitment to the organization; these negative attitudes are associated with turnover intention(45,46).\u003c/p\u003e\u003cp\u003eIn terms of individual wealth, most of the working and receiving population (approximately 24.5%) belongs to the higher wealth quintile. Individuals belonging to the higher wealth quintile are 2.9 times more likely to work and receive income than those belonging to the lower wealth quintile. This result is channelled as individuals from higher wealth quintiles can easily access education and skills, job opportunities, asset ownership and investment, health and well-being, social capital and networks, risk aversion, and policy and economic environment(47,48). Hence, understanding these factors highlights the complex interplay between wealth, socioeconomic opportunities, and income generation. This result is consistent with findings from a previous by (49), who found that individual wealth heterogeneity matters for aggregate fluctuations in employment, output, and wages based on the overall employment shape. The employment rate across the wealth quintile is nearly flat, especially among households, except for the first wealth quintile.\u003c/p\u003e\u003cp\u003eIn terms of viral load level, finding that individuals with suppressed viral load have 2.1 times higher odds of working and receiving income aligns with broader evidence linking viral suppression to improved socioeconomic outcomes. A study in Zambia found that longer ART duration and multi-month drug dispensation significantly increased viral suppression, which in turn correlated with better employment stability(50). Similarly, research in Chile has shown that unemployment is associated with higher odds of having a detectable viral load, reinforcing the bidirectional relationship between health and economic productivity(51). Moreover, a meta-analysis revealed that monetary incentives improved viral suppression and retention in care, indirectly enhancing economic participation(52). These findings suggest that viral suppression not only improves clinical outcomes but also facilitates labor market engagement, especially in resource-limited settings.\u003c/p\u003e\u003cp\u003eIn terms of several partners, the majority of working and receiving incomes, about 59.8% belong to one partner. However, multipartners were found to be 1.3 times more likely to work and receive income compared to an individual with no partner. These results are channelled in several ways, firstly, having multiple partners could increase financial responsibilities and obligations, motivating individuals to seek employment or work actively to meet these obligations(53). Secondly, a study conducted by (54)found that individuals with multiple partners may exhibit characteristics associated with risk-taking behavior, which can extend to their economic activities. This behavior may lead them to pursue more entrepreneurial opportunities or take on higher-risk but potentially higher-reward occupations. Lastly, a study conducted by (55)provides an argument that having multiple partners can expand an individual's social network and connections, which may provide access to employment opportunities, referrals, or business partnerships.\u003c/p\u003e\u003cp\u003eThe study found that 62.5% of individuals who never consume alcohol are employed and receive income. However, individuals who drink alcohol two to three times a week were found to be 1.27 times more likely to work and earn income compared to abstainers (56). Several arguments may explain this association. Firstly, in some cultural contexts, alcohol consumption is closely intertwined with social activities, networking, and business engagements. Supporting this, research by (57) suggests that certain occupations or industries may exhibit higher levels of alcohol use among workers. For example, in sectors such as hospitality, entertainment, or various skilled trades, socializing and networking over drinks may be common practice, potentially influencing employment patterns. Moreover, a study conducted by (58) notes that, in some cases, individuals with alcohol use disorders may engage in informal or irregular employment to sustain their drinking habits. While these forms of work may lack stability, they still contribute to income generation. Nonetheless, it is crucial to recognize that higher alcohol consumption is associated with negative health and behavioral outcomes. Alcohol intoxication can lead to behavioral problems and mental changes, including inappropriate behavior, mood instability, poor judgment, slurred speech, memory issues, attention deficits, and impaired coordination.\u003c/p\u003e\u003cp\u003eIn terms of household size, most individuals who are employed and earn income, approximately 73.4% belong to households with a size below the national average. Conversely, individuals from households above the national average in size were found to be 0.82 times less likely to work and receive income compared to those from smaller households. This pattern may be influenced by several factors. First, larger households often face challenges in accessing education and skill development opportunities, which are essential for improving employability and income-generation prospects(59). Second, they typically experience greater economic pressures due to increased consumption needs and expenses. These financial constraints may limit opportunities for household members, including adults, to engage in paid employment(60).\u003c/p\u003e\u003cp\u003eLastly, in terms of residence location, most individuals who are employed and receive income, approximately 58.0% reside in rural areas. Furthermore, individuals from rural areas were found to be 1.2 times more likely to work and earn income compared to those from urban areas. This finding is largely attributed to the agricultural sector, which employs over 60% of the population in Sub-Saharan African countries and is predominantly concentrated in rural areas. Consequently, rural residents may have greater employment opportunities compared to their urban counterparts.\u003c/p\u003e"},{"header":"5.0 Conclusions","content":"\u003cp\u003eThis study highlights the complex factors influencing income generation among individuals living with HIV in Sub-Saharan Africa. Socio-demographic and health-related variables such as age, gender, education, wealth, marital status, alcohol use, household size, partner count, and residence significantly affect labor force participation. Notably, viral load suppression is positively linked to economic engagement, emphasizing the dual benefit of consistent ART use. The findings advocate for policies that combine viral monitoring with economic empowerment, particularly for vulnerable groups. Women, those who are married, and individuals from larger households face unique constraints. Addressing these disparities calls for integrated interventions. HIV programs should embed livelihood strategies to support inclusive socioeconomic recovery.\u003c/p\u003e"},{"header":"6.0 Study Limitations","content":"\u003cp\u003eThese findings should be interpreted with caution due to certain study limitations. Our recruitment strategy was restricted to 12 out of 54 Sub-Saharan African countries, primarily because the HIV Impact Assessment Survey (PHIA) was not conducted across the entire region. This constraint reflects the availability of data rather than a deliberate exclusion.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eART: \u0026nbsp; \u0026nbsp;\u0026nbsp;Antiretroviral therapy\u003c/p\u003e\n\u003cp\u003ePHIA: \u0026nbsp; Population HIV Impact Assessment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAIDS: \u0026nbsp; Acquired Immunodeficiency Syndrome\u003c/p\u003e\n\u003cp\u003eAOR: \u0026nbsp; \u0026nbsp;Adjusted Odds Ratio\u003c/p\u003e\n\u003cp\u003eSSA: \u0026nbsp; \u0026nbsp; \u0026nbsp;Sub-Saharan African Countries\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHIV: Human Immunodeficiency Virus\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements-\u003c/strong\u003eThe author gratefully acknowledges the Centers for Disease Control and Prevention (CDC) for their valuable support in providing access to datasets from twelve countries upon request through their website.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions: -\u003c/strong\u003eConceptualization, B.Y. and J.M.; methodology, B.Y; software, B.Y; validation, B.Y., J.M. and A.M.; formal analysis, A.M.; investigation, B.Y.; resources, B.Y.; data curation, B.Y.; writing\u0026mdash;original draft preparation, B.Y.; writing\u0026mdash;review and editing J.M. and A.M.; All authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo funding \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eThe study datasets and materials for all twelve sub-Saharan African countries are available on the website of the Centre for Disease Control (CDC). \u003cstrong\u003ehttps://phia-data.icap.columbia.edu/datasets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol underwent a thorough review process, ensuring ethical clearance was obtained from the respective Ministries of Health in each country. Additionally, informed consent was secured from all participants before to their interviews.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve a clinical trial and, therefore, was not registered in any clinical trial registry. This study did not involve a clinical trial and, therefore, was not registered in any clinical trial registry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eAll authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBloom D, Canning D, Kotschy R, Prettner K, Sch\u0026uuml;nemann J. Health and Economic Growth: Reconciling the Micro and Macro Evidence. Cambridge, MA; 2019 Jun.\u003c/li\u003e\n\u003cli\u003eOgundari K, Awokuse T. Human capital contribution to economic growth in Sub-Saharan Africa: Does health status matter more than education? Econ Anal Policy. 2018 Jun;58:131\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003ePuiu IA, B\u0026icirc;lb\u0026icirc;ie A. Measuring productivity in the healthcare sector: a bibliometric and content analysis. Health Econ Rev. 2025 Mar 18;15(1):24.\u003c/li\u003e\n\u003cli\u003ePuiu IA, B\u0026icirc;lb\u0026icirc;ie A. Measuring productivity in the healthcare sector: a bibliometric and content analysis. Health Econ Rev. 2025 Mar 18;15(1):24.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Global action plan on physical activity 2018-2030: more active people for a healthier world. World Health Organization. 2019.\u003c/li\u003e\n\u003cli\u003eRoziana Baharin, Rizqon Halal Syah Aji, Ishak Yussof, Nasir Mohd Saukani. Impact of Human Resource Investment on Labor Productivity in Indonesia. Iranian Journal of Management Studies (IJMS). 2019;\u003c/li\u003e\n\u003cli\u003eKokabisaghi F. Assessment of the Effects of Economic Sanctions on Iranians\u0026rsquo; Right to Health by Using Human Rights Impact Assessment Tool: A Systematic Review. Int J Health Policy Manag. 2018 Jan 20;7(5):374\u0026ndash;93.\u003c/li\u003e\n\u003cli\u003eLibois F, Somville V. Fertility, household size and poverty in Nepal. World Dev. 2018 Mar;103:311\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eMora Z, Suharyanto A, Yahya M. Effect of Work Safety and Work Healthy Towards Employee\u0026rsquo;s Productivity in PT. Sisirau Aceh Tamiang. Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences. 2020 May 8;3(2):753\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eBrunner B, Igic I, Keller AC, Wieser S. Who gains the most from improving working conditions? Health-related absenteeism and presenteeism due to stress at work. The European Journal of Health Economics. 2019 Nov 15;20(8):1165\u0026ndash;80.\u003c/li\u003e\n\u003cli\u003eTzenios N. The Impact of Health Literacy on Employee Productivity: An Empirical Investigation. 2019.\u003c/li\u003e\n\u003cli\u003eAWOGBEMI TO. HUMAN CAPITAL DEVELOPMENT AND NIGERIA\u0026rsquo;S ECONOMIC GROWTH. Journal of Public Administration, Finance and Law. 2023;(27):67\u0026ndash;76.\u003c/li\u003e\n\u003cli\u003eAchdou Y, Han J, Lasry JM, Lions PL, Moll B. Income and Wealth Distribution in Macroeconomics: A Continuous-Time Approach. Rev Econ Stud. 2022 Jan 10;89(1):45\u0026ndash;86.\u003c/li\u003e\n\u003cli\u003eAsare Vitenu-Sackey P, Barfi R. The Impact of Covid-19 Pandemic on the Global Economy: Emphasis on Poverty Alleviation and Economic Growth. The Economics and Finance Letters. 2021;8(1):32\u0026ndash;43.\u003c/li\u003e\n\u003cli\u003eSriram S, Khan MM. Effect of health insurance program for the poor on out-of-pocket inpatient care cost in India: evidence from a nationally representative cross-sectional survey. BMC Health Serv Res. 2020 Dec 7;20(1):839.\u003c/li\u003e\n\u003cli\u003eDean LT, Moss SL, Ransome Y, Frasso-Jaramillo L, Zhang Y, Visvanathan K, et al. \u0026ldquo;It still affects our economic situation\u0026rdquo;: long-term economic burden of breast cancer and lymphedema. Supportive Care in Cancer. 2019 May 18;27(5):1697\u0026ndash;708.\u003c/li\u003e\n\u003cli\u003eKaranika-Murray M, Biron C. The health-performance framework of presenteeism: Towards understanding an adaptive behaviour. Human Relations. 2020 Feb 18;73(2):242\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eWolkoff P, Azuma K, Carrer P. Health, work performance, and risk of infection in office-like environments: The role of indoor temperature, air humidity, and ventilation. Int J Hyg Environ Health. 2021 Apr;233:113709.\u003c/li\u003e\n\u003cli\u003eMominur Rahman Md, Islam F, Saidur Rahaman Md, Sultana NA, Fahim NF, Ahmed M. Studies on the prevalence of HIV/AIDS in Bangladesh including other developing countries. Advances in Traditional Medicine. 2023 Sep 20;23(3):647\u0026ndash;58.\u003c/li\u003e\n\u003cli\u003eGovender RD, Hashim MJ, Khan MA, Mustafa H, Khan G. Global Epidemiology of HIV/AIDS: A Resurgence in North America and Europe. J Epidemiol Glob Health. 2021;11(3):296.\u003c/li\u003e\n\u003cli\u003eMukiri E. HIV Oral Self-testing (HiVST) Strategy for Increased HIV Testing Among Female Sex Workers (Fsw) in Nairobi County, Kenya. 2023.\u003c/li\u003e\n\u003cli\u003eUSMAN SUNUSI USMAN, ALIYU MUHAMMAD MAIGORO, GANA MUHAMMAD LAWAN, JIBRIN ADAMU DAMAZAI, ABUBAKAR MUHAMMAD KURFI, IBRAHIM ADAM ABDULLAHI, et al. Comparative study on sero-prevalence and risk factors of HIV/AIDs between intra-city and long-distance commercial drivers in Kano State. International Journal of Science and Research Archive. 2023 Jul 30;9(2):077\u0026ndash;89.\u003c/li\u003e\n\u003cli\u003eFaria NR, Vidal N, Lourenco J, Raghwani J, Sigaloff KCE, Tatem AJ, et al. Distinct rates and patterns of spread of the major HIV-1 subtypes in Central and East Africa. PLoS Pathog. 2019 Dec 6;15(12):e1007976.\u003c/li\u003e\n\u003cli\u003eCollier DA, Monit C, Gupta RK. The Impact of HIV-1 Drug Escape on the Global Treatment Landscape. Cell Host Microbe. 2019 Jul;26(1):48\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eChilunda V, Calderon TM, Martinez-Aguado P, Berman JW. The impact of substance abuse on HIV-mediated neuropathogenesis in the current ART era. Brain Res. 2019 Dec;1724:146426.\u003c/li\u003e\n\u003cli\u003ePortilla-Tamarit J, Reus S, Portilla I, Fuster Ruiz-de-Apodaca MJ, Portilla J. Impact of Advanced HIV Disease on Quality of Life and Mortality in the Era of Combined Antiretroviral Treatment. J Clin Med. 2021 Feb 11;10(4):716.\u003c/li\u003e\n\u003cli\u003eSapiano TN. Fiscal Decentralization and the HIV Treatment Cascade in Low-and Middle-Income Countries: Identifying Opportunities to Sustain Progress Made Under the President\u0026rsquo;s Emergency Plan for Aids Relief . 2023.\u003c/li\u003e\n\u003cli\u003eZhang Q, Yang H, Fan J, Duan L, Chen D, Feng X, et al. Older people living with human immunodeficiency virus/acquired immune deficiency syndrome in Chinese rural areas: perceived stigma and associated factors. Trans R Soc Trop Med Hyg. 2019 Aug 1;113(8):477\u0026ndash;82.\u003c/li\u003e\n\u003cli\u003eLiu XJ, McGoogan JM, Wu ZY. Human immunodeficiency virus/acquired immunodeficiency syndrome prevalence, incidence, and mortality in China, 1990 to 2017: a secondary analysis of the Global Burden of Disease Study 2017 data. Chin Med J (Engl). 2021 May 20;134(10):1175\u0026ndash;80.\u003c/li\u003e\n\u003cli\u003eDavy-Mendez T, Napravnik S, Wohl DA, Durr AL, Zakharova O, Farel CE, et al. Hospitalization Rates and Outcomes Among Persons Living With Human Immunodeficiency Virus in the Southeastern United States, 1996\u0026ndash;2016. Clinical Infectious Diseases. 2020 Oct 23;71(7):1616\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eKim JM, Kim NJ, Choi JY, Chin BS. History of Acquired Immune Deficiency Syndrome in Korea. Infect Chemother. 2020;52(2):234.\u003c/li\u003e\n\u003cli\u003eMcinziba A. Exploring how the management of household incomes impact on antiretroviral therapy adherence behaviour of people living with HIV in the Western Cape, South Africa. 2021.\u003c/li\u003e\n\u003cli\u003eFauk NK, Ward PR, Hawke K, Mohamadi L, Mwanri L. A narrative systematic review of quantitative and qualitative evidence on the impacts of HIV on women living with HIV and their families in developing countries. 2019.\u003c/li\u003e\n\u003cli\u003eOsobase AO. Factors Influencing Food Expenditure Patterns Among HIV/AIDS Affected Households in Lagos State of Nigeria. T\u0026uuml;rk Akademik Sosyal Bilimler Araştırma Dergisi. 2019;\u003c/li\u003e\n\u003cli\u003eBoateng EY, Abaye DA. A Review of the Logistic Regression Model with Emphasis on Medical Research. Journal of Data Analysis and Information Processing. 2019;07(04):190\u0026ndash;207.\u003c/li\u003e\n\u003cli\u003eOsobase AO, Dauda RO, Nwakeze NM. The Impact of HIV/AIDS on Household Income: A case study of Lagos State, Nigeria. 2018.\u003c/li\u003e\n\u003cli\u003eRaghupathi V, Raghupathi W. The influence of education on health: an empirical assessment of OECD countries for the period 1995\u0026ndash;2015. Archives of Public Health. 2020 Dec 6;78(1):20.\u003c/li\u003e\n\u003cli\u003eMusyafaah NL, Novitasari Y, Rahmawati TL. Division the Husband and Wife Roles to Live a Domestic Life During the Pandemic Covid 19 in the Mubadala Perspective. Ulul Albab: Jurnal Studi dan Penelitian Hukum Islam. 2022 Jan 8;5(1):19.\u003c/li\u003e\n\u003cli\u003eSancak. Factors that Influence Employment of Women in the City of Van. In Women\u0026rsquo;s Economic Empowerment in Turkey (pp. 17-27). Routledge. Routledge. 2019;\u003c/li\u003e\n\u003cli\u003eAburto JM, Villavicencio F, Basellini U, Kj\u0026aelig;rgaard S, Vaupel JW. Dynamics of life expectancy and life span equality. Proceedings of the National Academy of Sciences. 2020 Mar 10;117(10):5250\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eWang X, Cheng Z. Cross-Sectional Studies. Chest. 2020 Jul;158(1):S65\u0026ndash;71.\u003c/li\u003e\n\u003cli\u003eUgargol AP, Bailey A. Family caregiving for older adults: gendered roles and caregiver burden in emigrant households of Kerala, India. Asian Popul Stud. 2018 May 4;14(2):194\u0026ndash;210.\u003c/li\u003e\n\u003cli\u003eSzromek AR, Wolniak R. Job Satisfaction and Problems among Academic Staff in Higher Education. Sustainability. 2020 Jun 15;12(12):4865.\u003c/li\u003e\n\u003cli\u003eGuerrero M, Urbano D, Cunningham JA, Gaj\u0026oacute;n E. Determinants of Graduates\u0026rsquo; Start-Ups Creation across a Multi-Campus Entrepreneurial University: The Case of Monterrey Institute of Technology and Higher Education. Journal of Small Business Management. 2018 Jan;56(1):150\u0026ndash;78.\u003c/li\u003e\n\u003cli\u003eAbate J, Schaefer T, Pavone T. UNDERSTANDING GENERATIONAL IDENTITY, JOB BURNOUT, JOB SATISFACTION, JOB TENURE AND TURNOVER INTENTION. Journal of Organizational Culture, Communications and Conflict. 2018;\u003c/li\u003e\n\u003cli\u003eBhagwandeen TP. Relationship between intrinsic job satisfaction, extrinsic job satisfaction, and employee turnover intentions. 2021.\u003c/li\u003e\n\u003cli\u003ePietrolucci A, Albertini M. Not all wealth is the same: types and levels of wealth and children\u0026rsquo;s university enrolment. Eur Sociol Rev. 2023 Oct 20;39(5):789\u0026ndash;803.\u003c/li\u003e\n\u003cli\u003ePrieto J. New approaches to measuring economic and social well-being in Chile (Doctoral dissertation, London School of Economics and Political Science). 2020;\u003c/li\u003e\n\u003cli\u003ePietrolucci A, Albertini M. Not all wealth is the same: types and levels of wealth and children\u0026rsquo;s university enrolment. Eur Sociol Rev. 2023 Oct 20;39(5):789\u0026ndash;803.\u003c/li\u003e\n\u003cli\u003eLamba L, Kazonga E, Nyirenda C, Chilyabanyama R. Viral Load Suppression Among Adults with HIV on Antiretroviral Therapy: Outcomes from a Lusaka District Hospital, Zambia. International Journal of Translational Medical Research and Public Health. 2025 Feb 14;9:e004.\u003c/li\u003e\n\u003cli\u003eLeiva-Escobar I, Cortes CP, Lamadrid A. Employment Status and HIV Viral Load in Chilean Adult Population: A Propensity Score Analysis. AIDS Behav. 2025 Apr 9;29(4):1256\u0026ndash;65.\u003c/li\u003e\n\u003cli\u003eZhu Z, Guo L, Yang M, Cheng J. The effectiveness of monetary incentives in improving viral suppression, treatment adherence, and retention in care among the general population of people living with HIV: a systematic review and meta-analysis. AIDS Res Ther. 2025 Jun 2;22(1):57.\u003c/li\u003e\n\u003cli\u003eBalven R, Fenters V, Siegel DS, Waldman D. Academic Entrepreneurship: The Roles of Identity, Motivation, Championing, Education, Work-Life Balance, and Organizational Justice. Academy of Management Perspectives. 2018 Feb;32(1):21\u0026ndash;42.\u003c/li\u003e\n\u003cli\u003eCzerwonka M. Cultural, cognitive and personality traits in risk-taking behaviour: evidence from Poland and the United States of America. Economic Research-Ekonomska Istraživanja. 2019 Jan 1;32(1):894\u0026ndash;908.\u003c/li\u003e\n\u003cli\u003eNeumeyer X, Santos SC. Sustainable business models, venture typologies, and entrepreneurial ecosystems: A social network perspective. J Clean Prod. 2018 Jan;172:4565\u0026ndash;79.\u003c/li\u003e\n\u003cli\u003eGeusens F, Bigman-Galimore CA, Beullens K. A cross-cultural comparison of the processes underlying the associations between sharing of and exposure to alcohol references and drinking intentions. New Media Soc. 2020 Jan 3;22(1):49\u0026ndash;69.\u003c/li\u003e\n\u003cli\u003ePachito D V., Pega F, Bakusic J, Boonen E, Clays E, Descatha A, et al. The effect of exposure to long working hours on alcohol consumption, risky drinking and alcohol use disorder: A systematic review and meta-analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury. Environ Int. 2021 Jan;146:106205.\u003c/li\u003e\n\u003cli\u003eDordoye EK, Afun LA, Alalbila TM. Perception and Attitude of Employers towards Employees with AUD in an Emerging Economy: A Qualitative Enquiry. Open J Psychiatr. 2021;11(02):107\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eUgargol AP, Bailey A. Family caregiving for older adults: gendered roles and caregiver burden in emigrant households of Kerala, India. Asian Popul Stud. 2018 May 4;14(2):194\u0026ndash;210.\u003c/li\u003e\n\u003cli\u003eTrlifajov\u0026aacute; L, Hurrle J. Work must pay: Does it? Precarious employment and employment motivation for low-income households. J Eur Soc Policy. 2019 Jul 25;29(3):376\u0026ndash;95.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Working and receiving income, Viral Load Suppression, Socioeconomic Determinants and sub-Saharan Africa, Multiple logistic analysis","lastPublishedDoi":"10.21203/rs.3.rs-7283006/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7283006/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e HIV/AIDS is the leading cause of death in sub-Saharan Africa. Families facing numerous challenges, often exacerbated by the pandemic, frequently turn to their communities and external economic support for assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The PHIA project data (2015-2022) from 12 sub-Saharan countries focused on both adolescents and adults aged \u0026gt; 15 years and above. This study examined income after work, with HIV status, viral load, and sociodemographic factors as predictors. Descriptive statistics and econometric modelling were used to analyze the determinants of working and receiving income.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOur analysis revealed a strong connection between health status and labor force engagement among individuals in Sub-Saharan Africa. Suppressed viral load in HIV-positive individuals significantly increased the odds of working and earning income by 2.1 times, underscoring the economic value of ART adherence. Behavioral factors such as alcohol use also played a role: those consuming alcohol frequently had 1.27 times higher odds of economic participation. Conversely, poor health outcomes and larger household sizes diminished employment prospects. Age, gender, and marital status influenced economic engagement, with older adults, females, and married individuals showing reduced odds. Higher education and wealth status substantially boosted labor participation. Rural residents were more economically active than urban dwellers. These findings emphasize the critical intersection between health and socioeconomic resilience\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e \u0026nbsp;Our study demonstrates that health status, particularly viral suppression among HIV-positive individuals, plays a pivotal role in enhancing income generation. Behavioral and demographic factors also shaped employment outcomes, with younger, educated, wealthier individuals showing higher odds. Poor health, female gender, older age, and urban residence reduced income opportunities. These findings highlight the vital link between clinical well-being and socioeconomic empowerment.\u003c/p\u003e","manuscriptTitle":"Economic Consequences of HIV Status and Viral Load Level Among Households: Evidence from Twelve Sub-Saharan African Countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 16:32:24","doi":"10.21203/rs.3.rs-7283006/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"755e4443-02c0-4e2f-a2e4-e2aa931ce0ee","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-18T05:08:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 16:32:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7283006","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7283006","identity":"rs-7283006","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}