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. 2018 Aug 27;21(8):1093–1102. doi: 10.1093/ntr/nty176

Smoking and HIV in Sub-Saharan Africa: A 25-Country Analysis of the Demographic Health Surveys

John D Murphy 1,✉, Benmei Liu 2, Mark Parascandola 3
PMCID: PMC6941705  PMID: 30165688

Abstract

Background

Having HIV/AIDS has been associated with a higher prevalence of smoking. Moreover, evidence suggests that people with HIV/AIDS who smoke have poorer treatment and survival outcomes. The HIV–smoking relationship is understudied in sub-Saharan Africa, where tobacco use patterns and HIV prevalence differ greatly from other world regions.

Methods

Cross-sectional data from the Demographic Health Surveys and AIDS Indicator Surveys, representing 25 sub-Saharan African countries, were pooled for analysis (n = 286850). The association between cigarette smoking and HIV status was analyzed through hierarchical logistic regression models. This study also examined the relationship between smokeless tobacco (SLT) use and HIV status.

Results

Smoking prevalence was significantly higher among men who had HIV/AIDS than among men who did not (25.90% vs 16.09%; p < .0001), as was smoking prevalence among women who had HIV/AIDS compared with women who did not (1.15% vs 0.73%; p < .001). Multivariate logistic regression revealed that the odds of smoking among people who had HIV/AIDS was 1.12 times greater than among people who did not when adjusting for socioeconomic, demographic, and sexual risk factors (adjusted OR = 1.12, 95% CI = 1.04% to 1.21%; p < .001). Similarly, multivariate logistic regression revealed that HIV-positive individuals were 34% more likely to use SLT than HIV-negative individuals (adjusted OR = 1.34, 95% CI = 1.17% to 1.53%).

Conclusion

Having HIV was associated with a greater likelihood of smoking cigarettes as well as with using SLT in sub-Saharan Africa. These tobacco use modalities were also associated with male sex and lower socioeconomic status.

Implications

This study shows that in sub-Saharan Africa, as in more studied world regions, having HIV/AIDS is associated with a higher likelihood of smoking cigarettes when adjusting for demographic, socioeconomic, and sexual risk factors. This study also supports the literature stating that cigarette smoking is inversely associated with socioeconomic status, as evidenced by higher smoking prevalence among poorer individuals, less educated individuals, and manual and agricultural laborers.

Introduction

Data from North America and Europe suggest that tobacco use is higher among persons living with HIV/AIDS (PLWHA) compared with their HIV-negative counterparts.1–6 In addition, a range of studies, largely from high-income countries (HICs), have documented that PLWHA who smoke tobacco products suffer greater morbidity and mortality than their nonsmoking counterparts. Tobacco control among PLWHA has become more relevant as HIV infection has largely shifted from an acute disease to a chronic condition because of the implementation of novel antiretroviral treatments. PLWHA who smoke have increased risks of developing lung and other forms of cancer and are more likely to develop pneumonia, chronic obstructive pulmonary disease, and cardiovascular disease.7,8 For example, a cohort study in Denmark found that more than 60% of deaths in the HIV-infected population were associated with smoking and that the number of life years lost in association with smoking was larger than that associated with HIV-related factors among 35-year-old HIV-infected smokers.9 There is also some evidence that tobacco smoking increases the risk of HIV/AIDS infection and disease progression. Tobacco smoking appears to be an independent and important risk factor for contracting HIV.10,11 Research suggests that PLWHA who smoke have a greater rate of progression from HIV infection to AIDS and have a poorer response to antiretroviral therapy (ART). In a longitudinal study of a large HIV-infected cohort, Feldman et al. found that smokers receiving ART had poorer immunological responses and a greater risk of rebound compared with nonsmokers.12 PLWHA who smoke have also been found to have lower therapeutic adherence than PLWHA who do not smoke.13 Smoking also increases the risk of tuberculosis (TB) infection and disease progression, a leading coinfection in PLWHA.14,15

Worldwide there are currently an estimated 37 million people living with HIV, with 15.8 million on ART.16 Sub-Saharan Africa is the region most affected, accounting for around 70% of HIV infections. In South Africa alone, there were 1.4 million new HIV infections in 2014. While the rate of new infections has decreased by 35% since 2000, the number of persons living with HIV continues to grow because of both new infections and improved treatments. In addition, many low- and middle-income countries (LMICs) are now facing a “double burden” of disease characterized by a rise in the prevalence of noncommunicable diseases (NCDs), such as cancer and heart disease, alongside a persistent burden of infectious diseases such as HIV.

Prevalence of cigarette smoking in sub-Saharan Africa tends to be lower than in other parts of the world. However, while smoking rates are falling in many countries worldwide, they are increasing and projected to continue to increase on the African continent.17–18 Currently, sub-Saharan Africa exhibits substantial variation in smoking rates among countries; according to an earlier review of the literature, prevalence of smoking may range from 1.8% in Zambia to 25.8% in Sierra Leone, though limited data make these estimates uncertain.19

There are a mounting number of studies on the relationship between HIV and smoking in HICs. However, this relationship is still understudied within the context of sub-Saharan Africa. In addition, there is little discussion in the literature to date on tobacco use patterns and their correlates in Africa. A recent study by Mdege et al. found a positive association between HIV and tobacco; however, its data were not analyzed at the individual level and did not control for covariates such as socioeconomic status (SES). Our study aims to fill these gaps, which may shed light on how best to address these overlapping epidemics.

Methods

Study Design and Sample

This study analyzed the association between smoking and HIV in sub-Saharan Africa using data from the Demographic and Health Surveys (DHS) and AIDS Indicator Surveys (AIS),20 which are USAID programs. These surveys ask a variety of health-related questions from nationally representative probability samples of men and women ages 15–59 in LMICs. Sample weights were developed to obtain nationally representative estimates. In addition, the AIS generated a separate weight for the sample of respondents who participated in HIV testing. This weight was used to develop nationally representative HIV prevalence estimates that attempt to account for nonresponse in HIV testing.21–23 To preserve the hierarchical data structure, we treated each country as a higher level sampling stratum and stacked them together so that the unified dataset represents the population from the 25 countries being included, while the original sampling information within each country remained the same. The country name was combined with the original strata variable to form a new strata variable for this unified analytic dataset. The primary sampling units (PSUs) and sample weights within each country were kept in their original form in the unified dataset.

The three most recent, simultaneously collected individual-level datasets from each country—the women’s dataset, the men’s dataset, and the individual HIV test results dataset—were merged using the person ID and appended into a unified, “overall” dataset including individual men and women and their HIV test results from each country in the study. This final unified dataset included 286850 individuals from 25 countries in sub-Saharan Africa, using data from the most recent surveys taken between 2005 and 2015 (see Supplementary Table 6 for a full list of included countries).

Selection of Variables

Variables of interest included HIV test results and self-reported cigarette smoking and smokeless tobacco (SLT) use responses from the following three survey questions, which were included in all of the surveys used:

1. Do you currently smoke cigarettes?

2. Do you currently smoke or use any (other) type of tobacco?

3. What (other) type of tobacco do you currently smoke or use?

The DHS literature suggests that “current smoking” was not asked in a defined way (eg, whether the participant smoked daily or had smoked in the preceding 30 days).24 Sociodemographic variables were included in the analysis because they have been associated with smoking status. Such variables included the highest level of education completed (no education, primary, secondary, and higher), wealth index (a complex variable designed for the DHS, with quintiles for lowest wealth, lower wealth, middle wealth, higher wealth, and highest wealth),25 occupation (a categorical variable with the following levels: not working, agricultural, manual labor, services, and professional), sex, age (analyzed as a continuous variable), urban/rural residence, marital status (never married or in union, currently married or in union, formerly married or in union), number of sexual partners, and recent sexual activity (never had sex, sexually active in the last 4 weeks, and not sexually active in the last 4 weeks), which has been found to be positively associated with smoking.26 Within the occupation categorical variable, “manual” included both skilled and unskilled manual laborers, and “services” included household/domestic, clerical, and sales work. Alcohol use was examined in only five of the countries in this study and therefore was not included in the analysis.

Statistical Analysis

Both descriptive and logistic regression analyses were conducted overall and by country for the variables of interest. Global chi-squared tests were used for testing independence among variables. The sample weight adjusting for HIV nonrespondents was used to produce nationally representative estimates. The stratum and PSU information were incorporated in variance estimation to account for the complex sampling design. All data manipulation and statistical analyses were performed in Stata 14 statistical software, following instructions from the DHS Program Users’ Forum.27

Associations between current cigarette smoking (yes/no) and HIV status (positive/negative) were examined through hierarchical logistic regressions: Step 1 was the simple bivariate logistic regression of current cigarette smoking on HIV status. Step 2 adjusted for socioeconomic factors as represented by wealth index. Step 3 further adjusted for demographic characteristics including sex, area of residence (urban/rural), and country. Finally, Step 4 further adjusted for sexual risk behaviors including number of sexual partners in the last 12 months and recent sexual activity. This study did not distinguish between HIV1 and HIV2 positivity; both were simply considered “HIV positive.” A similar approach was used to examine the association between SLT use (yes/no) and HIV status (positive/negative).

Results

Demographic Characteristics of Respondents (25 Countries Combined)

Of the 286850 respondents in our analytic sample, 48% were males, 79% were aged 39 or under, 62% lived in rural areas, about one third were not working and one third were doing agricultural work, and about half of the respondents had received no education (Table 1). Fifty-eight percentage of respondents reported being married or in union, 61% reported having only one sexual partner within the last 12 months, and 52% were sexually active in the last 4 weeks. About 5.90% were HIV positive, with women having a higher HIV-positive prevalence (6.95%) than men (4.75%).

Table 1.

Sample Size and Weighted Percentage by Variables of Interest for Total Sample and by Sex

Variable Total
(n = 286850)
n (%)		 Male (n = 131496)
n (%)		 Female (n = 155354)
n (%)
HIV status
HIV negative 269718 (94.10) 125313 (95.25) 144405 (93.05)
HIV positive 17132 (5.90) 6183 (4.75) 10949 (6.95)
Demographic characteristics
Age group (years)
 15–19 62086 (21.51) 28660 (21.64) 33426 (21.39)
 20–29 95726 (33.82) 39530 (30.79) 56196 (36.57)
 30–39 70037 (24.45) 30120 (22.97) 39917 (25.79)
 40–49 46286 (15.77) 21251 (15.74) 25035 (15.79)
 50–59 12378 (4.35) 11802 (8.77) 576 (0.34)
 ≥60 337 (0.11) 133 (0.09) 204 (0.12)
Area of residence
 Urban 105165 (38.38) 47973 (38.92) 57192 (37.89)
 Rural 181685 (61.62) 83523 (61.08) 98162 (62.11)
Socioeconomic status
Level of education completed
 No education 147686 (50.24) 60060 (44.82) 87626 (55.16)
 Primary 109623 (38.65) 54321 (41.23) 55302 (36.29)
 Secondary 15827 (5.85) 8923 (7.16) 6904 (4.66)
 Higher 13671 (5.26) 8171 (6.78) 5500 (3.88)
Wealth index
 Poorest 57621 (16.96) 25742 (16.24) 31879 (17.62)
 Poorer 54312 (18.33) 25101 (18.29) 29211 (18.37)
 Middle 54600 (19.36) 25261 (19.50) 29339 (19.23)
 Richer 55734 (21.27) 25669 (21.48) 30065 (21.08)
 Richest 64583 (24.08) 29723 (24.49) 34860 (23.71)
Occupation
 Not working 85867 (29.74) 23124 (18.09) 62743 (40.21)
 Agricultural 96316 (33.48) 54488 (40.76) 41828 (26.94)
 Manual 33029 (12.37) 23196 (18.82) 9833 (6.57)
 Services 54064 (19.98) 19727 (16.28) 34337 (23.31)
 Professional 12285 (4.43) 7785 (6.05) 4500 (2.98)
Sexual risk behaviors
Marital status
 Never in union 98293 (34.96) 55403 (42.61) 42890 (28.00)
 Currently in union 169126 (58.46) 71012 (53.59) 98114 (62.88)
 Formerly in union 19429 (6.58) 5081 (3.80) 14348 (9.12)
No. of sexual partners, last 12 months
 0 73565 (25.55) 31395 (24.04) 42170 (26.92)
 1 175809 (60.90) 66746 (50.55) 109063 (70.31)
 2 or more 37476 (13.55) 33355 (25.41) 4121 (2.77)
Recent sexual activity
 Never had sex 44779 (16.69) 21586 (18.46) 23193 (15.25)
 Active in last 4 weeks 140966 (52.02) 62841 (53.06) 78125 (51.18)
 Not active in last 4 weeks 86580 (31.29) 33492 (28.49) 53088 (33.57)
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Overall Smoking Prevalence (25 Countries Combined)

The overall cigarette smoking prevalence and SLT use prevalence were 8.28% and 1.89% respectively (Table 2). Cigarette smoking prevalence was far higher among men than among women (16.55% vs 0.76%). Cigarette smokers were skewed toward the older age groups, with people older than 50 having the highest smoking prevalence (20.33%). Young people smoked the least; only 1.83% of teenagers smoked, and only 7.32% of adults aged 20–29. Overall, HIV-positive individuals had a higher cigarette smoking prevalence than those who were HIV negative (10.63% vs 8.14%, Table 2). A linear decrease in smoking prevalence by wealth index from poorest (10.82%) to richest (6.17%) in the overall dataset was observed. Education was also inversely correlated with cigarette smoking overall. Rural dwellers were significantly more likely to use cigarettes than urban dwellers; however, the difference was small (8.61% vs 7.77%; p < .0001). Cigarette smoking prevalence was higher among those doing manual labor or agricultural work compared with other occupational categories. SLT use varied similarly by socioeconomic factors. However, the difference in SLT use between men and women was not as stark as that of cigarette smoking (Table 2).

Table 2.

Weighted Cigarette Smoking and Smokeless Tobacco (SLT) Use Prevalence by Participant Characteristics, Overall and stratified by Sex

Variable %Total cigarette smokers (95% CI) %total SLT users
(95% CI)		 %Male cigarette smokers (95% CI) %Male SLT users
(95% CI)		 %Female cigarette smokers (95% CI) %Female SLT users
(95% CI)
Total 8.28 (8.12, 8.45) 1.89 (1.79, 1.98) 16.55 (16.24, 16.88) 2.33 (2.19, 2.48) 0.76 (0.70, 0.83) 1.48 (1.38, 1.59)
HIV status
HIV negative 8.14**** (7.97, 8.31) 1.85**** (1.76, 1.95) 16.09**** (15.77, 16.41) 2.33 (2.18, 2.49) 0.73*** (0.67, 0.80) 1.40**** (1.30, 1.51)
HIV positive 10.63 (10.06, 11.22) 2.44 (2.18, 2.73) 25.90 (24.56, 27.30) 2.28 (1.88, 2.75) 1.15 (0.91, 1.44) 2.54 (2.21, 2.92)
Demographic characteristics
Area of residence
 Urban 7.77**** (7.48, 8.06) 1.08**** (0.97, 1.21) 15.06**** (14.54, 15.60) 1.48**** (1.29, 1.69) 0.95**** (0.83, 1.08) 0.72**** (0.61, 0.84)
 Rural 8.61 (8.41, 8.81) 2.38 (2.25, 2.52) 17.51 (16.24, 16.88) 2.87 (2.67, 3.08) 0.65 (0.58, 0.73) 1.95 (1.81, 2.10)
Age group (years)
 15–19 1.83**** (1.67, 2.00) 0.33**** (0.27, 0.41) 3.50**** (3.22, 3.80) 0.47**** (0.38, 0.59) 0.30**** (0.20, 0.44) 0.21**** (0.15, 0.29)
 20–29 7.32 (7.08, 7.56) 1.06 (0.97, 1.16) 16.07 (15.56, 16.58) 1.52 (1.36, 1.71) 0.62 (0.53, 0.71) 0.71 (0.62, 0.81)
 30–39 10.76 (10.45, 11.08) 2.25 (2.09, 2.43) 22.99 (22.34, 23.65) 2.78 (2.51, 3.07) 0.85 (0.75, 0.98) 1.83 (1.65, 2.02)
 40–49 11.94 (11.55, 12.33) 4.13 (3.88, 4.40) 23.63 (22.87, 24.41) 3.99 (3.64, 4.37) 1.33 (1.17, 1.52) 4.25 (3.92, 4.61)
 50–59 20.33 (19.47, 21.23) 5.65 (5.14, 6.22) 20.81 (19.92, 21.73) 5.59 (5.06, 6.18) 9.14 (6.92, 11.96) 7.12 (5.10, 9.85)
 ≥60 16.58 (12.09, 22.32) 6.33 (4.07, 9.72) 28.40 (19.25, 39.77) 4.09 (1.70, 9.49) 8.78 (5.60, 13.49) 7.81 (4.62, 12.92)
Socioeconomic status
Level of education completed
 No education 8.51**** (8.30, 8.73) 2.59**** (2.46, 2.74) 18.88**** (18.42, 19.36) 3.20**** (2.98, 3.44) 0.85** (0.76, 0.94) 2.15**** (1.99, 2.31)
 Primary 8.26 (8.00, 8.52) 1.27 (1.17, 1.39) 15.65 (15.20, 16.12) 1.78 (1.60, 1.98) 0.61 (0.52, 0.73) 0.75 (0.65, 0.87)
 Secondary 8.42 (7.84, 9.05) 1.12 (0.89, 1.42) 13.80 (12.85, 14.82) 1.59 (1.27, 2.00) 0.92 (0.64, 1.31) 0.47 (0.24, 0.95)
 Higher 6.12 (5.63, 6.64) 0.46 (0.33, 0.63) 9.53 (8.77, 10.36) 0.69 (0.49, 0.97) 0.70 (0.46, 1.06) 0.09 (0.04, 0.21)
Wealth index
 Poorest 10.82**** (10.46, 11.19) 3.90**** (3.64, 4.19) 22.58**** (21.84, 23.33) 4.69**** (4.30, 5.12) 0.95* (0.81, 1.11) 3.24**** (2.94, 3.58)
 Poorer 9.40 (9.07, 9.75) 2.29 (2.10, 2.48) 18.94 (18.27, 19.63) 2.80 (2.50, 3.13) 0.76 (0.63, 0.91) 1.82 (1.63, 2.03)
 Middle 8.44 (8.11, 8.78) 1.82 (1.67, 1.99) 16.86 (16.23, 17.52) 2.33 (2.08, 2.60) 0.67 (0.53, 0.85) 1.36 (1.20, 1.55)
 Richer 7.55 (7.23, 7.89) 1.22 (1.08, 1.38) 14.82 (14.19, 15.48) 1.45 (1.24, 1.69) 0.81 (0.69, 0.96) 1.01 (0.84, 1.22)
 Richest 6.17 (5.89, 6.45) 0.80 (0.68, 0.94) 12.05 (11.52, 12.60) 1.20 (0.97, 1.47) 0.65 (0.54, 0.77) 0.43 (0.34, 0.53)
Occupation
 Not working 2.73**** (2.56, 2.90) 0.93**** (0.85, 1.03) 7.91**** (7.42, 8.42) 0.91**** (0.78, 1.07) 0.63**** (0.54, 0.73) 0.94**** (0.84, 1.06)
 Agricultural 10.93 (10.63, 11.24) 3.08 (2.88, 3.28) 18.41 (17.92, 18.91) 3.37 (3.11, 3.65) 0.76 (0.64, 0.89) 2.69 (2.45, 2.95)
 Manual 15.70 (15.12, 16.29) 1.89 (1.67, 2.14) 21.08 (20.33, 21.84) 1.82 (1.59, 2.09) 1.85 (1.49, 2.30) 2.06 (1.63, 2.62)
 Services 7.09 (6.76, 7.43) 1.49 (1.35, 1.65) 17.29 (16.53, 18.07) 2.30 (2.00, 2.65) 0.68 (0.57, 0.80) 0.98 (0.86, 1.13)
 Professional 8.21 (7.54, 8.94) 1.08 (0.85, 1.37) 12.21 (11.20, 13.29) 1.51 (1.17, 1.94) 0.91 (0.61, 1.34) 0.30 (0.18, 0.51)
Sexual risk behaviors
Marital status
 Never in union 5.63**** (5.41, 5.85) 0.67**** (0.60, 0.75) 9.37**** (9.03, 9.72) 0.92**** (0.80, 1.04) 0.44**** (0.35, 0.56) 0.33**** (0.26, 0.42)
 Currently in union 9.59 (9.38, 9.80) 2.49 (2.36, 2.63) 20.92 (20.48, 21.37) 3.34 (3.12, 3.57) 0.80 (0.72, 0.88) 1.84 (1.71, 1.98)
 Formerly in union 10.83 (10.22, 11.47) 2.95 (2.67, 3.27) 35.59 (33.82, 37.40) 4.01 (3.40, 4.72) 1.47 (1.24, 1.74) 2.56 (2.25, 2.90)
No. of sexual partners, last 12 months
 0 3.64**** (3.46, 3.83) 1.08**** (0.97, 1.19) 7.55**** (7.17, 7.96) 0.97**** (0.83, 1.15) 0.46**** (0.38, 0.54) 1.16**** (1.02, 1.31)
 1 8.70 (8.51, 8.90) 2.05 (1.94, 2.16) 20.84 (20.40, 21.30) 2.78 (2.58, 2.98) 0.76 (0.69, 0.84) 1.57 (1.46, 1.69)
 2 or more 15.17 (14.61, 15.75) 2.69 (2.43, 2.98) 16.54 (15.93, 17.17) 2.73 (2.45, 3.04) 3.75 (2.70, 5.19) 2.36 (1.85, 3.01)
Recent sexual activity
 Never had sex 1.54**** (1.39, 1.70) 0.30**** (0.24, 0.38) 3.01**** (2.73, 3.32) 0.46**** (0.36, 0.60) 0.08**** (0.04, 0.16) 0.14**** (0.08, 0.23)
 Active in last 4 weeks 10.56 (10.33, 10.80) 2.24 (2.11, 2.38) 21.89 (21.43, 22.35) 2.98 (2.77, 3.20) 0.95 (0.85, 1.07) 1.62 (1.49, 1.76)
 Not active in last 4 weeks 8.42 (8.15, 8.69) 2.08 (1.93, 2.23) 19.42 (18.83, 20.03) 2.34 (2.11, 2.59) 0.78 (0.69, 0.88) 1.89 (1.74, 2.07)
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*p < .05; **p < .01; ***p < .001; ****p < .0001; p values are from a Pearson chi squared test assessing the independence of the outcome variable from participant characteristics.

Logistic Regression Analyses (25 Countries Combined)

HIV-positive respondents had significantly higher odds of smoking cigarettes than HIV-negative respondents with or without adjusting for confounding factors (Table 3). The fully adjusted effect of HIV status on likelihood of smoking was slightly smaller than the crude effect (crude OR = 1.34 from Step 1 vs adjusted OR = 1.12 from Step 4; both p values <.001). Consistent with the bivariate analysis, the adjusted regression model (Step 4) revealed a significant inverse relationship between smoking prevalence and wealth (Table 2). However, when wealth index was added to the model, the effect of urban versus rural residence changed so that rural dwellers had a 16% lower odds of tobacco use compared with urban dwellers (OR = 0.84, p < .001). In addition, those who were sexually active or had two or more sexual partners were significantly more likely to smoke than those who had never had sex.

Table 3.

Odd Ratios and 95% Confidence Intervals From Hierarchical Logistic Regressions of Cigarette Smoking Status on HIV Status in Sub-Saharan Africa, With Fully Adjusted Model of Total Samplea and Stratified by Sex

Variable (Ref) Step 1 Step 2 Step 3 Step 4
Total Male Female
Intercept 0.09*** 0.06*** 0.05*** 0.02*** 0.02*** 0.00***
HIV status (HIV negative)
 HIV positive 1.34*** (1.25, 1.42) 1.17*** (1.08, 1.26) 1.16*** (1.07, 1.26) 1.12** (1.04, 1.21) 1.13** (1.04, 1.23) 1.13 (0.89, 1.44)
Demographic characteristics
(Not shown: country)
Sex (male)
 Female 0.04*** (0.035, 0.043) 0.04*** (0.035, 0.042) 0.037*** (0.033, 0.040) N/A N/A
 Age 1.04*** (1.037, 1.041) 1.04*** (1.038, 1.041) 1.023*** (1.022, 1.026) 1.02*** (1.021, 1.024) 1.04*** (1.03, 1.05)
Area of residence (urban)
 Rural 1.26*** (1.19, 1.32) 0.83*** (0.78, 0.89) 0.84*** (0.79, 0.90) 0.85*** (0.79, 0.91) 0.72** (0.58, 0.90)
Socioeconomic status
Wealth index (richest)
 Richer 1.33*** (1.24, 1.43) 1.35*** (1.25, 1.45) 1.35*** (1.25, 1.45) 1.30* (1.02, 1.66)
 middle 1.59*** (1.47, 1.73) 1.63*** (1.50, 1.77) 1.67*** (1.54, 1.82) 1.13 (0.81, 1.59)
 Poorer 1.87*** (1.72, 2.04) 1.91*** (1.75, 2.07) 1.95*** (1.79, 2.13) 1.33 (0.97, 1.81)
 Poorest 2.39*** (2.19, 2.60) 2.45*** (2.25, 2.67) 2.51*** (2.30, 2.74) 1.82*** (1.34, 2.48)
Sexual risk behaviors
No. of sexual partners, including spouse, in last 12 months (0)
 1 1.06 (0.99, 1.15) 1.10* (1.02, 1.19) 0.85 (0.66, 1.10)
 2 or more 1.23*** (1.13, 1.34) 1.23*** (1.12, 1.35) 3.35*** (2.24, 4.99)
Recent sexual activity (never had intercourse)
 Active in last 4 weeks 5.37*** (4.70, 6.13) 5.33*** (4.66, 6.10) 5.56*** (2.51, 12.34)
 Not active in last 4 weeks 5.19*** (4.59, 5.86) 5.27*** (4.66, 5.97) 3.72** (1.74, 7.95)
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aSteps 1–3 apply to the total sample (ie, men and women combined).

*p < .05; **p < .01; ***p < .001.

Given the stark difference in smoking prevalence by sex (Table 2), a logistic regression model of smoking on HIV status stratified by sex was run to see whether the resulting odds ratios would differ greatly from that of the model that included both sexes. However, the results were almost identical (OR = 1.12 for both sexes and 1.13 for men only; p < .01 for both models). For women only, the odds ratio (OR = 1.13) was not significant, but this is likely due to the relatively small number of female smokers. Women were very under-represented among participants aged 50–59. However, a sensitivity analysis excluding participants aged 50 or older revealed logistic regression results almost identical to the results using the whole sample (OR = 1.12 when including all participants, OR = 1.14 when excluding participants ≥50; p < .01 for both models).

A separate multivariate logistic regression showed that HIV-positive individuals were significantly more likely to use SLT than HIV-negative individuals (Supplementary Table 1) (OR = 1.34; 95% CI = 1.17% to 1.56%). Notably, this relationship appears stronger than that for cigarette smoking and was significant in both men and women when stratified by sex. As with cigarette smoking, an inverse relationship was seen between wealth and SLT use, while urban versus rural residence did not show a significant effect.

Country-Level Analyses

Prevalence of cigarette smoking and SLT use varied widely across countries included in the analysis, as did HIV prevalence. Cigarette smoking prevalence ranged from 2.37% in Ghana to 19.90% in Lesotho (Figure 1, Supplementary Table 6), while SLT use ranged from 0.16% in Burundi to 5.94% in Democratic Republic of Congo. The HIV prevalence ranged from 0.36% in Niger to 25.88% in Swaziland. Among men, the highest cigarette smoking prevalence was also observed in Lesotho (41.3%, 95% CI = 39.05% to 43.60%), while the lowest was in Ghana (4.68%, 95% CI = 3.99 % to 5.48%). The highest smoking prevalence among women (Sierra Leone, 4.66%; 95% CI = 3.94 % to 5.49%) was very similar to the lowest prevalence among men. To evaluate those results, we compared our smoking prevalence data with those of available countries from the Global Adult Tobacco Surveys (GATS) and World Health Organization STEPwise approach to Surveillance (STEPS; Supplementary Table 7). These surveys are the most widely used sources for international comparisons of smoking prevalence, especially in LMICs.

Figure 1.

Figure 1.

Open in a new tab

Maps of HIV prevalence and smoking prevalence in 25 sub-Saharan African countries using Demographic and Health Surveys and AIDS Indicator Surveys data. This figure only reflects data that fit the criteria for inclusion in our study.

Over half (14/25) of the countries examined showed a higher smoking prevalence in the HIV-positive group than in the HIV-negative one (Supplementary Table 3). However, chi-squared analysis showed that this difference was significant in only five countries: Gambia, Niger, Swaziland, Zambia, and Zimbabwe. When smoking prevalence was examined by socioeconomic factors, individual countries showed contrasting trends. Country-level logistic regression analyses of smoking status on HIV status (Supplementary Table 6) revealed qualitatively different odds ratios depending on the country. Five countries (Ethiopia, Liberia, Namibia, Swaziland, and Zambia) showed fully adjusted odds ratios that were statistically significantly different from the null. Three of these countries (Liberia, Swaziland, and Zambia) showed a higher likelihood of smoking among HIV-positive individuals than among HIV-negative individuals. Ethiopia and Namibia, however, showed a lower likelihood of smoking among HIV-positive individuals. With regard to occupation, country-level analyses consistently showed higher smoking prevalence among agricultural and manual laborers (Supplementary Table 5).

SLT use prevalence was higher among HIV-positive individuals than among HIV-negative ones in the Southern African countries (Supplementary Table 6), but not elsewhere on the continent. Logistic regression of SLT use on HIV status (Supplementary Table 2) showed similar results—significantly higher odds of using SLT among HIV-positive individuals were reported for Lesotho, Swaziland, Zambia, and Zimbabwe. However, Ethiopia and the Democratic Republic of Congo showed lower odds of using SLT among HIV-positive individuals. The fully adjusted regression model showed a significantly higher odds of using SLT among HIV-positive individuals than among HIV-negative individuals in Zambia overall and among Zambian women (but not among men). HIV-positive women in Swaziland were also found to have a higher odds of using SLT than HIV-negative women.

Discussion

Multivariate logistic regression across all countries included in the analysis revealed that respondents who were HIV positive were significantly more likely to smoke cigarettes and to use SLT than those who were HIV negative (adjusted OR cigarettes = 1.12, p < .01; SLT = 1.34, p < .01). This finding is consistent with previous research. A positive association between cigarette smoking and HIV/AIDS has previously been reported in other world regions, though data have primarily been limited to HICs and have not included SLT use.1–3,6,28,29 In our study, SLT use showed an even stronger relationship with HIV status than cigarette smoking, and this relationship was significant among both men and women. Another recent study conducted in India found that HIV-positive men had a 1.48 times higher odds of using tobacco (in any form) than HIV-negative men.30

However, the positive relationship between HIV and cigarette smoking observed in our study was more modest than in previous literature examining other parts of the world and also more modest than the relationship reported in a recent study by Mdege et al. that also used data from the DHS and AIS but was not limited to sub-Saharan Africa.29–31 The weaker relationship in our study may be due in part to the fact that HIV is more prevalent in sub-Saharan Africa than in any other world region, meaning that the HIV-positive population is not markedly different from the general population, as well as different patterns of tobacco use behavior compared with other regions. Other potential reasons for the difference in regression results between our study and that of Mdege et al. may be the different statistical methodology used (multivariate logistic regression yielding an odds ratio in our study vs random effect meta-regression yielding a risk ratio in Mdege et al.’s study) and the inclusion of demographic characteristics, socioeconomic risk factors, and sexual risk factors in our multivariate logistic regression model. The stronger relationship with SLT use may also be influenced by different characteristics of smokeless versus cigarette users in Africa; for example, compared with cigarette smoking, SLT use may be more closely linked to lower education or economic status and, in turn, to HIV status. We were not able to explore these issues further in our study because of limitations in the DHS data to provide a more detailed characterization of patterns of tobacco use behavior, but this is an important avenue for future research.

There was considerable variation in the proportion of smokers as well as HIV-positive individuals among countries. By region, Southern Africa contained the highest proportion of smokers. It also included Lesotho, the country with the highest smoking prevalence of all. This is particularly noteworthy given that Southern Africa is the site of the highest prevalence and incidence of HIV on the continent.32 When adjusted logistic regression of smoking status on HIV status was performed separately for each country, just five countries (Ethiopia, Liberia, Namibia, Swaziland, and Zimbabwe) showed a statistically significant difference in smoking prevalence between HIV-positive and HIV-negative groups in the fully adjusted model. The direction of the effect of HIV on likelihood of smoking also appeared to vary by country: HIV-positive persons had higher odds of smoking in Liberia, Swaziland, and Zimbabwe. However, HIV-positive persons had lower odds of smoking in Ethiopia and Namibia. Differences in patterns of tobacco use and HIV status across countries, along with other factors, may influence the relationship between tobacco and HIV.

In addition to the relationship with HIV, the data in this study also shed light on demographic patterns of tobacco use in Africa. While overall smoking prevalence remains low in most African countries compared with other parts of the world, it varies substantially by demographic factors. Most notably, more than 95% of the smokers in this dataset were male. For example, Niger reported no women smokers at all. While this seems unlikely, data from the World Bank have reported similarly low smoking levels among Nigerienne women.33 A similar trend has been observed in other studies of smoking in sub-Saharan African countries, including a recent study by Uthman et al. of 19 sub-Saharan African countries using DHS data that showed that men had a 62 times higher odds of smoking than women.20,34,35 Socioeconomic factors—level of education, wealth, occupation, and urban versus rural residence—were also strongly associated with both smoking status and SLT use. This agrees with recent research on tobacco use and SES conducted in various LMICs including Ghana, Zimbabwe, and Côte d’Ivoire,36–38 as well as in HICs.39,40 The link between poverty and tobacco consumption has also been extensively documented in a 2004 WHO report as well as in a 2011 systematic review.41,42 Interestingly, our study showed lower smoking prevalence among people with no education compared with people with a primary education. However, this was likely due to the disproportionate representation of women (who largely did not smoke) in the no education group. When smoking was examined by educational level among only men, a clearer inverse relationship was found—the no education group had the highest smoking prevalence, followed by the primary school group, then secondary school, and finally higher education. It is noteworthy that the opposite trend was found comparing characteristics of PLWHA and non-PLWHA: PLWHA were found, on average, to be more educated and wealthier than non-PLWHA (table not reported here). A similar trend has been observed in previous studies using DHS data from sub-Saharan Africa.43,44 The relationship persisted when HIV status was regressed on wealth and educational attainment (in separate models and in the same model) while controlling for age. As suggested by Hajizadeh et al., this could be a result of survivor bias: HIV-positive individuals of lower SES may have been more likely to die before they could be surveyed or have been too ill to answer questions. Despite these seemingly divergent findings, we still observed a relationship between smoking and HIV status when controlling for wealth and education.

Overall, these findings highlight the importance of addressing tobacco use in the HIV-positive population in sub-Saharan Africa. Existing HIV prevention and treatment infrastructure in sub-Saharan Africa provides a unique opportunity for implementing low-cost tobacco interventions, including cessation services, community participation, and public health outreach to affected families. Such integration may bring economic benefits as well, including reduced health care costs, reductions in family poverty, and improved results of HIV/AIDS and TB programs in already overburdened countries. In addition, stronger implementation of evidence-based tobacco control policies could help reduce tobacco use and prevent a projected future increase among HIV-positive people as well as the general population.45 As we noted in the Introduction, tobacco use is associated with higher morbidity and mortality in PLWHA and is also associated with the increased risk of infection and TB coinfection. Sub-Saharan Africa faces a growing burden of NCDs,46 and as HIV-positive people survive longer, behavioral risk factors for NCDs are increasingly important as health determinants.

Limitations

The DHS data are cross-sectional and therefore cannot prove a causal relationship between smoking and HIV status. The study was also limited by the number of countries in which the DHS Program collected HIV data and allowed for linking of HIV test results to other questionnaires. Thus, there are several omissions from the overall dataset, such as Nigeria, the most populous country in sub-Saharan Africa.47

A further limitation of the DHS questionnaire was the vagueness of the question used to determine current smoking status (the primary exposure of this study). Participants were simply asked, “Do you currently smoke cigarettes?” By contrast, the GATS questionnaire distinguishes between current daily smoking (defined as “smoking at least one tobacco product every day or nearly every day over a period of a month or more”) and current occasional smoking (people who responded “non-daily” to the question “Do you currently smoke tobacco on a daily basis, less than daily, or not at all?”).48 The STEPS questionnaire similarly distinguishes between daily and nondaily current smoking.49 These definitions might classify current smokers more accurately than the DHS questionnaire could. When we compared our current smoking prevalence data with the latest GATS and STEPS data for available countries, we found the GATS numbers to be generally lower or similar to our numbers. By contrast, the STEPS smoking prevalence data were generally higher than what we found using DHS data. However, all but three of the STEPS questionnaires for the countries in our study surveyed adults ages 25–64, while our data surveyed individuals ages 15–60. The fact that smoking prevalence is higher at higher ages in sub-Saharan Africa (with a peak between the ages of 25–35) may explain some of this discrepancy.50 Inclusion of more detailed questions about tobacco use behavior in future DHS instruments would allow for more in-depth characterization of tobacco use behavior and its relationship with HIV status. Despite these limitations, a unique advantage of our study’s data was its linkage of HIV status and smoking status data.

Another issue was the asynchronous timing of the surveys. The DHS Program does not carry out each type of survey every year in every country. This study included data that were as much as 12 years old. To attempt to control for this, and following a technique used in a recent study using DHS data by Mdege et al.,31 a binary variable was made that was equal to 0 if the survey was collected before 2012 (the median year of survey collection in our dataset) and equal to 1 if the survey was collected during or after 2012. However, when this variable was included in the adjusted logistic regression model, it had no effect on the odds ratio of smoking comparing HIV-positive and HIV-negative groups. Adjusting for the year of survey completion (as a continuous variable) similarly had no effect on the results.

Several variables pertinent to tobacco use and HIV status were not collected by the DHS Program. Drinking alcohol may also be related to tobacco use and HIV status, but alcohol use data were only collected in five of the countries included in this study (Ethiopia, Liberia, Namibia, Swaziland, and Zambia). When alcohol use was integrated into the multivariate logistic regression analysis of smoking status on HIV status for those countries, its effect on the respective odds ratios was negligible. Participants were also not questioned about their sexual orientation, which has been shown to be associated with HIV status and tobacco use in HICs.51–54 However, this factor may not be relevant to the sub-Saharan African setting, where the dynamics of HIV/AIDS proliferation differ from those of HICs. Data were not collected on whether PLWHA were receiving treatment for their infection. Treatment-seeking behavior may be associated with other healthy behaviors such as avoiding smoking and therefore should be included in a future study.

Conclusion

This study showed that having HIV was associated with a greater likelihood of smoking cigarettes as well as with using SLT in sub-Saharan Africa. These tobacco use modalities were also associated with male sex and lower SES, as evidenced by higher smoking prevalence among people who had little education, who were impoverished, and who performed manual labor. PLWHA who smoke experience a double burden of disease, suffering the consequences of living with HIV as well as the harmful effects of tobacco use. However, the existing global programs and infrastructure for addressing the HIV epidemic also provide a unique opportunity to intervene to address these combined health threats. Incorporating smoking prevention and cessation strategies into the existing global HIV control infrastructure would help reduce the burden of disease caused by tobacco use. By the same token, implementing tobacco control measures such as the WHO’s Framework Convention on Tobacco Control and MPOWER could reduce smoking among the HIV-positive population.55

Declaration of Interests

None declared.

Supplementary Material

nty176_suppl_Supplement-Material
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Acknowledgments

The first author would like to thank his mentor, Dr. Andrew Freedman, for helping to edit this paper and for invaluable guidance during his time as a research fellow at the National Cancer Institute.

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