Association between Risky Sexual Behaviour and having STIs
or HIV among young persons aged 15-24 years in Uganda
Gideon Rutaremwa§1, Peninah Agaba2, Elizabeth Nansubuga2 and Olivia Nankinga2
§
1
Correspondence: [email protected]
United Nations Economic Commission for Africa (ECA), Social Development Policy
Division, P.O.Box 3001, Addis Ababa, Ethiopia
2
Center for Population and Applied Statistics (CPAS), and Department of Population
Studies, Makerere University, P.O.Box 7062, Kampala, Uganda
Author contacts
GR: [email protected]
PA: [email protected]
EN: [email protected]
ONJ: [email protected]
Abstract
Adolescent pregnancy is often discussed in literature as causes of health concern and as a
social problem. Taking these accounts as a starting point, this paper uses the 2011 Uganda
AIDS Indicator Survey (UAIS) data to explore the factors related to sexual behaviour and
risk of STI and HIV infection among youths in Uganda. A total of 2,491 males and female
young persons were selected for this study. A complementary log-log regression model
was used to examine the association between women’s risk y sexual behaviour and
having an STI, HIV, and any STI including HIV. Female youths were more likely to
contract an STI and HIV compared to their male counterparts (OR=2.1; 95% CI=1.2-3.6).
The risk of contracting STIs was higher in Western Region of Uganda compared to
Central region (OR=1.4; 95% CI=1.0-1.9). At the same time youths in Eastern Uganda
had the least odds (OR=0.63) of contracting STIs. Furthermore, youth with multiple sex
1
partners were more likely to contract STIs including HIV compared to those who had a
single partner. Finally, young persons from the top two wealth quintiles were more likely
to test positive for HIV compared to those who belonged to the lowest wealth quintile. The
discourse in this paper shows that the youthful age category is a serious policy
intervention target that requires redress.
Keywords: Sexual behavior, sexually transmitted infections, HIV/AIDS, Women, Uganda
Background
Risky sexual behaviour are of particular concern to reproductive health practitioners and
other primary care clinicians in that they can lead to serious consequences both for the
individual and their sexual partners. Both STIs and HIV have potential to undermine
development in many ways, including loss of productivity, supply of human capital,
agricultural productivity and food security (Muthengi, 2009). Researchers,
development practitioners, and medical personnel are faced with 3 challenges: First,
how to understand this behaviour, second, how to identify risky sexual behaviour, and
third what to do about it. Risky sexual behaviour can take several forms, including
unprotected intercourse, multiple or concurrent sexual partners and unsafe sexual
intercourse under the influence of substances such as alcohol (Kalichman & Simbayi,
2011; Rosenberg et al., 2015).
Among the risk factors for contracting STIs are sexual violence and mental health. Studies
indicate that intimate partner violence is frequently associated with increased HIV risk in
women. This observation is partly because men who abuse their wives also exhibit
riskier sexual behaviour ( Du d e, 201 1; S i l v er m a n, D e ck e r, S a ggu rt i , &
Don t a , 2 00 8) . Factors found to be associated with sexual violence have also been
identified as risk factors for contracting an STI. Data on the relationship between mental
health most commonly identified as depression and STIs is mixed, but most studies
report a positive correlation between depression and risky sexual behaviour, an
established precursor to STIs (Buffardi, Kathy, King, & Manhart, 2008; Shrier, Harris,
Sternberg, & Beardslee, 2001). Similarly, research suggests a positive relationship
2
between alcohol use (Burns, 2015; Kalichman & Simbayi, 2011; Ritchwood, Ford,
DeCoster, Lochman, & Sutton, 2015; Rosenberg et al., 2015; Yi et al., 2014), drug and
other substance use (Lansford, Dodge, Fontaine, Bates, & Pettit, 2014; Manhart et al.,
2006), multiple sexual partners (Chimoyi & Musenge, 2014; Kalichman & Simbayi,
2011) and STI risk.
Concerning age, risky sexual behaviour is known to cut across all age groups. Reports
of adults having had two or more sexual partners in the previous year are also
common ( M i n i s t r y o f H e a l t h - U g a n d a a n d I C F M a c r o I n t e r n a t i o n a l
C a l v e r t o n M a r yl a n d U S A , 2 0 1 2 ) . Why should researchers care about risky
sexual behaviour among young ages? One reason why this issue is important is that
risky sexual behaviour increases the likelihood of contracting an STI including
HIV/AIDS. It is clear that sexual activity is common among individuals in the
reproductive ages, and many of the behaviour that they engage in put women at risk for
contracting STIs or HIV. This paper seeks to contribute to the concern of risky sexual
behaviour and its link with STIs and HIV among young persons in Uganda.
Uganda has long been regarded as an HIV success story. This was because the
Ugandan government initiated a robust response to the epidemic that was praised as a
model response to the HIV epidemic bringing about a substantial fall in HIV
prevalence (Kibombo, Neema, & Ahmed, 2007; Kilian et al., 1999; Kirby, 2008). HIV
prevalence declined from a peak of 15% in 1990/91 (Kibombo et al., 2007; Murphy,
Greene, Mihailovic, & Olupot-Olupot, 2006) to a low of 6.2% in 1999/2000 before
increasing to 6.4% in 2004/05. The fall in HIV prevalence observed during the 1990s
was statistically significant (Shafer et al., 2011). Factors influencing the recent trends of
the epidemic are not yet clear, but there are indications that the observed changes may
partly be explained by the increased risky sexual behaviour. A recent Ugandan study
(Tumwesigye et al., 2012), showed that 63% of men and 59% of females had
unprotected sex during their last sexual encounter. No doubt, the prevalence for
HIV/AIDS in Uganda continued to increase to 6.7% from 6.4% (Ministry of HealthUganda and ICF Macro International Calverton Maryland USA, 2012) .
3
It is against this background that this research was conducted to contribute to the current
debate of why there is seemingly a reversal in the earlier gains in the fight against
HIV/AIDS in Uganda. It is our belief that an improved understanding of factors
associated with STIs or HIV infection among young people of Uganda will lead to
improved policy frameworks and programming, ultimately reducing the cases of STIs or
HIV infection in this country, which has for two decades been negatively affected by
STIs including HIV/AIDS.
Methods
Data Source
Data from the 2011 Uganda AIDS Indicator Survey (AIS) were used for this study. The
AIS data provide information on knowledge, attitudes and sexual behaviour related to
HIV/AIDS and other indicators such as HIV testing, access to antiretroviral therapy,
services for treating sexually transmitted infections, and coverage of interventions to
prevent mother to child transmission of HIV. Informed consent was sought from all
study respondents. The protocol for the blood specimen collection and analysis was
developed jointly by all parties to the survey and was reviewed and approved by the
Science and Ethics Committee of the Uganda Virus Research Institute (UVRI), ICF
Macro’s Institutional Review Board and a review committee at the Centers for Disease
Control and Prevention (CDC) in Atlanta. It was also cleared by the Ethics Committee
of the Uganda National Council of Science and Technology (Ministry of HealthUganda and ICF Macro International Calverton Maryland USA, 2012). Furthermore,
permission was sought from ICF Macro International to use the AIS data set.
Explanatory variables
The independent variables considered in the analysis included respondent’s: age,
residence, marital status, wealth status, region, educational level attainment, age at first
sexual intercourse, consistent condom use with the last three sexual partners, alcohol
4
intake by either the respondent or partner before sexual intercourse and number of life
time partners. The association between these predictor variables and STI or HIV
infection was the primary relationship of interest.
Dependent variable
The dependent variable is dichotomous, that is, whether the respondent had an STI or
HIV infection or not. Women were asked if they had ever suffered from any STI in
addition to experiencing any genital discharge or genital sores in the year preceding the
survey or during the last 12 months. In addition, these women were tested for HIV/AIDS.
Sampling
This study is based on data from a w e i g h t e d sample of 2 ,491 young persons, both
male and female, aged between 15-24 years who had ever had sex i n t h e 4 w e e k s
p r e c e d i n g t h e A IS S u r v e y and were tested for HIV/AIDS during the Uganda AIDS
Indicator Survey (AIS) of 2011. The 2011 AIS was a nationally representative population
based sample, designed to obtain national and sub-national estimates of the prevalence of
HIV and syphilis infection. Information was collected on both the covariates and
dependent variable used in the current study. The sample for the Uganda AIS of 2011 is
deemed adequate to allow for analysis, comparisons and is also useful in identification of
important factors associated with sexual behavior in the era of HIV/AIDS in Uganda.
Statistical analyses
Analysis in this study was done in two stages, first the descriptive analysis to describe
the characteristics of youth and their sexual behaviour. Secondly, to examine the
association between risky sexual behaviour and having STIs or HIV, three models were
fitted including STI (Model 1), HIV (Model 2) and any STI including HIV (Model 3).
The likelihood of having an STI or HIV was fitted using a complementary log-log
regression reporting odds ratios based on the 95% confidence interval (Table 3). The
significance level of the predictor variables was set at p<0.05 for the regression model
coefficients. Typically this model is used when the positive (or negative) outcome is rare
as is the case with the current data, and is an alternative to logit and probit analysis.
Formally, the equation fitted to the data may be expressed as follows:
log{-log(1-p i )} = α + ∑ X β
(1)
5
Where pi is the probability of an adolescent being pregnant or ever had a child in the 5
years preceding the survey; and Xβ refers to regression estimates for the set of explanatory
variables included in the models. The complementary log-log model is derived from the
assumption that the error distribution (or distribution of the latent variable) follows a
standard extreme value distribution (Powers & Xie, 2000). For individual level data, the
parameters have a similar interpretation to those from the logistic regression model.
For purposes of accounting for the complex sample design including clustering and
design effect used in the AIS, we weighted the data using the HIV sample weight
provided for in the data set. To declare survey design for dataset, svyset command was
run. We also tested for multi-collinearity of the variables using the Variance inflation
factor (VIF) and none of the variables in the model had a threshold of 10. The goodness
of fit for the model was tested using the link test.
Results
Descriptive results
Results in Table 1 show that more than two-thirds of the respondents were females (69%),
had primary education (66%), 80% resided in rural areas, were currently in union (78.1%)
and 29.8% were from the Eastern region. Majority (87.8%) of the youths did not use a
condom at their last sexual encounter and 15% had sex under the influence of alcohol. The
HIV/AIDS prevalence among the youths was 5.3% and 17.4% of the youths had an STI
during the past year as shown in Table 1.
At bivariate level, having an STI was higher among females (74.5%), those with primary
education (70.1%), rural residents (74.8%), currently in union (79.6%) and respondents
from Western region (27.2%). Except alcohol intake before last sexual encounter and
condom use at last sexual encounter, all other demographic and risky sexual behaviors
were significantly associated (p < 0.05) with having an STI among youths as shown in
Table 2. More than three quarters (77.4%) of the youths who were HIV positive were
females. Notably, the HIV sero-status distribution of respondents across the socio6
demographic and risky sexual behavior is similar to that observed among respondents with
an STI as shown in Table 2. Furthermore, the distribution between HIV status and condom
use at last sexual encounter, alcohol intake before last sexual encounter, age at first union
and residence were not significantly associated (p > 0.05).
Regression model results
Results in Table 3 show that females are at a greater risk of having an STI or HIV/AIDS.
The odds of females having an STI during the past survey year are higher by 1.7 times
compared to males. Additionally, females are twice more likely to have HIV/AIDS
compared to males as shown in Model 2 (Table 3). Model 3 also shows that females are
still at a higher risk of having either an STI or HIV/AIDS.
Youths with primary education were twice as likely to have an STI as compared to
uneducated youths. Similarly, these youths were also twice as likely to have HIV/AIDS as
compared to their uneducated counterparts as shown in Table 3 (Model 3).
The odds of having an STI among youths from Eastern and Northern regions are lower by
37% and 51% respectively compared to youths from the Central region. However, the
odds of youths from Western having an STI are higher by 41% compared to their
counterparts from the Central region as shown in Table 3. A similar pattern is observed
with regards to having HIV/AIDS across the different regions. Youths from the Eastern
and Northern region have lower odds of being infected with HIV/AIDS compared to
youths from the Central region. In addition, youths from Western Uganda have an
increased risk of having HIV/AIDS (OR 1.4) compared to youths from Central Uganda.
Notably, marital status had no relationship with having an STI during the past survey year.
However, the odds of formerly married and married youths having HIV/AIDS were higher
by 6.7 times and 4.7 times respectively compared to never married youths. Similarly, the
odds of having either an STI or HIV/AIDS are still higher among formerly married youths
and married youths compared to never married youths as shown in Table 3 (Model 3).
Furthermore, the number of lifetime sexual partners is significantly associated with having
and STI, HIV and either an STI or HIV/AIDS. Table 3 shows that youth who had 4 or
7
more lifetime partners had increased odds of having an STI (OR 2.6); and having
HIV/AIDS (OR 4.5); and either an STI or HIV/AIDS (OR 2.8) in comparison. A similar
trend is observed for youths who had 2–3 lifetime sexual partners being at a higher risk of
having an STI, HIV, or either HIV or an STI in comparison to having one lifetime sexual
partner. Notably having more lifetime partners has the greatest effect on having
HIV/AIDS compared to having an STI.
Youths belonging to richer (OR 2.7) and richest categories (OR=2.9) households had
higher odds of having an STI compared to youths belonging to the poorest households.
There was no significant effect of wealth status on having an STI or HIV/AIDS among the
respondents.
Discussion
This manuscript examined the risk factors that were most associated with having an STI
or HIV among young persons aged 15-24 years in Uganda in the 12 months preceding
the UAIS exercise. As noted earlier the HIV prevalence rates in Uganda have remained
high. This research suggests that a number of factors including: sex of young person,
region, educational level attainment, wealth status and number of lifetime partners were
significant predictors of STI or HIV infection among young persons in the country.
All regions of Uganda exhibited lower odds of STI or HIV infection compared to
Western region. The findings suggest that some geographical clustering’s are often
associated with higher risk of HIV prevalence (Ramjee & Wand, 2014). Such regional
disparities in the risk of contacting an STI or HIV could be attributed to the differences in
traditions and culture relating to sex in the different regions of Uganda including wife
inheritance, female genital mutilation and polygamy (Kibombo et al., 2007).
The findings with regard to education level attainment are contrary to the hypothesis
that persons with more education are better equipped to make necessary decisions that
help reduce the risk of STI or HIV infection (Muthengi, 2009). The current findings
8
suggest that higher levels of education are associated with deleterious health outcomes
including STIs and HIV/AIDS among young persons.
The current study showed that the richer category of young people tended to have
increased risks of having an STI and HIV in the one-year period prior to the survey. The
findings are consistent with other literature on this subject (Johnson & Way, 2006).
Evidence suggests that wealth is associated with risky sexual behaviour, including
individuals having multiple sex partners (Chimoyi & Musenge, 2014). Indeed the rich in
society tend to have well developed social and sexual networks (Kuhanen, 2010), which
could ultimately enable STI and HIV transmission among the young persons.
Also importantly, having more sexual partners increases the risk of STI or HIV
infection. This finding is consistent with expectation and also with literature (Chimoyi &
Musenge, 2014; Fenton et al., 2001; Singh, Darroch, & Bankole, 2004). The apparent
reductions in HIV prevalence rates in Uganda in early 1990s has been attributed to
reduction in risky sexual behaviour and specifically reduction in the number of sexual
partners (Kilian et al., 1999; Kirby, 2008; Murphy et al., 2006).
Study Limitations
The current study was based on cross-sectional data and therefore it is difficult to ascertain
the association between explanatory variables and STI or HIV, since they were both
measured at one point in time. Besides the study did not address the important question of
knowledge of disease symptoms and diagnosis related factors that may affect survey
outcomes in population surveys. Furthermore, the accuracy of information on STI
infection may be based on women’s recall and description of discharge. The study lacked
information on cultural and social practices among the various population groups of
women that influence their sexual behaviour. Despite these limitations the data used for
this study are reliable and appropriate analysis procedures were used, hence the findings
are deemed to be reflecting accurately on the study topic.
9
Conclusions
Given that this research explores the factors associated with the risk of having an STI or
HIV among young persons in Uganda, conclusions can be harnessed from the findings
and the discussion. Sexually transmitted infections and HIV vary significantly by sex,
education, and region of residence, wealth, marital status and number of sexual
partners. It is therefore important to understand these differentials in order to ensure that
prevention policy and programme efforts are targeted towards the groups that are at
greatest risk.
The findings from this study suggest very wide regional variations in the risks of STI or
HIV infection. The latter implies that any intervention programs targeting STI and HIV
should focus on Western Uganda, given that the region is most at risk as indicated earlier.
Further, the analyses suggest that female young persons are most at risk of contracting
STIs and HIV compared to their male counterparts. The implication for this finding is
that any programs aimed at prevention of such infection should target especially the
female young persons.
In addition, high risk of infection was observed among young persons with more
education and those in higher income households. In this regard, efforts by the
Government and program implementers should target these categories of young
persons for prevention and treatment. Finally, a high number of lifetime partners
among young persons were significantly associated with high risk of STI or HIV
infection. Public health campaigns targeting the most at risk population groups would be
an effective strategy. An improved understanding of factors associated with STIs and
HIV infection among young persons in Uganda will lead to improved intervention
policy frameworks and programing, ultimately reducing the cases of STIs and HIV and
generally improving the health of the generations to come.
Abbreviations
10
UAIS: Uganda AIDS Indicator Survey; HIV: Human Immune-deficiency Virus; AIDS:
Acquire Immune-Deficiency Syndrome; OR: Odds Ratio; VIF: Variance Inflation factor;
STIs: Sexually Transmitted Infections; DHS: Demographic and Health Survey; CI:
Confidence interval; UBOS: Uganda Bureau of Statistics; UVRI: Uganda Virus Research
Institute; CDC: Centers for Disease Control and Prevention; CoBAMS: College of
Business and Management Sciences – Makerere University. ECA: United Nations
Economic Commission for Africa.
Competing interests
The authors declare that they have no competing interest.
Authors’ contributions
GR, EN, PA, and ON participated in the conceptualization of the study, acquisition of data
and revision of the manuscript. All authors determined the design and performed the
statistical analysis. All authors interpreted the data and drafted the manuscript. All authors
read and approved the final manuscript.
Acknowledgements
We acknowledge the support received from the institutions of affiliation, Economic
Commission for Africa and Makerere University College of Business and Management
Sciences (CoBAMS). We are also grateful to all members of staff of the Department of
Population Studies for their valuable comments. We are also grateful to the Uganda
Bureau of Statistics (UBOS) and ICF Macro International Inc. for providing and
authorization of use of the dataset. The contents are solely the responsibility of the
authors and do not necessarily represent the official views of the supporting institutions.
11
Author details
1
United Nations Economic Commission for Africa (ECA), Social Development Policy
Division, P.O.Box 3001, Addis Ababa, Ethiopia.
2
Center for Population and Applied
Statistics (CPAS), and Department of Population Studies, Makerere University, Uganda.
12
References
Buffardi, L. A., Kathy, K. T., King, K. H., & Manhart, L. E. (2008). Moving Upstream:
Ecosocial and Psychosocial Correlates of Sexually Transmitted Infections Among
Young Adults in the United States. American Journal of Public Health, 98(6), 1128–
1136. doi:10.2105/AJPH.2007.120451
Burns, S. (2015). Sexual health, alcohol and the university environment: is there a need for
sexual health promotion intervention? Sexual Health.
Chimoyi, L. A., & Musenge, E. (2014). Spatial analysis of factors associated with HIV
infection among young people in Uganda, 2011. BMC Public Health, 14(1), 555.
Cook, R. L., & Clark, D. B. (2005). Is there an association between alcohol consumption
and sexually transmitted diseases? A systematic review. Sexually Transmitted
Diseases, 32(3), 156–164.
Dude, A. M. (2011). Spousal intimate partner violence is associated with HIV and other
STIs among married Rwandan women. AIDS and Behavior, 15(1), 142–152.
Fenton, K. a, Korovessis, C., Johnson, a M., McCadden, a, McManus, S., Wellings, K., …
Erens, B. (2001). Sexual behaviour in Britain: reported sexually transmitted
infections and prevalent genital Chlamydia trachomatis infection. Lancet, 358(9296),
1851–4. doi:10.1016/S0140-6736(01)06886-6
Johnson, K., & Way, A. (2006). Risk factors for HIV infection in a national adult
population: evidence from the 2003 Kenya Demographic and Health Survey. JAIDS
Journal of Acquired Immune Deficiency Syndromes, 42(5), 627–636.
Kalichman, S. C., & Simbayi, L. C. (2011). Multiple-Recent Sexual Partnerships and
Alcohol Use among Sexually Transmitted Infection Clinic Patients, Cape Town
South
Africa.
Sexually
Transmitted
Diseases,
38(1),
18–23.
doi:10.1097/OLQ.0b013e3181e77cdd.Multiple-Recent
Kibombo, R., Neema, S., & Ahmed, F. H. (2007). Perceptions of risk to HIV infection
among adolescents in Uganda: are they related to sexual behaviour? African Journal
of Reproductive Health, 11(3), 168.
Kilian, A. H. D., Gregson, S., Ndyanabangi, B., Walusaga, K., Kipp, W., Sahlmüller, G.,
… Weis, P. (1999). Reductions in risk behaviour provide the most consistent
explanation for declining HIV-1 prevalence in Uganda. Aids, 13(3), 391–398.
Kirby, D. (2008). Changes in sexual behaviour leading to the decline in the prevalence of
HIV in Uganda: confirmation from multiple sources of evidence. Sexually
Transmitted Infections, 84(Supplement 2). doi:10.1136/sti.2008.029892
13
Kuhanen, J. (2010). Sexualised space, sexual networking & the emergence of AIDS in
Rakai, Uganda. Health Place, 16(2), 226–235.
Lansford, J. E., Dodge, K. A., Fontaine, R. G., Bates, J. E., & Pettit, G. S. (2014). Peer
rejection, affiliation with deviant peers, delinquency, and risky sexual behavior.
Journal of Youth and Adolescence, 43(10), 1742–1751.
Manhart, L. E., Holmes, K. K., Koutsky, L. A., Wood, T. R., Kenney, D. L., Feng, Q., &
Kiviat, N. B. (2006). Human papillomavirus infection among sexually active young
women in the United States: Implications for developing a vaccination strategy.
Sexually Transmitted Diseases, 32(8), 502–508.
Ministry of Health-Uganda and ICF Macro International Calverton Maryland USA.
(2012). AIDS Indicator Survey (AIS) 2011. Kampala.
Murphy, E. M., Greene, M. E., Mihailovic, A., & Olupot-Olupot, P. (2006). Was the
“ABC” Approach (Abstinence, Being Faithful, Using Condoms) Responsible for
Uganda’s Decline in HIV? PLoS Medicine, 3(9). doi:10.1371/journal.pmed.0030379
Muthengi, E. (2009). Socioeconomic status and HIV infection among women in Kenya.
African Population Studies, 23(Supplement), 161–177.
Powers, D. A., & Xie, Y. (2000). Statistical Methods for Categorical Data Analysis. New
York: Academic Press.
Ramjee, G., & Wand, H. (2014). Geographical clustering of high risk sexual behaviors in
“hot-spots” for HIV and sexually transmitted infections in Kwazulu-Natal, South
Africa. AIDS and Behavior, 18(2), 317–322.
Ritchwood, T. D., Ford, H., DeCoster, J., Lochman, J. E., & Sutton, M. (2015). Risky
sexual behavior and substance use among adolescents: A meta-analysis. Children and
Youth Services Review, 52, 74–88.
Rosenberg, M., Pettifor, A., Van Rie, A., Thirumurthy, H., Emch, M., Miller, W. C., …
Laeyendecker, O. (2015). The relationship between alcohol outlets, HIV risk
behavior, and HSV-2 infection among South African young women: A crosssectional study.
Shafer, L. A., White, R. G., Nsubuga, R. N., Chapman, R., Hayes, R., & Grosskurth, H.
(2011). The role of the natural epidemic dynamics and migration in explaining the
course of the HIV epidemic in rural Uganda: a modelling study. International
Journal of Epidemiology, 40(2), 397–404.
Shrier, L. A., Harris, S. K., Sternberg, M., & Beardslee, W. R. (2001). Associations of
depression, self-esteem, and substance use with sexual risk among adolescents.
Preventive Medicine, 33(3), 179–89.
14
Silverman, J., Decker, M., Saggurti, N., & Donta, B. A. (2008). Married Indian Women
Intimate Partner Violence and HIV Infection Among. Journal of American Medical
Association, 300, 703–710.
Singh, S., Darroch, J. E., & Bankole, A. (2004). A, B and C in Uganda: the roles of
abstinence, monogamy and condom use in HIV decline. Reproductive Health
Matters,
12(23),
129–31.
Retrieved
from
http://www.ncbi.nlm.nih.gov/pubmed/15242220
Tumwesigye, N., Atuyambe, L., Wanyenze, R., Kibira, S., Li, Q., Wabwire-Mangen, F., &
Wagner, G. (2012). Alcohol consumption and risky sexual behaviour in the fishing
communities: evidence from two fish landing sites on Lake Victoria in Uganda. BMC
Public Health, 12(1069).
Yi, S., Tuot, S., Yung, K., Kim, S., Chhea, C., & Saphonn, V. (2014). Factors Associated
with Risky Sexual Behavior among Unmarried Most-at-Risk Young People in
Cambodia. American Journal of Public Health Research, 2(5), 211–220.
15
Table 1: Percentage distribution of respondents by selected explanatory variables
Variable/category
Sex of respondent
Male
Female
Education attainment
None
Primary
Secondary +
Rural/urban residence
Urban
Rural
Region
Central
Kampala
Eastern
Northern
Western
Marital Status
Never in union
Currently in union
Formerly in union
Age at first sex
<14 years
15-17 years
18 years plus
Age at first union
Not in married
<18 years
18 years plus
Number of lifetime sex partners
Only 1
2-3
4+
Alcohol before sex
No
Yes
Condom use at last sex
None
Yes
Wealth Status
Poorest
Poorer
Middle
Richer
Richest
HIV Test Result
Negative
Positive
Had an STI during past year
No
Yes
16
Number
Percentage
764
1,716
30.8
69.2
108
1,647
725
4.4
66.4
29.2
487
1,993
19.7
80.4
534
203
738
398
606
21.5
8.2
29.8
16.1
24.5
459
1938
84
18.5
78.1
3.4
465
1,186
829
18.8
47.8
33.4
459
1,013
1008
18.5
40.8
40.7
772
1,133
576
31.1
45.7
23.2
2,168
367
85.5
14.5
2,176
303
87.8
12.2
447
526
479
427
601
18.0
21.2
19.3
17.2
24.2
2,349
131
94.7
5.3
2,050
431
82.6
17.4
Table 2: Distribution of respondents by selected background factors and by contraction of an
STI during past year and by HIV status
Had STI in past year
HIV test Status
(n-2535)
(n=2491)
Variable/category
Sex of respondent
Male
Female
Education attainment
None
Primary
Secondary +
Rural/urban residence
Urban
Rural
Region
Central
Kampala
Eastern
Northern
Western
Marital Status
Never in union
Currently in union
Formerly in union
Age at first sex
<14 years
15-17 years
18 years plus
Age at first union
Not in married
<18 years
18 years plus
Number of lifetime sex partners
Only 1
2-3
4+
Alcohol before sex
No
Yes
Condom use at last sex
None
Yes
Wealth Status
Poorest
Poorer
Middle
Richer
Richest
No
Yes
p
Negative
Positive
p
32.5
67.5
25.5
74.5
0.01
31.5
68.5
22.6
77.4
0.04
4.9
65.3
29.8
2.2
70.1
27.7
4.4
66.0
29.7
0.9
74.8
24.4
19.0
81.0
25.3
74.8
19.6
80.4
24.4
75.7
17.6
10.4
31.9
21.3
18.8
25.7
13.0
24.3
9.8
27.2
18.8
10.8
31.5
19.2
19.7
24.4
12.2
16.5
19.1
27.8
0.01
20.2
76.8
3.1
14.2
79.6
6.1
0.00
19.7
77.0
3.3
7.0
83.5
9.6
0.00
17.6
49.1
33.2
23.3
46.6
30.2
0.03
18.4
48.8
32.8
20.9
45.2
33.9
0.71
20.2
40.2
39.6
14.2
44.4
41.4
0.02
19.7
41.1
39.2
7.0
40.0
53.0
0.00
32.9
45.0
22.1
21.8
46.1
32.1
0.00
31.5
45.8
22.7
17.4
41.7
40.9
0.00
86.0
14.0
83.1
16.9
0.13
86.1
13.9
81.7
18.3
0.19
86.4
13.6
89.4
10.6
0.09
86.9
13.1
89.6
10.4
0.40
20.3
23.3
18.5
15.3
22.6
15.7
16.2
18.9
19.9
29.4
19.8
22.5
18.8
15.8
23.2
13.9
15.7
14.8
25.2
30.4
17
0.03
0.00
0.00
0.00
0.06
0.21
0.01
Table 3: Regression model predicting the log-odds of having an STI or testing HIV positive among
youths in Uganda (2011)
Variable/category
Sex of respondent
Male
Female
Education attainment
None
Primary
Secondary +
Rural/urban residence
Urban
Rural
Region
Central
Kampala
Eastern
Northern
Western
Marital Status
Never in union
Currently in union
Formerly in union
Age at first sex
<14 years
15-17 years
18 years plus
Number of lifetime sex
partners
Only 1
2-3
4+
Wealth Status
Poorest
Poorer
Middle
Richer
Richest
Model 2
Odds
Ratio
95% CI
Model 1
Odds
Ratio
95% CI
1.0
1.68*
[1.3-2.3]
1.0
2.11*
1.0
2.35*
2.02
[1.2-4.8]
[0.9-4.3]
-
1.0
0.71
[0.5-1.1]
1.0
0.74
0.63*
0.49*
1.41*
-
Model 3
Odds
Ratio
95% CI
1.0
1.70*
[1.3-2.2]
-
1.0
2.80*
2.51*
[1.4-5.6]
[1.2-5.2]
-
-
1.0
0.82
[0.5-1.2]
[0.4-1.2]
[0.5-0.9]
[0.3-0.7]
[1.0-1.9]
1.0
0.72
0.68
1.96
1.73
-
1.0
0.78
0.63*
0.68*
1.41*
[0.5-1.2]
[0.5-0.9]
[0.5-1.0]
[1.1-1.9]
1.0
1.37
1.68
[1.0-1.9]
[1.0-2.9]
1.0
4.66*
6.70*
1.0
1.66*
2.20*
[1.2-2.3]
[1.3-3.6]
1.0
0.74*
0.83
[0.6-1.0]
[0.6-1.1]
-
-
1.0
0.74*
0.81
[0.6-1.0]
[0.6-1.1]
1.0
1.41*
2.57*
[1.1-1.9]
[1.8-3.7]
1.0
2.05*
4.47*
-
1.0
1.52*
2.78*
[1.2-2.0]
[2.0-3.9]
1.0
0.82
0.99
1.09
1.02
[0.6-1.2]
[0.7-1.4]
[0.7-1.6]
[0.6-1.6]
1.0
1.36
1.12
2.71*
2.90*
1.0
0.87
1.02
1.24
1.20
[0.6-1.2]
[0.7-1.4]
[0.9-1.8]
[0.8-1.9]
[1.2-3.6]
[0.3-1.6]
[0.3-1.5]
[0.9-4.4]
[0.9-3.2]
[2.1-10.2]
[2.5-18]
[1.1-3.7]
[2.3-8.6]
[0.6-2.9]
[0.5-2.5]
[1.2-6.0]
[1.2-7.1]
0.06*
[0.0-0.1] 0.002*
[0.0-0.01] 0.04*
[0.0-0.1]
Model Constant
Note: * Represents significant results at p<0.05; n=2491 for all the 3 models; Model 1= STI
model; Model 2= HIV Model; Model 3= Combined model (either STI or HIV). All the three
models were tested for goodness of fit using the link test and all passed the test.
18