HIV 837

ORIGINAL ARTICLE

HIV and the urban homeless in Johannesburg

 

G M Lohrmann, B Botha, A Violari, G E Gray

 

Chris Hani Baragwanath Academic Hospital, Johannesburg

G M Lohrmann, MB BCh

 

Trinity Health Services, Johannesburg

B Botha, MPhil

 

Perinatal HIV Research Unit, Chris Hani Baragwanath Academic Hospital, Johannesburg

A Violari, MD, FCPaed (SA)

G E Gray, MB BCh, FCPaed (SA)


Corresponding author: G M Lohrmann (graham.lohrmann@gmail.com)

 

Background. There are few data on HIV prevalence and risk factors among inner-city homeless and marginally housed individuals in South Africa.

Methods. We recruited 136 adults from a Johannesburg inner-city homeless clinic; mean age was 32.4 years, 129 (95%) were male, and 90 (66%) were of South African nationality. Participants were tested for HIV and answered a short demographic survey. Descriptive statistics and uni- and multivariate regression analyses were used for data analysis.

Results. The HIV prevalence in the cohort was 23.5%. Transactional sex, relationship status, number of concurrent sexual partners, condom usage and history of previously treated sexually transmitted infections (STIs), living on the street, the use of alcohol or drugs, and previous exposure to voluntary counselling and testing (VCT), were not significant risk factors for HIV-positivity. Statistically significant HIV risk factors on multivariate analysis included the presence of an STI (odds ratio (OR) 5.6; p<0.01) and unemployment (OR 6.7; p<0.01). South African nationality was a significant risk factor on univariate analysis (OR 2.99; p<0.05), but not on multivariate analysis (OR 2.2; p=0.17).

Conclusion . The HIV prevalence in the sample did not differ appreciably from HIV prevalence estimates in other at-risk populations in similar settings, suggesting that homelessness in a South African city alone may not be a significant risk factor for HIV infection. HIV prevention efforts cannot be restricted to behaviour change programmes, but must be more holistic, recognising the protective role that employment has on HIV incidence.

S Afr J HIV Med 2012;13(4):174-177. DOI:10.7196/SAJHIVMED.837


South Africa (SA) bears a significant burden of the HIV epidemic, with an estimated 18.8% of the adult population (aged 15 - 49 years) infected.1 However, the prevalence of HIV within an essentially homeless population, as found in the city centre of Johannesburg, is not known. Research performed predominantly in developed countries suggests a substantially higher HIV and sexually transmitted infection (STI) burden among the homeless and marginally housed.2 , 3 , 4

The relationship between HIV infection, employment status and homelessness is complex. It is well documented that HIV leads to neuropsychological impairment,5 potentially leading to decreased job performance and unemployment, with up to 65% of HIV-infected individuals unemployed, even in developed countries.6 , 7 Unemployment in turn leads to food insecurity, which has been associated with an increased risk of contracting HIV and lower CD4 counts.8 The homeless are also at risk of psychiatric illness and have a decreased awareness of HIV,3 both of which may affect uptake of prevention and/or treatment interventions.9 In addition, the majority of the homeless in SA urban settings are male, and hence more difficult to reach with HIV interventions.10 To gain a better understanding of these issues and to inform future HIV prevention strategies, we investigated the HIV prevalence and risk factors among urban homeless individuals in Johannesburg.

Methods

We performed a cross-sectional survey of 136 adults attending a Johannesburg inner-city centre soup kitchen and clinic, operated by a local non-governmental organisation. Participants were sampled conveniently and classified as homeless or marginally housed. Data were collected for 1 year commencing 1 April 2010. HIV testing was performed using 2 rapid finger prick tests from separate manufacturers. A third confirmatory test was used in the case of discordant results. We also included 6 individuals who had proof of a previous HIV-positive test result. Exclusion criteria included: (i) earnings in excess of R5 000/month; and (ii) residing in a house defined on the grounds of a solid permanent structure with basic amenities (water, electricity, sewerage) and that is under the ownership or rental of the participant or family (excluding anything considered to be a form of shelter).

Ethics approval was obtained from the Human Research Ethics Committee of the University of the Witwatersrand. HIV risk factors were identified by uni- and multivariate logistic regression analysis. Data were represented as odds ratios (ORs) with 95% confidence intervals (CIs).

Results

Sample characteristics are summarised in Table 1. The HIV prevalence was 23.5% (95% CI 16.4% - 30.7%). Mean participant age was 32.4 years with 129 (95%) of the adults being male and 46 (35%) being foreigners. The mean monthly income for the HIV-positive and -negative groups was R 485 (95% CI R176 - R794) and R 863 (95% CI R540 - R1 186), respectively. There was no relationship between HIV-seropositivity and relationship status, educational level, number of sexual partners, smoking or use of alcohol, illicit drugs or condoms.

Previous exposure to voluntary counselling and testing (VCT) was not protective against HIV in this sample, as the number of previous tests was marginally higher in the HIV-positive group (1.6 v. 1.2; p>0.05). Of patients who tested HIV-positive, 36.3% had had a previous positive test result elsewhere, but had never been followed up in terms of CD4 count or antiretroviral therapy (ART) initiation. During post-test counselling, both HIV-positive and -negative groups reported unwillingness to disclose their status to others (59% and 50%, respectively).

The results of logistic regression modelling are shown in Table 2. There was a significant association between HIV-positive status and unemployment (OR 6.7; 95% CI 1.8 - 25.6; p=0.006), and current STI (OR 5.6; 95% CI 1.5 - 20.7; p=0.010). South African nationality was associated with HIV seropositivity on univariate analysis (OR 2.2; 95% CI 1.12 - 7.9; p=0.028), but not multivariate analysis (OR 2.2; 95% CI 0.7 - 7.0; p=0.17).

Table 1. Sample characteristics

Characteristic

N ( % )

Total

HIV-positive

HIV-negative

136 (100)

32 (100)

104 (100)

HIV test result

Positive

Negative

 

32 (24)

104 (76)

 

32 (100)

0 (0)

 

0 (0)

104 (100)

Gender

Male

Female

 

129 (95)

7 (5)

 

29 (91)

3 (9)

 

100 (96)

4 (4)

Residence

Street

Shelter

Informal settlement

House

 

68 (51)

30 (22)

8 (6)

28 (21)

 

21 (66)

6 (19)

2 (6)

3 (9)

 

47 (45)

24 (23)

6 (6)

25 (24)

Marital status

Married

Single

Widowed

 

14 (11)

91 (73)

20 (16)

 

3 (10)

21 (72)

5 (17)

 

11 (11)

70 (73)

15 (16)

Nationality

South African

Other

 

87 (65)

46 (35)

 

26 (84)

5 (16)

 

61 (60)

41 (40)

Employment status

Employed

Unemployed

 

55 (42)

77 (58)

 

7 (22)

25 (78)

 

52 (50)

52 (50)

Education level

<Grade 12

Grade 12

Tertiary

 

70 (62)

48 (38)

9 (7)

 

15 (54)

13 (46)

0 (0)

 

55 (56)

35 (35)

9 (9)

Reason for testing

Multiple partners

Partner HIV-positive or advised by family/friends

Feeling unwell or advised by health worker

Retesting (confirming HIV-positive test)

Curious about status

Partner’s behaviour or stopping condoms

Other

 

6 (4)

5 (4)

42 (31)

3 (2)

96 (71)

14 (10)

8 (6)

 

3 (9)

0 (0)

22 (68)

3 (9)

15 (47)

1 (3)

1 (3)

 

3 (3)

5 (5)

20 (19)

0 (0)

81 (78)

13 (13)

7 (7)

Result of most recent HIV test

Positive

Negative

Did not collect

 

12 (16)

61 (82)

1 (1)

 

11 (55)

8 (40)

1 (5)

 

1 (2)

52 (98)

0 (0)

Current exposures

Current STI

Cough for >1 week

Alcohol on most days

Smoking on most days

Recreational drug use in preceding week

Condom use at last sexual encounter

 

23 (17)

27 (20)

40 (30)

65 (48)

16 (12)

91 (51)

 

11 (35)

10 (32)

10 (31)

19 (59)

3 (9)

21 (44)

 

12 (12)

17 (17)

30 (29)

46 (45)

13 (13)

70 (53)


Table 1. Sample characteristics (continued)

Characteristic

N ( % )

Total

HIV-positive

HIV-negative

136 (100)

32 (100)

104 (100)

VCT

Previous HIV testing and counselling

 

65 (49)

 

15 (50)

 

50 (49)

Exposure in preceding 12 months

Vaginal or anal intercourse

Blood transfusion or medical injection

Intercourse after alcohol or illicit drugs

Transactional sexual intercourse

Had oral sex

Sexual intercourse without a condom

Symptoms of an STI

Diagnosed with an STI

 

114 (84)

45 (33)

65 (48)

18 (13)

28 (21)

55 (40)

26 (19)

16 (12)

 

26 (81)

11 (34)

17 (50)

7 (22)

6 (19)

14 (44)

11 (34)

7 (22)

 

88 (85)

34 (33)

48 (46)

11 (11)

22 (21)

41 (39)

15 (14)

9 (9)

STI = sexually transmitted infection; VCT = voluntary counselling and testing.

Table 2. Risk factors for HIV using logistic regression*

Risk factor

Univariate

 

Multivariate

OR (95% CI)

p -value

 

OR (95% CI)

p -value

Unemployment

3.7 (1.4 - 9.8)

0.009

 

6.7 (1.8 - 25.6)

0.006

South African nationality

2.99 (1.12 - 7.9)

0.028

 

2.2 (0.7 - 7.0)

0.170

Current STI

3.7 (1.4 - 9.8)

0.008

 

5.6 (1.5 - 20.7)

0.010

STI = sexually transmitted infection.

*Statistically significant results are indicated in bold.

Discussion

The associations between homelessness and HIV in this study contrast markedly with international literature, in terms of absolute risk and behaviour patterns. The HIV prevalence in our study was high by international standards, but not substantially greater than that of a general SA population of similar age and gender.12 Furthermore, no association was found between drug use and HIV in our sample, although this is a well-documented HIV risk factor in homeless populations in Europe and North America.2 , 3 , 4

HIV incidence in SA has only recently begun to plateau, with no consensus on the reason for this. It is unclear whether the successes of education and prevention programmes are finally being felt, or if increased access to ART and STI treatment are at play. Evidence suggests that HIV education and prevention programmes may not have had a significant effect on HIV status in our sample. For example, previous exposure to VCT did not appear to be protective against HIV, and the patterns of risk behaviour were similar between HIV-positive and -negative individuals. However, the association between HIV status and unemployment was significant, because it linked unemployment to HIV risk, rather than the more simplistic factor of homelessness.

Overall, the results of our study suggest that the dynamics of HIV among the urban homeless in SA may be different from other settings. High levels of inequality and unemployment, especially in an urban environment, lead to a unique set of risk factors which differ from that of the developed world. In SA, simply being homeless or marginally housed may not put individuals at increased risk of HIV compared with the general population. However, unemployment needs to be addressed to mitigate the effect of the HIV epidemic in this setting.

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Crossref Citations

1. Transactional sex and risk for HIV infection in sub-Saharan Africa: a systematic review and meta-analysis
Joyce Wamoyi, Kirsten Stobeanau, Natalia Bobrova, Tanya Abramsky, Charlotte Watts
Journal of the International AIDS Society  vol: 19  issue: 1  first page: 20992  year: 2016  
doi: 10.7448/IAS.19.1.20992