Copyright © NISC (Pty) Ltd
African Journal of AIDS Research 2014, 13(3): 271–279 Printed in South Africa — All rights reserved
AJAR
ISSN 1608-5906 EISSN 1727-9445 http://dx.doi.org/10.2989/16085906.2014.952651
Socio-economic and demographic factors related to HIV status in urban informal settlements in the Eastern Cape, South Africa Liana Steenkamp1*, Danie Venter2, Corinna Walsh3 and Pelisa Dana4 HIV&AIDS Research Unit, Nelson Mandela Metropolitan University, PO Box 77000, Port Elizabeth, 6031, South Africa Unit for Statistical Consultation, Nelson Mandela Metropolitan University, PO Box 77000, Port Elizabeth, 6031, South Africa 3Department of Nutrition and Dietetics, University of the Free State, PO Box 339, Bloemfontein, 9300, South Africa 4Eastern Cape AIDS Council, Postnet Vincent, P/Bag X9063, Suite No 3025246, Vincent, 5247, South Africa *Corresponding author, e-mail: [email protected]
1
2
The prevalence of HIV&AIDS is embedded in social and economic inequity and the relationship between social determinants and HIV incidence is well established. The aim of this study was to determine which socio-economic and demographic factors are related to HIV status in the age group 18 to 49 years in informal settlements in the Eastern Cape, South Africa. This cross-sectional study was conducted in 3 informal settlements (n = 752) during March 2013 within the Nelson Mandela Bay and Buffalo City districts. A proportional cluster sample was selected and stratified by area and formal plot/squatter households in open areas. Respondents who volunteered to participate had to provide informed written consent before trained, bilingual peer educators interviewed them and completed the structured questionnaire. HIV status was determined and information on demographic and socio-economic variables was included in the bivariate analysis. The prevalence of HIV was higher, at 17.3%, than the 2011 estimated national prevalence among the general population in South Africa. The level of education (χ2 = 5.50, df = 1, p < 0.05), geographical site (χ2 = 7.41, df = 2, p < 0.05), gender (χ2 = 33.10, df = 1, p < 0.0005), household food insecurity (χ² = 4.77, df = 1, p < 0.05), cooking with cast iron pots (χ2 = 15.0, df = 3, p < 0.05) and availability of perceived ‘wealth’ indicators like mobile telephones and refrigerators (χ² = 9.67, df = 2, p < 0.05) were significantly associated with HIV-status. No significant associations could be demonstrated between household income, the number of people living in the household and the availability of electricity/water and HIV status. As the observed levels of HIV prevalence underlined gender bias and failure to graduate from high school, future interventions should focus on HIV prevention in female schoolchildren. However, HIV infection is also prevalent among wealthier individuals in informal settlements, which indicates that renewed efforts should be made to improve sexual risk behaviour within this group. Keywords: HIV, employment status, level of education, food security, wealth, informal, urban
Introduction Domestically and internationally, the prevalence of HIV&AIDS is embedded in social and economic inequity (Phaswana-Mafuya et al. 2010). The current HIV prevalence of South Africans between the ages of 16 and 48 years is 15.9% and it increases to 17.4% among women within the same age group (Statistics South Africa 2013). The HIV prevalence from antenatal data in the Eastern Cape was reported as 29.3% during 2011 (Massyn et al. 2013). Inadequate socio-economic resources and unstable housing have been linked to riskier health behaviours (Aidala et al. 2005, Adler 2006, Ordónez and Marconi 2012) and individuals living in such environments seem more likely to be infected with HIV than individuals in more stable housing environments (Culhane et al. 2001). Developing countries are undergoing major demographic shifts from rural to urban, and in South Africa, it is mainly the African population that is experiencing rapid urbanisation. Benefits of urbanisation may include lowering of infant mortality rates
and longer life expectancies, as well as improved socioeconomic conditions and the wider availability of foods; however, urbanisation may be associated with poorer living conditions as well, especially in informal settlements (Bezuidenhout et al. 2009). Informal settlements, which have been part of South Africa’s landscape, have high HIV incidence rates resulting in a higher HIV prevalence than urban formal areas (HSRC 2005). Findings from earlier studies (HSRC 2002) estimate the annual HIV incidence in urban informal settlements in South Africa to be around 7% compared to 1.8% in urban formal areas. However, poverty as a driver of HIV-infection has been challenged by evidence from the demographic and health surveys (DHS) from eight African countries where a strong positive correlation was established between HIV status and wealth (Potts et al. 2008). As summarised by Fox (2010), a growing body of evidence indicate that ‘wealthier’ individuals in sub-Saharan Africa were more likely to be HIV infected than the poorest individuals. This phenomenon has become known as the positive wealth gradient
African Journal of AIDS Research is co-published by NISC (Pty) Ltd and Routledge, Taylor & Francis Group
272
in HIV infection (Fortson 2008). Rather than poverty itself being the main driver of HIV infection, economic and gender inequalities influencing sexual risk behaviour have been cited as the main causes (Fox 2010). Apart from gender inequalities, urban dwellers in some African countries like Ethiopia are eight times more likely to be HIV infected than people living in rural areas (Vinod et al. 2009). In South Africa, similar findings from the Assuring Health for All (AHA) study in the Free State indicated that participants in the Mangaung area which included an informal settlement, had an HIV prevalence of 40.8%, compared to only 17.1% in the rural areas (Groenewald et al. 2012). Clear evidence exists that the relationship between HIV and socio-demographic factors varies between countries, most likely as a result of cultural differences (Piot et al. 2007). In South Africa urban informal settlements tend to be situated close to business activities, formal housing areas and main roads. These settlements are characterised by a lack of basic services; limited access to piped water and electricity; informal housing structures (constructed from wood, tin and miscellaneous materials); and the general absence of proper infrastructure, including adequate sanitation. Findings from research in three South African informal settlements as summarised by Phaswana-Mafuya et al. (2010: 42) found that HIV risk was “embedded in various ‘social ills’ that included poor education, unemployment, discrimination, crime and violence”. However, according to a comprehensive report compiled by the Human Sciences Research Council (HSRC) for the Eastern Cape Socio Economic Consultative Council (Phaswana-Mafuya et al. 2010), the available evidence currently does not support the contention that a consistent relationship exists between poverty and HIV risk and more research about the drivers of HIV is needed in the South African context, especially in the Eastern Cape. The researchers therefore proposed to investigate associations between demographic and socioeconomic factors and HIV status in urban informal settlements in the Eastern Cape. Although HIV risk behaviour was also assessed as part of the study, detailed outcomes were not included in the analysis for this publication. Methodology A cross-sectional study design was applied to determine HIV status, demographic and socio-economic information in informal settlements in two health districts in the Eastern Cape, the Nelson Mandela Metropolitan Municipality (NMMM) and the Buffalo City Metropolitan Municipality (BCMM). For this study, an informal settlement referred to unplanned housing, which resulted in a lack of basic services and infrastructure (Working Group on Informal Settlements 2007). The study was conducted in March 2013 (n = 752). Ethical approval was obtained from the Ethics Committee (Human), Nelson Mandela Metropolitan University (NMMU) (H13-RTI-HIV-001). The districts were chosen because of the high reported HIV prevalence according to the 2011 National Antenatal Sentinel HIV & Syphilis Prevalence Survey (National Department of Health 2011). BCMM had the highest HIV prevalence in the Eastern Cape at 34.1%, compared to 28.3% for NMMM. Initially the study team planned to include
Steenkamp, Venter, Walsh and Dana
two informal settlements from each district, Duncan Village and Orange Grove in BCMM, and Walmer and Motherwell informal settlements in NMMM. However, due to unrest in the area and to ensure the safety of the field workers, Duncan Village was excluded. In included areas, permanent residents of the settlements, who had lived there for more than 6 months, were aged between 18 and 49 years and who gave written informed consent for both the HIV screening test and the interview were included in the sample. Before the research, ward councillors, as the community gatekeepers, were approached to inform the residents of the settlements about the study. A stratified proportional cluster sample, was selected, stratified by area and formal plot/squatter households in open areas. Fifty starting points were selected on randomly selected X and Y coordinates. From each starting point, one adult aged between 18 and 49 years, in up to 5 adjacent households were approached to participate. Refusal rates were reported to be very low, but were not recorded. Adjacent households to the initial five on the identified starting point were approached if the initially chosen household was empty or in the case of refusal. At each informal settlement, data were collected over a two to three day period. Bilingual peer educators (isiXhosa/English) recruited by NMMU (for the NMMM area) and by Walter Sisulu University (BCMM areas) were trained in the data collection procedures. A structured questionnaire was used to gather information from participants. The interview was conducted in their home language. A site supervisor/facilitator ensured that data collection procedures were followed and that coding of the HIV status and the questionnaire were correctly applied. HIV screening was done by a registered nurse from the HIV Counselling and Testing (HCT) unit in the area doing a finger prick rapid test (G-Ocean as currently available for the Eastern Cape on National Tender) in the privacy of the respondent’s household without any other family members present. Those that reported that they were HIV infected and currently attending an antiretroviral programme at a primary healthcare facility in the district were not retested. All participants received appropriate pre- and post-test counselling from a qualified health worker and were managed according to procedures as per awareness campaigns by the HCT units. All positive screening tests were referred to the nearest primary healthcare facility for a second confirmatory test (enzyme-linked immunosorbent assay by the National Health Laboratory Service) and assessment for antiretroviral therapy. To maintain confidentiality, outcomes of the second test were not incorporated into results of our study. Socio-demographic questions were chosen and adapted from the questionnaire used in the Assuring Health for All (AHA) study which was developed from the Prospective Urban Rural Epidemiology (PURE) study (Teo et al. 2009). Similar questions were used to enable researchers to make future comparisons between samples in these different geographical areas in Southern Africa. For this study, demographic and socio-economic variables included gender, area, dwelling, room density, education, employment and marital status of the participating individual, type of dwelling, water and sanitary conditions, household
African Journal of AIDS Research 2014, 13(3): 271–279
income, and the estimated amount of income spent on food. The amount spent on food was adapted to a per capita amount spent on food per day by using the total number of people in the household (which allowed for an adapted figure for children below 14 years of age). As more than 90% of respondents were Xhosa-speaking of black ethnic origin, language and race were excluded from data analysis. In this study, a Wealth Indicator Score was calculated reflecting the percentage of commodities that a household had access to. This score included eight commodities in total from which the percentage access to three broad categories, namely communication media, refrigeration and gas/electrical cooking facilities was determined. According to the participant’s access to ‘wealth’ commodities, they were grouped in a poor cluster with access to only 25% of the commodities, a middle cluster with between 25% and 75% of the commodities and a wealthy cluster with access to 75% or more of the commodities. Although this was not based on a validated tool, similar approaches to categorise household access to wealth in terms of quintiles have been described by other researchers (Howe et al. 2008). All the interviews were conducted by trained students and peer counsellors under supervision of trained facilitators/supervisors. A structured interviewing technique was used to complete the questionnaire once the participant had provided written informed consent. The data analysis was conducted by the Unit for Statistical Consultation, NMMU. The data was analysed using MS Excel (2012) and Statistica 10. In a few cases inconsistent responses were removed during data analysis, which resulted in missing values for some of the variables, as indicated in the tables. Frequencies and percentages were used to present categorical data. Subgroups were compared using the Pearson Chi-square test to test for statistical significant differences and Cramer’s V as an effect size measure to indicate practical significance. Results The sample comprised 752 adults (60.2% female), 505 from 2 informal settlements in the Nelson Mandela Bay district and 247 from one informal settlement in the Buffalo City district. Only 39% (n = 292) of participants were employed at the time of the study, with only 14.1% (n = 106) employed full time. Of those who participated, 43% (n = 327) were the head of the household, 16% the spouse (n = 124), 18% (n = 132) adult children still living with parents and 16% (n = 122) parents of the household head or spouse. The remainder had either no relation or a distant relationship with the family where they resided. Within the sample 22% (n = 164) were known to be HIV infected and on treatment and another 11% (n = 84) tested positive. This brought the total to 33% who were either known to be HIV infected or had a positive G-Ocean screening test. While no significant differences could be shown for HIV prevalence between the two districts, notably more participants from the Walmer informal settlement were HIV infected with 40% being either known HIV infected or tested positive (Table 1). The Cramer’s V (0.01)
273
suggested that this difference was of small practical significance. No statistically significant differences could be demonstrated between the gender distribution and the level of education between the Walmer site which had a higher HIV prevalence and the other two sites. Analysis of sexual risk behaviour indicated that significantly more (χ² = 45.15, df = 4, p < 0.0005) participants from Orange Grove reported multiple sexual partners in the last 12 months (35% versus
African Journal of AIDS Research 2014, 13(3): 271–279 Printed in South Africa — All rights reserved
AJAR
ISSN 1608-5906 EISSN 1727-9445 http://dx.doi.org/10.2989/16085906.2014.952651
Socio-economic and demographic factors related to HIV status in urban informal settlements in the Eastern Cape, South Africa Liana Steenkamp1*, Danie Venter2, Corinna Walsh3 and Pelisa Dana4 HIV&AIDS Research Unit, Nelson Mandela Metropolitan University, PO Box 77000, Port Elizabeth, 6031, South Africa Unit for Statistical Consultation, Nelson Mandela Metropolitan University, PO Box 77000, Port Elizabeth, 6031, South Africa 3Department of Nutrition and Dietetics, University of the Free State, PO Box 339, Bloemfontein, 9300, South Africa 4Eastern Cape AIDS Council, Postnet Vincent, P/Bag X9063, Suite No 3025246, Vincent, 5247, South Africa *Corresponding author, e-mail: [email protected]
1
2
The prevalence of HIV&AIDS is embedded in social and economic inequity and the relationship between social determinants and HIV incidence is well established. The aim of this study was to determine which socio-economic and demographic factors are related to HIV status in the age group 18 to 49 years in informal settlements in the Eastern Cape, South Africa. This cross-sectional study was conducted in 3 informal settlements (n = 752) during March 2013 within the Nelson Mandela Bay and Buffalo City districts. A proportional cluster sample was selected and stratified by area and formal plot/squatter households in open areas. Respondents who volunteered to participate had to provide informed written consent before trained, bilingual peer educators interviewed them and completed the structured questionnaire. HIV status was determined and information on demographic and socio-economic variables was included in the bivariate analysis. The prevalence of HIV was higher, at 17.3%, than the 2011 estimated national prevalence among the general population in South Africa. The level of education (χ2 = 5.50, df = 1, p < 0.05), geographical site (χ2 = 7.41, df = 2, p < 0.05), gender (χ2 = 33.10, df = 1, p < 0.0005), household food insecurity (χ² = 4.77, df = 1, p < 0.05), cooking with cast iron pots (χ2 = 15.0, df = 3, p < 0.05) and availability of perceived ‘wealth’ indicators like mobile telephones and refrigerators (χ² = 9.67, df = 2, p < 0.05) were significantly associated with HIV-status. No significant associations could be demonstrated between household income, the number of people living in the household and the availability of electricity/water and HIV status. As the observed levels of HIV prevalence underlined gender bias and failure to graduate from high school, future interventions should focus on HIV prevention in female schoolchildren. However, HIV infection is also prevalent among wealthier individuals in informal settlements, which indicates that renewed efforts should be made to improve sexual risk behaviour within this group. Keywords: HIV, employment status, level of education, food security, wealth, informal, urban
Introduction Domestically and internationally, the prevalence of HIV&AIDS is embedded in social and economic inequity (Phaswana-Mafuya et al. 2010). The current HIV prevalence of South Africans between the ages of 16 and 48 years is 15.9% and it increases to 17.4% among women within the same age group (Statistics South Africa 2013). The HIV prevalence from antenatal data in the Eastern Cape was reported as 29.3% during 2011 (Massyn et al. 2013). Inadequate socio-economic resources and unstable housing have been linked to riskier health behaviours (Aidala et al. 2005, Adler 2006, Ordónez and Marconi 2012) and individuals living in such environments seem more likely to be infected with HIV than individuals in more stable housing environments (Culhane et al. 2001). Developing countries are undergoing major demographic shifts from rural to urban, and in South Africa, it is mainly the African population that is experiencing rapid urbanisation. Benefits of urbanisation may include lowering of infant mortality rates
and longer life expectancies, as well as improved socioeconomic conditions and the wider availability of foods; however, urbanisation may be associated with poorer living conditions as well, especially in informal settlements (Bezuidenhout et al. 2009). Informal settlements, which have been part of South Africa’s landscape, have high HIV incidence rates resulting in a higher HIV prevalence than urban formal areas (HSRC 2005). Findings from earlier studies (HSRC 2002) estimate the annual HIV incidence in urban informal settlements in South Africa to be around 7% compared to 1.8% in urban formal areas. However, poverty as a driver of HIV-infection has been challenged by evidence from the demographic and health surveys (DHS) from eight African countries where a strong positive correlation was established between HIV status and wealth (Potts et al. 2008). As summarised by Fox (2010), a growing body of evidence indicate that ‘wealthier’ individuals in sub-Saharan Africa were more likely to be HIV infected than the poorest individuals. This phenomenon has become known as the positive wealth gradient
African Journal of AIDS Research is co-published by NISC (Pty) Ltd and Routledge, Taylor & Francis Group
272
in HIV infection (Fortson 2008). Rather than poverty itself being the main driver of HIV infection, economic and gender inequalities influencing sexual risk behaviour have been cited as the main causes (Fox 2010). Apart from gender inequalities, urban dwellers in some African countries like Ethiopia are eight times more likely to be HIV infected than people living in rural areas (Vinod et al. 2009). In South Africa, similar findings from the Assuring Health for All (AHA) study in the Free State indicated that participants in the Mangaung area which included an informal settlement, had an HIV prevalence of 40.8%, compared to only 17.1% in the rural areas (Groenewald et al. 2012). Clear evidence exists that the relationship between HIV and socio-demographic factors varies between countries, most likely as a result of cultural differences (Piot et al. 2007). In South Africa urban informal settlements tend to be situated close to business activities, formal housing areas and main roads. These settlements are characterised by a lack of basic services; limited access to piped water and electricity; informal housing structures (constructed from wood, tin and miscellaneous materials); and the general absence of proper infrastructure, including adequate sanitation. Findings from research in three South African informal settlements as summarised by Phaswana-Mafuya et al. (2010: 42) found that HIV risk was “embedded in various ‘social ills’ that included poor education, unemployment, discrimination, crime and violence”. However, according to a comprehensive report compiled by the Human Sciences Research Council (HSRC) for the Eastern Cape Socio Economic Consultative Council (Phaswana-Mafuya et al. 2010), the available evidence currently does not support the contention that a consistent relationship exists between poverty and HIV risk and more research about the drivers of HIV is needed in the South African context, especially in the Eastern Cape. The researchers therefore proposed to investigate associations between demographic and socioeconomic factors and HIV status in urban informal settlements in the Eastern Cape. Although HIV risk behaviour was also assessed as part of the study, detailed outcomes were not included in the analysis for this publication. Methodology A cross-sectional study design was applied to determine HIV status, demographic and socio-economic information in informal settlements in two health districts in the Eastern Cape, the Nelson Mandela Metropolitan Municipality (NMMM) and the Buffalo City Metropolitan Municipality (BCMM). For this study, an informal settlement referred to unplanned housing, which resulted in a lack of basic services and infrastructure (Working Group on Informal Settlements 2007). The study was conducted in March 2013 (n = 752). Ethical approval was obtained from the Ethics Committee (Human), Nelson Mandela Metropolitan University (NMMU) (H13-RTI-HIV-001). The districts were chosen because of the high reported HIV prevalence according to the 2011 National Antenatal Sentinel HIV & Syphilis Prevalence Survey (National Department of Health 2011). BCMM had the highest HIV prevalence in the Eastern Cape at 34.1%, compared to 28.3% for NMMM. Initially the study team planned to include
Steenkamp, Venter, Walsh and Dana
two informal settlements from each district, Duncan Village and Orange Grove in BCMM, and Walmer and Motherwell informal settlements in NMMM. However, due to unrest in the area and to ensure the safety of the field workers, Duncan Village was excluded. In included areas, permanent residents of the settlements, who had lived there for more than 6 months, were aged between 18 and 49 years and who gave written informed consent for both the HIV screening test and the interview were included in the sample. Before the research, ward councillors, as the community gatekeepers, were approached to inform the residents of the settlements about the study. A stratified proportional cluster sample, was selected, stratified by area and formal plot/squatter households in open areas. Fifty starting points were selected on randomly selected X and Y coordinates. From each starting point, one adult aged between 18 and 49 years, in up to 5 adjacent households were approached to participate. Refusal rates were reported to be very low, but were not recorded. Adjacent households to the initial five on the identified starting point were approached if the initially chosen household was empty or in the case of refusal. At each informal settlement, data were collected over a two to three day period. Bilingual peer educators (isiXhosa/English) recruited by NMMU (for the NMMM area) and by Walter Sisulu University (BCMM areas) were trained in the data collection procedures. A structured questionnaire was used to gather information from participants. The interview was conducted in their home language. A site supervisor/facilitator ensured that data collection procedures were followed and that coding of the HIV status and the questionnaire were correctly applied. HIV screening was done by a registered nurse from the HIV Counselling and Testing (HCT) unit in the area doing a finger prick rapid test (G-Ocean as currently available for the Eastern Cape on National Tender) in the privacy of the respondent’s household without any other family members present. Those that reported that they were HIV infected and currently attending an antiretroviral programme at a primary healthcare facility in the district were not retested. All participants received appropriate pre- and post-test counselling from a qualified health worker and were managed according to procedures as per awareness campaigns by the HCT units. All positive screening tests were referred to the nearest primary healthcare facility for a second confirmatory test (enzyme-linked immunosorbent assay by the National Health Laboratory Service) and assessment for antiretroviral therapy. To maintain confidentiality, outcomes of the second test were not incorporated into results of our study. Socio-demographic questions were chosen and adapted from the questionnaire used in the Assuring Health for All (AHA) study which was developed from the Prospective Urban Rural Epidemiology (PURE) study (Teo et al. 2009). Similar questions were used to enable researchers to make future comparisons between samples in these different geographical areas in Southern Africa. For this study, demographic and socio-economic variables included gender, area, dwelling, room density, education, employment and marital status of the participating individual, type of dwelling, water and sanitary conditions, household
African Journal of AIDS Research 2014, 13(3): 271–279
income, and the estimated amount of income spent on food. The amount spent on food was adapted to a per capita amount spent on food per day by using the total number of people in the household (which allowed for an adapted figure for children below 14 years of age). As more than 90% of respondents were Xhosa-speaking of black ethnic origin, language and race were excluded from data analysis. In this study, a Wealth Indicator Score was calculated reflecting the percentage of commodities that a household had access to. This score included eight commodities in total from which the percentage access to three broad categories, namely communication media, refrigeration and gas/electrical cooking facilities was determined. According to the participant’s access to ‘wealth’ commodities, they were grouped in a poor cluster with access to only 25% of the commodities, a middle cluster with between 25% and 75% of the commodities and a wealthy cluster with access to 75% or more of the commodities. Although this was not based on a validated tool, similar approaches to categorise household access to wealth in terms of quintiles have been described by other researchers (Howe et al. 2008). All the interviews were conducted by trained students and peer counsellors under supervision of trained facilitators/supervisors. A structured interviewing technique was used to complete the questionnaire once the participant had provided written informed consent. The data analysis was conducted by the Unit for Statistical Consultation, NMMU. The data was analysed using MS Excel (2012) and Statistica 10. In a few cases inconsistent responses were removed during data analysis, which resulted in missing values for some of the variables, as indicated in the tables. Frequencies and percentages were used to present categorical data. Subgroups were compared using the Pearson Chi-square test to test for statistical significant differences and Cramer’s V as an effect size measure to indicate practical significance. Results The sample comprised 752 adults (60.2% female), 505 from 2 informal settlements in the Nelson Mandela Bay district and 247 from one informal settlement in the Buffalo City district. Only 39% (n = 292) of participants were employed at the time of the study, with only 14.1% (n = 106) employed full time. Of those who participated, 43% (n = 327) were the head of the household, 16% the spouse (n = 124), 18% (n = 132) adult children still living with parents and 16% (n = 122) parents of the household head or spouse. The remainder had either no relation or a distant relationship with the family where they resided. Within the sample 22% (n = 164) were known to be HIV infected and on treatment and another 11% (n = 84) tested positive. This brought the total to 33% who were either known to be HIV infected or had a positive G-Ocean screening test. While no significant differences could be shown for HIV prevalence between the two districts, notably more participants from the Walmer informal settlement were HIV infected with 40% being either known HIV infected or tested positive (Table 1). The Cramer’s V (0.01)
273
suggested that this difference was of small practical significance. No statistically significant differences could be demonstrated between the gender distribution and the level of education between the Walmer site which had a higher HIV prevalence and the other two sites. Analysis of sexual risk behaviour indicated that significantly more (χ² = 45.15, df = 4, p < 0.0005) participants from Orange Grove reported multiple sexual partners in the last 12 months (35% versus
