Epidemiology and Social
Systematic review exploring time trends in the association between educational attainment and risk of HIV infection in sub-Saharan Africa
Hargreaves, James R; Bonell, Christopher P; Boler, Tania; Boccia, Delia; Birdthistle, Isolde; Fletcher, Adam; Pronyk, Paul M; Glynn, Judith R
From the London School of Hygiene and Tropical Medicine, Keppel Street, London, UK.
Received 26 July, 2007
Revised 19 September, 2007
Accepted 26 September, 2007
Correspondence to Dr J. Hargreaves, Infectious Disease Epidemiology Unit, London School of Hygiene and Tropical Medicine, Keppel St, London WC1E 7HT, UK. E-mail: firstname.lastname@example.org
Objective: To assess the evidence that the association between educational attainment and risk of HIV infection is changing over time in sub-Saharan Africa.
Design and methods: Systematic review of published peer-reviewed articles. Articles were identified that reported original data comparing individually measured educational attainment and HIV status among at least 300 individuals representative of the general population of countries or regions of sub-Saharan Africa. Statistical analyses were required to adjust for potential confounders but not over-adjust for variables on the causal pathway.
Results: Approximately 4000 abstracts and 1200 full papers were reviewed. Thirty-six articles were included in the study, containing data on 72 discrete populations from 11 countries between 1987 and 2003, representing over 200 000 individuals. Studies on data collected prior to 1996 generally found either no association or the highest risk of HIV infection among the most educated. Studies conducted from 1996 onwards were more likely to find a lower risk of HIV infection among the most educated. Where data over time were available, HIV prevalence fell more consistently among highly educated groups than among less educated groups, in whom HIV prevalence sometimes rose while overall population prevalence was falling. In several populations, associations suggesting greater HIV risk in the more educated at earlier time points were replaced by weaker associations later.
Discussion: HIV infections appear to be shifting towards higher prevalence among the least educated in sub-Saharan Africa, reversing previous patterns. Policy responses that ensure HIV-prevention measures reach all strata of society and increase education levels are urgently needed.
HIV has spread most rapidly in sub-Saharan Africa, the world's poorest region, where access to medical care for those infected is also most constrained . While the pandemic of HIV has been widely characterized as a disease of poverty, the association between socioeconomic status and risk of HIV infection is more complex than this suggests.
In 2001, we published a systematic review on the association between educational attainment and risk of HIV infection in developing countries . This examined studies reporting on data mostly collected before 1996. With one exception, in studies conducted in sub-Saharan Africa, there was either no association between educational attainment and HIV infection or individuals with the highest levels of education were more likely to be infected. Other authors have also reached this conclusion . This finding is likely to have reflected the higher socioeconomic status and greater mobility of those with greater levels of education, which, in turn, facilitated engagement in larger, more risky sexual networks.
We and others hypothesized that as the public-health response to HIV grew in sub-Saharan Africa, so the most educated, empowered members of society would be the first to adopt protective behaviours [2,4,5]. Since HIV campaigns are often delivered within schools, longer attendance at school might also increase contact with health-promotion messages and reduce risk of infection among young adults. In the absence of effective interventions that engaged the least educated, HIV would, over time, increasingly affect this group.
This article updates the systematic review to include new data published between 2001 and July 2006 and new data on time trends. The analysis explores the hypothesis that later studies of the association between educational attainment and HIV infection (those conducted from 1996 onwards) are more likely to show a lower risk of HIV infection among the most educated than earlier studies (those where data were collected before 1996).
Details of the search strategy employed in 2001 are published elsewhere . This strategy was repeated in July 2006, involving a comprehensive search of the major biomedical databases (Pubmed, Embase), hand searching key journals (AIDS, International Journal of STD & AIDS, Journal of Acquired Immune Deficiency Syndrome) and checking reference lists of relevant articles. Two modifications were made to the original strategy: the search only looked for data from sub-Saharan Africa and it included a search term for ‘sexual behaviour’. The search sought to identify articles reporting original data comparing individually measured educational attainment and HIV status among at least 300 individuals from population groups broadly representative of the general population. Studies restricted to high-risk groups and other unrepresentative sections of the population were excluded. Standardized forms were used to collect data from selected articles.
Only articles that presented appropriately analysed information on the association between schooling and HIV were reviewed. Appropriately analysed articles were those that adjusted for, or restricted by, at least setting (rural/urban), age and sex. Inappropriately analysed articles were those containing univariate analysis only or multivariate analysis that ‘over-adjusted’ for sexual behaviour or the presence of sexually transmitted infections, which might mediate any true effect of education on risk of HIV infection. Where necessary, authors were contacted to provide full details of analyses described, but not reported in full, in published articles.
In order to summarize the findings of the studies, discrete populations were identified in whom the association between educational attainment and HIV infection had been assessed. If appropriately adjusted analyses were stratified by sex, setting or time period, these were considered discrete populations. In many cases a single article reported on multiple populations while in some cases more than one article reported on the same population. The results of age-stratified analyses are described separately, with an emphasis on the results of studies conducted among younger groups, in whom patterns of prevalent infection are likely to most closely reflect incidence.
Analyses generally compared levels of HIV infection among individuals grouped by two to five categories of educational attainment, measured as years of school attended or grade achieved. First, we examined the comparison between the most educated and the least educated within each population. In each case the result was classified as a statistically significant (P < 0.05) increased risk of HIV infection among the most educated compared with the least educated, a significantly lower risk, or no association. Since only three articles assessed evidence for linearity or trend or presented the results of likelihood ratio tests examining the total evidence for an impact of education on risk of HIV infection, in most cases judgements about the type of association reported were made on the basis of the 95% confidence intervals around the odds ratio describing the comparison between the highest and lowest educational groups. Where this approach gave a misleading impression of the association across more than two educational categories, relevant additional data has been provided. Summarized results are presented by year of study, sex and type of study population, study design, setting and country. The year 1996 was chosen a priori as a cut-off to describe changes over time, since most articles included in the previous review presented data from before 1996 and since about half the data were collected either side of this date.
Second, situations were identified where successive cross-sectional data were available covering the same target population at different time points. To be included, analyses had to be conducted on a study population selected from the same, or similar, sampling frame in terms of geographic area and age group, and they had to use a comparable analytic approach at different time points. Data over time could be reported in the same article or across a number of articles. Values for the odds ratio and 95% confidence intervals are provided for the relative risk of HIV infection between the highest and lowest educational categories at the different time points and the trend in prevalence of HIV infection among the most and least educated groups was examined over time. Formal meta-analysis was not conducted since the association between educational attainment and HIV infection was not expected to be the same across different populations.
In 2001, approximately 2000 articles were reviewed, and 18 meeting the inclusion criteria were identified [6–23]. For this review, the titles and abstracts of a further 1830 articles published between January 2001 and June 2006 were also examined. The full texts of 335 articles were reviewed and a further 18 articles containing relevant data were identified [24–41]. Appropriately conducted analyses were available for 32 discrete populations for which data had been collected predominantly before 1996 and 40 populations where the data were collected from 1996 onwards. This covered a total of over 200 000 individuals from 11 countries. See Table 1 for details of all selected articles and populations.
Cross-sectional data came from general population surveys employing systematic random sampling [8–12,15–17,20–23,25–29,31–33,37–40], and from samples of young people , army recruits , residents of a sugar estate  and antenatal clinic surveillance [14,18,19,30,36]. Additionally, incident infections were identified in one factory-based cohort study  and one nested case–control study within a cluster-randomized trial among the general population . Studies reported on data collected from populations between 1987 and 2003. Information on HIV serostatus was collected from a variety of assays and algorithms, most commonly employing at least one enzyme-linked immunosorbent assay on a fresh blood sample. However, dried blood spots, oral fluid and urine samples were also collected and analysed.
Comparing the highest and lowest educational categories, increased educational attainment was not statistically significantly associated with risk of HIV infection in 44 populations, was associated with an increased risk of HIV infection in 20 populations and was associated with a decreased risk of infection in eight populations. Populations from which data were collected prior to 1996 were more likely to show a higher risk of HIV infection among the most educated (15/32 populations) than those studied from 1996 onwards (5/40) (Table 2). Only one pre-1996 population , compared with 7/40 from 1996 onwards, had a lower risk of HIV infection among the most educated. Similar patterns of results were seen in studies conducted in different settings, among different population types, with different study designs and in countries where data were available from both time periods. There was some suggestion that there was more likely to be a higher HIV risk among the most educated compared with the least educated among females (compared with males), antenatal clinic surveillance populations (compared with general population samples) and rural (compared with urban) populations (Table 2).
Eligible data from different time periods were available for 13 populations from five countries: Malawi (1 population), Tanzania (2 populations), Uganda (3 populations), Zambia (6 populations) and Zimbabwe (1 population) (Fig. 1). Downward trends in HIV prevalence in the most educated, together with a reduction in the relative HIV prevalence between the most and least educated were seen in rural (but not urban) Kagera, Tanzania; females (but not males) from Masaka, Uganda; antenatal clinic users in Fort Portal, Uganda, and in Manicaland, Zimbabwe; and among urban (but not rural) populations in Zambia. In Karonga, Malawi, a reduction in the relative prevalence of HIV between the early and late 1990s reflected an increase in prevalence in the least educated rather than a fall in the more educated. In Zimbabwe and Zambia, increases, or stabilization, in the HIV prevalence in the least educated contributed to the changing relative prevalence seen. Only in Masaka, Uganda, was there evidence of reduction in HIV prevalence in both the most and the least educated. In general, from 1996 onwards, changes in the adjusted odds ratio for HIV infection comparing groups of the highest and lowest levels of educational attainment tended to shift towards no association or a lower risk in the most educated (Fig. 2). Multiple studies were also conducted in Mwanza, Tanzania, and Rakai, Uganda, but these varied over time in sampling frame, age group, study design or analysis. In Mwanza, two cross-sectional studies conducted a single year apart showed a slightly higher adjusted odds ratio comparing the most and the least educated in 1991–2 than in 1990–1 for both sexes [6,8,12]. Data from a later nested case–control study of incident HIV infections between 1991 and 1994 in Mwanza, Tanzania, suggested a significantly lower risk of infection among the most educated among males, while among females there was no association with education .
Age-stratified or restricted analyses were available for 35 populations under 30 years of age (Table 1). In Kisumu, Kenya, and Ndola, Zambia, there was no association between educational attainment and HIV among males or females aged 15–24 years. In Yaounde, Cameroon, among females aged 15–24 years, the most educated were at the lowest risk of infection, while there was no association among young males. In Cotonou, Benin, the most educated males aged 15–24 years were at the lowest risk of infection, but there was no association among females. In Manicaland, Zimbabwe, there was a lower risk of infection among the most educated females aged 15–24 years, while among males aged 17–29 years there was no association. In Mwanza, Tanzania, ever having attended school was associated with a lower risk of infection among males, but not females, aged 15–19 years.
Time-series analyses were also available for some populations of young people. In Masaka, Uganda, there was no significant association among males aged 18–29 years in 1989–90 or 1999–2000. However, among females there was no association in 1989–90 but a significantly lower risk of infection among the most educated in 1999–2000. In Fort Portal, Uganda, there was no association among antenatal clinic attendees aged 15–24 years in 1991–4, whereas by 1995–7 there was a lower risk of infection among the most educated. Among Zambian antenatal clinic attendees aged 15–24 years, there was a higher risk of infection among the most educated in rural and urban areas in 1994, 1998 and 2002. In Zambian population-based surveys, educational attainment was not associated with risk of infection among rural males aged 15–24 years in any year. Among urban males, there was no association in 1995 and 1999, but by 2003 there was a lower risk of infection among the most educated. Among Zambian females from urban areas, there was no association among younger women in 1995 and 1999, whereas by 2003 a lower risk was seen among the most educated women aged 15–24 years. In rural Zambia, there was no association among younger women in 1995 and 2003, but a higher risk of infection among the most educated women in 1999.
We present evidence that the epidemiology of HIV infection in sub-Saharan Africa may be changing. Studies conducted before 1996 tended to find either no association with education level or a higher risk of HIV infection among the most educated. A larger proportion of studies conducted from 1996 onwards identified a lower risk of infection among the most educated. Where data over time were available, the trend was generally for strong positive associations to be replaced by weaker or negative associations. Across many settings, HIV prevalence fell more consistently among the higher educated than among the less educated groups, in whom prevalence sometimes rose even while overall population prevalence was falling. Taking these findings together, we suggest that new HIV infections occurring in the latter half of the 1990s and into the 21st century have been occurring disproportionately among the least educated members of society in many sub-Saharan African countries.
Our attempt to synthesize the available evidence might be subject to several limitations. There may have been limitations in the included studies . For instance, population studies may have been biased if eligible individuals excluded from the study differed from those included. However, response rates were high where they were reported. Studies of antenatal clinic attendees are generally unlinked–anonymized, with approximately 100% response rates, but include only pregnant women who attend antenatal clinics , thus excluding the sexually inactive  and those not using clinics, who may be less educated. Since delayed first pregnancy has become more common over time in many settings, young women who do become pregnant may be at particularly high risk of infection, perhaps explaining why antenatal clinic data more often suggested higher levels of infection among the most educated and less evidence of changes over time [40,45].
The synthesis of data from observational studies presents particular challenges . In this case, a standardized measure of exposure (educational attainment) was not available and a variety of scales were used. In addition, most studies used more than two educational groups in the analysis, making the appropriate choice of summary measure complex. It was only possible to identify associations between risk of HIV infection and relative level of education in each study. We chose to summarize studies descriptively, primarily on the basis of the comparison of HIV risk between the highest and lowest educational groups. This implicitly assumes a linear association between education and HIV risk, although in only a relatively small number of cases did we judge that such a comparison led to potentially misleading conclusions (see Table 1, footnote h). Estimates of effect were unstable if the highest and/or lowest groups had relatively small numbers for the calculation of HIV prevalence, and the interpretation of the ratio of highest to lowest education will differ across studies. In some settings, levels of secondary school attendance were very low, while in others these were much higher, and different cut-offs were used. The lowest category of educational level thus varied from no education to less than secondary in different studies (Table 1).
We only included studies that adjusted for (or restricted by) age and sex, and that did not adjust for sexual-behaviour factors that are likely to be on the causal pathway between education level and HIV. An exception was made for marital status, as many of the studies had controlled for this. In one study that explicitly explored the confounding effect of marriage on the association of education status and HIV risk in four different settings, it had little effect on the results .
We cannot exclude publication bias: perhaps studies finding a lower risk of HIV infection among more educated groups, or a shifting pattern of infection towards this trend, may have been more likely to be published between 2001 and 2006. However, education level was often not the main focus of the studies, so it is unlikely to have biased publication. Finally, data were only available from 11 of the countries in the region, a minority.
Overall, available data from a number of settings suggested that the trend seen in data collected before 1996, of a higher risk of infection among the most educated groups, was weakening, and in some cases reversing, over time. Since patterns of prevalent HIV infection are relatively slow to respond to changes in HIV incidence, it is possible that these patterns hide much greater relative differences in HIV incidence between the most and least educated in recent years. Where time-series data were available on younger age groups, in whom infection is likely to have been acquired recently, this trend was also seen. Previous studies have attempted to infer time trends by comparing the association between education and HIV between younger and older groups [15,27,32]. However, differential AIDS-related mortality in these groups makes these comparisons difficult  and a strength of our study was the inclusion of data collected in different time periods to address this question. Differential mortality could only explain the changes seen in different time periods if overall rates of AIDS-related mortality were changing rapidly in some age groups over time, which is unlikely as these studies predate the widespread accessibility of antiretroviral drugs.
While an overall pattern emerged, there was also some evidence of differences between population groups, for example with differences between rural and urban Zambia. Zambia was the only country with data from a wide range of regions within the country; other results came from single regions or towns, and for most countries of sub-Saharan Africa there were no data at all. It is possible that patterns in other countries may differ.
Strong supporting evidence of behaviour change among the most educated comes from studies that have consistently found higher levels of reported condom use among more educated individuals in a variety of contexts [28,45,47–53]. However, evidence with regard to other safer-sexual behaviours, such as delayed age at first sex and reduced partner numbers, is less consistent: some studies have suggested lower risk among the most educated but this has not been seen in other settings [28,31,45,48,53–56]. The plausibility of the hypothesis of change occurring earlier in those with more education is also supported by well-established theory. For example, the diffusion of innovations model predicts that more-educated, more-empowered members of a population will seek out information on new practices (such as condom use in sub-Saharan Africa in the latter part of the 20th century) and more readily adopt these .
The evidence presented here should highlight the importance of monitoring future trends within surveillance systems across sub-Saharan Africa. We believe there is already sufficient evidence to support consideration of policy responses to the changing epidemiology of HIV infection we outline in this paper, since such responses will need to be rapidly rolled out if the trends reported here are confirmed in ongoing surveillance. Current approaches, focusing primarily on the provision of information, distribution of condoms and treatment of sexually transmitted infections have worked and should continue. However, these efforts may effectively serve only some sections of the community, and health inequalities in sub-Saharan Africa may be set to increase. Additional efforts are needed to expand the reach of HIV prevention programmes to target socially vulnerable groups more effectively and to address social inequalities. As an example, recent interventions to improve school enrolment, such as the abolition of primary school fees, have met with dramatic success in Kenya, Malawi, Tanzania and Uganda as part of efforts to achieve universal access to primary education . Such interventions may have a role to play in complementing more traditional HIV prevention methods in reducing HIV incidence in all social groups.
We would like to thank Knut Fylkesnes, Simon Gregson, James Lewis and Andrew Nunn for providing further information on some of the studies described here. We would also like to thank Andrew Thomson for his assistance in producing the figures.
Sponsorship: James Hargreaves and Isolde Birdthistle are supported by ESRC/MRC interdisciplinary postdoctoral fellowships. Judith Glynn is funded by the UK Department of Health (Public Health Career Scientist Award). Chris Bonell is funded from London School of Hygiene & Tropical Medicine core funding. Adam Fletcher is supported by an MRC studentship.
1. UNAIDS. Report on the Global HIV/AIDS Epidemic
. Geneva: UNAIDS; 2006.
2. Hargreaves JR, Glynn JR. Educational attainment and HIV-1 infection in developing countries: a systematic review. Trop Med Int Health 2002; 7:489–498.
3. Wojcicki JM. Socioeconomic status as a risk factor for HIV infection in women in East, Central and Southern Africa: a systematic review. J Biosoc Sci 2005; 37:1–36.
4. Rogers EM. Diffusion of Innovations. New York: The Free Press; 1962.
5. Over M, Piot P. HIV infection and sexually transmitted diseases. In: Jamison DT, Mosley WH, Mensham AR, Bobadilla JL, editors. Disease Control Priorities in Developing Countries. Oxford: Oxford University Press; 1993. pp. 455–527.
6. Barongo LR, Borgdorff MW, Mosha FF, Nicoll A, Grosskurth H, Senkoro KP, et al
. The epidemiology of HIV-1 infection in urban areas, roadside settlements and rural villages in Mwanza Region, Tanzania. AIDS 1992; 6:1521–1528.
7. Fontanet AL, Woldemichael T, Sahlu T, van Dam GJ, Messele T, Rinke de Wit T, et al
. Epidemiology of HIV and Schistosoma mansoni
infections among sugar-estate residents in Ethiopia. Ann Trop Med Parasitol 2000; 94:145–155.
8. Grosskurth H, Mosha F, Todd J, Senkoro K, Newell J, Klokke A, et al
. A community trial of the impact of improved sexually transmitted disease treatment on the HIV epidemic in rural Tanzania: 2. Baseline survey results. AIDS 1995; 9:927–934.
9. Kwesigabo G, Killewo J, Godoy C, Urassa W, Mbena E, Mhalu F, et al
. Decline in the prevalence of HIV-1 infection in young women in the Kagera region of Tanzania. J Acquir Immune Defic Syndr 1998; 17:262–268.
10. Mnyika KS, Klepp KI, Kvale G, Ole-King'ori N. Risk factors for HIV-1 infection among women in the Arusha region of Tanzania. J Acquir Immune Defic Syndr 1996; 11:484–491.
11. Nunn AJ, Wagner HU, Okongo JM, Malamba SS, Kengeya-Kayondo JF, Mulder DW. HIV-1 infection in a Ugandan town on the trans-African highway: prevalence and risk factors. Int J STD AIDS 1996; 7:123–130.
12. Quigley MA, Morgan D, Malamba SS, Mayanja B, Okongo MJ, Carpenter LM, Whitworth JA. Case–control study of risk factors for incident HIV infection in rural Uganda. J Acquir Immune Defic Syndr 2000; 23:418–425.
13. Senkoro K, Boerma JT, Klokke A, Ng'weshemi JZL, Muro AS, Gabone R, Borgdorff MW. HIV incidence and HIV-associated mortality in a cohort of factory workers and their spouses in Tanzania, 1991 through 1996. J Acquir Immune Defic Syndr 2001; 23:194–202.
14. Fylkesnes K, Masonda RM, Kasumba K, Ndhlovu Z, Mluanda F, Kaetano L, Chipaila CC. The HIV epidemic in Zambia: sociodemographic prevalence patterns and indications of trends among childbearing women. AIDS 1997; 11:339–345.
15. Fylkesnes K, Musonda RM, Sichone M, Ndhlovu Z, Tembo F, Monze M. Declining HIV prevalence and risk behaviours in Zambia: evidence from surveillance and population based surveys. AIDS 2001; 15:907–916.
16. Gregson S, Anderson RM, Ndlovu J, Zhuwau T, Chandiwana SK. Recent upturn in mortality in rural Zimbabwe: evidence for an early demographic impact of HIV-1 infection? AIDS 1997; 11:1269–1280.
17. Gregson S, Mason PR, Garnett GP, Zhuwau T, Nyamukapa CA, Anderson RM, Chandiwana SK. A rural HIV epidemic in Zimbabwe? Findings from a population-based survey. Int J STD AIDS 2001; 12:189–196.
18. Gregson S, Zhuwau T, Anderson RM, Chimbadzwa T, Chiwandiwa SK. Age and religion selection biases in HIV-1 prevalence data from antenatal clinics in Manicaland, Zimbabwe. Cent Afr J Med 1995; 41:339–346.
19. Kilian AH, Gregson S, Ndyanabangi B, Walusaga K, Kipp W, Sahlmuller G, et al
. Reductions in risk behaviour provide the most consistent explanation for declining HIV-1 prevalence in Uganda. AIDS 1999; 13:391–398.
20. Kirunga CT, Ntozi JP. Socio-economic determinants of HIV serostatus: a study of Rakai District, Uganda. Health Transit Rev 1997; 7(Suppl):175–188.
21. Konde-Lule JK, Wawer MJ, Sewankambo NK, Serwadda D, Kelly R, Li C, et al
. Adolescents, sexual behaviour and HIV-1 in rural Rakai district, Uganda. AIDS 1997; 11:791–799.
22. Serwadda D, Wawer MJ, Musgrave SD, Sewankambo NK, Kaplan JE, Gray RH. HIV risk factors in three geographic strata of rural Rakai District, Uganda. AIDS 1992; 6:983–989.
23. Smith J, Nalagoda F, Wawer MJ, Serwadda D, Sewankambo N, Konde-Lule J, et al
. Education attainment as a predictor of HIV risk in rural Uganda: results from a population-based study. Int J STD AIDS 1999; 10:452–459.
24. Abebe Y, Schaap A, Mamo G, Negussie A, Darimo B, Wolday D, Sanders EJ. HIV prevalence in 72 000 urban and rural male army recruits, Ethiopia. AIDS 2003; 17:1835–1840.
25. Boisier P, Ouwe Missi Oukem-Boyer ON, Amadou Hamidou A, Sidikou F, Ibrahim ML, Elhaj Mahamane A, et al
. Nationwide HIV prevalence survey in general population in Niger. Trop Med Int Health 2004; 9:1161–1166.
26. Crampin AC, Glynn JR, Ngwira BM, Mwaungulu FD, Ponnighaus JM, Warndorff DK, Fine PE. Trends and measurement of HIV prevalence in northern Malawi. AIDS 2003; 17:1817–1825.
27. de Walque D, Nakiyingi-Miiro JS, Busingye J, Whitworth JA. Changing association between schooling levels and HIV-1 infection over 11 years in a rural population cohort in south-west Uganda. Trop Med Int Health 2005; 10:993–1001.
28. Glynn JR, Carael M, Buve A, Anagonou S, Zekeng L, Kahindo M, Musonda R. Does increased general schooling protect against HIV infection? A study in four African cities. Trop Med Int Health 2004; 9:4–14.
29. Glynn JR, Ponnighaus J, Crampin AC, Sibande F, Sichali L, Nkhosa P, et al
. The development of the HIV epidemic in Karonga District, Malawi. AIDS 2001; 15:2025–2029.
30. Gregson S, Terceira N, Kakowa M, Mason PR, Anderson RM, Chandiwana SK, Carael M. Study of bias in antenatal clinic HIV-1 surveillance data in a high contraceptive prevalence population in sub-Saharan Africa. AIDS 2002; 16:643–652.
31. Gregson S, Waddell H, Chandiwana SK. School education and HIV control in sub-Saharan Africa: from discord to harmony? J Int Dev 2001; 13:467–485.
32. Michelo C, Sandoy IF, Fylkesnes K. Marked HIV prevalence declines in higher educated young people: evidence from population-based surveys (1995–2003) in Zambia. AIDS 2006; 20:1031–1038.
33. Nyambi P, Zekeng L, Kenfack H, Tongo M, Nanfack A, Nkombe I, et al
. HIV infection in rural villages of Cameroon. JAIDS 2002; 31:506–513.
34. Obasi AI, Balira R, Todd J, Ross DA, Changalucha J, Mosha F, et al
. Prevalence of HIV and Chlamydia trachomatis
infection in 15–19-year olds in rural Tanzania. Trop Med Int Health 2001; 6:517–525.
35. Todd J, Grosskurth H, Changalucha J, Obasi A, Mosha F, Balira R, et al
. Risk factors influencing HIV infection incidence in a rural African population: a nested case–control study. J Infect Dis 2006; 193:458–466.
36. Fabiani M, Nattabi B, Opio AA, Musinguzi J, Biryahwaho B, Ayella EO, et al
. A high prevalence of HIV-1 infection among pregnant women living in a rural district of north Uganda severely affected by civil strife. Trans R Soc Trop Med Hyg 2006; 100:586–593.
37. Auvert B, Buve A, Ferry B, Carael M, Morison L, Lagarde E, et al
. Ecological and individual level analysis of risk factors for HIV infection in four urban populations in sub-Saharan Africa with different levels of HIV infection. AIDS 2001; 15(suppl 4):S15–S30.
38. Lagarde E, Congo Z, Meda N, Baya B, Yaro S, Sangli G, et al
. Epidemiology of HIV infection in urban Burkina Faso. Int J STD AIDS 2004; 15:395–402.
39. Bloom SS, Urassa M, Isingo R, Ng'weshemi J, Boerma JT. Community effects on the risk of HIV infection in rural Tanzania. Sex Transm Infect 2002; 78:261–266.
40. Sandoy IF, Kvale G, Michelo C, Fylkesnes K. Antenatal clinic-based HIV prevalence in Zambia: declining trends but sharp local contrasts in young women. Trop Med Int Health 2006; 11:917–928.
41. Fabiani M, Nattabi B, Ayella EO, Ogwang M, Declich S. Using prevalence data from the programme for the prevention of mother-to-child-transmission for HIV-1 surveillance in North Uganda. AIDS 2005; 19:823–827.
42. Mnyika KS, Klepp KI, Kvale G, Nilssen S, Kissila PE, Ole-King'ori N. Prevalence of HIV-1 infection in urban, semi-urban and rural areas in Arusha region, Tanzania. AIDS 1994; 8:1477–1481.
43. Michelo C, Sandoy IF, Dzekedzeke K, Siziya S, Fylkesnes K. Steep HIV prevalence declines among young people in selected Zambian communities: population-based observations (1995–2003). BMC Public Health 2006; 6:279.
44. Fylkesnes K, Ndhlovu Z, Kasumba K, Mubanga MR, Sichone M. Studying dynamics of the HIV epidemic: population-based data compared with sentinel surveillance in Zambia. AIDS 1998; 12:1227–1234.
45. Sandoy IF, Michelo C, Siziya S, Fylkesnes K. Associations between sexual behaviour change in young people and decline in HIV prevalence in Zambia. BMC Public Health 2007; 7:60.
46. Cochrane Collaboration. Cochrane Handbook for Systematic Reviews of Interventions
, version 4.2.5. Chichester, UK: John Wiley; 2005 www.cochrane.org/resources/handbook/
[updated May 2005].
47. Agha S, Hutchinson P, Kusanthan T. The effects of religious affiliation on sexual initiation and condom use in Zambia. J Adolesc Health 2006; 38:550–555.
48. Blanc AK. The Relationship Between Sexual Behavior and Level of Education in Developing Countries
. Columbia, MD: Blancroft Research International, for UNAIDS; 2001.
49. Camlin CS, Chimbwete CE. Does knowing someone with AIDS affect condom use? An analysis from South Africa. AIDS Educ Prev 2003; 15:231–244.
50. Kapiga SH, Lugalla JL. Sexual behaviour patterns and condom use in Tanzania: results from the 1996 Demographic and Health Survey. AIDS Care 2002; 14:455–469.
51. Kongnyuy EJ, Wiysonge CS, Mbu RE, Nana P, Kouam L. Wealth and sexual behaviour among men in Cameroon. BMC Int Health Hum Rights 2006; 6:11.
52. Lagarde E, Carael M, Glynn JR, Kanhonou L, Abega SC, Kahindo M, et al
. Educational level is associated with condom use within nonspousal partnerships in four cities of sub-Saharan Africa. AIDS 2001; 15:1399–1408.
53. Magnani RJ, Karim AM, Weiss LA, Bond KC, Lemba M, Morgan GT. Reproductive health risk and protective factors among youth in Lusaka, Zambia. J Adolesc Health 2002; 30:76–86.
54. Filmer D. The socioeconomic correlates of sexual behavior: a summary of results from an analysis of DHS data
. In: Ainsworth M, Fransen L, Over M, editors. Confronting AIDS: Public Priorities in a Global Epidemic
. New York: Oxford University Press; 1998.
55. Lugoe WL, Klepp KI, Skutle A. Sexual debut and predictors of condom use among secondary school students in Arusha, Tanzania. AIDS Care 1996; 8:443–452.
56. Meekers D, Ahmed G. Contemporary patterns of adolescent sexuality in urban Botswana. J Biosoc Sci 2000; 32:467–485.
57. UNICEF. The State of the World's Children
. New York: UNICEF; 2004.
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Between the cracks: out-of-school youth and discourses of HIV/AIDS
South African Journal of Psychology, 38(4):
Bmc Public HealthEffects of neighbourhood-level educational attainment on HIV prevalence among young women in ZambiaBmc Public Health
Croatian Medical JournalMeasurement of education as analytic variable in HIV research in AfricaCroatian Medical Journal
Exploring the role of economic empowerment in HIV prevention
AIDS and BehaviorPrevalence and Correlates of Concurrent Sexual Partnerships in ZambiaAIDS and Behavior
Education and vulnerability: the role of schools in protecting young women and girls from HIV in southern Africa
Reproductive Health Matters
HIV has shifted to the less educated in Africa
Reproductive Health Matters, 16():
AIDS Care-Psychological and Socio-Medical Aspects of AIDS/HIVIncreased risk of HIV-infection among school-attending orphans in rural ZimbabweAIDS Care-Psychological and Socio-Medical Aspects of AIDS/HIV
Public Health Reports
The Social Determinants of HIV Serostatus in Sub-Saharan Africa: An Inverse Relationship Between Poverty and HIV?
Public Health Reports, 125():
AIDS Research and TherapyIs the Sexual Behaviour of HIV Patients on Antiretroviral therapy safe or risky in Sub-Saharan Africa? Meta-Analysis and Meta-RegressionAIDS Research and Therapy
Health CommunicationDeterminants of HIV/AIDS-Related Information Needs and Media Use: Beyond Individual-Level FactorsHealth Communication
Bmj OpenThe relationship between age of coital debut and HIV seroprevalence among women in Durban, South Africa: a cohort studyBmj Open
Social Science & MedicineRealist randomised controlled trials: A new approach to evaluating complex public health interventionsSocial Science & Medicine
Journal of Epidemiology and Community HealthDoes the 'inverse equity hypothesis' explain how both poverty and wealth can be associated with HIV prevalence in sub-Saharan Africa?Journal of Epidemiology and Community Health
Bmc Public HealthHIV risk perception and distribution of HIV risk among African, Caribbean and other Black people in a Canadian city: mixed methods results from the BLACCH studyBmc Public Health
AIDS Care-Psychological and Socio-Medical Aspects of AIDS/HIVMeta-analysis on risky sexual behaviour of men: Consistent findings from different parts of the worldAIDS Care-Psychological and Socio-Medical Aspects of AIDS/HIV
Journal of Infection in Developing Countries
Baseline Findings of an HIV Incidence Cohort Study to Prepare for Future HIV Prevention Clinical Trials in Kisumu, Kenya
Journal of Infection in Developing Countries, 6():
Bmc Public HealthQualitative evaluation of the Teenage Mothers Project in Uganda: a community-based empowerment intervention for unmarried teenage mothersBmc Public Health
American Journal of Mens HealthDeterminants of Previous HIV Testing and Knowledge of Partner's HIV Status Among Men Attending a Voluntary Counseling and Testing Clinic in Dar es Salaam, TanzaniaAmerican Journal of Mens Health
Social Science & MedicineIs education the link between orphanhood and HIV/HSV-2 risk among female adolescents in urban Zimbabwe?Social Science & Medicine
Africa; education; HIV infection; poverty; systematic review
© 2008 Lippincott Williams & Wilkins, Inc.
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