Laura Robertson

Simon Gregson

Keywords: Orphans, HIV, Sexual Behaviour, Adolescents, Education Orphans, HIV, Adolescents, Sexual Behaviour, Education

AIDS Impact special issue 2009: Sexual risk among orphaned adolescents: is country-level HIV prevalence

an important factor?

1. The theoretical framework is pretentious. I say this because it is trivial at one level (countries with higher levels of HIV prevalence will have higher levels of orphanhood) and unexplored at another. The idea that sexual risk among orphans is attributable to 'poorer standards of care and guidance' is an interesting one, considering the fairly lengthy list of potential explanations advanced by Operario and Cluver, for example. The absence of household level effects in the analysis speaks against this, but the authors reflect no further on the strength of the framework in relation to the data analysis.

Several possibilities exist for exploring the 'low standards of care and guidance' hypothesis, including household composition and age of orphaning, and it's a pity the authors make no more of it.

Our theoretical framework has been developed using ideas from three previously published frameworks [START_REF] Boerma | Integrating demographic and epidemiological approaches to research on HIV/AIDS: the proximate-determinants framework[END_REF][START_REF] Poundstone | The social epidemiology of human immunodeficiency virus/acquired immunodeficiency syndrome[END_REF][START_REF] Mosley | An analytical framework for the study of child survival in developing countries[END_REF] that have been used for hypothesis generation and testing in epidemiological and demographic research, particularly HIV-related research [START_REF] Lewis | Evaluating the proximate determinants framework for HIV infection in rural Zimbabwe[END_REF][START_REF] Lopman | HIV incidence in 3 years of follow-up of a Zimbabwe cohort--1998-2000 to 2001-03: contributions of proximate and underlying determinants to transmission[END_REF][START_REF] Watts | Poorer health and nutritional outcomes in orphans and vulnerable young children not explained by greater exposure to extreme poverty in Zimbabwe[END_REF]. There was limited space to describe these in the paper. We have provided some new details (and references) to clarify the background of our framework in paragraph 1 of the Method section.

The reviewer suggests that our framework, and presumably the hypotheses we developed from it, are trivial on some levels. They suggest, as an example, the exploration of the relationship between country-level HIV prevalence and prevalence of orphaned children. We do not feel that this is a trivial point. Due to the long incubation period there is a lag between HIV incidence and mortality. As a result, HIV prevalence may increase in a region but mortality can take some time to catch up. Furthermore, in mature epidemics there may be a decline in prevalence due to increasing HIVrelated mortality. Central to our hypothesis regarding the relationship between country-level HIV prevalence and the effect of orphan status on HIV risk/sexual risk behaviour is that if a country is experiencing a greater burden of adult mortality (and morbidity), the extended family that traditionally cares for orphaned children will be under greater strain and will be less able to provide adequate care to orphaned children (this is what we mean by "poor care and guidance" and we have attempted to clarify this in paragraph 2 of the redrafted Methods section). Thus we are using country-level HIV prevalence, which is readily available from UNAIDS as a proxy for HIV mortality. It is therefore important that we check that within our sample of countries, where the progress of the epidemic may vary by country, that this is a reasonable assumption i.e. that orphan prevalence (parental (adult) death) is higher in countries with higher HIV prevalence. We have attempted to clarify this in paragraph 3 of the redrafted methods section.

We have added references to Operario, Cluver and colleagues work in our redrafted introduction and discussion (paragraph 1 and paragraph 3). Furthermore, we are aware Nyamukapa and colleagues have shown that psychosocial distress mediates the relationship between orphan status and sexual risk behaviour using national data from Zimbabwe [START_REF] Nyamukapa | HIV-associated orphanhood and children's psychosocial distress: theoretical framework tested with data from Zimbabwe[END_REF]. We have added psychosocial distress as an individual-level underlying determinant in our framework but we have not added an explicit hypothesis regarding this pathway as the necessary information to explore this is not available in our dataset.

The reviewer is correct to point out that there is no evidence from our analysis that increased sexual risk amongst orphans living in high HIV prevalence countries is due to household-level factors reflecting the strain on households following an adult death -the observed interaction remained significant after household level effects were included in the models. As we stated in our discussion, this could be due to our household-level indicators not capturing completely the burden of adult mortality on households. Cluver and colleagues have found that poverty mediates the relationship between orphanhood and poor psychological outcomes, which suggests that investment of resources plays a role. We feel that it is reasonable to test a variety of causal pathways but have added more emphasis in our discussion to the role of stigma as an alternative to our proposed mechanism (paragraph 3 of our redrafted discussion).

With regards to the reviewer's suggestion that we investigate the household composition in relation to orphan HIV risk under the low standards of care and guidance hypothesis -we have indeed attempted to do this. As stated, we added household level variables, including age and sex of the household head, wealth quintile and the number of individuals in the household, to the model examining the interaction between orphan status and country-level HIV prevalence with respect to having started sex. We did this to determine whether the addition of the household-level factors would remove or reduce the evidence for an interaction between HIV prevalence and orphan status.

We have added a brief discussion of other household level factors that were not available but may have been important in determining orphans' sexual risk in high HIV prevalence countries to our redrafted discussion (paragraph 2) . There are, of course, other possible pathways to be examined regarding the relationship between orphan status and HIV risk. We hope that we have captured many of these in our framework and we have stated in our discussion that we would like to use the framework to develop and test new hypotheses in the future. To have identified and tested a large number of hypotheses in this analysis would have been an unmanageably large task that would have involved a large number of statistical tests, which would invalidate the probabilities associated with any specific result.

We were not able to investigate the effect of age at orphaning as the necessary data were not available in the DHS dataset. We agree that this would have been useful and have emphasised this in our discussion of the limitations of our analysis (third paragraph from the end of the discussion)

2. Postulating a relationship between age of sexual activity amongst 15-17-year-old orphans and national prevalence is quite crude. National prevalence masks striking differences between counties (such as predominant religious affiliation, which affects both prevalence and sexual debut) as well as between districts, regions, provinces -and sex. Without knowing anything about national estimates of sexual debut, age trends in infection between men and women, I'm not sure that matching by cluster sorts this out, especially when the loss of matches is substantial -20-30%. I am also curious about the lack of matches -the authors offer no explanation why, in this number of clusters, no non-orphans, matched by age and sex, were identified.

It is important to note that we do not postulate a relationship between age of sexual activity amongst 15-17 year olds and national HIV prevalence. Our outcome variable was "Ever had sex?" and does not make reference to the age at which these sexual experiences occurred. We have attempted to clarify wherever this may have been misleading in the text by removing the word "early" from all occasions where sexual debut is mentioned. Matching by cluster was not intended to adjust for the possible biases mentioned by the reviewer.

The matching was necessary because of the underlying structure of our dataset and the types of outcome indicators used. Our binary indicators required the use of logistic regression models.

Hierarchical logistic regression models are computationally intensive and were difficult to run on a standard PC. The data were collected in the DHS surveys (our data source) using a stratified twostage cluster design -enumeration areas (or sample clusters) were drawn from available census files and a sample of households within these clusters was selected from a list of households. Furthermore, to ensure robust estimates of key demographic variables at the urban/rural and province/regional level, when population sizes in these areas vary dramatically, some areas were oversampled during the surveys. People within clusters tend to be more similar with respect to many indicators than would be found when comparing people from different clusters and this was found to be the case in this dataset for the outcome variables we were using. If we were to analyse the data without taking into account this clustering we would underestimate the variance (standard errors) in our outcome indicators and our statistical tests would not be correct. Normally we would deal with this by including sample cluster as a level (random intercept) in the hierarchical models. However, we have also pooled data from several different countries, which means that, for similar reasons to those described above, we need to include country as a level (random intercept) in the hierarchical model. This, combined with the fact that we were using logistic regression meant that our models were computationally intensive and time consuming to run and, when investigating the more complicated hypotheses, some of the models would not run. Thus we matched at the sample cluster level to remove the need to include sample cluster as a level in our hierarchical models. We have attempted to clarify this in the text of the redrafted paper (second paragraph from the end of the Methods section).

The sample clusters in the DHS surveys are relatively small. This was exacerbated by the need to perform the analyses separately by gender and also by the fact that only one child was selected per household (to remove the effects of correlation in the outcome indicators at the household level i.e. for similar reasons to why we needed to adjust for correlation at the sample cluster and country level). For female adolescents, the sample clusters contained between 1 and 15 individuals aged 15-17 years -20% contained only one child and 44% contained one or two children. It is therefore easy to see why matches were not available for some orphans. A similar pattern was found for male adolescents. We have clarified the reason for the lack of suitable matches in paragraph 2 and paragraph 6 of our redrafted Results section. The loss of children from the dataset due to the matching procedure is unfortunate and has been highlighted as a limitation in our discussion. We did not match by age, as stated by the reviewer, as this further restriction on suitable matches would have reduced even further the number of available matches for each orphan and therefore would have increased the losses from the dataset during the matching procedure. We did however adjust for age in our models to remove biases due to the association between age and orphanhood and age and sexual debut.

We agree that country-level HIV prevalence is a crude indicator that may mask differences at the regional level. It is also true that there may be other underlying differences between countries with high and low HIV prevalence that could be influencing the observed relationships between orphans and sexual risk e.g. differences in predominant religion. We have added a discussion of these limitations to the redrafted paper (paragraph 4 and paragraph 9 of the discussion. 3. The paper, like many analyses of orphan effects, adopts an uncritical approach to the meaning and rates of orphaning. For example, a child may have a father who is alive, but who does not acknowledge them, they have not seen for X number of years or who has not provided support for the child for X number of years. Clearly, age of orphaning is an important determinant of effects, as acknowledged by the authors, and rates of orphaning are quite dramatically affected by reporting biases. Demographers acknowledge that father deaths are over-reported in contexts with high father absence, and mother deaths are under-reported in contexts with extended family care and children orphaned at a young age.

In our redrafted paper we have acknowledged and discussed the limitations of our orphan data, including the potential biases in reports of parental deaths in demographic surveys (paragraph 9 of the discussion). Data were unfortunately not available on the level of support provided by living parents of single orphans or other relatives. Generally, the nuance of each individual orphan's situation cannot be captured completely within quantitative, cross-sectional, household, demographic surveys. Further qualitative work could obviously be useful to fill these gaps and we have now highlighted this in paragraph 9 of our discussion. Information was available in our dataset on the "fostering status" of adolescents i.e. whether they currently live with either or both of their parents. We attempted to perform a similar analysis to that presented in the paper examining the relationship between this "fostering status" 1 and sexual/HIV risk. Unfortunately, the losses during the matching procedure were much more severe when we attempted this analysis (around 50%) i.e. fewer suitable matches could be found for each fostered child, probably due to the higher prevalence of fostering compared to orphanhood. Thus this analysis had to be abandoned. As mentioned previously, data on age at orphaning were not available in our dataset.

4.

The results are marginally significant -borderline for female double orphans in the fully adjusted model, and the effects of orphan status did not vary by country on either of the two outcomes examined, early sexual activity or HIV status. The latter outcome was known for only such a small number that it really shouldn't be included in the paper, including with respect to an association with sexual activity at a young age.

Although the effect among maternally orphaned girls is significantly modified in high prevalence countries, the authors offer no explanation for the mechanism of effect apart from 'poor care and guidance' -which is not supported by the household level variables (age and sex of household head, number of household members and wealth quintile) which presumably were included because they were thought to be sensitive to the quality of care and guidance received by children. Education, also presumably a reflection of quality of care or investment in a child, did not modify the effect of orphan status on young age of starting sex.

1 Fostering status has been put in inverted comments as this is a crude title for the indicator we attempted to use. In particular, a child who lived with their mother but not their father (or vice versa) would have been considered "fostered" in this indicator but not under the general use of the term. The results of the unstratified models of the effects of orphan status on female adolescents ever having had sex were indeed marginally significant and this was acknowledged in the initial submission of our paper. In the stratified analysis, the effect of maternal orphan status in high HIV prevalence countries was not borderline significant (p<0.009). It is true that the effects of orphan status did not vary by country, which would be expected given the observed difference in relationship between orphan status and sexual risk by country-level HIV prevalence. A discussion of this has been included in our redrafted paper -paragraph 4 of the discussion.

We are reluctant to remove the results relating to HIV infection, particularly for the female adolescents where the sample size was larger and the direction and size of the effect is similar to those observed for ever having had sex, although, of course, they were not significant. This was not previously mentioned in the paper and has been added now (paragraph 3 of the results). The results regarding HIV infection in male adolescents have been kept for completeness.

Please see our response to the reviewer's first point regarding household level variables and the lack of support for their inclusion on the causal pathway between high country-level HIV prevalence and increased risk of having started sex amongst female adolescents. We have now also made reference to education as a proxy for household investment in orphaned children in the discussion (paragraph 6).

Further note

Some of the numbers in Table 2 had been copied down incorrectly in the version of this paper that was originally submitted. These have been corrected and they do not affect the pattern or statistical significance of our findings. particularly South Africa and Zimbabwe, where HIV prevalence is high (Birdthistle et al., 2009;Birdthistle et al., 2008;Gregson et al., 2005;Kang, Dunbar, Laver, & Padian, 2008). Three studies have used data on male and female adolescents from southern Africa (Nyamukapa et al., 2008;Operario, Pettifor, Cluver, MacPhail, & Rees, 2007;Thurman, Brown, Richter, Maharaj, & Magnani, 2006) These studies found that orphans were at increased risk of HIV infection (Birdthistle et al., 2009;Birdthistle et al., 2008;Gregson et al., 2005;Kang et al., 2008;Operario et al., 2007), STI infection (Birdthistle et al., 2009;Birdthistle et al., 2008), pregnancy (Gregson et al., 2005;Kang et al., 2008), sexual debut (Birdthistle et al., 2009;Birdthistle et al., 2008;Gregson et al., 2005;Kang et al., 2008;Nyamukapa et al., 2008;Operario et al., 2007;Thurman et al., 2006), multiple partners, unprotected sex (Birdthistle et al., 2008;Operario et al., 2007), transactional sex and forced sex (Kang et al., 2008;Thurman et al., 2006). Some studies have found maternal and/or double orphans to be particularly at risk (Birdthistle et al., 2008;Gregson et al., 2005;Kang et al., 2008). The aim of this analysis is to investigate the association between orphanhood and sexual health risk using pooled data from across sub-Saharan Africa. These investigations were guided by the development of a new theoretical framework from which specific hypotheses regarding potential causal pathways were generated and tested.

F

Methods

Theoretical Framework & Hypotheses

A number of theoretical frameworks have been developed to aid investigation of the complex risk factors that influence sexual health and HIV risk. Boerma & Weir (2005) 2004) developed a social epidemiological framework using an ecosocial approach. They state that "factors at multiple levels -from the microscopic to the societal" contribute to the distribution of HIV. Mosley & Chen (1984) developed a conceptual framework for child survival, which has been adapted and tested using data from Zimbabwe [START_REF] Watts | Poorer health and nutritional outcomes in orphans and vulnerable young children not explained by greater exposure to extreme poverty in Zimbabwe[END_REF]. They suggest that underlying determinants of mortality (and morbidity) exist at three levelsthe individual-level, the household-level and the community-level. In our framework (see Figure 1) we have expanded Boerma & Weir (2005)'s proximate determinants framework to make explicit the country/regional, household and individual level, underlying determinants of HIV.

We used our theoretical framework to develop specific hypotheses regarding the relationship between orphanhood and sexual debut and HIV infection. We expect that orphans, particularly maternal and double orphans, will experience increased sexual risk compared to non-orphans. We also expect that in high HIV prevalence countries there will be a greater burden of adult (parental) mortality and morbidity (Bicego, Rutstein, & Johnson, 2003;Hosegood et al., 2007;Monasch & Boerma, 2004). This will put strain on the extended family network that traditionally cares for orphaned children resulting in households being less able to provide adequate care to these children compared to households in countries with low HIV prevalence. Thus orphans living in countries with high HIV prevalence will receive a lower standard of care and guidance, which will put them at even greater risk of starting sex and becoming infected with HIV. We further hypothesise that the effect of country-level HIV prevalence on orphans' sexual risk will be mediated through household level variables that reflect the circumstances experienced by families affected by HIV e.g. female and elderly headed households, overcrowding and reduced socioeconomic status. We tested these hypotheses using a dataset compiled from Demographic and Health Surveys conducted in sub-Saharan African countries (see below).

Due to its long incubation period there is a lag between HIV incidence and AIDS-related mortality.

As a result, HIV prevalence may increase in a region but mortality can take some time to catch up (Bicego et al., 2003). Countries experiencing a mature epidemic may also experience a decline in prevalence due to increasing AIDS-related mortality. These issues are further complicated by the roll-out of anti-retroviral therapy, which will increase HIV prevalence and reduce mortality. It was therefore necessary to test our assumption that parental mortality (orphan prevalence) is higher in countries, from our sample, with higher HIV prevalence. Recent studies have found that adolescents attending school experience lower sexual risk (Birdthistle et al., 2009;Gregson et al., 2005;Gregson, Waddell, & Chandiwana, 2001;Hargreaves, Bonell et al., 2008;Hargreaves, Morison et al., 2008;Nyamukapa et al., 2008) However, a study using data on adolescent women from urban Zimbabwe (Birdthistle et al., 2009) found that differences in school participation and completion did not explain the increased sexual risk experienced by orphans i.e.

education did not seem to be on the causal pathway between orphan status and sexual risk. Other analyses of this dataset (Birdthistle et al., 2009;Birdthistle et al., 2008) found that married orphans experience reduced sexual risk compared to unmarried orphans. We investigated both these findings in our dataset.

Data Sources

Demographic and Health Surveys (DHS) are cross-sectional, nationally representative surveys that have collected a large quantity of data from countries in sub-Saharan Africa. They use a stratified, two-stage cluster sampling design -enumeration areas (or sample clusters) are selected from census files and then a sample of households is selected from within each cluster. To ensure robust estimates of key demographic variables at urban/rural and province/regional level, some areas are oversampled in the surveys i.e. the surveys are not self-weighting. We extracted data on adolescents aged 15-17 years from surveys that performed HIV testing and collected data on orphan status from children up to age 17 years.

If a country had more than one survey meeting these criteria, the most recent was selected. The outcome indicators that we used to represent sexual risk were "ever had sex?" and HIV status. Our orphan status categories were defined as:

• Non-orphan -both parents are alive

• Maternal orphan -mother deceased; father alive

• Paternal orphan -father deceased; mother alive

• Double orphan -both parents deceased DHS use principal components analysis to produce household wealth indices for each country's survey, based on data on household assets and utility. These are then used to divide the samples of households into wealth quintiles (described elsewhere (Rutstein & Johnson, 2004)). Following HIV-related shocks, some aspects of household socioeconomic status may be subject to change (e.g. household assets) whereas others will be unaffected (e.g. education level of the household head). We used the countryspecific wealth quintiles in our analyses because we required a socioeconomic measure that would be susceptible to change following HIV-related shocks. Data on marital status (ever married vs. never married), education status of the adolescent and household head (highest level of education achievednone, primary or secondary and above), number of people in the household (5 or less vs. more than 5), migration status (two or more years in current area vs. less than two years) sex and age of household head (60 years or younger vs. over 60 years) and urban/rural region were also used in our analyses. To increase statistical efficiency, in examining the effect of country-level HIV prevalence, we split the countries in our sample into a low HIV prevalence group (less than 5%) and a high prevalence group (5% or higher).

Statistical analysis

We calculated the prevalence of orphanhood amongst 0-17 year olds for each country, applying appropriate sample weights and adjustments for DHS sampling design. We used these to investigate the correlation between country-level HIV prevalence and prevalence of orphanhood. Pearson correlation coefficients were calculated using Microsoft Excel 2007. The rest of this analysis was conducted using Stata 10.0 and was performed separately for male and female adolescents. There were four levels of hierarchy in our dataset -country, sample cluster, household and individual.

Orphan status and our outcome variables were individual level variables so we had to have an individual level in all models. Since we had pooled data from a number of countries and due to the DHS sampling design, there was likely to be variation with respect to the outcomes (and possibly the effect of orphan status on those outcomes) at the country and sample cluster level. For this reason we also considered these levels in our models. Over 90% of households contributed only one adolescent, of each sex, to our dataset. Adding this fourth (household) level to the models, especially given the large dataset and the small number of households with two adolescents, would have made the models difficult, if not impossible, to run. In light of this, we selected one child at random from each household where more than one child was eligible for inclusion in the analysis. This removed the need for household to be included as a level in the models.

Hierarchical, logistic regression models were developed separately for each outcome -"ever had sex?" and HIV status. Random intercepts were added to the models at the country level to investigate the amount of variation in the outcome variables at this level. Since it was not possible, in Stata 10.0, to compare a "naïve" logistic regression model with a hierarchical logistic regression model using likelihood ratio tests, we assessed the importance of allowing the outcome variables to vary across the countries by comparing the variance of the country-level intercepts with the standard error of these variances. If the variance is more than double the standard error, the variance should be considered important (Twisk, 2006). Random intercepts at the sample cluster level were then added to the models to investigate the variation in the outcome variables at this level. Likelihood ratio tests were used to compare these models to the models with only country-level random intercepts and to assess the importance of including the sample cluster level intercepts in the models.

The hierarchical logistic regression models with random intercepts at the cluster-and country-level were computationally intensive and time consuming to run and, when investigating our hypotheses, some of the models would not run. We therefore simplified our models when investigating our hypotheses. A random sample of one orphan per sample cluster was selected and a random sample of non-orphans, matched on sample cluster, was selected as a comparison group. This removed the need for sample cluster to be included as a level in the model i.e. the matching procedure was necessary because of the underlying structure of our dataset. The rest of the analysis was conducted on this matched dataset. Age-adjusted, logistic regression models with a random intercept at the country-level were then used to investigate the effects of orphan status on each of the outcomes and the possible confounding of this effect by education status and marital status. We used likelihood ratio tests to investigate possible interactions, with respect to our outcome variables, between orphan status and country-level HIV prevalence, education status and marital status. We also investigated whether the inclusion, in the models, of household level indicators that reflect some of the effects of HIV-related shocks would modify the interaction between orphan status and country-level HIV prevalence. If the evidence of an interaction disappeared it would suggest that the household level variables were on the causal pathway between country-level HIV prevalence and increased sexual risk among orphans (Lewis et al., 2007;Twisk, 2006). Finally, we added a random coefficient (slope) for orphan status at the country level and used likelihood ratio tests to compare these models to models without a random slope. This allowed us to determine whether the effect of orphan status on our outcomes differed by country.

Results

The initial sample contained 9821 women and 6464 men aged 15-17 years. 30.6% (3002/9799) of women and 26.8% (1731/6457) of men had started sex. 2.5% (156/6219) of women and 1.4% (77/5546) of men were infected with HIV. Among women there was evidence that the proportion of adolescents that had started sex varied by country (ratio of variance to S.E. 2.21) and sample cluster (p<0.0001).

There was little evidence that the proportion infected with HIV varied by country (ratio of variance to S.E. 1.85) or sample cluster (p=0.106), although the number of adolescent girls that were infected with HIV was small. Amongst men there was evidence that the proportion of adolescents that had started sex varied by country (ratio of variance to S.E. 2.06) and sample cluster (p<0.0001). There was little evidence that the proportion infected with HIV varied by country (ratio of variance to S.E.1.37) but there was some evidence that it varied by sample cluster (p=0.018).

In our matched female sample, 1767 orphans were initially selected. The sample clusters were small, especially since only one eligible adolescent per household was included in the dataset. The clusters ranged in size from 1 to 15 female adolescents, with 44% containing only 1 or 2 adolescents. Thus matching non-orphans could not be found for 325 (18.4%) of the selected orphans. These orphans were dropped and the final sample contained 1442 orphans matched to 1442 non-orphans (total = 2884).

Amongst the orphans, 9.2% were maternal orphans, 29.2% were paternal orphans and 11.6% were 2). However, in countries with HIV prevalence lower than 5% (Liberia, Rwanda), there was no evidence that orphan status was associated with increased risk of having started sex. The interaction remained significant (p=0.036) after adjusting for household level variables (age and sex of the household head, number of people in the household and wealth quintile), which suggests that these variables were not on the causal pathway between country-level HIV prevalence and increased risk of starting sex amongst female orphans. It was not possible to perform the above analysis for our HIV infection outcome because the number of infections in the low HIV prevalence countries was too small (2 infections).

Adjusting for education status very slightly reduced the size of the effect of maternal and double orphan status on having started sex, although the pattern of effect across the orphan types remained the same and the association with double orphanhood remained significant (see Table 1). Education status was not a significant modifier of the effect of orphan status on having started sex (p=0.144). It was not possible to test for an interaction between orphan status and education with respect to risk of HIV infection due to small numbers of infected individuals. Marital status did not confound the relationship between orphanhood and risk of HIV infection and there was no evidence that it was an effect modifier (p=0.179).

In our matched male sample, 1248 orphans were initially selected. The sample clusters were small, especially since only one eligible adolescent per household was included in the dataset. The clusters ranged in size from 1 to 11 male adolescents, with around 60% containing only 1 or 2 adolescents. Thus matching non-orphans could not be found for 358 (28.7 %) of the selected orphans. These were dropped and the final sample contained 890 orphans matched to 890 non-orphans (total = 1780).

Amongst the orphans, 9.8% were maternal orphans, 29.4% were paternal orphans and 10.8% were double orphans. 25.2% had started sex and 1.6% were infected with HIV. Amongst non-orphans, 23.7% had started sex and 1.5% were infected with HIV.

Both the age adjusted and fully adjusted models indicated that there was no association between orphan status and risk of starting sex or HIV infection for male adolescents (see Table 3), although it was not possible to run the fully adjusted model of risk of HIV infection due to the small numbers of HIV infected cases in the sample (23 infections). There was little evidence that the effect of orphan status, on either outcome, varied by country (p>0.1 for all tests, except for the effect of maternal orphan status on risk of starting sex: p=0.061). Neither country-level HIV prevalence or education status was a significant modifier of the effect of orphan status on having started sex. Adjusting for education status did not alter the size of the effect of orphan status on risk of starting sex or HIV infection. It was not possible to run tests for interaction for the HIV infection models due to the small number of individuals infected with HIV.

We found a strong, positive correlation between the prevalence of maternal (Pearson correlation coefficient -PCC=0.89), paternal (PCC=0.70) and double (PCC=0.68) orphans in a country and the prevalence of HIV in that country (Figure 2)

Discussion

We developed a theoretical framework for investigating determinants of HIV risk and used it to generate specific hypotheses regarding the effects of orphan status on sexual health risk among adolescents. We hypothesized that in high HIV prevalence countries there would be a greater prevalence of orphanhood, which would increase pressure on the extended family network, which facilitates the care of orphans. Thus orphans living in countries with high HIV prevalence would receive a lower standard of care and guidance, and therefore increased sexual health risk, compared to orphans living in low HIV prevalence countries. In our dataset we found that the prevalence of orphanhood was higher in high HIV prevalence countries -a strong, positive correlation between country-level HIV prevalence and orphan prevalence was observed. We also found that, among female adolescents, maternal and double orphans experienced an increased sexual risk relative to non-orphans in high HIV prevalence countries but not in low HIV prevalence countries. These findings were consistent with our initial hypotheses and with previous studies using data from Zimbabwe (Birdthistle et al., 2009;Birdthistle et al., 2008;Gregson et al., 2005;Kang et al., 2008;Nyamukapa et al., 2008), South Africa (Operario et al., 2007;Thurman et al., 2006) -both countries with high HIV prevalence -and from across sub-Saharan Africa (Bicego et al., 2003;Hosegood et al., 2007;Monasch & Boerma, 2004).

We also hypothesized that the effect of country-level HIV prevalence on orphans' sexual risk would be mediated through household level variables that reflected the circumstances experienced by families affected by HIV. However, when the household level variables were added to the female adolescent model, the interaction between orphan status and country-level HIV prevalence, with respect to having started sex, remained significant. This suggests that high HIV prevalence was not affecting female orphans' sexual risk through these household level indicators. It may be that these indicators do not adequately reflect household level changes brought about by HIV related shocks. For example, the relationship of an adolescent to his/her primary caregivers, which was not available in our dataset, may better reflect a households' ability or willingness to invest in an orphaned child.

There may be some other aspect of the socioeconomic and cultural environment that was impacting negatively on orphans in countries with high HIV prevalence. Nyamukapa et al (2008) found evidence -using national data from Zimbabwe -supporting a causal pathway in which orphans suffer from psychosocial distress, which in turn leads to increased sexual risk behavior. Data from South Africa have indicated that the association between orphanhood and poor psychological health is mediated through poverty and stigma associated with AIDS orphans (Cluver, Gardner, & Operario, 2009;Cluver, Gardner, & Operario, 2008). It is possible that high levels of stigma around orphanhood in countries with high HIV prevalence could lead to psychosocial distress and increased sexual risk amongst adolescents. This is an interesting area for future investigation. We did not find that the effects of orphanhood on sexual risk or HIV infection varied significantly at the country-level, despite our finding that there was a difference in effect depending on country-level HIV prevalence. This may be because there is a threshold effect whereby a significant effect modification only occurs after a country reaches a sufficiently high prevalence i.e. above 5%. Our sample of countries was skewed towards those with high prevalence i.e. the majority of countries in our sample had a prevalence above 5%. Thus it may not have been possible to detect significant variation in the effect of orphan status across the countries in our small sample when this was modeled as a random coefficient at the country-level. It is also possible that there was some other difference between the high and low prevalence countries (e.g. in predominant religion, urbanisation etc) that produced our observed effect.

There was no evidence in our dataset that orphaned male adolescents experienced increased sexual risk.

This was surprising as analyses of data from South Africa and Zimbabwe suggest that adolescent, male orphans are at increased risk of having started sex (Nyamukapa et al., 2008;Operario et al., 2007;Thurman et al., 2006). This suggests that more work is needed to investigate the effects of orphanhood on male adolescents using data from a variety of contexts, especially since the majority of existing studies have focused on young women. It was also surprising that the increased risk of sexual debut amongst female double orphans did not seem to translate into an increased risk of HIV infection, especially given that having started sex was strongly associated with HIV infection amongst female adolescents in our large, unmatched dataset (results not shown). However, there were only a few infected women in our smaller, matched sample, which may have masked the effect of orphan status on this outcome.

Our results regarding the role of education in the causal pathway between orphanhood and sexual risk were consistent with findings from previous studies -differences in education status did not explain the increased risk among female orphans (Birdthistle et al., 2009). Adolescent educational achievement could also be an indicator of household investment in children suggesting that lower investment of resources in orphaned children may not explain their increased sexual risk. We did not find marital status to be a significant effect modifier of the relationship between orphan status and sexual risk for adolescents of either sex. A study of female adolescents in urban Zimbabwe found that married orphans were protected against the increased risk of HIV and/or HSV-2 experienced by unmarried orphans (Birdthistle et al., 2009;Birdthistle et al., 2008). It is likely that the effects of marriage would vary This study has some limitations. The statistical models were complex and this made the analysis difficult to run and, when numbers of HIV infected individuals were small, power to detect associations may have been reduced. In order to simplify our models we selected a matched sample. This forced us to exclude orphans from the analysis if we were unable to find a matching non-orphan in their area.

This process for excluding orphans is non-random -e.g. excluded orphans may be more likely to come from areas with fewer children -and this could have introduced bias to our sample. However, less than 20% of the female sample and less than 30% in the male sample were dropped. Thus we hope the extent of the bias is small.

The data we used were cross-sectional and information was not available on age at first sex or age of orphaning. It was therefore impossible to determine the temporal arrangement of events -e.g. it is not clear whether children were orphaned before or after beginning sex or becoming infected with HIV (e.g.

through vertical transmission)

. There may also be different effects on sexual risk depending on the time that has elapsed since a parental death - Birdthistle et al (2008) found that paternal orphans suffered increased sexual risk if their father died before they reached age 12 years but not if their father died after they reached this age.

Parental survival data is a relatively crude way of describing the varied experience of orphaned children -some may have living parents taking care of them or sending them money while others may be fostered within the extended family. Non-orphaned children may be at risk due to absent parents.

Further work investigating the effects of fostering and other childcare practices on sexual risk behavior is required. Qualitative work is also needed to explore the details of orphaned children's experiences that are not captured in quantitative data. There may be biases due to under-estimation of orphan prevalence due to foster parents being misreported as biological parents (Robertson et al., 2008;Timaeus, 1991). However, it is likely that such children are being cared for within the extended family and may themselves be unaware of their orphan status (Robertson et al., 2008)

F o r P e e r R e v i e w O n l y

15 Previous studies from sub-Saharan Africa have found that orphans experience increased sexual risk compared to non-orphans. We developed a theoretical framework for the investigation of determinants of HIV risk and used it to generate specific hypotheses regarding the effect of country-level HIV prevalence on the sexual risk experience of orphans. We expected that countries with high HIV prevalence would experience a higher prevalence of orphanhood. We further hypothesised that orphans in countries with high HIV prevalence would experience increased sexual risk because of pressure on the extended family network, which is primarily responsible for the care of orphans in sub-Saharan Africa, resulting in poorer standards of care and guidance. We used hierarchical logistic regression models to investigate this hypothesis using cross-sectional, Demographic and Health Survey data from 10 sub-Saharan African countries. We found that countries with high HIV prevalence did indeed have higher prevalence of orphanhood. We also found that, amongst female adolescents, maternal and double orphans were significantly more likely to have started sex than non-orphans in countries with high HIV prevalence but were not at increased risk in low HIV prevalence countries. This effect of country-level HIV prevalence on the sexual risk of orphans was not explained by household level factors such as wealth, overcrowding or age of the household head. The same pattern of risk was not observed for male adolescents -male orphans were not more likely to have started sex than nonorphans. This suggests that orphaned adolescent women are an important target group for HIV prevention and that efforts should be made to integrate prevention messages into existing support programmes for orphans and vulnerable children.

Keywords

Orphans, HIV, Adolescents, Sexual Behaviour, Education particularly South Africa and Zimbabwe, where HIV prevalence is high (Birdthistle et al., 2009;Birdthistle et al., 2008;Gregson et al., 2005;Kang, Dunbar, Laver, & Padian, 2008). Three studies have used data on male and female adolescents from southern Africa (Nyamukapa et al., 2008;Operario, Pettifor, Cluver, MacPhail, & Rees, 2007;Thurman, Brown, Richter, Maharaj, & Magnani, 2006) These studies found that orphans were at increased risk of HIV infection (Birdthistle et al., 2009;Birdthistle et al., 2008;Gregson et al., 2005;Kang et al., 2008;Operario et al., 2007), STI infection (Birdthistle et al., 2009;Birdthistle et al., 2008), pregnancy (Gregson et al., 2005;Kang et al., 2008), sexual debut (Birdthistle et al., 2009;Birdthistle et al., 2008;Gregson et al., 2005;Kang et al., 2008;Nyamukapa et al., 2008;Operario et al., 2007;Thurman et al., 2006), multiple partners, unprotected sex (Birdthistle et al., 2008;Operario et al., 2007), transactional sex and forced sex (Kang et al., 2008;Thurman et al., 2006). Some studies have found maternal and/or double orphans to be particularly at risk (Birdthistle et al., 2008;Gregson et al., 2005;Kang et al., 2008). The aim of this analysis is to investigate the association between orphanhood and sexual health risk using pooled data from across sub-Saharan Africa. These investigations were guided by the development of a new theoretical framework from which specific hypotheses regarding potential causal pathways were generated and tested.

Methods

Theoretical Framework & Hypotheses

A number of theoretical frameworks have been developed to aid investigation of the complex risk factors that influence sexual health and HIV risk. Boerma & Weir (2005) 2004) developed a social epidemiological framework using an ecosocial approach. They state that "factors at multiple levels -from the microscopic to the societal" contribute to the distribution of HIV. Mosley & Chen (1984) developed a conceptual framework for child survival, which has been adapted and tested using data from Zimbabwe [START_REF] Watts | Poorer health and nutritional outcomes in orphans and vulnerable young children not explained by greater exposure to extreme poverty in Zimbabwe[END_REF]. They suggest that underlying determinants of mortality (and morbidity) exist at three levelsthe individual-level, the household-level and the community-level. In our framework Figure 1) we have expanded Boerma & Weir (2005)'s proximate determinants framework to make explicit the country/regional, household and individual level, underlying determinants of HIV.

We used our theoretical framework to develop specific hypotheses regarding the relationship between orphanhood and sexual debut and HIV infection. We expect that orphans, particularly maternal and double orphans, will experience increased sexual risk compared to non-orphans. We also expect that in high HIV prevalence countries there will be a greater burden of adult (parental) mortality and morbidity (Bicego, Rutstein, & Johnson, 2003;Hosegood et al., 2007;Monasch & Boerma, 2004). This will put strain on the extended family network that traditionally cares for orphaned children resulting in households being less able to provide adequate care to these children compared to households in countries with low HIV prevalence. Thus orphans living in countries with high HIV prevalence will receive a lower standard of care and guidance, which will put them at even greater risk of starting sex and becoming infected with HIV. We further hypothesise that the effect of country-level HIV prevalence on orphans' sexual risk will be mediated through household level variables that reflect the circumstances experienced by families affected by HIV e.g. female and elderly headed households, overcrowding and reduced socioeconomic status. We tested these hypotheses using a dataset compiled from Demographic and Health Surveys conducted in sub-Saharan African countries (see below).

Due to its long incubation period there is a lag between HIV incidence and AIDS-related mortality.

As a result, HIV prevalence may increase in a region but mortality can take some time to catch up (Bicego et al., 2003). Countries experiencing a mature epidemic may also experience a decline in prevalence due to increasing AIDS-related mortality. These issues are further complicated by the roll-out of anti-retroviral therapy, which will increase HIV prevalence and reduce mortality. It was therefore necessary to test our assumption that parental mortality (orphan prevalence) is higher in countries, from our sample, with higher HIV prevalence. Recent studies have found that adolescents attending school experience lower sexual risk (Birdthistle et al., 2009;Gregson et al., 2005;Gregson, Waddell, & Chandiwana, 2001;Hargreaves, Bonell et al., 2008;Hargreaves, Morison et al., 2008;Nyamukapa et al., 2008) However, a study using data on adolescent women from urban Zimbabwe (Birdthistle et al., 2009) found that differences in school participation and completion did not explain the increased sexual risk experienced by orphans i.e.

education did not seem to be on the causal pathway between orphan status and sexual risk. Other analyses of this dataset (Birdthistle et al., 2009;Birdthistle et al., 2008) found that married orphans experience reduced sexual risk compared to unmarried orphans. We investigated both these findings in our dataset.

Data Sources

Demographic and Health Surveys (DHS) are cross-sectional, nationally representative surveys that have collected a large quantity of data from countries in sub-Saharan Africa. They use a stratified, two-stage cluster sampling design -enumeration areas (or sample clusters) are selected from census files and then a sample of households is selected from within each cluster. To ensure robust estimates of key demographic variables at urban/rural and province/regional level, some areas are oversampled in the surveys i.e. the surveys are not self-weighting. We extracted data on adolescents aged 15-17 years from surveys that performed HIV testing and collected data on orphan status from children up to age 17 years.

If a country had more than one survey meeting these criteria, the most recent was selected. The outcome indicators that we used to represent sexual risk were "ever had sex?" and HIV status. Our orphan status categories were defined as:

• Non-orphan -both parents are alive

• Maternal orphan -mother deceased; father alive

• Paternal orphan -father deceased; mother alive

• Double orphan -both parents deceased DHS use principal components analysis to produce household wealth indices for each country's survey, based on data on household assets and utility. These are then used to divide the samples of households into wealth quintiles (described elsewhere (Rutstein & Johnson, 2004)). Following HIV-related shocks, some aspects of household socioeconomic status may be subject to change (e.g. household assets) whereas others will be unaffected (e.g. education level of the household head). We used the countryspecific wealth quintiles in our analyses because we required a socioeconomic measure that would be susceptible to change following HIV-related shocks. Data on marital status (ever married vs. never married), education status of the adolescent and household head (highest level of education achievednone, primary or secondary and above), number of people in the household (5 or less vs. more than 5), migration status (two or more years in current area vs. less than two years) sex and age of household head (60 years or younger vs. over 60 years) and urban/rural region were also used in our analyses. To increase statistical efficiency, in examining the effect of country-level HIV prevalence, we split the countries in our sample into a low HIV prevalence group (less than 5%) and a high prevalence group (5% or higher).

Statistical analysis

We calculated the prevalence of orphanhood amongst 0-17 year olds for each country, applying appropriate sample weights and adjustments for DHS sampling design. We used these to investigate the correlation between country-level HIV prevalence and prevalence of orphanhood. Pearson correlation coefficients were calculated using Microsoft Excel 2007. The rest of this analysis was conducted using Stata 10.0 and was performed separately for male and female adolescents. There were four levels of hierarchy in our dataset -country, sample cluster, household and individual.

Orphan status and our outcome variables were individual level variables so we had to have an individual level in all models. Since we had pooled data from a number of countries and due to the DHS sampling design, there was likely to be variation with respect to the outcomes (and possibly the effect of orphan status on those outcomes) at the country and sample cluster level. For this reason we also considered these levels in our models. Over 90% of households contributed only one adolescent, of each sex, to our dataset. Adding this fourth (household) level to the models, especially given the large dataset and the small number of households with two adolescents, would have made the models difficult, if not impossible, to run. In light of this, we selected one child at random from each household where more than one child was eligible for inclusion in the analysis. This removed the need for household to be included as a level in the models.

Hierarchical, logistic regression models were developed separately for each outcome -"ever had sex?" and HIV status. Random intercepts were added to the models at the country level to investigate the amount of variation in the outcome variables at this level. Since it was not possible, in Stata 10.0, to compare a "naïve" logistic regression model with a hierarchical logistic regression model using likelihood ratio tests, we assessed the importance of allowing the outcome variables to vary across the countries by comparing the variance of the country-level intercepts with the standard error of these variances. If the variance is more than double the standard error, the variance should be considered important (Twisk, 2006). Random intercepts at the sample cluster level were then added to the models to investigate the variation in the outcome variables at this level. Likelihood ratio tests were used to compare these models to the models with only country-level random intercepts and to assess the importance of including the sample cluster level intercepts in the models.

The hierarchical logistic regression models with random intercepts at the cluster-and country-level were computationally intensive and time consuming to run and, when investigating our hypotheses, some of the models would not run. We therefore simplified our models when investigating our hypotheses. A random sample of one orphan per sample cluster was selected and a random sample of non-orphans, matched on sample cluster, was selected as a comparison group. This removed the need for sample cluster to be included as a level in the model i.e. the matching procedure was necessary because of the underlying structure of our dataset. The rest of the analysis was conducted on this matched dataset. Age-adjusted, logistic regression models with a random intercept at the country-level were then used to investigate the effects of orphan status on each of the outcomes and the possible confounding of this effect by education status and marital status. We used likelihood ratio tests to investigate possible interactions, with respect to our outcome variables, between orphan status and country-level HIV prevalence, education status and marital status. We also investigated whether the inclusion, in the models, of household level indicators that reflect some of the effects of HIV-related shocks would modify the interaction between orphan status and country-level HIV prevalence. If the evidence of an interaction disappeared it would suggest that the household level variables were on the causal pathway between country-level HIV prevalence and increased sexual risk among orphans (Lewis et al., 2007;Twisk, 2006). Finally, we added a random coefficient (slope) for orphan status at the country level and used likelihood ratio tests to compare these models to models without a random slope. This allowed us to determine whether the effect of orphan status on our outcomes differed by country.

Results

The initial sample contained 9821 women and 6464 men aged 15-17 years. 30.6% (3002/9799) of women and 26.8% (1731/6457) of men had started sex. 2.5% (156/6219) of women and 1.4% (77/5546) of men were infected with HIV. Among women there was evidence that the proportion of adolescents that had started sex varied by country (ratio of variance to S.E. 2.21) and sample cluster (p<0.0001).

There was little evidence that the proportion infected with HIV varied by country (ratio of variance to S.E. 1.85) or sample cluster (p=0.106), although the number of adolescent girls that were infected with HIV was small. Amongst men there was evidence that the proportion of adolescents that had started sex varied by country (ratio of variance to S.E. 2.06) and sample cluster (p<0.0001). There was little evidence that the proportion infected with HIV varied by country (ratio of variance to S.E.1.37) but there was some evidence that it varied by sample cluster (p=0.018).

In our matched female sample, 1767 orphans were initially selected. The sample clusters were small, especially since only one eligible adolescent per household was included in the dataset. The clusters ranged in size from 1 to 15 female adolescents, with 44% containing only 1 or 2 adolescents. Thus matching non-orphans could not be found for 325 (18.4%) of the selected orphans. These orphans were dropped and the final sample contained 1442 orphans matched to 1442 non-orphans (total = 2884).

Amongst the orphans, 9.2% were maternal orphans, 29.2% were paternal orphans and 11.6% were The age-adjusted model shown in Table 1 indicates that female double orphans were at increased risk of starting sex (OR 1.45;. This effect was reduced but remained borderline significant in the fully adjusted model (OR 1.34; 95% CI 1.00-1.84). Paternal and maternal orphans did not experience a significantly increased risk of having started sex. None of the orphan types experienced an increased risk of HIV infection, although the size and direction of the effect estimates were comparable to those in the sexual debut models. The number of infected women in the sample was small (56 infections). There was no evidence that the effect of orphan status, on either outcome, varied by country (p>0.1 for all tests).

The effect of orphan status on having started sex was significantly modified by country-level HIV prevalence (p=0.045; see Table 1). In countries with HIV prevalence greater than 5% (Cote d'Ivoire, Lesotho, Malawi, Swaziland, Tanzania, Zambia, Zimbabwe) there was evidence that maternal orphans (OR 1.61; 95% CI 1.13-2.29) and double orphans (OR 1.41; 95% CI 1.02-1.95), though not paternal orphans (OR 1.06; 95% CI 0.82-1.36) were at increased risk of having started sex (see Table 2 & Figure 2). However, in countries with HIV prevalence lower than 5% (Liberia, Rwanda), there was no evidence that orphan status was associated with increased risk of having started sex. The interaction remained significant (p=0.036) after adjusting for household level variables (age and sex of the household head, number of people in the household and wealth quintile), which suggests that these variables were not on the causal pathway between country-level HIV prevalence and increased risk of starting sex amongst female orphans. It was not possible to perform the above analysis for our HIV infection outcome because the number of infections in the low HIV prevalence countries was too small (2 infections).

Adjusting for education status very slightly reduced the size of the effect of maternal and double orphan status on having started sex, although the pattern of effect across the orphan types remained the same and the association with double orphanhood remained significant (see Table 1). Education status was not a significant modifier of the effect of orphan status on having started sex (p=0.144). It was not possible to test for an interaction between orphan status and education with respect to risk of HIV infection due to small numbers of infected individuals. Marital status did not confound the relationship between orphanhood and risk of HIV infection and there was no evidence that it was an effect modifier (p=0.179).

In our matched male sample, 1248 orphans were initially selected. The sample clusters were small, especially since only one eligible adolescent per household was included in the dataset. The clusters ranged in size from 1 to 11 male adolescents, with around 60% containing only 1 or 2 adolescents. Thus matching non-orphans could not be found for 358 (28.7 %) of the selected orphans. These were dropped and the final sample contained 890 orphans matched to 890 non-orphans (total = 1780).

Amongst the orphans, 9.8% were maternal orphans, 29.4% were paternal orphans and 10.8% were double orphans. 25.2% had started sex and 1.6% were infected with HIV. Amongst non-orphans, 23.7% had started sex and 1.5% were infected with HIV.

Both the age adjusted and fully adjusted models indicated that there was no association between orphan status and risk of starting sex or HIV infection for male adolescents (see Table 3), although it was not possible to run the fully adjusted model of risk of HIV infection due to the small numbers of HIV infected cases in the sample (23 infections). There was little evidence that the effect of orphan status, on either outcome, varied by country (p>0.1 for all tests, except for the effect of maternal orphan status on risk of starting sex: p=0.061). Neither country-level HIV prevalence or education status was a significant modifier of the effect of orphan status on having started sex. Adjusting for education status did not alter the size of the effect of orphan status on risk of starting sex or HIV infection. It was not possible to run tests for interaction for the HIV infection models due to the small number of individuals infected with HIV.

We found a strong, positive correlation between the prevalence of maternal (Pearson correlation coefficient -PCC=0.89), paternal (PCC=0.70) and double (PCC=0.68) orphans in a country and the prevalence of HIV in that country (Figure 2)

Discussion

We developed a theoretical framework for investigating determinants of HIV risk and used it to generate specific hypotheses regarding the effects of orphan status on sexual health risk among adolescents. We hypothesized that in high HIV prevalence countries there would be a greater prevalence of orphanhood, which would increase pressure on the extended family network, which facilitates the care of orphans. Thus orphans living in countries with high HIV prevalence would receive a lower standard of care and guidance, and therefore increased sexual health risk, compared to orphans living in low HIV prevalence countries. In our dataset we found that the prevalence of orphanhood was higher in high HIV prevalence countries -a strong, positive correlation between country-level HIV prevalence and orphan prevalence was observed. We also found that, among female adolescents, maternal and double orphans experienced an increased sexual risk relative to non-orphans in high HIV prevalence countries but not in low HIV prevalence countries. These findings were consistent with our initial hypotheses and with previous studies using data from Zimbabwe (Birdthistle et al., 2009;Birdthistle et al., 2008;Gregson et al., 2005;Kang et al., 2008;Nyamukapa et al., 2008), South Africa (Operario et al., 2007;Thurman et al., 2006) -both countries with high HIV prevalence -and from across sub-Saharan Africa (Bicego et al., 2003;Hosegood et al., 2007;Monasch & Boerma, 2004).

We also hypothesized that the effect of country-level HIV prevalence on orphans' sexual risk would be mediated through household level variables that reflected the circumstances experienced by families affected by HIV. However, when the household level variables were added to the female adolescent model, the interaction between orphan status and country-level HIV prevalence, with respect to having started sex, remained significant. This suggests that high HIV prevalence was not affecting female orphans' sexual risk through these household level indicators. It may be that these indicators do not adequately reflect household level changes brought about by HIV related shocks. For example, the relationship of an adolescent to his/her primary caregivers, which was not available in our dataset, may better reflect a households' ability or willingness to invest in an orphaned child.

There may be some other aspect of the socioeconomic and cultural environment that was impacting negatively on orphans in countries with high HIV prevalence. Nyamukapa et al (2008) found evidence -using national data from Zimbabwe -supporting a causal pathway in which orphans suffer from psychosocial distress, which in turn leads to increased sexual risk behavior. Data from South Africa have indicated that the association between orphanhood and poor psychological health is mediated through poverty and stigma associated with AIDS orphans (Cluver, Gardner, & Operario, 2009;Cluver, Gardner, & Operario, 2008). It is possible that high levels of stigma around orphanhood in countries with high HIV prevalence could lead to psychosocial distress and increased sexual risk amongst adolescents. This is an interesting area for future investigation. We did not find that the effects of orphanhood on sexual risk or HIV infection varied significantly at the country-level, despite our finding that there was a difference in effect depending on country-level HIV prevalence. This may be because there is a threshold effect whereby a significant effect modification only occurs after a country reaches a sufficiently high prevalence i.e. above 5%. Our sample of countries was skewed towards those with high prevalence i.e. the majority of countries in our sample had a prevalence above 5%. Thus it may not have been possible to detect significant variation in the effect of orphan status across the countries in our small sample when this was modeled as a random coefficient at the country-level. It is also possible that there was some other difference between the high and low prevalence countries (e.g. in predominant religion, urbanisation etc) that produced our observed effect.

There was no evidence in our dataset that orphaned male adolescents experienced increased sexual risk.

This was surprising as analyses of data from South Africa and Zimbabwe suggest that adolescent, male orphans are at increased risk of having started sex (Nyamukapa et al., 2008;Operario et al., 2007;Thurman et al., 2006). This suggests that more work is needed to investigate the effects of orphanhood on male adolescents using data from a variety of contexts, especially since the majority of existing studies have focused on young women. It was also surprising that the increased risk of sexual debut amongst female double orphans did not seem to translate into an increased risk of HIV infection, especially given that having started sex was strongly associated with HIV infection amongst female adolescents in our large, unmatched dataset (results not shown). However, there were only a few infected women in our smaller, matched sample, which may have masked the effect of orphan status on this outcome.

Our results regarding the role of education in the causal pathway between orphanhood and sexual risk were consistent with findings from previous studies -differences in education status did not explain the increased risk among female orphans (Birdthistle et al., 2009). Adolescent educational achievement could also be an indicator of household investment in children suggesting that lower investment of resources in orphaned children may not explain their increased sexual risk. We did not find marital status to be a significant effect modifier of the relationship between orphan status and sexual risk for adolescents of either sex. A study of female adolescents in urban Zimbabwe found that married orphans were protected against the increased risk of HIV and/or HSV-2 experienced by unmarried orphans (Birdthistle et al., 2009;Birdthistle et al., 2008). It is likely that the effects of marriage would vary This study has some limitations. The statistical models were complex and this made the analysis difficult to run and, when numbers of HIV infected individuals were small, power to detect associations may have been reduced. In order to simplify our models we selected a matched sample. This forced us to exclude orphans from the analysis if we were unable to find a matching non-orphan in their area.

This process for excluding orphans is non-random -e.g. excluded orphans may be more likely to come from areas with fewer children -and this could have introduced bias to our sample. However, less than 20% of the female sample and less than 30% in the male sample were dropped. Thus we hope the extent of the bias is small.

The data we used were cross-sectional and information was not available on age at first sex or age of orphaning. It was therefore impossible to determine the temporal arrangement of events -e.g. it is not clear whether children were orphaned before or after beginning sex or becoming infected with HIV (e.g.

through vertical transmission)

. There may also be different effects on sexual risk depending on the time that has elapsed since a parental death - Birdthistle et al (2008) found that paternal orphans suffered increased sexual risk if their father died before they reached age 12 years but not if their father died after they reached this age.

Parental survival data is a relatively crude way of describing the varied experience of orphaned children -some may have living parents taking care of them or sending them money while others may be fostered within the extended family. Non-orphaned children may be at risk due to absent parents.

Further work investigating the effects of fostering and other childcare practices on sexual risk behavior is required. Qualitative work is also needed to explore the details of orphaned children's experiences that are not captured in quantitative data. There may be biases due to under-estimation of orphan prevalence due to foster parents being misreported as biological parents (Robertson et al., 2008;Timaeus, 1991). However, it is likely that such children are being cared for within the extended family and may themselves be unaware of their orphan status (Robertson et al., 2008). Thus they may not represent an important risk group with respect to targeting of orphan-related interventions. Finally, our crude estimate of country-level HIV prevalence may mask heterogeneity at the sample cluster (or regional) level with respect to the effects of parental mortality on adolescent sexual risk behavior. It Despite these limitations, we have used hierarchical statistical modeling to investigate hypothesized causal relationships relating to HIV risk in pooled data from several sub-Saharan African countries and plausible results have been obtained. This methodology could therefore be used to further validate our theoretical framework through the generation and testing of new hypotheses -e.g. regarding the effects of migration, psychosocial distress or wealth distribution on HIV risk. Furthermore, analyses involving longitudinal data could provide additional temporal information and thus would allow more robust conclusions to be drawn regarding causal pathways. Our results suggest that adolescent orphans represent an important target group for HIV prevention and that efforts should be made to integrate prevention messages into existing support programmes for orphans and vulnerable children.

F o r P e e r R e v i e w O n l y

14

  issue 2009: Sexual risk among orphaned adolescents: is country-level HIV prevalence an important factor?

  -level) statistical modeling is used to analyse nested data where different levels of variability are associated with each level of nesting(Rabe-Hesketh & Skrondal, 2005; Twisk, 2006).

  cultural context and further investigation into the role of marriage in determining sexual risk among orphans is required.

Figure 2 :

 2 Figure2:A. The distribution of country orphan prevalence by country HIV prevalence. B. The effect of orphan status on sexual debut amongst female adolescents using data from all countries. C. The effect of orphan status on sexual debut amongst female adolescents in countries with HIV prevalence less than 5%. D. The effect of orphan status on sexual debut amongst female adolescents in countries with HIV prevalence greater than 5%.

  been published suggesting that orphans are at increased sexual health risk. The majority of these studies used cross-sectional data on female adolescents from southern Africa,

  Thus we included 10 surveys in our dataset -Democratic Republic of Congo 2007; Cote d'Ivoire 2005; Lesotho 2004; Liberia 2007; Malawi 2004; Rwanda 2005; Swaziland 2006; Tanzania 2003; Zambia 2007, Zimbabwe 2005/06. In some surveys, HIV testing was only carried out on a sub-sample of those surveyed. Data on country-level HIV prevalence were based on UNAIDS estimates from the year of the survey (UNAIDS). Data on HIV prevalence were not available for Demographic Republic of Congo so this country was excluded from analyses involving country-level HIV prevalence.

  -level) statistical modeling is used to analyse nested data where different levels of variability are associated with each level of nesting(Rabe-Hesketh & Skrondal, 2005; Twisk, 2006).

  cultural context and further investigation into the role of marriage in determining sexual risk among orphans is required.

  to investigate this here because we do not have reliable estimates of HIV prevalence at the sample cluster level.

Figure 1 Figure 2 :

 12 Figure 1: Theoretical Framework

1 Sexual risk among orphaned adolescents: is country-level HIV prevalence an important factor?

	Introduction
	Laura Robertson*, Simon Gregson* Ψ , Geoff Garnett* Several studies have been published suggesting that orphans are at increased sexual health risk. The
	majority of these studies used cross-sectional data on female adolescents from southern Africa,
	*Imperial College London, Department of Infectious Disease Epidemiology
	Ψ Biomedical Research & Training Institute, Zimbabwe
	o Contact Author: Ms Laura Robertson. Imperial College London, Department of Infectious Disease Epidemiology, St Mary's Campus, Norfolk Place, London W2 1PG. Telephone: 0207 594 3288. F o
	r Email: l.robertson06@imperial.ac.uk. r
	P P
	e e r e e r
	R e v i e w R e v i e w
	O n O n
	l y l y

  None of the orphan types experienced an increased risk of HIV infection, although the size and direction of the effect estimates were comparable to those in the sexual debut models. The number of infected women in the sample was small (56 infections). There was no evidence that the effect of orphan status, on either outcome, varied by country (p>0.1 for all tests).

	double orphans. 28.2% had started sex and 3.3% were infected with HIV. Amongst non-orphans,
	25.3% had started sex and 3.0% were infected with HIV.
	The age-adjusted model shown in Table 1 indicates that female double orphans were at increased risk of
	starting sex (OR 1.45; 95% CI 1.08-1.92). This effect was reduced but remained borderline significant
	in the fully adjusted model (OR 1.34; 95% CI 1.00-1.84). Paternal and maternal orphans did not
	experience a significantly increased risk of having started sex.
	F o
	r
	P
	e e r
	R e v i e w
	O n
	l y
	9

The effect of orphan status on having started sex was significantly modified by country-level HIV prevalence (p=0.045; see Table

1

). In countries with HIV prevalence greater than 5% (Cote d'Ivoire, Lesotho, Malawi, Swaziland, Tanzania, Zambia, Zimbabwe) there was evidence that maternal orphans (OR 1.61; 95% CI 1.13-2.29) and double orphans (OR 1.41; 95% CI 1.02-1.95), though not paternal orphans (OR 1.06; 95% CI 0.82-1.36) were at increased risk of having started sex (see Table

2 & Figure

Table 1 : The effects of orphan status on sexual risk among female adolescentsTable 2 : Age adjusted effects of orphan status on risk of having started sex amongst female adolescents by country HIV prevalence

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	N (Orphans)	OR	95% CI p-value

Table 3 : The effects of orphan status on sexual risk among male adolescents Page 22 of 46 http

 3

	Figure 1: Theoretical Framework
	1: Theoretical Framework

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Alcohol/Drug Use Migration/Mobility Orphan Status Gender Age Income/Assets Occupation HIV -Knowledge & Attitudes Ever Had an HIV Test

		New sex partner acquisition
		Coital Frequency
		Concurrency
		Abstinence	Exposure of Susceptible to
		Sexual Mixing	Infected Persons (C)
		Blood Transfusion
		Injection Drug-Use
		Medical Injections
		Condom Use
		Other STI	Efficiency of Transmission
		Circumcision	per Contact (β)
		Type of Sex
		Virus Load
		Biological Susceptibility
		Blood-Safety Practices
		Needle Safety
		Treatment with ARVs	HIV Prevalence (Y/N)
		Treatment of Opportunistic
		Infections
		Marital Status
	Psychosocial distress	Fostering Status

Table 1 : The effects of orphan status on sexual risk among female adolescentsTable 2 : Age adjusted effects of orphan status on risk of having started sex amongst female adolescents by country HIV prevalence

 12

	N (Orphans)	OR	95% CI p-value

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