Sex in the shadow of HIV:A systematic review of prevalence, risk factors, and interventions to reduce sexual risk-taking among HIVpositive adolescents and youth in sub-Saharan Africa

Background Evidence on sexual risk-taking among HIV-positive adolescents and youth in sub-Saharan Africa is urgently needed. This systematic review synthesizes the extant research on prevalence, factors associated with, and interventions to reduce sexual risk-taking among HIV-positive adolescents and youth in sub-Saharan Africa. Methods Studies were located through electronic databases, grey literature, reference harvesting, and contact with researchers. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. Quantitative studies that reported on HIV-positive participants (10-24 year olds), included data on at least one of eight outcomes (early sexual debut, inconsistent condom use, older partner, transactional sex, multiple sexual partners, sex while intoxicated, sexually transmitted infections, and pregnancy), and were conducted in sub-Saharan Africa were included. Two authors piloted all processes, screened studies, extracted data independently, and resolved any discrepancies. Due to variance in reported rates and factors associated with sexual risk-taking, meta-analyses were not conducted. Results 610 potentially relevant titles/abstracts resulted in the full text review of 251 records. Forty-two records (n=35 studies) reported one or multiple sexual practices for 13,536 HIV-positive adolescents/youth from 13 sub-Saharan African countries. Seventeen cross-sectional studies reported on individual, relationship, family, structural, and HIV-related factors associated with sexual risk-taking. However, the majority of the findings were inconsistent across studies, and most studies scored Conclusions Sexual risk-taking among HIV-positive adolescents and youth is high, with inconclusive evidence on potential determinants. Few known studies test secondary HIV-prevention interventions for HIV-positive youth. Effective and feasible low-cost interventions to reduce risk are urgently needed for this group.</p

Resource mobilization for Universities

This fourth volume of the University Vice Chancellors’ Forum Bulletin carries papers on the theme of University Governance with particular reference to UgandaHigher education, the world over, is dogged with the challenge of inadequate resources. Public universities, which receive Government subventions, have over the years experienced declining public funding both in actual amount and value. All private universities in Uganda suffer an inordinate dependence on student tuition, with its attendant uncertainties

Conditional inference

This thesis was submitted for the degree of Doctor of Philosophy in the Department of Statistics, University of Melbourne. Available at: https://minerva-access.unimelb.edu.au/handle/11343/36921Conditional inference is a branch of statistical inference in which observed data is reduced using either sufficient or ancillary statistics. This often simplifies inference about the parameters. In comparison to full likelihood methods, conditional inference theory’s performance still needs validating in many areas. Some of these are the concern of this thesis. While the definition of an ancillary statistic in single parameter models is unequivocal, the presence of accessory (or nuisance) parameters in a model presents problems in defining an ancillary statistic. Statistical literature abounds with definitions of ancillarity in this case. Some of the commonest and most useful of these are discussed and shown to be interrelated. This facilitates the choice of the strongest eligible ancillary in a problem, i.e. that which offers the biggest reduction of the sample space. The Pitman-Morgan test for variance ratios in bivariate normal populations with unknown correlation coefficient is shown to be a conditional test. We condition on sufficient statistics for the accessory parameters to eliminate them. The test statistic is then derived as an ancillary statistic for the accessory parameters. When a probability model depends on a number of accessory parameters which increases with the sample size, estimation methods based on the full likelihood will often be inconsistent. Using a partial likelihood instead has been suggested. Local maximum partial likelihood estimators are shown to exist, and to be consistent and asymptotically normal under mild conditions. These results also cover conditional and marginal likelihoods, thus considerably strengthening earlier results in this area. In planning statistical inferences, it is useful to choose a sampling scheme which provides only the essential data to our inferences. Jagers’ lemma proposes very general conditions under which maximum likelihood estimation from a subset of the data is identical with that from the full data. However, the lemma is incorrect as given. We show that an additional sufficiency condition repairs the lemma. It is further shown that this lemma cannot be extended to general exponential families

Conditional inference

This thesis was submitted for the degree of Doctor of Philosophy in the Department of Statistics, University of Melbourne. Available at: https://minerva-access.unimelb.edu.au/handle/11343/36921Conditional inference is a branch of statistical inference in which observed data is reduced using either sufficient or ancillary statistics. This often simplifies inference about the parameters. In comparison to full likelihood methods, conditional inference theory’s performance still needs validating in many areas. Some of these are the concern of this thesis. While the definition of an ancillary statistic in single parameter models is unequivocal, the presence of accessory (or nuisance) parameters in a model presents problems in defining an ancillary statistic. Statistical literature abounds with definitions of ancillarity in this case. Some of the commonest and most useful of these are discussed and shown to be interrelated. This facilitates the choice of the strongest eligible ancillary in a problem, i.e. that which offers the biggest reduction of the sample space. The Pitman-Morgan test for variance ratios in bivariate normal populations with unknown correlation coefficient is shown to be a conditional test. We condition on sufficient statistics for the accessory parameters to eliminate them. The test statistic is then derived as an ancillary statistic for the accessory parameters. When a probability model depends on a number of accessory parameters which increases with the sample size, estimation methods based on the full likelihood will often be inconsistent. Using a partial likelihood instead has been suggested. Local maximum partial likelihood estimators are shown to exist, and to be consistent and asymptotically normal under mild conditions. These results also cover conditional and marginal likelihoods, thus considerably strengthening earlier results in this area. In planning statistical inferences, it is useful to choose a sampling scheme which provides only the essential data to our inferences. Jagers’ lemma proposes very general conditions under which maximum likelihood estimation from a subset of the data is identical with that from the full data. However, the lemma is incorrect as given. We show that an additional sufficiency condition repairs the lemma. It is further shown that this lemma cannot be extended to general exponential families

Conditional inference

Deposited with permission of the author. © 1984 Dr. John Musisi Senyonyi-MubiruConditional inference is a branch of statistical inference in which observed data is reduced using either sufficient or ancillary statistics. This often simplifies inference about the parameters. In comparison to full likelihood methods, conditional inference theory’s performance still needs validating in many areas. Some of these are the concern of this thesis. While the definition of an ancillary statistic in single parameter models is unequivocal, the presence of accessory (or nuisance) parameters in a model presents problems in defining an ancillary statistic. Statistical literature abounds with definitions of ancillarity in this case. Some of the commonest and most useful of these are discussed and shown to be interrelated. This facilitates the choice of the strongest eligible ancillary in a problem, i.e. that which offers the biggest reduction of the sample space. The Pitman-Morgan test for variance ratios in bivariate normal populations with unknown correlation coefficient is shown to be a conditional test. We condition on sufficient statistics for the accessory parameters to eliminate them. The test statistic is then derived as an ancillary statistic for the accessory parameters. When a probability model depends on a number of accessory parameters which increases with the sample size, estimation methods based on the full likelihood will often be inconsistent. Using a partial likelihood instead has been suggested. Local maximum partial likelihood estimators are shown to exist, and to be consistent and asymptotically normal under mild conditions. These results also cover conditional and marginal likelihoods, thus considerably strengthening earlier results in this area. In planning statistical inferences, it is useful to choose a sampling scheme which provides only the essential data to our inferences. Jagers’ lemma proposes very general conditions under which maximum likelihood estimation from a subset of the data is identical with that from the full data. However, the lemma is incorrect as given. We show that an additional sufficiency condition repairs the lemma. It is further shown that this lemma cannot be extended to general exponential families

School, Supervision and Adolescent-Sensitive Clinic Care: Combination Social Protection and Reduced Unprotected Sex Among HIV-Positive Adolescents in South Africa

© 2016 The Author(s). Social protection can reduce HIV-risk behavior in general adolescent populations, but evidence among HIV-positive adolescents is limited. This study quantitatively tests whether social protection is associated with reduced unprotected sex among 1060 ART-eligible adolescents from 53 government facilities in South Africa. Potential social protection included nine ‘cash/cash-in-kind’ and ‘care’ provisions. Analyses tested interactive/additive effects using logistic regressions and marginal effects models, controlling for covariates. 18 % of all HIV-positive adolescents and 28 % of girls reported unprotected sex. Lower rates of unprotected sex were associated with access to school (OR 0.52 95 % CI 0.33–0.82 p = 0.005), parental supervision (OR 0.54 95 % CI 0.33–0.90 p = 0.019), and adolescent-sensitive clinic care (OR 0.43 95 % CI 0.25–0.73 p = 0.002). Gender moderated the effect of adolescent-sensitive clinic care. Combination social protection had additive effects amongst girls: without any provisions 49 % reported unprotected sex; with 1–2 provisions 13–38 %; and with all provisions 9 %. Combination social protection has the potential to promote safer sex among HIV-positive adolescents, particularly girls