Browsing by Author "Kassanjee, Reshma"

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    Open Access
    Interpreting HIV diagnostic histories into infection time estimates: analytical framework and online tool
    (2019-10-26) Grebe, Eduard; Facente, Shelley N; Bingham, Jeremy; Pilcher, Christopher D; Powrie, Andrew; Gerber, Jarryd; Priede, Gareth; Chibawara, Trust; Busch, Michael P; Murphy, Gary; Kassanjee, Reshma; Welte, Alex
    Abstract Background It is frequently of epidemiological and/or clinical interest to estimate the date of HIV infection or time-since-infection of individuals. Yet, for over 15 years, the only widely-referenced infection dating algorithm that utilises diagnostic testing data to estimate time-since-infection has been the ‘Fiebig staging’ system. This defines a number of stages of early HIV infection through various standard combinations of contemporaneous discordant diagnostic results using tests of different sensitivity. To develop a new, more nuanced infection dating algorithm, we generalised the Fiebig approach to accommodate positive and negative diagnostic results generated on the same or different dates, and arbitrary current or future tests – as long as the test sensitivity is known. For this purpose, test sensitivity is the probability of a positive result as a function of time since infection. Methods The present work outlines the analytical framework for infection date estimation using subject-level diagnostic testing histories, and data on test sensitivity. We introduce a publicly-available online HIV infection dating tool that implements this estimation method, bringing together 1) curatorship of HIV test performance data, and 2) infection date estimation functionality, to calculate plausible intervals within which infection likely became detectable for each individual. The midpoints of these intervals are interpreted as infection time ‘point estimates’ and referred to as Estimated Dates of Detectable Infection (EDDIs). The tool is designed for easy bulk processing of information (as may be appropriate for research studies) but can also be used for individual patients (such as in clinical practice). Results In many settings, including most research studies, detailed diagnostic testing data are routinely recorded, and can provide reasonably precise estimates of the timing of HIV infection. We present a simple logic to the interpretation of diagnostic testing histories into infection time estimates, either as a point estimate (EDDI) or an interval (earliest plausible to latest plausible dates of detectable infection), along with a publicly-accessible online tool that supports wide application of this logic. Conclusions This tool, available at https://tools.incidence-estimation.org/idt/ , is readily updatable as test technology evolves, given the simple architecture of the system and its nature as an open source project.
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    Open Access
    Prevalence and correlates of cyber-victimization in a nationally representative sample of South African youth
    (2025) Hlatshwayo, Lerato; Kassanjee, Reshma; Ward, Catherine
    Cyber-victimization is defined as “the experience of aggressive behaviours while using new electronic technologies, primarily mobile phones and the internet" (Álvarez-García et al., 2015a; Smith & Steffgen, 2013). Approximately 20 to 50% of adolescents have experienced online victimization globally (Zhu et al., 2021). This is a public health concern because cyber- victimization can harm the mental health of the victim thus leading to depressive symptoms such as anxiety, helplessness, distress, sadness, trauma symptoms, reduced self-esteem, feelings of isolation, fear of socialization, hopelessness, self-harm, or suicidal ideation (Hertz et al., 2015; Kim et al., 2022; Landoll et al., 2015; Mason et al., 2009). Research on the risk factors associated with cyber-victimization is relatively new and has some gaps and inconsistencies (Álvarez-García et al., 2015a; Zhu et al., 2021). This study will focus on analyzing the association of some demographic, psychological, educational, family factors and exposure to other forms of violence, with cyber-victimization, in a nationally representative sample of South African children. We aim to determine the lifetime prevalence and last-year prevalence (i.e., annual incidence) of cyber- victimization, as well as the association of cyber-victimization with its correlates, based on a nationally representative cross-sectional study of 15–17-year-old youth in South Africa. Method: This mini dissertation will use secondary data obtained, with permission, from the Optimus Study conducted in South Africa (Ward et al., 2018). The study drew on data from a population survey that was conducted with a sample of 15- to 17-year-old adolescents recruited nationally from schools (4 086 participants) as well as households (5 631 participants) (Ward et al., 2018). The aims of this study are as follows: To estimate the prevalence and incidence of cyber-victimization among South African youth as of 2013/2014, as well as in-person victimization. This will be achieved by reporting the relative frequencies with CI of both the lifetime and last-year prevalence, stratified by key demographic measures. We will also report the prevalence of each of the six types of cyberbullying. To measure the strength of association between cyber-victimization and potential risk/protective factors among South African youth. We will use logistic regression to estimate the association of each factor in table 1 with cyber-victimization, adjusting for the possible confounding factors listed. The associations will be expressed as odds ratios (ORs) with their 95% confidence intervals (Cis). The unadjusted odds ratios (OR) will be estimated using a univariable regression model for each correlate and adjusted OR (aOR) will be estimated using a multivariable regression model containing all correlates. To study the relationship between cyber-victimization and each of the potential consequences stratified by sex. For the factors, we will report differences in proportions, by cyber-victimization and CIs. The following correlates will be considered as consequences of cyber-victimization (table 2): Behavioral patterns (high-risky sexual behaviours, alcohol and substance misuse), educational (academic performance), and psychological (anxiety, depression, anger, and post-traumatic stress).
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    Open Access
    Prevalence and correlates of cyber-victimization in a nationally representative sample of South African youth
    (2025) Hlatshwayo, Lerato; Kassanjee, Reshma; Ward, Catherine
    Cyber-victimization is defined as “the experience of aggressive behaviours while using new electronic technologies, primarily mobile phones and the internet" (Álvarez-García et al., 2015a; Smith & Steffgen, 2013). Approximately 20 to 50% of adolescents have experienced online victimization globally (Zhu et al., 2021). This is a public health concern because cyber victimization can harm the mental health of the victim thus leading to depressive symptoms such as anxiety, helplessness, distress, sadness, trauma symptoms, reduced self-esteem, feelings of isolation, fear of socialization, hopelessness, self-harm, or suicidal ideation (Hertz et al., 2015; Kim et al., 2022; Landoll et al., 2015; Mason et al., 2009). Research on the risk factors associated with cyber-victimization is relatively new and has some gaps and inconsistencies (Álvarez-García et al., 2015a; Zhu et al., 2021). This study will focus on analyzing the association of some demographic, psychological, educational, family factors and exposure to other forms of violence, with cyber-victimization, in a nationally representative sample of South African children. We aim to determine the lifetime prevalence and last-year prevalence (i.e., annual incidence) of cyber-victimization, as well as the association of cyber-victimization with its correlates, based on a nationally representative cross-sectional study of 15–17-year-old youth in South Africa.
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    Sexual violence against children in South Africa: A nationally representative cross-sectional study of prevalence and correlates
    (The Lancet Global Health, 2018) Ward, Catherine L; Artz, Lillian; Leoschut, Lezanne; Kassanjee, Reshma; Burton, Patrick
    Background We could identify no nationally representative South African studies of sexual violence against children. Methods A multistage sampling frame, stratified by province, urban/rural and race group, selected households. Within households, children aged 15-17 years were interviewed after obtaining parental consent. The final sample was 5,631 (94.6% participation rate). Findings 9.99% (95%CI 8.65-11.47) of boys and 14.61% (95%CI 12.83-16.56) of girls reported some lifetime sexual victimisation. Physical abuse, emotional abuse, neglect, family violence, and other victimisations, were all strongly associated with sexual victimisation. The following were associated with greater risk of sexual abuse (adjusted OR); school enrolment (OR 2.12; 95%CI 1.29-3.48); urban dwelling (OR 0.59; 95%CI 0.43-0.80); having a flush toilet (OR 1.43; 95%CI 1.04-1.96); having a substance-misusing parent ( OR 2.37; 95%CI 1.67-3.36); being disabled (OR 1.42; 95%CI 1.10-1.82); female but not male caregivers’ poorer knowledge of the child’s whereabouts, friends and activities (OR 1.07; 95%CI 0.75-1.53) and poorer quality of the relationship with the child (OR 1.20; 95%CI 0.55-2.60). Respondents’ own substance misuse (OR 4.72; 95%CI 3.73-5.98) and high-risk sexual behaviour (OR 3.71; 95%CI 2.99-4.61) were the behaviours most frequently associated with sexual abuse, with mental health conditions far less prevalent but nonetheless strongly associated with sexual victimisation (PTSD OR 2.81, 95%CI 1.65-4.78; depression OR 3.43, 95% CI 2.26-5.19; anxiety OR 2.48, 95%CI 1.61-3.81). Interpretation Sexual violence is widespread among both girls and boys, and is associated with serious health problems. Associated factors require multi-sectoral responses to prevent sexual violence or mitigate consequences.