Exploring exposure to television alcohol advertising and harmful aicohol use among South African youth

A research report submitted to the School of Public Health, University of the Witwatersrand in partial fulfilment of the requirements for the degree of Master of Public Health 2 June 2016Youth are targets of alcohol marketing (World Health Organization, 2010a). Studies conducted in South Africa indicate an increasing trend in the prevalence of drinking among youth (Rehm et al., 2004). Nationally, 49.2% of learners had drunk one or more drinks of alcohol (e.g. a beer, a glass of wine, or a ‘tot’ of brandy) in their lifetime, with significantly more males (53.8%) than female (44.9%) learners consuming alcohol. (Omardien et al., 2013). The same study found that 12.4% of learners reported having the first drink before the age of 13 years. Early alcohol initiation remains a concern and binge drinking is increasing, especially among females (Ramsoomar and Morojele, 2012). One of the key considerations to reduce harmful alcohol use in South Africa is to place restrictions on alcohol advertising. This process was initiated in 1997 as part of a policy initiative to reduce the harmful impact of alcohol, but this was delayed (Parry, 2010). Hence, youth in South Africa are still exposed to alcohol marketing on television (TV) and other media. This study aimed to explore associations between TV alcohol advertising exposure and youth drinking patterns. Methods This is a secondary analysis of data from the National Communication Survey (NCS), a national cross sectional study that included data on TV exposure and alcohol-related behaviours in South Africa. The alcohol advertisements that were broadcast in each of the national television stations during the study period were also explored to establish the plausibility of exposure of youth to such advertisements. For this study, the sample was restricted to youth aged 16 - 24 years. Descriptive data on their alcohol use patterns and exposure to television were explored and tests of association were conducted for categorical outcomes, e.g. never having drunk, ever having drunk, binge drinking and problem drinking, and other variables, e.g. socio-demographic characteristics and TV viewership at the 95% confidence level. Results The prevalence for the three alcohol drinking patterns in the total sample of youth were 34.9% having ever drunk; 20.7% binge drinking; and 14.0 % problem drinking. Of those who had ever drunk, 60.5% engaged in binge drinking and 41.2% had problem drinking, with males outnumbering females. A number of other characteristics, beyond sex, were also associated with the drinking patterns, including age, education, employment, socio-economic status (SES), and race. The nature of these associations varied according to the drinking patterns, namely, having ever drunk, binge drinking and problem drinking. The frequency of television viewership was associated with the three drinking patterns of the youth. All four television channels had relatively high numbers of alcohol advertisements during the NCS data collection period, meaning that youth reporting television exposure were also likely to have seen alcohol advertisements. The implications of these findings are discussed in light of the broader literature on youth drinking patterns, television viewership patterns and alcohol advertising. Conclusions Youth drinking patterns, particularly binge drinking and problem drinking, are of concern. Given that exposure to TV was associated with drinking and all channels had high numbers of alcohol advertisements, restrictions on alcohol advertising on TV should be considered to prevent or reduce alcohol-related harm among South African youth.MB201

ClassTR: Classifying Within-Host Heterogeneity Based on Tandem Repeats with Application to Mycobacterium tuberculosis Infections.

Genomic tools have revealed genetically diverse pathogens within some hosts. Within-host pathogen diversity, which we refer to as "complex infection", is increasingly recognized as a determinant of treatment outcome for infections like tuberculosis. Complex infection arises through two mechanisms: within-host mutation (which results in clonal heterogeneity) and reinfection (which results in mixed infections). Estimates of the frequency of within-host mutation and reinfection in populations are critical for understanding the natural history of disease. These estimates influence projections of disease trends and effects of interventions. The genotyping technique MLVA (multiple loci variable-number tandem repeats analysis) can identify complex infections, but the current method to distinguish clonal heterogeneity from mixed infections is based on a rather simple rule. Here we describe ClassTR, a method which leverages MLVA information from isolates collected in a population to distinguish mixed infections from clonal heterogeneity. We formulate the resolution of complex infections into their constituent strains as an optimization problem, and show its NP-completeness. We solve it efficiently by using mixed integer linear programming and graph decomposition. Once the complex infections are resolved into their constituent strains, ClassTR probabilistically classifies isolates as clonally heterogeneous or mixed by using a model of tandem repeat evolution. We first compare ClassTR with the standard rule-based classification on 100 simulated datasets. ClassTR outperforms the standard method, improving classification accuracy from 48% to 80%. We then apply ClassTR to a sample of 436 strains collected from tuberculosis patients in a South African community, of which 92 had complex infections. We find that ClassTR assigns an alternate classification to 18 of the 92 complex infections, suggesting important differences in practice. By explicitly modeling tandem repeat evolution, ClassTR helps to improve our understanding of the mechanisms driving within-host diversity of pathogens like Mycobacterium tuberculosis

Comparison of clustering techniques for residential load profiles in South Africa

This work compares techniques for clustering metered residential energy consumption data to construct representative daily load profiles in South Africa. The input data captures a population with high variability across temporal, geographic, social and economic dimensions. Different algorithms, normalisation and pre-binning techniques are evaluated to determine their effect on producing a good clustering structure. A Combined Index is developed as a relative score to ease the comparison of experiments across different metrics. The study shows that normalisation, specifically unit norm and the zero-one scaler, produce the best clusters. Pre-binning appears to improve clustering structures as a whole, but its effect on individual experiments remains unclear. Like several previous studies, the k-means algorithm produces the best results. To our knowledge this is the first work that rigorously compares state of the art cluster analysis techniques in the residential energy domain in a developing country context

Modelling uncertain adaptive decisions: Application to KwaZulu-Natal sugarcane growers

A dynamic Bayesian decision network was developed to model the pre- harvest burning decision-making processes of sugarcane growers in a KwaZulu-Natal sugarcane supply chain and extends previous work by Price et al. (2018). This model was created using an iterative development approach. This paper recounts the development and validation process of the third version of the model. The model was vali- dated using Pitchforth and Mengersen (2013)’s framework for validating expert elicited Bayesian networks. During this process, growers and cane supply members assessed the model in a focus group by executing the model, and reviewing the results of a pre- run scenario. The participants were generally positive about how the model represented their decision-making processes. However, they identified some issues that could be addressed in the next iteration. Dynamic Bayesian decision networks offer a promising approach to modelling adaptive decisions in uncertain conditions. This model can be used to simulate the cognitive mechanism for a grower agent in a simulation of a sugarcane supply chain

Mapping awareness of breast and cervical cancer risk factors, symptoms and lay beliefs in Uganda and South Africa

Funder: Cancer Association of South AfricaFunder: University of Cape Town; funder-id: http://dx.doi.org/10.13039/501100007112Funder: South African Medical Research Council; funder-id: http://dx.doi.org/10.13039/501100001322Funder: National Department of Health; funder-id: http://dx.doi.org/10.13039/100009041Funder: UK Medical Research CouncilFunder: Newton Fund; funder-id: http://dx.doi.org/10.13039/100010897Background: Breast and cervical cancer are leading causes of cancer burden in Sub-Saharan Africa (SSA). We measured breast and cervical cancer symptom and risk factor awareness and lay beliefs in Uganda and South Africa (SA). Methods: Between August and December 2018 we conducted a cross-sectional survey of women ≥18 years in one urban and one rural site per country. Households were selected using systematic random sampling, then one woman per household randomly selected to participate. Data were collected by interviewers using electronic tablets customised with the locally validated African Women Awareness of Cancer (AWACAN) tool. This has unprompted questions (testing recall) followed by prompted questions (testing recognition) on risk factor, symptom awareness and lay beliefs for breast and cervical cancer. Mann Whitney and Kruskal Wallis tests were used to compare the association between socio-demographic variables and outcomes. Poisson regression with robust variance was conducted to identify independent socio-demographic predictors. Results: Of the 1758 women interviewed, 90.8% had heard of breast and 89.4% of cervical cancer. 8.7% recalled at least one breast risk factor and 38.1% recalled at least one cervical cancer risk factor. 78.0% and 57.7% recalled at least one breast/cervical cancer symptom respectively. Recognition of risk factors and symptoms was higher than recall. Many women were unaware that HPV, HIV, and not being screened were cervical cancer risk factors (23.7%, 46.8%, 26.5% respectively). In SA, urban compared to rural women had significantly higher symptom and risk factor awareness for both cancers. In Uganda married women/living with a partner had higher awareness of breast cancer risk factors and cervical cancer symptoms compared to women not living with a partner. Women mentioned several lay beliefs (e.g. putting money in their bra as a breast cancer risk factor). Conclusion: We identified gaps in breast and cervical cancer symptom and risk factor awareness. Our results provide direction for locally targeted cancer awareness intervention programs and serve as a baseline measure against which to evaluate interventions in SSA