The South African tuberculosis (TB) epidemic is driven mainly by HIV, and the TB disease burden is greater in males than females. Additional factors that drive the epidemic include undiagnosed and untreated TB, contributing to transmission; and highly prevalent TB risk factors such as alcohol misuse, smoking, diabetes, and undernutrition, which increase the risk of progression to TB disease. These factors are distributed differently by sex and likely explain the observed sex disparities in TB. The South African TB control programme has implemented multiple interventions, including directly observed therapy strategy (DOTS), antiretroviral therapy (ART), intensified screening activities, the provision of isoniazid preventative therapy (IPT) and the implementation of Xpert MTB/RIF as a first-line diagnostic tool. However, few analyses have quantified the historical impact of HIV and the combined impact of TB interventions on the South African TB epidemic at a national level. In addition, factors that influence sex disparities in the South African TB burden have not been explored thoroughly. Also, it remains uncertain whether, with existing interventions, it would be feasible for South Africa to meet the End TB targets to reduce TB incidence and mortality by 80% and 90% respectively (relative to 2015 levels) by 2030. This thesis aims to address the abovementioned gaps in knowledge and provide insights into understanding the population-level TB dynamics, using a mathematical model. The first objective is to quantify TB incidence and mortality due to HIV and assess the impact of interventions mentioned above on TB incidence and mortality between 1990 and 2019. The second objective is to explore the extent to which the following factors contribute to sex differences in TB: HIV, ART uptake, smoking, alcohol abuse, undernutrition, diabetes, health-seeking patterns, social contact rates and TB treatment discontinuation. The third objective is to project the future impact of increasing screening, improving linkage to TB care and retention, increasing preventative therapy, and reducing ART interruptions. An age- and sex-stratified dynamic tuberculosis transmission model for South Africa was developed. To dynamically model the effect of HIV and ART on TB incidence and mortality, the TB model was integrated into the Thembisa model, a previously developed HIV and demographic model. In addition, age- and sex-specific relative risks were applied to rates of progression to TB disease to capture age and sex differences in tuberculosis incidence. The model also included a diagnostic pathway representing health-seeking patterns and the sensitivity and specificity of the diagnostic algorithm. A Bayesian approach was used to calibrate the model to the numbers of people starting treatment from the electronic tuberculosis register, deaths from the vital register, microbiological tests, and the national tuberculosis prevalence survey. The model estimated rapid increases in TB incidence and mortality in the mid-to-late 1990s, influenced by HIV. Between 1990 and 2019, approximately eight million people developed tuberculosis, and two million died from TB; HIV accounted for at least half and two-thirds of the TB incidence and mortality, respectively. The TB epidemic peaked in the mid-to-late 2000s, followed by declines until 2019. The ART program and TB screening efforts, which were expanded in the mid-2000s, contributed the most to reductions in TB incidence and mortality, while other interventions had minor impacts. Due to the heavier HIV burden in women than men, women experienced greater HIV-associated TB incidence and mortality than men. However, because of the higher ART uptake among women than men, women experienced greater relative reductions in TB incidence and mortality over the period 2005– 2019. Consequently, the higher TB burden among men has been sustained; the estimated male-to-female ratios of TB incidence and mortality in 2019 were 1.7 and 1.65, respectively. Additional factors explaining the excess TB in men are smoking, alcohol abuse and delays in health-seeking patterns. Sex differences in undernutrition, social contact patterns, and treatment discontinuation had minimal effect on TB sex disparities. Projections of the model to 2030, considering the effects of COVID-19-related disruptions to TB care, suggest that increasing TB screening would be the most impactful among all interventions explored. However, the model also suggests that the 2030 End TB milestone is unlikely to be met by scaling up existing interventions. Other interventions that need to be explored include targeted universal TB testing and other diagnostic tests such as digital chest x-rays, urine Lipoarabinomannan, and biomarkers to identify individuals at risk of TB disease. Accelerating progress toward TB incidence and mortality reductions will require developing affordable and efficient rapid diagnostic tools to identify potential and active TB cases. Research and innovation efforts towards finding a vaccine effective in preventing TB disease are also critical. In addition, it is essential to improve the uptake of TB preventative therapy in HIV-positive individuals and perhaps further expand provision to other TB risk groups

Epidemiological models are used to inform health policy on issues such as target vaccination levels, comparing quarantine options and estimating the eventual size of an epidemic. Models that incorporate some elements of the social network structure are used for diseases where close contact is required for transmission. The motivation of this research is to extend epidemic models to include the relationship with a broader set of relevant real world network properties. The impact of degree distribution by itself is reasonably well understood, but studies with assortativity or clustering are limited and none examine their interaction. To evaluate the impact of these properties, I simulate epidemics on networks with a range of property values. However, a suitable algorithm to generate the networks is not available in the literature. There are thus two research aspects: generating networks with relevant properties, and estimating the impact of social network structure on epidemic behaviour. Firstly, I introduce a flexible network generation algorithm that can independently control degree distribution, clustering coefficient and degree assortativity. Results show that the algorithm is able to generate networks with properties that are close to those targeted. Secondly, I fit models that account for the relationship between network properties and epidemic behaviour. Using results from a large number of epidemic simulations over networks with a range of properties, regression models are fitted to estimate the separate and joint effect of the identified social network properties on the probability of an epidemic occurring and the basic reproduction ratio. The latter is a key epidemic parameter that represents the number of people infected by a typical initial infected person in a population. Results show that social network properties have a significant influence on epidemic behaviour within the property space investigated. Ignoring the differences between social networks can lead to substantial errors when estimating the basic reproduction ratio from an epidemic and then applying the estimate to a different social network. In turn, these errors could lead to failure in public health programs that rely on such estimates.

Utilizing a social construction perspective, this study uses a mixed method approach to examine the opioid epidemic. The study begins by identifying the numerous claims-making groups along with conducting a content analysis of the rhetoric and symbols used to legitimize the claims about the opioid epidemic. The data for the content analysis was obtained through a search of the websites, newsrooms, and pressrooms of claims-making groups. Additionally, the study examines and assesses the volume of money that is generated and allocated towards opioid research and prevention in an effort to determine who has more power to influence the policy initiatives. Findings show that the frequency of rhetoric and the number of claims-making groups releasing information about the opioid epidemic increased from 2010-2016. Most of the rhetoric consists of groups proposing resolution strategies and formulating new policies. Only a few claims-makers are making financial contributions towards opioid prevention initiatives and in most cases, it is a very small amount of money.

In the first half of 1955 an unusual infective disease appeared in my practice, which is centred on Dalston, a village which lies 4½ miles south-west of Carlisle, in the valley of the River Caldew. The condition became prevalent in February and March and affected a considerable number of my patients; there were no fatalities but the disease was the cause of much disability and loss of working time. The disease appeared to be infectious, and the illness was characterised by acute myalgia, disturbances of the reticulo endothelial system and central nervous system, and psychogenic sequelae which, in some instances, persisted for many months. Relapses, with recrudescences of symptoms, occurred in a proportion of those infected; in sane cases, several relapses occurred over a period of months, symptoms being minimal or absent between the recurrences. The clinical picture that emerged was one with which I was not familiar. At an early stage in the epidemic I considered the condition to be most like glandular fever, and therefore sent serum samples and blood films to the Pathological Laboratory of the Cumberland Infirmary for Paul Bunnell screening and examination of the films for the picture seen in glandular fever.

No description available.

Influenza is a respiratory virus that causes significant morbidity and mortality throughout the world every year. Seasonal epidemics of influenza occur in the late fall and winter in the United States annually, but there are variations in its timing from year to year. Further, although the timing of epidemic waves in the United States are similar, there is variation between different populations. It is not well understood why these differences exist. Understanding the spatial and temporal variation in the timing of influenza is important because it shapes our understanding of preventive actions that can be taken to limit the spread of the virus. Past studies that have examined the timing of influenza have been limited by the fact that they have used influenza-like illness (ILI) as an indicator of influenza. ILI has traditionally been the conventional indicator of influenza because the illness does not present unique symptoms. As such, spatial and temporal variation in the relative timing of influenza A, influenza B, and ILI have not been investigated extensively. Additionally, there has been concern raised about implications of the imprecise nature of ILI in treating patients that it is believed may have influenza. This study addressed this gap and concern by utilizing influenza-specific data from clinics and hospitals throughout the United States to evaluate spatial variation in the timing of influenza across the United States in the 2016-17 influenza season. Results from influenza rapid tests were aggregated by urban area as a means of evaluating associations between epidemic timing and independent variables in different locations. The timing of influenza A and B epidemics was tested for spatial autocorrelation and incorporated in regression models to identify potential relationships between epidemic timing and several variables (including dew point, temperature, population, population density, and cumulative seasonal vaccination rates). Forward stepwise regression was then conducted to identify a set of variables that may be best suited to explain the timing of these milestones, and spatial lag regression was conducted to account for spatial autocorrelation in these variables. This analysis indicated that higher average dew point and temperature and greater population and population density were both associated with earlier epidemic beginnings and later epidemic endings, while higher cumulative seasonal vaccination rates were associated with earlier epidemic endings for influenza A and B. Forward stepwise regression yielded models that generally differed for each epidemic milestone and type of influenza, indicating that different sets of variables might be best suited to explain different milestones of epidemics. Spatial lag regression improved model fit for the forward stepwise models for which there was residual spatial autocorrelation. This is one of the first studies to evaluate the timing of different points within an epidemic. The techniques I used to study timing are well-suited for the study of future epidemics of infectious diseases, including influenza, that seek to identify and clarify potential associations between independent variables and epidemic timing.

Thesis (M.Sc.)--York University, 2006. Graduate Programme in Science. / Typescript. Includes bibliographical references (leaves 62-65). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR29635

The aim of this research study was to investigate the effects of HIV/AIDS on teachers and learners of one Secondary School in Mthatha District of Eastern Cape, South Africa. The research design used was a case study; both quantitative and qualitative techniques were employed. The research was conducted in one secondary school, where data was collected using the interview schedules and the questionnaires. The teachers and learners were the respondents. Close-ended responses were analysed using Statistical Programme for Social Sciences (SPSS) computer software. Open-ended responses and interviews were analysed manually using sentence analysis, themes, categories and pattern. Interview responses were also analysed and interpreted using descriptions. From the analysis and interpretation of results, the following main findings emerged: HIV/AIDS has a devastating and deadly effect on both learners and teachers such as:-early sick pensions, redeployments, high teacher death rate, high learner death rate and learner absenteeism. Learner and teacher absenteeism trough HIV/AIDS epidemic, learner drop-out from school because of HIV/AIDS, non-fulfillment of future goals by the learners were discovered by the researcher as some of the findings from the study. From the findings it emerged that HIV/AIDS has devastating and deadly effect on both learners and educators. Support on those who are affected by HIV/AIDS epidemic is recommended by the researcher and the strategies that can be used to control HIV/AIDS epidemic.

Detecting change points in epidemic models has been studied by many scholars. Yao (1993) summarized five existing test statistics in the literature. Out of those test statistics, it was observed that the likelihood ratio statistic showed its standout power. However, all of the existing test statistics are based on an assumption that population variance is known, which is an unrealistic assumption in practice. To avoid assuming known population variance, a new test statistic for detecting epidemic models is studied in this thesis. The new test statistic is a parameter-free test statistic which is more powerful compared to the existing test statistics. Different sample sizes and lengths of epidemic durations are used for the power comparison purpose. Monte Carlo simulation is used to find the critical values of the new test statistic and to perform the power comparison. Based on the Monte Carlo simulation result, it can be concluded that the sample size and the length of the duration have some effect on the power of the tests. It can also be observed that the new test statistic studied in this thesis has higher power than the existing test statistics do in all of cases.

M.Cur / Department of Advanced Nursing Science