Botswana continues to have a high level of HIV prevalence, with about 17% of the population living with HIV AIDS (BAIS IV, 2013). Female sex workers are classified among the most at risk population group in the country. However, sub-national disaggregated data on new infections are not available. Hence, there is a need to focus great attention on other proxies of infection. The present study examines predictors of HIV testing among female sex workers (FSWs) in Botswana. The FSWs were recruited into the study using the time-location cluster sampling method (TLS) to collect data on prevalence and incidence of HIV and other STIs and their risk factors for HIV. The logistic regression analysis was performed to estimate crude odds ratios and identify the factors associated with having an HIV test among the FSWs. HIV prevalence among sex workers in Botswana was found to be 3 times higher than in the general population. Analysis of the results shows that the sex workers most likely to seek HIV testing were young women with no children. The odds of testing for HIV were almost 4 times more for FSWs who had first sex older as compared to the odds of testing for those who are 17 to 19 years old. Lack of or inconsistent condom use and currently having symptoms of STIs such as lower abdominal pain and genital ulcerations were also factors associated with HIV testing. Results further show that FSW hold little discrimination and stigma related attitudes towards PLWA. FSWs have little participation in the HIV prevention, treatment and care efforts currently accessed by the general population. It is recommended is that this framework should also be extended to FSW's and their clients in order to curb HIV and STIs.
Background: Predictive models for mortality due to human immunodeficiency virus (HIV) disease as a result of opportunistic infections, such as tuberculosis and pneumonia, have been developed. Methods: The data are taken from the Statistics South Africa multiple causes of death data for 2006 and 2007, which is available for public use. The dataset was compiled from death notifications, and contains up to five causes of death as well as some demographic characteristics of the deceased. The logistic regression modeling framework was used to model the presence or absence of HIV disease, given the predictive variables. Results: The higher the number of causes listed, the higher the likelihood that HIV would be a cause, with the percentage of notifications of HIV listed increasing from under 2% when only one cause is listed to almost 15% when 4-5 causes are listed. When the logit model was fitted to the multiple cause of death model, it was found that individual demographics were good predictors of the likelihood that the death notification would have HIV as one of the causes of death. Although there are highly significant differences in the likelihood that people of different demographics would die from HIV, the predictive power of these demographic factors on their own is very low, especially when there is only a single cause of death mentioned. With the full multiple cause of death model, two-way interactions between tuberculosis, pneumonia, and other opportunistic infections were highly significant, and their inclusion lead to significant improvements in the predictive power of the model.
Selected data transformation techniques in time series modeling are evaluated using real-life data on Botswana Gross Domestic Product (GDP). The transformation techniques considered were modified, although reasonable estimates of the original with no significant difference at 0.05 α = level were obtained: minimizing square of first difference (MFD) and minimizing square of second difference (MSD) provided the best transformation for GDP, whereas the Goldstein and Khan (GKM) method had a deficiency of losing data points. The Box-Jenkins procedure was adapted to fit suitable ARIMA (p, d, q) models to both the original and transformed series, with AIC and SIC as model order criteria. ARIMA (3, 1, 0) and ARIMA (1, 0, 0) were identified, respectively, to the original and log of the transformed series. All estimates of the fitted stationary series were significant and provided a reliable forecast.
This paper is focused on the analysis of categorical data in a 2 × c × K contingency table. The theoretical frame work of a 2 × c design is extended to 2 × c × K with provision for testing interactions among subsets of either lower or upper columns of the designated table. The developed chi square tests for the total interactions as well as for the partitions are shown to be significant and the degrees of freedom additive.
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