The geographical distribution and burden of trachoma in Africa.

BACKGROUND: There remains a lack of epidemiological data on the geographical distribution of trachoma to support global mapping and scale up of interventions for the elimination of trachoma. The Global Atlas of Trachoma (GAT) was launched in 2011 to address these needs and provide standardised, updated and accessible maps. This paper uses data included in the GAT to describe the geographical distribution and burden of trachoma in Africa. METHODS: Data assembly used structured searches of published and unpublished literature to identify cross-sectional epidemiological data on the burden of trachoma since 1980. Survey data were abstracted into a standardised database and mapped using geographical information systems (GIS) software. The characteristics of all surveys were summarized by country according to data source, time period, and survey methodology. Estimates of the current population at risk were calculated for each country and stratified by endemicity class. RESULTS: At the time of writing, 1342 records are included in the database representing surveys conducted between 1985 and 2012. These data were provided by direct contact with national control programmes and academic researchers (67%), peer-reviewed publications (17%) and unpublished reports or theses (16%). Prevalence data on active trachoma are available in 29 of the 33 countries in Africa classified as endemic for trachoma, and 1095 (20.6%) districts have representative data collected through population-based prevalence surveys. The highest prevalence of active trachoma and trichiasis remains in the Sahel area of West Africa and Savannah areas of East and Central Africa and an estimated 129.4 million people live in areas of Africa confirmed to be trachoma endemic. CONCLUSION: The Global Atlas of Trachoma provides the most contemporary and comprehensive summary of the burden of trachoma within Africa. The GAT highlights where future mapping is required and provides an important planning tool for scale-up and surveillance of trachoma control

Post-treatment drug use, recidivism, analogous behaviors, and perceptions of fairness: Examining whether parolees with low self-control will benefit from the Collaborative Behavioral Management intervention

This dissertation tested Gottfredson and Hirschi\u27s (1990) low self-control theory and its relationship with post-treatment outcomes by conducting a secondary-data analysis of a randomized controlled trial on parolees (n=569) called the Step\u27n Out study (2005). The Step\u27n Out study (2005) compared the results of a control group (standard parole) with an experimental treatment for parolees called the Collaborative Behavioral Management (CBM) intervention which was designed to improve substance-use treatment outcomes, reduce drug use, and reduce recidivism for parolees participating in the study. Low self-control theory states that individuals with character traits that are impulsive, risk-seeking, self-centered, and display volatile temper have a high likelihood of engaging in criminal and analogous (i.e. risky sexual practices) behaviors. Gottfredson and Hirschi\u27s (1990) theory makes the assumption that these traits are the result of parental socialization practices, are not able to be changed after the age of 8 or 10, and are stable across time. In order to measure low self-control for the present study, an exploratory factor analysis was conducted on 20 self-report items collected at intake from the parolees in the study and a unidimensional measure of low self-control was constructed. Based on low self-control theory, this study hypothesizes that parolees who self-report engaging in substance use, recidivism, and analogous behaviors after the end of the treatment intervention at the 3 and 9 month follow-up periods will have low self-control traits (measured at intake). Also based on the theory, this study hypothesizes that the treatment condition (control group vs. CBM group) will not moderate the relationship between low self-control traits and post-treatment outcomes even when controlling for demographic, risk-factors, peer-associations, and treatment dosage. The exploratory results from this study were reported using univariate, bivariate, and multivariate statistics. Also a confirmatory factor analysis was conducted to measure the direct and indirect effects of low self-control, peer-associations, and perceptions of fairness on post-treatment outcomes. The results from this dissertation study largely indicate that parolees across the self-control spectrum (low to high levels of self-control) are engaging in post-treatment outcomes (substance use, recidivism, and analogous behaviors) at the 3 and 9 month follow-up periods even when controlling for age, gender, race, age at first arrest, education status, dosage levels, and treatment condition. Therefore, based on the findings from this study, low self-control theory does not allow researchers to understand the causal mechanisms by which post-treatment outcomes occur for parolees. More theoretical refinement of the theory or alternative theories are needed in order to explain the post-treatment outcomes of parolees participating in the Step\u27n Out study. However, a particularly interesting finding that also has strong public policy implications indicates that parolees that self-reported physically or verbally threatening someone at both the 3 and 9 month follow-up periods had statistically significant lower mean levels of self-control compared to parolees who did not physically or verbally threaten someone

Remaining Useful Life Estimation Based on Detection of Explosive Changes: Analysis of Bearing Vibration

The monitoring of condition variables for maintenance purposes is a growing trend amongst researchers and practitioners where decisions are based on degradation levels. The two approaches in Condition-Based Maintenance (CBM) are diagnosing the level of degradation (diagnostics) or predicting when a certain level of degradation will be reached (prognostics). Using diagnostics determines when it is necessary to perform maintenance, but it rarely allows for estimation of future degradation. In the second case, prognostics does allow for degradation and failure prediction, however, its major drawback lies in when to perform the analysis, and exactly what information should be used for predictions. This encumbrance is due to previous studies that have shown that degradation variable could undergo a change that misleads these calculations. This paper addresses the issue of identifying explosive changes in condition variables, using Control Charts, to determine when to perform a new model fitting in order to obtain more accurate Remaining Useful Life (RUL) estimations. The diagnostic-prognostic methodology allows for discarding pre-change observations to avoid contamination in condition prediction. In addition the performance of the integration methodology is compared against adaptive autoregressive (AR) models. Results show that using only the observations acquired after the out-of-control signal produces more accurate RUL estimations