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The overall effect of non response biases on national HIV estimates tends to be small and remains inferior to sample variations. If adjustments need to be interpreted with caution due to the limited information available to predict the prevalence of non tested people, we can conclude that national population-based surveys can provide quality and representative national HIV prevalence estimates.
Sub-national estimates of HIV prevalence can inform the design of policy responses to the HIV epidemic. Such responses also benefit from a better understanding of the correlates of HIV status, including the association between HIV and geographical characteristics of localities. In recent years, several countries in Africa have implemented household surveys (such as Demographic and Health Surveys) that include HIV testing of the adult population, providing estimates of HIV prevalence rates at the sub-national level. These surveys are known to suffer from non-response bias, but are nonetheless thought to represent a marked improvement over alternatives such as sentinel surveys. At present, however, most countries are not in a position to regularly field such household surveys. This paper proposes a new approach to the estimation of HIV prevalence for relatively small geographic areas in settings where national population-based surveys of prevalence are not available. The proposed approach aims to overcome some of the difficulties with prevailing methods of deriving HIV prevalence estimates (at both national and sub-national levels) directly from sentinel surveys. The paper also outlines some of the limitations of the proposed approach.
The DHS Program has supported the conduct of numerous large-scale HIV seroprevalence surveys. Some of these surveys used a testing strategy based on enzyme-immunoassays (EIA) and recent concerns were raised that this algorithm could have led to overestimation of HIV prevalence. The present report investigated the impact of potential misclassification of samples on HIV prevalence estimates for 23 surveys conducted from 2010-2014. Along with visual inspection of laboratory results, we examined how accounting for potential misclassification of HIV status through Bayesian latent class models affected prevalence estimates. Two types of Bayesian models were specified: one that only uses the individual dichotomous test results and a continuous model that makes use of the quantitative information of the EIA (i.e., their signal-to-cutoff values). Overall, we found that adjusted prevalence estimates roughly matched the surveys’ original results, with overlapping uncertainty intervals, suggesting that misclassification of HIV status should not affect prevalence estimates in most surveys. Our analyses did, however, suggest that two surveys may be problematic; the Uganda AIDS Indicator Survey 2011 and the Zambia Demographic and Health Survey 2013-14, where prevalence could have been overestimated - the magnitude of which remains difficult to ascertain. Interpreting results from the Uganda survey is made difficult by the lack of internal quality control and potential violation of the multivariate normality assumption of the continuous Bayesian latent class model. In conclusion, despite limitations of our latent class models, our analyses suggest that prevalence estimates from most reviewed surveys are not overwhelmingly affected by sample misclassification.
A complete guide to carrying out complex survey analysis using R As survey analysis continues to serve as a core component of sociological research, researchers are increasingly relying upon data gathered from complex surveys to carry out traditional analyses. Complex Surveys is a practical guide to the analysis of this kind of data using R, the freely available and downloadable statistical programming language. As creator of the specific survey package for R, the author provides the ultimate presentation of how to successfully use the software for analyzing data from complex surveys while also utilizing the most current data from health and social sciences studies to demonstrate the application of survey research methods in these fields. The book begins with coverage of basic tools and topics within survey analysis such as simple and stratified sampling, cluster sampling, linear regression, and categorical data regression. Subsequent chapters delve into more technical aspects of complex survey analysis, including post-stratification, two-phase sampling, missing data, and causal inference. Throughout the book, an emphasis is placed on graphics, regression modeling, and two-phase designs. In addition, the author supplies a unique discussion of epidemiological two-phase designs as well as probability-weighting for causal inference. All of the book's examples and figures are generated using R, and a related Web site provides the R code that allows readers to reproduce the presented content. Each chapter concludes with exercises that vary in level of complexity, and detailed appendices outline additional mathematical and computational descriptions to assist readers with comparing results from various software systems. Complex Surveys is an excellent book for courses on sampling and complex surveys at the upper-undergraduate and graduate levels. It is also a practical reference guide for applied statisticians and practitioners in the social and health sciences who use statistics in their everyday work.
With insightful discussion of program evaluation and the efforts of the Centers for Disease Control, this book presents a set of clear-cut recommendations to help ensure that the substantial resources devoted to the fight against AIDS will be used most effectively. This expanded edition of Evaluating AIDS Prevention Programs covers evaluation strategies and outcome measurements, including a realistic review of the factors that make evaluation of AIDS programs particularly difficult. Randomized field experiments are examined, focusing on the use of alternative treatments rather than placebo controls. The book also reviews nonexperimental techniques, including a critical examination of evaluation methods that are observational rather than experimentalâ€"a necessity when randomized experiments are infeasible.
Highly recommended by the Journal of Official Statistics, The American Statistician, and other journals, Applied Survey Data Analysis, Second Edition provides an up-to-date overview of state-of-the-art approaches to the analysis of complex sample survey data. Building on the wealth of material on practical approaches to descriptive analysis and regression modeling from the first edition, this second edition expands the topics covered and presents more step-by-step examples of modern approaches to the analysis of survey data using the newest statistical software. Designed for readers working in a wide array of disciplines who use survey data in their work, this book continues to provide a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. An example-driven guide to the applied statistical analysis and interpretation of survey data, the second edition contains many new examples and practical exercises based on recent versions of real-world survey data sets. Although the authors continue to use Stata for most examples in the text, they also continue to offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book’s updated website.
The issue of universal and free access to treatment is now a fundamental goal of the international community. Based on original data and field studies from Brazil, Thailand, India and Sub-Saharan Africa under the aegis of ANRS (the French nationalagency for research on Aids and viral hepatitis, this timely and significant book both assesses the progress made in achieving this objective and presents a rigorous diagnosis of the obstacles that remain. Placing particular emphasis on the constraints imposed by TRIPS as well as the poor state of most public health systems in Southern countries, the contributing authors provide a comprehensive analysis of the huge barriers that have yet to be overcome in order to attain free access to care and offer innovative suggestions of how they might be confronted. In doing this, the book renews our understanding of the political economy of HIV/AIDS in these vast regions, where the disease continues to spread with devastating social and economic consequences. This volume will be a valuable addition to the current literature on HIV/AIDS in developing countries and will find widespread appeal amongst students and academics studying economics, sociology and public health. It will also be of interest to international organizations and professional associations involved in the fight against pandemics.
Infectious diseases are the leading cause of death globally, particularly among children and young adults. The spread of new pathogens and the threat of antimicrobial resistance pose particular challenges in combating these diseases. Major Infectious Diseases identifies feasible, cost-effective packages of interventions and strategies across delivery platforms to prevent and treat HIV/AIDS, other sexually transmitted infections, tuberculosis, malaria, adult febrile illness, viral hepatitis, and neglected tropical diseases. The volume emphasizes the need to effectively address emerging antimicrobial resistance, strengthen health systems, and increase access to care. The attainable goals are to reduce incidence, develop innovative approaches, and optimize existing tools in resource-constrained settings.
In addition to a comprehensive discussion of methods for gauging the extent of the epidemic and forecasting AIDS incidence, this book presents methods and results concerning the risks of HIV transmission, the incubation period of HIV infection, markers of disease progression, prevention strategies, including strategies to protect the blood supply, and the evaluation of treatments and vaccines. These topics are presented quantitatively, with an emphasis on the strengths and weaknesses of available data. The book highlights how a naive statistical approach to the design or analysis of such studies can lead to seriously misleading results. The various methods of monitoring and forecasting HIV disease and AIDS incidence are given thorough treatment.