{"title":"A METHOD FOR ESTIMATING THE PROPORTION OF HIV INFECTED PERSONS THAT HAVE BEEN DIAGNOSED AND APPLICATION TO CHINA.","authors":"Ron Brookmeyer, Zunyou Wu","doi":"10.1007/s12561-019-09240-8","DOIUrl":null,"url":null,"abstract":"<p><p>Estimation of the proportion of living HIV infected persons that have been diagnosed is critical for tracking progress toward meeting the UNAIDS goal that all persons who need HIV treatment receive it. The objective of this article is to develop a method for estimating that proportion. The methodological problem is that persons with undiagnosed HIV infection are not directly observable and are a \"hidden\" population. Here we propose a methodology for estimating the proportion diagnosed that is relatively simple to implement. The key idea is that in many settings certain health conditions such as pregnancy or an upcoming surgery lead to mandatory HIV tests. The size of the undiagnosed infected population can be estimated from the numbers of infected persons diagnosed by mandatory tests and an estimate of the rate that persons in the undiagnosed infected population receive mandatory tests. We discuss approaches for estimating the rate of mandatory testing in the undiagnosed population, such as surgical or pregnancy rates. We develop estimators of the proportion diagnosed and confidence interval procedures. Sample size considerations and sensitivity analyses to underlying assumptions are considered. The proposed methods can be performed at a local level and within demographic strata. Implementation of the method is simple and requires neither historical HIV/AIDS surveillance data nor biomarkers such as CD4 cell counts. The methods are applied to data from Dehong Prefecture in Yunnan Province, China.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":"267-278"},"PeriodicalIF":0.8000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s12561-019-09240-8","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics in Biosciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s12561-019-09240-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2019/4/29 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 1
Abstract
Estimation of the proportion of living HIV infected persons that have been diagnosed is critical for tracking progress toward meeting the UNAIDS goal that all persons who need HIV treatment receive it. The objective of this article is to develop a method for estimating that proportion. The methodological problem is that persons with undiagnosed HIV infection are not directly observable and are a "hidden" population. Here we propose a methodology for estimating the proportion diagnosed that is relatively simple to implement. The key idea is that in many settings certain health conditions such as pregnancy or an upcoming surgery lead to mandatory HIV tests. The size of the undiagnosed infected population can be estimated from the numbers of infected persons diagnosed by mandatory tests and an estimate of the rate that persons in the undiagnosed infected population receive mandatory tests. We discuss approaches for estimating the rate of mandatory testing in the undiagnosed population, such as surgical or pregnancy rates. We develop estimators of the proportion diagnosed and confidence interval procedures. Sample size considerations and sensitivity analyses to underlying assumptions are considered. The proposed methods can be performed at a local level and within demographic strata. Implementation of the method is simple and requires neither historical HIV/AIDS surveillance data nor biomarkers such as CD4 cell counts. The methods are applied to data from Dehong Prefecture in Yunnan Province, China.
期刊介绍:
Statistics in Biosciences (SIBS) is published three times a year in print and electronic form. It aims at development and application of statistical methods and their interface with other quantitative methods, such as computational and mathematical methods, in biological and life science, health science, and biopharmaceutical and biotechnological science.
SIBS publishes scientific papers and review articles in four sections, with the first two sections as the primary sections. Original Articles publish novel statistical and quantitative methods in biosciences. The Bioscience Case Studies and Practice Articles publish papers that advance statistical practice in biosciences, such as case studies, innovative applications of existing methods that further understanding of subject-matter science, evaluation of existing methods and data sources. Review Articles publish papers that review an area of statistical and quantitative methodology, software, and data sources in biosciences. Commentaries provide perspectives of research topics or policy issues that are of current quantitative interest in biosciences, reactions to an article published in the journal, and scholarly essays. Substantive science is essential in motivating and demonstrating the methodological development and use for an article to be acceptable. Articles published in SIBS share the goal of promoting evidence-based real world practice and policy making through effective and timely interaction and communication of statisticians and quantitative researchers with subject-matter scientists in biosciences.