A METHOD FOR ESTIMATING THE PROPORTION OF HIV INFECTED PERSONS THAT HAVE BEEN DIAGNOSED AND APPLICATION TO CHINA

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.

Keywords: AIDS; HIV; Hidden Population; Prevalence; Undiagnosed.

Figure 1:

Size (N) of the population of persons living with diagnosed HIV infection that is required for the 95% error bound for the estimated proportion that have been diagnosed $\left({\widehat{P}}_1\right)$ to be equal to .05.

Figure 2:

Asymptotic bias Δ of the proportion of HIV infections that have been diagnosed $(\widehat{P})$ versus P under different assumptions about the relative bias in the rate that persons with undiagnosed infections receive a mandatory test, $R=\frac{f^{*}}{f}$.