AIDS Incidence Data Research Articles

An analysis of AIDS incidence data by clustering trends

AIDS incidence trends vary greatly among geographic areas in the United States. We define clusters of areas within which AIDS incidence trends are similar, as areas within a cluster may have similar human immunodeficiency virus epidemic patterns and thus may lead to similar prevention/intervention strategies. Methods of exploratory data analysis are used to define such clusters from reported quarterly AIDS incidence to December 1990 (adjusted for estimated reporting delays) in homosexual and bisexual men not using intravenous drugs in 39 metropolitan statistical areas (MSAs) in the United States. After smoothing AIDS incidence in each MSA, we define groups from cluster analysis based on a measure of similarity between pairs of MSAs. A log-linear model gives estimates of the scale factors and the common trend for the MSAs in each group. Alternative metrics and simulated data suggest that the clustering is fairly robust to variations in AIDS incidence data. The resulting clusters separate MSAs with different trends, for example, MSAs in which AIDS incidence shows signs of reaching a plateau are separated from MSAs in which incidence continues to increase rapidly.

A method for estimating the age-specific relative risk of HIV infection from AIDS incidence data

Using a multiplicative model, the age-specific relative risk of infection with the human immunodeficiency virus, HIV, and the time-dependent HIV infection rate can be estimated simultaneously from acquired immunodeficiency syndrome, AIDS, incidence data. Non-parametric estimates are arrived at by an EM-algorithm, with smoothing, applied to a back-projection model. The method gives estimates which lie on a smooth curve in accordance with strong prior expectations. The approach is illustrated with an application to Australian AIDS data. It is found that the HIV infection rate tends to be estimated more precisely when age is included as a covariate.

How many drug injectors are there in New Haven? Answers from AIDS data

The number of drug injectors in a given community is an important quantity to determine, yet estimating this quantity has proven very difficult. This is not surprising given the nature of drug injecting behavior and its illicit status; direct enumeration of the population of drug injectors is not possible. This paper details an indirect approach grounded in the epidemiology of HIV infection, a disease inextricably linked to drug injecting behavior. The method consists of two parts. First, one determines from AIDS incidence data the aggregate rate of new HIV infections among drug injectors. Second, one determines a per capita HIV incidence measure among drug injectors (either empirically via a cohort study, or analytically via a mathematical model). Division of the first quantity by the second yields an estimate of the number of drug injectors in the community. Natural measures of variablility follow from this approach, enabling the construction of confidence intervals. When applied to New Haven, Connecticut (where the necessary data are available, in part owing to the evaluation of that city's legal needle exchange program), the technique estimates roughly 1,910 to 2,660 drug injectors, or between 1.5% and 2.1% of New Haven's total population.

Estimating the stage-specific numbers of HIV infection using a Markov model and back-calculation.

The back-calculation method has been used to estimate the number of HIV infections from AIDS incidence data in a particular population. We present an extension of back calculation that provides estimates of the numbers of HIV infectives in different stages of infection. We model the staging process with a time-dependent Markov process that partitions the HIV infectious period into the following progressive stages and/or substages: stage 1, infected but antibody negative; substages 2-3; antibody positive but asymptomatic; substages 4-6, pre-AIDS symptoms and/or abnormal haematologic indicator, stage 7, clinical AIDS. We also model an eight stage, decreased due to AIDS. The model allows for time-dependent treatment effects that slow the rate of progression in substages 4-7. We use the estimated AIDS incubation period distribution for the Markov model in back calculation from AIDS incidence data to estimate the total number of HIV infections and the parameters of the infection probability distribution. We then use these estimates in the Markov model to estimate the stage-specific numbers of HIV infections over the course of the epidemic in the population under study. Example calculations employ data for epidemic in San Francisco City, Clinic Cohort.

Short and medium term projections of the AIDS/HIV epidemic by a dynamic model with an application to the risk group of homo/bisexual men in Amsterdam.

Several methods exist for short term projection of the numbers of AIDS cases. Some use extrapolation of empirical curves fitted to data up to a given time, whereas others such as the popular method of 'back projection' or actuarial methods also use information about the process. In this paper we describe a dynamic model based on a distributed modelling technique allowing for variability both in infectiousness and in age distribution of the population at risk. Some model parameters are taken from the literature, others are estimated from AIDS incidence data from the homo/bisexual population in Amsterdam. The model described here simulates prevalence and incidence of HIV infection. We present prediction intervals for two years from January 1990 onwards. We discuss three scenarios based on the estimated model, two of which consider early treatment with anti-viral drugs. Given the model and the state of the epidemic in Amsterdam, early treatment intervention must be combined with very drastic measures for reducing infectivity in order to have any serious impact on the course of the epidemic.

Statistical Modelling of the AIDS Epidemic for Forecasting Health Care Needs

The objective of this paper is to develop statistical methods for estimating current and future numbers of individuals in different stages of the natural history of the human immunodeficiency (AIDS) virus infection and to evaluate the impact of therapeutic advances on these numbers. The approach is to extend the method of back-calculation to allow for a multistage model of natural history and to permit the hazard functions of progression from one stage to the next to depend on calendar time. Quasi-likelihood estimates of key quantities for evaluating health care needs can be obtained through iteratively reweighted least squares under weakly parametric models for the infection rate. An approach is proposed for incorporating into the analysis independent estimates of human immunodeficiency virus (HIV) prevalence obtained from epidemiologic surveys. The methods are applied to the AIDS epidemic in the United States. Short-term projections are given of both AIDS incidence and the numbers of HIV-infected AIDS-free individuals with CD4 cell depletion. The impact of therapeutic advances on these numbers is evaluated using a change-point hazard model. A number of important sources of uncertainty must be considered when interpreting the results, including uncertainties in the specified hazard functions of disease progression, in the parametric model for the infection rate, in the AIDS incidence data, in the efficacy of treatment, and in the proportions of HIV-infected individuals receiving treatment.

Determination of the spread of HIV from the AIDS incidence history

The relation between the incidence of HIV in the general population, the number of AIDS cases, and the incubation period for the disease is examined. The number of AIDS cases can be expressed in terms of a convolution integral over the incubation period distribution and the temporal history of HIV incidence. In order to determine the level of HIV incidence it is necessary to invert the convolution. In this manner, it is possible to determine the spread of HIV up to the present time from knowledge of the AIDS incidence history and the incubation period. We describe the inversion of the convolution in terms of a Laplace transform technique that is applicable for any given incubation period distribution. Substantial simplifications in the technique are found in the case of an Erlang distribution for the probability density. The spread of HIV infections in the United States is charted through 1988 using AIDS incidence data that are corrected for both the revised AIDS case definition and reporting time delays. The results are consistent with current estimates of the HIV incidence in the United States and show no evidence of saturation in the rate of new infections. Indeed, the rate of new infections still appears to be climbing as of that date. While the technique is unable to predict the future course of the epidemic, it may provide a useful benchmark for comparison with mathematical models of the epidemic. The techniques are conceptually applicable to diseases other than AIDS.

Statistical methods for short-term projections of AIDS incidence.

Short-term projections of AIDS incidence are critical for assessing future health care needs. This paper focuses on the method of back-calculation for obtaining short-term projections. The approach consists of back-calculating from AIDS incidence data through use of the incubation period distribution to obtain estimates of the numbers previously infected. The numbers previously infected are then projected forward to obtain short-term projections. An approach is suggested for accounting for new infections in short-term projections of AIDS incidence. Back-calculation requires accurate AIDS incidence data. A method which is computationally easy to implement is proposed for estimating the distribution of the delays in reporting AIDS cases. It was found that the reporting delay distribution in the United States varies by geographic region of diagnosis. Back-calculation also requires a reliable estimate of the incubation period distribution. Statistical issues associated with estimating the incubation period distribution are considered. The methods are applied to obtain short-term projections of AIDS incidence in the United States. The projected cumulative AIDS incidence in the U.S. by the end of 1992 was 287,100 under the assumption that there are no new infections after 1 July 1987, and 330,600 under the assumption that the infection rate remains constant. These projections do not account for the new broadened AIDS surveillance definitions or the underreporting of AIDS cases to the Centers for Disease Control.

The value of AIDS incidence data in assessing the spread of HIV infection.

Changes over time in the cumulative number of cases of AIDS depend in a complex way on several features of the epidemic, including the distribution of the induction time between infection with the human immunodeficiency virus (HIV) and onset of symptoms of AIDS, heterogeneity in such behaviours as sexual practices, selection of partners, and IV drug use, and changes over time in these behaviours. Consequently, the observed increase in the doubling time in cumulative AIDS incidence from 5 to 13 months, since AIDS was first recognized in 1982, demonstrates neither that the epidemic has begun to 'run its course' nor that behavioural changes have had a major impact in reducing incidence. Even in a homogeneous population with known induction distribution, AIDS incidence data currently are of little value in determining the number of persons infected with HIV unless additional information is available about the shape of cumulative incidence curve of HIV infection.